Material-based subdomain hybrid cellular automata algorithm for material optimization of thin-walled frame structures

Patent metadata

Field	Value
Publication number	US-12032885-B2
Application number	US-202117623617-A
Country	US
Kind code	B2
Filing date	Mar 23, 2021
Priority date	Mar 4, 2021
Publication date	Jul 9, 2024
Grant date	Jul 9, 2024

Abstract

The present invention provides a material-based subdomain hybrid cellular automata algorithm for solving material optimization of thin-walled frame structures, including an outer loop and an inner loop: the outer loop is to define and update the target cost for the inner loop; the inner loop is to adjust material using a PID control strategy according to the nominal flow stress of a current cell and the nominal flow stress of candidate materials, so that a current cost of the inner loop converges to the target cost. During the execution of the inner loop, the cellular material update rule based on the PID control strategy is employed to update cellular material, to define the candidate material library and the nominal flow stress, to update the nominal flow stress of current cell, to compare the nominal flow stress with the actual flow stress of each material in the candidate material library, to select the candidate material closest to the nominal flow stress as the selected material grade for the current cell and to replace the material parameters of the current cell with the mechanical parameters of the selected material. The present invention can efficiently solve nonlinear the dynamic response optimization problems containing a large number of material variables, significantly improving the robustness of the algorithm.

First claim

What is claimed is:

1. A material-based subdomain hybrid cellular automata method for solving material optimization of thin-walled frame structures, comprising the following steps:
S1: establishing, by a processor, an initial crash finite element model, constructing a subdomain cellular automata model, defining material variables and field variables of the thin-walled frame structures, and employing the initial crash finite element model for material and cost optimization;
S2: executing, by the processor, an outer loop: calculating a cell internal energy density and a constraint value at a current design point by finite element analysis and updating a target cost by a penalty function method according to an extent of the current design point violating a constraint boundary;
S3: executing, by the processor, an inner loop with the following steps:
S3.1: constructing a step internal energy density (IED) target (SIED*) function and updating a target IED;
S3.2: updating a cell material by a material updating rule based on a proportional integral derivative (PID) control strategy;
specifically: defining a candidate material library and a nominal flow stress of each material, updating a nominal flow stress of a current cell, comparing the nominal flow stress with a true flow stress of each material in the candidate material library, and selecting a candidate material closest to the nominal flow stress as a selected material of the current cell, replacing material parameters of the current cell with mechanical parameters of the selected material;
S3.3: executing S4 and exiting an inner loop if the inner loop is convergent, otherwise returning to S3.1;

S4: outputting, by the processor, optimal results if global convergence conditions in the outer loop are satisfied, otherwise returning to S2 for updating the cell material in the inner loop; and
S5: selecting materials in the optimal results and assembling the thin-walled frame structures with the selected materials;
wherein the thin-walled frame structures are selected from the group consisting of A-pillar B-pillar, sill, roof-rail, front door, rear door, rear side member, seat crossbeam, front side member rear section, seat rear crossbeam, rear floor front crossbeam, roof front crossbeam, roof middle crossbeam, and roof rear crossbeam.

2. The material-based subdomain hybrid cellular automata method for solving the material optimization of the thin-walled frame structures according to claim 1 , wherein the candidate material library is defined as follows:

Mat
=

{

Mat
⁡
(
1
)

,
…
,

Mat
⁡
(
s
)

,
…
,

Mat
⁡
(
l
)

}

,

1
≤
s
≤
l

=

{

(

ρ
1

,

E
1

,

σ

y
⁢
1

,

σ

u
⁢
1

,

σ

f
⁢
1

,
…

)

,
…
,

(

ρ
s

,

E
s

,

σ

y
⁢
s

,

σ

u
⁢
s

,

σ

f
⁢
s

,
…

)

…
,

(

ρ
l

,

E
l

,

σ

y
⁢
l

Assignees

• Univ Jiangsu

Inventors

• Duan Libin
• Xu Wei
• Liu Xing
• Luo Xin

Classifications

• G06F30/15 Primary

  Vehicle, aircraft or watercraft design · CPC title

• G06F2119/14

  Force analysis or force optimisation, e.g. static or dynamic forces · CPC title

• G06F2111/06

  Multi-objective optimisation, e.g. Pareto optimisation using simulated annealing [SA], ant colony algorithms or genetic algorithms [GA] · CPC title

• G06F2111/10

  Numerical modelling · CPC title

• Y02T90/00

  Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation · CPC title