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Computation of Scour Depth withinChannel Contraction using ANN
Submitted ByAvinash K. Hegde (2KL05ME012)Manoj V. Naik (2KL05ME032)Patel Pratik N. (2KL05ME046)
Vadher Ansh A. (2KL05ME075)
Under The Guidance ofDr. G. Ravindranath
Dr. R.V.Raikar
Project Report on
DEPARTMENT OF MECHANICAL ENGINEERINGK.L.E.SOCIETYS COLLEGE OF ENGINEERING AND
TECHNOLOGYUDYAMBAG, BELGAUM-590008
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INDEX
Objective, Scope & Abstract. Introduction to Scour.
Artificial Neural
Network(ANN).
Training Programs
Testing Programs
Results
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OBJECTIVE
To develop an ANN model for thecomputation of scour depth within a
channel.
SCOPE
Working with moderatedata.
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ABSTRACT
Due to change in flow props. ,conduitmay erode out
To study the behaviors of scour depthin
different areas
Extensive data of scour depth areanalyzed using
ANN in MATLAB environment.
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Introduction
Scour
Scour Depth
CLASSIFICATION:1. General bed
scour2.Local scour
Parameters influencing
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Scour within channel
contractions:
Channel contraction Geometry
(b)
21
1 2
h1
h2
dsc Bed sediment
(a)
b1 b2
L
dsc = f (d50 U1 h1 b2 )
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Methods:
Direct field measurement
Laboratory model study
Mathematical modeling
Applying soft computing techniques
to the
measured values.
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Prediction of operations of fluidmechanics
takes more time as it is extremely
complicated and non-linear.
ANN is adapted in this work for Scour
Depthstudies.Neural networks are promising due
to their ability to learn highly non-linear relationshi .
ArtificialNeural Network (ANN)
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ANN is capable of handling tasksinvolvingIncomplete data sets
Complex and ill-defined problemsNon-linear problemsSystems with many inter related
parameters
ANN takes a set of known patterns as
inputs androduces out uts which closel match
Prediction
through ANN
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Types of ANN
Multi layer perceptrons (MLPs)
Radial basis function network (RBFNNs)
Computer propagation neural networks(CPNNs)
Feed forward neural network consisting
Input layer
Output layer
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D50
h1
b2
u1/u
c
iwij
wjk
k
Architecture
dsc
j
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ANN MODEL:
METHOD: Feed forward Architecture Trainedby Back-Propagation Technique.
VARIABLES: d50 U1/Uc h1 b2
dsc
DATA: 99
INPUT: 4 (d50 U1/Uc h1 b2 )
OUTPUT: 1 (dsc
)
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ANN provides with built-in training
functions. No algorithm is best suited to alllocations and
selected judiciously for a particularapplication. Depends on complexity of problem,
number ofdata points in training set and errorgoal. Train GDX,GDA,CGF,CGP,CGB,SCG,OSS,LM
Training algorithm
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From graphs it is observed TRAINLM
is fastest method for training
moderate-sized feed-forward neural
network.
Training algorithm(Conti..)
e^-1traingdx e^-1traingd
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e^-1traingda e^-1trainoss
e^-1trainscg e^-1traincgb
^ 1 i f
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e^-1traincgf
e^-1traincgp
e^-1trainlm
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Training Methods :
Unsupervised or Adaptive
training
Supervised training
Here both inputs and outputs areprovided
During training default performance
function for
feed forward network is mean square
error (MSE).
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MATrix LABoratory(MATLAB):
It is a special purpose computer
program
It is used to perform engineering
and
scientific calculations
Graphical User Interface
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4.1 .95 .0849 .36;
4.1 .925 .1366 .36;
4.1 .931 .0692 .3;
4.1 .913 .101 .3;
4.1 .907 .1322 .3;
4.1 .932 .0903 .24;
4.1 .925 .1063 .24;
4.1 .944 .1242 .24;
5.53 .976 .0859 .42;
5.53 .936 .1048 .42;
5.53 .94 .122 .42;
5.53 .958 .0707 .36;
5.53 .938 .1077 .36;5.53 .929 .1269 .36;
5.53 .953 .071 .3;
5.53 .933 .0891 .3;
5.53 .922 .1277 .3;
5.53 .944 .0716 .24;
5.53 .941 .0835 .24;
5.53 .906 .107 .24;7.15 .956 .0786 .42;
7.15 .914 .1036 .42;
7.15 .917 .124 .42;
7.15 .957 .0677 .36;
7.15 .923 .0857 .36;
7.15 .936 .1219 .36;7.15 .939 .0675 .3;
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7.15 .93 .0817 .3;
7.15 .917 .1033 .3;
7.15 .951 .0853 .24;
7.15 .913 .102 .24;7.15 .926 .123 .24;
10.25 .918 .084 .42;
10.25 .901 .101 .42;
10.25 .904 .1215 .36;
10.25 .957 .077 .3;
10.25 .913 .0897 .3;10.25 .91 .121 .3;
10.25 .919 .0892 .24;
10.25 .91 .121 .24;
14.25 .923 .089 .42;
14.25 .941 .1045 .42;
14.25 .947 .104 .36;
14.25 .92 .1247 .36;14.25 .937 .088 .3;
14.25 .947 .122 .3;
14.25 .903 .108 .24;
14.25 .91 .121 .24];
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ttr=[.026;
.029;
.056;
.095;
.149;
.045;
.063;
.096;
.143;
.154;
.025;
.064;
.088;
.11;
.139;
.03;
.036;
.04;
.043;
.056;
.065;
.069;
.083;
.097;
.092;
.129;
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.131;
.037;
.041;
.044;
.051;
.07;.069;
.068;
.08;
.099;
.074;
.098;
.103;
.034;
.041;
.044;
.052;
.062;
.068;
.071;
.081;
.093;
.089;
.103;
.135;
.029;
.031;
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.069;
.071;
.073;
.104;
.087;
.138;
.05;
.052;
.073;
.074;
.081;
.108;
.118;
.144];
p=transpose(ptr);
t=transpose(ttr);
[pn,minp,maxp,tn,mint,maxt]=premnmx(p,t);
net=newff(minmax(pn),[1,1],{'tansig','purelin'},'trainlm');
net.trainParam.show=50;net.trainParam.lr=0.5;
net.trainParam.lr_inc=1.005;
net.trainParam.epochs=500;
net.trainParam.goal=1.0e-1;
[net,tr]=train(net,pn,tn);
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Testing Program
an = sim(net,pn);
a=postmnmx(an,mint,maxt);
at=transpose(a);
erg=[]; rs1=[]; rs2=[];
xaxis1=[1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25];
xaxis2=[1,2,3,4,5,6,7,8,9,10];
fprintf(' dsc(exp) dsc(predi) ERROR \n');
fprintf(' in percent \n');
fprintf('----------------------------------\n');for i=1:32
rs1(i,1)=ttr(i,1);
rs1(i,2)=at(i,1);
rs1(i,3)=abs(100*(rs1(i,1)-rs1(i,2))/rs1(i,1));
erg1(i)=rs1(i,3);
end
disp(rs1);reg1=[];
for i=1:32
reg1(i,1)=at(i,1);
end
regt1=transpose(reg1);
rs1=[];
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% Testing with new input patterns for validation
pnew=[.81 .963 .123 .42;
.81 .954 .094 .3;
.81 .964 .0937 .24;
1.86 .959 .093 .42;
1.86 .953 .13 .36;1.86 .954 .128 .3;
2.54 .944 .1273 .42;
2.54 .957 .1 .36;
2.54 .946 .127 .3;
4.1 .923 .1373 .42;
4.1 .923 .1115 .36;
4.1 .946 .0892 .3;4.1 .934 .0772 .24;
5.53 .927 .0726 .42;
5.53 .94 .0886 .36;
5.53 .907 .1079 .3;
5.53 .915 .1281 .24;
7.15 .956 .0833 .42;
7.15 .913 .1037 .36;
7.15 .927 .1229 .3;
7.15 .95 .0681 .24;
10.25 .9 .122 .42;
10.25 .921 .0793 .36;
10.25 .922 .089 .36;
10.25 .902 .101 .36;
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10.25 .906 .1018 .3;
10.25 .925 .079 .24;
10.25 .944 .1063 .24;
14.25 .947 .122 .42;
14.25 .952 .091 .36;
14.25 .911 .1072 .3;
14.25 .944 .0875 .24];
pnewt=transpose(pnew);
pnewn = tramnmx(pnewt,minp,maxp);
anewn = sim(net,pnewn);
anew = postmnmx(anewn,mint,maxt);
anewt = transpose(anew);
fprintf('dsc(exp)test dsc(predi)test ERROR \n');fprintf(' in percentage \n');
fprintf('--------------------------------------\n');
for i=1:32
res1(i,2)=anewt(i,1);
res1(i,3)=abs(100*(res1(i,1)-res1(i,2))/res1(i,1));
ergt1(i)=res1(i,3);
end
disp(res1);
regt1=[];
for i=1:32
regt1(i,1)=at(i,1);
end
regte1=transpose(regt1);
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Results
Graphs for 500
epochs: Training Graphs(Performance graphs)MSE Level e-2:
4 HiddenLayers
3 Hidden Layers
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1 Hidden Layer
MSE Level e-1: MSE Level e-3:
10 Hidden Layers
MSE Level e-2
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Testing(Performance graphs)
MSE Level e-1
MSE Level e-3
MSE Level e-2
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Error graphsa) Training
MSE Level e-1
MSE Level e-3
MSE Level e
MSE Level e-2
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b) Testing graphs:
MSE Level e-1
MSE Level e
MSE Level e-3
MSE Level e-2
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Comparison ofEXPERIMENTAL
Data with PREDICTABLEData
MSE Level e-1
MSE Level e
MSE Level e-3
MSE Level e-2
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MSE Level e-1
S e e e
MSE Level e-3
Regression graphs:
a) Training
MSE Level e-2
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MSE Level e-1
MSE Level e-3
b) Testing graphs:
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References
Ph.D thesis entitled characteristicsof flow
over gravel beds and scour within
contractions and at piers byDr.R.V.Raikar(IIT Kharagpur,2006) MATLAB programming for
Engineers-Stephan J Chapman 2nd Edition Singapore 2002.
Neural Networks- Simon Haykin 2nd
Edition New Jersey 1999
MATLAB manual.
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THANK
YOU