JP5774733B2 - 物理的構造/物体の設計の最適化 - Google Patents
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/06—Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA]
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
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Description
・進化的アルゴリズム(例えば、遺伝的アルゴリズム及び進化的戦略(evolution strategies)、差分進化(differential evolution))
・群ベースの最適化アルゴリズム(Swarm-based optimization algorithms)(例えば、粒子群最適化(particle swarm optimization)、複数群最適化(Multi-swarm optimization)及び蟻コロニー最適化(ant colony optimization))
トポロジー最適化 − 機械構造の設計に関する概念構造を自動で提供するプロセスをいう。
更新ストラテジ − トポロジー最適化における材料再配分のための更新信号に代わる感度を、利用可能なローカル情報に基づいて計算するのに用いられる、機能的関係。
勾配情報 − 探索空間における、設計品質が改善される方向。
Claims (12)
- 物理的物体のトポロジー最適化のためのコンピュータ支援の方法であって、
a)前記物理的物体の構造を構成することとなる局所部分を表す各セルの材料の充填状況を表す設計変数を定義するステップと、
b)停止基準が満たされるまで、更新ストラテジに基づいて前記各セルに対する材料の再配分を繰り返し行うステップと、
c)最適化された設計を表す信号を出力するステップと、
を有し、
前記更新ストラテジは、前記セルが表す前記物理的物体の構造の局所情報に基づいて前記材料の再配分を行うための更新信号を生成するのに用いられ、
前記停止基準が満たされたときの、前記セルが表す前記物理的物体の構造が収束基準を満たさないときは、前記更新ストラテジを更新して、当該更新された更新ストラテジに基づいてステップbを繰り返し、
前記収束基準が満たされたときに、前記物理的物体の構造の最適化された設計を表す信号を出力する、
ことを特徴とする、方法。 - 前記更新ストラテジは、機械学習又は確率最適化を用いて更新される、
請求項1に記載の方法。 - 前記更新ストラテジは、ニューラルネットワークであって、
前記ニューラルネットワークは、当該ニューラルネットワークの重みを最適化法又は学習法により変更することにより更新される、
請求項1に記載の方法。 - ステップbを最初に行う際に用いる更新ストラテジは、所与の更新ストラテジの初期セット又はランダムに選択された更新ストラテジのセットである、
請求項1ないし3のいずれか一項に記載の方法。 - 前記停止基準が満たされたときの前記セルが表す前記物理的物体の構造が収束基準を満たすか否かは、当該物理的物体の構造についての構造解析に基づいて判断され、
前記構造解析は、有限要素解析である、
請求項1ないし4のいずれか一項に記載の方法。 - 前記停止基準が満たされたときの前記セルが表す前記物理的物体の構造が収束基準を満たすか否かは、当該物理的物体の構造についての構造解析に基づいて得られる品質関数又は物理的特性に基づいて判断される、
請求項1ないし5のいずれか一項に記載の方法。 - 最適化された前記物理的物体の評価結果が、更新ストラテジを改良又は適合させるための情報として用いられる、
請求項1ないし6のいずれか一項に記載の方法。 - 前記設計変数は、前記各セル内の材料の量を表すものである、請求項1ないし7のいずれか一項に記載の方法。
- 前記物理的物体についての所望の質量、及び又は物理的物体の設計から算出される質量が、初期的に固定されるか又は固定されない、請求項1ないし8のいずれか一項に記載の方法。
- 前記物理的物体が、熱力学的、空気力学的、又は流体力学的なパラメータ、並びに重量及び機械的特性の、少なくとも一つの点において最適化される、請求項1ないし9のいずれか一項に記載の方法。
- 前記物理的物体は、陸上用、航空用、及び又は海洋用の乗り物、及び又はロボット、又はこれらの部分である、請求項1ないし10のいずれか一項に記載の方法。
- 物理的物体の設計の最適化のためのシステムであって、
前記物理的物体の設計変数を定義する手段と、
停止基準が満たされるまで、更新ストラテジに基づいて前記各セルに対する材料の再配分を繰り返し行う手段と、
最適化された設計を表す信号を出力する手段と、
を備え、
前記更新ストラテジは、前記セルが表す前記物理的物体の構造の局所情報に基づいて前記材料の再配分を行うための更新信号を生成するのに用いられ、
前記停止基準が満たされたときの、前記セルが表す前記物理的物体の構造が収束基準を満たさないときは、前記更新ストラテジを更新して、当該更新された更新ストラテジに基づいて前記材料の再配分を繰り返し行い、
前記収束基準が満たされたときに、前記物理的物体の構造の最適化された設計を表す信号を出力する、
ことを特徴とする、システム。
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EP13153179.0A EP2763058B1 (en) | 2013-01-30 | 2013-01-30 | Optimizing the design of physical structures/objects |
EP13153179.0 | 2013-01-30 |
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JP2014149818A JP2014149818A (ja) | 2014-08-21 |
JP5774733B2 true JP5774733B2 (ja) | 2015-09-09 |
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US (1) | US10853528B2 (ja) |
EP (1) | EP2763058B1 (ja) |
JP (1) | JP5774733B2 (ja) |
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EP2763058B1 (en) | 2021-10-20 |
US20140214370A1 (en) | 2014-07-31 |
US10853528B2 (en) | 2020-12-01 |
JP2014149818A (ja) | 2014-08-21 |
EP2763058A1 (en) | 2014-08-06 |
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