assessment of coastal processes and design of cutter suction dredging system for puducherry harbour

ASSESSMENT OF COASTAL PROCESSES AND DESIGN OF

CUTTER SUCTION DREDGING SYSTEM FOR

PUDUCHERRY HARBOUR

A thesis submitted in partial fulfillment of the requirements for the award of degree of

Master of Technology in Dredging and Harbour Engineering

by

SHAILESH SHUKLA

(Reg. No. 13400102004)

under the guidance of

Dr. K V K Ramkrishna Patnaik (Scientist-D, IMU Visakhapatnam Campus)

Dr. S V S Phani Kumar

(Scientist-E, OSS Group, NIOT Chennai)

Department of Dredging and Harbour Engineering

Indian Maritime University Visakhapatnam Campus

Visakhapatnam - 530005

11 May 2015

INDIAN MARITIME UNIVERSITY VISAKHAPATNAM CAMPUS

Department of Dredging and Harbour Engineering

CERTIFICATE

This is to certify that the thesis entitled “Assessment of Coastal Processes and Design

of Cutter Suction Dredging System For Puducherry Harbour” submitted by Shailesh

Shukla to Indian Maritime University Visakhapatnam Campus for the award of the degree in

Master of Technology in Dredging and Harbour Engineering, is a bonafide record of the

project work carried out by him under our supervision. The contents of this thesis, in full or in

parts have not been submitted to any other institute or University for the award of any degree

or diploma.

The Project has been carried out at Indian Maritime University Visakhapatnam Campus.

Dr. K V K R Patnaik Dr. B V R Rao Dr. S V S Phani Kumar

Project Co-Guide

Scientist - E

National Institute of Ocean

Technology, Chennai

Project Guide Academic Co-ordinator

Scientist - D Indian Maritime University.

Indian Maritime University.

Visakhapatnam Campus.

Visakhapatnam Campus.

EVALUATION SHEET

Name of the Candidate Shailesh Shukla

Title of the Project “Assessment of Coastal Processes and Design of Cutter

Suction Dredging System for Puducherry Harbour”

Specialization Dredging and Harbour Engineering

Date of Examination 11th May, 2015

This thesis is approved by the Board of Examiners

External Examiner :

Internal Examiner :

I

ACKNOWLEDGEMENTS

First of all I would like to thank my adviser Dr. M V Ramana Murthy (Project Director,

Scientist-G, OSS Group, NIOT) who not only gave me the great opportunity to pursue a project

at NIOT, but also provided invaluable advice, assistance, and encouragement throughout the

whole study. Further, I would like to thank Dr. U S Ramesh (Director In-charge, Scientist-E,

IMU) and Dr. B V L Rao (Academic Co-ordinator, IMU) for their continuous academic

support and inspiration throughout my M.Tech Programme.

I would like to express my gratitude to my external guide Dr. S V S Phani Kumar (Scientist-

E, NIOT), my internal guide Dr. K V K R Patnaik (Scientist-D, IMU) and Dr. U S Panda

(Scientist-D, ICMAM-PD), for their professional guidance, confident wisdom and generous

mentorship from the beginning.

I would especially like to thank Ms. D Shyamla Varthini (Scientist-C, NIOT), Mr. Satya

Kiran Raju Alluri (Scientist-C, NIOT) and Mr. Subrahmanyam Bhuktha BVK (Scientist-

B, IMU) for their consistent support throughout different phases of my project.

I feel very happy to say my deeply thanks to Mr. Ram Kumar J (Project Scientist-I, NIOT),

Mr. A Naveen (Scientific Asst, NIOT), Mr. Jarpula Laxman (Scientific Asst, NIOT), Mr. S

Murli (Project Scientific Asst, NIOT) and Mr. V Mathiyazhagan (Project Scientific Asst,

NIOT) for their familiar behaviour and valuable help to complete my thesis.

I would like to express my sincere thanks to all staff members of NIOT, Chennai and IMU

Visakhapatnam who have rendered their support for carrying out this project successfully. I

would like to express my boundless thanks and infinite gratitude towards my parents and my

friends who helped make this thesis and degree possible.

Above all, I owe it all to Almighty God for granting me the wisdom, health and strength to

undertake this task and enabling me to its completion.

SHAILESH SHUKLA

II

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS I

LIST OF TABLES V

LIST OF FIGURES VI

LIST OF ABBREVIATIONS VIII

ABSTRACT IX

CHAPTER

1 INTRODUCTION 1

1.1 Background 1

1.2 Problem Identification 4

1.3 Study Area 5

1.3.1 Physical Status 5

1.3.2 Environmental Conditions 6

1.3.3 Climate 6

1.3.4 Meteorological Conditions 6

1.3.5 Rainfall 6

1.3.6 Wind 7

1.3.7 Wave 7

1.3.8 Cyclones 7

1.4 Cutter Suction Dredger 7

1.4.1 Areas of application 8

1.4.2 Dredging at Puducherry Harbour 9

1.5 Objectives 9

1.6 Organisation of the Thesis 10

2 LITERATURE REVIEW 11

3 MATERIAL AND METHODOLOGY 15

3.1 Data Collection and Analysis 15

3.1.1 Bathymetry Data 15

3.1.2 Topographic Data 17

3.1.3 Waves 17

3.1.4 Wind 20

III

3.1.5 Sediment Sampling and Analysis 21

3.1.6 Sediment Table Generation 24

3.2 Calculation Of Sediment Transport Rate 25

3.3 Methodology 27

4 NUMERICAL MODELLING 29

4.1 Spectral Wave Model 29

4.1.1 Basic Formulation 30

4.1.2 Input Parameters 31

4.1.3 Energy Transfer 32

4.1.4 Calibration Parameters 32

4.1.5 Output Parameters 33

4.1.6 Results 34

4.2 Hydrodynamic Model (HD) 34





4.2.5 Results 36

4.3 Sediment Transport Model (ST) 36





4.3.5 Results 39

5 RESULTS AND DISCUSSIONS 40

5.1 Model Calibration 40

5.1.1 Tide Calibration 40

5.1.2 Wave Calibration 42

5.2 Model Results 43

5.2.1 Hydrodynamic Model Results 43

5.2.2 Spectral Wave Model Results 46

5.2.3 Sediment Transport Model Results 48

IV

5.3 Cutter Suction Dredger Design 49

6 CONCLUSIONS 59

REFERENCES 61

APPENDIX A (Ferret Code for Wind) 63

APPENDIX B (Mat lab Code for Wind) 64

APPENDIX C (Mat lab Code for Long Shore Transport Estimation) 66

V

LIST OF TABLES

Page

Table 1: Puducherry Harbour Sediment Analysis Results (Phase-I) 21

Table 2: Puducherry Harbour Sediment Analysis Results (Phase-II) 22

Table 3: Sediment Sample Test Results 23

Table 4: Long Shore Sediment Transport Rate across South Breakwater 26

Table 5: Constituents from Measured Tide Analysis 40

Table 6: CSD Design Parameters 50

Table 7: Comparison between Different Capacity Systems for Dredge

Pump Power 56

VI

LIST OF FIGURES

Page

Figure 1.1 Major eroding coasts of Indian peninsula 2

Figure 1.2 Puducherry coast changing pattern with time & structures built along

Coastline 2

Figure 1.3 Accretion and erosion around Puducherry Harbour breakwaters 3

Figure 1.4 Sand bypassing during 2002-2003 3

Figure 1.5 Sea wall and groin construction and their impact on shoreline 4

Figure 1.6 Study area, Puducherry Harbour 5

Figure 1.7 General Layout of a Cutter Suction Dredger 8

Figure 3.1 Surveyed bathymetry/topography along Puducherry coast 15

Figure 3.2 Bathymetry with nested mesh, interpolated mesh and with isolines 16

Figure 3.3 Wave rose plots for significant wave height at offshore boundary 18

Figure 3.4 Wave rose plots for Peak wave period at offshore boundary 18

Figure 3.5 Various field instruments, deployment scheme and location 18

Figure 3.6 Variation of significant wave height (Hs) 19

Figure 3.7 Variation of peak wave period (Tp) 19

Figure 3.8 Variation of mean wave direction (MWD) 19

Figure 3.9 Wind pattern during May 2010 to April 2011 20

Figure 3.10 Sediment sample locations and their sizes (Phase-I) and PSD curve 21

Figure 3.11 Van Veen grab sampler and ICMAM sieve analyzer 22

Figure 3.12 Sediment table generation command window view 24

Figure 3.13 Data flow diagram (DFD) for Mike 21/3 Coupled FM modelling. 28

Figure 5.1 Measured water elevation, astronomical tide and residual tide 41

Figure 5.2 Surface elevation validation during Oct, 12 with full view & high

resolution view 41

Figure 5.3 Wave height, wave period and wave direction validation during

Oct, 2012 42

Figure 5.4 Surface elevation at Puducherry Harbour mouth from May, 2010 to

April, 2012 43

Figure 5.5 Current speed throughout the study period at harbour mouth 43

Figure 5.6 Surface elevation (a). SW monsoon (b). NE monsoon 44

VII

Figure 5.7 Current flow during (a). Flood Tide (b). Ebb Tide 44

Figure 5.8 Current direction in (a). SW monsoon (b). NE monsoon 45

Figure 5.9 Tidal Prism for Pondicherry harbour inlet 45

Figure 5.10 Significant wave height & peak wave period at Pondicherry harbour 46

Figure 5.11 Wave propagation from offshore to the harbour 46

Figure 5.12 Significant wave height, peak wave period and mean wave direction

at Puducherry harbour mouth 47

Figure 5.13 Rate of bed level change 48

Figure 5.14 Cutter suction dredger and pipe line transport system layout 49

Figure 5.15 CSD IHC Beaver-40 and system layout of pipeline transport system 49

Figure 5.16 Filter arrangement in suction line and Booster Pump 57

VIII

LIST OF ABBREVIATIONS

I. DHI – Danish Hydraulic Institute

II. FM – Flow Model

III. HD – Hydrodynamic

IV. SW – Spectral Wave

V. ST – Sediment Transport

VI. LST – Long Shore Transport

VII. CERC – Coastal Engineering Research Centre

VIII. CSD – Cutter Suction Dredger

IX. HDPE – High Density Polyethylene (Pipe)

X. OSS – Offshore Structures

XI. NIOT – National Institute of Ocean Technology

XII. ICMAM (PD) – Integrated Coastal and Marine Area Management (Project Directorate)

XIII. INCOIS – Indian National Centre for Ocean Information Services

IX

ABSTRACT

Today many sites along Indian coastline are facing severe accretion and erosion caused by

natural effects as well as human intervention. Nearly all coastal states have to deal with the

problem of coastal erosion. Coastal erosion and accretion has always existed and contributed

to the shaping of the present coastlines. Although engineering projects are aimed at solving the

erosion problems, it has long been known that these projects can also contribute to creating

problems at other nearby locations. Puducherry, like all other coasts is undergoing continuous

changes due to developmental activities like construction of fishing harbour breakwaters, sea

wall and groins as well as cause of natural geological agents like wind, wave, tide and currents.

Puducherry harbour; being classified as a minor port, it has the potential for rapid development

in the near future.

The present thesis details about the assessment of wave climate and coastal processes around

Puducherry harbour located in the East Coast of India. With the advent of various numerical

models, understanding of coastal processes has become quite illustrative. DHI Mike 21/3

integrated Coupled Model FM (Integration of HD, SW & ST Models) was used to observe the

coastal processes. Calibration and validation of the models were performed; using data

collected during field surveys, secondary sources and global reanalysis data.

Maintaining sufficient navigational depth in the entrance channel at harbours is a major

operational challenge for harbour authorities. The results of the model were used to estimate

the wave climate and bed level changes around the harbour. Theoretical formulas were used to

predict the long-shore sediment transport amount going through south breakwater. Based on

littoral drift amount, a suitable Cutter Suction Dredger and pipeline transport system was

designed to pump the dredged material from harbour to a location 3 km away (Gandhi Statue)

which will not only keep the navigation and fishing activity round the clock but beach

nourishment will be also done at the disposal site.

Keywords: Puducherry Harbour, Mike 21/3 Coupled FM, Coastal Processes, Wave Climate,

Bed Level Changes, Long-shore Sediment Transport, Cutter Suction Dredger

1

CHAPTER - 1

INTRODUCTION

1.1 BACKGROUND

The shoreline is the margin between land and sea, where the coast is much broader extending

sufficiently landwards and seawards to constitute areas where processes affecting the shore

area are active. Coasts are generally highly scenic and contain plenty of natural resources.

That’s the reason why majority of the world population live close to the sea. Around 60% of

the world population lives in coastal region. But, coasts are among the most dynamic

geomorphological systems on Earth. The land and sea never meet at constant margins.

India has a rich coastline of about 7517 km in which 5423 km is along the main land and the

rest 2094 km along the Andaman Nicobar and Lakshadweep Islands. The coastline is rich with

large variety of geological features such as headlands, bays, promontories, rocky beaches,

sandy spits, barrier beaches, open sandy beaches, embayment, estuaries, inlets, marshy land,

offshore islands, etc. According to Naval Hydrographic charts, coasts of Indian peninsula

consist of nearly 43% sandy beaches, 11% rocky coasts with cliffs, and 46% mud flats and

marshy coasts. Nearly 250 million people live within a distance of 20km along Indian coast.

Multiple coastal issues are occurring along theses coastal areas.

The shoreline variation along Indian coastline is seasonal, in which some beaches regains its

original shape after the extreme seasons where some does not. The beaches which do not regain

its original shape are either undergoing accretion or erosion. Studies show that, at present

around 25% of the Indian coastline are undergoing erosion as a combined effect of both natural

and manmade activities (Pondy-CAN 2009). The Figure 1.1 shows the main eroding areas of

Indian coastline.

Development within coastal areas has increased interest in erosion problems; it has led to major

efforts to manage coastal erosion problems and to restore coastal capacity to accommodate

short and long-term changes induced by human activities, extreme events and sea level rise.

The erosion problem becomes worse whenever the countermeasures (i.e. hard or soft structural

options) applied is inappropriate, improperly designed, built or maintained and also if the

effects on adjacent shores are not carefully evaluated. Often erosion is addressed locally at

specific places or at regional or jurisdictional boundaries instead of at system boundaries that

reflect natural processes. This anomaly is mostly attributable to insufficient knowledge of

coastal processes and the protective function of coastal systems.

2

Figure 1.1: Major eroding coasts of Indian peninsula (Source: Pondy Citizens’ Action

Network-July 2009)

Figure 1.2 Puducherry coast changing pattern with time & structures built along coastline

3

Figure 1.3: Accretion and erosion around Puducherry Harbour breakwaters

Figure1.4: Sand bypassing during 2002-2003

A clear reflection of above described problem can be identified in Puducherry coast (Figure

1.2) and also around Puducherry Harbour breakwaters (Figure 1.3).

Prof. V Sundar

4

1.2 PROBLEM IDENTIFICATION

After construction of the Puducherry port in 1980s zones of erosion have increased to a

noticeable manner. For protecting the coast from the severe erosion, several methods were

adopted such as sand bypassing (Figure 1.4), sea wall, groin field (Figure1.5) etc. The sand

bypassing system was implemented by the harbour authorities for maintaining the navigation

channel as well as to counteract down-drift erosion.

Figure: 1.5: Sea wall with groin construction and their impact on shoreline

After discontinuation of the sand bypassing system a 6 km long seawall was constructed by the

Union Territory of Puducherry, which covers the city of Puducherry. In addition, groin fields

combined with seawalls of about 2 km in length were constructed by Tamil Nadu government

to protect the coastal stretch from Sodhanaikuppam to Thanthriyankuppam. However, the

seawalls constructed to protect the land structures have been partially or totally lost. Now the

coastal erosion problem has shifted further north and many houses and other buildings were

lost to sea. This situation demands for detailed field measurements as well as numerical

modelling studies for getting a better understanding of coastal processes and wave climate of

the area to develop a model for the protection of the Puducherry coast and harbour.

5

1.3 STUDY AREA

Figure 1.6: Study area, Puducherry Harbour

1.3.1 Physical Status

Puducherry (Also known as Pondicherry) city is the capital of the Union Territory of

Puducherry. In 2006 government changed its name to Puducherry from Pondicherry. Its main

economic activities are fishing, small scale industries and higher education. It was the capital

of former French India and was also held at times by the Dutch and British. In 1962 it became

part of the Union Territory of Puducherry. Puducherry region is situated on the Coromandel

Coast between 11°45’ to 12°03’N latitudes and 79°37’ to 79°53’E longitudes with an area of

293 km2. The existing port of Puducherry (11°56’N latitude and 79°50’E longitude) is situated

between two major ports namely, Chennai and Tuticorin.

The port is suitable for lighterage operations during fair weather months (February to

September). The coast is of open type with estuaries. Though the regional coastline appears to

be almost straight, it is a part of a larger concave coast. The potential for fisheries is substantial

in the region. The four regions of the Union Territory have a coastline of 45 km with 675 of

6

inshore waters, 1.35 hectares of inland water and 800 hectares of brackish water. 27 marine

fishing villages and 23 inland fishing villages are actively engaged in fishing.

1.3.2 Environmental Conditions

The elevation of Puducherry city varies between 2 m to 10 m from Mean Sea Level (MSL).

There are two major rivers draining this region 1). The Gingee River, which traverses the region

diagonally from north-west to south-east and 2). The Ponnaiyar (Penniyar) river, which forms

the southern border of the region. The river Gingee also known as the Varahanadi or

Sankaraparani which has its source in the hills of Malayanur of Villupuram district, Tamil

Nadu has a course of 34 km in this region before it confluences with the Bay of Bengal. The

river Ponnaiyar originates from the hills of Karnataka and enters the Puducherry region after

flowing through the districts of Dharmapuri, Salem, Vellore and Cuddalore of Tamil Nadu. All

the rivers are ephemeral in nature.

1.3.3 Climate

Climate at the Puducherry is hot and humid. The maximum and minimum temperature recorded

at the Puducherry is 35.70 C in the month of June and 20.90 C in January respectively. The

average maximum temperature is 31.50 C and average minimum temperature is 23.90 C.

1.3.4 Meteorological Conditions

Puducherry has hot and humid summer, cool winter and two distinct monsoon seasons (south-

westerly and north-easterly).

Hot: Summer (February)

Rainy season: South-West Monsoon (March-September)

Rainy Season: North-East Monsoon (October to December)

Cold: Winter (January)

1.3.5 Rainfall

The rainfall in the Puducherry is influenced by the Southwest and Northeast monsoon. Wet

season persists mainly during the north east monsoon period between October and December.

The average rainfall received in northeast monsoon is about 1300 mm. Southwest monsoon

starts in the month of March and rains still September.

7

1.3.6 Wind

The average wind speed during the southwest monsoon period is about 35 km/h (9.7 m/s),

frequently rising up to 45-55 km/h (12.5-15.3 m/s). The average wind speed during northeast

monsoon prevails around 20 km/h (5.6 km/s). During the cyclone period the winds are around

100 km/hr (Nisha 2008) and 140 km/hr (Thane 2011).

1.3.8 Cyclones

Puducherry, being a part of the Indian subcontinent, experiences tropical cyclones which

originate from the depression generated in the Bay of Bengal during the northeast monsoon

season (October to December). It experiences an average of 2 -3 cyclones annually. Highest

wind speed of 189 km/hr and the lowest wind speed of 83 km/hr have crossed the Puducherry

coast in the past. Puducherry is also affected by cyclone generated waves during this period.

Cyclone data over the Bay of Bengal since 1891 indicates that on average, a moderate to severe

cyclone hits Tamil Nadu and Puducherry coasts every two years.

1.3.7 Wave

As a part of Coromandel Coast, the Puducherry region is experiencing two different monsoon

seasons, North-East and South-West, annually. During South-West monsoon the waves are

approaching the coast from SE direction while during the following North-East monsoon the

wave direction is from NE and E. The normal wave climate in the Bay of Bengal is mild with

significant wave height varies from 1 m to 1.5 m and peak period varies from 7 sec to 9 sec,

but the wave climate is very severe during cyclone with significant wave heights ranging from

4 to 6 m and peak periods from 10 sec to 18 sec. The severe climate exists only for less than

1% a year, but from erosion point of view, its impacts need to be considered.

1.4 CUTTER SUCTION DREDGER

The cutter suction dredger is a stationary dredger equipped with a cutter device (cutter head)

which excavate the soil before it is sucked up by the flow of the dredge pump. During operation

the dredger moves around a spud pole by pulling and slacking on the two fore sideline wires.

This type of dredger is capable to dredge all kind of material and is accurate due to their

movement around the spud pole.

The spoil is mostly hydraulically transported via pipeline, but some dredgers do have barge-

loading facilities as well. Cutter power ranges from 50 kW up to 5000 kW, depending on the

8

type of soil to be cut. The ladder; the construction upon which the cutter head, cutter drive and

the suction pipe are mounted, is suspended by the pontoon and the ladder gantry wire. Seagoing

cutter suction dredgers have their own propulsion that is used only during mobilization. The

propulsion is situated either on the cutter head side or on the spud poles

side.

Figure 1.7: General Layout of a Cutter Suction Dredger

1.4.1 Areas of Application

Cutter suction dredgers are largely used in the dredging of harbours and fairways as well as

for land reclamation projects. In such cases the distance between the dredging and disposal

areas is usually smaller than the distances covered by trailing suction hopper dredgers. The

cutter suction dredger also has the advantage when an accurate profile has to be dredged.

The cutter suction dredger can tackle almost all types of soil, although of course this depends

on the installed cutting power. Cutter suction dredgers are built in a wide range of types and

sizes, the cutting head power ranges between 20 kW for the smallest to around 4,000 kW for

the largest. The dredging depth is usually limited; the biggest suction dredger can reach

depths between 25 and 30 m. The minimum dredging depth is usually determined by the

draught of the pontoon.

9

1.4.2 Dredging at Puducherry Harbour

Puducherry port is one of the 19 minor ports in India. After construction of the Puducherry port

in 1980s the shipping activity increased anonymously. Dredging was carried out at various

intervals to maintain the enough navigational depth for incoming cargo vessels as well as for

fishing boats. But barriers like lack of facilities, poor infrastructure, and presence of major

ports (Chennai Port and Tuticorin) nearby as well as many other factors caused slowdown of

the Puducherry Port. While in 2010 cause of technical problems, dredging also stopped around

harbour which caused severe changes in Puducherry shore line.

Puducherry harbour; being classified as a minor port, it has the potential for rapid development

in the near future. It is still a good growing fishing harbour which is increasing day by day.

Fishing boats needs minimum 4 m water depth for navigation. Dredging was carried out

recently in January 2015, an amount of 1.4 × 105 m3 sediment was dredged to maintain the

navigation for fishing boats and dredged material was discharged at nearby beach. Dredging at

harbour can benefit two things; first we could keep the enough navigation depth and second

we could use this dredged material to nourish the nearby beaches. The present thesis includes

design of a cutter suction dredging system to pump the dredged material from harbour to a

distance 3 km (Gandhi Statue) away for beach nourishment purpose.

1.5 OBJECTIVES

To maintain the inlet of the Puducherry harbour for navigational purpose and for other

activities, it is important to understand the coastal processes. Soft and hard solutions for

engineering interventions on short-term and long-term basis need to be addressed through

coastal & oceanographic data analysis and numerical modelling. To achieve this following

objective were set up for the present study.

1. Understanding and analysis of spatio-temporal variation of sediment characteristics off

Puducherry coast.

2. Study of oceanographic and surface meteorological parameters influencing the

hydrodynamics of the region.

3. Determination of spatio-temporal variation of hydrodynamics, waves and sediment

transport through numerical modeling.

4. Computation of sediment budget and load on seasonal and annual basis.

5. Design of an optimized and economical cutter suction dredger and pipeline system.

10

To achieve the above objectives following tasks were involved.

Oceanographic, bathymetry and meteorological data collection

Sediment sampling and analysis

Preparation of the wind and wave climate.

Estimation of long shore sediment transport

Computation of tidal prism for harbour inlet.

Numerical Modelling for hydrodynamics, waves and sediment transport.

Model data calibration and validation using observed and global re-analysis data

Computation of bed level change analysis around harbour.

Based on littoral drift amount; design of suitable CSD and pipe line transport system.

1.6 ORGANISATION OF THE THESIS

Chapter 1: Provides a brief description about the study area considered. The objectives of the

study are also explained.

Chapter 2: In this chapter references taken from various literatures related to wave

hindcasting, nearshore wave transformation, wave hydrodynamics and sediment transport are

briefly described.

Chapter 3: This chapter provide details about the equipment used for data collection and the

methodology obtained to solve the problem. The chapter also include estimation of long shore

sediment transport rate.

Chapter 4: The numerical models SW, HD and ST; integral part of Mike 21/3 Coupled FM

model are explained briefly in this chapter. Various input parameters, calibration parameters

and output parameters of the model are also explained in this chapter.

Chapter 5: This chapter discusses the model setups and results for the simulations conducted

for the study. It include two parts; Calibration runs – where the model was run for October,

2012 to tune the model to the data available and Main run – where the data available for the

site is used to arrive at an understanding of the hydrodynamic conditions, wave climate and

bed level changes at the Pondicherry Harbour. Chapter also describe design of CSD.

Chapter 6: Chapter concludes the thesis with brief interpretation of the results.

11

CHAPTER - 2

LITERATURE REVIEW

To understand the sediment dynamics in an area, it is necessary to have a quantitative

knowledge about the wave climate in the region. The field measurement of wave parameters is

difficult and costly due to the expensive equipments and expertise man power needed for it.

Thus, numerical models are used to hindcast the wave information in a region where

measurements are not available for suitable duration.

Sulis and Annis 2014 presented a case study on a comprehensive analysis of the shoreline

stability of a salient in the lee of an emergent natural reef at Sa Mesa Longa Beach, Sardinia,

Italy. The analysis included field surveys, geometrical and shape predictive formulas and

simulations on 2D/wave /current/sediment transport numerical model. The morphological

simulation results for the primary response showed agreement with field observations and

predictions from empirical formulas. Here empirical predictive formulas, field and imagery

derived survey methods were used as complementary tools with a morphological model to

obtain reliable results with respect to the shoreline evolution of a salient and the results were

promising in terms of applying to similar morphologies in the lee of natural reefs.

Ghasemizadeh and Tajziehchi 2013 developed a numerical model for studying the impact of

long jetties on shoreline evaluation on Eastern coast of Bandar Abbas. The long jetty at Eastern

coast of Bandar Abbas was designed to provide marine access from Bandar Abbas to Hormoz

Island. Due to local bathymetry, the jetty was designed with relatively large length that may

cause significant changes on the hydrodynamic and considerably morphological pattern on the

study area. Here tidal currents and wind generated waves were separately simulated using

MIKE 21 numerical model in a global scale. Global tidal and wave model were calibrated using

available field data and the output of the model was applied as the boundary conditions for the

main local model. MIKE 21/ 3 Coupled model was applied to simulate morphological variation

around study area. In the coupled model; HD (Hydrodynamic), SW (Spectral Wave) and

sediment transport models were run concurrently and the wave-wave and wave-current

interaction were considered. Shoreline changes due to jetty were studied and compared with

mathematical model LITSTP (LITPACK) for more verification of the coupled model. Then

wave propagation, wave induced currents; tidal currents and sedimentation and erosion patterns

before and after construction of jetty were simulated. The results were in good agreement and

it was observed that the direction of sediment transport was along dominant waves in the area.

12

ANIL ARI GÜNER et al. 2013 estimated wave parameters based on nearshore wind-wave

correlations. They developed a statistical wave model to generate long-term wave data in case

of the absence of any measured data or to fill the data gaps which occur in a measurement data.

Wind data, wave measurement data and a third generation numerical wave model (MIKE 21

SW) were used to develop the wave model. The Karaburun coastal zone located at the

southwest coast of the Black Sea was chosen as the study field. Wave measurement data was

used both for the development and calibration of the statistical wave model. The results

obtained from the statistical wave model have a Root Mean Square Error value of 0.29 m for

the significant wave heights which is very reasonable when the model is considered to be

simple and demanding less input data.

Nayak et al. 2013 investigated the influence of distant swells generated from Southern Ocean,

and their role in modifying local wind-waves at coastal Kalpakkam located at south-east coast

of India. They performed numerical experiments to understand the influence of distant swells

in modulating wind-waves. A multi scale nested approach was used to simulate the non-linear

interaction effects due to distinct swell wave systems in the coastal region of Kalpakkam. The

study was based using state-of-art numerical models like Wave Watch III (WW3), Simulating

WAves Nearshore (SWAN) forced by winds from Weather Research and Forecast (WRF)

model and NCEP Final analysis (FNL).The investigation shows that meso-scale phenomena

like land-sea breeze in Kalpakkam region can generate local wind-waves having a wide degree

of directional behaviour. The mixed sea state resulting from this meso-scale phenomenon and

by interacting with the long distant swells can lead into complex local wind-wave

characteristics through their non-linear interaction process.

Aydoğan et al. 2013 evaluated the wave energy potential of the Black Sea. Wave properties

were calculated using 3rd generation Spectral Wave Model for years 1996-2009 by using wind

data from European Centre for Medium-Range Weather Forecasts (ECMWF). The wave model

was calibrated using the wave measurements conducted at five different stations. Wave power

atlas displaying time – averaged wave power for entire Black sea was generated. Also wave

power roses and wave power distribution tables in terms of periods and heights for different

regions were presented. Wave energy found to be decreasing along the coast from west to east.

It was found that most energetic region is the South Western part of the sea. Annual wave

energies for different regions were also presented.

13

Aydoğan et al. 2013 assessed wave energy potential of the Eastern Mediterranean sea basin.

Wave fields obtained from 3rd generation spectral wave model for years 1994 - 2009 by using

wind data from European Centre for Medium-Range Weather Forecasts (ECMWF) were used

to calculate the wave powers. Wave model was calibrated using the wave measurements

available. Wave model simulated the wave characteristics such as significant wave height (Hs)

and mean wave period (Tm). Wave power atlas was generated based on 15-year time averaged

wave data. The most energetic coast of the Southern Mediterranean basin is Egyptian coast

lying between Nile Delta and the Libya boarder. The most energetic sea states have significant

wave heights between 1 to 4 m and wave energy periods 4 to 8 sec.

Ghasemi et al. 2012 formulated a numerical model for finding the factors causing

sedimentation at the entrance of Bassaidu fisheries port. In this study effects of wind, waves

and tidal currents were discussed. Modelling of this study were carried out using Spectral Wave

and Hydrodynamic modules of MIKE 21 software. The models were simulated and supported

by the collected data from the study area. The results were compared with field measurements

to assure the accuracy of the model.

Venugopal and Davey 2010 used SWAN, TOMAWAC and MIKE 21 to analyse wave

transformation process at Figueira da Foz on the Portuguese coast. The wave climate at the 70

m water depth was transformed to the shallow water buoys located in water depths of 20 m and

12.5 m over a distance of approximately 15 km. They ran the SWAN wave model in two stages,

one without the effect of wind (no wind forcing) on wave propagation and another including

wind effect on wave propagation. The processes shoaling, refraction, bottom dissipation and

wave breaking were included in the modelling. The computational mesh built for SWAN model

was converted into the TELEMAC format and TOMAWAC was run with default parameters.

An unstructured computational mesh was used for the MIKE 21 wave modelling. MIKE 21

produces similar results to SWAN and TOMAWAC and its user friendly graphical pre-

processing and post-processing aids much in quicker visualization and plotting output

parameters.

Kurian et al. 2008 studied the wind waves and sediment transport regime of south-central

Kerala coast. Wind waves were measured at four locations during different seasons.

Simultaneously, numerical models were simulated to generate wave climate and sediment

transport regime of the inner shelf. MIKE 21 Spectral Wave module was used for simulation

of wave climate in the nearshore area and Sediment Transport module was used for the

14

calculation of sediment transport. Surface elevation and current related outputs from the

Hydrodynamic model, wave data from MIKE 21 Spectral Wave model and seabed material

characteristics were given as the input for the Sediment Transport model. The salient

characteristics of the inner shelf waves were derived from the measured data as well as model

simulation. It reveals that apart from natural processes anthropogenic factors play an important

role in the erosion/accretion process of this coast. The correspondence between the erosion or

accretion pattern deduced from the model outputs and the physical scenario from the field was

excellent.

Moeini and Etemad-Shahidi 2007 used third generation wave models SWAN and MIKE 21

for the prediction of wave parameters of Lake Erie. Significant wave height (Hs), peak spectral

period (Tp) and mean wave direction were hindcasted. Both models were forced by temporarily

varying wind. The results show that average scatter index of SWAN was about 16% for Hs and

19% for Tp, while the average scatter index of MIKE 21 SW is about 20% and 13% for Hs and

Tp, respectively. The inconsistency between the models was found due to the differences

between the wind input parameterizations. They observed Komen’s formulation for the wind

input led to a more accurate prediction of Hs rather than using Janssen’s formulation for the

wind input. Both models were evaluated for the prediction of wave direction and it was found

that MIKE 21 SW results were slightly more accurate than those of SWAN.

15

CHAPTER - 3

MATERIAL AND METHODOLOGY

3.1 DATA COLLECTION AND ANALYSIS

3.1.1 Bathymetry Data

The data for bathymetry is obtained from Jeppesen charts; extracted using MIKE C-Map.

MIKE C-Map uses Jepessen charts to extract data for different locations. Jepessen charts are

available for the entire world with a better resolution of the area than available through open

source (NOAA website).

Figure 3.1: Surveyed bathymetry/topography along Puducherry coast

16

Figure 3.2: Bathymetry with nested mesh, interpolated mesh and with isolines

Offshore

Land

North

South

17

3.1.2 Topographic Data

Topographic survey was carried out for the entire Puducherry harbour with 75 transects of 250

m horizontal spacing. Real Time Kinematic Global Positioning System (RTK-GPS) was used

for near shore elevation data collection. RTKGPS survey is based on the use of carrier phase

measurements which provides improved location accuracy, from the 15-meter nominal GPS

accuracy to about 1 cm in case of the best implementations. The entire Puducherry coast was

surveyed using Trimble RTK GPS system. TRIMBLE R8 GNSS receiver used as a single

reference station which provides the real-time corrections to the mobile unit TRIMBLE TSC-

2. The reference base station was established at a known benchmark where the latitude,

longitude and elevation were marked on the ground. The mobile unit was carried along the

Harbour and the elevation data was recorded in the control unit. The land topography and C-

Map sea bathymetry data were merged in GIS and resultant map is shown in Figure 3.1.

3.1.3 Waves

The Bay of Bengal experiences three different weather conditions normally fair, southwest

monsoon and northeast monsoon. During fair weather monsoon (February-May), the sea

surface is usually calm and the coastal region is dominated by swells and to a smaller extent

by locally generated waves, during this period beach building takes place. Extreme weather

events are common during southwest monsoon (June-September) as well as in northeast

monsoon (October-January) seasons. SW monsoon is dominated by northerly drift and NE

monsoon by southerly drift.

Wave measurements were carried out off Puducherry coast by INCOIS during using a data

well Directional Wave Rider (DWR) buoy in 30 m water depth. The data were recorded for 20

min at every 3 h interval. Frequency distribution of wave heights show that the waves are

approaching from southeast by direction for about 9 months in a year and approaches from east

direction for the rest of the year. The wave heights range from 0.2 m to 2.0 m during southwest

monsoon and 0.2 to 2.3 m during North East monsoon. The wave rose diagram shows that the

wave approaches the Puducherry coast from NE with maximum significant wave height of 2.8

m. The wave period ranges between 3 sec to 8 sec during NE monsoon. The wave statistics

along Puducherry coast during the observation period are shown in Figures 3.3 to 3.8.

18

Figure 3.3: Wave rose plots for significant wave height at offshore boundary (a). SW monsoon

(June10 – Sept10) (b). NE monsoon (Oct10 – Jan11) (c). Summer monsoon (Feb11 - Apr11)

Figure 3.4: Wave rose plots for Peak wave period at offshore boundary (a). SW monsoon (b).

NE monsoon (c). Summer monsoon

19

Directional Wave Recorder (DWR), Aanderra Current Meter (RCM) and Acoustic Doppler

Velocimeter (ADV) are oceanographic devices deployed in ocean to study the ocean water

characteristics. DWR measures wave parameters like wave height, wave period, wave direction

as well as other parameters like water temperature, salinity etc. RCM usually measures current

speed and current direction while ADV is designed to record instantaneous velocity

components at a single point with a relative high frequency. Measurements are performed by

measuring the velocity of particles in a remote sampling volume based upon the Doppler shift

affect.

Figure 3.6: Variation of significant wave height (Hs)

Figure 3.7: Variation of peak wave period (Tp)

Figure 3.8: Variation of mean wave direction (MWD)

20

3.1.4 Wind

Wind data for area (780E to 830E and 90N to 140N) covering Puducherry was collected from

European Centre for Medium Range Weather Forecasts (ECMWF). The data from ECMWF

was obtained for a period from May 2010 to April 2011 with spatial resolution of 0.125 × 0.125

degree and temporal resolution of 3 hours. Typical wind pattern is shown in Figure 3.9. The

wind data consist of following three components;

i). 10 m U wind component (m/s)

ii). 10 m V wind component (m/s)

iii). Mean sea level pressure (Pa)

The wind data from open source ECMWF website could be get either in the ‘Netcdf’ format

or ‘Retrieve GRIB’ format for any time period which further needed to be converted in to the

Mike acceptable format (.txt). For this study wind Data from ECMWF was downloaded in the

Netcdf format (.nc) which was converted to .dat format using ‘Ferret’ software. After that ‘.dat’

files were converted to ‘.txt’ format using ‘Mat lab’ software. Then finally grid series wind file

was generated using ‘.txt’ file.

Figure 3.9: Wind pattern during May 2010 to April 2011

21

3.1.5 Sediment Sampling and Analysis

Sediment samples were taken at varios locations around harbour to determine type and size of

sediment. Van Veen grab sampler was used for taking the samples. Samples were taken in two

phases. In phase-I six samples were taken before the start of dredging. Location of samples are

shown in Figure 3.10. Three sand ponds were created to store the discharge material from

dredging during phase-II. In phase-II two samples were taken from each sand pond. Samples

were analysed in ICMAM geo technical lab. Sample results are shown in the tables 1 and table

2.

Figure 3.10: Sediment sample locations and their sizes (Phase-I) and PSD curve for sample-5

Table 1: Puducherry Harbour Sediment Analysis Results (Phase-I)

Puducherry Harbour Sediments (Phase-I)

Sample no. D10(mm)

D30(mm)

D50(mm)

D60(mm)

1. Discharge point 0.115 0.168 0.207 0.233

2. Discharge point 0.116 0.175 0.216 0.246

3. Tide gauge location Clay

4. Next to tide gauge location 0.088 0.117 0.149 0.163

5. At dredger location 0.095 0.136 0.167 0.178

6. Non dredging point 0.075 0.095 0.115 0.123

22

Table 2: Puducherry Harbour Sediment Analysis Results (Phase-II)

Puducherry Harbour Sediments (Phase-II)

Sample no. D10(mm)

D30(mm)

D50(mm)

D60(mm)

Pond1 0.153 0.195 0.249 0.283

Pond1 0.149 0.194 0.250 0.284

Pond2 0.117 0.170 0.195 0.205

Pond2 0.095 0.148 0.178 0.195

Pond3 0.091 0.134 0.170 0.182

Pond3 0.091 0.134 0.170 0.186

Van Veen Grab Sampler

The stainless steel Van Veen grab sampler (Figure 3.11) was used for taking samples from the

sea bottom at Puducherry Harbour. At the surface, the jaws were pushed open and kept in that

position by a hook. To keep the hook in the right position; the Van Veen grabs were sunk at a

steady, not too high pace. Both jaws were fitted with holes to allow air to escape during the

sinking. If these holes were not there, the air would not escape while taking the sample which

would result in interference with the sample. As soon as the jaws touch the bottom, the hook

loosens its grip so that when hoisting the rope again the jaws would shut tight because of the

leverage by the rods. Once surfaced, the grab is emptied and cleaned.

Figure 3.11: Van Veen grab sampler and ICMAM sieve analyzer

23

Sieve Analyzer

Sieve analysis is a practice or a procedure used to assess the particle distribution of a granular

material. Samples taken during field surveys were analyzed using sieve analyzer (Figure 3.11)

in ICMAM geo technical lab. Like a typical sieve analyzer, it consists of a nested column of

sieves with wire mesh cloth. It consists of 8 pans with sizes 2 mm, 1 mm, 0.6 mm, 0.425 mm,

0.3 mm, 0.212 mm, 0.15 mm, 0.075 mm and a bottom pan, called receiver. Each lower pan in

the column has smaller opening than the one above.

The column is typically placed in a mechanical shaker. Then definite amount of samples (300

gm) were poured into the top pan (2 mm). The shaker shakes the column, usually for some

fixed amount of time (10 minutes). After shaking is complete, the material on each sieve is

weighed. The results of the test were displayed on a connected computer screen and used to

describe the particle size distribution and sediment properties. Samples are required to be dried

in oven for 24 hours at 1000C before analysis. Particle distribution curve is shown in figure

3.10. Sediment test result for phase-I sample (Dredger location) is shown in Table 3.

Table 3: Sediment Sample Test Results

SEDIMENT SAMPLE TESTING

Sample No/Location 5 Latitude Longitude

Source : Puducherry 11.906093 79.828946

SAMPLING DATE : 22-01-2015

Date of Testing : 04-02-2015

Tested at : geo-tech lab Referred Standard IS 2720-4

Weight of sample : 300 gm

Sieve Size Retained Weight (gm) %Retained

Weight %Cumulative

Retained % Passing

2 0.03 0.01 0.01 99.99

1 0.34 0.11 0.12 99.88

0.6 0.93 0.31 0.43 99.57

0.425 0.88 0.29 0.73 99.27

0.3 4.80 1.60 2.32 97.68

0.212 32.73 10.90 13.23 86.77

0.15 152.14 50.67 63.90 36.10

0.075 107.47 35.79 99.69 0.31

0 0.92 0.31 100.00 0.00

Silt & Clay Sand

Gravel Fine Medium coarse

0.31 97.37 2.20 0.11 0.01

From graph D10

D30

D50

D60

0.095 0.136 0.167 0.178

Soil type Poorly graded sand Cu 1.882

Classification Fine sand Cc 0.010

24

3.1.6 Sediment Table Generation

Figure 3.12: Sediment table generation command window view

For combined wave and current; the sand transport rates are provided by interpolation in

sediment transport tables. The sediment transport table is generated using MIKE 21 Toolbox

programme "Generation of Q3D Sediment Tables". The table must be generated such that any

combination of bathymetry, current, wave and sediment conditions appearing in the simulation

are within the range defined in the transport table. During the simulation the transport rates are

found by linear interpolation in the transport tables using input parameters from the ongoing

simulation.

25

3.2 CALCULATION OF SEDIMENT TRANSPORT RATE

Reliable estimation of longshore sand transport remains of considerable practical importance

in coastal engineering applications such as the derivation of sediment budgets for coastal areas

with and without structures (breakwaters, groins) and long-term beach stability with and

without beach nourishments or coarse-grained beach protections. There are a number of

equations for calculating the longshore sediment transport rate among them CERC (1984)

equation and Kamphuis equation is the best known equation.

The model, based on the assumption that the total longshore sediment transport rate is

proportional to longshore energy flux, is given as:

𝑄 =𝐾

16 √γbρ𝑔3/2𝐻𝑠𝑏

5/2𝑠𝑖𝑛 2𝛼𝑏

Or (Assuming a dense sand with ρs =1800 kg/m3 and porosity with n =0.32)

𝑄 = 2.2𝑋106 𝐻𝑠𝑏

52

𝛾𝑠𝑏

12

sin 2𝛼𝑏 (𝑚3/𝑦𝑟)

Where Q is the submerged total longshore transport rate, K is an empirical coefficient, ρ is

density of water, g is acceleration due to gravity, Hsb is significant wave height at breaking, γb

is the breaker index, and 𝛼𝑏 is wave angle at breaking.

From experimental results it is found that CERC expression over predicts the value of Q,

particularly at high energy wave conditions. Therefore a more refined equation by Kamphuis

(1991) is using for the current study for finding the longshore sediment transport rate. It was

derived based on dimensional analysis and calibration using laboratory and field data. The

expression includes the effects of wave period, beach slope and grain size for the calculation

of sediment transport rate.

𝑄𝑠

𝜌𝐻𝑠𝑏3

𝑇𝑜𝑝

= 1.3𝑋10−3 [𝐻𝑠𝑏

𝐿𝑜𝑝]

−1.25

𝑚𝑏0.75 [

𝐻𝑠𝑏

𝐿𝑜𝑝]

0.25

𝑠𝑖𝑛0.6𝛼𝑏 (𝑚3/𝑦𝑟)

This reduces to

𝑄𝑘= 2.27 𝐻𝑠𝑏2𝑇𝑜𝑝

1.5𝑚𝑏0.75𝐷−0.25𝑠𝑖𝑛0.62𝛼

Where 𝑄𝑠 is in kg/s under water. This may be converted to

26

𝑄𝑘= 6.4 x104𝐻𝑠𝑏2𝑇𝑜𝑝

1.5𝑚𝑏0.75𝐷−0.25𝑠𝑖𝑛0.62𝛼 (𝑚3/𝑦𝑟)

Or

𝑄𝑘= 7.3𝐻𝑠𝑏2𝑇𝑜𝑝

1.5𝑚_𝑏^0.75𝐷−0.25𝑠𝑖𝑛0.62𝛼 (𝑚3/ℎ𝑟)

Where 𝐻𝑠𝑏is the breaking wave height, 𝑇𝑜𝑝 is the peak wave period, 𝑚𝑏 is the beach slope and

D is the grain size. 𝐻𝑠𝑏 can be obtained from the following expression,

𝐻𝑠𝑏=𝐻𝑠𝑘𝑟𝑘𝑠

Were 𝐻𝑠 is the significant wave height, 𝑘𝑟 𝑎𝑛𝑑 𝑘𝑠 are refraction and shoaling coefficient

respectively.

𝑘𝑟𝑘𝑠= √1

2

1

𝑛

1

tan 𝑘𝑑 √

𝑏0

𝑏 and n =

1

2 (1 +

2𝑘𝑑

sin ℎ 2𝑘𝑑)

k is the wave number and d is the depth of water and n is the breaker index. The long shore

transport results obtained for May 2010 to April 2011 are shown in Table 4.

Table 4: Long Shore Sediment Transport Rate across South Breakwater

Month Avg.

Hs

Avg.

Tp

Avg.

MWD

Sediment m3/month

(CERC Formula)

Sediment m3/month

(Kamphuis

Formula)

May-10 0.82 7.66 132.98 70470.13 20706.88

Jun-10 0.73 8.09 140.19 59098.48 19171.76

Jul-10 0.62 9.54 131.60 35732.22 17825.37

Aug-10 0.64 9.51 131.01 38052.09 18671.05

Sep-10 0.61 9.66 133.52 35811.11 18045.72

Oct-10 0.66 9.24 125.31 34467.00 17117.31

Nov-10 0.92 8.36 110.44 25433.81 13967.34

Dec-10 1.00 8.64 103.92 -392.55 -1251.95

Jan-11 1.03 7.05 97.21 -34712.90 -13248.17

Feb-11 0.80 6.19 100.29 -10157.13 -4596.20

27

Mar-11 0.62 7.52 112.22 12168.59 6386.46

Apr-11 0.53 6.62 130.51 22602.48 6877.79

Net Annual Sediment Transport (m3) 288573.33 119673.38

Thus net long shore sediment transport across south breakwater for a year (May10 - Apr11)

was estimated 2.89 × 105 m3 by CERC formula while 1.2 × 105 m3 was estimated from

Kamphuis formula, drifting towards north. The sediment amount for CSD design was taken

from Kamphuis formula i.e. 1.2 × 105 m3. On the basis of above estimation, suitable cutter

suction dredger and pipeline system was designed to pump dredged material from harbour to

the disposal site (Gandhi Statue) 3 km away.

3.3 METHODOLOGY

The methodology adopted for the study is shown in Figure 3.13. The bathymetry data was

collected from C-Map while for topographic data, survey was carried out. Tidal corrections

were applied during post processing using measured tide data from DWR. The depth values

were post processed, analysed and interpolated using HYPACK MAX software. The raw data

was interpolated and mesh was generated using MIKE Zero.

The sediment samples were collected using Van Veen grab sampler at various locations around

Puducherry harbour. The samples collected from the study area were analysed using sieve

analyzer and the data was processed using a computer programming called GRADISTAT. The

wave data for Puducherry coast was collected from Wave Rider Buoy of INCOIS. Long shore

sediment transport was estimated using CERC and Kamphuis formulas. These two studies help

in preliminary understanding of the problem.

Reference wave climates were obtained for SW Monsoon, NE Monsoons and summer

monsoons using weighted energy method. Spectral Wave Model was developed for Puducherry

coast and the wave radiation stresses were estimated for reference wave climate. Flow was

established in Hydrodynamic module using radiation stresses from Spectral Wave Model.

The hydrodynamic Model was validated for tidal elevations while spectral wave module for

significant wave height, peak wave period and peak wave direction and results shown good

agreement with field measurements. The HD and SW Models were further extended to ST

28

model to estimate bed level changes. Studies were carried out for reference wave climate from

May 2010 to April 2011 for actual condition.

Figure 3.13: Data flow diagram (DFD) for Mike 21/3 Coupled FM Model

Water Level Variation

Radiation Stress

Wave Field

Bed Level Changes

Wind

Hydrodynamic Model

Spectral Wave Model

Sediment Transport Model

Tide

Wave Climate

Coupled FM

Bathymetry

29

CHAPTER - 4

NUMERICAL MODELLING

Numerical models are effectively used all over the world for modelling different coastal

engineering problems. Environmental processes are simulated by mathematical equations,

solved with a numerical approximation scheme, over a discretized temporal and spatial domain.

Physical models, field investigations and numerical modelling provides profundity of the

problems and hence supports to take necessary decisions and policy making. .

There are number of numerical models available ranging from open source (DELFT 3D,

TELEMAC, ROMS, POMS, FVCOM etc.) to commercial (DHI MIKE, SMS, MOHID etc).

The choice for selection of MIKE 21/3 Coupled FM modelling suite was the provision of

flexible mesh which enables much more accurate representation of the actual area and its easy

user interface to handle the problems with better real-time scenario. The flexible mesh also

allows reduction of grid size locally at areas of special interest.

MIKE 21/3 Coupled FM module developed by DHI water and Environment was selected for

the study. Mike 21/3 Coupled FM module is integration of Mike 21 HD, SW and ST modules.

The MIKE 21 morphological model is combined wave/current/sediment transport numerical

models. A local model of the study area was generated for the purpose. 2D model domain was

considered to be sufficient to arrive at a reasonably accurate model of the area. Coupled FM

simulate all three models in parallel while interchanging various model outputs to required

model. Wave radiation stress obtained as the SW model output is fed to the hydrodynamic

model. Water level flow and current variation from HD model is provided to the ST model.

4.1 SPECTRAL WAVE MODEL (SW)

MIKE 21 SW is a state-of-the-art numerical tool for prediction and analysis of wave climates

in offshore and coastal areas (DHI 2007a). It includes a new generation spectral wind-wave

model based on unstructured meshes. The model simulates the growth, decay and

transformation of wind-generated waves and swells in offshore and coastal areas.

MIKE 21 SW includes the following physical phenomena:

1. Wave growth by action of wind

2. Non-linear wave-wave interaction

3. Dissipation due to white-capping

4. Dissipation due to bottom friction

30

5. Dissipation due to depth-induced wave breaking

6. Refraction and shoaling due to depth variations

7. Effect of time varying water depth and flooding and drying

4.1.1 Basic Formulations

Spectral Wave Model in MIKE 21 has two spectral formulations,

Directionally decoupled parametric formulation and

Fully spectral formulation.

In this study directionally decoupled parametric formulation was used as it is suitable for small

domain as well as takes less time for running model. It is based on a parameterization of the

wave action conservation equation. Following Holthuijsen et al. (1989) the parameterization is

made in the frequency domain by introducing the zeroth and first moment of the wave action

spectrum as dependent variables. It is based on the wave action balance equation where the

wave field is represented by the wave action density spectrum N (σ, θ). The independent phase

parameters have been chosen as the relative (intrinsic) angular frequency, σ=2πf and the

direction of wave propagation, θ. The relation between the wave energy density spectrum E (σ,

θ) and the wave action density spectrum is given by,

𝑵(𝛔, 𝛉) = 𝑬/𝛔

The spectral wave calculation is activated at a start time step relative to the start of the

simulation specified. The simulation time and accuracy can be controlled by specifying the

order of the numerical schemes which are used in the numerical calculations. The schemes for

discretization in the geographical domain and the spectral domain can be specified. In the

present study directional discretization, 360° rose with number of discrete directions as 16 is

considered.

The solution technique adopted is based on fractional step approach. Firstly, a propagation step

is performed calculating an approximate solution at the new time level by solving the basic

conservation equations without the source functions. Secondly, a source function step is

performed calculating the new solution from the estimated solution taking into account only

the effect of the source functions. The propagation step is carried out by an explicit Euler

scheme. To overcome the severe stability restriction, a multi-sequence integration scheme is

employed following (Feistauer et al. 1995). Here, the maximum time step is increased by

31

locally employing a sequence of integration steps, where the number of levels (steps) may vary

from element to element. The maximum number of levels in the propagation calculation is 32.

The source integration step is carried out using the method of (Komen 1994) and the number

of time steps in the source calculation is set as 1.

A variable time step interval is used in the time integration of the governing equations. The

time step is determined so that the CFL number is less than the maximum number of levels in

all computational nodes. The number of levels (and thereby the local time step) for each

element is then determined so that the local CFL number is less than 1. The minimum and

maximum time steps were set to 0.01 s and 1800 s respectively. The CFL number is defined as

𝑪𝑭𝑳 = |𝑪𝒙 𝚫𝐭

𝚫𝒙| + |𝑪𝒚

𝚫𝐭

𝚫𝒚| + |𝑪𝛔

𝚫𝐭

𝚫𝛔| + |𝑪𝛉

𝚫𝐭

𝚫𝛉|

where cx , cy , cσ and cθ are the propagation velocities of a wave group in the four dimensional

phase spaces x, y, σ and θ. Δx and Δy are characteristic length scale in the x and y-directions

for an element, Δσ and Δθ are discrete intervals in the direction and frequency spaces and Δt

is the time step interval.

4.1.2 Input Parameters

Wind is the basic input parameter for wave simulation. Successful wave hindcast and forecast

depend on accurate wind fields deduced from meteorological models and analysis. In the

present study ECMWF winds have been used. In the spectral wave model winds are spatially

interpolated to the respective grids over the Indian Ocean domain and directly used temporally.

Winds are applied as vector components (in the form of u and v velocities) varying in time and

space. For the air-sea interaction, a “coupled” formulation is applied according to the

formulation of Komen et al (1994). It means the momentum transfer from the wind to the waves

or drag depends not only on the wind but also on the waves. The applied background roughness

Charnock parameter is 0.01. The Charnock parameter is defined as,

𝒁𝒄𝒉 = 𝒈. 𝒁𝟎 / 𝑼∗𝟐

Where, U* is the friction velocity and Z0 is the sea roughness. The wind input source term is

parameterized following Janssen's formulation (Komen et al., 1994). For a given wind speed

and direction, the growth rate of waves of a given frequency and direction depends on the

friction velocity, U*, and sea roughness Z0. In principle, if the sea roughness is known or

assumed (e.g. the Charnock parameter may be assumed), the wind friction speed can be

estimated using the logarithmic wind profile. Thus, the growth rate of waves due to wind input

32

can be calculated. Komen et al. (1994) made a formulation by assuming a dimensionless sea

roughness (zch) of 0.0144, to fit the observations compiled by Plant (1982).

4.1.3 Energy Transfer

The nonlinear energy transfer amongst the different wave components of a directional

frequency spectrum plays a crucial role for the temporal and spatial evolution of a wave field.

A quadruplet-wave interaction, which is described by the accepted approximate Discrete

Interaction Approximate (DIA) (Komen et al. (1994), has been applied in the present study.

The quadruplet-wave interaction controls (i) the shape-stabilization of the High-frequency part

of the spectrum, (ii) the downshift of energy to lower frequencies and (iii) frequency-dependent

redistribution of directional distribution functions.

4.1.4 Calibration Parameters

Bottom Friction

As waves propagate into shallow water, the orbital wave velocities penetrate the water depth,

and the source function due to wave-bottom interaction becomes important. The dissipation

source function is based on the quadratic friction law and linear wave kinematic theory

(Johnson and Kofoed-Hansen 2000).

𝑺𝒃𝒐𝒕(𝝈, 𝜽) = −𝑪𝒇 𝒌

𝐬𝐢𝐧 𝒉𝟐𝒌𝒉𝑬(𝝈, 𝜽)

Where Cf is a dissipation coefficient (= fwUbm), which depends on the hydrodynamic and

sediment conditions. Here fw is the wave friction factor and Ubm is the maximum near-bed

particle velocity. In the present study, the bottom friction is considered according to Nikuradse

roughness, kN. It is a calibration factor and the value applied in the present study is 0.04 m.

Wave Breaking

Depth-induced breaking occurs when waves propagate into very shallow areas, and the wave

height can no longer be supported by the water depth. The formulation of wave breaking is

based on the breaking model by Battjes and Janssen (1978). The source term due to depth-

induced breaking can be written as,

𝑺𝒔𝒖𝒓𝒇(𝝈, 𝜽) = − ⍺�̅�𝑯𝒎

𝟐 𝑸𝒃

𝟖𝝅 𝑬(𝝈, 𝜽)

𝑬𝒕𝒐𝒕

Where, α (=1.0) is a calibration constant, Qb is the fraction of breaking waves, 𝜎 is the mean

relative frequency, Etot is the total wave energy and Hm = γ d is the maximum wave height.

Here, γ is the free breaking parameter (a wave height to depth ratio). The alpha (α) controls the

rate of dissipation and is a proportional factor to the wave breaking source function. Kaminsky

33

and Kraus (1993) found that γ values are in the range between 0.6 and 1.59 with an average of

0.79. In the present study, γ = 0.8 has been applied.

Initial Conditions

The initial conditions are applied by calculating the spectra from empirical formulations. In the

present study, JONSWAP fetch growth expression has been applied to calculate the spectra.

The following values are used for various parameters: maximum fetch length: 100 km;

maximum peak frequency: 0.4 Hz; maximum Philip’s constant: 0.0081; shape parameter, SA:

0.07; shape parameter, σb: 0.09; peakedness parameter, γ: 3.3

Boundary Conditions

For wave simulations, offshore boundary was selected as ‘Wave Parameter (version 1)’ where

wave climate data was given as varying in time and constant along line. North and south

boundaries were selected ‘Lateral Boundary’ as boundary lines are almost straight and the

depth contours is almost perpendicular to the line.

4.1.5 Output Parameters

The basic outputs from the simulations are integral wave parameters and spectral parameters.

Using the directionally decoupled parametric formulation, the integral parameters are

determined for the total spectral, the wind sea part or the swell part. The parameters can be

calculated for the entire frequency spectrum or for a specific frequency range. The important

integral parameters used in the present study are significant wave height (Hmo), peak wave

period TP, mean wave period Tm02, mean wave direction 𝜃𝑚 and directional standard deviation

(DSD).

Significant wave height, Hmo = 4 √m0

Peak wave period, TP = 1/fp

The peak frequency fp is calculated from the one-dimensional frequency spectrum using a

parabolic fit around the discrete peak.

Mean wave period, 𝑇02 = √𝑚0𝑚2

⁄

Mean wave direction, 𝜃𝑚 = 270 − tan−1 (𝑏𝑎⁄ )

Where, a=1/m0 ∫ ∫ cos(270 − 𝜃)∞

0

2𝜋

0 E (f, θ) df dθ

b = 1/m0 ∫ ∫ sin(270 − 𝜃)∞

0

2𝜋

0E(f, θ)dfdθ

34

4.1.6 Results

The results obtained are Significant wave height, Maximum wave height, Peakwave

height,Wave period, Wave direction, Mean wave direction, Directioal standard deviation and

Radiation stresses (Sxx, SXY, SYY). The wave radiation stress obtained was given as the input

for the Hydrodynamic module.

4.2 HYDRODYNAMIC MODEL (HD)

Hydrodynamic module is a part of MIKE 21/3 Coupled FM Model. The HD module simulates

water level variations and flows in response to a variety of forcing functions in lakes, estuaries

and in coastal regions. It simulates unsteady Two Dimensional flows in one layer (vertically

homogeneous) fluids and has been applied in a large number of studies (DHI 2007b).

4.2.1 Basic Formulation

The Hydrodynamic module is based on the numerical solution of two dimensional shallow

water equations i.e.; the depth integrated incompressible Reynolds averaged Navier-Stokes

equations. Thus the model consists of continuity, momentum, temperature, salinity and density

equations. The local continuity equation integrated over a depth (2D) can be written as:

𝝏𝒉

𝝏𝒕 +

𝝏𝒉�̅�

𝝏𝒙 +

𝝏𝒉�̅�

𝝏𝒚 = hS

Where h is the water depth and u and v are water particle velocities in x and y direction

respectively, S is the energy source-dissipation term.

The two depth averaged horizontal momentum equations for x and y directions are,

respectively:

𝝏𝒉�̅�

𝝏𝒕 +

𝝏𝒉𝒖𝟐̅̅̅̅

𝝏𝒙 +

𝝏𝒉𝒗𝒖̅̅ ̅̅

𝝏𝒚 = fh�̅�- gh

𝝏𝜼

𝝏𝒙 -

𝒉

𝝆𝟎

𝝏𝒑𝒂

𝝏𝒙 -

𝒈𝒉𝟐

𝟐𝝆𝟎

𝝏𝝆

𝝏𝒙 +

𝝉𝒔𝒙

𝝆𝟎 -

𝝉𝒃𝒙

𝝆𝟎 –

𝟏

𝝆𝟎(

𝝏𝑺𝒙𝒙

𝝏𝒙+

𝝏𝑺𝒙𝒚

𝝏𝒚) +

𝝏

𝝏𝒙 ℎ𝑇𝑥𝑥 +

𝝏

𝝏𝒚 ℎ𝑇𝑥𝑦 + h𝑢𝑠𝑆

𝝏𝒉�̅�

𝝏𝒕 +

𝝏𝒉𝒗𝒖̅̅ ̅̅

𝝏𝒙 +

𝝏𝒉𝒗𝟐̅̅̅̅

𝝏𝒚 = fh�̅�- gh

𝝏𝜼

𝝏𝒚 -

𝒉

𝝆𝟎

𝝏𝒑𝒂

𝝏𝒚 -

𝒈𝒉𝟐

𝟐𝝆𝟎

𝝏𝝆

𝝏𝒚 +

𝝉𝒔𝒚

𝝆𝟎 -

𝝉𝒃𝒚

𝝆𝟎 –

𝟏

𝝆𝟎(

𝝏𝑺𝒚𝒙

𝝏𝒙+

𝝏𝑺𝒚𝒚

𝝏𝒚) +

𝝏

𝝏𝒚 ℎ𝑇𝑥𝑦 +

𝝏

𝝏𝒚 ℎ𝑇𝑦𝑦 + h𝑣𝑠𝑆

35

Where t is the time; x and y are the Cartesian co-ordinates; η is the surface elevation; d is the

still water depth; h = η + d is the total water depth; u and v are velocity components in x and y

direction; f is the coriolis parameter; g is the gravitational acceleration; ρ is the density of water;

𝜏𝑠𝑥 and 𝜏𝑠𝑦 are the components of the bottom stresses; 𝑇𝑖𝑗 includes viscous friction, turbulent

friction and differential advection estimated using eddy viscosity formulation based on depth

averaged velocity gradients.

The right hand side of the above equations constitutes the input and boundary conditions

provided to model for calculating the current components and water particle velocities. The

spatial discretization of the primitive equations is performed using a cell centered finite volume

method. The spatial domain is discretized by subdivision of the continuum into non-

overlapping elements or cells. In the horizontal plane an unstructured grid is used comprising

of triangles or quadrilateral element. An approximate Reimann solver is used for computation

of convective fluxes, which makes it possible to handle discontinues solutions.


The same bathymetry was used by HD module like SW & ST models, provided to coupled FM.

Hydrodynamic model takes radiation stresses from SW model. The model is simulated for one

year with a time step of 1800 seconds. Different parameters were given as input for the model.

A low order, fast algorithm solution technique was applied with a CFL number of 0.8 kept as

default.


Coriolis force, wind and tidal components were neglected for HD simulation runs. Eddy

viscosity was applied using Smagorinsky formulation with the default constant coefficient of

0.28. Wave radiation was applied as varying in time and across the domain as a dynamic input

from SW simulation.

Boundary Conditions

In hydrodynamic simulation, offshore boundary was selected as ‘Specified Level’ where

predicted tide was given as varying in time and along boundary. North and south boundaries

were selected as ‘Specified Flux’ with zero flux. Land boundary was selected as land (zero

normal velocity). Predicted tide was only given to the offshore boundary. The reason behind

36

that as water flow will get accelerated for small domains, if we give tide on both north and

south boundary.


The important basic parameters used in the study were surface elevation, total water depth, U

velocity and V velocity. The additional parameters selected were current speed and current

direction.

4.2.5 Results

The results of the hydrodynamic module includes water discharge, current speed, current

direction, surface elevation are used to study the flow pattern near the interested site.

4.3 SEDIMENT TRANSPORT MODEL (ST)

MIKE 21/3 Coupled Flow Model – ST describes erosion, transport and deposition of sand

under the action of currents and wave or under pure current (Geils et al. 2001). It is specifically

suited for application to coastal engineering problems for studying sediment transport studies

of non-cohesive sediments. The hydrodynamic basis of ST module is calculated using HD

module of MIKE 21 Flow model FM. The sand transport calculations are carried out using a

mean horizontal velocity component.

The ST model can calculate sediment transport rates using two different model types:

Pure current

Combined wave and current

The sediment transport rates were calculated in two modes: bed load and suspended load. For

pure current model, the bed load and suspended load are calculated separately whereas for

combined wave and current actions, the total load is calculated.

In present study the total sediment transport 𝑞𝑡 is calculated as the sum of bed load transport

through the deterministic approach of (Frank and Jørgen 1975) and the sediment transport in

suspension where the concentration varying in time and over depth is obtained by an iterative

process of the vertical diffusion equation. A sediment continuity equation calculates the rate of

bed level change at each element. Sediment transport tables need to be generated for the general

spectrum of wave field.

37

4.3.1 Basic Formulation

Engelund & Fredsøe Transport Theory

The total-load transport rate qt is calculated as the sum of the bed-load transport qb and the

suspended-load transport rate qs.

qt = qb + qs

It is assumed that bed-load transport takes place in one single layer of

thickness equal to one grain diameter d. The bed-load transport qb is calculated as;

𝑞𝑏 = (√𝜃′ − 0.07√𝜃𝑐 )√[(𝑠 − 1)𝑔𝑑] if 𝜃′> 𝜃𝑐

where p is the probability that all particles in a single layer will be in motion, θ’ is the

dimensionless bed shear stress (shields parameter) related to skin friction, θc is the critical bed

shear stress for initiation of motion and s is the relative density of the bed material.

θ’ is defined as

𝜃′ = 𝑈′

𝑓2

(𝑠−1)𝑔𝑑

p is defined as

p = [1 + [𝜋

6𝛽

𝜃′− 𝜃𝑐 ]4]−1/4

With β = the dynamic friction coefficient.

Following the ideas of Einstein (1950), the suspended load qs is evaluated as

qs = 11.6 𝑈′𝑓 ∗ cb * a [I1*ln(30 ℎ

𝑘𝑁) + I2]

With cb = the bed concentration of suspended sediment, Uf’ = the shear velocity related to skin

friction, a = 2d = the reference level for cb, I1 and I2 = Einstein’s integrals, h = the water depth

and kN = Nikuradse’s equivalent roughness = 2.5d.

The integrals I1 and I2 are a function of the dimensionless reference level A = a/h and of the

Rouse number z = ws/κUf, where ws is the settling velocity of the suspended sediment and κ =

von Karman’s constant (≈0.40). I1 and I2 are integrated between y = a to y = h, where y is

measured upwards from the fixed bed level.

38

Engelund and Fredsøe developed a semi-empirical relation for the value of cb at a = 2d

cb = 0.65

(1+ λ/3)3

Where the linear concentration λ is given by

λ = √𝜃′− 𝜃𝑐−

𝜋𝑝𝛽

6

0.027𝑠𝜃′ if θ′ > 𝜃𝑐 + πpβ ⁄6


In this study the ST model was simulated for combined wave and current. The forcing

parameters from hydrodynamics and spectral wave model were incorporated in ST model for

creating a sediment transport model which includes the effect of both waves and currents.

Before starting modelling a sediment transport table is needed to be generated. It can be done

with the help of MIKE 21 tool box. The table should be generated such that any combinations

of bathymetry, current, wave and sediment conditions appearing in the simulation are within

the range defined in the transport table. Sediment properties like porosity was given constant

value 0.4, gran diameter 0.2 mm and grading coefficient was selected as 1.1.


The wave forcing was given from SW model simulation. Water flow and current variation were

given from HD model simulation. Bank erosion effect was neglected for the ST simulation.

Boundary Conditions

For ST simulation, ‘Zero sediment flux gradient for outflow, zero bed change for inflow’ was

selected for all three boundaries (offshore boundary, north boundary, south boundary).


2D field variables were selected as output. The important basic parameters used in the study

were bed level, bed level change, rate of bed level change, X and Y component of total load.

The additional parameters selected are total load magnitude, total load direction and X as well

as Y component of accumulated total load.

39

4.3.5 Results

The output of the sediment transport module includes bed level, bed level changes, rate of bed

level changes, total load magnitude, total load direction, acc. total load x-component and y

component are used to study the change in morphology and variation in sediment transport at

various points across the domain.

40

CHAPTER - 5

RESULTS AND DISCUSSIONS

5.1 MODEL CALIBRATION

MIKE21/3 Coupled FM model was calibrated for the period October, 2012. This period was

selected for calibration, as the measured parameters were available for this period. After

obtaining confidence on validation of model results, the model was set up for complete one

year i.e. for May, 2010 to April, 2011 to study the annual cycle of hydrodynamics, waves and

sediment transport along the Puducherry coast with special emphasis to the harbour region.

The offshore wave conditions were given to all model runs as per the wave data recorded from

INCOIS buoy which was deployed in 30 m water depth off Puducherry coast. The water level

and current variation across the domain were updated dynamically with the HD module. The

wind was applied as velocity components in both X and Y directions across the domain. Ice

coverage and diffraction were neglected. For wave breaking constant gamma value 0.8 was

selected. Bottom friction was applied as a function of sediment diameter (d50) kept as constant

value of 0.2 mm. The results were analysed and the calibrated input settings were used for

simulations in the main runs.

5.1.1 Tide Calibration

Table 5: Constituents from Measured Tide Analysis

Constituent Type Amplitude Phase

1. O1 0.0334 148.87

2. K1 0.0737 140.86

3. M2 0.3029 55.13

4. M3 0.0031 91.70

5. M4 0.0066 290.52

6. 2MK5 0.0022 12.54

7. 2SK5 0.0030 115.23

8. M6 0.0021 211.28

9. 3MK7 0.0025 161.44

10. M8 0.0018 27.25

41

Figure 5.1: Measured water elevation, astronomical tide and residual tide.

Figure 5.2: Surface elevation during Oct, 12 with full view & high resolution view.

Hydrodynamic model was validated using observed tide at station S3 near Light House.

Observed tide constituents (Table 5) were used to predict tide for whole time period. In order

to assess whether a suitable combination of the calibration parameters had been reached, the

computed surface elevations were compared to those measured at station S3 (Figure 5.2).

Measured time series have been plotted using blue lines while the simulated values are shown

in red. Good agreement between measured and calculated values can be seen.

Measured Tide [m]Astronomical Tide [m]Residual (Non-tidal component) [m]

00:002012-10-12

00:0010-14

00:0010-16

00:0010-18

00:0010-20

00:0010-22

00:0010-24

00:0010-26

-0.4

-0.2

0.0

0.2

0.4

0.6

Simulated_Surface elevation [m]Measured_Surface elevation [m]

00:002012-10-14

00:0010-16

00:0010-18

00:0010-20

00:0010-22

00:0010-24

00:0010-26

-0.4

-0.2

0.0

0.2

0.4

0.6

Simulated_Surface elevation [m]Measured_Surface elevation [m]

12:002012-10-14

00:0010-15

12:00 00:0010-16

12:00

-0.4

-0.2

0.0

0.2

0.4

0.6

42

5.1.2 Wave Calibration

Figure 5.3: Wave height, wave period and wave direction validation during Oct, 2012

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

14-10-2012 00:00 16-10-2012 00:00 18-10-2012 00:00 20-10-2012 00:00 22-10-2012 00:00 24-10-2012 00:00 26-10-2012 00:00 28-10-2012 00:00

in m

eter

s

Time

Comparision of Significant Wave Height, Hs Simulated DataObserved DataOffshore Wave Climate

0

2

4

6

8

10

12

14

16

18

20

14-10-2012 00:00 16-10-2012 00:00 18-10-2012 00:00 20-10-2012 00:00 22-10-2012 00:00 24-10-2012 00:00 26-10-2012 00:00 28-10-2012 00:00

in s

econds

Time

Comparision of Peak Wave Period,TpSimulated Data

Observed Data

Offshore Wave Climate

0

50

100

150

200

250

14-10-2012 00:00 16-10-2012 00:00 18-10-2012 00:00 20-10-2012 00:00 22-10-2012 00:00 24-10-2012 00:00 26-10-2012 00:00 28-10-2012 00:00

in d

egre

es

Time

Comparision of Mean Wave Direction, MWD

Simulated Data

Observed Data

Offshore Wave Climate

43

The wave model was validated against measured waves at station S3 near Light House. At S3

DWR buoy was deployed on 11 October 2012 and had measured wave heights and wave

periods continuously up to 26 October 2012. The DWR was deployed in approximately 4 m

water north of the Puducherry Harbour (Figure 3.5). In Figure 5.3 the comparison between

simulated and observed significant wave heights, peak wave periods and mean wave directions

at S3 location is presented. It can be seen that the considerable wave height reduction taking

place from offshore to the location of the wave buoy is captured correctly by the model and,

more essentially, all important spikes and lows found in the measured waves are well captured

by the model.

5.2 MODEL RESULTS

5.2.1 Hydrodynamic Model Results

Figure 5.4: Surface elevation at Puducherry Harbour mouth from May, 2010 to April, 2012

Figure 5.5: Current speed throughout the study period at harbour mouth

Simulated Surface elevation (372893.112000, 1317349.657000) [m]

May2010

Jun2010

Jul2010

Aug2010

Sep2010

Oct2010

Nov2010

Dec2010

Jan2011

Feb2011

Mar2011

Apr2011

-0.4

-0.2

0.0

0.2

0.4

May2010

Jun2010

Jul2010

Aug2010

Sep2010

Oct2010

Nov2010

Dec2010

Jan2011

Feb2011

Mar2011

Apr2011

0.00

0.05

0.10

0.15

0.20

0.25

0.30

44

Figure 5.6: Surface elevation (a). SW monsoon (b). NE monsoon

Figure 5.7: Current flow during (a). Flood Tide (b). Ebb Tide

Surface elevation at harbour mouth is shown in Figure 5.4 and 5.6 for SW and NE monsoon

respectively. It shows that tides arriving at harbour are predominantly semi-diurnal. Further

analysis of the current flow patterns during the ebb and flow tide are shown in figure 5.7 while

current direction in figure 5.8. During the falling period of the tide, the tide driven currents are

directed outwards from the harbour inlet. Currents were observed stronger during NE monsoon

than SW and summer monsoon. Also, the currents during the ebb tide are much stronger than

during the flood tide near the harbour. In general, the current speed varies near harbour in the

range of 0.01 to 0.6 m/s but it is seen that that value increases above 1 m/s around breakwaters.

45

Figure 5.8: Current direction in (a). SW monsoon (b). NE monsoon

Tidal Prism

Figure 5.9: Tidal Prism for Pondicherry harbour inlet

Tidal prism result got from HD model for which breakwater gap was taken as cross-section. It

is seen that water flow variation for harbour inlet is high during SW monsoon in comparison

to NE monsoon as well as to summer monsoon which causes more sedimentation in the harbour

during SW monsoon.

46

5.2.2 Spectral Wave Model Results

Figure 5.10: Significant wave height & peak wave period at Pondicherry harbour mouth (a).

SW monsoon (b). NE monsoon

Figure 5.11: Wave propagation from offshore to the harbour

Figure 5.11 shows the waves approaching to the coast at a particular time step. The wave

heights near the coast have an average value of 0.85 m with a peak period of 10 seconds. The

waves arriving at the coast are short in nature and mainly wind generated.

47

Figure 5.12: Significant wave height, peak wave period and mean wave direction at

Puducherry harbour mouth

From SW model output wave data was extracted for a point at harbour mouth to study the wave

climate near harbour. The results shows, significant wave height ranging from 0.2 m to 1.9 m,

peak wave period from 2 sec to 18 sec and mean wave direction varies between ENE to SSE

direction (470 to 1510). It was observed that waves were very high during months of November

to January with short wave period in comparison to rest months of the year. Hs, was found to

have a minimum value of' 0.12 m and a maximum value of 1.97 m while minimum and

maximum value of Tp was found 2.38 sec and 17.59 respectively. The wave direction changes

from NE to SW during the month of February and from SW to NE during the month of

November.

48

5.2.3 Sediment Transport Model Results

Rate of Bed Level Change

Figure 5.13: Rate of bed level change at a). South of harbour b). Harbour mouth c). North of

harbour

Rate of bed level change for whole study period is shown in figure 5.13 which shows that area

south of the harbour breakwater is getting deposition while north is getting eroded. The graph

shows that rate of bed level change is nearly same 0.4 m/day throughout the year south side of

the harbour. At harbour mouth rate of bed level change is very less. The area north of the

harbour is stable for first three months (May to July). It gets erosive rate of bed level change

during NE monsoon while gets positive rate of bed level change during summer monsoon.

South_Rate of bed level change (372695.676211, 1316038.420137) [m/day]

May2010

Jun2010

Jul2010

Aug2010

Sep2010

Oct2010

Nov2010

Dec2010

Jan2011

Feb2011

Mar2011

Apr2011

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Harbour Inlet_Rate of bed level change (372801.203060, 1316477.411830) [m/day]

May2010

Jun2010

Jul2010

Aug2010

Sep2010

Oct2010

Nov2010

Dec2010

Jan2011

Feb2011

Mar2011

Apr2011

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

North_Rate of bed level change (372649.244397, 1316983.940707) [m/day]

May2010

Jun2010

Jul2010

Aug2010

Sep2010

Oct2010

Nov2010

Dec2010

Jan2011

Feb2011

Mar2011

Apr2011

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

49

5.3 CUTTER SUCTION DREDGER DESIGN

Figure 5.14: Cutter suction dredger and pipe line transport system layout

Figure 5.15: CSD IHC Beaver-40 and system layout of pipeline transport system

Puducherry harbour is mainly a fishing harbour. Fishing boats need 4 m navigation depth in

the channel. To maintain the enough navigational depth dredging is required to be done at

proper time intervals. As Puducherry coast line is also facing severe erosion, we could use the

dredged material for beach nourishment. In recently dredging had been carried out using cutter

suction dredger ‘IHC Beaver-40’ by Ocean Sparkle’s Limited in two phases. In phase-I 55,507

m3 sediment amount was dredged and beach adjacent to the harbour was nourished. While in

phase-II 84,815 m3 sediment amount was dredged and discharged into three nearby ponds. To

counteract the coastal line changes and for nourishment of the eroding beaches, we could pump

the dredged material at those places with optimized pipeline transport system.

50

In present thesis, the study is carried out to transport the dredged material from harbour to the

one of the tourist attraction places in Puducherry district i.e. Gandhi Statue. The purpose is to

save the sea front at Gandhi Statue from erosion and enrich the beach through beach

nourishment. The distance of Gandhi statue is around 3 km from the harbour. As per the

Kamphuis formula, net annual long shore sediment transport through south breakwater is 1.2

× 105 m3 towards north. As per the above conditions, total transport amount was taken 1.2 ×

105 m3 while pipeline length was considered 3100 m. As per the availability of HDPE pipes in

the market, three pipe sizes with internal diameter 200 mm, 390 mm and 630 mm were selected

for transport purpose. The design of cutter suction dredger was done following the Prof. W J

Vlasblom’s theory (Design of Dredging Equipment : Course Home 2015

(http://ocw.tudelft.nl/courses/offshore-engineering/design-of-dredging-equipment/course-

home/).

The mixture capacity of the CSD used at Puducherry harbour, was varying between 16 to 20%.

So CSD design was chosen for 14%, 17% and 20% mixture capacity. Here one example of

CSD design with 390 mm inner diameter pipe for 17% mixture capacity system is shown with

target to finish the project in 40 days. The particle diameter was taken constant (0.2 mm) for

all cases as per the dominant size found in field surveys. Water depth at harbour was taken 4

m while elevation of the discharge pipeline taken 2 m to cover all pipe line layout scenario.

The input parameters and formulas used are shown in Table 6.

Table 6: CSD Design Parameters

Parameters Quantity Unit

Total amount to be dredged, Q 120000 m3

Inner Diameter of discharge pipeline, D 0.390 m

Water Depth, hdepth

4 m

Discharge Elevation, helevation

2 m

Particle Diameter, dmf

0.2 mm

SEC for fine sand 0.7 MJ/m3

Pipe Roughness, k 0.00002 m

Kinematic Viscosity, ʋf 0.000001 m

2

/s

Gravitational Acceleration, g 9.81 m/s2

Density of mixture, ρm

1300 kg/m3

Density of sand, ρs 2650 kg/m

3

Density of seawater, ρw 1025 kg/m

3

51

Parameters Formula Quantity Unit

Relative Density of sand, Ss SS = ρs/ρw 2.585 -

Reynold Number, Re Re = (Vm*D)/ʋf 1407235.4 -

Estimated Time (Days) 40 -

Available Hours TAvailable = Days*24 960 hr

General Delays + Dredging

Delays TDelay = 10% + 20%

0.300

-

Non-dredging Hours TIdle = Tavailable*Tdelay 288 hr

Dredging Hours TDredging = TAvailable - TIdle 672 hr

Estimated Spillage 25% 0.250 -

Required Hourly Output,

Qdredged QDredged = (1+Spillage)*Q/TDredging

223.214

m3/hr

Required Hourly Output,

Qdredged

0.062

m3/s

Time losses due to

stepping, spud changes TLoss = 15%

0.150

-

Solid Flow Rate, Qs Qs = QDredged/(1-TLoss) 0.073 m3/s

Required Cutter Power, Pc Pc = Qs*SEC*1000 51.06 kW

Volumetric Concentration,

Cvd Cvd = (ρm - ρm) / (ρs - ρw)

0.169

-

Flow Rate of mixture, Qm Qm = Qs / CVD 0.431 m3/s

Mean velocity of mixture

in a pipeline, Vm Vm = 4Qm / πD2 3.608

m/s

Deposition Limit Velocity,

Vdl

Vdl=1.7*{5(1/√dmf)}*√D{Cvd/(Cvd+0.1

)}1/6*{(SS-1)/1.65}

2.609

m/s

Critical Velocity of

mixture, Vcrit Vcrit = 1.1*Vdl

2.870

m/s

´Manometric Pressure Of

The Pump, Pman Pman = hdepth * (ρm-ρf)*g + PTotalLoss

The pressure drop due to

total losses, PTotalLoss PTotalLoss = PMinorLoss + PMajorLoss

Minor-loss coefficients for different pipeline sections:

52

Suction pipeline; pipe entrance, ξ 0.400 -

all bends, joints etc., ξ 0.300 -

Floating pipeline; all bends, joints etc., ξ 0.800 -

Shore pipeline; all bends, joints etc., ξ 1.500 -

Total Value, Σξ 3.000 -

Minor Loss, Pminor Pminor = ∑ξ*(Vm2/2)*ρm 25.389 -

Friction Coefficient, λf λf = 8 [(8/Re)12 + (X+Y)-1.5]1/12 0.012 -

X X = {-2.457*ln[(7/Re)0.9 + 0.27k/D]}16

3.25733E

+22 -

Y Y = (37530/Re)16

6.54921E-

26 -

Major Loss: (A). Inclined Pipeline:

Friction head loss (Darcy-

Weisbach equation), If If = (λf/D)*(Vm2/2g)

0.0209

V50 V50 ≈ 3.93*(d50)0.35*{(SS-1)/1.65}0.45 2.198 m/s

Im (Im - If )/Cvd(SS-1) = 0.22(Vm/V50)-M 0.046 -

Imω (Imω- If)/(Im -If) = (cosω)(1+Mγ) 0.029 -

Manometic Gradient, Imhω Imhω = Imω+ Cvd (SS-1) sinω 0.258 -

Water Depth, hdepth 4 m

Discharge

Elevation,helevation

2

m

The Required Head, Hman Hman = Imhω * Linc = Imhω * ∆hdepth/Sinω 1.212 mwc

The Required Head, Hman 11.888 kPa

(B). Horizontal Pipeline:

For Suction Line Length 3.000 m

For Water Flow, ∆Phor

∆Phor, fwater flow = λf*(Lhor /

D)*(Vm2/2)*ρf

0.62999

kPa

For Mixture Flow, ∆Phor

∆Phor, fmixture flow = 0.1127 *Cvd(SS-

1)g*ρf*Lhor + ∆Phor,fwater flow

1.542

kPa

(C). Horizontal Pipeline:

For Suction Line Length 3100 m

53

For Water Flow, Phor

∆Phor, fwater flow = λf*(Lhor /

D)*(Vm2/2)*ρf

650.99469

kPa

For Mixture Flow, Phor

∆Phor, fmixture flow = 0.1127 *Cvd(SS-

1)g*ρf*Lhor + ∆Phor,fwater flow

1593.508

kPa

Total Loss, PTotalLoss ∆PTotalLoss = ∆Pminor + ∆Pmajor 1632.326 kPa

Total Verticle Height, ∆h ∆h = hdepth + Helevation 6 m

Manometric Pressure Of

The Pump, Pman Pman = ∆h * (ρm- ρf) * g + ∆PTotalLoss

1648.513

kPa

Pman 168.044 mwc

Power Required to Pump

mixture,P

P = Qm*Pman 710.58 kW

For 17% mixture capacity system with 390 mm inner diameter pipe; it is required to pump the

mixture with higher velocity than the critical velocity 2.87 m/s to avoid deposition of sand in

pipeline. Pumping below critical velocity could also lead to the blockage of the pipeline,

resulting in loss of labour and time. With above design to finish the project in 40 days; various

parameters achieved are-

1. Mixture velocity to pump 3.61 m/s

2. Minimum cutter power required 51.06 kW

3. Minimum dredge pump power required 710.58 kW

Similarly other cases were also considered to pump the mixture for same distance and

conditions. The main parameters for design of different capacity system are shown below.

Case – 1: Mixture Capacity 20% (1350 kg/m3)

Pipe Diameter: 200 mm (Vcrit = 2.076 m/s)

Estimated Time (Days) 110 100 90 80 70

Solid Flow Rate (m3/sec), Qs 0.03 0.03 0.03 0.04 0.04

Required Cutter Power, Pc (kW) 18.57 20.42 22.69 25.53 29.18

Minimum Velocity of mixture, Vm (m/s) 4.22 4.64 5.16 5.80 6.63

Total Loss, PTotalLoss (kPa) 3109.53 3505.79 4038.04 4777.35 5848.48

Manometric Pressure of the Pump, Pman (kPa) 3128.66 3524.92 4057.17 4796.48 5867.60

Power Required to Pump mixture, P (kW) 414.95 514.26 657.67 874.71 1222.91

54


Estimated Time (Days) 50 40 30

Solid Flow Rate (m3/sec), Qs 0.06 0.07 0.10

Required Cutter Power, Pc (kW) 40.85 51.06 68.08

Minimum Velocity of mixture, Vm (m/s) 2.44 3.05 4.07

Total Loss, PTotalLoss (kPa) 1451.76 1621.91 1982.76

Manometric Pressure of the Pump, Pman (kPa) 1470.89 1641.04 2001.89

Power Required to Pump mixture, P (kW) 429.18 598.53 973.53


Estimated Time (Days) 10

Solid Flow Rate (m3/sec), Qs 0.29

Required Cutter Power, Pc (kW) 204.25

Minimum Velocity of mixture, Vm (m/s) 4.68

Total Loss, PTotalLoss (kPa) 1782.01

Manometric Pressure of the Pump, Pman (kPa) 1801.14

Power Required to Pump mixture, P (kW) 2627.72










55


























56

















Table 7: Comparison between Different Capacity Systems for Dredge Pump Power

Pipe Diameter: 200 mm


Mixture Capacity 14% 920 1182 1568 2163 3132





Mixture Capacity 14% 608 942 1771



57



Mixture Capacity 14% 4462



Table 7 shows the dredge pump power required for different capacity system to finish the

dredging project in different time intervals. As CSD used by Ocean Sparkle Limited at

Puducherry harbour is having mixture capacity of 16 to 20%, so it will be good to go for 17%

mixture capacity case design outputs. To go with 630 mm diameter pipe, we have to choose a

bigger cutter suction dredger.

From all conditions, the most feasible and economic condition is to select 390 mm inner

diameter HDPE pipe. To finish the dredging project in 40 days with 17% mixture capacity and

390 mm pipe, we can dredge and pump 1.2 × 105 m3 sediment quantity up to Gandhi Statue by

using 711 kW dredge pump. We can use the same dredger IHC Beaver 40 (Pump power - 447

kW) with an additional booster pump after 2 km distance. The booster pump should have

minimum 270 kW installed power. With above combination we can finish the dredging project

economically in minimum time as well as beach reclamation will be also done which will

ultimately encourage more tourist attraction to the Gandhi Statue in the city.

Figure 5.16: Filter arrangement in suction line and Booster Pump

58

From dredge site to the discharge location, the dredged material is pumped through pipelines.

The pipeline is combination of many small pipe sections, connected in series. Most frequent

problem seen while pumping for long distances is the chocking of the pipeline. It happens when

plastic bags, bottles, leather or large size particles like gravels, pebbles and boulders get enter

into the pipeline through cutter head and suction line. Blockage of pipeline stop all the

operation and dredger becomes idle for that period of time, resulting in loss of time as well as

loss of money.

In case of choking, pipelines are needed to inspect from one end to another end and choked

pipes are removed. Either we can replace the chocked pipes with fresh pipes otherwise we have

to clean the pipe in case of limited number of pipes. It requires lot of labour to clear the chocked

pipes. To tackle the problem of chocking, a filter could be placed between suction line and

dredge pump to prevent the large size particles and garbage from entering to the discharge

pipeline. We could clean the filter at the regular time interval and keep the dredging operation

continue.

59

CHAPTER - 6

CONCLUSIONS

The shoreline of the country is undergoing a major change because of a large number of

construction activities in the coastal region, these developments have all led to serious threats

to the coast, as especially beaches face severe erosion and shorelines are visibly changing. The

coastline of Pondicherry and the neighbouring Tamil Nadu coastline have suffered from severe

coastal erosion due to natural and anthropogenic activities. Initially, sand bypassing was carried

out by harbour authorities to prevent down drift erosion and to maintain channel free from

siltation. Later, discontinuing of sand bypassing due to various technical reasons, lead to

erosion on Pondicherry city.

UT Pondicherry and Tamil Nadu State Government resorted to short term measures to protect

the coast from erosion using hard solutions like sea walls and groin series. Though these

protection measures have offered some relief to the coast under threat but adjacent parts of

coast areas are eroding, more unstable and are constantly under threat. Therefore there is a need

for integrated long-term solution for protection and restoration of beach along Pondicherry

coast. Dredging at proper time interval will not only keep the navigation of fishing boats round

the clock but will also nourish the beach. In order to counteract on shore erosion is to supply

of sand to that place. In this way dredging will do complete both tasks; maintaining enough

navigational depth in channel and beach reclamation will be also done.

A coupled hydrodynamic-wave-sediment transport model was employed to understand the

inter-intra annual variability of coastal processes along Puducherry coast. The coast

geomorphology in and around the Puducherry harbour region is dynamic with apparent

seasonal and annual variability of sediment transport. The coast experienced semidiurnal tide.

Significant waves ranging from 0.2 to 1.9 m, peak wave period 2 to 18 sec and mean wave

direction varies between ENE to SSE direction. Net sediment transport was estimated to 1.2 ×

105 m3. On the basis of above estimation, suitable cutter suction dredger and pipeline system

was designed to pump dredged material from harbour to the disposal site (Gandhi Statue) 3 km

away. The dredge pump power required for different capacity system to finish the dredging

project in different time period, got from the design.

60

As CSD used by Ocean Sparkle Limited at Puducherry harbour is having mixture capacity of

16 to 20%, so it will be good opt to go for 17% mixture capacity case design outputs. The most

feasible and economic condition is to select 390 mm inner diameter HDPE pipe. To finish the

dredging project in 40 days with 17% mixture capacity and with 390 mm inner diameter pipe,

we can dredge and pump 1.2 × 105 m3 sediment quantity up to Gandhi Statue by using 711 kW

dredge pump. We can use the same dredger IHC Beaver 40 (Pump power - 447 kW) with an

additional booster pump after 2 km distance. The booster pump should have minimum 264 kW

power. With above combination the dredging project will be completed economically in

minimum time with best results.

61

REFERENCES

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wave parameters based on nearshore wind―wave correlations. Ocean engineering 63.

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13 years hindcasted wave data. Renewable Energy 57: 436–447.

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Design of Dredging Equipment : Course Home. 2015. http://ocw.tudelft.nl/courses/offshore-

engineering/design-of-dredging-equipment/course-home/. Accessed May 7.

DHI. 2007a. MIKE 21 - Spectral Wave Module - Scientific Document. Hørsholm, Denmark.

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Feistauer, Miloslav, Jiří Felcman, and Mária Lukáčová-Medvid’ová. 1995. Combined finite

element-finite volume solution of compressible flow. Journal of Computational and

Applied Mathematics 63: 179–199. doi:10.1016/0377-0427(95)00051-8.

Frank, Engelund, and Fredsøe Jørgen. 1975. A Sediment Transport Model for Straight

Alluvial Channels. IWA Publishing.

Geils, Jan, O Stoschek, and A Matheja. 2001. 4 th DHI Software Conference M IKE 21 / M

IKE3 for Modeling Hydrodynamics in a Brackish Tidal Environment by. Coastal

Engineering: 1–22.

Ghasemi, M, M Tajziehchi, and a Sadeghi. 2012. Numerical Modeling of Effective Factors

on Sediment Transport of the Entrance of Bassaidu Fishery Port , Qeshm 2: 5685–5693.

Ghasemizadeh, Najmieh, and Mojtaba Tajziehchi. 2013. Impact of Long Jetties on Shoreline

Evaluation ( Case Study : Eastern Coast of Bandar Abbas ). J. Basic. Appl. Sci. Res., 3:

1256–1266.

Hasselmann, Klaus. 1974. On the spectral dissipation of ocean waves due to white capping.

Boundary-Layer Meteorology 6. doi:10.1007/BF00232479.

Janssen, Peter A. E. M. 1989. Wave-Induced Stress and the Drag of Air Flow over Sea

Waves. Journal of Physical Oceanography 19: 745–754. doi:10.1175/1520-

0485(1989)019<0745:WISATD>2.0.CO;2.

Johnson, Hakeem K., and Henrik Kofoed-Hansen. 2000. Influence of Bottom Friction on Sea

Surface Roughness and Its Impact on Shallow Water Wind Wave Modeling. Journal of

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Kaminsky, G.M. and Kraus, N.C. 1993: Evaluation of depth-limited wave breaking criteria,

Proc. Of 2nd Int. Symposium on Ocean Wave Measurement and Analysis, New Orleans,

180-193.

Komen, G. 1994. Dynamics and modelling of ocean waves. Cambridge ;;New York NY

USA: Cambridge University Press.

Kurian, N. P., K. Rajith, T. S. Shahul Hameed, L. Sheela Nair, M. V. Ramana Murthy, S.

Arjun, and V. R. Shamji. 2008. Wind waves and sediment transport regime off the

south-central Kerala coast, India. Natural Hazards 49: 325–345. doi:10.1007/s11069-

008-9318-3.

Moeini, M.H., and A. Etemad-Shahidi. 2007. Application of two numerical models for wave

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63

APPENDIX - A

FERRET CODE

The following code was used in ‘Ferret’ (Source: Ferret Manuals) for converting ECMWF file

(.ncfile) to .dat format;

! NOAA/PMEL TMAP

! FERRET v6.85

! Linux 2.6.32-358.23.2.el6.x86_64 64-bit - 11/12/13

! 30-Apr-15 13:51

set data PuducherryWindECMWF.nc

sh da

sh grid U10

vector/L=1/colour=red U10,V10

go land

plot/x=80/y=10/l=:1460

frame/file=WindArea.gif

vector/L=1/colour=red U10,V10,MSL

vector/L=1/colour=red U10,V10

go land

plot/x=80/y=10/l=1:1460/colour=blue U10

frame/file=U2010-11.gif

plot/x=80/y=10/l=1:1460/colour=red V10

frame/file=V2010-11.gif

list/file=U2010-11_timeseries.dat U10

set memory/size=500

list/file=U2010-11.dat/format=(161F11.5)/nohead/append U10[d=1]

list/file=V2010-11.dat/format=(161F11.5)/nohead/append V10[d=1]

list/file=MSL2010-11.dat/format=(161F20.5)/nohead/append MSL[d=1]

q

64

APPENDIX - B

MAT LAB CODE

The following code was used in Mat lab to convert .dat files into one .txt file; codes are

developed by Mr. Reddy ([email protected]). clear all; clc; % --------------------------------- u=load('U2010-11.dat'); v=load('V2010-11.dat'); MSL=load('MSL2010-11.dat'); % ---------------------------------- fname='PuducherryWind2010-11'; latdif=41;londif=41; t=1460; last=londif*t; o=1; % ---------------------------------- for k=1:t ugrid(:,:,k)=u(o:o+londif-1,:); p=1; for i=londif:-1:1 for j=1:latdif b(p,j,k)=ugrid(i,j,k); end p=p+1; end vgrid(:,:,k)=v(o:o+londif-1,:); q=1; for i=londif:-1:1 for j=1:latdif c(q,j,k)=vgrid(i,j,k); end q=q+1; end MSLgrid(:,:,k)=MSL(o:o+londif-1,:); r=1; for i=londif:-1:1 for j=1:latdif d(r,j,k)=vgrid(i,j,k); end r=r+1; end o=o+londif; end %------------------------------------ ffname=[fname,'.txt']; fid = fopen(ffname,'w'); fprintf(fid,'"Title" ""'); fprintf(fid,'\n'); fprintf(fid,'"Dim" 2'); fprintf(fid,'\n'); fprintf(fid,'"Geo" "UTM-44" 78 9 7.27196E+00'); fprintf(fid,'\n'); fprintf(fid,'"Time" "EqudistantTimeAxis" "'); fprintf(fid,fname); fprintf(fid,'" "00:00:00" ');

65

fprintf(fid,'%d', t); fprintf(fid,' 21600'); fprintf(fid,'\n'); fprintf(fid,'"NoGridPoints" '); fprintf(fid,'%d',latdif); fprintf(fid,' '); fprintf(fid,'%d',londif); fprintf(fid,'\n'); fprintf(fid,'"Spacing" 13890 13890'); fprintf(fid,'\n'); fprintf(fid,'"NoStaticItems" 0'); fprintf(fid,'\n'); fprintf(fid,'"NoDynamicItems" 3'); fprintf(fid,'\n'); fprintf(fid,'"Item" "U-comp" "Wind speed" "m/s"'); fprintf(fid,'\n'); fprintf(fid,'"Item" "V-comp" "Wind speed" "m/s"'); fprintf(fid,'\n'); fprintf(fid,'"Item" "MSL-Pressure" "MSL pressure" "pascal"'); fprintf(fid,'\n'); fprintf(fid,'NoCustomBlocks 1'); fprintf(fid,'\n'); fprintf(fid,'"M21_Misc" 1 7 7.27196E+00 -1E-030 -900 10 -1E-030 -1E-030 -

1E-030'); fprintf(fid,'\n'); fprintf(fid,'"Delete" -1E-030'); fprintf(fid,'\n'); fprintf(fid,'"DataType" 0'); for k=1:t % time steps p=k-1; fprintf(fid,'\n'); fprintf(fid,'\n'); fprintf(fid,'"tstep"\t%d"item " 1\t"layer " 0',p); for i=1:londif fprintf(fid,'\n'); for j=1:latdif fprintf(fid,'%f\t',b(i,j,k)); end end fprintf(fid,'\n'); fprintf(fid,'\n'); fprintf(fid,'"tstep"\t%d"item " 2 \t"layer " 0',p);

for i=1:londif fprintf(fid,'\n'); for j=1:latdif fprintf(fid,'%f\t',c(i,j,k)); end end fprintf(fid,'\n'); fprintf(fid,'\n'); fprintf(fid,'"tstep"\t%d"item " 3 \t"layer " 0',p);

for i=1:londif fprintf(fid,'\n'); for j=1:latdif fprintf(fid,'%f\t',d(i,j,k)); end end end fclose(fid);

66

APPENDIX - C

Computing the longshore sediment transport using Mat lab codes for CERC and Kamphuis

equations and these codes are developed by Mr. Satya Kiran Raju Alluri (NIOT).

%matlabpool open 4;

clear all;

close all;

clc;

%syms W_L;

HH=double(zeros(1,9));

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%loading Data

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

input=load('Wave.dat');

shore_ang=14:1:14;

len=length(shore_ang);

len1=length(input);

sediment1=(zeros(len1,1));

shore_slop=0.0166;

HHH=(zeros(len1,len));

depth=30;

eff_dia=0.2/1000;

shore_slop=0.0166;

for ii=1:len1

ii

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%Input for Parameters

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

Hs=input(ii,2); %Significant Wave Height

Tp=input(ii,3); %Peak Period

fp=1/Tp; %Peak frequency

O_P1=input(ii,4); %Peak Direction

% O_S=input(ii,5); %Directional Spread

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

%Estimating Breaker Height for each event

%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

for i=1:len

O_P2=O_P1-shore_ang(i);

if (O_P2<=90)

O_P=90-O_P2;

else

O_P=O_P2-90;

end

if (or(O_P>=90,O_P<=0))

O_P=00;

else

w_lo=1.56*Tp^2;

fun = @(W_L)

Hs*sqrt((w_lo/W_L)/(2*(0.5*(1+((2*(2*pi/W_L)*((W_L/(2*pi))*atanh(W_L/(1.56*

Tp^2))))/sinh(2*(2*pi/W_L)*((W_L/(2*pi))*atanh(W_L/(1.56*Tp^2)))))))))*((1-

(sin(O_P*pi/180))^2)/(1-

((W_L/w_lo)*sin(O_P*pi/180))^2))^0.25/((W_L/(2*pi))*atanh(W_L/(1.56*Tp^2)))

-0.56*exp(3.5*shore_slop);

S = fzero(fun,w_lo/2.0);

S = double(S);

S(2)=((S/(2*pi))*atanh(S/(1.56*Tp^2)));

k_s=sqrt((w_lo/S(1))/(2*(0.5*(1+((2*(2*pi/S(1))*S(2))/sinh(2*(2*pi/S(1))*S(

2)))))));

67

k_r=((1-(sin(O_P*pi/180))^2)/(1-

((S(1)/w_lo)*sin(O_P*pi/180))^2))^0.25;

O_PP=asind(double((S(1)/w_lo)*sin(O_P*pi/180)));

HH(i)=Hs*k_s*k_r;

end

if (O_P==0)

sediment1(ii,i)=0;

sediment2(ii,i)=0;

sediment3(ii,i)=0;

elseif (O_P2<=90)

sediment1(ii,i)=(-1)*0.39*1035*9.81^(0.5)/(16*0.78^(0.5)*(2650-

1025)*(1-0.4))*HH(i)^(5/2)*sin(2*O_PP*pi/180)*60*60*24*30.5;

sediment2(ii,i)=(-

7.3*24*30.5)*HH(i)^2*Tp^1.5*(shore_slop)^0.75*eff_dia^(-

0.25)*(sin(2*O_PP*pi/180))^0.6;

else

sediment1(ii,i)=(1)*0.39*1035*9.81^(0.5)/(16*0.78^(0.5)*(2650-

1025)*(1-0.4))*HH(i)^(5/2)*sin(2*O_PP*pi/180)*60*60*24*30.5;

sediment2(ii,i)=(7.3*24*30.5)*HH(i)^2*Tp^1.5*(shore_slop)^0.75*eff_dia^(-

0.25)*(sin(2*O_PP*pi/180))^0.6;

end

end

% HHH(ii,:)=HH(:);

end

sediment1(isnan(sediment1)) = 0;

Sediment_Angles=sum(sediment1);

%matlabpool close;

assessment of coastal processes and design of cutter suction dredging system for puducherry harbour

Engineering

s v s phani kumar project

s v s phani kumar scientiste

s v s phani kumar scientist

imu visakhapatnam campus

u s panda scientistd

r patnaik scientistd

ram kumar j project

ramkrishna patnaik scientistd