Finite Element Approximation for Optimal Shape Design: Theory and ApplicationsExplains how to speed the optimal shape design process using a computer. Outlines the problems inherent in optimal shape design and discusses methods of their solution. Concentrates on finite element approximation and describes numerical realization of optimization techniques. Treats optimal design problems via the optimal control theory when the state systems are governed by variational inequalities. Provides useful background information, followed by numerous approaches to optimal shape design, all supported by illustrative examples. Appendices provide algorithms and numerous examples and their calculations are included. |
From inside the book
Results 1-3 of 24
Page 10
... positive constant C = C ( N ) , such that ( 1.12 ) || u || 1,22 ≤ C | u | 1,02 holds for all u € V = { u € H1 ( N ) | u = 0 on г1 } , where Ã1 is a part of ÎN with meas F1 > 0 . The analogue of Friedrichs ' inequality is Theorem 1.8 ...
... positive constant C = C ( N ) , such that ( 1.12 ) || u || 1,22 ≤ C | u | 1,02 holds for all u € V = { u € H1 ( N ) | u = 0 on г1 } , where Ã1 is a part of ÎN with meas F1 > 0 . The analogue of Friedrichs ' inequality is Theorem 1.8 ...
Page 38
... ( positive constant ) . The radius Ro of the mounting surface E1 is fixed so that the boundary surface Σ1 is fixed in the design problem . Using the axial symmetry of our problem , one can consider the situation in R2 ( see Figure 2.5 b ) ...
... ( positive constant ) . The radius Ro of the mounting surface E1 is fixed so that the boundary surface Σ1 is fixed in the design problem . Using the axial symmetry of our problem , one can consider the situation in R2 ( see Figure 2.5 b ) ...
Page 226
... positive constants . In this case V = H2 ( ( 0,1 ) ) , U = C ( [ 0,1 ] ) , K = { y Є V | | y ( x ) | ≤ r in [ 0 , 1 ] } , B = 0 , y = 0 , au ( y , v ) = √ = [ ' bu3y " v " dx . ( P2 ) has at least one solution , by Theorem 10.1 a ) ...
... positive constants . In this case V = H2 ( ( 0,1 ) ) , U = C ( [ 0,1 ] ) , K = { y Є V | | y ( x ) | ≤ r in [ 0 , 1 ] } , B = 0 , y = 0 , au ( y , v ) = √ = [ ' bu3y " v " dx . ( P2 ) has at least one solution , by Theorem 10.1 a ) ...
Contents
Preliminaries | 1 |
Abstract setting of optimal shape design problem and | 28 |
Optimal shape design of systems governed by a unilateral | 53 |
Copyright | |
10 other sections not shown
Other editions - View all
Common terms and phrases
adjoint algorithm Appendix applied approximation boundary value problem C₁ Céa compute constraints contact problems convex convex set cost functional defined denote design sensitivity analysis differentiable discrete domain elastic exist a subsequence Figure Find finite element follows formula given Gm(a H¹(Î Haslinger Haug Hlaváček I₁ Ir(an ITERATION jEJk Komkov Lagrange multipliers least one solution Lemma lim inf lim sup linear Lipschitz Lipschitz continuous lower semicontinuous mapping material derivative matrix method minimization Nečas Neittaanmäki nodes nonlinear nonlinear programming nonsmooth Numerical results obtain optimal control optimal design optimal pair optimal shape design parameter Pironneau Proof results for Example Section sensitivity analysis sequence shape design problems Shape optimization Sokolowski solves P(a subgradient subset T₁ Theorem triangulation un(an unilateral boundary value variational inequality vector w₁ Zolesio г₁