125 results about "Design domain" patented technology

The invention discloses a circuit EMI suppression-oriented multi-knowledge collaborative distillation and optimization method and system, and the method comprises the steps: constructing a basic large language model based on the circuit design of the electronic industry as a student model, and carrying out the adaptive optimization of the circuit design field. Thirdly, integrating the formalized EMI rule base, the simulation data / model base and the expert experience case base, constructing a multi-source heterogeneous teacher knowledge system, and generating a comprehensive guidance signal G; then, candidate circuit design generated by a student model is evaluated through the system, and a multi-objective loss function including basic distillation, rule conformity, performance fitting and expert experience consistency is constructed; according to the method, the universality is reserved by quantifying differences, compliance is ensured through punishment, the performance difference is reduced, dynamic weight adjustment is introduced, and the model performance reaches a preset target through iterative distillation. The model can understand and process terminologies and design rules in the field of circuit design, and the defect that a general AI model lacks domain knowledge is overcome.

The invention relates to the technical field of lattice structure topological optimization, and discloses a multi-scale hybrid lattice structure double-variable topological optimization method, which comprises the steps of defining double variables, including a first design variable as a scaling scale and a second design variable as a relative density; determining a basic unit cell of the hybrid lattice structure and obtaining a corresponding unit cell size, wherein the second design variable is determined based on the unit cell size; obtaining a first filling unit cell and a second filling unit cell; determining a design domain, and filling the first filling unit cells and the second filling unit cells into the design domain by taking the flexibility minimization of the hybrid lattice structure as a target and the volume fraction as a constraint; and optimizing the filling process based on a moving asymptote optimization algorithm so as to carry out topological optimization of the hybrid lattice structure based on double variables, and outputting an optimized multi-scale hybrid lattice structure. According to the industrial bearing structure, higher structural specific stiffness, specific strength and energy absorption efficiency are achieved, and the requirements for light weight, self-adaption and high performance in an existing industrial bearing structure can be met.

The invention discloses a thin-wall structure topological optimization design method, device and system, and a storage medium. The method comprises the following steps: calculating any unit density in a design domain according to a structure topological optimization model; calculating a displacement vector according to the density of any unit in the design domain, the total stiffness matrix and the external load vector; calculating the structural flexibility according to the element stiffness matrix and the displacement vector; according to the unit density and the structural flexibility, obtaining the sensitivity of the target function and the constraint function to design variables; establishing a topological optimization model by taking the minimum structural flexibility as a target function, taking the welding spot area as a constraint condition and taking a design variable as a normalized height; and according to the sensitivity of the target function and the constraint function to the design variables and the topological optimization model, performing updating iteration on the design variables by utilizing an MMA algorithm to obtain an optimized structure. By the adoption of the technical scheme, on the premise that the technological constraint of the superplastic forming technology is met, the structure with the excellent mechanical property is obtained.

A method includes defining a design domain for a meta-element configured to be attached to an edge of thin wall structure with an arbitrary boundary condition, and executing a topological optimization process on the design domain and providing a topology optimized shape for the meta-element. The topological optimization process includes an objective function that minimizes a reflection coefficient of flexural waves in the audible frequency range propagating towards and impinging the meta-element.

The present invention discloses a multi-objective thermal-fluid topological design optimization method for the cooling element of a precision gear grinding machine tool, comprising the following steps: Step 1: Define the design domain: Expand the cooling element into a plane, define a two-dimensional design domain representing the cooling element's geometric structure, and discretize it using the finite element method; Step 2: Numerical modeling: Model the cooling element as a porous medium; Under the conditions of laminar incompressible flow, the fluid dynamics are governed by the dimensionless forms of the continuity equation and the momentum equation; The fluid-solid coupled heat transfer model is governed by the dimensionless energy conservation equation; Step 3: Construct an objective function: The optimization objective is defined as a weighted function of heat transfer and fluid dissipated power, and a fluid-solid heat transfer topological optimization model is constructed; Step 4: Solve the objective function; Step 5: Restore the shape and structure of the cooling element. The present invention also discloses a precision gear grinding machine tool and a cooling element.

This invention discloses an optimized design method for support structures to control thermal stress in metal additive manufacturing, relating to the field of metal additive manufacturing. The method includes the following steps: defining a design domain and a non-design domain; initializing design variables; converting design variables into physical variables through density filtering and projection; establishing an interpolation model between the physical variables and the overall structural stiffness; performing finite element simulation on the layer-by-layer additive manufacturing process of the overall structure to obtain the thermal deformation of the overall structure; and calculating the maximum thermal stress σ of the structure. pn The proposed method involves calculating the volume fraction V of the supporting structure and the suspension angle constraint response G; determining the sensitivity; updating the design variables to solve the optimization model; determining whether convergence has occurred; and post-processing the optimization results. The structure designed by this method ensures that the thermal stress of the overall structure is controllable after additive manufacturing, avoiding cracking and delamination caused by excessive thermal stress.

The invention relates to the field of computer-aided engineering and intelligent manufacturing, and provides a topological optimization design method integrating subjective preferences. The method comprises the following steps: initializing material density distribution in a design domain, and intelligently recommending an initial density distribution scheme according to a subjective preference type selected by a user; calculating an objective response value and sensitivity thereof; calling a pre-trained subjective preference scoring model to subjectively evaluate the current material density distribution, outputting a subjective evaluation value, and calculating the sensitivity of the subjective evaluation value based on a numerical perturbation method; the objective response value, the sensitivity of the objective response value, the subjective evaluation value and the sensitivity of the subjective evaluation value are input into an MMA optimizer for iterative calculation, and material density distribution is dynamically updated to balance subjective preference and objective constraint; and judging whether the updated material density distribution meets a convergence condition set by a user, if so, performing post-processing visualization on the optimized material density distribution, and outputting a final topological configuration, otherwise, returning to a new round of iteration.

The invention relates to the technical field of lattice structure optimization, and discloses a topological optimization-based lattice structure, a design method and application, a topological optimization model is constructed, and physical parameters and optimization parameters of the topological optimization model are initialized; defining a structural design domain, a load and a boundary condition corresponding to the topological optimization model, and establishing a finite element model; the defined load and boundary conditions are applied to the topological optimization model, finite element analysis is carried out, displacement response of each node on a design domain is obtained, and sensitivity of structural flexibility and volume about design variables is analyzed; filtering the sensitivity, and filtering the material density represented by the design variable by adopting a projection method based on a Heaviside function so as to inhibit a checkerboard phenomenon; updating a design variable according to the filtered sensitivity and material density; and checking whether the current iteration result meets a convergence condition, if so, outputting the result, and if not, returning to continue iteration.

The invention relates to the field of model design, and provides an aircraft multi-objective optimization design method and system based on semanteme in order to improve the calculation efficiency of multi-objective balance. Firstly, original design parameters and design rules are obtained, obtained data are converted into semantic vectors through a semantic embedding function, and adjacency matrixes are obtained; generating a semantic gene map based on the semantic vector and the adjacent matrix, constructing a comprehensive objective function, and performing optimization solution based on the semantic gene map; and creating a target mapping function, and mapping an optimized result into a specific engineering target based on the target mapping function. By introducing a semantic modeling and cognitive enhancement mechanism, aircraft structure optimization is improved from traditional numerical drive to semantic drive, a prediction and search process is accelerated by using semantic space and cognitive reasoning, and global optimization and traceability are realized in combination with crowd intelligence evolution and a cross-domain hypernetwork; not only are efficiency and robustness improved, but also engineering feasibility and compliance credibility of a design result are ensured.

The invention provides a neural network multi-objective optimization and FPGA hardware acceleration collaborative design method, and belongs to the field of deep learning model compression and hardware collaborative design. The method comprises the following steps: constructing a joint optimization space containing a neural network compression parameter and an FPGA hardware design parameter; a multi-target Bayesian optimization search strategy is adopted, iterative search is carried out in the joint optimization space, model precision, FPGA resource occupation and reasoning delay are synchronously optimized, and optimal candidate configuration is obtained; matching the compressed network structure with the FPGA parallel architecture by using a hardware-perceived pruning and quantification strategy; a multi-task performance prediction model is adopted to quickly predict the precision, resource occupation and delay of the optimal candidate configuration so as to accelerate the search process; according to the optimal configuration, a hardware accelerator code facing the target FPGA is automatically generated, and integration and implementation are completed. According to the method, collaborative optimization of neural network compression and hardware design is achieved, FPGA resource occupation can be remarkably reduced, the reasoning speed can be increased, and meanwhile the model precision is kept.

The invention discloses a structural topology optimization design method for additive manufacturing and a related device, which can be used in the field of structural design, in the method, based on the shape of a structural design domain and a parameterized density field function including the structural design domain, a structural density field is determined, and a suspension angle constraint function at a structural boundary is constructed; constructing a boundary gradient constraint function based on a normal vector pointing to the interior of the structural design domain on the boundary of the structural design domain; discretizing the structural design domain into a plurality of finite element units, and constructing a material stiffness matrix to calculate an overall stiffness matrix; and constructing a topological optimization mathematical model according to the finite element balance constraint, the volume constraint, the suspension angle constraint function and the boundary gradient constraint function based on the overall stiffness matrix, and solving to obtain a structural topological optimization result. Therefore, through multi-constraint collaborative optimization, the conflict problem of suspended constraint and material anisotropy on the forming direction is solved, and a structural topological optimization result considering manufacturability and structural performance is obtained.

The invention discloses a direct topology and shape optimization method for an existing engineering structure model, and relates to the technical field of topology optimization and shape optimization, and the method comprises the steps: carrying out the mapping through a regular hexahedron unit based on a finite element simulation model of an original engineering structure, and determining an approximate optimization simulation model; based on the approximate optimization simulation model, determining a typical design mode of multi-component non-interference optimization by transforming the state and range of a design domain; based on the typical design mode, the same discrete adjoint equation is adopted to convert the sensitivity of independent or combined application under different sensitivity algorithms, and a compatible unified optimization framework is determined; and determining a variable grouping strategy based on the compatible unified optimization framework so as to uniformly apply various manufacturing process constraints, and obtaining a structure improvement scheme with high manufacturability. The typical design mode is realized by reconstructing the original optimization simulation model, the unified discrete adjoint equation is constructed to realize free combination of different optimization algorithms, and the optimization efficiency and the engineering practicability of multi-component design are improved.

The invention relates to the field of scroll compressor design, and discloses a full-meshing double-vortex-ring scroll compressor diffusion hole machining design method which comprises the steps of constructing an initial design domain, defining a multi-working-condition working point set and a fluid dynamic control equation determined by compressor operation parameters to construct a multi-working-condition analysis model, establishing a solid isotropic material penalty model, and designing a diffusion hole of a full-meshing double-vortex-ring scroll compressor according to the solid isotropic material penalty model. Multi-working-condition robustness topological optimization with the total pressure loss weighted sum under all the weighted working conditions as the target is executed, a discrete pseudo-density field is obtained, parameterized geometric reconstruction is conducted on the discrete pseudo-density field, and a diffuser hole parameterized geometric model controlled by parameter vectors is obtained. By introducing multi-working-condition robustness topological optimization and integrated dynamic machining constraint linkage re-optimization, remarkable robustness of aerodynamic performance of the diffusion hole in a wide working range is achieved, it is ensured that the diffusion hole meets the high-quality and high-efficiency numerical control machining requirements congenitally, and the design efficiency and manufacturing feasibility of the diffusion hole of the scroll compressor are greatly improved.

The embodiment of the invention provides a method and equipment for automatically designing a domain model, and a storage medium. The method comprises the following steps: acquiring a plurality of business object names input by a user; constructing a first prompt instruction used for prompting design of the domain models, and inputting the first prompt instruction and the plurality of business object names into the first large language model to obtain JSON description of the domain models corresponding to the business object names and JSON description of a data flow relationship between the domain models; and constructing a second prompt instruction used for prompting design of a model field of the domain model, and inputting the second prompt instruction, the JSON description of the domain model and the JSON description of the data flow relationship related to the domain model into a second large language model to obtain the JSON description of the model field of the domain model. The domain model design is automatically generated through a large language model, a user only needs to input a business object name, a system can automatically output the domain model in a JSON format, a model data flow relation and a model field definition, and the domain model and related description thereof are efficiently output.

This invention belongs to the field of artificial intelligence hardware architecture design and discloses a method, system, device, and medium for cross-layer optimization of multi-core packaging and optical interconnect clusters for large language model training. The method includes: constructing a design space, including a core hardware architecture layer, an optical interconnect network layer, and a training parallel strategy layer; performing an outer-layer search to search for the architecture parameters of the multi-core modules within the core hardware architecture layer; for each outer-layer search sampling point, performing an inner-layer search to collaboratively optimize the optical interconnect network topology and training parallel strategy within the optical interconnect network layer and the training parallel strategy layer; and outputting the Pareto optimal design point on the training performance and cluster cost plane. The technical solution described in this invention can guide the design of related training clusters, thereby fully leveraging the advantages and potential of core technology and optical interconnect technology in large language model training.

The application provides a three-period minimal surface intervertebral fusion cage design method, relates to the technical field of porous structure design, and determines a design domain of a three-period minimal surface TPMS porous structure region, combines individual vectors of modeling parameters of corresponding TPMS of each region, randomly generates a plurality of individual vectors, and forms an initial population; finite element simulation is carried out on the TPMS porous model corresponding to each individual vector, so as to divide the initial population into a load advantage population and a pore advantage population; the individual vectors are selected from the load advantage population and the pore advantage population respectively to perform improved neighborhood directional crossover operation; an optimal three-period minimal surface TPMS porous structure region is obtained through iterative genetic algorithm; through improved crossover operation and neighborhood directional crossover between the load advantage population and the pore advantage population, the new generation of individuals can consider both load performance and pore performance, and optimization is avoided around a single performance.

The invention discloses a topological optimization method and device considering volume constraints, boundary conditions and a design domain based on deep learning, and belongs to the technical field of topological optimization design. According to the method, training data sets under various constraint conditions and the design domain are constructed, a full convolutional neural network is adopted for offline training, and the training data sets are optimized; and obtaining a full convolutional neural network model capable of representing a topological optimization design rule. In practical application, only corresponding boundary conditions and design domain information need to be input, a topological optimization result can be rapidly output through the trained neural network, and therefore a traditional iterative calculation process is replaced. According to the method, the calculation complexity and the time consumption are remarkably reduced while the topological optimization precision is kept, high efficiency and intelligence of topological optimization are realized, and the method has a good engineering application prospect.

A 4D printing intelligent structure design method based on a convolutional neural network comprises the following steps: parametric modeling and data generation: controlling finite element software through a script to construct a'material parameter field-target deformation field 'sample data set in batches; data preprocessing and geometric standardization: uniformly converting a complex design domain into a standard rectangular domain by adopting a conformal mapping technology, and realizing high-fidelity standardization of physical field data; constructing a U-Net + ResNet hybrid convolutional neural network, and combining with composite loss function training to synchronously optimize global and key region deformation prediction precision; performing standardization processing on the new design after model deployment to realize second-level high-precision deformation prediction; the physical space design is restored through inverse mapping, multi-material 4D printing manufacturing is driven, and a deformation verification closed loop is completed. According to the method, the limitation of complex geometric boundaries is broken through, the nonlinear and multi-physics field coupling characteristics of the intelligent material can be accurately represented, and the practicability of the scheme is ensured by the constructed'design-prediction-manufacturing 'integrated process.

The invention discloses a topological optimization method and device based on a CutFEM and an MMC, a medium and equipment. The method comprises the steps that a design domain is defined, a plurality of initial moving deformation assemblies are arranged in the design domain, and material parameters of the moving deformation assemblies are set; carrying out structured background grid unit division on the design domain; dividing each background grid unit into an entity unit, a blank unit and a cutting unit, and performing sub-grid division on a material area of the cutting unit; identifying boundary lines between the material areas and the material-free areas of the cutting units as inner boundaries and outer boundaries; carrying out stiffness matrix assembly on the material areas of the entity unit and the cutting unit, forming and solving a total stiffness matrix equation, and obtaining node displacement; generating and solving a topological optimization column formula; and calculating the sensitivity, and carrying out iterative optimization. The method can solve the problems that an existing MMC method is low in efficiency and limited in precision under the large-scale complex geometric boundary condition.

The present invention provides a method for failure-safe topology optimization design under dynamic load, comprising: step 1, defining the design domain and design parameters of the structure; step 2, performing transient dynamic analysis on the structure; step 3, calculating the equivalent static load of the dynamic load; step 4: performing failure-safe topology optimization design on the model under equivalent static load; minimizing the maximum flexibility of the structure under local failure as the optimization goal; and using the moving asymptote method to iteratively optimize the design variable unit density until the inner loop failure-safe topology optimization process is completed; step 5: judging whether the outer loop convergence condition is met based on the updated design variables after the failure-safe topology optimization. The present invention fully considers the possible local failure of the structure during the design process, thereby improving the performance of the structure under local failure conditions; converting the dynamic load under the original working condition into a static load through the equivalent static load method, effectively improving the computational efficiency of the optimization process.

The application discloses a kind of based on NiTi alloy's chip liquid cooling plate flow channel topological optimization design method, comprising: obtaining chip heat source distribution, determine two-dimensional design domain and establish flow-thermal coupling numerical model;Determine material, environment and boundary parameter;Based on solid isotropic material interpolation format, express physical property parameter as the function of design variable, construct topological optimization mathematical model;With minimum fluid viscosity energy dissipation rate and design domain average temperature as joint target, with fluid volume fraction and temperature interval as constraint, carry out iterative solution, obtain two-dimensional optimal flow channel topological configuration.Wherein, NiTi shape memory alloy unit is arranged on the wall surface of flow channel, adaptive flow regulation based on temperature sensing can be realized.The application realizes the collaborative optimization of heat dissipation performance and flow resistance, can effectively suppress hot spot, and design efficiency is high.

The application discloses a multi-constraint geometric nonlinear level set topology optimization method, device and medium, and aims to solve the problem that the current linear condition level set method is difficult to well optimize the large deformation of a to-be-optimized component. The method divides a design domain corresponding to the to-be-optimized component into a plurality of finite element units, and establishes corresponding level set radial basis functions based on volume constraints; according to the relationship between the element strain increment and the node displacement increment of the finite element unit, displacement constraint terms and frequency constraint terms are introduced to extend the Lagrange function; according to the nonlinear element unbalanced force, the element thermal load and the overall thermal load are calculated; based on the extended Lagrange function and the overall thermal load of the to-be-optimized component under the set temperature, the design domain is topologically optimized. The application considers the displacement constraint and the frequency constraint under the nonlinearity, is suitable for the to-be-optimized component under the large deformation condition, and can achieve an optimal topology result.

The invention relates to the field of composite material structure design and gear structure design, in particular to a multi-material on-demand fixed-point design method based on heterogeneous interface action. In order to solve the bottleneck of multifunctional collaborative design, the method comprises the following steps of: 1, determining a model type by utilizing structures and material types in a database according to expected requirements, and establishing a three-dimensional model; 2, systematically researching the influence rule of the stress and the performance of the heterogeneous interface on the three-dimensional model by adopting a finite element and micro-scale experiment combination mode; 3, revealing the action mechanism of interface macroconfiguration, microstructure and chemical bond connection, and constructing a theoretical model and a design criterion of a multi-material heterogeneous interface fusion process; 4, establishing a multi-material digital model by combining solid-liquid interface fluid dynamics and a chemical energy transfer rule; and 5, in combination with multi-material voxelization modeling and a three-dimensional gridding technology, developing a distribution model of the adjustable material.

The application discloses a kind of topological optimization method, device, medium and equipment based on CutFEM and SIMP, method includes: definition design domain, discrete design domain into background grid unit, with node density as design variable;Node density is converted into node level value;According to node level value, unit is divided into entity, blank and cutting unit;Subgrid division is carried out to cutting unit;Boundary is identified as inner boundary and outer boundary;Stiffness matrix assembly is carried out to the material area unit of entity unit and cutting unit, obtains node displacement;Solving topological optimization formula, calculating sensitivity updates design variable, until optimal structure is obtained.The application combines CutFEM and SIMP, can solve the problem of insufficient calculation accuracy and efficiency of existing SIMP method under complex geometry and boundary conditions.

This invention relates to the field of industrial design and intelligent manufacturing technology, specifically disclosing a digital twin AI-based industrial design and production process collaborative optimization system. The system includes a multi-source heterogeneous industrial data spatiotemporal synchronous fusion module, a full-element dynamic digital twin construction module, a design domain multi-agent cluster, a process domain multi-agent cluster, a design-process collaborative game negotiation engine, and an edge-cloud collaborative real-time scheduling and execution module that communicates bidirectionally with all the aforementioned modules. This invention breaks through the technical barriers of traditional serial R&D models. Through the design-process collaborative game negotiation engine, it constructs a non-cooperative game optimization model between the design and process domains, achieving forward adaptation of design parameters to process solutions and reverse correction of process constraints to design optimization, forming a bidirectional closed-loop iterative optimization mechanism.

The invention discloses a multi-source heterogeneous data automatic structuring method in the field of engineering investigation and design, and the method comprises the steps: recognizing the type of a file uploaded by a user, extracting effective data according to the type of the file, and carrying out the preliminary cleaning of the data; constructing a knowledge graph in the field of engineering investigation and design, including standardized entities and relationships among the standardized entities, matching the preliminarily cleaned data with the knowledge graph, and mapping the data to the standardized entities; performing real-time verification on the data mapped to the standardized entity, wherein the real-time verification comprises category verification, unit conversion and consistency verification and relevance verification; and based on the overall structure of the engineering project, performing intelligent association and combination on the data from different files after real-time verification to generate a complete structured data entry. According to the multi-source heterogeneous data automatic structuring method in the field of engineering investigation and design provided by the embodiment of the invention, automatic conversion from multi-source heterogeneous data to structured data is realized, and the efficiency and quality of data processing are improved.

The application discloses a design method of a high-rebound-performance honeycomb structure based on TPU, and is characterized by comprising the following steps: S1, adopting a topological optimization method to perform structure design, the size of a topological optimization design domain is consistent with the size of the honeycomb structure, the design domain is divided into a limited number of microstructure units, and the attribute of each unit is described by the position and relative density thereof; S2, taking the relative density of the unit as a design variable, introducing an equivalent elastic modulus model into the topological optimization model, thereby connecting the design variable with the in-plane mechanical performance as a target function; S3, finally, obtaining the numerical form of the optimization result, namely, the relative density matrix of the unit, through an optimization solving algorithm; and S4, on the basis of the discretization of the design space, performing finite element analysis on the honeycomb structure.

The invention relates to the technical field of mechanical design, in particular to a mechanical structure global intelligent design system based on topological evolution optimization. Comprising a global parametric modeling module configured to construct an initial population of a mechanical structure by using an explicit geometric description method based on a preset design domain, a mechanical boundary condition and a manufacturing process constraint, each individual in the initial population being uniquely represented by a group of geometric parameter vectors, according to the method, a closed-loop feedback mechanism of a physical perception agent model and active learning evolution is constructed, so that the inherent contradiction of huge time consumption of traditional evolution optimization calculation and insufficient physical precision of a pure data driving model is effectively overcome; according to the system, the deep neural network is used for replacing high-frequency finite element calculation, the global search efficiency of a complex mechanical structure is improved by two orders of magnitude, meanwhile, real-time error calibration is carried out on an agent model through online high-fidelity verification, and it is ensured that the mechanical property prediction error of an optimal solution is lower than a preset threshold value.

The invention discloses a power system blade structure optimization design method which comprises the following steps: acquiring a digital model of a blade, and dividing the digital model into a non-design domain shell and a design domain space; performing section taking processing on the design domain space of the digital model to obtain a plurality of sections of the design domain space; performing triangulation on the layered boundary of the design domain space again to obtain a lofting surface corresponding to each section; reducing the plurality of lofting surfaces obtained by triangulation into an optimized design domain space; and filling the optimized design domain space by adopting a microstructure filling method to obtain a blade structure subjected to optimization design. The functional metamaterial can be effectively used in the structure of a complex curved surface structure, and the integrated design of the impact resistance function and the bearing performance is achieved.

The invention discloses a structural reliability optimization design method based on a probability and probability box hybrid model, and the method comprises the steps: building a solving model of a reliability index through employing a quadratic four-moment method based on a maximum entropy principle, so as to improve the calculation efficiency of reliability analysis; the classification capability of a K-nearest neighbor algorithm model is adopted to replace a reliability evaluation process in constraint conditions, and design variables are classified so as to reduce the calculation cost of the constraint conditions in the optimization design process; according to the method, a complex nested optimization design problem can be converted into a single-layer deterministic optimization design problem, the optimization calculation efficiency can be improved essentially, and the method has wide engineering application value in the field of engineering mechanical structure design.