365 results about "Design space" patented technology

The invention discloses an automobile front subframe lightweight design method considering modal constraint. The method comprises the following steps: (1) constructing a front subframe parameterized model and a weight-modal simulation model by adopting CATIA (Computer-graphics Aided Three-dimensional Interactive Application) and Altar Optistruct; (2) establishing a population in a design space based on a space filling Latin hypercube sampling method; (3) constructing a classification cooperation speed updating formula based on a particle swarm algorithm to generate offspring particle individuals; (4) constructing a Gaussian process machine learning model and a fitness evaluation function, and screening an optimal offspring particle individual based on a feasibility rule; and (5) constructing a comprehensive score calculation formula according to a statistical ranking method to determine the updating condition of the population individuals, updating the database and skipping to the step (3) until the optimized structure meets the requirement. According to the method, the coverage capability of the filial generation in the target space is enhanced through the classification collaborative speed updating formula, and the prediction precision of the Gaussian process machine learning model is continuously improved, so that the optimization performance of the lightweight of the front subframe of the automobile is improved.

The invention relates to a spinel multi-objective reverse design method based on machine learning and a Bayesian optimization algorithm, electronic equipment and a storage medium, and the design method comprises the steps: firstly extracting related data of a spinel material from a database, and constructing a balanced data set through preprocessing; feature engineering is carried out, a comprehensive feature set is constructed, and key features are reserved; then training a multi-target prediction model through hyper-parameter optimization by using a multi-task gradient elevator model; and finally, integrating to a Bayesian reverse design framework, expanding a design space through a specific encoder, and combining a Gaussian process proxy function and an expected hyper-volume improvement criterion to screen candidate materials meeting conditions and verify performance, thereby completing reverse design optimization. Compared with the prior art, the intelligent and efficient spinel novel multi-target reverse design method can be used for solving the problem of data scarcity, and development of high-performance spinel solar cell materials is accelerated.

The invention discloses an aircraft aerodynamic performance analysis method, which comprises the following steps: constructing a multi-dimensional parameterized model integrating aerodynamic / structure / thermal control, and outputting a global design space; based on a global design space, fusing sparse fluid dynamics and engineering data to train an agent model, and verifying the cross-speed domain reliability; decoupling the centroid / lift-drag ratio / thermal load conflict by using the proxy model, and generating a speed domain adaptive configuration library; executing fluid-structure-thermal field strong coupling simulation by utilizing a shock wave-boundary layer self-adaptive grid technology aiming at the speed domain self-adaptive configuration library; based on a fluid-structure-thermal field strong coupling mechanism, utilizing a dynamic sensing technology to predict the aerodynamic performance of the aircraft; and integrating dynamic prediction data and digital simulation, calibrating model parameters, and outputting an aerodynamic performance report and an optimization scheme. According to the method, the problems of insufficient multi-physical field integration, low cross-speed domain reliability, poor configuration self-adaption and low coupling simulation precision in aerodynamic performance analysis of a traditional aircraft are solved.

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 provides a centrifugal pump performance optimization method and device, computer equipment and a storage medium, and belongs to the field of centrifugal pump design. The method comprises the steps that a parameterized model of a centrifugal pump impeller is established to define the geometrical shape of the centrifugal pump impeller, a design space is determined, and a plurality of sample points are generated; for each sample point, performing computational fluid dynamics simulation, adjusting inlet pressure, monitoring a ratio of simulation lift to rated lift, determining critical cavitation pressure corresponding to the sample point, and storing flow field data to construct a data set; training an agent model by using the data set; the lift, the efficiency and the critical cavitation pressure serve as targets, design variables serve as optimization variables, performance is evaluated through an agent model, global optimization is conducted through a multi-target optimization algorithm, and a target design scheme is selected from a Pareto solution set obtained through optimization. Therefore, the centrifugal pump performance under different design variables can be quickly and accurately predicted, multiple targets are effectively balanced, and a comprehensive optimization solution is provided.

The innovation is the Landscape Syntax Algorithm, a new analytical framework to transcend the age-old limitations of space syntax. It integrates the highly versatile idea of landscape. Space syntax is largely objective and deals with the arrangement of space, along with little or no consideration for the more subjective parts of human aspects and experiences. Thus, the Landscape Syntax Algorithm fills that gap by providing a model that marries objective spatial measures with the more subjective cognitive and experiential aspects of landscape. This is carried out by infusing perceptual cognition and history / contextual layers within spatial analysis. Hence, there is a thorough reading of the relation between human experience and the built world. It opens up the opportunity to unify the most objective spatial configuration and subjective perceptual cognition to provide a powerful resource for the architect, urban planner, or researcher interested in analyzing and designing spaces that connect to the human experience in a more profound and mature sense. This invention enables quantification and analysis of landscape as moving and interactive phenomena towards a much more human-oriented contextual design of space.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures include: obtaining a design space, design criteria, and boundary conditions for numerical simulation; defining an application of the boundary conditions to a voxelized mesh, including specifying a distribution of a load to nodes of voxels in the voxelized mesh that correspond to a surface of preserve geometry; and iteratively modifying a generatively designed three dimensional shape in the design space in accordance with the design criteria and a physical response of the modeled object determined by the numerical simulation performed using the application of the boundary conditions to the voxelized mesh, wherein the distribution of the total loading value during determination of the physical response ensures an equivalence between the total loading value and a sum of loading values distributed to the respective nodes of the voxels that correspond to the surface.

The invention discloses a wireless power transmission coil optimization method and a related system, which can realize automation of coil structure parameters and multi-target global optimization by acquiring a coil geometric parameter space, constructing a comprehensive optimization function and performing iterative search in the coil geometric parameter space. The problems that in traditional design, the number of simulation iterations is large, design efficiency is low, local optimum is prone to occurring, and multiple performance indexes are difficult to balance are effectively solved, coil design efficiency and precision are remarkably improved, and dependence on artificial experience is reduced. Therefore, the number of manual intervention and simulation is remarkably reduced, the coil design efficiency is improved, and the prototype development period of the WPT system is shortened. Besides, iterative search is carried out in the parameter space, so that the whole design space can be effectively explored, a locally optimal solution trap can be jumped out, and a globally optimal or approximately globally optimal coil structure parameter combination can be obtained more possibly.

The invention discloses a power turbine optimization method and system based on data driving, and the method comprises the steps: selecting a plurality of optimization working condition points, and determining turbine operation working condition parameters under each optimization working condition point; obtaining a blade profile optimization geometric parameter, and determining the dimension and the variation range of the blade profile optimization geometric parameter; forming a plurality of groups of geometric parameter schemes based on the blade profile optimization geometric parameters, and establishing a turbine three-dimensional model and dividing computational grids; cFD simulation is carried out on the turbine under each group of geometric parameter schemes, aerodynamic performance parameters of each geometric parameter scheme under each working condition are obtained, and a sample library is formed based on the blade profile optimization geometric parameters, turbine operation working condition parameters and the aerodynamic performance parameters so as to establish a neural network prediction model; and performing multi-objective optimization on the blade profile optimization geometric parameters by adopting a neural network prediction model to determine optimal blade profile geometric parameters. By means of the scheme, the optimal blade profile geometric parameter combination meeting the multiple performance targets at the same time can be efficiently searched in the design space.

An acoustic vibration coupling plane structure topological optimization method based on an energy finite element is characterized in that discretization processing and unit assembly of the energy finite element are carried out on an acoustic system by utilizing a unit independent grid method, an energy finite element model is constructed, and then the optimization of the acoustic performance of an acoustic vibration system is realized by combining the energy finite element model with the topological optimization method; according to the method, a unified analysis framework is provided for an acoustic vibration system, the dynamic change of a discontinuous interface of an energy density field in iterative optimization can be adapted, and a high-degree-of-freedom design space is provided; the calculation cost is reduced, the calculation efficiency is improved, and the accuracy of an analysis result is ensured.

The invention discloses a physical perception-based generative three-dimensional reverse design method and system, which realize efficient exploration and optimization in a three-dimensional design space by constructing a unified physical-geometric potential representation and combining a two-stage optimization strategy. According to the method, shape and physical field data combined training is utilized to obtain potential representation, so that a geometric structure and physical constraints are compactly represented; on the basis, two stages of physical perception optimization are adopted: in the first stage, global exploration is performed in a potential manifold through a gradient-guided diffusion model, and an initial grid is generated; and in the second stage, the initial grid is locally optimized by utilizing target-driven topology maintenance optimization, so that the initial grid gradually approaches the target performance requirement. According to the method, the high-fidelity three-dimensional geometric design can be directly generated, the target performance and the result diversity of the design are remarkably improved, the method can be widely applied to aerospace, energy equipment and complex engineering structure design, and a new technical approach is provided for efficient implementation of the three-dimensional intelligent design.

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 discloses an outer rotor permanent magnet synchronous motor torque optimization method, which comprises the following steps: 1) design parameter selection: selecting key characteristics of a motor stator slot as design parameters, including slot shoulder height Hs1, tooth length Hs2, chord length Bs0 and slot shoulder width Bs1; 2) finite element model construction: establishing a multi-target finite element model with cogging torque, torque pulsation and average torque as targets; 3) establishing an analytic model: establishing the analytic model of the motor torque by using an energy method and a repetitive unit method, and comparing the analytic model with the finite element model to verify the accuracy of the analytic model; 4) response surface model construction: based on the simulation data of the finite element model, performing finite element analysis by adopting a central composite design CCD, constructing a response surface model, performing sensitivity analysis on design variables, and determining the influence of each variable on an optimization target; and 5) optimization efficiency improvement: determining optimal configuration in a design space by using an adaptive genetic aggregation algorithm AGA, and achieving a multi-objective optimization effect.

The invention discloses a rotor blade optimization design method which comprises the following steps: firstly, establishing a multi-target blade layout optimization problem, performing parametric modeling on an initial blade, extracting blade layout parameters as design variables, then determining sampling points through experimental design, and calculating target values and constraint values; carrying out variance analysis to obtain sensitivity information of an optimization target to a design variable, carrying out multi-target optimization by adopting an evolutionary algorithm, setting an iteration stop condition to obtain a Pareto leading-edge solution set, carrying out design variable analysis on the selected individual to obtain a design variable analysis result, and based on the design variable analysis result and the sensitivity information, carrying out multi-target optimization on the Pareto leading-edge solution set; and dynamically adjusting the design space until an iteration stop condition is met, and outputting a final optimization scheme. A sand blind suppression target is embedded into a multi-target optimization framework, and a design space dynamic adjustment strategy is introduced, so that the conventional pneumatic / structural performance is ensured, the sand and dust concentration is remarkably reduced, and the environmental adaptability and the flight safety of a rotor wing are improved.

The invention discloses a bridge splicing part route optimization design method based on a constraint condition genetic algorithm, and relates to the technical field of bridge engineering design, and the method comprises the steps: building a hard constraint and soft constraint double-layer constraint system based on a standardized data set and a parameterized model in a step 2, and associating the system to fitness calculation of chromosomes; and based on a constraint processing result, integrating multi-objective optimization requirements, and constructing a fitness function. According to the method, a route is abstracted into a third-order cubic B-spline curve and parameterized coding is carried out, iteration is carried out in combination with a genetic algorithm, a design space can be globally explored in an optimizable control point range, and compared with traditional experience design, a better global solution can be found, and the problem of local optimum caused by experience limitation of engineers is avoided. The fitness function realizes multi-target quantitative integration through normalized indexes and adjustable weights, the weights of cost, smoothness and environmental influence can be flexibly adjusted according to project scenes, the problems of multi-target qualitative and difficult comparison in traditional design are solved, and the scheme adaptability is remarkably improved.

The invention discloses a front subframe lightweight design method and device based on RMNN, a medium and a program product, and the method comprises the steps: (1) constructing three simulation models of weight, stress and modal of a front subframe, and deducing a lightweight design model containing stress and modal constraints; (2) generating a population based on a uniform experimental design and a correlation criterion, performing simulation evaluation on the population, and constructing a database; (3) constructing a network architecture and training an RMNN; (4) generating a candidate frame set according to a random preferential strategy and the RMNN; and (5) establishing a radial basis function model, screening an optimal frame, performing simulation evaluation, updating the database and the population, returning to the step (3) until the optimization target reaches the standard, and outputting an optimal solution of the optimization parameter. According to the method, the feasible region is quickly searched through the RMNN, the problems that the high-dimensional design space search efficiency is low and the feasible region is difficult to recognize under multiple constraints are further solved in combination with the radial basis function model, and quick optimization can be carried out for the lightweight design of the front subframe.

The invention discloses a variant reconstruction AUV (Autonomous Underwater Vehicle) shape optimization method based on proxy-assisted multi-starting-point space reduction. The variant reconstruction AUV shape optimization method comprises the following steps: establishing a parameterized model of a variant reconstruction AUV; constructing a variant reconstruction AUV shape modeling-grid division-performance simulation automation framework, realizing input of design variables, and automatically performing variant reconstruction AUV shape modeling and watershed grid division; a variant reconstruction AUV shape hydrodynamic force simulation calculation framework is built, and assessment of variant reconstruction AUV hydrodynamic force performance is achieved; generating a plurality of groups of initial samples for a design space of design variables, calculating target function values corresponding to the samples by utilizing the simulation automation framework and the simulation calculation framework, and importing the samples and corresponding sailing resistance into a sample library; and performing optimization in the design space of the design variables by utilizing an agent-assisted multi-starting-point space reduction method to obtain an optimal solution of the variant reconstruction AUV shape design problem. According to the method, the reliability of an optimization result and the design efficiency can be improved, and the optimization time is shortened.

Disclosed are a neuromorphic chip compiling method and compiling system. The compiling method comprises: acquiring a pre-trained model and a data set of a neuromorphic application; generating an initial neuromorphic chip architecture and a network spiking input; performing an optimization and adjustment operation, performing simulation, and evaluating performance; and determining whether an optimization objective is satisfied, and performing loop optimization until an optimal neuromorphic chip architecture is obtained. In the method, by means of iterative optimization and effective evaluation, an optimal neuromorphic chip architecture is efficiently explored in a vast design space, thereby improving design quality and efficiency. The present application further provides a corresponding compiling system, comprising a generator, a simulator, and a compiler, thereby achieving automated compilation and deployment, and providing support for the design of large-scale multi-core neuromorphic chips.

The invention discloses a digital circuit design method and device, equipment and a medium, and relates to the technical field of digital circuits, and the method comprises the steps: determining a target type of a to-be-designed digital circuit, and collecting a first training set corresponding to the target type; each training sample in the first training set comprises an input feature item used for designing a target type of historical digital circuit and the actual result quality of the historical digital circuit, and the input feature item comprises a high-level language source code and an HLS constraint parameter; constructing an initial prediction model based on the multi-layer perceptron mixed structure, training the initial prediction model by using the first training set, and searching and optimizing the model architecture based on the network structure to obtain a target prediction model; and predicting the actual result quality corresponding to each parameter combination in a target design space of a to-be-designed digital circuit by using the model, and designing the digital circuit by using the parameter combination of which the quality meets the Pareto frontier. And predicting an accurate actual quality result by utilizing a prediction model without intermediate representation so as to carry out digital circuit design.

The invention discloses a rear positioning structure of a sliding rail and a machining process of an elastic piece of the rear positioning structure, and belongs to the technical field of sliding rails, the rear positioning structure comprises a shifting plate, a rear positioning plate and an elastic piece, the shifting plate is rotationally connected to an outer rail, and at least part of the shifting plate is located in a mounting groove of the outer rail; a main body structure of the elastic piece is installed on the outer rail, and at least one elastic piece is integrally formed on the main body structure of the elastic piece; when the middle rail is located at the retraction point position, the elastic piece is in elastic contact with the shifting plate, so that the shifting plate abuts against the middle rail; when the middle rail is located at the extending point position, the shifting plate is pushed by the elastic piece to be arranged in the locking groove in the middle rail. According to the rear positioning structure, the shifting plate is rotationally connected to the outer rail, the main body structure of the elastic piece is also mounted on the outer rail, a key part of the positioning structure is transferred to the outer rail, and the outer rail generally has a larger space and a larger mounting position relative to the middle rail, so that the designable space of the whole rear positioning structure is increased.

The invention discloses a PPA prediction method for designing a few-sample processor based on RAG. According to the technical scheme, an RAG-based few-sample processor design PPA prediction system composed of a processor design normal form, an EDA tool module, an RAG module, a to-be-tested processor design input module and a PPA prediction module is constructed. The RAG module samples a design space of a processor design normal form, obtains a processor design sample, encodes feature vectors of the sample, obtains a PPA value of the design sample by using the EDA tool module, and constructs a knowledge base required by PPA prediction; and the RAG module detects knowledge matched with the processor design code of the PPA value to be predicted in the knowledge base, and generates a cue word based on the matched knowledge. And the PPA prediction module interacts with the cue word to obtain a PPA prediction value designed by the PPA value processor to be predicted. Accurate PPA prediction can be carried out based on a small number of samples, and the design period is shortened.

The invention discloses an accelerator architecture design method and electronic equipment, relates to the technical field of computers, and performs dynamic fractal space coding on a design parameter matrix of an accelerator to obtain an analog coding vector. And performing meta-learning dynamic weight entropy search on the analog coding vector by using a historical task data set to determine a new design scheme. And when the difference between the sensitivity parameters in the new design scheme and the sensitivity parameters in the neighbor set meets a difference condition, performing multi-objective optimization simulation on the new design scheme. And when the difference does not meet the difference condition, multiplexing the simulation result of the neighbor set. And performing iterative search according to Bayesian optimization, and determining a final accelerator architecture design scheme in the selected design scheme and the simulation result thereof. Dimension reduction is performed on a high-dimensional discrete space, and a search direction is guided by means of an efficient meta-learning dynamic weight entropy acquisition function. And rapid exploration of an accelerator architecture design space under multi-objective optimization is realized.

The invention relates to the technical field of impeller design optimization, in particular to a fuel cell compressor impeller design optimization method, which comprises the following steps of: firstly constructing a parameter system of compressor impeller design, defining a design variable set and generating an impeller geometric model through parametric modeling; establishing a multi-objective function system, coordinating aerodynamic efficiency, structural strength and quality objective conflicts, and setting manufacturing and safety constraint conditions; then, key design variables are screened through global sensitivity analysis, and the space is optimized in a dimensionality reduction mode; a Pareto optimal solution set is generated by adopting a gradient enhanced NSGA-II algorithm, and convergence is accelerated in combination with gradient information; and finally, verifying the candidate solution through a multi-level verification system, and correcting the design space. According to the method, the design efficiency and performance of the fuel cell compressor impeller can be effectively improved, the method has wide application prospects in high-speed impeller mechanical design in the fields of aviation, automobiles and the like, and cross-platform multiplexing design criteria and empirical formula libraries are supported.

According to the technical scheme, the large-scale logic optimization multiplier verification method is characterized in that a four-stage pipeline processing mode is adopted, the first stage is a partial adder tree recovery stage, and the second stage is a partial adder tree recovery stage; the second stage is an architecture generation stage; the third stage is a function verification stage; and the fourth stage is an equivalence checking stage. The verification capability is improved in a breakthrough manner and is far better than that of an existing method; the innovative three-stage decomposition strategy significantly reduces the calculation complexity, the ILP algorithm optimizes resource allocation, the multi-stage scheduling balances the calculation load, and the QACO algorithm efficiently explores the design space, so that the RefSCAT-2.0 framework provided by the invention becomes the most effective solution for verifying a large-scale logic optimization multiplier at present. From the perspective of practice, the technical blank of large-scale logic optimization multiplier verification is filled up, and important tool support is provided for reliability guarantee of key computing systems such as artificial intelligence chips, high-performance CPUs and GPUs.

The invention relates to the technical field of electric power engineering, in particular to an electric power construction acceptance method and system based on an image processing technology, and the method comprises the steps: receiving an electric power construction drawing, recognizing an electric power equipment mark and an association mark through a deep neural network, and generating a design parameter set; a multi-view depth image of a construction site is collected, point cloud data and topology rules are fused to construct a live-action three-dimensional model, and an actual measurement parameter set is extracted; carrying out space coordinate system alignment on the design space coordinates and the actually measured space coordinates, calculating size and angle deviations and forming a comprehensive deviation data set; and performing compliance judgment according to a safety threshold value of the power equipment, marking a standard exceeding position, generating a deviation thermodynamic diagram, generating a dynamic rectification scheme in combination with a historical acceptance database and construction environment information, and finally outputting a power acceptance report containing the thermodynamic diagram and rectification suggestions. According to the invention, automatic identification and intelligent rectification of the power construction deviation can be realized, and the acceptance efficiency and the quality traceability are improved.

The invention provides a transparent antenna lens and intelligent glasses, and belongs to the technical field of intelligent glasses. The antenna transparent substrate is arranged on the lens body. The plurality of first metal structures are arranged on the surface of the antenna transparent substrate and are far away from the lens body, so that the lens body and the transparent antenna are combined and can support each other, and the transparent antenna is arranged on the lens of the glasses. The design space of the transparent antenna is not limited by the size of the glasses legs any more and is far away from the head of the human body, the problem that the electromagnetic performance is low due to the fact that the human body absorbs and scatters electromagnetic waves and is limited by the size is solved, and the electromagnetic performance of the transparent antenna is greatly improved. The multiple first metal structures are connected and surround to form the irregular polygonal metal grid, so that the problems of bright diffraction fringes and dark areas caused by coherent interference generated by the periodic metal grid can be solved, and a user can see a clearer and more accurate image.

Methods, systems, and apparatus, including medium-encoded computer program products, for computer aided design of physical structures using three-dimensional model synthesis processes. A method includes: obtaining a build angle, a manufacturing direction, a design space, and one or more design criteria for use in a shape synthesis process, the build angle and the manufacturing direction being for an additive manufacturing process; performing the shape synthesis process including applying a build-angle filter to a boundary-based computer-data representation of an intermediate version of the shape of the modeled object during multiple iterations, including removing a portion of material from the intermediate version of the shape in accordance with the build angle and the manufacturing direction to make the intermediate version of the shape self-supporting in a vicinity of the portion of material; and providing the shape of the modeled object for use in manufacturing using the additive manufacturing process.

The invention discloses an organic Rankine cycle optimal configuration search method and device based on PINN and multi-objective optimization, and the method comprises the steps: firstly constructing a thermodynamic model of an organic Rankine cycle system, constructing a training data set and physical constraints based on the thermodynamic model, and then training a PINN pair network through the training data set, thereby obtaining the optimal configuration of the organic Rankine cycle system. Embedding a physical constraint in the loss function; the method comprises the following steps: training a PINN (Plural Index Neural Network), predicting a to-be-predicted design parameter by using the trained PINN to obtain a predicted cycle state parameter, finally calculating a plurality of organic Rankine cycle evaluation indexes according to the predicted cycle state parameter, constructing a target function, and carrying out multi-target optimization search by minimizing the target function to obtain final optimal configuration. According to the method, the calculation efficiency of system performance evaluation is remarkably improved, the optimal performance configuration of the ORC system under various working medium and operation conditions can be efficiently obtained by searching in a wide design space, and unification of optimization efficiency and prediction accuracy is achieved.

The invention discloses a micro-channel heat exchanger topological optimization design method, which comprises the following specific steps of: 1, executing key working condition parameter sampling in a design space, constructing a topological optimization mathematical model, carrying out iterative computation, and establishing a topological optimization database comprising a topological field and corresponding temperature field, flow field and pressure field data; 2, realizing unified order reduction of a topological field and each physical field by adopting intrinsic orthogonal decomposition, and converting a complex high-dimensional structure into low-dimensional modal representation; 3, constructing a multi-task learning neural network, taking the multi-target constraint parameters as input, taking the topological field modal coefficient and each physical field modal coefficient as output, and adopting a PyTorch deep learning framework to train a topological structure generation model; and 4, a model is generated based on the trained topological structure, the high-resolution topological structure can be predicted within second-level time, and the thermal-fluid performance of the high-resolution topological structure can be synchronously fed back. The method has a remarkable calculation speed-up ratio and good engineering mobility, and an efficient solution is provided for micro-channel heat dissipation design of a high-power electronic device and a new energy system.

The invention is suitable for the field of automobile energy absorption structures, and provides a quantitative characterization method for the oblique pressure stability of a thin-walled tube, and the method comprises the steps: determining a segmentation n value, and dividing the thin-walled tube into n segments; establishing quantitative characterization of the instability behavior; establishing a critical instability angle data set; and constructing an instability prediction model. According to the method, the oblique pressure instability behavior is accurately judged by adopting a quantitative index and a threshold expression form thereof. On one hand, a correlation mechanism of the structural parameters and the instability behaviors is established, so that the instability condition is accurately predicted when the structural parameters change, and the deformation stability and high energy absorption of the structure are ensured. And on the other hand, customized design based on the specific loading angle working condition can be achieved, under the condition that the critical instability angle is given, global regulation and control are conducted in the design space, optimal configuration of the structure is achieved, and design insufficiency and design redundancy are avoided.