1134 results about "Optimal design" patented technology

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention relates to the technical field of three-dimensional reconstruction and simulation, and discloses a foam material porous structure three-dimensional reconstruction and simulation system, which comprises a scanning imaging module used for carrying out scanning imaging on a foam material sample to obtain pore image data; the pore boundary recognition module is used for performing pore boundary recognition based on the pore image data to obtain a pore boundary recognition result; the three-dimensional reconstruction module is used for executing three-dimensional reconstruction of bubble evolution physical constraints according to the pore boundary recognition result to obtain a three-dimensional reconstruction model; the coupling simulation module is used for performing grid division and coupling simulation on the three-dimensional reconstruction model to obtain mechanical simulation data; and the parameter solving module is used for carrying out density field optimization and parameter solving based on the mechanical simulation data to obtain optimal gap design parameters, so that seamless connection from simulation analysis to optimization design is realized, the structural design period of the foaming material is shortened, and the design precision and consistency are improved.

The invention discloses an optimization design method of a large-model-driven mechanical part, which comprises the following steps of: performing knowledge retrieval according to a design task and a constraint condition, and generating text description of a structure design scheme by fusing the retrieved knowledge through a large-scale language model (LLM); the LLM performs task decomposition and modeling code mapping on the structure design scheme described by the text, and generated modeling codes are directly executed by modeling software to generate a three-dimensional model; the LLM generates a reasoning track and calls a finite element analysis tool set to perform performance analysis on the generated three-dimensional model; and according to optimization requirements and design constraints, the LLM optimizes the three-dimensional model to generate an optimal three-dimensional model. The invention discloses an optimization design system of a large model driven mechanical part. The optimization design system comprises a design scheme generation module, a parametric modeling module, a design scheme verification module and a structure parameter optimization module. The method and the system provided by the invention can save code quantity, reduce manual intervention and improve design efficiency.

The invention discloses an optimal design method for a floating wind power-wave energy multi-energy complementary power generation platform, which comprises the following steps of: constructing an integrated and parameterized system model which is a fully-coupled and parameterized numerical model comprising all key components of the floating wind power-wave energy multi-energy complementary power generation platform; all key design parameters influencing the system performance are set as parameterized variables; establishing a multidisciplinary coupling dynamic simulation model; defining a multi-objective optimization problem including decision variables, objective functions and constraint conditions; the decision variable selects a group of core variables from the parameterized variables as optimization input; and combining the multi-objective optimization problem with a multidisciplinary coupling dynamic simulation model, executing a multi-objective optimization cycle, and generating and deciding a Pareto optimal solution set to obtain typical design schemes with different characteristics. According to the method, the global optimization design of the floating wind power-wave energy multi-energy complementary power generation platform can be realized.

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

The invention discloses a building facade optimization method and system based on sustainable analysis, and relates to the technical field of data processing. According to the method, parametric modeling is carried out on the external facade of a target building, and a design space is defined; training a machine learning model integrating energy consumption, carbon emission, cost and lighting prediction functions by using historical data; building an optimization problem by taking external facade parameters as decision variables and taking minimization of energy consumption, carbon emission and cost and maximization of lighting as targets, and automatically generating an optimal scheme set by adopting a multi-target optimization algorithm; a decision maker is assisted to select a final scheme through a visual interface, and a corresponding BIM model is output; the method effectively solves the problems that a traditional design method is difficult to coordinate multi-target conflicts, depends on experience and is low in efficiency, and can automatically and intelligently generate an optimal design scheme which is energy-saving, low-carbon, low-cost and good in lighting performance.

The invention relates to the technical field of automobile design, and particularly discloses an automobile wind resistance optimization design method based on a machine learning model, and the method comprises the steps: collecting data of different automobile types, and carrying out the cleaning and standardization processing; constructing a wind resistance prediction model, training the model by using the preprocessed data, and minimizing a prediction error by adjusting model parameters; key design variables are selected, the variation range of the key design variables is determined, and a parameterized model is constructed; selecting sample points in a design variable space, generating a corresponding geometric model, calculating a wind resistance coefficient and establishing a sample point database; dividing sample point data into a training set and a test set, optimizing a machine learning model, searching an optimal solution in combination with an optimization algorithm, and iteratively updating the model by increasing sample points until the precision requirement is met; an optimal design variable combination is determined, an optimized automobile geometric model is generated, CFD simulation verification is carried out, and a design scheme containing detailed parameters and optimization results is output.

The present disclosure relates to a field of a fuel cell test, and in particular, to a method and an apparatus for optimizing design based on performance evaluation of a gas diffusion layer of a fuel cell. The method includes: determining an overall porosity of the gas diffusion layer of the fuel cell according to production requirements, and obtaining a plurality of porosity structures with the overall porosity; obtaining performance evaluation indexes of the gas diffusion layer of the fuel cell, and constructing a performance evaluation system for the gas diffusion layer of the fuel cell; calculating, with reference to evaluation functions and index weight ratios, performance comprehensive scores of the plurality of porosity structures in the performance evaluation system of the gas diffusion layer of the fuel cell; determining an optimal design scheme in the plurality of porosity structures according to the performance comprehensive scores.

The invention belongs to the field of aero-engines and turbines, and provides a quick prediction method, system and device for a two-dimensional viscous compressible flow field of a gas compressor and a medium, and the method comprises the steps: obtaining CFD simulation data of a two-dimensional blade profile of the gas compressor under multiple working conditions, and carrying out the preprocessing to obtain a standardized training data set; on the basis of the data set, constructing a hybrid neural network model which takes a blade profile geometric parameter and an operation condition parameter as input and takes a two-dimensional viscous compressible flow field as output; combining mean square error, gradient loss and physical consistency loss based on RANS equation residual error to construct a total loss function, and performing end-to-end training on the model; during reasoning, to-be-predicted parameters are input into the trained model according to the same preprocessing mode, and then a flow field prediction result can be rapidly output. According to the method, the problems of long consumed time, low precision and poor consistency of traditional CFD simulation can be solved, millisecond-level flow field prediction is achieved, high precision and high physical consistency are achieved, and the method is suitable for efficient optimization design of the blade profile of the gas compressor.

The invention discloses a bipolar plate structure optimization method and system based on flow field data analysis, and relates to the technical field of fuel cells. The method comprises the following steps: establishing a parameterized bipolar plate runner geometric solid model; performing compression deformation simulation of a gas diffusion layer on the bipolar plate flow channel geometric solid model to obtain physical field data of the gas diffusion layer, and calculating a porosity distribution field and a local mass transfer impedance coefficient field based on the physical field data; based on the porosity distribution field and the local mass transfer impedance coefficient field, multi-physics field coupling simulation is carried out, and a local performance contribution degree field is obtained; constructing a comprehensive loss function according to the local mass transfer impedance coefficient field and the local performance contribution degree field; and with the purpose of minimizing the comprehensive loss function, iterative optimization is carried out on the design variables through an optimization algorithm until convergence conditions are met, and an optimal design variable combination is output. Through parametric modeling, multi-physics field coupling simulation and comprehensive loss function evaluation, efficient optimization of the bipolar plate design is realized.

The invention discloses an optimization design method for improving the environmental performance of a building site, and relates to the technical field of site design. The method comprises the following steps: S1, collecting and preprocessing basic information of a building site; s2, building a multi-dimensional digital model of the building site; s3, site environment performance simulation and index quantification are carried out; s4, environment performance parameters are intelligently optimized; s5, verifying and finally determining an optimization scheme; and S6, optimizing effect tracking and dynamic adjustment. According to the method, adjustable variables of a building candidate model serve as genes, binary crossover and polynomial variation are simulated through iteration of 100-200 sets of initial populations, and a multivariable combination space is automatically traversed; according to the method, planning specifications and a cost threshold value are converted into algorithm constraints, it is ensured that an output scheme is compliant and economical, 3-5 groups of non-inferior solution schemes are screened out in 3-5 rounds of iteration, standardized values of environmental performance parameters after optimization are all larger than or equal to 0.7, local optimum is avoided, and optimization efficiency and scheme comprehensiveness are greatly improved.

The invention discloses a Bayesian algorithm-based swivel bridge spherical hinge structure optimization design method, which is characterized in that a parameterized model of a swivel bridge spherical hinge structure is constructed, and a finite element simulation technology and a Bayesian optimization algorithm are combined, so that multi-target global optimization design is realized. The method specifically comprises the following steps: establishing a refined finite element model of the swivel bridge spherical hinge; defining input design variables (spherical radius, supporting radius, pin roll radius and the like) and output optimization targets (maximum contact stress, horizontal and vertical friction moment); adopting Latin hypercube sampling (LHS) to generate a plurality of groups of initial parameter combinations; dynamically selecting a high-value parameter combination through a Bayesian optimization framework to carry out finite element simulation; training a Gaussian process agent model and carrying out iterative optimization; and quantizing the parameter sensitivity and outputting a Pareto optimal solution set. According to the method, the simulation frequency can be remarkably reduced, the design efficiency is effectively improved, and the problem that traditional experience design is prone to falling into local optimum is solved.

The invention provides a tensor segmentation and mapping method of a neural network operator on a wafer chip, and the method comprises the steps: expanding a forward calculation graph into a training calculation graph containing forward propagation, back propagation and gradient updating, and employing different operators in the calculation of these stages; carrying out dimension segmentation on the input tensor of each operator in the training calculation graph to generate a plurality of global candidate segmentation strategies; aiming at each candidate segmentation strategy, enumerating feasible resource mapping strategies of all operators, and combining to generate candidate design points; and screening an optimal design point through cost evaluation, and outputting a corresponding candidate segmentation strategy and a resource mapping strategy. According to the method, forward and backward operators in a neural network training process are decoupled through expansion of a calculation graph, and a segmentation scheme for avoiding tensor copying in forward and backward calculation in the training process and a resource mapping strategy corresponding to the segmentation scheme are explored, so that tensor copying is avoided, global resource occupation is reduced, and the training efficiency is improved. Therefore, the same wafer-level chip can support larger model training.

The invention discloses a hypergraph neural network-based PVA fiber toughened UHPC material formula design method. The method comprises the steps of 1, multi-modal data acquisition and processing, 2, hypergraph structure construction, 3, HGNN model construction, 4, model training, and 5, performance optimization and reverse ratio design. According to the method, the high-order relationship among the multi-modal data such as the raw material, the proportion and the microstructure can be efficiently modeled, the precise prediction of the performance of the PVA fiber toughened UHPC and the optimal design of the proportion are realized, and the method is suitable for the performance prediction and proportion recommendation of various ultra-high performance concrete systems, and has wide engineering application prospects and industrial popularization values.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention discloses a comprehensive support hanger section multi-objective optimization method based on NSGA-II and an optimal determination method. The comprehensive support hanger section multi-objective optimization method comprises the following four steps: establishing a multi-objective optimization mathematical model of a comprehensive support hanger steel section; establishing a non-dominated sorting genetic algorithm; the NSGA-II is adopted to solve the multi-objective optimization mathematical model; establishing implementation of a multi-objective decision-making method; evaluating, sorting and selecting solutions in the approximate Pareto non-inferior solution set by adopting a multi-objective decision-making method; according to the optimized design scheme, final design parameters of the comprehensive support hanger steel section are determined. The method is directed to comprehensive supports and hangers used in construction machinery, electrical and piping systems, aiming at simultaneously achieving material cost minimization and bending performance maximization. According to the method, the defects in the aspects of multi-objective optimization and comprehensive decision making in the prior art are overcome, and an efficient and practical solution with multi-objective comprehensive performance is provided for the design of the steel section of the comprehensive support hanger.

The invention discloses a cargo floor structural strength design optimization method and system based on load prediction, and belongs to the technical field of freight vehicle structural design. The maximum equivalent stress of each node in a floor grid area is calculated and normalized by constructing a load prediction model of the dynamic distribution state of cargoes in a cargo tank; forming a stress intensity coefficient matrix; identifying a high-risk area and constructing a reinforced target area set; obtaining an initial parameter of the bottom plate structure, establishing a finite element model, and setting an adjustable structure parameter in the target area; a candidate structure design scheme set is generated, simulation calculation is executed, the total structure mass, the maximum stress value and the first-order inherent frequency of each scheme are extracted, and a performance evaluation vector is formed; selecting an optimal design scheme through a multi-objective optimization algorithm, and outputting structural parameters of the optimal design scheme as a final design result; according to the method, the structural optimization design of the cargo tank bottom plate under the complex load working condition is achieved, and the method has high engineering adaptability and practical value.

The invention relates to a multi-target aerodynamic-structure optimization method based on a deep neural network proxy model, and solves the problem that an existing method is insufficient in efficiency, feasibility and credibility, and the method comprises the steps: firstly, carrying out the sampling in a parameterized design target, building an aerodynamic-structure database through CFD / FEA high-fidelity simulation; the method comprises the steps that firstly, a multi-task neural network is used for predicting the lift-drag ratio, quality and stress at the same time, uncertainty is output, then, in NSGA-III multi-target optimization, a proxy model is used for rapidly evaluating and searching out a Pareto leading edge meeting constraints, finally, the precision of the model is gradually improved through high-fidelity checking and incremental updating, and an optimal design scheme is output. According to the method, sub-optimization and repeated iteration caused by'pneumatic first and then structure 'are avoided; the design universality and robustness are improved; a compromise scheme set of the system is directly obtained; and the optimization cost and the optimization period can be obviously reduced.

The invention belongs to the field of filter optimization of coupling matrixes and neural networks, and discloses a filter optimization design method of a neural network based on guidance of a coupling matrix, and physical elements and mechanisms are introduced into the neural network. The method comprises the following steps: firstly, determining filter optimization indexes, randomly generating sample data in a range near the size of the optimization indexes, and screening data samples by adopting a sample screening method to obtain high-quality samples; then, extracting a coupling matrix from the obtained S parameter according to a vector fitting algorithm, and forming new coupling information M'by combining the center frequency f0 and the bandwidth BW; a full-connection neural network is used as a medium of filter device size parameters and output frequency response, the size of a to-be-optimized device of the filter is input, and coupling information M'representing S parameters is output. And the forward simulator is connected with a genetic optimization algorithm GA to realize optimization design of a specific index parameter filter. According to the method, the optimization design time is effectively saved, the optimization efficiency is improved, and the optimization dimension is reduced.

The invention discloses a quantitative recognition device for the number and size of bubbles at a rotating impeller of a multiphase pump. The quantitative recognition device comprises a two-phase flow experiment system, the multiphase pump, a high-speed camera, a light source and the like. The preprocessing and data analysis system and the deep learning system are arranged in the computer; the two-phase flow experiment system and the image acquisition system are electrically connected with the computer through lines. The invention further discloses a quantitative recognition method for the number and size of the bubbles at the rotating impeller of the multiphase pump. According to the method, the problems of low blistering recognition precision, low efficiency and large error caused by the existing manual measurement method are solved, and a reference method is provided for exploring the influence of gas distribution at the impeller in the multiphase pump on pump operation and optimizing design of the multiphase pump.

The invention belongs to the technical field of advanced manufacturing and intelligent design, and provides a topological optimization method and system for a liquid cooling plate of a lithium ion battery pack, and the method comprises the steps: building a parameterized simulation model of a battery pack liquid cooling system, constructing a multi-dimensional design variable space of the liquid cooling plate, and formally defining a multi-objective optimization problem; on the basis of the initial training data set, independently constructing a probabilistic agent model capable of predicting a target value and quantifying uncertainty for each optimization target function; performing quantum behavior enhanced multi-universe optimizer iterative optimization on the constructed agent model, selecting new sample points from a multi-dimensional design variable space of the liquid cooling plate according to a set criterion after a set number of iterations is completed, performing high-fidelity CFD simulation to obtain real data, supplementing the real data to a data set, and updating or reconstructing the agent model; and judging whether an iterative optimization condition is met or not, and if so, screening and outputting a final Pareto optimal solution set from all samples subjected to high-fidelity simulation verification. And efficient multi-objective optimal design of the liquid cooling plate is realized.

The invention discloses a small modular prismatic high-temperature gas cooled reactor core optimization design method and system based on data driving. The method comprises the following steps: constructing a training data set; a data-driven agent model is trained to replace and execute high-cost simulation calculation; and exploring a design space and generating a group of Pareto optimal candidate solutions by using a multi-objective evolution algorithm and taking the proxy model as a fitness function evaluator. The key feature of the method is a closed loop of iterative verification and model updating: verifying the candidate solution through high-fidelity simulation, if the prediction precision does not meet the convergence criterion, expanding the verified new data point to a training data set, and retraining the agent model. According to the method, the calculation cost can be remarkably reduced, a complex design space can be efficiently explored, and a physically reliable reactor design scheme with better performance can be obtained.

The invention provides a gas-liquid-solid multi-phase conveying centrifugal pump performance prediction method and an optimization design method. According to the method, the performance of the centrifugal pump is accurately predicted and the design efficiency of the centrifugal pump is improved through numerical simulation and optimization design in combination with a gas-liquid-solid three-phase flow dynamic model. Firstly, blade parameters are determined according to design requirements, a three-dimensional model is established, and grid division is carried out; then, the VOF-Level Set multiphase flow model, the particle model and the gas-liquid-solid coupling model are corrected, factors such as surface tension, particle immersion force and drag force are considered, and lift and efficiency curves are calculated through numerical simulation; and finally, a particle accumulation area is analyzed based on the particle distribution characteristic graph, and the optimal lift efficiency combination is obtained by adjusting parameters such as the inlet angle, the inlet width, the wrap angle and the blade number of the blades and optimizing design. According to the method, the performance of the centrifugal pump in the gas-liquid-solid multiphase flow environment can be improved, and a new design thought is provided.

The invention discloses a shale gas well fracturing parameter optimization design method and system based on a depth Q network, and relates to the technical field of shale gas development, and the method comprises the following steps: S1, collecting geological parameters, fracturing construction parameters and productivity data of a shale gas well in advance; s2, constructing an agent model and performing environment simulation; and S3, constructing a DQN model. According to the method, a LightGBM algorithm is adopted to construct a data-driven proxy model to simulate a real fracturing environment, an optimization model is constructed based on a deep Q network (DQN), state, action, reward and epsilon-greedy strategies are defined, and technologies such as a variable step size search mechanism, experience playback and target network soft update are combined, so that the real fracturing environment is simulated. The problems that a traditional optimization method is weak in local search capability and low in convergence speed are effectively solved, multivariable synchronous optimization of the unit perforation length proppant dosage and the fracturing fluid dosage is achieved, and a global optimal parameter combination can be rapidly explored.

The invention discloses a structural vibration reduction topological optimization design method based on anti-resonant frequency constraint. The method comprises the following steps: constructing a finite element model corresponding to structure parameters; obtaining an initial structure and an optimization column formula according to the finite element model; obtaining an objective function value through finite element analysis; obtaining an anti-resonance frequency correlation characteristic value through generalized asymmetric characteristic value analysis; and the design variables are updated in combination with optimization column constraints, and a topological optimization design configuration is obtained after convergence judgment. According to the method, explicit calculation and precise control of the dynamic compliance anti-resonant frequency are achieved, optimization and advanced convergence are avoided through anti-resonant frequency constraint, a clear vibration reduction topological configuration can be obtained under high-frequency excitation without additional mass, the vibration reduction performance and the static force bearing capacity of the structure are both considered, and the method is suitable for large-scale popularization and application. And the design requirements for light weight and high vibration attenuation of the structure in the fields of aerospace and the like are met.

This invention relates to the field of hydraulic engineering, and more particularly to a method for safety assessment and optimization design of large-flow aqueduct structures. The method includes: collecting displacement, strain, and temperature monitoring sequences based on a triple probe, completing grouping, coordinate and time registration, and establishing a monitoring baseline; constructing a spatial model including support nonlinearity and fluid-structure boundary conditions, applying loading conditions, and obtaining response predictions; aligning monitoring and prediction in time and location, performing hierarchical Bayesian filtering inversion, and obtaining parameter posteriors and structural health; under the integration of in-service monitoring and accelerated degradation characteristics, using multi-task learning to generate material evolution and lifespan intervals, and automatically generating and simulating optimization schemes under reliability and maintainability constraints. This application forms a closed loop from assessment to design, effectively enhancing data comparability, traceability of uncertain information, and adaptability of the scheme to engineering projects.

The invention discloses a multi-disciplinary and multi-working-condition intelligent collaborative optimization design method for a generator. The method comprises the following steps: determining initial geometric and physical model parameters of the generator; geometric parametric modeling is carried out on related parts of the generator; carrying out parametric modeling on material attributes, physical processes and operation conditions of the generator; establishing a generator model, preliminarily screening design parameters, and checking whether the generator meets feasibility constraints or not under the multi-constraint condition; determining an optimization target and an optimization space; selecting a sampling algorithm and a parameter optimization range, and constructing a test design sample library; selecting an intelligent optimization design method to find a global final solution meeting the design requirements, meeting the design requirements of multiple targets and multiple working conditions of the generator, and completing the optimization process; and determining various parameters and design schemes of the generator, and performing performance evaluation and verification. According to the invention, multi-parameter, multi-target, multi-working-condition and multi-disciplinary optimal design searching of the generator is realized.

The invention provides a multi-objective optimization method fusing a classifier and an active learning strategy, and belongs to the technical field of artificial intelligence and optimization design, and the method specifically comprises the steps: obtaining a sample data set containing a plurality of target performance function values; performing non-dominated sorting on the sample data set to extract a Pareto frontier point set, and extracting geometric features from the point set as a geometric feature vector sequence; using the geometric features and the corresponding convex marks to train a classifier model of a Transform architecture, wherein the classifier model supports a multi-head attention mechanism and a position coding structure; judging the convex confidence coefficient of the current Pareto front by using a classifier model, and obtaining a candidate scheme; and inputting the candidate scheme into a double-attention mechanism prediction model based on context reasoning, and predicting the multi-target performance of the candidate scheme. According to the method, the experiment frequency can be reduced, the target performance can be accelerated to be achieved, and the intelligence and efficiency of the multi-target optimization process are improved.

The invention relates to the technical field of heat exchanger flow channel form design, and discloses an intelligent optimization method and system for heat exchanger flow channel form design based on multi-source data, and the method comprises the steps: obtaining flow channel form data, and carrying out the iterative optimization to generate various layout candidates; turbulence effect simulation is carried out on the candidate schemes to obtain thermal performance indexes; performing multi-target evaluation and comprehensive ranking under a complex working condition, and selecting an optimal design scheme; analyzing the balance state of the heat transfer efficiency and the fluid resistance, and extracting form adjustment parameters; combining processing feasibility standard comparison, and screening to obtain a feasible layout subset; backtracking thermal performance and analyzing and manufacturing constraint fusion consistency to form an adjustment parameter set for verification; and finally, machining and performance matching are confirmed through simulation iteration, and diversified runner layouts suitable for complex working conditions are obtained. The method can solve the problem that the thermal performance and the manufacturing process constraint are difficult to consider in the runner design.

The invention relates to the technical field of data processing, and discloses a shipyard steel framed bent scheme automatic optimization method and system based on a genetic algorithm. The method comprises the following steps: acquiring shipyard steel bent frame structure parameters and establishing an engineering experience database to obtain initial component section parameters; carrying out structure parametric modeling based on the initial parameters, generating a structure calculation model and carrying out load condition analysis; importing finite element software to carry out internal force analysis, and carrying out component standard checking calculation to obtain a stress ratio; performing multi-objective optimization through an NSGA-II algorithm to obtain a Pareto optimal solution set; and finally carrying out three-dimensional modeling and document generation to obtain an optimization design result. According to the method, the design scheme of the steel bent frame structure is automatically optimized while multiple load working conditions and design targets are considered, and it is ensured that the design result meeting the safety standard and with the optimal cost is obtained within the shortest time. By solving the problem, the design efficiency can be greatly improved, the design cost is reduced, and the structural safety is ensured.