Adjoint variable‐based shape optimization with bounding surface constraints

2020 ◽  
Author(s):  
Marios G. Damigos ◽  
Evangelos M. Papoutsis‐Kiachagias ◽  
Kyriakos C. Giannakoglou
Keyword(s):  
Shape Optimization ◽  
Adjoint Variable ◽  
Bounding Surface ◽  
Surface Constraints

1905 Shape Optimization Using Parameter-Survey-Type Parallel Adjoint Variable Method

2005 ◽  
Vol 2005.18 (0) ◽  
pp. 561-562
Author(s):  
Kazunori Shinohara ◽  
Hiroshi Okuda ◽  
Norihiro Nakajima ◽  
Masato Ida
Keyword(s):  
Shape Optimization ◽  
Variable Method ◽  
Adjoint Variable Method ◽  
Adjoint Variable

Shape optimization using adjoint variable method for reducing drag in Stokes flow

2008 ◽  
Vol 58 (2) ◽  
pp. 119-159 ◽  
Author(s):  
Kazunori Shinohara ◽  
Hiroshi Okuda ◽  
Satoshi Ito ◽  
Norihiro Nakajima ◽  
Masato Ida
Keyword(s):  
Shape Optimization ◽  
Stokes Flow ◽  
Variable Method ◽  
Adjoint Variable Method ◽  
Adjoint Variable

scholarly journals Shape Optimization Using Adjoint Variable Method for Reducing Drag

2007 ◽  
Vol 1 (2) ◽  
pp. 166-177
Author(s):  
Kazunori SHINOHARA ◽  
Hiroshi OKUDA ◽  
Satoshi ITO ◽  
Norihiro NAKAJIMA ◽  
Masato IDA
Keyword(s):  
Shape Optimization ◽  
Variable Method ◽  
Adjoint Variable Method ◽  
Adjoint Variable

Shape Optimization Using an Adjoint Variable Method in ITBL Grid Environment

2006 ◽  
Author(s):  
K. Shinohara ◽  
H. Okuda ◽  
S. Ito ◽  
N. Nakajima ◽  
M. Ida
Keyword(s):  
Shape Optimization ◽  
Parallel Algorithm ◽  
Drag Force ◽  
Large Scale ◽  
Adjoint Equation ◽  
Variable Method ◽  
Steepest Descent Method ◽  
Adjoint Variable Method ◽  
Adjoint Variable ◽  
Fluid Drag

To decrease the fluid drag force on the surface of a specified object subjected to an unsteady flow, under a constant volume condition, the adjoint variable method is formulated by using FEM. Based on the Lagrange multiplier method (a conditional variational principle), this method consists of the state equation, the adjoint equation and the sensitivity equation. To solve the equations effectively using the steepest descent method, a parallel algorithm that finds the Armijo’s line-search step size is constructed. The shape optimization code for solving a large scale 3D problem using a parallel algorithm was implemented on ITBL [1,2] using the HPC-MW library [3]. Results show that, by using shape optimization, the fluid drag force on the object can be reduced by about 17.5%.

scholarly journals Adjoint Variable Method for Multi-Objective Sizing and Shape Optimization

2009 ◽  
Vol 3 (1) ◽  
pp. 275-286 ◽  
Author(s):  
Chen Jian Ken LEE ◽  
Wataru FURUYA ◽  
Masato TANAKA ◽  
Naoki TAKANO
Keyword(s):  
Shape Optimization ◽  
Variable Method ◽  
Adjoint Variable Method ◽  
Adjoint Variable ◽  
Multi Objective

Acoustic Shape Optimization Based on Isogeometric Wideband Fast Multipole Boundary Element Method with Adjoint Variable Method

2020 ◽  
Vol 28 (02) ◽  
pp. 2050015
Author(s):  
Jie Wang ◽  
Changjun Zheng ◽  
Leilei Chen ◽  
Haibo Chen
Keyword(s):  
Boundary Element Method ◽  
Shape Optimization ◽  
Boundary Element ◽  
Acoustic Scattering ◽  
Variable Method ◽  
Optimization Approach ◽  
Adjoint Variable Method ◽  
Adjoint Variable ◽  
Fast Multipole ◽  
Element Method

A shape optimization approach based on isogeometric wideband fast multipole boundary element method (IGA WFMBEM) in 2D acoustics is developed in this study. The key treatment is shape sensitivity analysis by using the adjoint variable method under isogeometric analysis (IGA) conditions. A set of efficient parameters of the wideband fast multipole method has been identified for IGA boundary element method. Shape optimization is performed by applying the method of moving asymptotes. IGA WFMBEM is validated through an acoustic scattering example. The proposed optimization approach is tested on a sound barrier and two multiple structures to demonstrate its potential for engineering problems.

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