68 results about "Design domain" patented technology

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 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 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.

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 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 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 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 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.

The application discloses a kind of topological optimization methods of support position and structure topology simultaneous optimization, and relates to the technical field of topological optimization.The method specifically includes: determining the initial structure design domain of topological optimization, support design domain and boundary condition;Using variable density method to establish the topology optimization model of minimum compliance;The sensitivity information of objective function and constraint is calculated;Based on gradient optimization method and discrete particle swarm algorithm, structure design domain and support design domain are solved;The effectiveness of the method is verified by numerical case.The present application can effectively determine the optimal support position by discrete particle swarm algorithm;Using variable density method to establish structure topology optimization model, density variables in design domain can be efficiently processed.

The invention provides an intelligent structure design generation method and system, and is suitable for a generative design task in the field of complex structure design, the method introduces a semantic interpretation mechanism based on a sketch graph structure, combines multi-scale texture perception and a graph neural network embedding strategy, and can improve the structure design efficiency under the premise of not depending on a traditional topological optimization solution framework. And efficient generation and diversified control of the structural form are realized. According to the method, through gesture sketch input, fuzzy structure intention map construction, wavelet scale regulation and control and structure solution set evolution screening strategies oriented to man-machine collaboration, the controllability of understanding is enhanced, the diversity of understanding sets is remarkably improved, and therefore the requirements for multi-target comprehensive optimization of aesthetics, technology, mechanics and the like in actual design are met.

The invention provides a quick layout method of a functional device oriented to structural circuit integrated printing in an irregular design domain, and relates to the field of additive manufacturing aided design. The method comprises the following steps: calculating a symbol distance field for an irregular design domain, and performing three-dimensional sampling to generate a candidate point set; grouping all functional devices according to functional similarity and determining a global layout sequence; for each to-be-placed functional device, dynamically generating a search list sorted based on an adjacent principle, traversing each position in the list and an allowed rotation angle around a preset axial direction, and searching and locking a first compliant pose through collision detection and border crossing detection based on a symbol distance field; and traversing all functional devices and then outputting an overall layout scheme. According to the method, automatic and compliant rapid arrangement of functional devices in a complex irregular design domain is realized, and the problems that a traditional layout method is low in efficiency and difficult to meet additive manufacturing process constraint and electrical function association requirements are effectively solved.

The invention belongs to the technical field of structure topological optimization, and discloses a heat dissipation structure design method based on topological optimization, which comprises the following steps: defining a three-dimensional design domain and boundary conditions of a heat dissipation structure, converting the three-dimensional design domain into a two-dimensional model, and establishing a load uncertainty model according to the boundary conditions and heat source temperature distribution; performing robustness topological optimization on the two-dimensional model according to the load uncertainty model to obtain a two-dimensional flow channel topological structure; mapping the two-dimensional flow channel topological structure into a three-dimensional flow channel structure; and based on a gate sponge iteration principle, constructing fractal lattices with different volume fractions, endowing the constructed fractal lattices with material attributes, and embedding the fractal lattices into the three-dimensional flow channel structure to obtain an optimized heat dissipation structure. The invention further discloses a heat dissipation structure design system based on topological optimization. According to the method and the system, pseudo three-dimensional modeling, a fractal lattice structure and an efficient sampling strategy are fused, so that efficient design and performance optimization of a heat transfer channel are realized.

The application discloses a direct topology and shape optimization method for an existing engineering structure model, relates to the technical field of topology optimization and shape optimization, and comprises the following steps: mapping a finite element simulation model of an original engineering structure based on a regular hexahedron element to determine an approximate optimization simulation model; determining a typical design mode of multi-component non-interference optimization by transforming a design domain state and range based on the approximate optimization simulation model; determining a compatible unified optimization framework by converting the sensitivity of independent or combined application under different sensitivity algorithms based on the typical design mode; and determining a variable grouping strategy based on the compatible unified optimization framework to uniformly apply various manufacturing process constraints to obtain a structure improvement scheme with strong manufacturability. The application realizes a typical design mode by reconstructing an original optimization simulation model, realizes free combination of different optimization algorithms by constructing a unified discrete adjoint equation, and improves optimization efficiency and engineering practicability of multi-component design.

The invention discloses a multi-stage self-supporting constraint topological optimization method and system combining projection and a virtual pyramid. The method comprises the following steps: initializing a design domain and generating a non-uniform gradient initial density field; applying a total load vector formed by superposing a bearing working condition and a guide load, and delimiting a key protection area; calculating the projection support quantity of each grid unit and applying punishment; constructing a multi-layer virtual pyramid support domain which extends downwards in a conical manner, calculating a virtual pyramid support quantity and applying cross-layer penalty; superposing the two penalty terms to the original sensitivity to obtain corrected sensitivity; and iteratively updating the design variables and carrying out protective correction on the key region until convergence and outputting an optimized structure. Through a multi-stage self-supporting constraint mechanism, the overhanging defect in a traditional topological optimization structure is effectively eliminated, addition of a large number of external supports in additive manufacturing is avoided, material consumption and post-treatment difficulty are remarkably reduced, and collaborative improvement of mechanical properties and manufacturability is achieved.

The invention relates to a connection hole site iterative optimization method based on multi-dimensional stress characteristic driving. The problems existing in layout optimization of connecting hole sites of plate / shell components at present are solved. Firstly, the center position of a design domain inner hole is parameterized; calculating at least two of equivalent stress, stress gradient, principal stress direction and strain energy density under a predefined load; calculating a comprehensive influence function through a method of normalizing and weighting the multi-dimensional features; carrying out iterative optimization on the position of the guide hole in the first principal stress direction, calculating a multi-dimensional stress feature and a comprehensive influence function in each iteration, and carrying out dynamic updating on a feasible region by considering geometric and process constraints at the same time; after iterative calculation convergence, a calculation result is output, and key index verification is carried out; checking whether geometric constraints and process manufacturing conditions are met or not; and generating a sample piece and carrying out physical test verification. According to the method, optimization of multiple performance targets such as strength, rigidity and fatigue life can be achieved, and the method can also be closely combined with engineering practical application.

The application relates to the technical field of vehicle engineering, and particularly discloses a load distribution optimization method and system for a lattice frame design, which comprises the following steps: firstly, an initial three-dimensional design domain of the lattice frame is established, and a composite dynamic load working condition obtained based on multi-body dynamics simulation is applied; then, a material density distribution cloud picture is obtained through macroscopic topological optimization, a main force transmission path is identified and extracted, and the design domain is divided into a core bearing area, a transition area and a non-key area; in the non-key area and the transition area, a point array structure with gradient material properties is designed according to a stress state, and continuous change of the material properties is realized by adjusting the point array type, arrangement direction and control parameters; finally, performance verification and parameter iterative optimization are carried out on the overall structure containing the core bearing area and the point array filling area.

The application discloses a rear baffle disc partition topology optimization method and system based on multi-feature K means, and the method comprises the following steps: firstly, discretizing a rear baffle disc design domain and initializing variables; in iteration, generating physical density by density filtering and Heaviside projection, and solving a displacement field; in the initial stage, global maximum stress is taken as a constraint for optimization, and after the change rate of the structure mass is stabilized, a multi-feature K means partition model is switched; the model fuses unit stress, density and geometric distance features to carry out clustering partition, predicts stress evolution trend based on a transition factor, and dynamically regulates stress constraints of each sub-area by using a sub-area relaxation coefficient and an iterative correction mechanism; and finally, a lightweight topology structure with clear partition and uniform stress distribution is output. Through the density optimization, feature K means dynamic partition and adaptive stress regulation technology guided by the transition factor, the application realizes the lightweight topology design of the rear baffle disc with clear partition, uniform stress and strong manufacturability.

The application relates to the technical field of lattice structure optimization, and discloses a lattice structure based on topological optimization, a design method and application, a topological optimization model is constructed, and physical parameters and optimization parameters of the topological optimization model are initialized; a structure design domain corresponding to the topological optimization model, a load and boundary conditions are defined, and a finite element model is established; the defined load and boundary conditions are applied to the topological optimization model, finite element analysis is carried out, displacement responses of each node on the design domain are obtained, and the sensitivity of structural flexibility and volume to design variables is analyzed; the sensitivity is filtered, and the material density represented by the design variable is filtered by adopting a projection method based on a Heaviside function to inhibit the chessboard phenomenon; the design variable is updated according to the filtered sensitivity and the material density; whether the current iteration result meets a convergence condition is checked, if yes, the result is output, and if not, iteration is continued.

The application provides an industrial mold structure parameter optimization method and system based on multi-source data, relates to the field of industrial mold design, and has the technical scheme as follows: original parameters are extracted from multi-source mold data and converted into parameter records to ensure the consistency of data sources; a local object set is obtained through extension and a correlation between structure objects is established to determine a parameter allowable range and lay a foundation for subsequent optimization; when a parting boundary is adjusted, the alignment deviation of a guide part, the fitting length of a mold locking fitting boundary, and the connection state of an exhaust passage can be calculated according to the correlation to realize linkage analysis; a candidate scheme is generated by adjusting parameters within the parameter allowable range under the condition of meeting a preset hard constraint condition, a target scheme is determined through a preset scoring rule, and intelligent optimization is realized; finally, the landing point of the target scheme in multi-source mold data is checked reversely and written back synchronously to ensure the consistency of data and the executability of the scheme.

This invention discloses a topology optimization method for an electrothermal driven compliant mechanism based on minimum size control. It defines the design domain and boundary conditions of the electrothermal driven compliant mechanism, initializes design parameters, and uses a sequential coupling method to solve the multi-physics structural response of the electrothermal-structural fields. The optimization objective is to maximize the output displacement of the mechanism. By introducing a structural indicator function, minimum size constraint functions for real and empty phase material structures are constructed, establishing a topology optimization model for the electrothermal driven compliant mechanism based on minimum size control. The moving asymptotic algorithm is used to solve the topology optimization problem, determining whether the convergence criterion is met, and finally outputting the optimal topology configuration of the electrothermal driven compliant mechanism. This method effectively suppresses the occurrence of small-sized structures, facilitating processing and manufacturing.

The embodiment of the invention provides a catalytic reactor channel design method and system based on multi-objective topological optimization, and the method comprises the steps: simplifying a three-dimensional catalytic micro-reactor with consistent cross sections into a two-dimensional design domain containing an inlet and outlet domain and a reaction domain based on design basic data such as inlet and outlet positions and physical property parameters; establishing a multi-physical field control equation of fluid flow, catalytic reaction, heat release and transfer and substance conservation, constructing a multi-objective comprehensive utility function of coupling maximization global average reaction rate, minimization of flow energy dissipation and maximization of heat transfer rate by adopting a normalized linear weighted sum method, and setting a pseudo design domain and volume constraint; then, a topological optimization model is established with the density design variable as the core, and an optimal topological structure is obtained through an optimization solver after regularization processing; and finally, carrying out geometric reconstruction on the optimal topological structure to generate a two-dimensional molded line, stretching the two-dimensional molded line into a three-dimensional model, constructing a finite element model to carry out multi-physics field simulation, and calculating key indexes such as flow energy consumption to verify the performance.

The invention provides a workpiece design method and device, and relates to the technical field of aero-engines. The workpiece design method comprises the following steps: determining a design domain of a topological model of a workpiece; under the condition of loading the statics load, optimizing the topological model of the workpiece in the design domain by using a statics topological optimization algorithm to obtain a first optimization result; under the condition of loading a dynamic load, optimizing the topological model of the workpiece in the design domain by utilizing a dynamic topological optimization algorithm to obtain a second optimization result; extracting a first topological feature set from the first optimization result, and extracting a second topological feature set from the second optimization result; fusing the first topological feature set and the second topological feature set to obtain a fused topological feature set; and according to the fused topological feature set, constructing an optimized topological model of the workpiece. By means of the method, the workpiece modeling optimization effect can be improved while workpiece modeling optimization is efficiently carried out.

The invention provides a wind tunnel balance topology optimization design method and device, a storage medium and electronic equipment, and the method comprises the steps: defining a design domain of a wind tunnel balance topology, carrying out the discretization, and setting the attribute parameters of the design domain; setting an optimization target and constraint conditions of the wind tunnel balance topology; based on the design domain, establishing a finite element calculation model with a stress relaxation and density-stiffness material interpolation model, and initializing density field design variables and optimization parameters; and solving the finite element model, and carrying out optimization iteration on density field design variables by adopting a moving asymptotic algorithm or an improved algorithm of the moving asymptotic algorithm. According to the method, balance multi-target and multi-constraint optimization design under multi-dimensional force transmission path minimization coupling measurement is realized, the problems of rigidity-sensitivity contradiction and measurement coupling interference of balance design are solved, and the bottleneck problem that the existing design quality based on engineering experience and analogy depends on personal experience and level of designers is broken through; and the design level of the balance is greatly improved.