1707 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 data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

The invention provides an airfoil profile adaptive design and optimization method based on reinforcement learning and a conditional generative adversarial network, and belongs to the field of airfoil profile optimization design. The method aims at achieving the two core functions of self-adaptive optimal airfoil generation and rapid optimization. According to the first function, through interaction of a reinforcement learning agent and a conditional generative adversarial network generator, generation strategies for generating optimal airfoil profile potential variables under different design conditions are continuously learned, so that the airfoil profile with the optimal aerodynamic performance is adaptively generated, intelligent and adaptive design of the airfoil profile is achieved, and the optimization period is remarkably shortened. Secondly, a self-adaptive agent model based on meta-learning is introduced, the model not only establishes a mapping relation among design conditions, aerodynamic performance and potential variables and directly predicts the optimal potential variables of a specific airfoil profile, but also can quickly adapt to an updated database through simple adjustment; the agent model predicts the optimal potential variable corresponding to the specific airfoil profile for the self-adaptive generation framework, so that the agent can quickly optimize the specific airfoil profile under the condition of fine adjustment.

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 belongs to the technical field of industrial waste heat recovery, and discloses a flue gas waste heat recovery system optimization design method considering external parameter changes. The method comprises the steps of system monitoring data acquisition and feature extraction, thermodynamic modeling and parameter sensitivity analysis, external environment parameter and system performance coupling mechanism modeling, equipment failure prediction and reliability constraint determination, multi-objective optimization and self-adaptive control strategy generation and the like. Optimized operation of the flue gas waste heat recovery system under the condition of dynamic change of external environment parameters is achieved. According to the method, a dynamic influence matrix of external environment parameters and system performance is initiatively established, the reliability of the system is evaluated in combination with a stress-life analysis technology, efficiency and cost targets are balanced through a multi-target optimization algorithm, and a self-adaptive control strategy library for different working conditions is generated. The adaptability and stability of the system under complex working conditions are improved, the service life of equipment is prolonged, and the operation cost is reduced.

Systems and methods for asset life cycle optimization and management are provided. A probabilistic, physics-based, causal method for predicting the evolution of damage and failure time of an aging asset. The method comprises providing a probabilistic, physics-based, causal network, comprising a plurality of random-variable nodes, wherein the nodes represent at least one of: damage initiation time, damage state, damage rate, damage causal factors, observations, human expert knowledge, failure state, and failure time. The method further comprises applying the probabilistic physics-based causal network to an aging asset; predicting the evolution of damage and failure time of the aging asset; and using this knowledge to make optimal design, inspection, maintenance, and operational life cycle decisions for the aging asset.

The invention belongs to the cross technical field of the fusion graph attention network and the ship resistance field, and discloses a ship resistance intelligent forecasting method and system, and the method comprises the steps: constructing nodes and edges in a graph attention network, and carrying out the preprocessing of data; designing and improving a loss function; a dynamic weight multi-attention mechanism is established, and attention weights are automatically distributed according to different ship type features; applying a training strategy of an Adam and SGD combined optimizer to obtain a graph attention network model; and carrying out ship resistance prediction evaluation through the dynamic weight map attention network model. By introducing the ship resistance intelligent forecasting method based on the dynamic weight map attention network and the physical constraint, the ship resistance intelligent forecasting method has the advantages of simple structure and clear algorithm, is specially designed for forecasting resistance under different ship types and solving the matching problem of propellers and power devices, shows remarkable innovativeness and practical value, and is suitable for popularization and application. Powerful support is provided for optimization design, energy conservation and emission reduction and safe navigation of the ship.

The invention discloses a personalized design intelligent interaction method based on topological optimization, and the method comprises the steps: S1, constructing a user behavior monitoring system based on multi-modal data collection, and generating a high-dimensional data set containing time sequence features; s2, based on an adaptive deep neural network model, performing dynamic prediction and hierarchical modeling on the personalized demand of the user; s3, generating an optimal solution of the design parameters in real time through a dynamic topology reconstruction algorithm; s4, iteratively updating a design scheme generation rule through a reinforcement learning mechanism; s5, constructing a multi-objective design space exploration and optimization framework supported by the neural network, and rapidly screening and optimizing design parameters; s6, dynamic updating and local optimization of the design scheme are achieved through multi-dimensional data mapping and parameterization regulation and control means; and S7, establishing a data-driven continuous learning and evolution mechanism, continuously optimizing the user demand prediction model and designing an optimization algorithm. The method has the advantages of being high in dynamic adaptability, high in intelligent level and high in personalized meeting precision.

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 relates to the technical field of phase change heat storage, in particular to a test method of a composite nanoparticle latent heat system based on a Fibonacci fractal tree-shaped fin. The test method comprises the following steps: firstly, establishing a physical model of a nanoparticle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fin; secondly, performing two-dimensional simulation analysis on the physical model by adopting a two-dimensional model, simulating the melting and solidification process of the PCM, analyzing the heat transfer performance and energy storage efficiency of the system, building an experimental device of a nano-particle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fins, selecting proper PCM and nano-particles, simulating different working conditions, and establishing a three-sleeve vertical latent heat system based on the nano-particle three-sleeve vertical latent heat system based on the Fibonacci fractal tree-shaped fins. And finally, comparing a simulation result with an experimental result, adjusting parameters of a simulation model, improving the accuracy of the simulation result, analyzing the influence of different fin bifurcation angles, nanoparticle types, wall surface temperatures and fin materials on the heat transfer performance and energy storage efficiency of the system, and providing guidance for optimal design of the system.

The invention discloses a common concrete and ultra-high performance concrete performance prediction and mix proportion multi-objective optimization design method and system, and the method comprises the steps: obtaining a comprehensive data set which covers the mix proportion, performance data and cost data of common and ultra-high performance concrete; correlation analysis is carried out, and influence factors strongly correlated with the target variable are determined; constructing a concrete performance prediction model by using a machine learning algorithm, training and testing, then carrying out interpretability analysis, and selecting an optimal concrete performance prediction model; constructing a mix proportion optimization design objective function and an ultra-high performance concrete cost function; establishing a multi-objective optimization design mathematical model in combination with constraint conditions, a cost function and an optimization objective function; and solving by adopting a multi-objective evolutionary algorithm, generating an approximate Pareto frontier solution set, and determining an optimal mix proportion optimization design scheme. According to the method, the concrete performance can be predicted, the mix proportion with the optimal comprehensive performance and cost effectiveness is obtained, and multi-objective optimization design of the concrete mix proportion is achieved.

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 discloses an ML-based rear subframe structure lightweight and modal optimization target design method, which comprises the following steps: (1) through three-dimensional modeling, statics analysis and modal analysis, constructing a mathematical model corresponding to the weight of a rear subframe and first-order inherent frequency simulation; (2) generating a population based on a Maximin criterion and a Latin hypercube, and establishing a radial basis function machine learning model; (3) generating candidate offspring individual vectors by adopting a Pbest-driven DPM evolutionary strategy based on an Eplison function; (4) constructing a minimum and maximum Pareto front lifting function to screen real offspring individual vectors; and (5) performing simulation evaluation on a real offspring individual vector, adaptively switching the reference vector type based on a reverse generation distance updating condition, returning to the step (3) until an optimization target meets a design requirement, and outputting an optimal optimization design parameter value. According to the method, the evolution direction is adaptively adjusted according to the population simulation result, and the optimization design effect on the two targets of lightweight and modal optimization is good.

The invention discloses a multi-working-condition multi-objective optimization design method for a water bucket structure of an impulse water turbine, which comprises the following steps: constructing a three-dimensional numerical model of an original water bucket structure of the impulse water turbine, and obtaining parameterized optimization design variables; modeling is conducted through the parameterized optimization design variables, a three-dimensional model of the impulse turbine runner bucket subjected to preliminary optimization design is obtained, the coordinate change direction and range of a control point of the three-dimensional model are restrained, and the restrained optimization design variables are obtained; according to the parameterized optimization design variable and the constrained optimization design variable, constructing a sample space of optimization design, and constructing a response surface model between the constrained optimization design variable and the optimization objective function; and performing global optimization on the response surface model to obtain an optimal solution set, and obtaining an optimal solution from the optimal solution set. According to the method, actual engineering requirements are considered when the optimization parameter range is set, and the phenomenon that the bucket blade is excessively deformed in the multi-working-condition optimization design process is effectively avoided.

The invention discloses an inclined shaft formwork design method based on digital modeling and sliding mode optimization, and the method comprises the steps: drawing a complete three-dimensional digital model of an inclined shaft through employing a geographic information system and a building information model, obtaining the high-precision inclined shaft data through combining with the technologies of laser scanning, an unmanned aerial vehicle and the like, and guaranteeing the accuracy of the model; an included angle between a tangent line and a normal line of each segment is calculated through an inclined shaft curve equation, and a precise geometric basis is provided for optimization design of the formwork based on the divided pouring areas and bin division starting and ending points; the optimal scheme of sliding mode segmentation is calculated by adopting an optimization algorithm, the structural deformation of the template is emphatically optimized, and the rationality of the position, angle and transition part of each segment is ensured; finally, through the accurate three-dimensional inclined shaft model and optimal design, the overall construction formwork is constructed, the precision and stability of inclined shaft formwork design are improved, it is ensured that in the construction process, stress of the formwork is uniform, deformation is minimized, and the construction efficiency and safety are remarkably improved.

Disclosed in the present invention are an intelligent liquid cooling plate optimization method and apparatus based on CFD numerical simulation. The method comprises: first, establishing a 3D battery pack model on the basis of liquid cooling plate design parameters and other component design parameters; then, on the basis of the 3D battery pack model, performing thermal simulation modeling and thermal simulation calculation on a battery pack; on the basis of a thermal simulation result, using a particle swarm multi-objective optimization algorithm to optimally calculate locally optimal solutions of the liquid cooling plate design parameters, and determining whether the locally optimal solutions converge; and using a loop iteration mode to calculate the locally optimal solutions of the liquid cooling plate design parameters until the locally optimal solutions converge, and then obtaining globally optimal solutions. Three-dimensional CAD software, CFD simulation software and an optimization interface are interconnected to each other, so that the operation of the whole process of design, simulation and optimization of a liquid cooling plate can be completely and intelligently realized simply by means of inputting key design parameters of the liquid cooling plate in an initial design stage; and an optimal design scheme is obtained by means of implanting the particle swarm multi-objective optimization algorithm. The present invention achieves a high speed and high efficiency.

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 invention relates to the technical field of pilot working state evaluation, and discloses an interface interaction cognitive load evaluation method based on pilot behavior data, which comprises the following steps: real-time data acquisition; discrete information processing; evaluating weights of perception type and mental movement type factors; and deducing and evaluating a cognitive factor weight and outputting a cognitive load value. The technical problems that the degree of association between the evaluated cognitive load and the interface is not high, and optimization design of an aviation interface cannot be effectively guided are solved, and by collecting observable visual, auditory and mental movement data, evaluation of the load level of an unobservable cognitive factor is achieved. And the accuracy of pilot real-time cognitive load quantitative evaluation is improved.

The invention provides an urban block form optimization design decision-making method and system based on generation of a large model. The method comprises the steps that multi-mode green performance data influencing local heat island intensity and wind environment performance under the city block scale are collected and preprocessed; converting the preprocessed data into RDF triples according to entities, relationships and events, and constructing a multi-modal knowledge graph; performing modular clustering on knowledge in the multi-modal knowledge graph, obtaining a structured sub-graph, improving the knowledge retrieval efficiency based on the structured sub-graph, and obtaining a building green performance data vector database for establishing an external knowledge base enhanced large language model; and providing guidance and receiving feedback for a machine learning model for executing urban block form optimization design through a knowledge representation enhanced large language model, and forming a large and small model collaborative end-cloud collaborative architecture. According to the method and the system, the multi-modal data processing capability of a traditional single machine learning method can be improved, and the efficiency and the precision of optimization design decision making are improved.

The invention provides an optimal design method of an ultralow-temperature multi-load heat insulation support, a support assembly and an experimental research method, and relates to the field of large superfluid helium low-temperature distribution systems. Comprising the following steps: selecting thickness parameters of a casing pipe in each stage of temperature zone of a supporting assembly and a supporting panel as optimization design parameters to carry out parametric modeling, and introducing a stress maximum value and a heat leakage value as target values; and based on the thickness parameters of the cold shield sleeve and the supporting panel of each stage of temperature zone, taking a genetic algorithm as an optimization function, performing iterative calculation in combination with simulation software, obtaining the thickness parameters of the cold shield sleeve and the supporting panel under the optimal boundary, determining the heat leakage value and the maximum stress value of the supporting assembly according to the optimal boundary, and then processing the supporting assembly. By combining various simulation technical means and experimental research, the problems of poor performance of the support assembly, lack of process verification and the like caused by the adoption of a classic model are solved, and the rigidity and robustness of the large-scale superfluid helium transmission and distribution system are effectively optimized and improved.

The invention belongs to the field of water pump runner blade design, and particularly relates to a centrifugal pump runner blade parameterization modeling and optimizing method and system.The method comprises the steps that firstly, a runner three-dimensional initial model is built according to parameters such as the inlet diameter and the outlet diameter of a runner in combination with a traditional unary theory design method; secondly, flow paths of an inlet, an outlet and front and rear cover plates of the section of a runner shaft are extracted, multiple streamlines on the runner blades are extracted at equal intervals, multiple control curves of the front and rear cover plates of the runner, blade bone lines, thickness and the like are expressed through a Bezier fitting line method, and a parameterized model of the runner blades is constructed; establishing a numerical model, and performing analog calculation to obtain multiple groups of runner external characteristics; and finally, performing iterative optimization in combination with a multi-target genetic algorithm to obtain an optimal solution. According to the method, the hydraulic optimization process of the rotating wheel is accelerated, and technical reference is provided for external characteristic optimization of the centrifugal pump.

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 discloses a complex part molded line optimization design method based on multi-field node feature weighted aggregation. Comprising the following steps: firstly, performing finite element batch simulation according to a geometric design variable, a temperature and a contact surface friction coefficient of a to-be-optimized complex part molded line to obtain a training data set; then, constructing a depth map sampling aggregation neural network fusing space and temperature attention, then training the depth map sampling aggregation neural network fusing space and temperature attention by using the training data set, and obtaining a stress prediction model after training is completed; and finally, in combination with the stress prediction model, performing iterative optimization by taking minimization of the maximum equivalent stress of the complex part as an optimization target, and obtaining the optimal geometric parameters of the molded line of the current complex part in the target temperature interval. According to the method, rapid reconstruction of the stress distribution state of the complex part is achieved, the optimization speed is increased, meanwhile, the precision of an optimization result is fully guaranteed, and meanwhile calculation resources are greatly saved.

The invention discloses an engine turbine shaft sequential optimization design method based on ensemble learning, and the method comprises the steps: carrying out the parametric modeling of an aero-engine turbine shaft through employing a finite element method, and determining a design variable; determining an optimized objective function and constraint conditions based on the turbine shaft structure; constructing a training point set by using the target function and the constraint condition, and establishing an ensemble learning agent model by using the training point set through an ensemble learning algorithm; executing a sequential optimization design process based on the ensemble learning agent model; searching a current optimal sequence design point by utilizing an intelligent algorithm and a sequential updating criterion, and judging whether the sequence design point meets a threshold value requirement or not; if the current optimal sequence design point meets the threshold requirement, outputting the current optimal sequence design point as a design result; otherwise, adding the sequence design points as new training points into the training point set to update the training point set, updating the ensemble learning agent model by using the updated training point set, and iteratively calculating the sequence design points.

The invention provides an ultra-high performance concrete proportion design method based on an intelligent algorithm and functional requirements. The method comprises the following steps: S1, constructing an ultra-high performance concrete performance prediction database; s2, automatically selecting key variables based on function requirements; s3, training and predicting a machine learning model based on function requirements; and S4, optimal design of the ratio of the ultra-high performance concrete. According to the unsupervised anomaly detection method based on the isolation forest, the database is preprocessed, and the ultra-high performance concrete proportion design key variables are selected through the automatic variable selection method, so that rapid mapping from the mixing amount of each component of the ultra-high performance concrete to each function demand index is realized; through the particle swarm optimization (PSO), the ultra-high performance concrete with specific function requirements is subjected to proportioning design, and finally the ultra-high performance concrete proportioning value closest to the function requirement index is obtained, so that the problems of time and labor consumption, low efficiency and resource waste are solved.

The invention provides a heat exchanger design method based on a mixed TPMS structure, and relates to the technical field of heat exchange, and the method comprises the following steps: 1, combining a sigmoid function to realize smooth transition of two original typical TPMS structure curved surfaces so as to construct a flexible mixed TPMS lattice structure mathematical expression; 2, lattice units are output in three-dimensional modeling software according to the obtained flexible mixed TPMS lattice structure mathematical expression; thirdly, periodic arrays of the target lattice units in a space coordinate system are achieved according to the structural requirements of a heat exchanger shell, independence of a hot flow basin and a cold flow basin is achieved by sealing opposite fluid areas at a hot fluid inlet and a cold fluid outlet respectively, and finally a flexible mixed heat exchange core structure is obtained and filled into the heat exchange shell to obtain the heat exchanger; and 4, flow and heat transfer characteristic analysis is carried out on the TPMS heat exchanger with different flexible mixing so as to obtain the optimal design. And by adopting the flexible TPMS structural design, the heat exchange area can be increased under the condition that the size is kept unchanged, so that the heat exchange performance is improved.

The invention discloses a method for rapidly optimizing the wave absorption performance of an underwater ecological submerged dike, and belongs to the technical field of ocean engineering, and the method comprises the following steps: taking an oyster reef as a research object, comprehensively considering marine environment factors and ecological submerged reef arrangement which influence the wave absorption performance of the ecological submerged reef, carrying out dimensionless processing, and determining seven independent variable parameters: relative water depth, water depth, water depth, water depth and water depth; the wave steep, the relative immersion depth of the oyster reefs, the relative length of the oyster reefs, the relative distance between the oyster reefs, the riverbed gradient and the number of the oyster reefs are determined. According to the method, nonlinear influences between all variable parameters are considered, a second-order optimal design model is suggested, an information matrix representing how changes of all factors affect variances and covariances of model parameters is calculated, and an optimal design scheme is obtained by maximizing eigenvalues of an inverse matrix. Compared with the traditional full-factor test design, the method has the advantages that the number of design groups is reduced on the premise of ensuring that the test combination is as comprehensive as possible, and the time of physical model experiment and numerical simulation calculation is greatly shortened.

The invention relates to the technical field of simulation optimization design, in particular to a simulation optimization design method and system for a stay cable tower top hanging bracket. According to the scheme, the corresponding finite element model is established firstly, then different working conditions are set according to actual conditions, the dynamic load is determined based on historical hoisting parameters under different working conditions and applied to the corresponding node for simulation finite element analysis and calculation, and finally the envelope value is extracted for analysis and checking calculation. According to the scheme, the dynamic stress change condition in the stay cable hoisting process can be more accurately simulated, the force borne by the tower top hanging bracket, the generated deformation and the counter-acting force of the fulcrum can be accurately calculated, and the data of the whole process can be visually displayed, so that the simulation model better fits the actual working condition, and the simulation efficiency is improved. And a more reliable basis and firm and effective technical support and guarantee are provided for design, construction, safety evaluation and the like of the tower top hanging bracket.