49 results about "Design cycle" patented technology

The application discloses an OTR tire pattern heat dissipation hole parameter design and heat dissipation strengthening method, and belongs to the tire structure design field.The method firstly establishes a tire parameterized model containing a heat dissipation hole, determines the length, width, depth and inclination angle of the heat dissipation hole as key design variables; subsequently, sample points are generated in the design space by using Latin hypercube sampling, and the average convective heat transfer coefficient corresponding to each sample is calculated through numerical simulation; then, a radial basis function proxy model with the design variables as input and the heat transfer coefficient as response is constructed, and the accuracy thereof is verified; thereafter, the design variables are globally optimized by using an adaptive simulated annealing algorithm with the maximization of the average convective heat transfer coefficient as the target, and the optimal structure parameters are obtained; finally, the optimization scheme is modeled and numerically verified.The application significantly improves the heat dissipation efficiency of the OTR tire through multi-parameter collaborative optimization, has the advantages of short design cycle, strong optimization ability and good versatility, and is helpful to prolong the tire life.

The present application belongs to the technical field of photonic device design, and specifically relates to a micro-ring resonator reverse design optimization method based on deep learning. The present application comprises: obtaining different micro-ring resonator structure parameters and corresponding free spectral range and quality factor, and processing and dividing the data set; building a cascade neural network model, wherein the reverse network takes the target performance index as the input to predict the structure parameter, the forward network inputs the structure parameter and outputs the performance index, and is used to constrain and correct the reverse prediction result; after training, the test set is evaluated to measure the reverse effect by the average absolute percentage error index, the weight in the loss function is changed, and multiple rounds of training are performed to determine the optimal weight configuration and final model of the reverse design. The present application has the advantages of high calculation efficiency, excellent design precision and strong physical realizability, can reduce the dependence on large-scale simulation and multiple iteration optimization, effectively shorten the device design cycle, and has high application value.

This invention relates to a rapid prediction system and method for combustion characteristics of pure hydrogen micro-mixed combustors based on CNN. The invention addresses the problem of long design cycles for micro-mixed pure hydrogen combustors. By constructing a dataset representing the relationship between "inlet conditions and combustion characteristics," this invention enables rapid prediction of the combustion characteristics of pure hydrogen micro-mixed combustors based solely on the inlet conditions, significantly accelerating the variable operating condition design cycle. The prediction system includes a data acquisition module, a data preprocessing module, a dataset partitioning module, a prediction model construction module, a combustion characteristic prediction module, and a model update and optimization module. This invention belongs to the field of micro-mixed combustion technology.

The application relates to a parallel multi-objective optimization method and system for engineering product CAE simulation, and belongs to the technical field of simulation optimization. The method solves the problems that the simulation-driven design cycle is long and it is difficult to obtain a high-quality multi-objective trade-off optimization scheme. The method comprises the following steps: based on multiple objective functions and constraint conditions, initial sample data is obtained through two-stage collaborative optimization and is put into a sample library; a joint surrogate model is constructed and trained, and a hierarchical error compensation mechanism is initialized; multiple rounds of iterative optimization are performed until a preset optimization termination condition is met; each round of iterative optimization comprises the following steps: based on the current joint surrogate model and the hierarchical error compensation mechanism, multiple candidate design points are generated in parallel, and then the multiple candidate design points are distributed to multiple computing nodes for parallel simulation calculation to obtain new sample data which is put into the sample library; the joint surrogate model and the hierarchical error compensation mechanism are updated; and a multi-objective optimization design scheme set of a product to be optimized is obtained from the final sample library. The optimization efficiency is improved.

This application discloses a method, apparatus, equipment, and medium for flutter analysis of UAV wings during the design phase, relating to the field of aircraft design. The method includes: generating a first component by calling a corresponding structural component simulation model from a finite element structure module library based on the shape parameters of the wing to be analyzed; constructing a skin finite element mesh model based on the first component; determining the installation position of a second component at the node positions of the skin finite element mesh model; generating the second component by calling a corresponding structural component simulation model from the finite element structure module library; installing the second component at the corresponding node positions of the skin finite element mesh model based on the installation position to obtain a target wing model; performing aerodynamic modeling using the skin finite element mesh model to obtain a flutter calculation file; performing flutter analysis based on the flutter calculation file and the target wing model; and optimizing the wing to be analyzed that has a flutter risk based on the flutter analysis results. This application shortens the UAV design cycle.

The application belongs to the technical field of transformer three-dimensional design, and discloses a transformer three-dimensional parameterized design method based on Autodesk Inventor, which is realized based on an iLogic programming platform embedded in Autodesk Inventor, and the design steps are as follows: step one, establishing a standard component template; step two, parameterized design; step three, three-dimensional model generation; step four, two-dimensional drawing generation; and step five, version management and updating. The sub-modules are set as the standard component template, and the parameterized mode is used, so that the designer only needs to input design data in the input interface of the parameterized standard component template during design, then calls the program to calculate, and the transformer three-dimensional model and two-dimensional drawing can be obtained, thereby greatly reducing the workload and shortening the design cycle, and more importantly, the algorithm of the parameterized standard component template eliminates the quality problems caused by human factors and controls the design quality at a high level.

The application discloses a kind of circulator insertion loss quadratic response surface optimization methods.The method includes: obtaining the target performance of circulator, and determining the adjustable factor set of circulator according to target performance;Adopt experimental design method in adjustable factor set Significant factor is screened;According to significant factor, a quadratic response surface proxy model is constructed, and the minimum insertion loss is used as the optimization target to train the model;The trained model is used to execute multi-objective optimization with constraints, and the optimal parameter combination is obtained;After verifying the optimal parameter combination, the final design parameters are output.The application filters significant factors and constructs a quadratic response surface proxy model to minimize IL within the constraint space for fast optimization.The total amount of experiments is reduced by 70%, the verification error is less than 5%, the design cycle is significantly shortened, and the insertion loss is reduced, which is suitable for precise determination of low-loss process window for any-band self-biased circulator.

This invention discloses a diffuser design method based on variable variable gradient, relating to the field of diffuser design. The method includes: establishing a one-dimensional flow analysis model containing mass, momentum, and energy conservation equations; specifying a variable variable gradient objective function along the flow direction, defined as the spatial rate of change of fluid momentum along the flow direction; coupling the objective function with the mass conservation equation, and iteratively solving the closed equation set in conjunction with boundary conditions to obtain the variation law of the diffuser cross-sectional area along the flow direction; generating the profile curve accordingly, and iteratively optimizing it through three-dimensional numerical simulation. This invention directly couples the flow field control objective to the geometric profile design process, realizing active control of boundary layer growth and pressure distribution, effectively suppressing flow separation, improving the pressure recovery coefficient and outlet flow field uniformity, and has a short design cycle and wide applicability.

This application discloses a method, system, device, and storage medium for determining the feasibility of parameters, relating to the field of microfluidics. The method includes: acquiring design parameters to be determined, wherein the design parameters include liquid physical properties, nozzle structural parameters, and driving parameters for driving the liquid ejection; calculating state determination values ​​related to the atomization process based on the design parameters; comparing the state determination values ​​with preset state determination thresholds to determine the atomization state corresponding to the design parameters; and determining that the design parameters pass the feasibility judgment when both the atomization state corresponding to the design parameters and the driving parameters in the design parameters meet their respective preset feasibility judgment conditions. This application eliminates the need to fabricate physical samples of the design parameters and verify the atomization effect through actual testing, avoiding the time and resource consumption caused by repeatedly fabricating samples and conducting repeated tests, thereby shortening the design cycle and reducing development costs.

This invention provides a method for generating process routes, an electronic device, and a storage medium, relating to the field of computer-aided design and manufacturing technology. The method for generating process routes includes configuring the product type of the target product and the factory information for manufacturing the target product; obtaining a 3D model of the part to be processed and a preset process database; parsing the 3D model based on geometric topological relationships to obtain feature information of the part to be processed; obtaining processing instructions for the part to be processed in response to received process type instructions; matching and obtaining corresponding process data from the process database based on the feature information and processing instructions; determining an initial process route based on the process data and product type; adjusting the initial process route according to processing constraints and received editing instructions to obtain a transitional process route; and instantiating the transitional process route according to factory information to obtain a deployment process route. This invention shortens the design cycle of process routes.

The application provides a digital integrated circuit TMR reinforcement decision method based on multi-target parameter optimization. Original data of a designed circuit is acquired, a complex nonlinear relationship between a replacement decision and circuit parameters is accurately captured through a feedforward neural network, network parameters are optimized in combination with a genetic algorithm, the prediction accuracy and convergence speed of the feedforward neural network are significantly improved to obtain four cost functions, minimization of the four cost functions is taken as an optimization target, a multi-target evolutionary algorithm NSGA-II is used to solve the optimization target to obtain a Pareto solution set, the limitation of traditional single-target optimization is overcome, and a Pareto solution with the minimum comprehensive evaluation value is selected from the Pareto solution set to finally determine a final replacement scheme. The application predicts a replacement time-consuming simulation through a cost function, automatically searches an optimal solution in combination with an optimization algorithm, greatly shortens a design cycle, reduces manual intervention, and in addition, an optimization direction can be flexibly adjusted through a weight adjustment mechanism, so that diversified design requirements are met.

The application relates to the field of aero-engine pipeline fixing structure design, and discloses a continuous fiber composite single-union clamp structure design method and system. A single-union clamp simulation model is constructed to accurately obtain the inner radius of a deformation zone of a single-union clamp after the single-union clamp is closed after being sleeved on a guide pipe. A first analysis model of the single-union clamp bending moment is constructed based on the deformation zone thickness, the skeleton forming radius and the inner radius of the deformation zone in the deformation process. The single-union clamp under different combination conditions is analyzed, and the bending moment of the single-union clamp after being closed under the corresponding combination condition can be obtained. By screening the deformation zone thickness and the skeleton forming radius combination value meeting the given bending moment value range, scientific and reasonable single-union clamp structure design parameters can be determined, the iteration number of the continuous fiber composite single-union clamp structure design can be reduced, the design cycle can be shortened, the design quality and performance of the single-union clamp can be ensured, and the design efficiency can be improved.

The application provides a smart lightning rod arrangement visualization design method based on a rolling ball method, comprising the following steps: S1: a building contour drawing step; S2: a lightning rod arrangement step, based on the rolling ball method, using a fillPolygonWithCircles algorithm to automatically generate lightning rod center points; and S3: a coverage verification and downlead generation step. The application aims to overcome the defects of the prior art and provide a real-time interactive visualization lightning rod arrangement system, which ensures 100% coverage of the building by allowing the user to hand draw the building contour, automatically calculating the rolling ball method protection area, and optimizing the lightning rod position; meanwhile, the downlead path and material list are generated, the design cycle is shortened to the minute level, the accuracy and practicability of the lightning protection scheme are improved, and the engineering error rate is reduced.

The application provides a kind of simulation design method of integral mud pump piston based on multi-physical field coupling, on the basis of completely retaining the original integral mud pump piston structure, coupling structure field, flow field and wear field for the first time, a complete simulation system including Ogden super-elastic constitutive model, sealing contact pressure model, Archard wear model and coupling control equation is established. Through finite element simulation, the minimum contact pressure, maximum wear depth, predicted service life and leakage rate and other key design indicators can be quantitatively output, and the rubber hardness, arc curvature radius and interference amount and other parameters can be iteratively optimized accordingly. This method can accurately simulate the sealing performance degradation and wear process of the piston under different working conditions in a virtual environment without repeated trial production of physical objects, significantly shortening the design cycle, improving the design quality and reliability of the piston, and having a clear mathematical model and reproducible calculation process, facilitating industry application.

The application relates to the technical field of ice wind tunnel test, in particular to a method for matching test working conditions of a propeller ice wind tunnel test and a storage medium. A first calculation model is established based on a forward ratio similarity criterion and a first similarity criterion of the propeller, and a second calculation model is established based on a plurality of set similarity criteria. Then, the basic parameters of a target propeller are acquired, the basic parameters comprising the size, a first angular velocity and a first incoming flow wind speed of the target propeller, and a scale reduction ratio of the target propeller for the ice wind tunnel test. The first angular velocity and the scale reduction ratio are input into the first calculation model to obtain a second angular velocity after scale reduction. Finally, the basic parameters and the second angular velocity are input into the second calculation model to obtain test working condition parameters of the target propeller for the ice wind tunnel test. In this way, the design cycle of matching the test working conditions of the propeller ice wind tunnel test is shortened, and the quality of the test working condition data after scale reduction is ensured.

This invention discloses a low-background lead chamber optimization design method based on finite element simulation, specifically relating to the field of lead chamber simulation design technology. The method includes the following steps: establishing a three-dimensional parametric geometric model of the lead chamber, acquiring lead chamber parameters, predicting and analyzing lead creep through simulation, determining lead creep early warning, optimizing the anti-creep structure, and verifying the optimized scheme through simulation. This invention establishes a three-dimensional parametric geometric model of the lead chamber, acquires creep parameters of the low-background lead chamber, calculates the relative creep error and creep increment, evaluates the design based on simulation results, generates early warning results, optimizes the anti-creep structure, verifies the optimized scheme, updates the model, and re-simulates. It compresses the timescale of simulation calculations in a virtual environment, shortening the design cycle; it can output key creep data at multiple time points, providing a full lifecycle perspective for design iteration; and it uses a parametric geometric model to achieve model updates and scheme iterations, improving the efficiency of scheme achievement.

This invention provides an intelligent assisted design optimization system and method for military power modules. The system includes an intelligent design and manufacturing database for storing production line big data of packaged power modules. This production line big data includes successful design data that meets design requirements, manufacturing processes and their corresponding measured performance data, as well as negative sample data. A design module, trained based on the successful design data in the intelligent design and manufacturing database, receives target electrical performance parameters and outputs preliminary circuit design parameters and initial manufacturing process parameters. A collaborative optimization module receives the circuit design parameters and initial manufacturing process parameters and outputs predicted electrical performance parameters. The output of the design module is connected to the input of the collaborative optimization module to form a design and verification closed loop. This invention solves the technical problems of long design cycles, high verification costs, and difficulty in collaborative optimization of design and manufacturing in the development of military packaged power modules.

This application relates to a method for optimizing bridge vase-shaped pier parameters based on mixed-integer programming, comprising: constructing a multi-objective optimization model with the design cost and total construction cost of the vase-shaped pier as objectives; establishing mixed-integer programming constraints based on the multi-objective optimization model; and calculating the global optimal solution of the multi-objective optimization model to obtain the optimized parameters of the vase-shaped pier. This application comprehensively considers both design cost and total construction cost, combines mixed-integer programming to solve the problem, and ensures that the optimized parameters meet design requirements through constraints, achieving quantitative balance and realizing global optimization of key parameters of the vase-shaped pier. This effectively shortens the design cycle, reduces the total construction cost, reduces the types of construction formwork, and simultaneously balances the economy of concrete usage and construction convenience.

The present application belongs to the field of combustion chamber and additive manufacturing technology, and discloses a heat transfer performance and forming deformation amount collaborative optimization method and system of a combustion chamber flow channel. The optimization method comprises the following steps: constructing a training sample set, each sample in the training sample set being characterized by flow channel structure parameters, forming placement mode and cooling fluid Reynolds number and being labeled with heat transfer performance indicators and SLM deformation amount; training a neural network to obtain a prediction model; coupling the prediction model with a multi-objective optimization model; inputting the constraint conditions corresponding to the application scenario, calling the prediction model for iterative optimization under the constraint conditions until the optimization target is met, and obtaining an optimal solution set, and screening a final solution from the optimal solution set. An optimization system is obtained based on the prediction model and the multi-objective optimization model. The heat transfer performance of the flow channel and the SLM forming deformation amount are collaboratively optimized, the product quality and reliability are improved, the design cycle is greatly shortened, and the design efficiency and quality are improved.

This invention provides a method and system for generating casting process models for automotive chassis components based on multimodal feature recognition. The entire process is automated, effectively addressing the industry pain points of traditional process modeling, such as excessive reliance on human experience, long design cycles, and poor consistency. This invention integrates multi-strategy feature recognition algorithms based on rules, cross-section analysis, and topology spread. It can accurately extract key process features such as spindle holes, machined holes, and complex curved surfaces, and establish a unified local coordinate system, significantly improving the accuracy of complex structural feature recognition. Through a standardized process rule library and standard parts library, it achieves automatic blank processing, automatic selection and assembly of standard parts, and automatic mold generation, compressing the traditional modeling cycle of several days to several hours. Furthermore, this invention performs specific optimizations after selecting chassis components, ensuring that the process scheme closely matches actual production, effectively guaranteeing the consistency and reliability of the process model, and significantly reducing casting process design costs and the risk of human error.

The application provides a smart railway system architecture document generation method, device, equipment and medium, the method comprises the steps: obtaining the design specification information and design requirement information of the current smart railway system; inputting the design specification information and design requirement information of the current smart railway system into an architecture document generation model to obtain the architecture document of the current smart railway system. In the embodiment of the application, the design specification information and design requirement information of the current smart railway system are inputted into the architecture document generation model to obtain the architecture document of the current smart railway system, which can reduce the design cost of the smart railway architecture, improve the design efficiency, shorten the design cycle, and basically meet the needs of rapid iteration and upgrading of the architecture of the smart railway system.

Embodiments of the present application provide a data optimization method, device and equipment and a storage medium. The method comprises: obtaining an initial population data set, the initial population data set comprising at least one individual data, the individual data comprising at least one design parameter; performing reverse learning processing on the initial population data set to obtain a first population data set; performing differential evolution processing and crossover processing on the first population data set to obtain a second population data set; performing non-dominated sorting processing and clustering processing on the second population data set to obtain a target population data set, the target population data set being used for multi-objective optimization. Through multi-working-condition constraint processing and multi-modal solution set maintenance, the output target population data set can meet the design requirements under different working conditions, provide diversified candidate schemes for engineers, shorten the design cycle and improve the comprehensive performance of the suspension system.

The application discloses a wide-adapted multi-element free-flowing ecological fishway and a design method thereof, and the core guide ridge, the side edge guide rest board and the flow velocity grading fin are cooperatively arranged to form a layered gradient flow field, which can meet the efficient upstream migration demand of strong swimming adult fish, and can also adapt to the low-speed passing of juvenile fish and bottom-dwelling weak fish species, so that the fish species adaptation range is greatly widened. Meanwhile, a low-disturbance rest area is created in the plate fin combined area to relieve the fatigue of fish upstream migration, and the fish passing success rate is significantly improved. The application combines CFD numerical simulation with high-precision coupling formula to calculate the error, and the structure parameters are optimized through ecological target reverse iteration, so that the whole flow field is accurately controlled, the design cycle is effectively shortened, the engineering cost is reduced, the overall ecology and practicability are prominent, and the application has important popularization value for river aquatic biodiversity protection.

The application provides a control surface structure flutter margin optimization method based on a Kriging surrogate model, comprising the following steps: establishing a control surface structure reference model; performing quality discrete processing on the reference model, updating the quality of each point of the control surface structure reference model according to the perturbation method, performing control surface structure flutter analysis, performing sensitivity analysis on each quality point of the control surface, obtaining the positions of a plurality of quality points with the greatest influence on the quality point discrete flutter speed, and marking the plurality of quality points as flutter significant points; updating the quality data of the plurality of flutter significant points in batches, establishing a flutter speed surrogate model based on the Kriging method; testing the flutter speed surrogate model by using a test data set; and performing rudder surface flutter optimization analysis based on the control surface flutter speed surrogate model and a genetic algorithm. The technical scheme of the application is used to solve the technical problem that the traditional design method usually depends on experience, experiments or a large amount of calculation, and the design cycle is long and the cost is high.

The present application provides a kind of vertical steering cam mechanism and profile curve generation method, belong to logistics automation technical field.For the problems of large pressure angle fluctuation, significant dynamic impact, design dependent experience and poor adaptability of high speed and high load of existing shuttle car vertical steering mechanism, the present application adopts kinematics reverse method, by constructing mathematical model, establishes global and cam motion coordinate system, sets rotation ratio n, guide rod length L and other parameters, analyzes cam-follower relative motion (coordinate translation and rotation transformation), generates cam groove track point cloud, calculates and controls pressure angle (peak value ≤45 °), completes optimization verification by combining three-dimensional modeling and dynamics simulation, realizes the accurate design of cam groove profile curve.The present application can make the shuttle car follower complete 90 ° steering stably, effectively inhibit the self-locking and dynamic impact of mechanism, shorten the design cycle, improve the operation reliability under high speed and high load working condition, meet the frequent vertical steering demand of automatic warehouse shuttle car.

This application discloses a deep learning-based DTCO cross-layer performance matching method and related products, applicable to the semiconductor technology field. The method includes: obtaining target electrical performance indicators of the target bottom-layer bulk silicon device; performing a logarithmic transformation on the off-state leakage current in the target electrical performance indicators, then normalizing the target electrical performance indicators to obtain target feature data; inputting the target feature data into a target matching model to obtain target process parameters corresponding to the target top-layer low-temperature SOI device; the target matching model is a four-layer fully connected feedforward neural network with inverse mapping capability from electrical performance to process parameters. Thus, by introducing a four-layer fully connected feedforward neural network, accurate inverse mapping from electrical performance to process parameters is achieved, effectively shortening the design cycle and improving design efficiency while ensuring device electrical compatibility.