US 9,852,235 B2 · Multi-objective design optimization using adaptive classification

Abstract

Definition of a design space and an objective space for conducting multi-objective design optimization of a product is received in a computer system having a design optimization application module installed thereon. Design space is defined by design variables while objective space is defined by design objectives. First set of designs in the design space is selected. Each of the first set is evaluated in the objective space for non-dominance. Design space is partitioned into first and second regions using a multi-dimensional space division scheme (e.g., SVM). The first region is part of the design space containing all of the non-dominated design alternatives while the second region contains remaining of the design space. Second set of designs is selected within the first region. Each of the second set and existing non-dominated design alternatives are evaluated for non-dominance. Multi-objective optimization repeats the partition and evaluation until an end condition is reached.

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Claims17

Dependent claims

A method of conducting multi-objective design optimization of a product using adaptive classification for selecting design alternatives comprising:

(a) receiving a definition of a design space and an objective space for conducting multi-objective design optimization of a product in a computer system having a design optimization application module installed thereon, the design space being defined by a plurality of design variables while the objective space being defined by a plurality of design objectives;
(b) initially selecting, by the design optimization application module, a first set of design alternatives within the design space;
(c) determining, by the design optimization application module, whether each of the first set is a non-dominated design alternative by evaluating said each of the first set in the objective space, whereby the non-dominated design alternative does not improve all of the design objectives;
(d) partitioning, by the design optimization application module, the design space into first and second regions using a multi-dimensional space division scheme, the first region being part of the design space containing all of the non-dominated design alternatives while the second region containing remaining of the design space;
(e) selecting, by the design optimization application module, a second set of design alternatives within the first region according to a predetermined selection scheme;
(f) determining, by the design optimization application module, whether each of the second sets and the previously-determined non-dominated design alternatives is non-dominated in the objective space;
(g) repartitioning, by the design optimization application module, the design space into the first region and the second region using the multi-dimensional space division scheme with respect to the newly-determined non-dominated design alternatives;
and (h) repeating (e)-(g) until an end condition of the multi-objective design optimization is reached;
wherein the first region is increased by a buffer region along the first and second region's boundary.

6 more elements

A system for conducting multi-objective design optimization of a product using adaptive classification for selecting design alternatives comprising:

a main memory for storing computer readable code for a design optimization application module;
at least one processor coupled to the main memory, said at least one processor executing the computer readable code in the main memory to cause the design optimization application module to perform operations by a method of:
(a) receiving a definition of a design space and an objective space for conducting multi-objective design optimization of a product, the design space being defined by a plurality of design variables while the objective space being defined by a plurality of design objectives;
(b) initially selecting a first set of design alternatives in the design space;
(c) determining whether each of the first set is a non-dominated design alternative by evaluating said each of the first set in the objective space, whereby the non-dominated design does not improve all of the design objectives;
(d) partitioning the design space into first and second regions using a multi-dimensional space division scheme, the first region being part of the design space containing all of the non-dominated design alternatives while the second region containing remaining of the design space;
(e) selecting a second set of design alternatives within the first region according to a predetermined selection scheme;
(f) determining whether each of the second sets and the previously-determined non-dominated design alternatives is non-dominated in the objective space;
(g) repartitioning the design space into the first region and the second region using the multi-dimensional space division scheme with respect to the newly-determined non-dominated design alternatives;
and (h) repeating (e)-(g) until an end condition of the multi-objective design optimization is reached;
wherein the first region is increased by a buffer region along the first and second region's boundary.

8 more elements

A non-transitory computer readable storage medium containing instructions for conducting multi-objective design optimization of a product using adaptive classification for selecting design alternatives by a method comprising:

(a) receiving a definition of a design space and an objective space for conducting multi-objective design optimization of a product in a computer system having a design optimization application module installed thereon, the design space being defined by a plurality of design variables while the objective space being defined by a plurality of design objectives;
(b) initially selecting, by the design optimization application module, a first set of design alternatives within the design space;
(c) determining, by the design optimization application module, whether each of the first set is a non-dominated design alternative by evaluating said each of the first set in the objective space, whereby the non-dominated design alternative does not improve all of the design objectives;
(d) partitioning, by the design optimization application module, the design space into first and second regions using a multi-dimensional space division scheme, the first region being part of the design space containing all of the non-dominated design alternatives while the second region containing remaining of the design space;
(e) selecting, by the design optimization application module, a second set of design alternatives within the first region according to a predetermined selection scheme;
(f) determining, by the design optimization application module, whether each of the second sets and the previously-determined non-dominated design alternatives is non-dominated in the objective space;
(g) repartitioning, by the design optimization application module, the design space into the first region and the second region using the multi-dimensional space division scheme with respect to the newly-determined non-dominated design alternatives;
and (h) repeating (e)-(g) until an end condition of the multi-objective design optimization is reached.

5 more elements

Dependencies are inferred from the claim text.

Description3,788 words

Summary720 words

#The present invention generally relates to engineering design optimization, more particularly a multi-objective design optimization using adaptive classification for selecting design alternatives.

#Today, computer aided engineering (CAE) has been used for supporting engineers in tasks such as analysis, simulation, design, manufacture, etc. In a conventional engineering design procedure, CAE analysis (e.g., finite element analysis (FEA), finite difference analysis, meshless analysis, computational fluid dynamics (CFD) analysis, modal analysis for reducing noise-vibration-harshness (NVH), etc.) has been employed to evaluate responses (e.g., stresses, displacements, etc.). Using automobile design as an example, a particular version or design of a car is analyzed using FEA to obtain the responses due to certain loading conditions. Engineers will then try to improve the car design by modifying certain parameters or design variables (e.g., thickness of the steel shell, locations of the frames, etc.) based on specific objectives and constraints. Another FEA is conducted to reflect these changes until a “best” design has been achieved. However, this approach generally depends on knowledge of the engineers or based on a trial-or-error method.

#Furthermore, as often in any engineering problems or projects, these objectives and constraints are generally in conflict and interact with one another and design variables in nonlinear manners. Thus, it is not very clear how to modify them to achieve the “best” design or trade-off. This situation becomes even more complex in a multi-discipline optimization that requires several different CAE analyses (e.g., FEA, CFD and NVH) to meet a set of conflicting objectives. To solve this problem, a systematic approach to identify the “best” design, referred to as design optimization, is used.

Brief description of the drawings10 figures

#These and other features, aspects, and advantages of the present invention will be better understood with regard to the following description, appended claims, and accompanying drawings as follows:

#FIG. 1A is a diagram showing a tubular member (an example product) to be optimized using thickness as design variable;

#FIG. 1B is a diagram showing example solutions including Pareto optimal solutions in a multi-objective objective space;

Detailed description2,802 words

#In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. However, it will become obvious to those skilled in the art that the present invention may be practiced without these specific details. The descriptions and representations herein are the common means used by those experienced or skilled in the art to most effectively convey the substance of their work to others skilled in the art. In other instances, well-known methods, procedures, and components have not been described in detail to avoid unnecessarily obscuring aspects of the present invention.

#Reference herein to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Further, the order of blocks in process flowcharts or diagrams representing one or more embodiments of the invention do not inherently indicate any particular order nor imply any limitations in the invention.

#Embodiments of the present invention are discussed herein with reference to FIGS. 1A-7. However, those skilled in the art will readily appreciate that the detailed description given herein with respect to these figures is for explanatory purposes as the invention extends beyond these limited embodiments.

Facts

Application

14/502,715Filed 30 Sep 2014

Published

US 2016/0092606 A131 Mar 2016

Granted

26 Dec 2017

Term ends

1 Jun 2036

Estimated · how

20 years from earliest known nonprovisional domestic filing plus recorded patent term adjustment, including 610 days of adjustment. Maintenance-fee lapses, terminal disclaimers and statutory extensions have not been checked. The continuity chain may be incomplete; this is not an enforceability determination.

Classification4 codes

G06F 30/20
Computer-aided design [CAD]: design optimisation, verification or simulation
G06F 30/00
Computer-aided design [cad]
G06F 2111/06
Details relating to CAD techniques: multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms…
G06F
Computing and data processing

Timeline

30 Sep 2014Application filedUS 14/502,715
30 Sep 2014Assigned to Livermore Software Technology Corporationassignment of assignor's interest
31 Mar 2016PublishedUS 2016/0092606 A1
26 Dec 2017GrantedUS 9,852,235 B2
4 Feb 2020Assigned to Livermore Software Technology LLCmerger and change of name
16 Nov 2022Assigned to Ansys, Inc.merger and change of name
12 Oct 2026TodayWithin its term
1 Jun 2036Term endsEstimated, includes 610 days of adjustment

Multi-objective design optimization using adaptive classification

Life of this patent

Multi-objective design optimization using adaptive classification

Life of this patent

Life of this patent

Abstract

Drawings

Drawings

Claims17

Description3,788 words

Drawings

Classification4 codes

Life of this patent

Description3,788 words