SlideShare a Scribd company logo
Choice of weighting function and
expansion function
MT15CMN008
28 October 2016 1Mohit Chimankar-MT15CMN08
INTRODUCTION
• In last decade various numerical techniques
have been developed to solve the
electromgnetic field problem which has given
the user advantage of solving the complex
problem more easily.
• A particular technique which has been applied
with great success is method of moments.
• It converts the general equation in the matrix
form which can be easily solved on computer.
Example:
• Given an operator equation
The MOM starts with expanding X in terms of set of known functions {Xi} with
unknown coefficients ‘αi’
Using exp function we are approx X with linear combination of
Substituting (2) in (1)
We want Rn to be minimal.
Hence,it is weighted to zero w.r.t. certain weighing
function {Wj} i.e.
By further simplifications we get
Now a set of weighting function{Wj} is desired
Since the objective is to solve for X which is approximated by a linear combination of
the expansion functions { x i } it is clear that for sufficiently large N , the
expansion functions must form a basis for D(A) (Only then theexpansion functions will
be able to represent any element X, in D(A)). Then, by definition Y, is in R(A) since it is
obtained by applying A to X,, and {Axi} must span R(A), as any Y, in R(A) can be written
as a linear combination of {Axi}. It is the duty of the weighting functions to make the
difference Y - Y, small.
Since the weighing function weights the residual to zero, we have
<Rn,Wj>=0
As error Yn-Y is orthogonal to the weighing functions we can say that weighting
function should be able to reproduce Yn and to some extent Y.
These requirements poses conditions on the weighing function, which are
a) The weighing function must be in the range of operator,more generally in the
domain of adjoint operator
b) As the weighing functions are orthogonal to error approximation, Wj should span
Yn.
c) As N->infinity, Yn->Y, therefore the weighing functions should be able to represent
the excitation Y in the limit.
About Adjoint operator:
The adjoint operator A * is defined by
(AJ, W) = (J,A*W) for all J in D(A) and W in D(A*). It is known from operator theory that
the domain of A* denoted by D(A*) covers R(A).
In particular from the orthogonal decomposition theorem
D(A*) = N(A *) +closure of R(A)
here N(A*) denotes the null space of A*. (i.e.. ifA* W = 0 then
W belongs to N(A*)).
A Numerical Example:
Let us try to solve with MOM with two different weighting functions. Choose the
expansion function as:
And the solution of αi gives the classical least squares, Fourier Series Solution
Above solution is a least square solution because Wj is
proportional to Axi
We obtain the approximate solution by assuming
Now if we were to apply Gallerkin method or weighting function would be
With this method some approximate solution may be obtained .
Then how close this solution is to the exact solution can be observed in the
graph..
1)We have observed that the rate of convergence of the solution depends on the
choice of weighting functions.
2) As mentioned in the previous section, the weighting functions should be in the
range of the operator or, more generally, in the domain of the adjoint operator.
So we will check if the weighting function chosen in the gallerkin method
satisfies the condition
It is easy to show that the adjoint operator for the problem is
and the adjoint boundary condition is p(z = 1) = 0
Since for
odd j , sin (jπz)/2 is nonzero at z = 1
We can conclude that the
weighting function used here is not in the domain of the adjoint operator and hence
Galerkin’s method should not be applied.
Expansion Function
• Now we will see what mathematical
requirement should the expansion function
satisfy
• For this again we would use MOM
• First lets see which all conditions should
expansion functions satisfy
On the choice of Expansion Function
• The expansion functions should be in the domain of the operator in some
sense, i.e., they should satisfy the differentiability criterion and they must
satisfy the boundary conditions for an integro-differential operator. It is
also required that the total solution should also satisy the boundary
condition
• The expansion functions must be such that Axi form a complete set for the
range of the operator
It really does not matter whether the expansion functions are complete in
the domain of the operator.
What is important is that xi must be chosen in such a way that Axi is
complete
This will be demonstrated by an example.
A numerical example:
For the given boundary condition the obvious choice would be
Therefore, our approximation will be
Note that this expansion function satisfy both differentiability criteria and the
boundary condition
(1)
The above choice of expansion function leads to the solution
Looking at the solution it is quite clear that this solution doesn’t satisfy equation (1)
What could be the problem?
Perhaps the set {sin (iz)) does not form a complete set, even though they are
orthogonal in the interval [0, 2π]. Therefore, in addition to the sin terms,
we add the constant and the cos terms. This results in
where ao, ai, and bi are constants to be solved for. Now the total solution obtained has to
satisfy the boundary conditions of the problem
Now if we solve the problem again by Galerkin’s method or by the method of least
squares, we still obtain the solution as
But we already know that this equation doesn’t satisfy the equation (1)
The problem is that even though xi{ 1 , sin (iz), cos (iz)} form a complete set, Axi do not.
This is because Axi are merely {cos (iz), sin (iz)} .
The constant term is missing from Axi. Hence the representation in is not proper.
To have the constant term in Axi, the representation of ‘In’ must be of
the form
The final solution obtained using above equation gives us exact solution
So from this example we can say that its just not enough to choose the expansion functions
to be in a set of complete functions in domain of the operator but certain completeness
have to be satisfied for Axi.
CONCLUSION
• It is shown from a mathematical standpoint that there are certain rules
that should be followed in the choice of the weighting functions, and for a
given problem it is the operator that dictates which method (e.g.>
Galerkin‘s method or least squares method) to apply and it is not the
computational considerations.
• It is concluded that the weighting functions must be in the range of the
operator or, more generally, in the domain of the adjoint operator.
• Some of the mathematical restrictions on the expansion functions are
discussed. An example is given to illustrate that it is just not enough for
the expansion functions xi to be in the domain of the operator A. In
addition, it is required that Axj form a complete set.
REFERENCES
[1] “A Note on the Choice Weighting Functions in theMethod
of Moments” by T.K. Sarkar.
[2] “On the Choice of Expansion and Weighting Functions in the
Numerical Solution of Operator Equations” by T.K. Sarkar.
[3] “Electromagnetic Modelling and Measurements for Analysis
and Synthesis Problems - Google Books_files”
THANK YOU

More Related Content

What's hot

Operators n dirac in qm
Operators n dirac in qmOperators n dirac in qm
Operators n dirac in qmAnda Tywabi
 
Chapter 8 principle of virtual work
Chapter 8 principle of virtual workChapter 8 principle of virtual work
Chapter 8 principle of virtual workramana4uiitm
 
The Laplace Transform of Modeling of a Spring-Mass-Damper System
The Laplace Transform of Modeling of a Spring-Mass-Damper System The Laplace Transform of Modeling of a Spring-Mass-Damper System
The Laplace Transform of Modeling of a Spring-Mass-Damper System Mahmoud Farg
 
Laplace transformations
Laplace transformationsLaplace transformations
Laplace transformations
Pradipta Sarkar
 
Laplace Transformation & Its Application
Laplace Transformation & Its ApplicationLaplace Transformation & Its Application
Laplace Transformation & Its Application
Chandra Kundu
 
Numerical disperison analysis of sympletic and adi scheme
Numerical disperison analysis of sympletic and adi schemeNumerical disperison analysis of sympletic and adi scheme
Numerical disperison analysis of sympletic and adi schemexingangahu
 
Laplace transforms
Laplace transformsLaplace transforms
Laplace transforms
wilmersaguamamani
 
Ch6 root locus method
Ch6 root locus methodCh6 root locus method
Ch6 root locus method
Elaf A.Saeed
 
Charge Quantization and Magnetic Monopoles
Charge Quantization and Magnetic MonopolesCharge Quantization and Magnetic Monopoles
Charge Quantization and Magnetic Monopoles
Arpan Saha
 
Integral Transform
Integral  TransformIntegral  Transform
Integral Transform
SheharBano31
 
On the Configuration-LP of the Restricted Assignment Problem
On the Configuration-LP of the Restricted Assignment ProblemOn the Configuration-LP of the Restricted Assignment Problem
On the Configuration-LP of the Restricted Assignment Problem
Arash Pourdamghani
 
Laplace transformation
Laplace transformationLaplace transformation
Laplace transformation
Santhanam Krishnan
 
Av 738- Adaptive Filtering - Wiener Filters[wk 3]
Av 738- Adaptive Filtering - Wiener Filters[wk 3]Av 738- Adaptive Filtering - Wiener Filters[wk 3]
Av 738- Adaptive Filtering - Wiener Filters[wk 3]
Dr. Bilal Siddiqui, C.Eng., MIMechE, FRAeS
 
Introduction to perturbation theory, part-1
Introduction to perturbation theory, part-1Introduction to perturbation theory, part-1
Introduction to perturbation theory, part-1
Kiran Padhy
 
Magnetic monopoles and group theory.
Magnetic monopoles and group theory.Magnetic monopoles and group theory.
Magnetic monopoles and group theory.Renata Queiroz
 
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correctionTime Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
James Salveo Olarve
 
Production Engineering - Laplace Transformation
Production Engineering - Laplace TransformationProduction Engineering - Laplace Transformation
Production Engineering - Laplace Transformation
EkeedaPvtLtd
 
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
AI Robotics KR
 
Av 738- Adaptive Filtering - Background Material
Av 738- Adaptive Filtering - Background MaterialAv 738- Adaptive Filtering - Background Material
Av 738- Adaptive Filtering - Background Material
Dr. Bilal Siddiqui, C.Eng., MIMechE, FRAeS
 

What's hot (20)

Operators n dirac in qm
Operators n dirac in qmOperators n dirac in qm
Operators n dirac in qm
 
Chapter 8 principle of virtual work
Chapter 8 principle of virtual workChapter 8 principle of virtual work
Chapter 8 principle of virtual work
 
The Laplace Transform of Modeling of a Spring-Mass-Damper System
The Laplace Transform of Modeling of a Spring-Mass-Damper System The Laplace Transform of Modeling of a Spring-Mass-Damper System
The Laplace Transform of Modeling of a Spring-Mass-Damper System
 
Laplace transformations
Laplace transformationsLaplace transformations
Laplace transformations
 
Numeros complejos y_azar
Numeros complejos y_azarNumeros complejos y_azar
Numeros complejos y_azar
 
Laplace Transformation & Its Application
Laplace Transformation & Its ApplicationLaplace Transformation & Its Application
Laplace Transformation & Its Application
 
Numerical disperison analysis of sympletic and adi scheme
Numerical disperison analysis of sympletic and adi schemeNumerical disperison analysis of sympletic and adi scheme
Numerical disperison analysis of sympletic and adi scheme
 
Laplace transforms
Laplace transformsLaplace transforms
Laplace transforms
 
Ch6 root locus method
Ch6 root locus methodCh6 root locus method
Ch6 root locus method
 
Charge Quantization and Magnetic Monopoles
Charge Quantization and Magnetic MonopolesCharge Quantization and Magnetic Monopoles
Charge Quantization and Magnetic Monopoles
 
Integral Transform
Integral  TransformIntegral  Transform
Integral Transform
 
On the Configuration-LP of the Restricted Assignment Problem
On the Configuration-LP of the Restricted Assignment ProblemOn the Configuration-LP of the Restricted Assignment Problem
On the Configuration-LP of the Restricted Assignment Problem
 
Laplace transformation
Laplace transformationLaplace transformation
Laplace transformation
 
Av 738- Adaptive Filtering - Wiener Filters[wk 3]
Av 738- Adaptive Filtering - Wiener Filters[wk 3]Av 738- Adaptive Filtering - Wiener Filters[wk 3]
Av 738- Adaptive Filtering - Wiener Filters[wk 3]
 
Introduction to perturbation theory, part-1
Introduction to perturbation theory, part-1Introduction to perturbation theory, part-1
Introduction to perturbation theory, part-1
 
Magnetic monopoles and group theory.
Magnetic monopoles and group theory.Magnetic monopoles and group theory.
Magnetic monopoles and group theory.
 
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correctionTime Independent Perturbation Theory, 1st order correction, 2nd order correction
Time Independent Perturbation Theory, 1st order correction, 2nd order correction
 
Production Engineering - Laplace Transformation
Production Engineering - Laplace TransformationProduction Engineering - Laplace Transformation
Production Engineering - Laplace Transformation
 
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
Sensor Fusion Study - Ch3. Least Square Estimation [강소라, Stella, Hayden]
 
Av 738- Adaptive Filtering - Background Material
Av 738- Adaptive Filtering - Background MaterialAv 738- Adaptive Filtering - Background Material
Av 738- Adaptive Filtering - Background Material
 

Viewers also liked

ECMAScript 2015 Tips & Traps
ECMAScript 2015 Tips & TrapsECMAScript 2015 Tips & Traps
ECMAScript 2015 Tips & Traps
Adrian-Tudor Panescu
 
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...dominion
 
Government Facilities - Turnkey Energy Solutions
Government Facilities - Turnkey Energy SolutionsGovernment Facilities - Turnkey Energy Solutions
Government Facilities - Turnkey Energy Solutions
McKenney's Inc
 
INSTRUCTIONS FAUCET ROTATOR
INSTRUCTIONS FAUCET ROTATORINSTRUCTIONS FAUCET ROTATOR
INSTRUCTIONS FAUCET ROTATOR
Jhoan Mancilla
 
Continuous Liquid Level Controller
Continuous Liquid Level ControllerContinuous Liquid Level Controller
Continuous Liquid Level Controller
Rahul Kalra
 
Как научить ученого?
Как научить ученого?Как научить ученого?
Как научить ученого?
Georgy Ayzel
 
Tutorial - Screen Flow For Web ScrapPad
Tutorial - Screen Flow For Web ScrapPadTutorial - Screen Flow For Web ScrapPad
Tutorial - Screen Flow For Web ScrapPad
Max Lam
 
Российское общество гидрологии
Российское общество гидрологииРоссийское общество гидрологии
Российское общество гидрологии
Georgy Ayzel
 
Sukob sa Staljinom. Hrvatsko proljeće
Sukob sa Staljinom. Hrvatsko proljećeSukob sa Staljinom. Hrvatsko proljeće
Sukob sa Staljinom. Hrvatsko proljeće
Vale Shau
 
New Radio Platforms and Applications Trends March 2011
New Radio Platforms and Applications Trends March 2011New Radio Platforms and Applications Trends March 2011
New Radio Platforms and Applications Trends March 2011
Francois Lefebvre
 
Как научить ученого
Как научить ученогоКак научить ученого
Как научить ученого
Georgy Ayzel
 
REGLAMENTO ESTUDIANTIL.
REGLAMENTO ESTUDIANTIL.REGLAMENTO ESTUDIANTIL.
REGLAMENTO ESTUDIANTIL.
Míshell K'lderón
 
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
Georgy Ayzel
 
Model monitoring & alerting
Model monitoring & alertingModel monitoring & alerting
Model monitoring & alerting
robert_zaremba
 
Doba dana
Doba danaDoba dana
Doba dana
Ivana Crnjac
 
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral AffairsFernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
Sustainable Prosperity
 
Water indicator Circuit to measure the level of any liquid
Water indicator Circuit to measure the level of any liquidWater indicator Circuit to measure the level of any liquid
Water indicator Circuit to measure the level of any liquid
Barani Tharan
 

Viewers also liked (18)

ECMAScript 2015 Tips & Traps
ECMAScript 2015 Tips & TrapsECMAScript 2015 Tips & Traps
ECMAScript 2015 Tips & Traps
 
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...
Lotusphere 2007 BP312: Trap and Manage Your Errors Easily, Efficiently and Re...
 
Government Facilities - Turnkey Energy Solutions
Government Facilities - Turnkey Energy SolutionsGovernment Facilities - Turnkey Energy Solutions
Government Facilities - Turnkey Energy Solutions
 
INSTRUCTIONS FAUCET ROTATOR
INSTRUCTIONS FAUCET ROTATORINSTRUCTIONS FAUCET ROTATOR
INSTRUCTIONS FAUCET ROTATOR
 
Continuous Liquid Level Controller
Continuous Liquid Level ControllerContinuous Liquid Level Controller
Continuous Liquid Level Controller
 
Как научить ученого?
Как научить ученого?Как научить ученого?
Как научить ученого?
 
Tutorial - Screen Flow For Web ScrapPad
Tutorial - Screen Flow For Web ScrapPadTutorial - Screen Flow For Web ScrapPad
Tutorial - Screen Flow For Web ScrapPad
 
dean list
dean listdean list
dean list
 
Российское общество гидрологии
Российское общество гидрологииРоссийское общество гидрологии
Российское общество гидрологии
 
Sukob sa Staljinom. Hrvatsko proljeće
Sukob sa Staljinom. Hrvatsko proljećeSukob sa Staljinom. Hrvatsko proljeće
Sukob sa Staljinom. Hrvatsko proljeće
 
New Radio Platforms and Applications Trends March 2011
New Radio Platforms and Applications Trends March 2011New Radio Platforms and Applications Trends March 2011
New Radio Platforms and Applications Trends March 2011
 
Как научить ученого
Как научить ученогоКак научить ученого
Как научить ученого
 
REGLAMENTO ESTUDIANTIL.
REGLAMENTO ESTUDIANTIL.REGLAMENTO ESTUDIANTIL.
REGLAMENTO ESTUDIANTIL.
 
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
Технология оперативного прогноза волнения высокого разрешения (Мысленков Стан...
 
Model monitoring & alerting
Model monitoring & alertingModel monitoring & alerting
Model monitoring & alerting
 
Doba dana
Doba danaDoba dana
Doba dana
 
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral AffairsFernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
Fernando Gonzalez Saiffe, Counselor, Political and Multilateral Affairs
 
Water indicator Circuit to measure the level of any liquid
Water indicator Circuit to measure the level of any liquidWater indicator Circuit to measure the level of any liquid
Water indicator Circuit to measure the level of any liquid
 

Similar to Choice of weighting function and expansion function in cem

Quantum algorithm for solving linear systems of equations
 Quantum algorithm for solving linear systems of equations Quantum algorithm for solving linear systems of equations
Quantum algorithm for solving linear systems of equations
XequeMateShannon
 
3. Weighted residual methods (1).pptx
3. Weighted residual methods (1).pptx3. Weighted residual methods (1).pptx
3. Weighted residual methods (1).pptx
Deepu Sivakumar
 
Statistical computing with r estatistica - maria l. rizzo
Statistical computing with r   estatistica - maria l. rizzoStatistical computing with r   estatistica - maria l. rizzo
Statistical computing with r estatistica - maria l. rizzo
André Oliveira Souza
 
LP linear programming (summary) (5s)
LP linear programming (summary) (5s)LP linear programming (summary) (5s)
LP linear programming (summary) (5s)
Dionísio Carmo-Neto
 
Inverse trig functions
Inverse trig functionsInverse trig functions
Inverse trig functionsJessica Garcia
 
Metodos jacobi y gauss seidel
Metodos jacobi y gauss seidelMetodos jacobi y gauss seidel
Metodos jacobi y gauss seidelCesar Mendoza
 
Metodos jacobi y gauss seidel
Metodos jacobi y gauss seidelMetodos jacobi y gauss seidel
Metodos jacobi y gauss seidelCesar Mendoza
 
Lightening & Darkening of Grayscale Image
Lightening & Darkening of Grayscale ImageLightening & Darkening of Grayscale Image
Lightening & Darkening of Grayscale Image
Saad Al-Momen
 
PRML Chapter 4
PRML Chapter 4PRML Chapter 4
PRML Chapter 4
Sunwoo Kim
 
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
MrGChandrasekarmecha
 
Fmincon
FminconFmincon
Domain and range (linear, quadratic, rational functions)
Domain and range (linear, quadratic, rational functions)Domain and range (linear, quadratic, rational functions)
Domain and range (linear, quadratic, rational functions)
Rose Mary Tania Arini
 
B.Tech-II_Unit-III
B.Tech-II_Unit-IIIB.Tech-II_Unit-III
B.Tech-II_Unit-IIIKundan Kumar
 
Ijetr021210
Ijetr021210Ijetr021210
Ijetr021210
Ijetr021210Ijetr021210
Ijetr021210
ER Publication.org
 
Chapter 3
Chapter 3Chapter 3
Chapter 3
Eka Puspita Sari
 
6 logistic regression classification algo
6 logistic regression   classification algo6 logistic regression   classification algo
6 logistic regression classification algo
TanmayVijay1
 
A Regularized Simplex Method
A Regularized Simplex MethodA Regularized Simplex Method
A Regularized Simplex Method
Gina Brown
 
Introduction to finite element method
Introduction to finite element methodIntroduction to finite element method
Introduction to finite element method
shahzaib601980
 
Unit 1 - Optimization methods.pptx
Unit 1 - Optimization methods.pptxUnit 1 - Optimization methods.pptx
Unit 1 - Optimization methods.pptx
ssuser4debce1
 

Similar to Choice of weighting function and expansion function in cem (20)

Quantum algorithm for solving linear systems of equations
 Quantum algorithm for solving linear systems of equations Quantum algorithm for solving linear systems of equations
Quantum algorithm for solving linear systems of equations
 
3. Weighted residual methods (1).pptx
3. Weighted residual methods (1).pptx3. Weighted residual methods (1).pptx
3. Weighted residual methods (1).pptx
 
Statistical computing with r estatistica - maria l. rizzo
Statistical computing with r   estatistica - maria l. rizzoStatistical computing with r   estatistica - maria l. rizzo
Statistical computing with r estatistica - maria l. rizzo
 
LP linear programming (summary) (5s)
LP linear programming (summary) (5s)LP linear programming (summary) (5s)
LP linear programming (summary) (5s)
 
Inverse trig functions
Inverse trig functionsInverse trig functions
Inverse trig functions
 
Metodos jacobi y gauss seidel
Metodos jacobi y gauss seidelMetodos jacobi y gauss seidel
Metodos jacobi y gauss seidel
 
Metodos jacobi y gauss seidel
Metodos jacobi y gauss seidelMetodos jacobi y gauss seidel
Metodos jacobi y gauss seidel
 
Lightening & Darkening of Grayscale Image
Lightening & Darkening of Grayscale ImageLightening & Darkening of Grayscale Image
Lightening & Darkening of Grayscale Image
 
PRML Chapter 4
PRML Chapter 4PRML Chapter 4
PRML Chapter 4
 
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
Finite element analysis sdfa ggq rsd vqer fas dd sg fa sd qadas casdasc asdac...
 
Fmincon
FminconFmincon
Fmincon
 
Domain and range (linear, quadratic, rational functions)
Domain and range (linear, quadratic, rational functions)Domain and range (linear, quadratic, rational functions)
Domain and range (linear, quadratic, rational functions)
 
B.Tech-II_Unit-III
B.Tech-II_Unit-IIIB.Tech-II_Unit-III
B.Tech-II_Unit-III
 
Ijetr021210
Ijetr021210Ijetr021210
Ijetr021210
 
Ijetr021210
Ijetr021210Ijetr021210
Ijetr021210
 
Chapter 3
Chapter 3Chapter 3
Chapter 3
 
6 logistic regression classification algo
6 logistic regression   classification algo6 logistic regression   classification algo
6 logistic regression classification algo
 
A Regularized Simplex Method
A Regularized Simplex MethodA Regularized Simplex Method
A Regularized Simplex Method
 
Introduction to finite element method
Introduction to finite element methodIntroduction to finite element method
Introduction to finite element method
 
Unit 1 - Optimization methods.pptx
Unit 1 - Optimization methods.pptxUnit 1 - Optimization methods.pptx
Unit 1 - Optimization methods.pptx
 

Recently uploaded

Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
Massimo Talia
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
MdTanvirMahtab2
 
CME397 Surface Engineering- Professional Elective
CME397 Surface Engineering- Professional ElectiveCME397 Surface Engineering- Professional Elective
CME397 Surface Engineering- Professional Elective
karthi keyan
 
Planning Of Procurement o different goods and services
Planning Of Procurement o different goods and servicesPlanning Of Procurement o different goods and services
Planning Of Procurement o different goods and services
JoytuBarua2
 
weather web application report.pdf
weather web application report.pdfweather web application report.pdf
weather web application report.pdf
Pratik Pawar
 
ethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.pptethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.ppt
Jayaprasanna4
 
Runway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptxRunway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptx
SupreethSP4
 
road safety engineering r s e unit 3.pdf
road safety engineering  r s e unit 3.pdfroad safety engineering  r s e unit 3.pdf
road safety engineering r s e unit 3.pdf
VENKATESHvenky89705
 
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdfWater Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation & Control
 
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdfGen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdf
gdsczhcet
 
English lab ppt no titlespecENG PPTt.pdf
English lab ppt no titlespecENG PPTt.pdfEnglish lab ppt no titlespecENG PPTt.pdf
English lab ppt no titlespecENG PPTt.pdf
BrazilAccount1
 
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
H.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdfH.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdf
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
MLILAB
 
DESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docxDESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docx
FluxPrime1
 
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean LockwoodArchitectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
seandesed
 
Fundamentals of Electric Drives and its applications.pptx
Fundamentals of Electric Drives and its applications.pptxFundamentals of Electric Drives and its applications.pptx
Fundamentals of Electric Drives and its applications.pptx
manasideore6
 
Hierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power SystemHierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power System
Kerry Sado
 
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
bakpo1
 
space technology lecture notes on satellite
space technology lecture notes on satellitespace technology lecture notes on satellite
space technology lecture notes on satellite
ongomchris
 
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
obonagu
 
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Dr.Costas Sachpazis
 

Recently uploaded (20)

Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024Nuclear Power Economics and Structuring 2024
Nuclear Power Economics and Structuring 2024
 
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)
 
CME397 Surface Engineering- Professional Elective
CME397 Surface Engineering- Professional ElectiveCME397 Surface Engineering- Professional Elective
CME397 Surface Engineering- Professional Elective
 
Planning Of Procurement o different goods and services
Planning Of Procurement o different goods and servicesPlanning Of Procurement o different goods and services
Planning Of Procurement o different goods and services
 
weather web application report.pdf
weather web application report.pdfweather web application report.pdf
weather web application report.pdf
 
ethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.pptethical hacking-mobile hacking methods.ppt
ethical hacking-mobile hacking methods.ppt
 
Runway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptxRunway Orientation Based on the Wind Rose Diagram.pptx
Runway Orientation Based on the Wind Rose Diagram.pptx
 
road safety engineering r s e unit 3.pdf
road safety engineering  r s e unit 3.pdfroad safety engineering  r s e unit 3.pdf
road safety engineering r s e unit 3.pdf
 
Water Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdfWater Industry Process Automation and Control Monthly - May 2024.pdf
Water Industry Process Automation and Control Monthly - May 2024.pdf
 
Gen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdfGen AI Study Jams _ For the GDSC Leads in India.pdf
Gen AI Study Jams _ For the GDSC Leads in India.pdf
 
English lab ppt no titlespecENG PPTt.pdf
English lab ppt no titlespecENG PPTt.pdfEnglish lab ppt no titlespecENG PPTt.pdf
English lab ppt no titlespecENG PPTt.pdf
 
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
H.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdfH.Seo,  ICLR 2024, MLILAB,  KAIST AI.pdf
H.Seo, ICLR 2024, MLILAB, KAIST AI.pdf
 
DESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docxDESIGN A COTTON SEED SEPARATION MACHINE.docx
DESIGN A COTTON SEED SEPARATION MACHINE.docx
 
Architectural Portfolio Sean Lockwood
Architectural Portfolio Sean LockwoodArchitectural Portfolio Sean Lockwood
Architectural Portfolio Sean Lockwood
 
Fundamentals of Electric Drives and its applications.pptx
Fundamentals of Electric Drives and its applications.pptxFundamentals of Electric Drives and its applications.pptx
Fundamentals of Electric Drives and its applications.pptx
 
Hierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power SystemHierarchical Digital Twin of a Naval Power System
Hierarchical Digital Twin of a Naval Power System
 
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
一比一原版(SFU毕业证)西蒙菲莎大学毕业证成绩单如何办理
 
space technology lecture notes on satellite
space technology lecture notes on satellitespace technology lecture notes on satellite
space technology lecture notes on satellite
 
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
在线办理(ANU毕业证书)澳洲国立大学毕业证录取通知书一模一样
 
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...
 

Choice of weighting function and expansion function in cem

  • 1. Choice of weighting function and expansion function MT15CMN008 28 October 2016 1Mohit Chimankar-MT15CMN08
  • 2. INTRODUCTION • In last decade various numerical techniques have been developed to solve the electromgnetic field problem which has given the user advantage of solving the complex problem more easily. • A particular technique which has been applied with great success is method of moments. • It converts the general equation in the matrix form which can be easily solved on computer.
  • 3. Example: • Given an operator equation The MOM starts with expanding X in terms of set of known functions {Xi} with unknown coefficients ‘αi’ Using exp function we are approx X with linear combination of Substituting (2) in (1)
  • 4. We want Rn to be minimal. Hence,it is weighted to zero w.r.t. certain weighing function {Wj} i.e. By further simplifications we get Now a set of weighting function{Wj} is desired
  • 5. Since the objective is to solve for X which is approximated by a linear combination of the expansion functions { x i } it is clear that for sufficiently large N , the expansion functions must form a basis for D(A) (Only then theexpansion functions will be able to represent any element X, in D(A)). Then, by definition Y, is in R(A) since it is obtained by applying A to X,, and {Axi} must span R(A), as any Y, in R(A) can be written as a linear combination of {Axi}. It is the duty of the weighting functions to make the difference Y - Y, small.
  • 6. Since the weighing function weights the residual to zero, we have <Rn,Wj>=0 As error Yn-Y is orthogonal to the weighing functions we can say that weighting function should be able to reproduce Yn and to some extent Y. These requirements poses conditions on the weighing function, which are a) The weighing function must be in the range of operator,more generally in the domain of adjoint operator b) As the weighing functions are orthogonal to error approximation, Wj should span Yn. c) As N->infinity, Yn->Y, therefore the weighing functions should be able to represent the excitation Y in the limit. About Adjoint operator: The adjoint operator A * is defined by (AJ, W) = (J,A*W) for all J in D(A) and W in D(A*). It is known from operator theory that the domain of A* denoted by D(A*) covers R(A). In particular from the orthogonal decomposition theorem D(A*) = N(A *) +closure of R(A) here N(A*) denotes the null space of A*. (i.e.. ifA* W = 0 then W belongs to N(A*)).
  • 7. A Numerical Example: Let us try to solve with MOM with two different weighting functions. Choose the expansion function as:
  • 8. And the solution of αi gives the classical least squares, Fourier Series Solution Above solution is a least square solution because Wj is proportional to Axi We obtain the approximate solution by assuming
  • 9. Now if we were to apply Gallerkin method or weighting function would be With this method some approximate solution may be obtained . Then how close this solution is to the exact solution can be observed in the graph..
  • 10. 1)We have observed that the rate of convergence of the solution depends on the choice of weighting functions. 2) As mentioned in the previous section, the weighting functions should be in the range of the operator or, more generally, in the domain of the adjoint operator. So we will check if the weighting function chosen in the gallerkin method satisfies the condition It is easy to show that the adjoint operator for the problem is and the adjoint boundary condition is p(z = 1) = 0 Since for odd j , sin (jπz)/2 is nonzero at z = 1 We can conclude that the weighting function used here is not in the domain of the adjoint operator and hence Galerkin’s method should not be applied.
  • 11.
  • 12. Expansion Function • Now we will see what mathematical requirement should the expansion function satisfy • For this again we would use MOM • First lets see which all conditions should expansion functions satisfy
  • 13. On the choice of Expansion Function • The expansion functions should be in the domain of the operator in some sense, i.e., they should satisfy the differentiability criterion and they must satisfy the boundary conditions for an integro-differential operator. It is also required that the total solution should also satisy the boundary condition • The expansion functions must be such that Axi form a complete set for the range of the operator It really does not matter whether the expansion functions are complete in the domain of the operator. What is important is that xi must be chosen in such a way that Axi is complete This will be demonstrated by an example.
  • 14. A numerical example: For the given boundary condition the obvious choice would be Therefore, our approximation will be Note that this expansion function satisfy both differentiability criteria and the boundary condition (1)
  • 15. The above choice of expansion function leads to the solution Looking at the solution it is quite clear that this solution doesn’t satisfy equation (1) What could be the problem? Perhaps the set {sin (iz)) does not form a complete set, even though they are orthogonal in the interval [0, 2π]. Therefore, in addition to the sin terms, we add the constant and the cos terms. This results in where ao, ai, and bi are constants to be solved for. Now the total solution obtained has to satisfy the boundary conditions of the problem Now if we solve the problem again by Galerkin’s method or by the method of least squares, we still obtain the solution as
  • 16. But we already know that this equation doesn’t satisfy the equation (1) The problem is that even though xi{ 1 , sin (iz), cos (iz)} form a complete set, Axi do not. This is because Axi are merely {cos (iz), sin (iz)} . The constant term is missing from Axi. Hence the representation in is not proper. To have the constant term in Axi, the representation of ‘In’ must be of the form The final solution obtained using above equation gives us exact solution So from this example we can say that its just not enough to choose the expansion functions to be in a set of complete functions in domain of the operator but certain completeness have to be satisfied for Axi.
  • 17. CONCLUSION • It is shown from a mathematical standpoint that there are certain rules that should be followed in the choice of the weighting functions, and for a given problem it is the operator that dictates which method (e.g.> Galerkin‘s method or least squares method) to apply and it is not the computational considerations. • It is concluded that the weighting functions must be in the range of the operator or, more generally, in the domain of the adjoint operator. • Some of the mathematical restrictions on the expansion functions are discussed. An example is given to illustrate that it is just not enough for the expansion functions xi to be in the domain of the operator A. In addition, it is required that Axj form a complete set.
  • 18. REFERENCES [1] “A Note on the Choice Weighting Functions in theMethod of Moments” by T.K. Sarkar. [2] “On the Choice of Expansion and Weighting Functions in the Numerical Solution of Operator Equations” by T.K. Sarkar. [3] “Electromagnetic Modelling and Measurements for Analysis and Synthesis Problems - Google Books_files”