665 results about "Optimal design" patented technology

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 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 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 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 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 provides a terahertz tunable polarization converter and a design method thereof. The polarization converter is composed of three layers of metasurface structures. A Fabry-Perot cavity is formed through an orthogonal grating, horizontal polarization destructive interference and cross polarization constructive interference are achieved through polarization selectivity, and therefore the cross polarization average conversion efficiency up to 95% or above is achieved. Meanwhile, the working frequency of the device can be effectively adjusted by adjusting the interlayer spacing. The design method comprises the specific implementation steps that the initial structure size and the material attribute of the three-layer metasurface are set; setting an objective function representing the performance of the terahertz polarization converter, and importing the initial structure of the polarization converter into electromagnetic field simulation software for simulation to obtain an objective function value of the initial structure of the polarization converter; and performing iterative optimization on the three-layer metasurface initial structure by using a multi-target particle swarm optimization algorithm until the optimized structure meets the target requirement, and obtaining the optimal design parameters of the metasurface structure.

The invention relates to the technical field of grouting construction parameter design, in particular to a large-section tunneling type rectangular tunnel grouting construction parameter optimization design method and system.The method comprises the following steps that modeling is conducted according to design sizes of a rectangular jacking pipe, a pipe joint and a stratum to obtain a rectangular jacking pipe model, a pipe joint model and a stratum model; wherein a construction gap is formed between the pipe joint model and the stratum model; setting slurry simulation parameters and contact surface simulation parameters; setting different hole site slurry injection ratios, performing slurry filling simulation through SPH according to the set different hole site slurry injection ratios, so as to simulate the process of filling the construction gap with slurry and filling the construction gap, and obtaining a corresponding simulation result; and selecting different hole site slurry injection ratios with the shortest time for filling the construction gap and smooth circumferential pressure distribution curve from the simulation result, and outputting the different hole site slurry injection ratios. According to the method, adverse disturbance to the stratum can be reduced to a certain extent through optimization.

The invention relates to the field of mechanical engineering, in particular to a hydraulic-structure collaborative optimization design method for a shield pump, and aims to solve the problems of load distortion and structure matching imbalance caused by traditional staged optimization. The method comprises the following steps: constructing a fluid-solid bidirectional coupling simulation model, and extracting transient hydraulic loads under multiple working conditions; establishing a structure dynamic response agent model based on modal superposition and Gaussian process regression; key coupling parameters such as the impeller outlet width and the volute base circle diameter are recognized through a Sobol method; and the efficiency, the lift fluctuation rate, the stress and the vibration are taken as multiple targets, and a Pareto optimal solution is searched through an improved genetic algorithm. Manufacturing constraints are embedded in the optimization process, the single period is shorter than 48 hours, and fatigue life evaluation and knowledge base intelligent recommendation are supported. According to the method, closed-loop cooperation of hydraulic excitation and structural response is achieved, the vibration prediction error is lower than 10%, the optimization dimension is compressed to be within 5 dimensions, the efficiency is improved by 20 times or above, and the performance reliability and the design intelligence level of the shield pump are remarkably improved.

The invention belongs to the technical field of aircraft structure design, and particularly relates to an aircraft arbitrary space four-bar mechanism parameter optimization method, which comprises the following steps: firstly, determining input conditions or preposed requirements of a four-bar mechanism, secondly, establishing a mathematical model of a four-bar mechanism optimization problem, and then, carrying out derivation and analysis to obtain a four-bar mechanism parameter optimization result. The method comprises the following steps: establishing an analytic expression of parameter variables of the four-bar mechanism and compiling a corresponding calculation program, then carrying out iterative optimization on the parameters of the four-bar mechanism by utilizing an optimization algorithm, and finally outputting an optimization result to obtain an optimal design parameter.

The invention discloses a design method of an aero-engine wheel disc simulation part, which fully considers the influence of a main stress gradient path of fastest speed reduction of a damage parameter in a dangerous area of a wheel disc characteristic part in a three-dimensional space and a secondary stress gradient path of sub-fast speed reduction on the fatigue life in the design process of the simulation part. According to the method, the fatigue life prediction precision of the simulation part and the stability of a prediction result are greatly improved, a gradient path is automatically formed through a search program, the final configuration of the simulation part is obtained through optimization design, the design method has operability and repeatability, standardization of simulation part design is facilitated, the design process is simple, the design efficiency is high, and the method is suitable for popularization and application. The device is high in practicability and suitable for wide popularization and application.

The invention provides a method and system for generating cultural and creative contents based on large model fine tuning, and the method comprises the steps: processing cultural symbols in text description data through employing a text analysis technology according to an initial feature vector, and building a correlation mapping relation between visual features and cultural semantics; obtaining similar design element clusters according to different culture backgrounds through the structured design resource library, and judging design logic consistency in the clusters; according to the design logic consistency judgment result, processing the similar design element cluster by adopting an iterative optimization technology to obtain an optimal design element combination scheme; if semantic conflicts exist in the optimization design element combination scheme, combination parameters are adjusted, and a final adaptive design intention expression form is determined; and through the final adaptive design intention expression form, generating a creative and cultural design content fusing traditional and modern styles, and obtaining an output result with uniqueness.

The invention discloses a composite cutter bar design parameter optimization method. The method comprises the following steps: constructing a low-fidelity data set, a high-fidelity data set and a dual-stage deep learning model; in the first stage, low-fidelity features are learned based on an LSTM network, and a cutter bar performance prediction value is output; in the second stage, high-fidelity data and a low-fidelity prediction result are input for residual learning, and a comprehensive prediction model is obtained. Performing global optimization in the cutter bar design parameter space through a particle swarm optimization algorithm to obtain optimized design parameters; performing experimental verification to obtain actually-measured dynamic performance parameters and errors between the calculated predicted values and the actually-measured dynamic performance parameters; when the error does not meet the preset threshold value, according to the error distribution extension parameter value interval, new high-fidelity data is generated, then the comprehensive prediction model is updated, optimization verification is repeated, and final cutter bar design parameters are output. According to the method, efficient optimization and dynamic performance improvement of cutter bar design parameters are achieved.

The invention belongs to the technical field of aircrafts, and discloses a trigger-containing aircraft composite material energy absorption structure analysis method, which comprises the following steps: establishing an external trigger composite material structure model according to actual working conditions; constructing an intra-layer and inter-layer damage coupled progressive damage model; configuring finite element model parameters adaptive to the composite material; integrating simulation results; the crashworthiness of the coupling trigger structure is adjusted and optimized in combination with basic parameters; repeating the steps to verify the optimization effect; and predicting the strength of the composite material by coupling stress strain and damage transfer, revealing a damage failure mechanism and determining an optimal design. The method has the beneficial effects that the failure mode and the load response of the aircraft structure under different impact loads can be accurately captured, and low initial peak load and high specific energy absorption requirements are considered through multi-type triggering collaborative design; reliable technical support is provided for crashworthiness design of composite aircraft structures of aircraft fuselage frames, wing supports and other key structures in the aerospace field.

The invention discloses a multi-objective optimization design method for a PBL shear connector based on machine learning, and the method is characterized in that the method comprises the following steps: collecting the shear bearing capacity of a plurality of PBL shear connectors containing input parameters and output parameters, and deducing test data; firstly, a Catboost machine learning model is trained by utilizing the deduced test data, then the trained Catboost machine learning model is adopted to predict the shear bearing capacity of the PBL shear connector, the sensitivity of input parameters is analyzed in combination with an SHAP method, and finally, a multi-target genetic algorithm is introduced into the verified Catboost machine learning model, so that the shear bearing capacity of the PBL shear connector is predicted. And a multi-objective optimization model with the minimum hole opening number and the minimum steel quality as objective functions is established, and PBL shear connector optimization design is conducted through the multi-objective optimization model. The lightweight and efficient construction of the PBL shear connector can be realized on the premise of meeting the shear bearing capacity.

Disclosed in the present invention are a data-driven multi-objective optimization method for an environment-friendly gas arc-extinguishing system, and a related device. The method comprises: firstly, inputting collected key design variables of an environment-friendly gas arc-extinguishing system into a parametric circuit breaker cold-state simulation model for simulation, so as to output objective function values; using the key design variables and the corresponding objective function values to construct and train a data-driven model, so as to predict and evaluate a design space; and finally, combining the data-driven model with an optimization objective function for representing the breaking capability of a circuit breaker, and using a multi-objective genetic algorithm to output optimal design variables, so as to realize the optimization of the environment-friendly gas arc-extinguishing system. The method solves the problems of reliability and efficiency in traditional designs on the basis of multi-objective collaborative optimization and a data-driven model, and makes minor modifications to the structure of a circuit breaker to significantly improve the breaking performance of the circuit breaker, thereby improving the reliability and efficiency of circuit breaker design processes.

The invention relates to the technical field of extremely-small curved surface structures, in particular to an optimal design method, system and equipment for an extremely-small curved surface structure and a storage medium. The minimum curved surface structure optimization design method comprises the steps that a multi-objective optimization problem is determined according to design variables and optimization objectives of a three-period minimum curved surface lattice structure; constructing an approximate function relationship between the optimization target and the design variable by utilizing a self-adaptive multi-target engineering optimization method of a Kriging agent model; latin hypercube sampling and a sequential quadratic programming optimization algorithm are combined to carry out optimization design on the three-period minimal curved surface lattice structure. According to the design method, the light / bearing / wave-absorbing minimum curved surface lattice sandwich structure with good structure-performance dual characteristics can be designed, the wave-absorbing structure is filled with the minimum curved surface lattice structure in a shape follow-up mode, design and manufacturing of a wave-absorbing and bearing integrated structure are achieved, and rapid iteration and performance improvement of design of the wave-absorbing and bearing structure are supported.

The invention provides a motor stator potting structure failure risk assessment and optimization design method. The method comprises the following steps: firstly, establishing a simplified numerical calculation domain based on a potting structure, carrying out grid and time step length independence verification, carrying out parameter sensitivity analysis and experimental measurement, and establishing a high-precision thermal-mechanical coupling numerical calculation model; then, in combination with the simulation result and the temperature change constitutive characteristics of the ultimate strength of the material, a node-level failure risk calculation method is provided, failure risk indexes of different stress forms are obtained, and a weighted comprehensive failure risk evaluation standard is determined according to the contribution degree of the stress forms to failure behaviors; and finally, proposing a parameterized structure optimization scheme according to a dangerous area and a dominant failure mechanism, carrying out quantitative evaluation and optimization on the candidate scheme under a weighted comprehensive failure risk evaluation standard, and determining a final structure optimization scheme. The method has the advantages of closed flow loop and quantifiable indexes, and is suitable for reliability design and life evaluation of the motor encapsulation structure.

The invention provides a jacket type offshore electrical platform optimization method based on a sequential quadratic programming algorithm. The jacket type offshore electrical platform optimization method comprises the following steps: S1, establishing a parameterized offshore electrical platform structure analysis model; s2, calculating an environmental load borne by the offshore electrical platform by adopting an equivalent static load method; s3, establishing an offshore electrical platform optimization model which comprises a target function and a constraint function; s4, performing optimization design by adopting a sequential quadratic specification algorithm; and S5, performing dynamic analysis on the optimal structure, judging whether the optimal structure meets a constraint condition or not under a dynamic load working condition, if so, ending optimization, and if not, manually adjusting the size of the component which does not meet the constraint until the constraint condition is met. According to the method, the sequential quadratic programming algorithm is adopted, the optimization efficiency is high, the engineering optimization requirement is met, the size optimization of the offshore structure considering the pile-soil interaction under wind, wave and flow loading can be achieved, and the cost is further reduced on the premise that the structural stability and safety are guaranteed.

Embodiments of the present disclosure provide enhanced systems and methods for predicting optimal design flow parameters for optimized output targets for physical design synthesis of a given IC design. A Variational Autoencoder (VAE) along with a regression network are trained using a dataset comprising synthesis design construction flows from historical IC designs to provide a training data representation of the dataset constrained to a latent space of the VAE. The system generates feature vectors based on the training data representation of the dataset and updates the feature vectors with initial design characteristics of the given IC design. The system iteratively performs an input gradient search of the updated feature vectors to optimize an objective function of the design targets to identify locally optimal design parameters. The system identifies globally optimal design flow parameters for optimized design targets based on locally optimal design parameters.

The invention discloses a motor multi-objective optimization design method and system based on a Kriging agent model. Comprising the following steps: constructing an initial data set through an experimental design method and finite element simulation calculation, so as to train an initial Kriging agent model which takes a motor design variable combination as an input variable and takes motor performance as an output response; the proxy model serves as a target function, a multi-target optimization problem is solved, the proxy model is updated, and an updating strategy is as follows: after each round of optimization is finished, a high-error solution in a current Pareto solution set is screened based on a Kriging model prediction mean square error, a finite element response of the high-error solution is obtained and supplemented to a data set, and the proxy model is updated and trained; and repeating the process until the optimization result converges. According to the method, high-uncertainty region samples are selectively supplemented, the calculation cost is remarkably reduced while the local prediction precision of the proxy model at the Pareto leading edge is improved, and the method has good practicability and economical efficiency.

The invention provides an optimized design method for an impact type rotating wheel bucket blade structure based on entropy production analysis. The method comprises the following steps: firstly, generating a sample space through parametric modeling and optimal Latin hypercube experimental design of a rotating wheel; then, acquiring internal flow field data through numerical simulation; identifying a key loss area based on entropy yield flow loss; then, a Gaussian process regression agent model is constructed by taking the geometric dimension parameters of the bucket blade as input and hydraulic efficiency and entropy production as output, multi-objective optimization is carried out by adopting an improved NSGA-II algorithm, and the accuracy of the agent model is improved through a dynamic updating strategy; and finally, carrying out numerical simulation verification on the Pareto optimal solution, and screening out a runner design scheme with excellent comprehensive performance. The flow loss can be effectively reduced, the hydraulic efficiency and the operation stability of the impact type runner are improved, and technical support is provided for optimization of the hydraulic performance of the impact type runner.

The invention discloses a parameterization design method for a speed brake mechanism applied to a wide-area aircraft. The parameterization design method comprises the steps that the maximum resistance increment and the target resistance increment of the speed brake mechanism are determined; determining constraint conditions of reference design parameters and dynamic design parameters, and constructing a resistance increment mathematical model based on a radial basis function; the maximum resistance increment is substituted into the mathematical model, the extension length of the auxiliary board and the opening angle of the main board are made to be maximum values of constraint conditions, an optimization equation is built, and an optimal reference design parameter combination meeting the maximum resistance increment requirement is obtained; the target resistance increment is substituted into the mathematical model, an optimization equation is constructed by taking the reference design parameters as optimal reference design parameters, and an optimal dynamic design parameter combination meeting the target resistance increment requirement is obtained; and performing simulation verification on the optimal design parameter combination until the design is verified to be qualified, and outputting final design parameters. Wide-range adjustment of resistance can be achieved, accurate control over resistance errors is achieved, and the wide-range aircraft resistance increasing requirement is met.

The invention relates to a converter valve water-cooling voltage-sharing electrode charged particle distribution simulation method and system. The method comprises the following steps: constructing a three-dimensional geometric model of multiple types of voltage-sharing electrodes; constructing a charged particle multi-physics field coupling model based on the three-dimensional geometric model, wherein the coupling model comprises a flow field control equation, an electric field control equation and a particle dynamics model; simulation boundary conditions and initial conditions are set, the boundary conditions comprise flow field boundary conditions and electric field boundary conditions, and the initial conditions comprise particle release initial conditions; on the basis of the coupling model, the simulation boundary condition and the initial condition, particle tracking and distribution characteristic resolving are executed, a simulation result is obtained, and the simulation result comprises a particle motion trail, a particle capture rate and a capture area. Compared with the prior art, the method has the advantages of breaking through macroscopic monitoring limitation, realizing microcosmic dynamic quantitative analysis, analyzing a multi-field force competition mechanism, revealing a local scaling source, verifying the advantages of a multi-electrode structure, supporting optimal design and the like.

The invention provides a cross-scale model enhanced fatigue damage prediction method and system, and belongs to the field of material fatigue performance prediction. The method comprises the following steps: constructing a discrete dislocation kinetic model and a cyclic crystal plastic model, and calibrating parameters of the discrete dislocation kinetic model; determining to-be-optimized parameters of the cyclic crystal plastic model and inverting the optimal values of the to-be-optimized parameters to obtain a calibrated cross-scale mechanical model; importing the initial microstructure features of the material into a cross-scale mechanical model to obtain an extended data set containing multi-scale features and fatigue damage results; and constructing a full-connection neural network prediction model, carrying out training based on the extended data set, calculating the Shapley value of the input feature by using a model interpretability tool, and outputting the contribution degree of the multi-scale microstructure feature to the fatigue damage. According to the method, cross-scale interpretable prediction from a micromechanism to macroscopic performance is realized, and a new direction is provided for accurate evaluation of the fatigue life of the heterogeneous material and optimization design of a microstructure.

The invention relates to the technical field of data processing, in particular to an elastic frame optimization design method and system based on the finite element theory, and the method comprises the steps: constructing a nonlinear finite element model of an elastic frame, and for each node, determining the sealing reliability of the node based on the normal contact stress value, fluid pressure value and displacement vector of the node; for each unit, determining a fluid permeation trend factor of the unit based on the sealing reliability of the nodes in the unit, remodeling a material relative density iterative updating formula of the optimization criterion method by using the fluid permeation trend factor, updating the material relative density of each unit, generating an optimal topological configuration, and carrying out the optimal topological configuration; therefore, the optimal design of the elastic frame is completed. According to the method, the problem of high-pressure leakage caused by neglecting microscopic sealing in traditional topological optimization is solved, and synchronous optimization of the structural rigidity and the sealing performance under the ultrahigh-pressure working condition is achieved.

The invention discloses an underwater wireless power transmission system parameter identification method based on machine learning, which comprises the following steps: constructing a data set through finite element simulation, and obtaining original input parameters including geometry, electromagnetism and position offset and corresponding key output parameters; feature engineering is executed, and derived features with physical significance are extracted; performing standardized preprocessing on the data; for each key output parameter, independently training an XGBoost regression model; carrying out five-fold cross validation on the training set by adopting a Bayesian optimization algorithm, and carrying out independent hyper-parameter optimization on each XGBoost model; and carrying out interpretability analysis on the trained agent model by utilizing SHAP analysis, quantifying the contribution degree of each input feature to a prediction result, and providing physical insight. According to the method, high-precision prediction of the electromagnetic parameters under the complex working condition is achieved, complex electromagnetic field calculation is avoided, the system design efficiency is remarkably improved, and the efficiency and interpretability of intelligent optimization design of the UWPT system are remarkably improved.