beam design for geometric nonlinearities jordan radas kantaphat sirison wendy zhao
TRANSCRIPT
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BEAM DESIGN FOR GEOMETRIC NONLINEARITIES
Jordan RadasKantaphat SirisonWendy Zhao
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PREMISE
Large deflection
Linear assumptions no longer apply
Is necessary form many real life applications
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DESIGN OVERVIEW
Nonlinear Linear
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GEOMETRIC NONLINEARITY ASSUMPTIONS Large deformation
Plane cross section remains plane
Linear elastic material
Constant cross section
ux1
ux2
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KINEMATICS
Location of particle at deformed configuration relative to displacement and original configuration
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KINEMATICS
e
(1 u'x )cos u'y sin 1 1(1 u'x )sin u'y cos
'
Characterize axial strain, shear strain and curvature in terms of the derivatives of the displacement
Green Lagrange Strain Tensor
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STRAIN DISPLACEMENT MATRIX
[h]31 [B]36[d]61
e
de
dux1...
de
du 2ddux1
...ddu 2
ddux1
...ddux1
31
ux1...
u 2
61
[B]The components of the strain displacement matrix can be
determined explicitly by differentiation.
B cos sin N1a cos sin N2a
sin cos N1b sin cos N2b
0 0 1 0 0 1
where
a (1 u'x )sin u'y cosb (1 u'x )cos u'y sin
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TANGENT STIFFNESS MATRIX
dV ubdx uAtV 0
V 0
(EA GA EI)dx uyqV 0
[K][d]
R
Through discretization and linearization of the weak form
k BTDBdx kgeometricV 0
kmaterial
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kg EAL
GAL
sin2 cos2 0 sin2 cos2 0
cos2 sin2 0 cos2 sin2 0
0 0 0 0 0 0
sin2 cos2 0 sin2 cos2 0
cos2 sin2 0 cos2 sin2 0
0 0 0 0 0 0
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NEWTON-RAPHSON METHOD
k ttu R tt
F tt R tt
F tt F t ku
k(i 1)u( i) R F ( i 1)
u(i) u(i 1) u( i)
Displacement
Load
F 4 R tt
F 0,u0
F1
F 2
F 3
K1
K 2
u1
u2
u3
u4
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NEWTON-RAPHSON METHOD
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RESTORING LOAD
dVBF iTii )1()1()1(
Xu
IX
xF
URF
]ln[U
3
1
lni
Tiii ee
Definition of deformation gradient
Spatial Decomposition
Corresponds to element internal loads of current stress state.
From right polar decomposition theorem
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INCREMENTAL APPROXIMATION
1 nnn
][][ nDed
2/12/1 RR nT
n
]ln[ U
From
11
nn FFURF
With
With
Xu
IURF
2/12/12/12/1
12/1 2
1 nn uuu
nn uB 2/1~ Wit
h 12/1 2
1 nn XXX
B Evaluated at midpoint geometry
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NONLINEAR SOLUTION LEVELS
Load steps:
Adjusting the number of load steps account for: abrupt changes in loading on a structure
specific point in time of response desired
Substeps:
Application of load in incremental substeps to obtain a solution within each load step
Equilibrium Iterations:
Set maximum number of iterations desired
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SUBSTEPSEquilibrium iterations performed until convergence
Opportunity cost of accuracy versus time
Automatic time stepping featureChooses the size and number of substeps to optimize
Bisections methodActivates to restart solution from last converged step if a solution does not converge within a substep
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MODIFIED NEWTON-RAPHSON
Incremental Newton-Raphson Initial-Stiffness Newton-Raphson
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DISPLACEMENT ITERATION
As opposed to residual iteration
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ANSYS FEATURES
Predictor
Line Search Option
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ANSYS FEATURESAdaptive Descent
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DESIGN CHALLENGE: OLYMPIC DIVING BOARD
L = 96in
b = 19.625in
h = 1.625in
P = -2500lbs
Al 2024 – T6 (aircraft alloy)
E = 10500ksi
v = .33
Yield Strength = 50ksi
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SOLID BEAM: LINEAR/NONLINEAR
Mesh Size Linear Nonlinear
.125in 8.1043in 2.1671 2.2383 8.1082in 2.1757 2.2442
.25in 8.0255in 2.2170 2.2675 7.9951in 2.2254 2.2749
.5in 8.0566in 2.3051 2.3144 8.0282in 2.3151 2.3195
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OPTIMIZATION PROBLEM
ANSYS Goal Driven Optimization is used to create a geometry where hole diameter is the design variable.
Goals include minimizing volume and satisfying yield strength criterion.
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OPTIMIZATION AND ELEMENT TECHNOLOGY Optimization samples points in the user specified design space.
The number of sampling points is minimized using statistical methods and an FEA calculation is made for each sample.
Samples are chosen based on goals set for output variables, such as volume and safety factor.
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OPTIMIZATION RESULTS
Problem Method Volume Deflection
Max Tensile
Von Mises
Solid Beam
Linear3061.5in3
8.0566in 2.3051 2.3144
Nonlinear 8.0282in 2.3151 2.3195
Optimized
Beam
Linear2719.5in3
8.7993in 1.2754 1.2555
Nonlinear 8.7576in 1.2756 1.2757
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CONCLUSION
Analysis serves as a proof of concept that real-world situations involving large structural displacements benefit from nonlinear modeling considerations
Extra computing power and time is worth it
Recommendations/suggestions
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QUESTIONS?