SlideShare a Scribd company logo
1 of 33
Download to read offline
134
UNIT– 5
DynamicAnalysis
Axi-symmetricAnalysis
Cylindrical coordinates:
x  rcos; y  r sin; z  z
r, , z
• quantities depend on rand zonly
• 3-D problem 2-D problem
135
Axi-symmetricAnalysis
136
Axi-symmetric Analysis – Single-VariableProblem
00
22
11 


 
 z
u(r, z)   a u  f (r, z)  0
z

r
u(r, z)  
 a

1   ra
r r

Weakform:
e
wqn ds
e


  
 z

z



 r
u 
wa
 r

wa u  a wu  wf (r,z)rdrdz
0  00
22
11
where nz
137
z
r
u(r, z)
u(r, z)
nr  a22
qn  a11
FiniteElement Model – Single-VariableProblem
u  u jj
j
Ritzmethod:
e
n
e e
K u  f Qe
 ij j i i
j1
where j (r, z) j (x, y)
w i
Weakform
22
138
e
j j

a
i i
K e
  a  a 

rdrdz
00 i j 
r r z z
  
ij   11
where
i i
e
f e
  frdrdz

i i n
e
Qe
  q ds

Single-Variable Problem – HeatTransfer
Weakform
Heat Transfer:

1  rk
T(r, z)  
 k
T(r, z)   f (r, z)  0
 
z  z
r r  r
   
e


0 
wk
T  
wk
T   wf (r, z) rdrdz
   
 
 r  r  z  z 
wqn ds
e
nz
139
T(r, z) T(r, z)
qn  k nr  k
r z
where
3-Node Axi-symmetricElement
T (r, z)  T11 T22 T33
1
2
3

140

1 2 3
e
1 r z
2A
r2z3  r3z2
 
 
  z  z
 
 
r r
 3 2 
  3 1 1 3
2 3 1
e
1 r z
2A
r z  r z
 
 z 
   z
 
 
r  r
 1 3 
  1 2 2 1
3 1 2
e
1 r z
2A
r z  r z
 
 
  z z
 
 
r r
 2 1 
4-Node Axi-symmetricElement
T (r, z)  T11 T22  T33  T44
1 2
3
4
a
b


r
141
z
2
3
a b
  1
 1
   
 1

1     
 a  b  a  b 
 
 
  1
 
4  a  b
 
Single-Variable Problem –Example
Step1: Discretization
Step2: Element equation
e
142
j
i
ij 

 
K e
  i
j 
rdrdz
r r z z 
  

i i
e
f e
  frdrdz
 i i n
e
Qe
  q ds

Reviewof CSTElement
• Constant Strain Triangle (CST)- easiest and simplest
finite element
– Displacement field in terms ofgeneralized coordinates
– Resulting strain field is
– Strains do not vary within the element. Hence, the name
constant strain triangle(CST)
• Other elements are not solucky.
• Canalso be called linear triangle becausedisplacement field is
linear in xand y - sidesremainstraight.
143
Constant Strain Triangle
• Thestrain field from the shapefunctions lookslike:
– Where, xi and yi are nodal coordinates (i=1, 2,3)
– xij =xi - xj and yij=yi - yj
– 2Ais twice the area of the triangle, 2A=x21y31-x31y21
• Node numbering is arbitrary except that the sequence123
must go clockwise around the element if Ais tobe positive.
144
145
Constant Strain Triangle
• Stiffness matrix for element k =BTEBtA
• TheCSTgivesgood results in regions of theFE
model where there is little straingradient
– Otherwise it does notwork well.
Linear Strain Triangle
• Changesthe shape functions and results in
quadratic displacement distributions and
linear strain distributions within theelement.
146
Linear Strain Triangle
• Will this element work better for the problem?
147
ExampleProblem
• Consider the problem we were lookingat:
5in.
1in.
0.1in.
I  0.113
/12  0.008333 in4
 
M  c

1 0.5
I 0.008333
 60 ksi
E
 
  0.00207
2
 
ML

25
2EI 2  29000  0.008333
 0.0517in.
148
1k
1k
Bilinear Quadratic
• TheQ4element is aquadrilateral element
that hasfour nodes. In terms ofgeneralized
coordinates, its displacement field is:
149
Bilinear Quadratic
• Shapefunctions and strain-displacement
matrix
150
ExampleProblem
• Consider the problem we were lookingat:
5in.
1in.
0.1in.
E
151
I 0.008333
 0.0345in.
3EI 3290000.008333
0.2125

 60ksi
10.5
I  0.1 13
/ 12 0.008333in4
 
PL3
 
 0.00207
 
M  c

0.1k
0.1k
Quadratic Quadrilateral Element
• The8 noded quadratic quadrilateral element
usesquadratic functions for thedisplacements
152
Quadratic Quadrilateral Element
• Shapefunction examples:
• Strain distribution within theelement
153
154
Quadratic Quadrilateral Element
• Should we try to use this element to solve our
problem?
• Or try fixing the Q4element for our purposes.
– Hmm…toughchoice.
155
Isoparametric Elements and Solution
• Biggestbreakthrough in the implementation of the
finite element method is the development of an
isoparametric element with capabilities to model
structure (problem) geometries of any shapeandsize.
• Thewhole idea works onmapping.
– Theelement in the real structure is mapped to an
‘imaginary’ element in an ideal coordinatesystem
– Thesolution to the stress analysis problem is easyand
known for the ‘imaginary’element
– Thesesolutions are mapped back to the element inthe
real structure.
– All the loads and boundary conditions are also mapped
from the real to the ‘imaginary’ element in this approach
Isoparametric Element
3
1
(x1,y1)
2
(x2,y2)
(x3,y3)
4
(x4,y4)
X,u
Y
,v


2
(1,-1)
1
(-1,-1)
4
(-1, 1)
3 (1, 1)
156
Isoparametric element
Y
1 2 3 4
0 0 0 N1
X N
0
  

• The mappingfunctions areq
x1u
 ite simple:
4

 x 
2
x3 
N N N 0 0 0 0  x 
N2 N3 N4 y1 
y2

 
1
4
N  1 (1)(1)
2
4
N 
1
(1 )(1)
3
4
N  1 (1 )(1 )
4
4
N 
1
(1)(1 )
y3 

y4


Basically, thex and y coordinatesof any point in the
elementare interpolations of the nodal (corner)
coordinates.
Fromthe Q4 element,thebilinear shape functions
are borrowed to be used as the interpolation
functions. They readily satisfy the boundaryvalues
too. 157
Isoparametric element
v
1 2 3 4
0 0 0 N1
u N
0
    

• Nodal shape functions for d
u
i1s
placements
4

u 
2
u3
N N N 0 0 0 0 u 
N2 N3 N4 v1 
v2

 
v3
v4


1
4
N  1 (1)(1)
2
4
N 
1
(1 )(1)
3
4
N  1 (1 )(1 )
4
4
N 
1
(1)(1 ) 158
• Thedisplacement strain relationships:
x

u

u

 
u


X  X  X
y

v

v

 
v


Y  Y  Y
x


 
 
 xy 
y

u
X
v
Y

Y X
   





 u





v 
Y Y

   0



0


0
 


 
X X
0

   
Y Y X X 



 u 
  v 
 
 u 
 
 
 

 
 v 

But,it is too difficult to obtain
 and

X X 159
Isoparametric Element
 X  Y 
u
u





  
X Y u 
X Y u 


X 
  Y 
  
It is easier to obtain
X
and
Y
 
X Y
 
J  X Y Jacobian
 
 
defines coordinate transformation
Hencewe will do itanother way
u

u

X

u

Y
 X  Y 
u

u

X

u

Y
X
 
Ni
X
  i
Y N

 

i
Yi
X N
 
i X i
 
Y N
 
i Yi
 
X
 
u
 
Y 
u 
 
1 
u
   J  
 
u


169
161
GaussQuadrature
• Themapping approach requires usto be able to
evaluate the integrations within the domain (-1…1)of
the functionsshown.
• Integration canbe done analytically by usingclosed-
form formulas from atable ofintegrals (Nah..)
– Or numerical integration can be performed
• Gaussquadrature is the more common form of
numerical integration - better suited for numerical
analysis and finite elementmethod.
• Itevaluated the integral of afunction asasum of a
becomes
n
I  Wii
i1
finite numb1er of terms
I   d
1
GaussQuadrature
• Wi is the ‘weight’ and i is the value of f(=i)
162
Numerical Integration
Calculate:
b
I   f xdx
a
• Newton – Cotesintegration
• Trapezoidalrule – 1storderNewton-Cotesintegration
• Trapezoidalrule – multipleapplication
1
b a
f (x)  f (x)  f (a) 
f (b)  f (a)
(x a)
2
f (a)  f (b)
b b
I   f (x)dx   f1(x)dx  (b  a)
a a
b
163
xn1
xn x1 x2 xn
a x0 x0 x1
I   f (x)dx   fn (x)dx   f (x)dx   f (x)dx    f
(x)dx





2 
n1
i1
i
f (x )  f (b)
f (a)  2
h 
I 
Numerical Integration
Calculate:
b
I   f xdx
a
• Newton – Cotesintegration
• Simpson1/3 rule – 2ndorderNewton-Cotesintegration
2
2
0 1
1
0 2
0 1
2 f (x )
(x2  x0 )(x2  x1)
(x  x0 )(x  x1)
f (x )  f (x )
(x0  x1)(x0  x2 ) (x  x )(x  x )
(x  x1)(x  x2 ) (x  x )(x  x )
f (x)  f (x) 
b b
a a
6
f (x )  4 f (x )  f (x )
I   f (x)dx   f2 (x)dx  (x2  x0 ) 0 1 2
164
Numerical Integration
Calculate:
b
I   f xdx
2

(b  a)
f (a) 
(b  a)
f(b)
2 2
165
I  (b  a)
f (a)  f (b)
I  c0 f (x0 )  c1 f (x1)
a
• GaussianQuadrature
Trapezoidal Rule: GaussianQuadrature:
Choose according to certaincriteria
c0,c1,x0 , x1
Numerical Integration
Calculate:
b
I   f xdx
a
• GaussianQuadrature
   

3
 1 
 1
1
1
I   f xdx  f 
3
  f 
1
• 3pt GaussianQuadrature
I   f xdx 0.55 f 0.77 0.89  f 0 0.55 f0.77
1
1
1
• 2pt GaussianQuadrature
I   f xdx  c0 f x0  c1 f x1  cn1 f xn1
b  a
~
x 1
2(x  a)
Let:
b 1

1
a
166
~ ~
1 1 1
 f (x)dx 
2
(b  a) f 2
(a  b) 
2
(b  a)xdx

More Related Content

Similar to UNIT I_5.pdf

Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Fabian Pedregosa
 
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...Kasun Ranga Wijeweera
 
Conversion of transfer function to canonical state variable models
Conversion of transfer function to canonical state variable modelsConversion of transfer function to canonical state variable models
Conversion of transfer function to canonical state variable modelsJyoti Singh
 
Lecture 9-online
Lecture 9-onlineLecture 9-online
Lecture 9-onlinelifebreath
 
Modeling Transformations
Modeling TransformationsModeling Transformations
Modeling TransformationsTarun Gehlot
 
Shape drawing algs
Shape drawing algsShape drawing algs
Shape drawing algsMusawarNice
 
Chap-2 Preliminary Concepts and Linear Finite Elements.pptx
Chap-2 Preliminary Concepts and  Linear Finite Elements.pptxChap-2 Preliminary Concepts and  Linear Finite Elements.pptx
Chap-2 Preliminary Concepts and Linear Finite Elements.pptxSamirsinh Parmar
 
Maths iii quick review by Dr Asish K Mukhopadhyay
Maths iii quick review by Dr Asish K MukhopadhyayMaths iii quick review by Dr Asish K Mukhopadhyay
Maths iii quick review by Dr Asish K MukhopadhyayDr. Asish K Mukhopadhyay
 
Journey to structure from motion
Journey to structure from motionJourney to structure from motion
Journey to structure from motionJa-Keoung Koo
 
Seismic data processing lecture 3
Seismic data processing lecture 3Seismic data processing lecture 3
Seismic data processing lecture 3Amin khalil
 
07 cie552 image_mosaicing
07 cie552 image_mosaicing07 cie552 image_mosaicing
07 cie552 image_mosaicingElsayed Hemayed
 
2D Finite Element Analysis.pptx
2D Finite Element Analysis.pptx2D Finite Element Analysis.pptx
2D Finite Element Analysis.pptxDrDineshDhande
 
An Algorithm to Find the Largest Circle inside a Polygon
An Algorithm to Find the Largest Circle inside a PolygonAn Algorithm to Find the Largest Circle inside a Polygon
An Algorithm to Find the Largest Circle inside a PolygonKasun Ranga Wijeweera
 
Non Linear Dynamics Basics and Theory
Non Linear Dynamics Basics and TheoryNon Linear Dynamics Basics and Theory
Non Linear Dynamics Basics and TheoryAnupama Kate
 
Three dimensional transformations
Three dimensional transformationsThree dimensional transformations
Three dimensional transformationsNareek
 

Similar to UNIT I_5.pdf (20)

Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3Random Matrix Theory and Machine Learning - Part 3
Random Matrix Theory and Machine Learning - Part 3
 
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...
Convex Partitioning of a Polygon into Smaller Number of Pieces with Lowest Me...
 
Week6.ppt
Week6.pptWeek6.ppt
Week6.ppt
 
ME Reference.pdf
ME Reference.pdfME Reference.pdf
ME Reference.pdf
 
Conversion of transfer function to canonical state variable models
Conversion of transfer function to canonical state variable modelsConversion of transfer function to canonical state variable models
Conversion of transfer function to canonical state variable models
 
Lecture 9-online
Lecture 9-onlineLecture 9-online
Lecture 9-online
 
2.circle
2.circle2.circle
2.circle
 
Modeling Transformations
Modeling TransformationsModeling Transformations
Modeling Transformations
 
Shape drawing algs
Shape drawing algsShape drawing algs
Shape drawing algs
 
Chap-2 Preliminary Concepts and Linear Finite Elements.pptx
Chap-2 Preliminary Concepts and  Linear Finite Elements.pptxChap-2 Preliminary Concepts and  Linear Finite Elements.pptx
Chap-2 Preliminary Concepts and Linear Finite Elements.pptx
 
Maths iii quick review by Dr Asish K Mukhopadhyay
Maths iii quick review by Dr Asish K MukhopadhyayMaths iii quick review by Dr Asish K Mukhopadhyay
Maths iii quick review by Dr Asish K Mukhopadhyay
 
Journey to structure from motion
Journey to structure from motionJourney to structure from motion
Journey to structure from motion
 
CLIM Fall 2017 Course: Statistics for Climate Research, Spatial Data: Models ...
CLIM Fall 2017 Course: Statistics for Climate Research, Spatial Data: Models ...CLIM Fall 2017 Course: Statistics for Climate Research, Spatial Data: Models ...
CLIM Fall 2017 Course: Statistics for Climate Research, Spatial Data: Models ...
 
Seismic data processing lecture 3
Seismic data processing lecture 3Seismic data processing lecture 3
Seismic data processing lecture 3
 
Virtual reality
Virtual realityVirtual reality
Virtual reality
 
07 cie552 image_mosaicing
07 cie552 image_mosaicing07 cie552 image_mosaicing
07 cie552 image_mosaicing
 
2D Finite Element Analysis.pptx
2D Finite Element Analysis.pptx2D Finite Element Analysis.pptx
2D Finite Element Analysis.pptx
 
An Algorithm to Find the Largest Circle inside a Polygon
An Algorithm to Find the Largest Circle inside a PolygonAn Algorithm to Find the Largest Circle inside a Polygon
An Algorithm to Find the Largest Circle inside a Polygon
 
Non Linear Dynamics Basics and Theory
Non Linear Dynamics Basics and TheoryNon Linear Dynamics Basics and Theory
Non Linear Dynamics Basics and Theory
 
Three dimensional transformations
Three dimensional transformationsThree dimensional transformations
Three dimensional transformations
 

Recently uploaded

Internship report on mechanical engineering
Internship report on mechanical engineeringInternship report on mechanical engineering
Internship report on mechanical engineeringmalavadedarshan25
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxPoojaBan
 
Concrete Mix Design - IS 10262-2019 - .pptx
Concrete Mix Design - IS 10262-2019 - .pptxConcrete Mix Design - IS 10262-2019 - .pptx
Concrete Mix Design - IS 10262-2019 - .pptxKartikeyaDwivedi3
 
Risk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdfRisk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdfROCENODodongVILLACER
 
Churning of Butter, Factors affecting .
Churning of Butter, Factors affecting  .Churning of Butter, Factors affecting  .
Churning of Butter, Factors affecting .Satyam Kumar
 
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHIntroduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHC Sai Kiran
 
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...VICTOR MAESTRE RAMIREZ
 
pipeline in computer architecture design
pipeline in computer architecture  designpipeline in computer architecture  design
pipeline in computer architecture designssuser87fa0c1
 
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfCCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfAsst.prof M.Gokilavani
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerAnamika Sarkar
 
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)Dr SOUNDIRARAJ N
 
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)dollysharma2066
 
Artificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxArtificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxbritheesh05
 
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile servicerehmti665
 
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxDecoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxJoão Esperancinha
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...VICTOR MAESTRE RAMIREZ
 

Recently uploaded (20)

young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Serviceyoung call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
young call girls in Rajiv Chowk🔝 9953056974 🔝 Delhi escort Service
 
Internship report on mechanical engineering
Internship report on mechanical engineeringInternship report on mechanical engineering
Internship report on mechanical engineering
 
Heart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptxHeart Disease Prediction using machine learning.pptx
Heart Disease Prediction using machine learning.pptx
 
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
🔝9953056974🔝!!-YOUNG call girls in Rajendra Nagar Escort rvice Shot 2000 nigh...
 
Concrete Mix Design - IS 10262-2019 - .pptx
Concrete Mix Design - IS 10262-2019 - .pptxConcrete Mix Design - IS 10262-2019 - .pptx
Concrete Mix Design - IS 10262-2019 - .pptx
 
Risk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdfRisk Assessment For Installation of Drainage Pipes.pdf
Risk Assessment For Installation of Drainage Pipes.pdf
 
Churning of Butter, Factors affecting .
Churning of Butter, Factors affecting  .Churning of Butter, Factors affecting  .
Churning of Butter, Factors affecting .
 
young call girls in Green Park🔝 9953056974 🔝 escort Service
young call girls in Green Park🔝 9953056974 🔝 escort Serviceyoung call girls in Green Park🔝 9953056974 🔝 escort Service
young call girls in Green Park🔝 9953056974 🔝 escort Service
 
Introduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECHIntroduction to Machine Learning Unit-3 for II MECH
Introduction to Machine Learning Unit-3 for II MECH
 
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
VICTOR MAESTRE RAMIREZ - Planetary Defender on NASA's Double Asteroid Redirec...
 
pipeline in computer architecture design
pipeline in computer architecture  designpipeline in computer architecture  design
pipeline in computer architecture design
 
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdfCCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
CCS355 Neural Networks & Deep Learning Unit 1 PDF notes with Question bank .pdf
 
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube ExchangerStudy on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
Study on Air-Water & Water-Water Heat Exchange in a Finned Tube Exchanger
 
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
UNIT III ANALOG ELECTRONICS (BASIC ELECTRONICS)
 
Design and analysis of solar grass cutter.pdf
Design and analysis of solar grass cutter.pdfDesign and analysis of solar grass cutter.pdf
Design and analysis of solar grass cutter.pdf
 
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)
Call Us ≽ 8377877756 ≼ Call Girls In Shastri Nagar (Delhi)
 
Artificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptxArtificial-Intelligence-in-Electronics (K).pptx
Artificial-Intelligence-in-Electronics (K).pptx
 
Call Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile serviceCall Girls Delhi {Jodhpur} 9711199012 high profile service
Call Girls Delhi {Jodhpur} 9711199012 high profile service
 
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptxDecoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
Decoding Kotlin - Your guide to solving the mysterious in Kotlin.pptx
 
Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...Software and Systems Engineering Standards: Verification and Validation of Sy...
Software and Systems Engineering Standards: Verification and Validation of Sy...
 

UNIT I_5.pdf

  • 2. Axi-symmetricAnalysis Cylindrical coordinates: x  rcos; y  r sin; z  z r, , z • quantities depend on rand zonly • 3-D problem 2-D problem 135
  • 4. Axi-symmetric Analysis – Single-VariableProblem 00 22 11       z u(r, z)   a u  f (r, z)  0 z  r u(r, z)    a  1   ra r r  Weakform: e wqn ds e       z  z     r u  wa  r  wa u  a wu  wf (r,z)rdrdz 0  00 22 11 where nz 137 z r u(r, z) u(r, z) nr  a22 qn  a11
  • 5. FiniteElement Model – Single-VariableProblem u  u jj j Ritzmethod: e n e e K u  f Qe  ij j i i j1 where j (r, z) j (x, y) w i Weakform 22 138 e j j  a i i K e   a  a   rdrdz 00 i j  r r z z    ij   11 where i i e f e   frdrdz  i i n e Qe   q ds 
  • 6. Single-Variable Problem – HeatTransfer Weakform Heat Transfer:  1  rk T(r, z)    k T(r, z)   f (r, z)  0   z  z r r  r     e   0  wk T   wk T   wf (r, z) rdrdz        r  r  z  z  wqn ds e nz 139 T(r, z) T(r, z) qn  k nr  k r z where
  • 7. 3-Node Axi-symmetricElement T (r, z)  T11 T22 T33 1 2 3  140  1 2 3 e 1 r z 2A r2z3  r3z2       z  z     r r  3 2    3 1 1 3 2 3 1 e 1 r z 2A r z  r z    z     z     r  r  1 3    1 2 2 1 3 1 2 e 1 r z 2A r z  r z       z z     r r  2 1 
  • 8. 4-Node Axi-symmetricElement T (r, z)  T11 T22  T33  T44 1 2 3 4 a b   r 141 z 2 3 a b   1  1      1  1       a  b  a  b        1   4  a  b  
  • 9. Single-Variable Problem –Example Step1: Discretization Step2: Element equation e 142 j i ij     K e   i j  rdrdz r r z z      i i e f e   frdrdz  i i n e Qe   q ds 
  • 10. Reviewof CSTElement • Constant Strain Triangle (CST)- easiest and simplest finite element – Displacement field in terms ofgeneralized coordinates – Resulting strain field is – Strains do not vary within the element. Hence, the name constant strain triangle(CST) • Other elements are not solucky. • Canalso be called linear triangle becausedisplacement field is linear in xand y - sidesremainstraight. 143
  • 11. Constant Strain Triangle • Thestrain field from the shapefunctions lookslike: – Where, xi and yi are nodal coordinates (i=1, 2,3) – xij =xi - xj and yij=yi - yj – 2Ais twice the area of the triangle, 2A=x21y31-x31y21 • Node numbering is arbitrary except that the sequence123 must go clockwise around the element if Ais tobe positive. 144
  • 12. 145 Constant Strain Triangle • Stiffness matrix for element k =BTEBtA • TheCSTgivesgood results in regions of theFE model where there is little straingradient – Otherwise it does notwork well.
  • 13. Linear Strain Triangle • Changesthe shape functions and results in quadratic displacement distributions and linear strain distributions within theelement. 146
  • 14. Linear Strain Triangle • Will this element work better for the problem? 147
  • 15. ExampleProblem • Consider the problem we were lookingat: 5in. 1in. 0.1in. I  0.113 /12  0.008333 in4   M  c  1 0.5 I 0.008333  60 ksi E     0.00207 2   ML  25 2EI 2  29000  0.008333  0.0517in. 148 1k 1k
  • 16. Bilinear Quadratic • TheQ4element is aquadrilateral element that hasfour nodes. In terms ofgeneralized coordinates, its displacement field is: 149
  • 17. Bilinear Quadratic • Shapefunctions and strain-displacement matrix 150
  • 18. ExampleProblem • Consider the problem we were lookingat: 5in. 1in. 0.1in. E 151 I 0.008333  0.0345in. 3EI 3290000.008333 0.2125   60ksi 10.5 I  0.1 13 / 12 0.008333in4   PL3    0.00207   M  c  0.1k 0.1k
  • 19. Quadratic Quadrilateral Element • The8 noded quadratic quadrilateral element usesquadratic functions for thedisplacements 152
  • 20. Quadratic Quadrilateral Element • Shapefunction examples: • Strain distribution within theelement 153
  • 21. 154 Quadratic Quadrilateral Element • Should we try to use this element to solve our problem? • Or try fixing the Q4element for our purposes. – Hmm…toughchoice.
  • 22. 155 Isoparametric Elements and Solution • Biggestbreakthrough in the implementation of the finite element method is the development of an isoparametric element with capabilities to model structure (problem) geometries of any shapeandsize. • Thewhole idea works onmapping. – Theelement in the real structure is mapped to an ‘imaginary’ element in an ideal coordinatesystem – Thesolution to the stress analysis problem is easyand known for the ‘imaginary’element – Thesesolutions are mapped back to the element inthe real structure. – All the loads and boundary conditions are also mapped from the real to the ‘imaginary’ element in this approach
  • 24. Isoparametric element Y 1 2 3 4 0 0 0 N1 X N 0     • The mappingfunctions areq x1u  ite simple: 4   x  2 x3  N N N 0 0 0 0  x  N2 N3 N4 y1  y2    1 4 N  1 (1)(1) 2 4 N  1 (1 )(1) 3 4 N  1 (1 )(1 ) 4 4 N  1 (1)(1 ) y3   y4   Basically, thex and y coordinatesof any point in the elementare interpolations of the nodal (corner) coordinates. Fromthe Q4 element,thebilinear shape functions are borrowed to be used as the interpolation functions. They readily satisfy the boundaryvalues too. 157
  • 25. Isoparametric element v 1 2 3 4 0 0 0 N1 u N 0       • Nodal shape functions for d u i1s placements 4  u  2 u3 N N N 0 0 0 0 u  N2 N3 N4 v1  v2    v3 v4   1 4 N  1 (1)(1) 2 4 N  1 (1 )(1) 3 4 N  1 (1 )(1 ) 4 4 N  1 (1)(1 ) 158
  • 26. • Thedisplacement strain relationships: x  u  u    u   X  X  X y  v  v    v   Y  Y  Y x        xy  y  u X v Y  Y X           u      v  Y Y     0    0   0       X X 0      Y Y X X      u    v     u            v   But,it is too difficult to obtain  and  X X 159
  • 27. Isoparametric Element  X  Y  u u         X Y u  X Y u    X    Y     It is easier to obtain X and Y   X Y   J  X Y Jacobian     defines coordinate transformation Hencewe will do itanother way u  u  X  u  Y  X  Y  u  u  X  u  Y X   Ni X   i Y N     i Yi X N   i X i   Y N   i Yi   X   u   Y  u    1  u    J     u   169
  • 28. 161 GaussQuadrature • Themapping approach requires usto be able to evaluate the integrations within the domain (-1…1)of the functionsshown. • Integration canbe done analytically by usingclosed- form formulas from atable ofintegrals (Nah..) – Or numerical integration can be performed • Gaussquadrature is the more common form of numerical integration - better suited for numerical analysis and finite elementmethod. • Itevaluated the integral of afunction asasum of a becomes n I  Wii i1 finite numb1er of terms I   d 1
  • 29. GaussQuadrature • Wi is the ‘weight’ and i is the value of f(=i) 162
  • 30. Numerical Integration Calculate: b I   f xdx a • Newton – Cotesintegration • Trapezoidalrule – 1storderNewton-Cotesintegration • Trapezoidalrule – multipleapplication 1 b a f (x)  f (x)  f (a)  f (b)  f (a) (x a) 2 f (a)  f (b) b b I   f (x)dx   f1(x)dx  (b  a) a a b 163 xn1 xn x1 x2 xn a x0 x0 x1 I   f (x)dx   fn (x)dx   f (x)dx   f (x)dx    f (x)dx      2  n1 i1 i f (x )  f (b) f (a)  2 h  I 
  • 31. Numerical Integration Calculate: b I   f xdx a • Newton – Cotesintegration • Simpson1/3 rule – 2ndorderNewton-Cotesintegration 2 2 0 1 1 0 2 0 1 2 f (x ) (x2  x0 )(x2  x1) (x  x0 )(x  x1) f (x )  f (x ) (x0  x1)(x0  x2 ) (x  x )(x  x ) (x  x1)(x  x2 ) (x  x )(x  x ) f (x)  f (x)  b b a a 6 f (x )  4 f (x )  f (x ) I   f (x)dx   f2 (x)dx  (x2  x0 ) 0 1 2 164
  • 32. Numerical Integration Calculate: b I   f xdx 2  (b  a) f (a)  (b  a) f(b) 2 2 165 I  (b  a) f (a)  f (b) I  c0 f (x0 )  c1 f (x1) a • GaussianQuadrature Trapezoidal Rule: GaussianQuadrature: Choose according to certaincriteria c0,c1,x0 , x1
  • 33. Numerical Integration Calculate: b I   f xdx a • GaussianQuadrature      3  1   1 1 1 I   f xdx  f  3   f  1 • 3pt GaussianQuadrature I   f xdx 0.55 f 0.77 0.89  f 0 0.55 f0.77 1 1 1 • 2pt GaussianQuadrature I   f xdx  c0 f x0  c1 f x1  cn1 f xn1 b  a ~ x 1 2(x  a) Let: b 1  1 a 166 ~ ~ 1 1 1  f (x)dx  2 (b  a) f 2 (a  b)  2 (b  a)xdx