coastal modeling – indispensable design tool
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Coastal Modeling – Indispensable Design tool . J. W. Kamphuis Queen’s University Kingston, ON, Canada K7L 3N6. This paper will be posted on your website. Coastal Modeling – Indispensable Design tool . 1. Coastal Design. 2. The Learning Curve. 3. How do we proceed?. 4. Integration. - PowerPoint PPT PresentationTRANSCRIPT
J.W. Kamphuis Coastal Modeling Tool
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Coastal Modeling – Indispensable Design tool
J. W. KamphuisQueen’s University
Kingston, ON, CanadaK7L 3N6
This paper will be posted on your website
September 2010
J.W. Kamphuis Coastal Modeling Tool
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1. Coastal Design
September 2010
2. The Learning Curve
3. How do we proceed?
4. Integration
Coastal Modeling – Indispensable Design tool
J.W. Kamphuis Coastal Modeling Tool
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1. Coastal Design
a. Complexb. Use of Modelsc. Uncertainties
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1a. ComplexProcesses are difficult to comprehend and not
clearly understood. Bottom shear stress wave impact and energy dissipation erosion, accretion transport of sediment, pollutants, nutrients.
Designs are subject to difficult combinations of inputs
water levels, waves, tides currents and windModel outputs are difficult to interpret
morphology, environmental impact, water quality, etc.
Coastal Design
September 2010
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Desk Study
Knowledge (Theory and Experience), Prototype Data
PreliminaryDesign
Modeling Design ImplementationPost-
ImplementationMonitoring
Interpretation
Trial and Error !
1b. Use of ModelsTraditional Design
Coastal Design
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ApprovalApproval
Knowledge (Theory and Experience) and Prototype Data
Post-Implementation
Design
Desk Study
Modeling Design ImplementationPreliminary
Design ModelingPreliminary
Design ModelingPreliminary
Design
Trial and Error !
Coastal Design
Contemporary Design
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ApprovalApproval
Knowledge (Theory and Experience) and Prototype Data
Post-Implementation
Design
Desk Study
Modeling Design ImplementationPreliminary
Design ModelingPreliminary
Design ModelingPreliminary
Design
Trial and Error !
Coastal Design
Models: A tool for design optimization through trial and error
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1c. UncertaintiesThe reason for the trial and error is the high
uncertainties in: Data Understanding of coastal processes
Design by simply combining existing knowledge with data (“desk study” in the figure) can only produce very approximate answers – conservative designs.
Coastal Design
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To improve design, uncertainties must be reduced in three areas: data, understanding of the coastal processes modeling techniques.
Moreover, clients, stakeholders, legislators, lawyers, etc. continue to press for (essentially zero) uncertainty in our design and in our understanding of the environment.
Coastal Design Uncertainties
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Further, acceptable levels of uncertainty have been reduced rapidly with time and hence reduction of uncertainties has become urgent.
But, is major reduction of uncertainty even possible? Also, it is a difficult task, since our tools were developed for an earlier time (BL)*[1] when uncertainties were accepted as part of life
*Before Lawyers
Coastal Design Uncertainties
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Growth of Uncertainty
Uncertainty
Primary ProductionFish, Birds,Mammals
Sediment,Morphology
Flow
Fisheries, etc
Far-FieldNear-Field
Flow
Site
Coastal Design
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Model Domains
Uncertainty
Primary ProductionFish, Birds,Mammals
Sediment,Morphology
Fisheries, etc
Far-FieldNear-Field
Flow
Site
Traditional Models
Coastal Design
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Model Domains
Uncertainty
Primary ProductionFish, Birds,Mammals
Sediment,Morphology
Fisheries, etc
Far-FieldNear-Field
Flow
Site
New Models
Coastal Design
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2. The Learning Curve
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Learning (or Development) Curve
Time
Dev
elop
men
t(K
now
ledg
e, R
elig
ion,
Bus
ines
s)
Rap
id
Prog
ress
Oops !
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Ages of the Learning Curve
Time
Infa
ncy
Old age
Maturit
y
Dev
elop
men
t(K
now
ledg
e, R
elig
ion,
Bus
ines
s)
Learning Curve
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The Learning Curve of Knowledge
Time
Kno
wle
dge
Yes.
we
can
!
Modern Era Postmodern
? ?
20001900180017001600
Enl
ight
enm
ent
Learning Curve
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Ages of Knowledge Learning Curve {+ Kuhn (1977)}
Time
Kno
wle
dge
Solution of pressing practical problems Much empiricism
Pressing problems are solvedDevelopment of sophisticated theoriesParadigm is articulated
Science becomes subculture (talks to itself)Work is addressed to peers and adjudicated by peersChallenges are internally imposed(Improvement of theories, validating paradigm)
Infa
ncy
Old ageM
atur
ity
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Paradigm Shift
Time
Paradigm Shift (Sharp Break with the Old)
Dev
elop
men
t(K
now
ledg
e, R
elig
ion,
Bus
ines
s)
Learning Curve
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Learning Curve - Photography
Time
Phot
ogra
phy
Plates
Dig
ital
Colo
ur
Film
Paradigm Shift
Learning Curve
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Possible Decline
Time
Paradigm Shift
Knowledge and development can decline after shift
Dev
elop
men
t(K
now
ledg
e, R
elig
ion,
Bus
ines
s)
Learning Curve
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Coastal Learning Curves ??D
evel
opm
ent
(% o
f pot
entia
l)
Time
Numerical Modeling
ProcessKnowledge(Theory) Field Data
Collection
Today
Physical Modeling
1970
0
75
50
25
100
1950 1970 1990 20101910 1930
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1970: Learning curves are all quite steep, except
for the learning curve for Physical Modelling. But it had produced the major tool of coastal engineering design.
The steepness of the numerical modeling, process knowledge and field measurement curves resulted from the introduction of computers.
Learning Curve
Dev
elop
men
t(%
of
pote
ntia
l)
Time
Numerical Modeling
ProcessKnowledge(Theory) Field
DataCollectio
n
Today
Physical Modeling
1970
0
75
50
25
100
1950 1970 1990 20101910 1930
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1970: To advance in 1970, it made sense to
concentrate on the steepest learning curves. Numerical modeling, process theory and field measurement methods and instrumentation were developed at the cost of physical modeling.
We took full advantage of the opportunities provided by the computer.
Learning Curve
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2010: All learning curves are quite flat.This is the “Old Age” of Coastal EngineeringTherefore, we can not - must not - continue
along the old paths followed since 1970.
Learning Curve
Dev
elop
men
t(%
of
pote
ntia
l)
Time
Numerical Modeling
ProcessKnowledge(Theory) Field
DataCollectio
n
Today
Physical Modeling
1970
0
75
50
25
100
1950 1970 1990 20101910 1930
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Another View of Coastal Learning Curve ?
Time
Decline in knowledge and development of
Physical Modeling
Dev
elop
men
t(K
now
ledg
e, R
elig
ion,
Bus
ines
s)
Paradigm Shift ?? (Introduction of
computers)
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3. How do we proceed?
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Without question, the Numerical Model has become the tool of choice in today’s Coastal Engineering design.
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Proceed?
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Rapid advancement can only occur through a paradigm shift we should be so fortunate!
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Proceed?
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In the meantime, to advance at all (to be able to deal with the present and future design complexities, uncertainties and the approvals process) we need a concerted effort on all fronts – process knowledge (theory), physical modeling, numerical modeling, field measurement.
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Proceed?
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We must take advantage of the particular strengths of each element.
We must integrate science and engineering, re-integrate theory and practice and integrate all our tools, people and facilities.
We must have (more?) open, frank communication between ‘silos of expertise’ and generate (more?) mutual appreciation, understanding and help.
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Proceed?
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To be able to solve practical problems, we must eventually (further?) break down the various ‘expert silos’ and revert to more generalist coastal engineering.
Very difficult, since career advancement is generally based on specialization, publication, etc.
Proceed?
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4. Integration
(of expertise, tools and people)
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4a. Closer (physical) integration of cultures
1. Theory ↔ Practice
Integration
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2. University education ↔ engineering: Define what we want in an engineering education –
theory + application + problem solving + skills. Interaction on the shop floor – Students and
Professors spend time regularly in industry – “Scholarships and sabbaticals in practice, etc.”.
Get engineers into the universities “Engineers in Residence, Educational leaves, etc.”
Industrial Academies: e.g. Coastal software courses run by the software developers are a great idea to be further explored. Such courses must to be technically broad and teach theory as well as skills.
Integration
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(physical) integration of cultures
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3. Physical ↔ Numerical ModelersThis is happening.
4. Physical Modelers - Join forces, co-operate, share facilities and expertise.
HYDRALAB is an excellent example.Problematic because of intellectual property and
perceived leading positions.
Integration
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(physical) integration of cultures
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This integration will cost money.Therefore, we must immediately be able to
show added value: Better, more relevant education, More competent engineers, Shorter project approval periods, Academic rewards for engineering as well as
research, etc, etc, etc.
Integration
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(physical) integration of cultures
Comment
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Numerical models are it! These models are usually are quite integrated
with theoretical development. But the ‘users’ are not! Numerical models need to be more integrated
with data acquisition and with physical models (another type of data acquisition)
Hybrid Modeling. What do we mean?
Integration
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Finally: Integrated Modeling
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Prototype Measurements
Cal.-Ver.Input Monitoring
Physical ModelPhysical ModelPhysical Model(s)
Numerical Model(s)
Output
Integration
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Hybrid Modeling
Usual Interpretation
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Prototype Measurements
Cal.-Ver.Input Monitoring
Physical ModelPhysical ModelPhysical Model(s)
Computational
Module
Numerical Model(s)
Output
Integration
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Hybrid Modeling
My Interpretation
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Integration !
Computational ModuleNumerical ModelPhysical Model(s)Prototype Data
Integration
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Hybrid Modeling
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CommentOur models must be validated properly.If this cannot be done by Field Measurements
alone, calibrate and verify with results from large process experiments.
Improve Field Measurements (more and better data) by: Reduction of mobilization and other costs Making experiments more transportable Use new or different technologies Develop some common standards
Integration
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Hybrid Modeling
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Therefore:Hybrid Modeling =
Field Measurements + Physical Modeling + Numerical Model +
Computational Module
Level 2000 ModelsComposite Models
Integration
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Hybrid Modeling
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Level 2000 ModelFar field is modelled numerically.Near field is NM or PM with far field
introduced as boundary conditions Any PMs are of small prototype sectionsVery Large Physical Models (VLPM) can
achieve n = 1 to 5.n = 1 to 5 is already possible in oscillating
tunnels and wave flumes; we need a basin.
Integration
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Prototype Data
Cal-VerInput Monitoring
Near Field Physical Model
Computational
Module
Far Field Numerical Model
Model Output
B.C for PM
Input
B.C from NM
Output
Integration
Near Field Numerical Model
B.C for NM
Output
Near Field VLPM
B.C for VLPM
B.C for VLPM
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Level 2000 Model
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Very Large Physical Models (Aside)Needed to:
Advance understanding (scientific justification) Reduce scale and laboratory effects Provide “Field Measurements” under controlled,
repeatable conditionsBut
Who will build them? Are they economically justified? Commercially not viable !
Integration
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Level 2000 Model
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And….
VLPM experiments are like field experiments: Extensive planning and long lead times Collective exhaustion and recovery of year(s)? Therefore extensive downtime of facility
Financing for Experiments? Facilities? Downtime?
Integration
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Level 2000 Model
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Therefore….
We want only one or two excellent, world class VLPM facilities (rather than a number of inadequate or barely adequate, locally financed facilities).
Given the economic environment and recent history, the success of this approach is in doubt.
What else can we do?
Integration
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Level 2000 Model
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Process Model
Computational
Module
B.C.Model
Results
Output
Prototype Data
Cal-VerInput Monitoring
IntegrationComposite Model
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Process models are Physical or numerical models must be trustworthy representations simple, inexpensive, easy to understand tests generic tests and test results repeatable results
Process model results form the “bricks”
Integration Composite Model
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Computational module forms the “mortar” not necessarily a numerical model Mostly, simple accounting of results Calibration is done here (inexpensive) What if? Is done here (inexpensive)
Not necessarily same provider for two partsWe could develop a world-wide Bank of
process model “bricks” (I wish)
Integration Composite Model
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Integration
Example: Sacrificial Beach Drilling Islands in the Arctic
Ocean, 1980 • Ice is Nice• 9 mo. instead of 2 mo.• No Protection
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Composite Model
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Arctic IslandModels
5 m
Island Modeln=100
WaveGuide
Absorbing Beach
Wave Generator
HalfIsland Model
n=100
WaveBasin
WaveAbsorber
Composite Model
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The Process Model
Integration
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Table of Test Parameters
Model Scales – n 200 100 75 50 Model Particle Sizes - D50 (mm) 0.56 0.18 0.11 Prototype Wave Heights (m) 6.5 4.75 3.0 Prototype Wave Periods (sec) 8.0 10.0 Wave Types Regular Irregular
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Composite Model
The Process Model
Integration
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Composite ModelIntegration
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Composite ModelIntegration
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Composite ModelIntegration
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Composite ModelIntegration
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Composite ModelIntegration
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Composite ModelIntegration
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Composite ModelIntegration
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IslandModels
5 m
Island Modeln=100
WaveGuide
Absorbing Beach
Wave Generator
HalfIsland Model
n=100
WaveBasin
WaveAbsorber
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Composite Model
The Process Model
Integration
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Island Model Calibration
0
100
200
300
400
500
600
700
800
0 20 40 60 80 100 120
Time (hrs)
Eros
ion
Volu
me
(m3/
m)
ObservedCalculated
Field !
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Composite Model
The Computational Module
Integration
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Thank You
This paper will be posted on:www.civil.queensu.ca
September 2010