21 results about "Hypercube" patented technology

This application discloses a method, device, medium, and product for optimizing the structure of a shaped charge shroud, relating to the field of structural design. The method includes: constructing an objective function with structural parameters as design variables and maximizing performance index values ​​as the objective; determining multiple initial sample points using a Latin hypercube sampling method based on the range of structural parameter values; constructing an initial sample library; constructing a surrogate model based on the initial sample library; determining candidate sample points and their corresponding performance index values ​​using a Bayesian optimization loop based on the surrogate model and the range of structural parameter values; updating the initial sample library and the surrogate model; continuing until a termination condition is met; and using the sample point corresponding to the maximum performance index value as the target structural parameter; and optimizing the design of the shaped charge shroud based on the target structural parameter. This application can reduce the computational cost of determining the structural parameters of a shaped charge shroud and improve the efficiency and intelligence of shaped charge shroud structure optimization.

The invention provides a tunnel blasting equivalent load prediction method and system based on an artificial neural network for tunnel blasting dynamic response rapid evaluation. The method comprises the following steps: firstly, establishing engineering parameters for describing a single-hole blasting working condition of a tunnel, and converting the engineering parameters into dimensionless input parameter vectors; aiming at continuity parameters and grading parameters, generating sample working conditions by adopting an orthogonal test and Latin hypercube combined sampling mode, carrying out numerical simulation on each working condition, extracting a blasting triangular wave speed-time history curve of a representative measuring point in the model, and establishing a blasting triangular wave speed-time history curve with engineering parameters of a single-hole blasting working condition as input; the database takes blasting triangular wave parameters as output; an artificial neural network is trained under the loss function, rapid prediction of triangular wave parameters is achieved, and triangular waves can serve as blasting equivalent load time history to be applied to the normal direction of the plane where representative measuring points are located and used for tunnel blasting dynamic response calculation. According to the method, the calculation cost and the modeling complexity can be remarkably reduced, and the numerical calculation stability and the engineering applicability are improved.

This invention discloses a heat sink structure optimization design method based on a non-dominated sorting genetic algorithm, belonging to the field of heat sink optimization design technology. The method first determines the structural parameters to be optimized (heat sink length, width, height, fin thickness, fin spacing, substrate thickness) and the optimization objective function (maximum junction temperature of power devices, heat sink mass, heat sink entropy productivity). Then, Latin hypercube sampling is used to sample parameters and establish a geometric model. Sample data is constructed through thermal simulation, and a surrogate model is established using response surface methodology. Finally, multi-objective optimization is performed based on the non-dominated sorting genetic algorithm to obtain the Pareto optimal solution set, and the best solution is selected through comprehensive performance evaluation indicators. This invention effectively reduces heat sink mass and cost while ensuring heat dissipation performance, shortens the R&D cycle, and is applicable to the heat dissipation optimization design of power devices in power electronic systems.

The application discloses a method and device for constructing a folded hypercube edge-disjoint Hamiltonian cycle, and relates to the technical field of interconnection network topology. The method comprises the following steps: recursively constructing a first Hamiltonian cycle on an n-dimensional folded hypercube; applying a mapping f to each vertex u in the first Hamiltonian cycle, and sequentially connecting the mapped vertices in the original order to obtain a second Hamiltonian cycle; and the first Hamiltonian cycle and the second Hamiltonian cycle do not have any common edge in the n-dimensional folded hypercube. The above method provides key technical support for the topology design and routing protocol of a high-performance parallel computing system.

This invention belongs to the field of electromagnetic device design technology, and relates to a method and apparatus for inverse design of electromagnetic devices based on bijective differentiable projection and neural networks. The method includes: determining the structural variables to be designed in the electromagnetic device and their inequality constraints; constructing a convex feasible region space composed of the inequality constraints; establishing a bijective mapping function from a hypercube domain to the convex feasible region space; establishing a neural network for generating the structural variables of the electromagnetic device; concatenating the bijective mapping function in the output layer of the neural network to form a neural network with a bijective differentiable projection layer and training it; inputting the target electromagnetic parameters into the trained neural network with the bijective differentiable projection layer to generate structural variables that satisfy the inequality constraints, thereby realizing the inverse design of the electromagnetic device. This invention accurately projects the output of the neural network into a preset convex feasible region, achieving rapid inverse design of electromagnetic devices that satisfy complex constraints.

This invention discloses a UAV 3D path planning method based on a multi-strategy improved lemming optimization algorithm, comprising the following steps: S1, constructing a 3D task space model; S2, constructing a flight cost function; S3, initializing the population using Latin hypercube sampling; S4, iterative global exploration and local exploitation with adaptive parameters; S5, updating the population using a bidirectional population evolution strategy and a memory mechanism; S6, evaluating fitness and selecting the current optimal individual; S7, iterative judgment and outputting the optimal path. This invention improves upon the UAV 3D path planning problem in complex environments by employing multi-strategy collaborative improvement, using Latin hypercube sampling to initialize the population, designing an adaptive parameter adjustment strategy to balance exploration and exploitation, and introducing a bidirectional population evolution strategy and a memory mechanism to enhance optimization performance. Through boundary reflection processing and flight cost function evaluation, the optimal flight path is obtained from the population, thus effectively solving the UAV 3D path planning problem in complex environments.

This invention discloses an optimization design method for lightweight torsion springs used in robots. The method includes the following steps: First, setting design objectives and constraints, with minimizing spring mass as the optimization objective. Constraints include stiffness, maximum stress, torque capacity, and maximum thickness. Then, material selection and topology design are performed, using chromium vanadium steel and designing three sets of symmetrically distributed wave-shaped spring structures. Next, a three-dimensional parametric model is established in a finite element environment, and simulation analysis is conducted to obtain key performance indicators. Further, based on the Latin hypercube sampling method and the Kriging surrogate model, the spring thickness and transition fillet radius are modeled and predicted, and a multi-objective genetic algorithm is used for global optimization to obtain the optimal structural parameters. Finally, physical processing and experimental verification are performed based on the optimization results. This invention can achieve significant weight reduction, reduce stress concentration, and extend service life while ensuring low stiffness and high torque capacity.

The present application relates to the technical field of bridge engineering, and provides a cable-stayed bridge completion cable force multi-objective intelligent optimization method: a finite element model of the cable-stayed bridge is established, the minimum bending energy method is adopted, and the cable force is initially uniform according to the principle of "long cable force is large, and short cable force is small"; a small sample support vector machine regression proxy model based on a radial basis kernel function is constructed, grid search and leave-one-out cross-validation are adopted to optimize the super parameters, and the mapping relationship between the cable force and the bending moment, displacement and other structural responses is represented; a multi-objective optimization model with bending strain energy and combined displacement as optimization objectives is established; and a multi-objective intelligent optimization method based on a double optimization strategy is used to guide the general cable force to approach the two types of excellent cable forces, the iteration optimization is adjusted through a direction factor and a strength factor, the Pareto optimal cable force candidate set is updated through a non-dominated sorting, a hypercube grid and a crowding degree strategy, and finally a comprehensive optimal cable force is obtained as the cable-stayed bridge completion cable force.

The application provides a full-size subsonic wind tunnel loop aerodynamic profile multi-objective optimization method based on steady CFD, and belongs to the technical field of wind tunnels.The application reduces the initial geometric parameters to a sensitive parameter subspace through principal component analysis, generates initial sample points by using Latin hypercube sampling, extracts response values by executing steady Reynolds average Navier-Stokes equation solving, constructs a Kriging surrogate model, performs multi-start parallel search based on an expected improvement function, extracts a separation zone length and a backflow zone height to input an unsteady correction factor model to calculate a correction coefficient for correcting a diffusion section pressure recovery coefficient, searches a Pareto front solution set by using a non-dominated sorting genetic algorithm, selects an optimal parameter combination through a comprehensive performance index, and reconstructs the wind tunnel loop aerodynamic profile through a hierarchical parameterization method and performs verification calculation, so that the problem that the steady calculation framework cannot accurately predict the separation flow and the optimization result deviates from the true performance is solved.

This application relates to the field of turbocharger efficiency prediction technology, and discloses a turbocharger efficiency evaluation method and device based on variable structure turbine data. The method first establishes a reliable radial turbine numerical model through high-precision three-dimensional simulation and experimental verification; then, it uses sensitivity analysis and Latin hypercube sampling to screen key impeller geometric parameters and construct an efficient sample dataset; the core is the application of a neural network algorithm to establish a nonlinear prediction model with turbine reduced speed and key geometric parameters as input and turbine efficiency as output, thereby quickly and accurately obtaining the variable structure turbine efficiency characteristics under all operating conditions; finally, by dynamically identifying the real-time operating conditions of the turbocharger, and combining the turbine efficiency predicted by the neural network with the compressor efficiency calculated by actual measurements, an accurate and efficient evaluation of the overall efficiency of the variable structure turbocharger is achieved, effectively solving the problems of high prediction difficulty and low accuracy of traditional methods.

A method and system(s) for processing queries to sparsely populated datasets. The method includes (i) receiving a query from a client system, (ii) determining, based at least in part on the query, a plurality of partitions of a hypercube that are expected to comprise information used in generating a response for the query, (iii) communicating a set of requests to a data source storing the hypercube, the set of requests comprising one or more requests corresponding to the plurality of partitions of the hypercube, (iv) obtaining, from the data source, a set of results to the set of requests, the set of results comprising data stored in the plurality of partitions of the hypercube, (v) determining a plurality of virtual objects for the set of results, (vi) combining the plurality of virtual objects into a response to the query, and (vii) providing the response to the query.

This invention belongs to the field of composite material structure optimization design technology, and discloses an integrated design method for composite material pipe winding parameters based on surrogate models and multi-objective optimization. The steps include: First, through parametric modeling and automated finite element simulation, a rapid mapping from design variables to performance responses is achieved; second, samples are generated based on optimal Latin hypercube sampling, and a high-precision Kriging surrogate model is constructed to replace time-consuming high-fidelity simulation; third, using the surrogate model as an evaluation tool, the NSGA-II multi-objective genetic algorithm is used for optimization, obtaining a series of Pareto optimal solution sets that achieve the best trade-off among multiple performance objectives; finally, the entropy weight method is used to objectively calculate the weights of each performance index, and the TOPSIS method is combined to make a comprehensive decision on the Pareto solution set, selecting the final optimal parameter combination. This achieves efficient, high-precision, and automated multi-objective optimization design of composite material pipe winding parameters, and ensures the comprehensive optimality and objectivity of the design scheme.

The application discloses an arc steel gate optimization design method based on an adaptive Kriging surrogate model, and comprises the following steps: obtaining basic information of an arc steel gate, establishing an arc steel gate parameterized finite element model and an arc steel gate structure optimization design mathematical model; inputting relevant parameters, and generating initial sample points by using a Latin hypercube sampling method; calling the arc steel gate finite element model to calculate target function values and constraint function values of the initial sample points, and establishing a sample library; constructing Kriging surrogate models of the arc steel gate target function and the constraint function by using sample points in the sample library; proposing a constraint global optimization method based on a confidence boundary, using an intelligent optimization algorithm to minimize corresponding optimization functions to find points, and then adding the sample points to the sample library to update the surrogate model; calling the arc steel gate finite element model to calculate target function values and constraint function values of new sample points until an algorithm termination condition is met.

This invention provides a state encoding method and apparatus based on hypercube space, belonging to the field of data structure technology. The method includes: obtaining a first state vector to be encoded; performing binarization mapping on the first state vector based on a preset mapping rule to determine the binary vector corresponding to the first state vector; mapping the binary vector corresponding to the first state vector to a hypercube space to determine a first integer index of the first state vector in the hypercube space; and storing or querying the first integer index as the first state vector. Using this invention can effectively reduce storage and computational overhead.

This application discloses a method for calculating and optimizing the magnetic field of high-current lead wires based on the response surface methodology, comprising: constructing a parameterized three-dimensional nonlinear electromagnetic-thermal bidirectional coupled finite element model; selecting representative sample points in the design space using the optimal Latin hypercube sampling method, training a surrogate model based on the sample points and dynamically adding points, and outputting a set of representative sample points; performing finite element calculations on each output representative sample point, extracting the performance indicators corresponding to each sample point, and then constructing a Kriging gradient-enhanced hybrid response surface model; establishing an optimization objective function to guide the optimization direction, and then using a multi-objective optimization algorithm to quickly optimize the high-precision hybrid response surface model, generating a Pareto optimal solution set; selecting the optimal design scheme from the Pareto optimal solution set based on the entropy weight-TOPSIS dynamic decision method and performing double verification, and outputting optimization parameters and a performance report after successful verification. This application achieves synergistic optimization of performance, cost, and reliability.

The invention provides a construction and operation method, unit and platform for digital twins of pipeline equipment, and belongs to the technical field of intelligent operation of oil and gas pipe networks. Comprising the steps of obtaining static data of pipeline equipment, and constructing a pipeline equipment geometric model; meshing division is carried out on the pipeline equipment geometric model, physical attributes and loading boundary conditions are given, and a pipeline equipment simulation model is obtained; generating multiple groups of training data by utilizing a Latin hypercube sampling method based on the static data; according to the multiple groups of training data, the pipeline equipment simulation model is compressed based on a singular value decomposition method, and a pipeline equipment reduced-order model is obtained; dynamic data of the pipeline equipment are obtained, the pipeline equipment order reduction model is input, the pipeline equipment order reduction model is solved, and a solving result is obtained and serves as an operation result of the digital twin of the pipeline equipment. By means of twin mapping of the pipeline, the performance of the pipeline equipment is monitored, predicted and evaluated, safe operation of the equipment is guaranteed, and data is provided for structural optimization of the pipeline equipment.

The application discloses a kind of nanocrystalline high-frequency transformer multi-physics field multi-objective robust optimization design method and system, belong to high-frequency transformer technical field, method includes: constructing multi-objective optimization function;Introduce the coupling calculation model of core loss and winding loss;Establish multidimensional constraint condition;Identify the high sensitivity parameter and low sensitivity parameter that optimization target is significantly influenced, simplify optimization dimension;Construct Kriging surrogate model, generate sample point using Latin hypercube sampling;Determine the optimization of Kriging surrogate model, obtain Pareto optimal solution set;Divide control factor and noise factor, use Taguchi method to design orthogonal experiment;Introduce support vector regression to construct robust optimization surrogate model, optimize control factor in combination with NSWOA algorithm.The application uses the above method and system, improves the anti-interference ability of equipment, solves the performance dispersion problem under high-frequency working condition, guarantees the stability of nanocrystalline high-frequency transformer long-term operation.

The application discloses a kind of fast topology optimization methods based on two-stage deep probability model, the steps of this method are as follows: determine the case to be generated design;According to the actual working condition of case, determine the load and boundary condition required in generation design;The combination form of load and boundary condition is sampled using Latin hypercube sampling;According to prior constraint, construct data set;Two-stage deep probability model is constructed, and the first stage design generation is carried out using probability eigenvalue orthogonal decomposition, and the second stage enhancement is carried out based on coding-decoding convolutional neural network;Two-stage deep probability model is trained based on the constructed data set;The trained model is used to carry out generation design under unknown load and boundary condition for case;The structure predicted using model is used as the basis for subsequent further topology optimization, so the optimal topology structure can be obtained.The method of the application is suitable for two-dimensional design and three-dimensional design, and can greatly shorten the optimization time while ensuring the design accuracy.

The application provides a high-power electric power cooling multi-channel single-phase heat transfer and energy efficiency topology multi-objective optimization method and system, and the method comprises the following steps: obtaining a design scene input condition, establishing a topology optimization model, solving a model output fluid and solid material distribution and a two-dimensional topology model; reconstructing a two-dimensional model to obtain a three-dimensional microchannel area model; sampling three-dimensional topology model variables, obtaining an optimal Latin hypercube after optimization, constructing a surrogate model; selecting new filling points according to a criterion to calculate and update a sample set and the surrogate model; optimizing total thermal resistance and pumping power conflict targets to obtain a Pareto optimal solution set. In the topology optimization stage, a microchannel two-dimensional section is selected as a design domain, a density method is used, average temperature and energy consumption are minimized as targets, an optimal material distribution configuration is generated, and a three-dimensional model is reconstructed. In the multi-objective optimization stage, a sampling is performed to construct a surrogate model, an EGO-PEI algorithm is used for optimization, and a Pareto optimal solution set is obtained.