CN114491996A - Optimization method of truss structure design, processor and storage medium - Google Patents
Optimization method of truss structure design, processor and storage medium Download PDFInfo
- Publication number
- CN114491996A CN114491996A CN202210035344.0A CN202210035344A CN114491996A CN 114491996 A CN114491996 A CN 114491996A CN 202210035344 A CN202210035344 A CN 202210035344A CN 114491996 A CN114491996 A CN 114491996A
- Authority
- CN
- China
- Prior art keywords
- design
- optimization
- truss
- final
- initial
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013461 design Methods 0.000 title claims abstract description 242
- 238000005457 optimization Methods 0.000 title claims abstract description 147
- 238000000034 method Methods 0.000 title claims abstract description 58
- 238000003860 storage Methods 0.000 title claims abstract description 23
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 55
- 238000004364 calculation method Methods 0.000 claims abstract description 36
- 238000012360 testing method Methods 0.000 claims abstract description 33
- 238000012795 verification Methods 0.000 claims description 37
- 238000013401 experimental design Methods 0.000 claims description 13
- 230000002068 genetic effect Effects 0.000 claims description 13
- 238000006073 displacement reaction Methods 0.000 claims description 12
- 238000009826 distribution Methods 0.000 claims description 12
- 238000010206 sensitivity analysis Methods 0.000 claims description 6
- 230000015654 memory Effects 0.000 description 46
- 230000006870 function Effects 0.000 description 23
- 238000010586 diagram Methods 0.000 description 11
- 238000012545 processing Methods 0.000 description 11
- 238000004590 computer program Methods 0.000 description 10
- 230000008569 process Effects 0.000 description 7
- 230000001360 synchronised effect Effects 0.000 description 6
- 238000011161 development Methods 0.000 description 3
- 238000002474 experimental method Methods 0.000 description 3
- 230000003287 optical effect Effects 0.000 description 3
- 230000003068 static effect Effects 0.000 description 3
- 238000005516 engineering process Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000003491 array Methods 0.000 description 1
- 230000005540 biological transmission Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000013400 design of experiment Methods 0.000 description 1
- 235000019800 disodium phosphate Nutrition 0.000 description 1
- 238000011156 evaluation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
- 230000005012 migration Effects 0.000 description 1
- 108090000623 proteins and genes Proteins 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
Images
Classifications
-
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- 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
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/04—Constraint-based CAD
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Artificial Intelligence (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Medical Informatics (AREA)
- Biomedical Technology (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Biophysics (AREA)
- Mathematical Optimization (AREA)
- Health & Medical Sciences (AREA)
- Computational Linguistics (AREA)
- Computational Mathematics (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Data Mining & Analysis (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Complex Calculations (AREA)
Abstract
The embodiment of the application provides an optimization method of a truss structure design, a processor and a storage medium. The optimization method comprises the following steps: determining initial design variables and constraint conditions according to typical working conditions of the truss; determining an objective function according to the optimization target of the truss; establishing an initial optimization task model according to the initial design variables; performing test design on the initial optimization task model by using a preset test design algorithm, and determining design variables meeting preset conditions in the initial design variables as final design variables; establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function; and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
Description
Technical Field
The application relates to the technical field of engineering machinery, in particular to an optimization method of a truss structure design, a processor and a storage medium.
Background
The truss is a structure formed by connecting rod pieces at two ends by hinges, can fully utilize the strength of materials, saves materials compared with a solid web beam when the span is large, reduces the dead weight and increases the rigidity, and is widely applied to the field of engineering machinery. The truss structure is often stressed greatly, has multiple working conditions, is complex in structure, has severe working conditions, requires light weight and works reliably, so that the truss structure is necessary to be reasonably designed and optimized.
The existing optimization mode of truss structure design usually adopts parameter optimization, parametric modeling is carried out through finite element software, design parameters are corrected by utilizing multi-parameter optimization software or an optimization algorithm, then the corrected parameters are returned to an output file, and the next iteration is carried out until an optimal result is obtained. However, the structure of the truss is complex, and the optimization calculation amount required by using the traditional parameter optimization algorithm is too much, so that the processing time is long and the efficiency is low.
Disclosure of Invention
An object of the embodiments of the present application is to provide an optimization method, a processor, and a storage medium for a truss structure design.
In order to achieve the above object, a first aspect of the present application provides a method for optimizing a truss structure design, including:
determining initial design variables and constraint conditions according to typical working conditions of the truss;
determining an objective function according to the optimization target of the truss;
establishing an initial optimization task model according to the initial design variables;
performing test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable which meets a preset condition in the initial design variables as a final design variable;
establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function;
and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
In the embodiment of the present application, a preset trial design algorithm is used to perform trial design on an initial optimization task model, and a design variable meeting a preset condition in initial design variables is determined as a final design variable, including:
carrying out test design on the initial optimization task model by using an optimal Latin hypercube algorithm to obtain a test design result;
sensitivity analysis is carried out on the test design result, and the influence coefficient of each design variable in the initial design variables is determined;
and selecting the design variables with the influence coefficients larger than the preset value in the initial design variables as final design variables.
In this embodiment of the present application, after selecting, as a final design variable, a design variable having an influence coefficient greater than a preset value among initial design variables, the optimization method further includes:
and determining the value boundary of the final design variable.
In the embodiment of the present application, performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain an optimal value of a final design variable, including:
performing optimization calculation on the final optimization task model by using a multi-island genetic algorithm to obtain an optimal solution of a final design variable;
verifying the optimal solution by using a preset multi-dimensional working condition to obtain a verification result;
and determining the optimal value of the final design variable according to the verification result.
In the embodiment of the present application, determining the optimal value of the final design variable according to the verification result includes:
determining whether the verification result meets a preset standard;
determining the optimal solution as the optimal value of the final design variable under the condition that the verification result meets the preset standard;
and under the condition that the verification result does not meet the preset standard, optimizing and calculating the final optimization task model by reusing the multi-island genetic algorithm.
In an embodiment of the present application, the initial design variables include a chord size parameter of the truss, a chord distribution parameter of the truss, a web size parameter of the truss, and a web distribution parameter of the truss.
In the embodiment of the present application, the constraint condition includes formula (1):
wherein σiRepresenting the stress of the ith frame beam unit of the truss,
represents allowable stress, umaxRepresents the maximum displacement of the truss or trusses,
representing allowable displacement, λminRepresents the minimum buckling characteristic value of the truss,λrepresenting the allowable buckling eigenvalues.
In an embodiment of the present application, the objective function includes formula (2):
wherein Mass represents the Mass of the truss, rho represents the density, n represents the number of frame beam units of the truss, and liLength of i-th frame beam unit representing truss, AiRepresenting the cross-sectional area of the ith frame beam unit of the truss.
A second aspect of the present application provides a processor configured to perform the above-described method for optimizing a truss structure design.
A third aspect of the present application provides a machine-readable storage medium having stored thereon instructions that, when executed by a processor, cause the processor to be configured to perform the above-described method of optimizing a truss structure design.
Through the technical scheme, the initial design variables and the constraint conditions are determined according to the typical working conditions of the truss, the objective function is determined according to the optimization target of the truss, the initial optimization task model is established according to the initial design variables, then the initial optimization task model is subjected to experimental design by using the preset experimental design algorithm, the design variables meeting the preset conditions in the initial design variables are determined as the final design variables, the final optimization task model is established according to the final design variables, the constraint conditions and the objective function, the final optimization task model is subjected to optimization calculation by using the preset optimization algorithm to obtain the optimal values of the final design variables, and the design variables meeting the preset conditions in the initial design variables are screened out by using the preset experimental design algorithm as the final design variables in the way, so that the optimization calculation amount required in the subsequent optimization calculation is greatly reduced, the processing efficiency is improved.
Additional features and advantages of embodiments of the present application will be described in detail in the detailed description which follows.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the disclosure and are incorporated in and constitute a part of this specification, illustrate embodiments of the disclosure and together with the description serve to explain the embodiments of the disclosure, but are not intended to limit the embodiments of the disclosure. In the drawings:
fig. 1 is a schematic flow chart of a method for optimizing a truss structure design provided in an embodiment of the present application;
fig. 2 is a schematic flowchart of step S14 in the optimization method for truss structure design provided in the embodiment of the present application;
fig. 3 is a schematic flowchart of step S16 in the optimization method for truss structure design provided in the embodiment of the present application;
fig. 4 is a schematic flowchart of step S163 in the optimization method for truss structure design provided in the embodiment of the present application;
fig. 5 is an internal structural diagram of a computer device provided in the embodiment of the present application.
Detailed Description
To make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it should be understood that the specific embodiments described herein are only used for illustrating and explaining the embodiments of the present application and are not used for limiting the embodiments of the present application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a schematic flow chart of a method for optimizing a truss structure design provided in an embodiment of the present application. As shown in fig. 1, in an embodiment of the present application, a method for optimizing a truss structure design is provided, which includes the following steps:
step S11: determining initial design variables and constraint conditions according to typical working conditions of the truss;
step S12: determining an objective function according to the optimization target of the truss;
step S13: establishing an initial optimization task model according to the initial design variables;
step S14: performing test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable which meets a preset condition in the initial design variables as a final design variable;
step S15: establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function;
step S16: and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
Specifically, in steps S11 and S12, the initial design variables and constraints of the truss and the objective function are determined first, and it is understood that the number of design variables included in the initial design variables of the truss is large. In step S13, after the initial design variables, the constraint conditions, and the objective function of the truss are determined, common finite element software, such as abaqus, ansys, optistruct, etc., may be called according to the initial design variables to establish an initial optimization task model. In step S14, the DOE (Design of Experiments, chinese full name) selects relatively fewest sample points on the basis of completing the experiment objective to save the experiment cost and maximize the amount of information about the unknown space, and performs the experiment Design on the initial optimization task model to determine the Design variables meeting the preset conditions in the initial Design variables and use them as the final Design variables, which can be understood that the number of the Design variables included in the final Design variables is relatively small. In step S15, a final optimization task model is built using the determined final design variables. In step S16, since the number of design variables included in the final design variables is relatively small, the amount of optimization calculation required when performing the optimization calculation on the final optimization task model using the preset optimization algorithm is greatly reduced. Through the mode, the design variables meeting the preset conditions are screened out from the initial design variables by using the preset experimental design algorithm to serve as the final design variables, so that the optimized calculation amount required in the subsequent optimized calculation is greatly reduced, and the processing efficiency is improved.
Referring to fig. 2, fig. 2 is a schematic flowchart illustrating step S14 in the method for optimizing a truss structure design according to the embodiment of the present application. The performing a test design on the initial optimization task model by using a preset test design algorithm in step S14, and determining a design variable meeting a preset condition in the initial design variables as a final design variable, may include the following steps:
step S141: carrying out test design on the initial optimization task model by using an optimal Latin hypercube algorithm to obtain a test design result;
step S142: sensitivity analysis is carried out on the test design result, and the influence coefficient of each design variable in the initial design variables is determined;
step S143: and selecting the design variables with the influence coefficients larger than the preset value in the initial design variables as final design variables.
Specifically, in step S141, the optimal latin hypercube algorithm is a common design algorithm in the experimental design, when the experimental design is performed, latin letters represent processing factors, rows and columns represent the other two factors, respectively, and the test units are arranged into latin squares to arrange the test, so as to obtain the experimental design result. In step S142, through sensitivity analysis of the obtained experimental design results, influence coefficients of each design variable in the initial design variables on the truss structure performance can be determined. In step S143, design variables having influence coefficients greater than a preset value may be screened from the initial design variables, that is, the design variables having relatively largest influence on the truss structure performance are selected as final design variables.
In one embodiment, after selecting the design variables with influence coefficients larger than the preset value from the initial design variables as the final design variables in step S143, the optimization method further includes:
and determining the value boundary of the final design variable.
Specifically, the value of the final design variable is necessarily adjustable within a certain range, but the adjustment range needs to be limited, so that the optimization calculation amount required in the subsequent optimization calculation is further reduced.
Referring to fig. 3, fig. 3 is a schematic flowchart illustrating step S16 in the method for optimizing a truss structure design according to the embodiment of the present application. Performing optimization calculation on the final optimization task model by using the preset optimization algorithm in step S16 to obtain an optimal value of the final design variable, which may include the following steps:
step S161: performing optimization calculation on the final optimization task model by using a multi-island genetic algorithm to obtain an optimal solution of a final design variable;
step S162: verifying the optimal solution by using a preset multi-dimensional working condition to obtain a verification result;
step S163: and determining the optimal value of the final design variable according to the verification result.
Specifically, in step S161, the multi-island genetic algorithm is one of the solutions proposed for the early maturity of the genetic algorithm, and by adding a plurality of "islands" on the basis of the genetic algorithm, each island has some individuals, and assuming that the individuals can migrate between each island, the individuals with excellent migration ability, i.e. elite individuals, which have excellent genes, can help the algorithm jump out of the local optimal point to achieve global optimal. In step S162, the preset multidimensional operating condition may include a normal operating condition, a limit operating condition, and the like, the optimal solution of the final design variable is used to establish a geometric model of the truss optimized structure, and the preset multidimensional operating condition is loaded, so that the evaluation parameters such as quality, displacement, maximum stress, and the like may be output, and the optimal solution is verified to obtain a verification result. Further, in step S163, based on the verification result, the optimum values of the final design variables can be determined.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating a step S163 in the optimization method for truss structure design provided in the embodiment of the present application. The determining of the optimal values of the final design variables according to the verification result in step S163 may include the steps of:
step S1631: determining whether the verification result meets a preset standard;
step S1632: determining the optimal solution as the optimal value of the final design variable under the condition that the verification result meets the preset standard;
step S1633: and under the condition that the verification result does not meet the preset standard, the multi-island genetic algorithm is reused for carrying out optimization calculation on the final optimization task model.
Specifically, in step S1632, if the verification result meets the preset criterion, the currently obtained optimal solution may be determined as the optimal value of the final design variable. In step S1633, if the verification result does not meet the preset standard, the multi-island genetic algorithm needs to be reused to perform optimization calculation on the final optimization task model to obtain a new optimal solution, the new optimal solution is verified by using the preset multi-dimensional working condition to obtain a new verification result, and then whether the verification result meets the preset standard is determined again, until the new verification result meets the preset standard, the new optimal solution is determined as the optimal value of the final design variable.
In one embodiment, the initial design variables include a chord size parameter of the truss, a chord distribution parameter of the truss, a web size parameter of the truss, and a web distribution parameter of the truss.
In particular, the truss may be a crane fly jib, which is mainly composed of chords and web members. The chord dimension parameters of the crane auxiliary arm can comprise an upper chord dimension parameter and a lower chord dimension parameter of the crane auxiliary arm; the chord member distribution parameters of the crane jib can comprise rear end width parameters of the crane jib, front end width parameters, rear end height parameters, front end height parameters, length parameters of the crane jib and the like, the web member distribution parameters of the crane jib can comprise rear end to nearest vertical web member distance parameters of the crane jib, span parameters of the web members, distance parameters between two web members, distance parameters between the front end to the nearest web member, distance parameters of a first vertical web member and an inclined web member, variation parameters of the span of the web members, variation parameters of the distance between two web members and the like.
In one embodiment, the constraints include formula (1):
wherein σiRepresenting the stress of the ith frame beam unit of the truss,
represents allowable stress, umaxRepresents the maximum displacement of the truss or trusses,
representing allowable displacement, λminRepresents the minimum buckling characteristic value of the truss,λrepresenting the allowable buckling eigenvalues.
In one embodiment, the objective function includes formula (2):
wherein Mass represents the Mass of the truss, rho represents the density, n represents the number of frame beam units of the truss, and liLength of i-th frame beam unit representing truss, AiRepresenting the cross-sectional area of the ith frame beam unit of the truss.
Specifically, optimization of the truss belongs to discrete body optimization, the embodiment of the application takes the lightest mass of the truss as an objective function, and takes the stress condition limit of each frame beam unit of the truss, the maximum displacement condition limit of the truss and the minimum buckling characteristic value condition limit of the truss as constraint conditions, so that the optimization design of the truss of discrete variables can be efficiently solved by using a genetic algorithm.
In practical application, the Python can be used for carrying out secondary development on finite element software such as abaqus, ansys, optistruct and the like in advance (wherein abaqus can be directly used for carrying out secondary development, software such as ansys, optistruct and the like can also generate corresponding secondary development languages through Python and be executed through batch processing), so that input files such as txt, excel and the like can be automatically read, a model is established and calculation is completed, output files such as txt, csv and the like can be output, a library pydo capable of carrying out DOE and a library capable of carrying out scientific calculation are arranged in Python, previous input files and previous output files can be directly read, an abaqu solver is called to carry out modeling and optimization, the optimal value of a final design variable is obtained, and finally the optimal value of the final design variable is transmitted back to abaqus to carry out modeling, a geometric model of a truss optimization structure is output, and an optimization report document is automatically generated at the same time. The optimization process of the truss structure design can be completed through python, only a finite element software solver needs to be called in the whole process, switching between multiple pieces of software is not needed, the operation from parameter input to geometric model and report obtaining can be completed in one key, and the operation is convenient and rapid.
Through the technical scheme, the initial design variables and the constraint conditions are determined according to the typical working conditions of the truss, the objective function is determined according to the optimization target of the truss, the initial optimization task model is established according to the initial design variables, then the initial optimization task model is subjected to experimental design by using the preset experimental design algorithm, the design variables meeting the preset conditions in the initial design variables are determined as the final design variables, the final optimization task model is established according to the final design variables, the constraint conditions and the objective function, the final optimization task model is subjected to optimization calculation by using the preset optimization algorithm to obtain the optimal values of the final design variables, and the design variables meeting the preset conditions are screened from the initial design variables by using the preset experimental design algorithm in the mode to serve as the final design variables, so that the optimization calculation amount required in the subsequent optimization calculation is greatly reduced, the processing efficiency is improved.
An embodiment of the present application further provides a processor, where the processor is configured to execute the following method: determining initial design variables and constraint conditions according to typical working conditions of the truss; determining an objective function according to the optimization target of the truss; establishing an initial optimization task model according to the initial design variables; performing test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable which meets a preset condition in the initial design variables as a final design variable; establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function; and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
In one embodiment, the performing a test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable meeting a preset condition in the initial design variables as a final design variable includes: carrying out test design on the initial optimization task model by using an optimal Latin hypercube algorithm to obtain a test design result; sensitivity analysis is carried out on the test design result, and the influence coefficient of each design variable in the initial design variables is determined; and selecting the design variables with the influence coefficients larger than the preset value in the initial design variables as final design variables.
In one embodiment, after selecting as the final design variables the design variables of which the influence coefficients are greater than the preset value among the initial design variables, the method further comprises: and determining the value boundary of the final design variable.
In one embodiment, the optimizing calculation of the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable includes: performing optimization calculation on the final optimization task model by using a multi-island genetic algorithm to obtain an optimal solution of a final design variable; verifying the optimal solution by using a preset multi-dimensional working condition to obtain a verification result; and determining the optimal value of the final design variable according to the verification result.
In one embodiment, determining optimal values for the final design variables based on the verification results includes: determining whether the verification result meets a preset standard; determining the optimal solution as the optimal value of the final design variable under the condition that the verification result meets the preset standard; and under the condition that the verification result does not meet the preset standard, the final optimization task model is subjected to optimization calculation by reusing the preset optimization algorithm.
In one embodiment, the initial design variables include a chord size parameter of the truss, a chord distribution parameter of the truss, a web size parameter of the truss, and a web distribution parameter of the truss.
In one embodiment, the constraints include formula (1):
wherein σiRepresenting the stress of the ith frame beam unit of the truss,
represents allowable stress, umaxWhich represents the maximum displacement of the truss or trusses,
representing allowable displacement, λminRepresents the minimum buckling characteristic value of the truss,λrepresenting the allowable buckling eigenvalues.
In one embodiment, the objective function includes formula (2):
wherein Mass represents the Mass of the truss, rho represents the density, n represents the number of frame beam units of the truss, and liLength of i-th frame beam unit representing truss, AiRepresenting the cross-sectional area of the ith frame beam unit of the truss.
It should be noted that, the specific process of the processor executing the above operations is shown in the method embodiment, and is not described herein again.
The method disclosed in the embodiments of the present application may be applied to a processor, or may be implemented by a processor. The processor may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in a processor or instructions in the form of software. The Processor may be a general purpose Processor, a Digital Signal Processor (DSP), or other programmable logic device, discrete gate or transistor logic device, discrete hardware components, or the like. The processor may implement or perform the methods, steps, and logic blocks disclosed in the embodiments of the present application. A general purpose processor may be a microprocessor or any conventional processor or the like. The steps of the method disclosed in the embodiments of the present application may be directly implemented by a hardware decoding processor, or implemented by a combination of hardware and software modules in the decoding processor. The software modules may be located in a storage medium having a memory and a processor reading the information in the memory and combining the hardware to perform the steps of the method.
In an exemplary embodiment, the processor may be implemented by one or more Application Specific Integrated circuits (as ICs), DSPs, Programmable Logic Devices (PLDs), Complex Programmable Logic Devices (CPLDs), Field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, Micro Controllers (MCUs), microprocessors (microprocessors), or other electronic components for performing the foregoing methods.
An apparatus is also provided in an embodiment of the present application, where the apparatus includes a processor, a memory, and a program stored in the memory and capable of being executed on the processor, and the processor implements the method according to any one of the above embodiments when executing the program.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The kernel can be set to be one or more, and the method provided by one or more technical schemes is realized by adjusting kernel parameters.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip.
It will be appreciated that the memory of embodiments of the present application can be either volatile memory or nonvolatile memory, and can include both volatile and nonvolatile memory. Among them, the nonvolatile Memory may be a Read Only Memory (ROM), a Programmable Read Only Memory (PROM), an Erasable Programmable Read Only Memory (EPROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a magnetic random access Memory (FRAM), a magnetic random access Memory (Flash Memory), a magnetic surface Memory, an optical Disc, or a Compact Disc Read Only Memory (CD ROM); the magnetic surface storage may be disk storage or tape storage. The volatile Memory may be a Random Access Memory (RAM) which serves as an external cache. By way of illustration and not limitation, many forms of RAM are available, such as Static Random Access Memory (SRAM), Synchronous Static Random Access Memory (SSRAM), Dynamic Random Access Memory (DRAM), Synchronous Dynamic Random Access Memory (SDRAM), Double Data Rate Synchronous Dynamic Random Access Memory (DDRSDRAM), Enhanced Synchronous Dynamic Random Access Memory (ESDRAM), Enhanced Synchronous Dynamic Random Access Memory (Enhanced DRAM), Synchronous Dynamic Random Access Memory (SLDRAM), Direct Memory (DRmb Access), and Random Access Memory (DRAM). The memories described in the embodiments of the present application are intended to comprise, without being limited to, these and any other suitable types of memory.
Embodiments of the present application also provide a machine-readable storage medium having stored thereon instructions, which when executed by a processor, cause the processor to perform the following method: determining initial design variables and constraint conditions according to typical working conditions of the truss; determining an objective function according to the optimization target of the truss; establishing an initial optimization task model according to the initial design variables; performing test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable which meets a preset condition in the initial design variables as a final design variable; establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function; and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
In one embodiment, the performing a test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable meeting a preset condition in the initial design variables as a final design variable includes: carrying out test design on the initial optimization task model by using an optimal Latin hypercube algorithm to obtain a test design result; sensitivity analysis is carried out on the test design result, and the influence coefficient of each design variable in the initial design variables is determined; and selecting the design variables with the influence coefficients larger than the preset value in the initial design variables as final design variables.
In one embodiment, after selecting as the final design variables the design variables of which the influence coefficients are greater than the preset value among the initial design variables, the method further comprises: and determining the value boundary of the final design variable.
In one embodiment, the optimizing calculation of the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable includes: performing optimization calculation on the final optimization task model by using a multi-island genetic algorithm to obtain an optimal solution of a final design variable; verifying the optimal solution by using a preset multi-dimensional working condition to obtain a verification result; and determining the optimal value of the final design variable according to the verification result.
In one embodiment, determining optimal values for the final design variables based on the verification results includes: determining whether the verification result meets a preset standard; determining the optimal solution as the optimal value of the final design variable under the condition that the verification result meets the preset standard; and under the condition that the verification result does not meet the preset standard, the final optimization task model is subjected to optimization calculation by reusing the preset optimization algorithm.
In one embodiment, the initial design variables include a chord size parameter of the truss, a chord distribution parameter of the truss, a web size parameter of the truss, and a web distribution parameter of the truss.
In one embodiment, the constraints include formula (1):
wherein σiRepresenting the stress of the ith frame beam unit of the truss,
represents allowable stress, umaxRepresents the maximum displacement of the truss or trusses,
representing allowable displacement, λminRepresents the minimum buckling characteristic value of the truss,λrepresenting the allowable buckling eigenvalues.
In one embodiment, the objective function includes formula (2):
wherein Mass represents the Mass of the truss, rho represents the density, n represents the number of frame beam units of the truss, and liLength of i-th frame beam unit representing truss, AiRepresenting the cross-sectional area of the ith frame beam unit of the truss.
In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as shown in fig. 5. The computer apparatus includes a processor a01, a network interface a02, a display screen a04, an input device a05, and a memory (not shown in the figure) connected by a system bus. Wherein processor a01 of the computer device is used to provide computing and control capabilities. The memory of the computer device comprises an internal memory a03 and a non-volatile storage medium a 06. The nonvolatile storage medium a06 stores an operating system B01 and a computer program B02. The internal memory a03 provides an environment for the operation of the operating system B01 and the computer programs B02 in the non-volatile storage medium a 06. The network interface a02 of the computer device is used for communication with an external terminal through a network connection. The computer program is executed by the processor a01 to implement the method of any of the above embodiments. The display screen a04 of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device a05 of the computer device may be a touch layer covered on the display screen, a button, a trackball or a touch pad arranged on a casing of the computer device, or an external keyboard, a touch pad or a mouse.
Those skilled in the art will appreciate that the architecture shown in fig. 5 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A method for optimizing a truss structure design, comprising:
determining initial design variables and constraint conditions according to typical working conditions of the truss;
determining an objective function according to the optimization target of the truss;
establishing an initial optimization task model according to the initial design variables;
performing test design on the initial optimization task model by using a preset test design algorithm, and determining a design variable which meets a preset condition in the initial design variables as a final design variable;
establishing a final optimization task model according to the final design variables, the constraint conditions and the objective function;
and performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal value of the final design variable.
2. The optimization method according to claim 1, wherein the performing a trial design on the initial optimization task model by using a preset trial design algorithm, and determining design variables meeting preset conditions in the initial design variables as final design variables comprises:
carrying out experimental design on the initial optimization task model by using an optimal Latin hypercube algorithm to obtain an experimental design result;
sensitivity analysis is carried out on the test design result, and the influence coefficient of each design variable in the initial design variables is determined;
and selecting the design variables of which the influence coefficients are larger than a preset value in the initial design variables as final design variables.
3. The optimization method according to claim 2, wherein after the selecting, as a final design variable, a design variable of the initial design variables whose influence coefficient is greater than a preset value, the optimization method further comprises:
and determining the value boundary of the final design variable.
4. The optimization method according to claim 1, wherein the performing optimization calculation on the final optimization task model by using a preset optimization algorithm to obtain the optimal values of the final design variables comprises:
performing optimization calculation on the final optimization task model by using a multi-island genetic algorithm to obtain an optimal solution of the final design variable;
verifying the optimal solution by using a preset multi-dimensional working condition to obtain a verification result;
and determining the optimal value of the final design variable according to the verification result.
5. The optimization method of claim 4, wherein said determining optimal values of the final design variables according to the verification results comprises:
determining whether the verification result meets a preset standard;
determining the optimal solution as the optimal value of the final design variable under the condition that the verification result meets the preset standard;
and under the condition that the verification result does not meet the preset standard, the multi-island genetic algorithm is reused for carrying out optimization calculation on the final optimization task model.
6. The optimization method according to claim 1, wherein the initial design variables include a chord size parameter of the truss, a chord distribution parameter of the truss, a web size parameter of the truss, and a web distribution parameter of the truss.
7. The optimization method according to claim 1, wherein the constraint condition comprises formula (1):
wherein σiThe stress of the i-th frame beam unit representing the truss,
represents allowable stress, umaxRepresents the maximum displacement of the truss or trusses,
representing allowable displacement, λminRepresents a minimum buckling characteristic value of the truss,λrepresentsAllowable buckling characteristic values.
8. The optimization method according to claim 1, wherein the objective function comprises formula (2):
wherein Mass represents the Mass of the truss, rho represents the density, n represents the number of frame beam units of the truss, and liLength of i-th frame beam unit representing the truss, AiRepresents the cross-sectional area of the ith frame beam unit of the truss.
9. A processor configured to perform the method of optimizing a truss structure design of any of claims 1-8.
10. A machine readable storage medium having instructions stored thereon, wherein the instructions, when executed by a processor, cause the processor to be configured to perform a method of optimizing a truss structure design according to any of claims 1-8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210035344.0A CN114491996A (en) | 2022-01-13 | 2022-01-13 | Optimization method of truss structure design, processor and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210035344.0A CN114491996A (en) | 2022-01-13 | 2022-01-13 | Optimization method of truss structure design, processor and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN114491996A true CN114491996A (en) | 2022-05-13 |
Family
ID=81512560
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210035344.0A Pending CN114491996A (en) | 2022-01-13 | 2022-01-13 | Optimization method of truss structure design, processor and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114491996A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796405A (en) * | 2023-02-03 | 2023-03-14 | 阿里巴巴达摩院(杭州)科技有限公司 | Solution report generation method for optimization model and computing equipment |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR363196A (en) * | 1905-02-18 | 1906-07-23 | Jean Delporte | Improvements to roof structures |
| GB250588A (en) * | 1925-04-13 | 1926-12-30 | Stanley Macomber | Improvements in trussed structural units |
| CN106845006A (en) * | 2017-02-15 | 2017-06-13 | 中车株洲电力机车有限公司 | Rail vehicle weight center of gravity design optimization method and system based on multiple-objection optimization |
| CN109697294A (en) * | 2017-10-20 | 2019-04-30 | 国家电投集团科学技术研究院有限公司 | Truss Structural Optimization Method |
| CN112528540A (en) * | 2020-12-08 | 2021-03-19 | 中国航空工业集团公司沈阳飞机设计研究所 | Structure optimization method based on force-heat coupling calculation |
| CN113221415A (en) * | 2021-05-13 | 2021-08-06 | 广东省科学院智能制造研究所 | Truss girder structure optimization method and device based on ABAQUS |
| CN113449454A (en) * | 2021-07-21 | 2021-09-28 | 中交第三公路工程局有限公司 | Topological optimization method of novel steel truss structure |
| CN113609601A (en) * | 2020-09-29 | 2021-11-05 | 中国电建集团华东勘测设计研究院有限公司 | Optimal design method for truss type fan foundation structure in medium-depth sea area |
-
2022
- 2022-01-13 CN CN202210035344.0A patent/CN114491996A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| FR363196A (en) * | 1905-02-18 | 1906-07-23 | Jean Delporte | Improvements to roof structures |
| GB250588A (en) * | 1925-04-13 | 1926-12-30 | Stanley Macomber | Improvements in trussed structural units |
| CN106845006A (en) * | 2017-02-15 | 2017-06-13 | 中车株洲电力机车有限公司 | Rail vehicle weight center of gravity design optimization method and system based on multiple-objection optimization |
| CN109697294A (en) * | 2017-10-20 | 2019-04-30 | 国家电投集团科学技术研究院有限公司 | Truss Structural Optimization Method |
| CN113609601A (en) * | 2020-09-29 | 2021-11-05 | 中国电建集团华东勘测设计研究院有限公司 | Optimal design method for truss type fan foundation structure in medium-depth sea area |
| CN112528540A (en) * | 2020-12-08 | 2021-03-19 | 中国航空工业集团公司沈阳飞机设计研究所 | Structure optimization method based on force-heat coupling calculation |
| CN113221415A (en) * | 2021-05-13 | 2021-08-06 | 广东省科学院智能制造研究所 | Truss girder structure optimization method and device based on ABAQUS |
| CN113449454A (en) * | 2021-07-21 | 2021-09-28 | 中交第三公路工程局有限公司 | Topological optimization method of novel steel truss structure |
Non-Patent Citations (3)
| Title |
|---|
| 孙仁范, 牟在根, 颜谋, 陈云周: "遗传算法在桁架结构优化设计中的应用", 建筑结构学报, no. 03, 10 June 2004 (2004-06-10) * |
| 崔新涛;毕凤荣;王树新;张连洪;: "桁架材料和结构组合多目标优化设计", 天津大学学报, no. 04, 15 April 2007 (2007-04-15) * |
| 王连广;侯献语;冯刚;: "基于遗传算法的预应力钢桁架优化设计", 沈阳建筑大学学报(自然科学版), no. 04, 25 August 2006 (2006-08-25), pages 567 - 570 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115796405A (en) * | 2023-02-03 | 2023-03-14 | 阿里巴巴达摩院(杭州)科技有限公司 | Solution report generation method for optimization model and computing equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Qian et al. | A sequential constraints updating approach for Kriging surrogate model-assisted engineering optimization design problem | |
| JP6031196B2 (en) | Tuning for distributed data storage and processing systems | |
| US8364450B2 (en) | Multi-objective optimization design support apparatus and method | |
| Li et al. | Software defect prediction based on ensemble learning | |
| CN110135635A (en) | A kind of region electric power saturation load forecasting method and system | |
| CN109145320B (en) | Static time sequence analysis method and device in chip hierarchical physical design | |
| Aulig et al. | State-based representation for structural topology optimization and application to crashworthiness | |
| Zhao et al. | Overhead-conscious format selection for spmv-based applications | |
| CN114491996A (en) | Optimization method of truss structure design, processor and storage medium | |
| Zhou et al. | Enabling runtime spmv format selection through an overhead conscious method | |
| CN114881343B (en) | Short-term load prediction method and device for power system based on feature selection | |
| CN110990135A (en) | Spark operation time prediction method and device based on deep migration learning | |
| CN111026603B (en) | On-chip network temperature prediction method and device, equipment and storage medium | |
| CN117318052B (en) | Reactive power prediction method and device for phase advance test of generator set and computer equipment | |
| CN114168318A (en) | Training method of storage release model, storage release method and equipment | |
| JP2009032237A (en) | Method and apparatus for calculating credit risk of portfolio | |
| CN116932174B (en) | Dynamic resource scheduling method, device, terminal and medium for EDA simulation task | |
| Zhang et al. | An efficient adaptive reliability analysis method based on Kriging and weighted average misclassification rate improvement | |
| CN117150669A (en) | Mechanical structure system failure probability estimation method, system and readable storage medium | |
| CN112308337A (en) | Prediction method, prediction device and processor for probabilistic short-term load of power system | |
| CN114896250B (en) | Key value separated key value storage engine index optimization method and device | |
| Cao et al. | A boundary identification approach for the feasible space of structural optimization using a virtual sampling technique-based support vector machine | |
| KR102498206B1 (en) | Method for placement of enegy sources using lyapunov exponent and computing device for executing the method | |
| CN112329267B (en) | Combined geometric neutron transport processing method and device based on characteristic line method | |
| CN114048544A (en) | Aircraft structure response characteristic prediction method, device, equipment and medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |