assessment 3 (2)

JEE309 – Subsea Engineering

Assessment Task 3 – Data Processing

Alex Fuglsang

Lecturer: Dr Alex Forrest

Date Due: 15/10/2015

1 SCENARIO 1: BAKU HARBOUR

A survey of an area of a harbour in Baku, Azerbaijan is required to ensure the safe transit of an oversized jacket. The required survey area is 600m2 at 49.663°E, 40.253°N, which is located in UTM zone 39T. A small AUV with bathymetric sonar sampling of 16Hz is to be used for the survey. Due to external pressure the survey must be run without harbour master approval, so care must be taken to keep the AUV submerged, and only a single run will be attempted. The AUV has an endurance of 3 hours, limited by the battery life.

In addition it is required to conduct a benthic sub-survey of a 100m2 area within the main area. This smaller survey does not require 100% overlap, however the main survey does, in addition to two tie lines.

It is assumed that this is a small AUV with the following unspecified specifications:

Minimum turning circle of30m

Benthic sampling rate of 4Hz

Operational speed of 1.5m/s

1.1 AUV MISSION PATH

The mission path is designed on the 60m side-to-side bathymetric swaths the AUV is capable of, and the minimum turning circle of 15m for the small section. Figure 1-2 displays the area in the harbour that will be surveyed, with the planned AUV path overlaid in red. 11 sweeps are planned in the main survey to provide an overlap of approximately 10%; with 54.5m between each sweep. Two orthogonal tie lines are planned for this survey, and divide the survey area into thirds. Due to the turning circle and battery limitations of the AUV, a complex course consisting of 19 sweeps is planned for the benthic sub-survey. The total length of the planned track is 15.9km, including the return to base. A minimum average velocity of 1.47m/s is required for the AUV to complete this path within 3 hours.

Figure 1-1 shows the field of view that the camera mounted in the AUV possesses. The altitude, a, is 2.5m for the sub-survey, and the half angle field of view (ϴ) is 22.5°.

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JEE309: Subsea Engineering – Assessment 3

Figure 1-1: A graphical representation of the field of view for the camera mounted ion the AUV

Thus, the length (l) of seafloor that each image will capture is:

11\* MERGEFORMAT ()

Therefore, with a sampling rate of 4Hz for the camera, the maximum speed to achieve 40% coverage can be calculated:

22\* MERGEFORMAT ()

This distance is the maximum distance travelled per image to achieve 40% overlap. The sampling period is 0.25s, so the maximum speed is 4.96m/s. This is well above the AUV’s operational speed, so by travelling at a speed of 1.5m/s there will be adequate time to complete the survey shown in Figure 1-2 and return to base without surfacing. This shows that the AUV’s speed is not governed by the camera’s sampling rate. If this AUV is a Gavia model, it is likely to be capable of up to 2.8m/s (Geomares Publishing bv., 2015) and a turning radius down to 10m. This track is unlikely to be difficult for a small AUV to achieve in 3 hours.

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Figure 1-2: The planned path for the AUV displayed in metres. The small focus area is located in the bottom right corner, and provision is made to return to base.

Figure 1-3: The AUV's planned path plotted in Google Earth is displayed in the left plot. The right plot displays precise latitude and longitude coordinates of the survey area

1.2 AREA OF IMAGE COVERAGE

The length of the track the AUV covers in the benthic sub-section is 3128m. Assuming that the cross field of view is similar to the fwd/aft field of view, the cross section covered on each sweep will be 2.07m as shown in Equation 1. This will give a total area of image coverage of 6474.96m2.

1.3 PROVIDED SPATIAL RESOLUTION

Bathymetric sampling is 16 Hz at 6m altitude. From the planned mission path, the mean velocity is 1.5 m/s. The minimum acceptable resolution is 10 samples per bin.

33\* MERGEFORMAT ()



From Equation 3, the spatial resolution per sample is 0.094m. This gives a spatial resolution per bin of 0.9375m.

2 SCENARIO 3: LAKE TAHOE

With the current drought in California, accessing and maintaining the water quality in catchments is vital. The task considered here is specified by The Tahoe Planning Agency to assess the quality of the drinking water in Lake Tahoe in relation to a recent algal bloom. An AUV equipped with an ultra-short baseline (USBL) tracking device and chlorophyll-a measurement equipment is used to investigate the spread of this bloom through the detection of phytoplankton.

1.4 PATH OF THE AUV

The area being surveyed is just offshore of Marla Bay at 119.955°W, 39.029°N. A 10-point moving average filter has been used to extract the actual vehicle path from the USBL data. The track of the vehicle is shown in red in Figure 1-4 (left) with the raw USBL data overlaid in white. Figure 1-4 (right) displays the actual track of the AUV in comparison with both the raw data, and the planned path. There is a small amount of deviation from the planned path that increases as the survey progresses. This survey begins at the southern end of the track.

Figure 1-4: The track of the AUV in Lake Tahoe. On the left is the actual path of the AUV in the lake, displayed in red. The white track is the raw USBL data. The right plot compares the planned path with the AUV track and the raw USBL data

It can be seen in Figure 1-4 that the AUV follows a zig-zag path that runs parallel with the coastline in a northerly direction.

CONTOUR PLOT OF CHLOROPHYLL-A

The algal outbreak is displayed in contour plots in Figure 1-5. It can be seen that along the track of the vehicle (displayed in red) there are higher levels of resolution in the contour steps due to the more refined and less interpolated data along the AUV’s path. The highest levels



of chlorophyll-a are found to be between 0.03 and 0.035mg/l peaking at the eastern edge of the surveyed area, and increasing further north compared the southern section.

CONCLUSION

This investigation provides data relating to the vessel’s tracking ability and the concentration of chlorophyll-a in this area. It can be seen in this survey of a swath approximately 5km north-south by 800m east-west that the highest concentrations of chlorophyll-a occur towards the shore, in the northern half. This suggests that the current flow in the lake runs in a northerly direction. The higher concentrations closer to the shore are likely to be due to onshore winds pushing the bloom towards shore.

The USBL data displays high levels of noise, resulting in the need for an averaging filter. The averaged data shows that the vehicle follows the planned path with some divergence as the survey continues and some higher frequency track hunting.

It is recommended that further surveying is conducted to the north and east of this survey area to investigate the extent of the higher chlorophyll-a levels.

Appendix A

% =========================================================================

% JEE309 Subsea Engineering

% Assignment 3

% Scenario 1

%


Figure 1-5: The contour plot of the chlorophyll-a measured by the AUV. The left plot is overlaid on Google Earth with the vehicle’s track in red. The plot on the right displays the precise latitude and longitude and the actual chlorophyll-a levels in mg/l in a colour bar.


% Alex Fuglsang

% ID: 155229

%

% October 2015

% =========================================================================

% Loading Waypoint Data

clc;close all;clf;clear all;

load('trk2.mat');

X = track2(:,1);

Y = track2(:,2);

plot(X,Y);

axis equal

addpath('googleearth','deg2utm','utm2deg');

%%-------------------------------------------------------------------------

% Location of Baku Harbour, Azerbaijan

Lat = 40.253; %°N

Long = 49.663; %°E

% converting to utm:

[x,y,utmz] = deg2utm(Lat,Long);

% rotation matrix:

theta = -60;

r = [cosd(theta) -sind(theta); sind(theta) cosd(theta)];



% Setting Origin Point at bottom left of square

orig = [-300; -300];

orig_r = r*orig;

%% ------------------------------------------------------------------------

% setting boundary:

bX = [82.11; 82.11; 682.11; 682.11; 82.11;];

bY = [0; 600; 600; 0; 0;];

b = [bX'; bY'];

b_r = r*b;

bX_r = x+orig_r(1,:) + b_r(1,:)';

bY_r = y+orig_r(2,:) + b_r(2,:)';

utmz_b = [utmz; utmz; utmz; utmz; utmz];

% Small focus area

sX = [655; 555; 555; 655; 655;];

sY = [124; 124; 24; 24; 124;];

s = [sX'; sY'];

s_r = r*s;

sX_r = x+orig_r(1,:) + s_r(1,:)';

sY_r = y+orig_r(2,:) + s_r(2,:)';

utmz_s = [utmz; utmz; utmz; utmz; utmz];

% converting back to lon/lat

[lat_b,lon_b] = utm2deg(bX_r,bY_r,utmz_b);

[lat_s,lon_s] = utm2deg(sX_r,sY_r,utmz_s);



%% ------------------------------------------------------------------------

% Loading the waypoints for survey path

wp = load('way4.txt'); % waypoint matrix

wpx = wp(:,1);

wpy = wp(:,2);

WP = wp';

% rotating path:

wp_r = r*WP;

wpX_rot = x + orig_r(1,:) + wp_r(1,:)';

wpY_rot = y + orig_r(2,:) + wp_r(2,:)';

% must have same number of points in utmzone field:

l = length(wpx);

for i = 1:l;

utmz(i,1:4) = utmz(1,1:4);

end

% Converting the planned path into Lat/Lon

[lat,lon] = utm2deg(wpX_rot,wpY_rot,utmz);

%% ------------------------------------------------------------------------

% Displaying planned survey areas and tracks

figure(1)

plot(bX,bY,'b-.');hold on

plot(sX,sY,'k-*')



plot(wpx,wpy,'r')

axis([0 800 -100 800]);

xlabel('Distance (m)');

ylabel('Distance (m)');

legend('Survey Area Boundary','Sub-Survey Boundary','Planned Path');

figure(2)

plot(lon_b,lat_b,'b-.');hold on

plot(lon_s,lat_s,'k-*')

plot(lon,lat,'r')

legend('Survey Area Boundary','Sub-Survey Boundary','Planned Path');

axis([49.658 49.669 40.248 40.258]);

xlabel('Longitude °E');

ylabel('Latitude °N');

%% Plotting Path with Google Maps

bound = ge_plot(lon_b,lat_b,'lineColor','FF0000FF','lineWidth',1);

small = ge_plot(lon_s,lat_s,'lineColor','FF000000','lineWidth',1);

track = ge_plot(lon,lat,'lineColor','ffff0000','lineWidth',2);

kmlFileName = 'AUV_Track_Plan.kml';

ge_output(kmlFileName,[track,bound,small],'name',kmlFileName,'msgToScreen',true);

Appendix B

% =========================================================================

% JEE309 Subsea Engineering



% Assignment 3

% Scenario 3

%

% Alex Fuglsang

% ID: 155229

%

% October 2015

% =========================================================================

% Loading Data

clc;close all;clf;

load('A3dataset.mat');

%% Filtering the USBL data & extracting path

for i = 1:length(A3dataset.lon_usbl)-9;

USBL.X(i,1) = sum(A3dataset.lon_usbl(i:i+9))/10;

USBL.Y(i,1) = sum(A3dataset.lat_usbl(i:i+9))/10;

end;

dist = A3dataset.timestamp_chla; % the entire domain

x = A3dataset.conc_chla; % the dependant vector

d = A3dataset.timestamp_chla; % the independant vector

binsize = 30/60/60/24; % 1/30Hz original, converting to seconds

[chlora, meand, bins] = binavgf(x,d,binsize,dist);

pX = A3dataset.lon_plan;

pY = A3dataset.lat_plan;



%% Contour Plotting

lth_chlora = length(chlora);

chlora(lth_chlora-7:lth_chlora) = [];

s = USBL.X(2)-USBL.X(1);

xmesh = min(USBL.X):s:max(USBL.X);

ymesh = min(USBL.Y):s:max(USBL.Y);

[dx,dy] = meshgrid(xmesh, ymesh);

M = TriScatteredInterp(USBL.X,USBL.Y,chlora);

chlora_z = M(dx,dy);

figure(1);

pcolor(xmesh,ymesh,chlora_z);

shading interp;hold on

plot(USBL.X, USBL.Y,'r','LineWidth',1.5);

% plot(USBL.X, USBL.Y,'k--','LineWidth',1.5);

tsz = 12;

colormap(jet);c = colorbar;

% hcolor = colorbar;

ylabel(c,'Chlorophyll-a Concentration (mg/l)','FontSize',tsz,'FontWeight','bold')

xlabel('Longitude (deg)','FontSize',tsz,'FontWeight','bold');

ylabel('Latitude (deg)','FontSize',tsz,'FontWeight','bold');

set(gca,'FontSize',tsz);

axis equal

% figure(2)



% ylabel('Chlorophyll-a Concentration (mg/l)','FontSize',10,'FontWeight','bold')

% xlabel('Longitude (deg)','FontSize',tsz,'FontWeight','bold');

% ylabel('Latitude (deg)','FontSize',tsz,'FontWeight','bold');

% set(gca,'FontSize',tsz);

% axis equal;

% dz = griddata(USBL.X,USBL.Y,chlora,dx,dy,'linear');

% contourf(dx,dy,dz);colorbar;

% colormap(jet)

% %% Saving contour plot into Google Earth

% k = kml('Chloro');

% k.contourf(dx,dy,dz,'numberOfLevels',100);

% % Save the kml and open it in Google Earth

% k.run;

%

% %% Plotting tracking data and sending to Google

% figure(3);

% plot(pX,pY,'g','LineWidth',2);

% hold on;

% plot(A3dataset.lon_usbl,A3dataset.lat_usbl);

% plot(USBL.X,USBL.Y,'r-','LineWidth',1);

% legend('Planned Track','Raw Data','Filtered Data','Location','Northwest');

% xlabel('Longitude (deg)','FontSize',10,'FontWeight','bold'); ylabel('Latitude (deg)','FontSize',10,'FontWeight','bold');

% axis equal;

% set(gca,'FontSize',10);

%



% %% Plotting Path with Google Maps

% raw_path = ge_plot(A3dataset.lon_usbl,A3dataset.lat_usbl,'lineColor','FF00FF00','lineWidth',0.5);

% path = ge_plot(USBL.X,USBL.Y,'lineColor','ffff0000','lineWidth',2);

% planned = ge_plot(pX,pY,'lineColor','FFFFFFFF','lineWidth',1.5);

% kmlFileName = 'path_on_earth.kml';

% ge_output(kmlFileName,[path,raw_path,planned],'name',kmlFileName,'msgToScreen',true);

Geomares Publishing bv. (2015). Geo-matching.com. Gavia. Retrieved 13/10/2015, 2015, from www.geo-matching.com/products/id1976-gavia.html


http://www.geo-matching.com/products/id1976-gavia.html

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