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be precise: p(t,x) = 0 when t < 0; p(0,0) = 1; p(t,0) = 0 at t > 0 . (2.13) These boundary conditions when applied to Equation 2.12 suggest that the we can define the one dimensional forward traveling wave: as: p(t,x) = f + ζ( ); where ζ = t − x /c ) with f+(ζ) = 0 for ζ< 0, and ζ> 0 and f+(ζ) = 1 for ζ= 0 . (...
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: ( p(t, x) = P+ cos ω(t − x /c) + ∠P+ )+ P− cos ω(t + x /c) + ∠P− ( ). How does this equivalence come about? Equations 2.15 and 2.16 can also be written in terms of the variable k= ω/c = 2π/λ, where k has units of radians per meter and is sometimes called the wave number, length constant or spatial frequency : p(...
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E R U S S E R P D N U O S 1.0 0.5 0.0 -0.5 -1.0 0.00 0.25 0.50 1.00 X = DISTANCE FROM REFERENCE POINT 1.50 1.25 0.75 1.75 2.00 As time progresses, the “wave-front” (here defined as the location of maximum pressure) travels across the room with a velocity c. Now suppose we place a microphone at various locations in th...
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case of the sinusoidal steady state: we can factor out ejωt, p(t, x) = Real P+e j (ωt−kx) + P−e j (ωt+kx) { } { p(t, x) = Real e jωt P(x) P(x) = P+e− jkx + P−e jkx }, where ) ( (2.19) (2.20) In a wide open environment with no reflection, we can define the spatial dependence of a forward traveling plane wave, as ...
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7. Reflections at Rigid Boundaries Suppose our propagating plane wave hits a rigid wall placed orthogonally to the direction of propagation, where the wall dimensions are much larger than the wavelength. The interaction will produce a reflected wave that appears as a backward traveling wave in a one-dimensional syst...
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+kx) { } where: } { p(t, x) = Real e jωt P(x) P(x) = P+e− jkx + P−e jkx (2.22) (2.23) In the case of a forward traveling wave with a rigid boundary at x=0 where P+ = P− , as in Figure 2.6, Equation 2.23 simplifies via Euler’s equations to P(x) = 2P+ cos kx( ), (2.24) Note that Equation 2.24: (a) Is dependent ...
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) V x(x) = 1 z0 ( P+e− jkx − P−e jkx )= −2 j sin(kx) P+ z0 ( Note that for x < 0; ∠Vx(x) =( π 2 + ∠P+ of 2 |P+|/z0 at x=–λ/4, –3λ/4, –5λ/4, –7λ/4 … The ratio of P(x) and Vx(x) defines the spatially varying specific acoustic impedance ZS(x). In the case of rigid boundary reflection: (2.25) ), has a magnitude of 0 a...
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Harvard-MIT Division of Health Sciences and Technology HST.951J: Medical Decision Support, Fall 2005 Instructors: Professor Lucila Ohno-Machado and Professor Staal Vinterbo 6.873/HST.951 Medical Decision Support Spring 2005 Variable Compression: Principal Components Analysis Linear Discriminant Analysis Lucila Ohno-...
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n − 1 n ∑ ( X − X i )( X i − X ) σ = XX i= 1 n − 1 st _ deviation n ∑ X ( i 1 = − X )( X i i − X ) n − 1 Xσ = Y Covariance and Correlation Matrices ⎡σXX σXY ⎤ cov = ⎢ ⎥ σYX σYY ⎦ ⎣ ⎡ 1 ρ⎤ ⎣ρ 1 ⎥ ⎦ corr = ⎢ n n σ = XY i=1 σ = i=1 XX ∑( X − i X Y )( − i Y ) ∑( X − i X )...
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variables (transformation vectors) • First component should represent the direction with largest variance • Second component is orthogonal to (independent of) the first, and is the next one with largest variance • and so on… Y X and Y are not independent (covariance is not 0) Y = ( X * 4) + e cov = ≠ 0 σ ...
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the direction of largest variance in the data. If q > m, then [a b]T is PC1 Principal Components 27 .1 ⎡ ⎢ . .5 12 21 ⎣ .1 ⎡ ⎢ . .5 12 21 ⎣ 27 .5 12 ⎡ ⎤ ⎢ ⎥ 65 ⎣ ⎦ .0 23⎤ ⎥ .0 97 ⎦ .0 97 .5 12 ⎡ ⎤ ⎥ ⎢ 65 − .0 ⎦ ⎣ ⎤ ⎥ 23 ⎦ = 22.87 ⎡ ⎢ ⎣ = . 0 05 .0 23⎤ ⎥ .0 97 ⎦ . 0 97 ⎡ ⎢ ⎣ − ⎤ ⎥ ⎦ 23 .0 Total varian...
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11 x11 + − 97.0 23.0 x 12 x 12 P C 2 ax31 cx 31 + + 23.0 97.0 bx32 ⎤ ⎥ dx32 ⎦ 97.0 x + 21 23.0 x − 21 x x 22 22 23.0 97.0 x31 x31 P C 1 + − 97.0 .0 23 x32 ⎤ ⎥ 32 x ⎦ X2 0 X1 Total variance is 22.92 Variance of PC1 is 22.87, so it captures 99% of the variance. PC2 can be discarded with li...
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point that best separates data Fisher’s Linear Discriminant • Use classes to define discrimination line, but criterion to maximize is: – ratio of (between classes variation) and (within classes variation) • Project all objects into the line • Find point in the line that best separates classes Sw is the sum of...
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normal Eigenvector with max eigenvalue Classification Models • Quadratic Discriminant Analysis • Partial Least Squares – PCA uses X to calculate directions of greater variation – PLS uses X and Y to calculate these directions • It is a variation of multiple linear regression Var(Xα), Corr2(y,Xα)Var(Xα) PCA maxi...
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2.094— Finite Element Analysis of Solids and Fluids — Fall ‘08 — MIT OpenCourseWare Contents 1 Large displacement analysis of solids/structures 1.1 Project Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Large Displacement analysis . . . . . . . . . . . . . . . . . . ....
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F.E. displacement formulation, cont’d 6 Finite element formulation, example, convergence 3 3 4 4 5 5 6 7 8 8 10 14 19 23 6.1 Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 6.1.1 F.E. model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
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. . . . . . . . . . . 61 15.1.2 Principle of virtual temperatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 15.2 Inviscid, incompressible, irrotational flow . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 16 F.E. analysis of Navier-Stokes fluids 17 Incompressible fluid flow and heat transfer, c...
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18.1 Slender structures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 19 Slender structures 20 Beams, plates, and shells 21 Plates and shells 81 85 90 2 2.094 — Finite Element Analysis of Solids and Fluids Fall ‘08 Lecture 1 - Large displacement analysis of solids/structures...
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necessarily For example: Snap-through problem The same load. Two different deformed configurations. 4 MIT 2.094 1. Large displacement analysis of solids/structures Column problem, statics Not physical tR is in “direction” of bending moment Not in equilibrium. ⇒ 1.2.2 Requirements to be fulfilled by solution a...
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= tτ11 tτ22 tτ33 tτ12 tτ23 tτ31 � (For i = 1, 2, 3) tτij,j + tfi tτij (e.g. tfi t nj = Sf = B = 0 in tV (sum over j) tfi tτi1 Sf on tSf (sum over j) t n2 + tτi3 t n1 + tτi2 t n3 ) And: tτ11 tn1 + tτ12 Sf tn2 = tf1 • Compatibility The displacements tui need to be continuous and zero on tSu. • Stre...
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tV = tV t V Derivation of (2.9) tτ11,1 + tf B 1 = 0 by (2.2) � tτ11,1 + tf B � u1 = 0 1 ∗or Principle of Virtual Displacements tSf 8 (2.6) (2.7) (2.8) (2.9) (2.10) (2.11) MIT 2.094 2. Finite element formulation of solids and structures Hence, � � tV tτ11,1 + tf1 � B u1 d tV = 0 11u � 1 � t...
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14) or � tV � tV u1,1 tτ11d V = u1 t tSf � � tτ11 tS u � − tV u1 tτ11,1d V = t � tV u1 B t Sf tf1 d V + u1 � � Sf tτ11,1 + tf1 d V + u1 B t � � tSf = 0 tPr − tτ11 u1 x Now let u1 = x 1 − t L � � � tτ11,1 + tf1 � B , where tL = length of bar. Hence we must have from (2.16) tτ11,1 +...
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d Vt = � tV ui tf B i d Vt + � tSf ui|tSf ft Sf i d St f teij = � ∂ u i t x ∂ j 1 2 + � ∂u j tx ∂ i Reading: Sec. 6.1-6.2 (3.1) (3.2) (3.3) (3.4) • If (3.3) holds for any continuous virtual displacement (zero on tS ), then (3.1) and (3.2) hold and vice versa. u • Refer to Ex. 4.2 in the text...
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094 3. Finite element formulation for solids and structures u(r) = t u(r) = u(r) = 1 2 1 2 1 2 (1 + r) u1 + 1 2 (1 + r) t u1 + 1 2 (1 + r) u1 + 1 2 (1 − r) u2 (1 − r) t u2 (1 − r) u2 Suppose we know tτ11, tV , tSf , tu ... use (3.6). For element 1, te11 = ∂u ∂ tx = B(1) � � u1 u2 T t te11t τ11d ...
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f · f 1 tS ⎤ ⎦ Now apply, = � U T 1 0 0 � then, then, T U � = 0 1 0 T U � = 0 0 1 � � 12 (3.7) (3.8) (3.9) (3.10) (3.11) (3.12) (3.13) (3.14) (3.15) (3.16) (3.17) (3.18) (3.19) (3.20) MIT 2.094 This gives, 3. Finite element formulation for solids and structures � t ˆF (1) 0 � �...
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II. Variational formulation (Principle of virtual displacements) (or weak formulation) We developed the governing F.E. equations for a sheet or bar We obtained tF tR= (4.1) where tF is a function of displacements/stresses/material law; and tR is a function of time. Assume for now linear analysis: Equilibrium wit...
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(4.6b) (4.6c) (4.6d) MIT 2.094 4. Finite element formulation for solids and structures Principle of Virtual Work: � V � �T τ dV = Uˆ f B dV V T (4.7) can be rewritten as � � m V (m) �(m)T τ (m)dV (m) = � � m V (m) Uˆ (m)T f B(m) dV (m) Substitute (4.6a) to (4.6d). � T � � Uˆ B(m)T � τ (m) dV (...
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(m) H (m)T f B(m) dV (m) 16 (4.7) (4.8) (4.9) (4.10) (4.11) (4.12) (4.13) (4.14) (4.15) (4.16) (4.17) MIT 2.094 4. Finite element formulation for solids and structures Example 4.5 textbook E = Young’s Modulus Mathematical model Plane sections remain plane: F.E. model ⎤ ⎡ U = ⎣ U2 ⎦ U1 U3 Element ...
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80 �� B(2) Then, K = ⎡ ⎣ E 100 1 −1 0 −1 1 0 0 0 0 ⎤ ⎦ + 13E 240 ⎡ ⎣ 0 0 0 ⎤ ⎦ 0 1 −1 0 −1 1 where, � � AE L � ≡ � E(1) 100 E · 13 3 · 80 E 80 = 13 3 � �� � A∗ � � A∗ < A � η=80 4.333 < 9 � � A � η=0 1 < < 18 2.094 — Finite Element Analysis of Solids a...
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) dV (m) Surface loads Recall that in the principle of virtual displacements, “surface” loads = � T Sf U f Sf dSf u S (m) H S (m) Sf = H S (m) U � � � evaluated at the surface = H (m) 19 (5.1a) (5.1b) (5.1c) (5.1d) (5.1e) (5.1f) (5.1g) (5.1h) (5.2) (5.3) (5.4) MIT 2.094 5. F.E. displaceme...
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yy ⎠ = τxy E 1 − ν2 ⎡ 1 ν ⎣ ν 1 0 0 �xx ⎤ ⎛ ⎞ 0 0 ⎦ ⎝ �yy ⎠ ν 1− 2 γxy 20 (5.5) (5.6) (5.7) (5.8) (5.9) (5.10) (5.11) MIT 2.094 5. F.E. displacement formulation, cont’d � � u(x, y) v(x, y) ⎛ = H ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ u1 u2 u3 u4 v1 v2 v3 v4 If we can set ...
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xy = ∂u ∂x ∂v ∂y ∂u ∂y + ∂v ∂x 22 (5.16) (5.17) (5.18) (5.19) (5.20) (5.21) (5.22) ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ u1 u2 u3 u4 v1 v2 v3 v4 2.094 — Finite Element Analysis of Solids and Fluids Fall ‘08 Lecture 6 - Finite element formulation, example, convergence Prof. K.J. Bathe MIT OpenCourseWar...
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23 ← u1 . . . (6.4) 6. Finite element formulation, example, convergence MIT 2.094 In practice, � � K � el = � V ⎛ BT CB dV ; � = B ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ u1 . . . u4 v1 . . . v4 where K is 8x8 and B is 3x8. Assume we have K (8x8) for el. (2) ⎡ ↓ ↓ · · · ↓ · · · ↓ U11 ↓ U1 ...
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⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ u1 u2 . . . u4 v1 . . . v4 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ � ← ← u(x, y) v(x, y) 24 U1← . . . . . . U11← . . . . . . U18← (6.5) (6.6) (6.7) (6.8) (6.9) MIT 2.094 6. Finite element formulation, example, convergence ⎤ ⎦ (6.10) (6.11) (6.12) (6.13) � H S = H � � y=+1 1 2 (1 + x) ...
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5 =1 hiui. i hi = 1 at node i and 0 at all other nodes. h5 = 1 (1 − x2)(1 + y) 2 25 MIT 2.094 6. Finite element formulation, example, convergence (6.14) (6.15) (6.16) (6.17) (6.18) (6.19) (6.20) (6.21) (6.22) (6.23) h1 = h2 = h3 = h1 = 1 4 1 4 1 4 1 4 (1 + x)(1 + y) − h5 (1 − x)(1 + y) − ...
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) B(m)T τ (m) d V (m) Two properties I. The sum of the F (m)’s at any node is equal to the applied external forces. 26 MIT 2.094 6. Finite element formulation, example, convergence II. Every element is in equilibrium under its F (m) � T T ˆU F (m) = ˆU � � = V (m) �� =�(m)T �(m)T B(m)T τ (m) d V (...
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4 h2 = . . . u(r, s) = 4 � hiui v(r, s) = i=1 4 � hivi i=1 28 Reading: Sec. 5.1-5.3 (7.1) (7.2) (7.3) (7.4) (7.5) (7.6) MIT 2.094 7. Isoparametric elements � = Buˆ uˆT = u1 u2 � � · · · v4 ⎡ ⎢ ⎢ ⎢ ⎢ ⎣ ⎛ ⎝ ⎛ ⎝ ⎤ ⎥ ⎥ ⎥ = Buˆ ⎥ ⎦ ∂u ∂x ∂v ∂y ∂u + ∂v ∂x ∂y ⎞ ⎡ ⎠ = ⎣ � ⎞ ⎠ = J −1 ...
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) (Ch. 5.5) � � K ∼ = t BT ij CBij det(Jij ) × (weight i, j) i j 2x2 Gauss integration, (i = 1, 2) (j = 1, 2) (weight i, j = 1 in this case) 29 (7.11) (7.12) (7.13) (7.14) (7.15) MIT 2.094 9-node element 7. Isoparametric elements 9 � hixi x = i=1 9 � hiyi i=1 9 � hiui i=1 9 � hivi i=1 y...
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any continuous function that satisfies boundary conditions), satisfying � V �T C� dV = a(u, v) = (f , v) � �� � R(v) � �� � bilinear form Example: for all v, an element of V. (7.22) Finite Element problem Find uh ∈ Vh, where Vh is F.E. vector space such that a(uh, vh) = (f , vh) ∀vh ∈ Vh (7.23) Size of Vh ⇒ ...
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4) (7.25) (7.26) (7.27) (7.28) (7.29) (7.30) (7.31) 32 2.094 — Finite Element Analysis of Solids and Fluids Fall ‘08 Lecture 8 - Convergence of displacement-based FEM Prof. K.J. Bathe (A) Find u ∈ V such that a(u, v) = (f , v) ∀v ∈ V (Mathematical model) a(v, v) > 0 ∀v ∈ V, v =� 0. where (8.2) implies t...
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! (Recall eh = u − uh) Proof: Pick wh ∈ Vh. a(eh + wh, eh + wh) = a(eh, eh) + ������� 0 2a(eh, wh) ( ) + a w wh, h � � �� ≥0 Equality holds for (wh = 0) a(eh, eh) ≤ a(eh + wh, eh + wh) = a(u − uh + wh, u − uh + wh) Take wh = uh − vh. a(eh, eh) ≤ a(u − vh, u − vh) (8.7) (8.8) (8.9) (8.10) (8.11) Using pr...
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meshes and plot log(E − Eh) vs. log h We need to use graded meshes if we have high stress gradients. Example Consider an almost incompressible material: �V = vol. strain or � · v → very small or zero We can “see” difficulties: p = −κ�V κ = bulk modulus As the material becomes incompressible (ν = 0.3 → 0.4999) � ...
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�V 3 δij τij = −pδij + 2G�� ij � p = − τkk 3 � (9.2) becomes � V � ��T C��� dV + � �V κ�V dV = R V � ��T C��� dV − �T V p dV = R V V MIT OpenCourseWare Reading: Sec. 4.4.3 (9.1) (9.2) (9.3) (9.4) (9.5) (9.6) (9.7) (9.8) (9.9) (9.10) (9.11) 37 MIT 2.094 9. u/p formulation We need ano...
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Kuu Kup Kpu Kpp uˆ pˆ = R 0 Kuu = DC�BD dV Kup = − BV T Hp dV Kpu = − Hp T BV dV � BT V � V � V � V T Kpp = − Hp 1 κ Hp dV 38 (9.12) (9.13) (9.14) (9.15) (9.16) (9.17) (9.18) Plane strain (�zz 4/1 element = 0) Reading: Ex. 4.32 in the text (9.19) (9.20) (9.21) (9.22) (9.23) (9.24a) ...
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9.26) Reading: Sec. 4.5 ⎤ ⎡ =�V � �� � � Vol qh � · vh dVol ⎥ ⎥ ⎥ v h ⎦ q � � � h �� � for normalization ⎢ ⎢ ⎢ ⎣ � � sup inf ���� ���� qh∈Qh vh∈Vh ≥ β > 0 (9.27) Qh: pressure space. If “this” holds, the element is optimal for the displacement assumption used (ellipticity must also be satisfied). Note: ...
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equation) Case 2 βh = small = � � · uh|i = 0 ⇒ uh|i = 0 ∴ � · ph|j + uh|i · α = Rh|i Rh| i � ph|j = ⇒ � ⇒ displ. = 0 pressure → large � as � is small The behavior of given mesh when bulk modulus increases: locking, large pressures. See Example 4.39 textbook. 40 � 2.094 — Finite Element Analysis of Soli...
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10.3) (10.4) (10.5) (10.6) (10.7) (10.8) MIT 2.094 10. F.E. large deformation/general nonlinear analysis Then we can write t+ΔtF = tF + F t+ΔtU = tU + U where only tF and tU are known. F ∼ = tK ΔU , tK = tangent stiffness matrix at time t From (10.8), tK ΔU = t+ΔtR − tF (10.9) (10.10) (10.11) (10.12)...
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< �F � � � t+ΔtR − Note: �a�2 = �� (ai)2 i ΔU (i) = U � i=1,2,3... ΔU (1) in (10.13) is ΔU in (10.12). (10.13) is the full Newton-Raphson iteration. How we could (in principle) calculate tK Process • Increase the displacement tUi by �, with no increment for all tUj , j = i calculate t+�F • • the i-th colum...
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) The strain measure energy-conjugate to the 2nd P-K stress 0 Then, � tSij δ 0 0 t�ij d V = R 0 t tSij is the Green-Lagrange strain 0 t�ij (10.21) (10.22) 0V Also, � 0V Example t+Δt S δ 0 ij t+Δt 0 � d V = 0 ij t+Δt R 43 MIT 2.094 10. F.E. large deformation/general nonlinear analysis (10.23) ...
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use � tV tτij δ eij d Vt = t � 0V tS t� 0 ij δ 0 ij d 0V = tR The deformation gradient We use txi = 0xi + tui tX0 = ⎡ ⎢ ⎢ ⎢ ⎢ ⎢ ⎢ ⎣ ∂ t x1 ∂ 0x1 ∂ t x2 0 x ∂ 1 ∂ x t 3 ∂ x0 1 ∂ t x1 ∂ 0x2 ∂ t x2 0 x ∂ 2 ∂ x t 3 ∂ x0 2 ⎤ ⎥ ⎥ ⎥ ⎥ ⎥ ⎥ ⎦ ∂ t x1 ∂ 0x3 ∂ t x2 x ∂ 3 0 ∂ x t 3 ∂ x0 3 dt ...
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ˆts · d st ∴ cos tθ = tC 0n 0nˆ T 0 tλˆ tλ Also, tρ = 0ρ det 0 tX (see Ex. 6.5) Example 46 (11.15) (11.16) (11.17) (11.18) Reading: Ex. 6.6 in the text MIT 2.094 11. Deformation, strain and stress tensors 1 4 (1 + x1)(1 + x2) 0 0 h1 = . . . t t 0 xi = xi + ui 4 � = hk t xi k , (i = 1, ...
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δij + t ∂ ui ∂ 0x j We find that 1 � 2 t� = 0 ij t u + t u + t u 0 j,i 0 i,j � t u 0 k,i 0 k,j where t∂ u i t ui,j = 0 ∂ xj 0 , sum over k = 1, 2, 3 47 (11.19) (11.20) (11.21) (11.22) (11.23) (11.24) (11.25) (11.26) (11.27) MIT 2.094 11. Deformation, strain and stress tensors Polar decomposi...
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rigid-body rotation. 48 2.094 — Finite Element Analysis of Solids and Fluids Fall ‘08 Lecture 12 - Total Lagrangian formulation Prof. K.J. Bathe MIT OpenCourseWare We discussed: � tX = 0 � i j ⇒ dt x = tX d0 x, 0 d0 x = � tX �−1 dt x 0 t ∂ x 0 x ∂ tC = tX T tX 0 0 0 d0 x = 0 tX dt x where 0 tX = � ...
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t xi = 0 k hk xi + t k hk ui = = k � hk k � k k + t ui � 0 xi k � hk k t xk k 49 (12.6) (12.7) (12.8) (12.9) (12.10) MIT 2.094 E.g., k = 4 12. Total Lagrangian formulation 2nd Piola-Kirchhoff stress 0ρ tX tτ 0 0 tS = tρ 0 tX T → components also independent of a rigid body rotation (12.1...
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2.25) (12.26) (12.27) (12.28) (12.29) MIT 2.094 12. Total Lagrangian formulation Left hand side as before but using (k − 1) and right hand side is (12.30) (12.31) (12.32) � t+Δt R − 0V = gives t+Δt S δ 0 ij t+Δt (k−1) 0 � 0 ij d V t+Δt (k−1) 0 F tF (k−1) t+ΔtR − t+Δ 0 In the full N-R iteration,...
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0 ���� vector The iteration (full Newton-Raphson) is � t+ΔtK (i−1) + t+ΔtK (i−1) ΔU (i) = t+ΔtR 0 N L 0 L � − t+ΔtF (i−1) 0 t+ΔtU (i) = t+ΔtU (i−1) + ΔU (i) Truss element example (p. 545) Here we have to only deal with 0 tS11, 0e11, 0η11 0e11 = 0η11 = ∂u1 + ∂ 0x1 � ∂uk 1 2 ∂ 0x1 t ∂ uk ∂ 0x1 · ...
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=u1 t 2 =u2 t ∂u1 + ∂ 0x1 � 0L 0L ∂ u1 ∂u1 + ∂ 0x1 ∂ 0x1 � cos θ − 0L � + ΔL sin θ + ΔL � t ∂ u2 ∂u2 ∂ 0x1 ∂ 0x1 � −1 0 1 0 uˆ � 1 0e11 = 0L ⎛ ⎞ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ � ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ � + 0L + ΔL 0L � 1 0L −1 0 1 0 � uˆ ⎟ ⎟ ⎟ · cos θ − 1 ⎟ ⎟ ⎟ � ⎠ �� ∂ tu1 0∂ x1 ⎞ ⎟ ⎟ ⎟ · sin θ ⎟ ⎟ ⎟ � ⎠ ...
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1 � uˆ � 55 � (13.20g) (13.21a) (13.21b) (13.21c) (13.21d) (13.21e) MIT 2.094 13. Total Lagrangian formulation, cont’d 0C = E tSˆ = tS 0 0 11 Assume small strains tK = 0 EA 0L ⎡ cos2 θ cos θ sin θ sin2 θ = ⎢ ⎢ ⎣ � sym − cos2 θ − sin θ cos θ cos2 θ − cos θ sin θ − sin2 θ sin θ cos θ sin2...
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teij d t+Δt V = t+Δt R t+Δt 0Sij δ t+Δt R t+Δt 0 0�ij δ V = ⏐ ⏐ � linearization � t+ΔtR − 0 ij δ V + tSij δ ηij δ V = 0 0 0 tS 0 ij δ 0e 0 ij δ V 0V � 0V � 0V 0Cijrs 0ers 0e δ Note: δ 0eij t = δ � 0 ij varying with respect to the configuration at time t. F.E. discretization 0 xi = � 0 k hk ...
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� sin θ cos θ − cos2 θ − cos θ sin θ − sin2 θ sin θ cos θ sin2 θ = ⎢ ⎢ ⎣ cos2 θ cos θ sin θ − cos2 θ − cos θ sin θ ⎡ + tP 0L ⎢ ⎢ ⎣ − cos θ sin θ − sin2 θ 0 −1 0 1 0 −1 0 1 1 0 0 0 −1 1 0 −1 ⎤ ⎥ ⎥ ⎦ (notice that the both matrices are symmetric) � u1 v1 � � = cos θ sin θ − sin θ cos θ � ...
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(14.12) (14.13) (14.14) (14.15) (14.16) (14.17) (14.18) (14.19) MIT 2.094 14. Total Lagrangian formulation, cont’d t+Δt � = 0 33 tu + u1 1 0 x1 + � 1 2 tu + u1 1 0 x1 �2 t�33 − 0 0�33 = t+Δ 0 u1 t�33 = 0x1 tu + 0x 1 1 u1 · 0x1 + � 1 u1 0x1 2 �2 (14.20) (14.21) How do we assess the accu...
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Lecture 15 - Field problems Prof. K.J. Bathe MIT OpenCourseWare Heat transfer, incompressible/inviscid/irrotational flow, seepage flow, etc. Reading: Sec. 7.2-7.3 • Differential formulation • Variational formulation • Incremental formulation • F.E. discretization 15.1 Heat transfer Assume V constant for now...
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∂x � ��dx + q dx = 0 B �� � � k ∂θ ∂x ∂ ∂x B = −q We also need to satisfy k ∂θ ∂n = q S on Sq. 15.1.2 Principle of virtual temperatures � � � ∂ k θ ∂x ∂θ ∂x ( θ� = 0 and θ to be continuous.) � + q B = 0 · · · + � Sθ � V � θ � k ∂ ∂x ∂θ ∂x � + � + q B dV = 0 · · · 62 (15.4) (15.5...
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θr)4 − � = κ∗ � (θr)2 + � � � = κ θr − θS θS �4 � θS �2 � � θr + θS � � θr − θS � where κ = κ(θS ) and θr is given temperature of source. At time t + Δt � �T θ t+Δtk t+Δtθ�dV = θ t+Δt q B dV + S t+Δt q S dSq θ � � V V Sq Let t+Δtθ = tθ + θ or with t+Δtθ(i) = t+Δtθ(i−1) + Δθ(i) t+Δtθ(0) = tθ Fr...
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16) (15.17) (15.18) (15.19) (15.20) (15.21) (15.22) (15.23) (15.24) MIT 2.094 F.E. discretization 15. Field problems for 4-node 2D planar element t+Δtθ� t+Δt t+Δt θˆ θ = H1x4 · 4x1 t+Δtθˆ 2x1 = B2x4 · t+ΔtθS = H S t+Δtθˆ · 4x1 For (15.23) � V � V � V � �T θ t+Δt (i−1)Δθ�(i) k � ⎛ dV = ⎝ BT t...
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S ���� � �� � Sq 4x1 1x4 ⎝ − ⎝ t+Δtθˆe � �� � 4x1 t+Δtθˆ(i−1) +Δ θˆ(i) ���� � � 4x1 �� 4x1 dSq = ⇒ ⎞⎞ ⎠⎠ dSq 15.2 Inviscid, incompressible, irrotational flow 2D case: vx, vy are velocities in x and y directions. or � · v = 0 ∂vy = 0 ∂y ∂vy ∂x = 0 ∂vx + ∂x ∂vx ∂y − (incompressible) (irro...
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Table 7.3 (16.1) (16.2) (16.3) (16.4) Incompressible flow with heat transfer We recall heat transfer for a solid: Governing differential equations (kθ,i),i + q B = 0 in V � � θ � Sθ is prescribed, k � ∂θ � � ∂n Sq = q S � � � Sq Sθ ∪ Sq = S Sθ ∩ Sq = ∅ Principle of virtual temperatures � � � θ,i...
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ivi = 0 Principle of virtual temperatures is now (use (16.4)) � � � � θ,ikθ,idV + θ (ρcpθ,ivi) dV = θqB dV + S q S dSq θ V V V Sq Navier-Stokes equations • Differential form τij,j + f B i = ρvi,j vj with ρvi,j vj like term (A) in (16.6) = ρ(v · �)v in V . τij = −pδij + 2µeij eij = � ∂vi ∂xj 1 ...
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vifi B dV + � Sf Sf fi vi Sf dSf (16.13) (16.14) • F.E. solution We interpolate (x1, x2, x3), vi, vi, θ, θ, p, p. Good elements are ×: linear pressure : biquadratic velocities ◦ (Q2, P1), 9/3 element 9/4c element Both satisfy the inf-sup condition. So in general, Example: For Sf e.g. τnn = 0, ∂vt = 0;...
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16.16) Reading: p. 683 (16.17) (16.18) (16.19) (16.20) MIT 2.094 16. F.E. analysis of Navier-Stokes fluids • F.E. discretization θ�� = Peθ� � 1 � θ 0 � 1 θ�dx + Pe 0 θθ�dx = 0 + { effect of boundary conditions = 0 here} (16.22) (16.21) Using 2-node elements gives 1 (h∗)2 (θi+1 − 2θi + θi−1) = Pe 2...
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(unreasonable). (16.30) 70 2.094 — Finite Element Analysis of Solids and Fluids Fall ‘08 Lecture 17 - Incompressible fluid flow and heat transfer, cont’d Prof. K.J. Bathe MIT OpenCourseWare 17.1 Abstract body Reading: Sec. 7.4 Fluid Flow Sv, Sf Sv ∪ Sf = S Sv ∩ Sf = 0 Heat transfer Sθ, Sq Sθ ∪ Sq = S ...
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Model problem 1D equation, dθ ρcpv = k dx d2θ dx2 (v is given, unit cross section) Non-dimensional form (Section 7.4) dθ Pe = dx d2θ dx2 72 (17.1) (17.2) (17.3) (17.4) (17.5) (17.6) (17.7) (17.8) (17.9) (17.10) MIT 2.094 17. Incompressible fluid flow and heat transfer, cont’d θ∗ is non-dimensio...
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1) 1 (h∗)2 θi+1 − θi−1 2h∗ 73 (17.11) (17.12) (17.13) (17.14) (17.15) (17.16) (17.17) (17.18) MIT 2.094 17. Incompressible fluid flow and heat transfer, cont’d Considered θi+1 = 1, θi−1 = 0. Then θi = 1 − (Pee/2) 2 (17.19) Physically unrealistic solution when Pee > 2. For this not to happen, we should...
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“A Flow-Condition-Based Interpolation Finite Element Procedure for Incompressible Fluid Flows.” Computers & Structures, 80:1267–1277, 2002. [2] H. Kohno and K.J. Bathe. “A Flow-Condition-Based Interpolation Finite Element Procedure for Triangular Grids.” International Journal for Numerical Methods in Fluids, 51:673–...
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compressible and Compressible Fluid Flows with Free Surfaces and Structural Interactions.” Computers & Structures, 56:193–213, 1995. [2] K.J. Bathe, H. Zhang and S. Ji. “Finite Element Analysis of Fluid Flows Fully Coupled with Structural Interactions.” Computers & Structures, 72:1–16, 1999. [3] K.J. Bathe and H. ...
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− ν) , (1 + ν) (1 − 2ν) (ν = 0.3) t� = 0 1 �� tU − I ⇒ 0 0 2 �2 � t�11 = �� 0L + tu �2 0L 1 2 � − 1 = 1 2 �� tu �2 1 + 0L � − 1 where 0 tU is the stretch tensor. tS = 0 11 0ρ tρ 0X T tτ 0X t 11 11 t 11 with 0 tX11 = 0L , 0L + tu 0ρ 0L = tρ tL ⇒ 0 tL tS11 = 0L � �2 0L tL 0L tτ11 ...
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∂ � 0 ji t � ∂ S ij ij + 0 t ∂ � 0 sr 77 (18.5) (18.6) (18.7) (18.8) (18.9) (18.10) (18.11) (18.12) (18.13) (18.14) (18.15) MIT 2.094 Plasticity 18. Solution of F.E. equations • yield criterion • flow rule • hardening rule � tτ = t−Δtτ + t dτ t−Δt Solution of (18.1) (similarly (18.2) and (18....
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+ΔtR − t+ΔtF (i−1) t+ΔtK (i−1) = ∂ t+ΔtF ∂U Physically � � � � t+Δt (i−1) U = � ∂F ∂U �� � � � t+Δt (i−1) U t+Δt (i−1)K11 = � (i−1) � Δ t+ΔtF1 Δu 78 (18.16) (18.17) (18.18) (18.19) (18.20) (18.21) (18.22) MIT 2.094 18. Solution of F.E. equations Pictorially for a single degree of freedom ...
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1 e.g. L = 100 (4-node el.) The element does not have curvature a spurious shear strain → we have (9-node el.) → → like ill-conditioning. We do not have a shear (better) But, still for thin structures, it has problems We need to use beam elements. For curved structures also spurious membrane strain can be ⇒ pr...
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into �xx, γxy → �xx = st 2L γxy = ∂u 2 · t ∂s 1 = − 2 (1 − r) v(r) = 1 2 (1 − r)v2 results into γxy → γxy = − 1 L 82 (19.8) (19.9) (19.10) (19.11) (19.12) (19.13) (19.14) (19.15) (19.16) (19.17) (19.18) (19.19) MIT 2.094 19. Slender structures For a pure bending moment, we want 1 − L ...
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= 3 2 · V A � t �2 − y2 2 � t �2 2 and V is the shear force. ⇒ k = 5 6 dAs 83 (19.21) (19.22) (19.23) (19.24) (19.25) (19.26) Reading: p. 400 Reading: Ex. 5.23 (19.27) MIT 2.094 Now interpolate w(r) = h1w1 + h2w2 β(r) = h1θ1 + h2θ2 Revisit the simple case: w = β = 1 + r 2 1 + r 2...
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inciple of virtual displacement (Linear Analysis) �T � − β dw dx � L EI � �T � β 0 � L � β�dx + (Ak)G 0 dw dx Two-node element: Three-node element: For a q-node element, · · · wq θ1 · · · θq �T ˆu = � w1 w = Hw ˆu β = Hβ ˆu Hw = � h1 Hβ = � 0 dx dr J = · · · · · · hq 0 0 h1 · · · 0 ·...
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inside the bracket in (20.10) and R is a result of the right hand side. For the 2-node element, w1 = θ1 = 0 w2, θ2 = ? γ = w2 L − 1 + r 2 θ2 (20.12) (20.13) (20.14) We cannot make γ equal to zero for every r (page 404, textbook). Because of this, we need to use about 200 elements to get an error of 10%. ...
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�, above � Vol v2 = 0 ⇒ � · vh for both elements is positive and the same. Now, if I choose pressures as qh�vhdVol = 0, hence (20.18) is not satisfied! (20.19) 9/3 element 9/4-c satisfies inf-sup satisfies inf-sup Getting back to beams � L EI � β 0 � L � βdx + (AkG) 0 � − β γAS dx = R w d dx � γAS γ − γ...
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(shear strain) is equal to the displacement-based shear strain at the middle of the beam. Use γAS in (20.20) to obtain a powerful element. For “our problem”, γAS = 0 hence w2 = L 2 θ2 � L EI ⇒ 0 � � β�dx = M β� x=L β �� �2 � 1 L ⇒ EI · L θ2 = M ⇒ θ2 = M L , EI w2 = M L2 2EI (exact solutions) ...
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Reissner-Mindlin plate theory For plates, and shells, w, βx, and βy as independent variables. w = displacement of mid-surface, w(x, y) A = area of mid-surface p = load per unit area on mid-surface w = w(x, y) w(x, y, z) = w(x, y) The material particles at “any z” move in the z-direction as the mid-surface. u(x, y,...
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⎞ ⎛ τxx ⎝ τyy ⎠ = τxy E 1 − ν2 ⎡ 1 ν ⎣ ν 1 0 0 (plane stress) ⎞ ⎤ ⎛ 0 �xx 0 ⎦ ⎝ �yy ⎠ = C � ν 1− γxy 2 · � � τxz = τyz E 2(1 + ν) � � γxz = Gγ γyz Principle of virtual work for the plate: � � t 2+ � A − t 2 �xx �yy γ xy ⎡ ⎣ � E 1 − ν2 � � k t 2+ � A − t 2 γ xz γ Consider a flat...
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.13) (21.14) 21. Plates and shells = · · · (21.15) MIT 2.094 For a flat element: � ⇒ 0 Kb 0 Kpl. str. ⎛ � ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ w1 θ1 x θ1 y . . . θ4 y u1 v1 . . . v4 ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠ � � � u = v = w = hiui hivi hiwi � i hiθy βx = − � βy = i hiθx From (...
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+ (1 + r)γC yz yz where γA yz = � ∂w ∂y − βy �� � � � evaluated at A from the w, βy displacement interpolations. γxz = (1 − s)γB + (1 + s)γD xz xz 1 2 1 2 1 2 1 2 (21.22) (21.23) (21.24) with this mixed interpolation, the element works. Called MITC interpolation (for mixed interpolated- tensio...
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fficulties to develop a good With Reissner-Mindlin theory, we independently interpolate rotations such that this problem does not arise. For flat structures, we can superimpose the plate bending and plane stress element stiffness. For shells, curved structures, we need to develop/use curved elements, see references. R...
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2.092/2.093 — Finite Element Analysis of Solids & Fluids I Fall ‘09 Lecture 6 - Finite Element Solution Process Prof. K. J. Bathe MIT OpenCourseWare In the last lecture, we used the principle of virtual displacements to obtain the following equations: KU = R (1) K = Σ K(m) m ; K(m) = � V (m) B(m)T C(m)B(m)d...
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) = 1 − 100 1 100 0 � � �� B(1) ⎤ ⎡ ⎥ � ⎢ ⎢ 1 ⎥ ⎣ 100 ⎦ − 100 0 1 − 100 1 � 100 K = E 1 · · 0 ⎡ � ⎣ � ⎡ � ⎣ � U1 U2 U3 U1 U2 U3 ⎤ ⎦ ⎤ ⎦ ; u(2)(x) = 0 � � � � x 1 − 80 �� H (2) ; ε(2)(x) = 0 � � − 1 80 �� B(2) ⎤ ⎦ ⎡ � U1 x ⎣ U2 80 � U3 ⎤ ⎡ � U1 1 ⎣ U2 80 � U3 ⎦ �...
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2 is A = 3 cm . This equivalent area must lie between the areas of the end faces A = 1 and A = 9. 13 2 2 Lecture 6 Finite Element Solution Process 2.092/2.093, Fall ‘09 ⎡ K = E 240 ⎢ ⎢ ⎣ 2.4 −2.4 0 −2.4 15.4 −13 0 −13 13 ⎤ ⎥ ⎥ ⎦ We note: • Diagonal terms must be positive. If the diagonal terms are z...
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πx C = ⎡ ⎣ E 1 − ν2 � 1 ν ν 1 � u x = ⎤ ⎦ f B = ρω2R N/cm3 ; R = x 4 MIT OpenCourseWare http://ocw.mit.edu 2.092 / 2.093 Finite Element Analysis of Solids and Fluids I Fall 2009 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.
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6.825 Techniques in Artificial Intelligence Planning • Planning vs problem solving • Situation calculus • Plan-space planning Lecture 10 • 1 We are going to switch gears a little bit now. In the first section of the class, we talked about problem solving, and search in general, then we did logical representatio...
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as a problem solving problem. Remember when we talked about problem solving, we were given a start state, and we searched through a tree that was the sequences of actions that you could take, and we tried to find a nice short plan. So, planning problems can certainly be viewed as problem-solving problems, but it ma...
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ving B A • Planning: DC • Start state (S) • Goal state (G) • Set of actions • Can be cast as “problem- solving” problem • But, what if initial state is not known exactly? start in bottom row in 4x4 world, with goal being C. • Do search over “sets” of E.g. E G I J K F H L Actions: N,S,E,W {I, J, K, L} ...
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A DC • Planning: • Start state (S) • Goal state (G) • Set of actions • Can be cast as “problem- solving” problem • But, what if initial state is E.g. not known exactly? start in bottom row in 4x4 world, with goal being C. • Do search over “sets” of underlying (atomic) states. E G I J K F H L Actions: N,...
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Lecture 10 • 10 One of our arguments for moving to logical representations was to be able to have a compact way of describing a set of states. You should be able to describe that set of states IJKL by saying, the proposition "bottom row" is true and you might be able to say, well, if "bottom row", and no obstacles ...
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and the idea is to use first-order logic, exactly as you know about it, to do planning. 12 Planning as Logic • The problem solving formulation in terms of sets of atomic states is incredibly inefficient because of the exponential blowup in the number of sets of atomic states. • Logic provides us with a way of d...
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