Topology Sensistivity Miloslav Capek Department of - PowerPoint PPT Presentation

Topology Sensistivity Miloslav ˇ Capek Department of Electromagnetic Field Czech Technical University in Prague, Czech Republic miloslav.capek@fel.cvut.cz Seminar Lund, Sweden October 24, 2018 Miloslav ˇ Capek Topology Sensistivity 1 / 31

Outline Shape Synthesis 1 Discretization of a Model 2 Shape Synthesis Techniques 3 Topology Sensitivity: Motivation 4 Topology Sensitivity: Derivation 5 Topology Sensitivity: Examples 6 Conversion to a Graph: Greedy Algorithm 7 Concluding Remarks and Future Work 8 This talk concerns: ◮ electric currents in vacuum, ◮ time-harmonic quantities, i.e. , A ( r , t ) = Re { A ( r ) exp (j ωt ) } . Miloslav ˇ Capek Topology Sensistivity 2 / 31

Shape Synthesis Analysis × Synthesis Miloslav ˇ Capek Topology Sensistivity 3 / 31

Shape Synthesis Analysis × Synthesis Analysis ( A ) ◮ Shape Ω is given, BCs are known, determine EM quantities. g = L { J ( r ) } = A f Ω, E i � � f ≡ , g ≡ { p i } Miloslav ˇ Capek Topology Sensistivity 3 / 31

Shape Synthesis Analysis × Synthesis ? Analysis ( A ) Synthesis ( S ≡ A − 1 ) ◮ Shape Ω is given, BCs are known, ◮ EM behavior is specified, neither Ω nor determine EM quantities. BCs are known. g = L { J ( r ) } = A f f = S g = A − 1 g Ω, E i � � f ≡ , g ≡ { p i } Miloslav ˇ Capek Topology Sensistivity 3 / 31

Shape Synthesis Synthesis How to get f = A − 1 g ? Questions inherently related to synthesis are 1 ( f ≡ Ω, E i � � , g ≡ { p i } ) 1. Can g be chosen arbitrary? 2. If g is such that there exists a solution f , is that solution unique? 3. If g is known only approximately, which is always the case, is the corresponding solution for f close to the exact one? 4. If f is not exactly realized what effect will this have on A f ? 1 G. Deschamps and H. Cabayan, “Antenna synthesis and solution of inverse problems by regularization methods”, IEEE Transactions on Antennas and Propagation , vol. 20, no. 3, pp. 268–274, 1972. doi : 10.1109/tap.1972.1140197 . [Online]. Available: https://doi.org/10.1109/tap.1972.1140197 Miloslav ˇ Capek Topology Sensistivity 4 / 31

Shape Synthesis Synthesis How to get f = A − 1 g ? Questions inherently related to synthesis are 1 ( f ≡ Ω, E i � � , g ≡ { p i } ) 1. Can g be chosen arbitrary? No. 2. If g is such that there exists a solution f , is that solution unique? No. 3. If g is known only approximately, which is always the case, is the corresponding solution for f close to the exact one? No. 4. If f is not exactly realized what effect will this have on A f ? Potentially huge. Generally, infinitely many possibilities and local minima → need for shape discretization. 1 G. Deschamps and H. Cabayan, “Antenna synthesis and solution of inverse problems by regularization methods”, IEEE Transactions on Antennas and Propagation , vol. 20, no. 3, pp. 268–274, 1972. doi : 10.1109/tap.1972.1140197 . [Online]. Available: https://doi.org/10.1109/tap.1972.1140197 Miloslav ˇ Capek Topology Sensistivity 4 / 31

Discretization of a Model Discretization σ → ∞ (PEC) Ω Original problem. Miloslav ˇ Capek Topology Sensistivity 5 / 31

Discretization of a Model Discretization ǫ 0 , µ 0 σ → ∞ (PEC) Ω Ω Original problem. Equivalent problem. Miloslav ˇ Capek Topology Sensistivity 5 / 31

Discretization of a Model Discretization ǫ 0 , µ 0 σ → ∞ (PEC) Ω Ω Ω T Triangularized domain Ω T . Original problem. Equivalent problem. Structure Ω → Ω T , current density in vacuum J ( r ), r ∈ Ω T . Miloslav ˇ Capek Topology Sensistivity 5 / 31

Discretization of a Model Operators Represented In RWG Basis Functions T Starting point in this work is a given discretization into T triangles t i , Ω T = � t i . i =1 Miloslav ˇ Capek Topology Sensistivity 6 / 31

Discretization of a Model Operators Represented In RWG Basis Functions T Starting point in this work is a given discretization into T triangles t i , Ω T = � t i . i =1 RWG basis functions { ψ n ( r ) } are applied as N l n P − T + ρ − � n n n J ( r ) ≈ I n ψ n ( r ) , ρ + n P + n =1 A − A + n n T − n n r where N is the number of all inner edges. z l n ρ ± ψ n ( r ) = y 2 A ± n x O n RWG basis function ψ n ( r ). Miloslav ˇ Capek Topology Sensistivity 6 / 31

Discretization of a Model Operators Represented In RWG Basis Functions T Starting point in this work is a given discretization into T triangles t i , Ω T = � t i . i =1 RWG basis functions { ψ n ( r ) } are applied as N l n P − T + ρ − � n n n J ( r ) ≈ I n ψ n ( r ) , ρ + n P + n =1 A − A + n n T − n n r where N is the number of all inner edges. z l n ρ ± ψ n ( r ) = y Matrix representation of the operators used 2 A ± n x O n � J , A J � = [ I ∗ m � ψ m , A ψ n � I n ] = I H AI . RWG basis function ψ n ( r ). Miloslav ˇ Capek Topology Sensistivity 6 / 31

Shape Synthesis Techniques Shape Synthesis: Properties and Approaches 1. Designers’ skill and knowledge. 2. Parametric sweeps. 3. Heuristic algorithms (global optimization). 4. Topology optimization (local optimization). Miloslav ˇ Capek Topology Sensistivity 7 / 31

Shape Synthesis Techniques Shape Synthesis: Properties and Approaches 1. Designers’ skill and knowledge. • Nonintuitive/complex design? 2. Parametric sweeps. • What parameters? How many? 3. Heuristic algorithms (global optimization). • Convergence. No-free-lunch. “Solution.” 4. Topology optimization (local optimization). • This talk. . . partly. Optimal solution: ◮ Combination of all approaches. Miloslav ˇ Capek Topology Sensistivity 7 / 31

Shape Synthesis Techniques Topology Optimization � minimize f = F ( ρ ( r )) d V Ω � subject to ρ d V − V 0 ≤ 0 Ω ◮ min. compliance → max. stiffness ◮ solved within FEM ◮ mesh dependence ◮ instability (chess board) Miloslav ˇ Capek Topology Sensistivity 8 / 31

Shape Synthesis Techniques Topology Optimization � minimize f = F ( ρ ( r )) d V Ω � subject to ρ d V − V 0 ≤ 0 Ω ◮ min. compliance → max. stiffness ◮ solved within FEM ◮ mesh dependence ◮ instability (chess board) 1216 × 3456 × 256 ≈ 1 . 1 · 10 9 unknowns, FEM 2 . 2 N. Aage, E. Andreassen, B. S. Lazarov, et al. , “Giga-voxel computational morphogenesis for structural design”, Nature , vol. 550, pp. 84–86, 2017. doi : 10.1038/nature23911 Miloslav ˇ Capek Topology Sensistivity 8 / 31

Shape Synthesis Techniques Topology Optimization in EM State-of-the-art in mechanics, serious problems in EM 3 ◮ “gray” elements, rounding yields different results, ◮ numerical oscillation (chessboard), ◮ more sensitive to local minima (current paths?), ◮ threshold function for MoM. Fundamental difference between EM vector field and stiffness in mechanics? Histogram of the best candidates found for min I Q , NSGA-II. 3 S. Liu, Q. Wang, and R. Gao, “A topology optimization method for design of small GPR antennas”, Struct. Multidisc. Optim. , vol. 50, pp. 1165–1174, 2014. doi : 10.1007/s00158-014-1106-y Miloslav ˇ Capek Topology Sensistivity 9 / 31

Topology Sensitivity: Motivation Topology Sensitivity Idea behind this work Let us accept NP-hardness of the problem, do brute force, but do it cleverly. . . Miloslav ˇ Capek Topology Sensistivity 10 / 31

Topology Sensitivity: Motivation Topology Sensitivity Idea behind this work Let us accept NP-hardness of the problem, do brute force, but do it cleverly. . . ◮ Inspired by pixeling 4 , but RWG functions are the unknowns ( T vs. N unknowns). ◮ Fixed mesh grid Ω T : operators calculated once, results comparable with the bounds. ◮ Woodbury identity employed: get rid of repetitive matrix inversion! ◮ Feeding is specified at the beginning. 4 Y. Rahmat-Samii, J. M. Kovitz, and H. Rajagopalan, “Nature-inspired optimization techniques in communication antenna design”, Proc. IEEE , vol. 100, no. 7, pp. 2132–2144, 2012. doi : 10.1109/JPROC.2012.2188489 Miloslav ˇ Capek Topology Sensistivity 10 / 31

Topology Sensitivity: Motivation Comparison of Pixeling Techniques Pixel removal   Z 11 Z 12 Z 13 · · · Z 1 N T 6 T 4   Z 21 Z 22 Z 23 · · · Z 2 N     T 1 T 8   Z 31 Z 32 Z 33 · · · Z 3 N   T 3 T 7    . . . .  ... . . . .   . . . .     T 2 T 5 Z N 1 Z N 2 Z N 3 · · · Z NN Classical pixeling removes metallic patches 5 . ( Z G + Z L ) I = ZI = V 5 Y. Rahmat-Samii, J. M. Kovitz, and H. Rajagopalan, “Nature-inspired optimization techniques in communication antenna design”, Proc. IEEE , vol. 100, no. 7, pp. 2132–2144, 2012. doi : 10.1109/JPROC.2012.2188489 Miloslav ˇ Capek Topology Sensistivity 11 / 31