Project Seminar Phase 2

8/14/2019 Project Seminar Phase 2

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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

Documents

Project Seminar Phase 2