14 results about "Robust design" patented technology

The invention discloses a rotor reliability constrained rolling bearing assembly parameter robust design method. The method comprises the following steps: constructing a dynamic model for an actual rotor-bearing system; constructing an uncertainty parameter vector and a design variable vector; a target function based on robustness and a constraint function based on reliability are constructed, so that an uncertainty optimization model is obtained; constructing an augmented input variable, and establishing a candidate orthogonal polynomial basis function set; on the basis, constructing and evaluating polynomial chaos-Kriging models for the target function and the constraint function respectively, and screening out an optimal polynomial chaos-Kriging model; calculating the expectation and the standard deviation of the target function and the failure probability of the constraint function under each design variable vector; and converting the uncertainty optimization model into an unconstrained single-target optimization model, randomly generating population individuals of a heuristic optimization algorithm in a feasible region of design variables, and iteratively searching an optimal solution of the unconstrained single-target optimization model as a rolling bearing assembly scheme.

The invention discloses an intelligent metasurface-based wave beam forming robust design method for a communication and induction integrated system, and belongs to the technical field of wireless communication. According to the method, aiming at the problems of imperfect channel state information and uncertainty of a target position in a communication and inductance integrated system, a worst-case robust optimization model is established by taking minimization of base station transmitting power as an optimization target. Firstly, an original non-convex problem is equivalently reconstructed by utilizing positive semidefinite programming; secondly, aiming at semi-infinite constraint introduced by uncertainty, solving by adopting a successive convex approximation method; meanwhile, interval uncertainty constraints are efficiently processed in combination with a sampling method; and finally, solving the coupling relationship among the optimization variables through an alternative optimization strategy. Simulation results show that the method can ensure the service quality of the communication and sensing target under multiple uncertainties, significantly improve the robustness of the system in an uncertain environment, and effectively reduce the transmitting power at the same time.

The invention discloses a nanocrystalline high-frequency transformer robust design method considering magnetic core uncertainty, and belongs to the technical field of high-frequency transformers, and the method comprises the steps: S1, carrying out the initial design of a nanocrystalline high-frequency transformer based on multiple physical fields; s2, defining an optimization target of the nanocrystalline high-frequency transformer and constructing a corresponding robust optimization design model; s3, after the robust optimization design model is determined, representative sample points are generated through orthogonal experimental design; s4, constructing a support vector regression agent model; s5, evaluating the uncertainty of the design through Monte Carlo analysis; and S6, obtaining a Pareto frontier through a non-dominated genetic algorithm-III, and then carrying out multi-objective optimization. According to the nanocrystalline high-frequency transformer robust design method considering the magnetic core uncertainty, the requirements of the fields of new energy, electric automobiles and the like are met by reducing energy loss, optimizing heat management and improving the structural stability.

The invention provides a robust design method and system for a fractional order PI controller of an inertia and delay fractional order object. The method comprises the following steps: describing a controlled object by adopting an inertia and delay fractional order transfer function; the feedback controller adopts a fractional order PI controller; a crossing frequency and a phase margin are given, and a proportional gain, an integral gain and a controller order of the fractional order PI controller are solely solved based on three constraint equations of the given crossing frequency, the given phase margin and zero slope of an open-loop phase of a closed-loop system at the crossing frequency; if a null set is solved, the values of the crossing frequency and the phase margin need to be given again, and solving is carried out; and traversing the range of the traversing frequency and the phase margin to obtain a feasible region of the fractional order PI controller. According to the method, the parameters and the feasible region of the fractional order PI controller can be directly calculated, it is guaranteed that the fractional order PI controller has high robustness, and the method has practical application value.

The present application relates to a kind of robustness design optimization methods of data-driven compressor blade, using the way of middle camber line superposition thickness distribution to construct blade, using NURBS curve to parameterize middle camber line of blade.4-point 3-order data-driven non-embedded polynomial chaos method is used to quantify the uncertainty of sparse sampling data, and four blade configuration modes are obtained.Latin hypercube method is used to sample blade design space, and sampling set is used to train Gaussian process regression model under each blade configuration mode;GPR proxy model at each blade configuration mode is obtained respectively.After training, multi-objective optimization algorithm NSGA II is used to optimize search with the statistical mean and standard deviation of blade total pressure loss coefficient as target;Robust compressor blade with better performance and greatly reduced sensitivity to input uncertainty is obtained.

The invention relates to an aerodynamic stability quantification method and system based on flow mechanism and deep learning coupling, and the method comprises the steps: compressing the high-dimensional point cloud data of a manufactured blade into a low-dimensional basic mode based on principal component analysis, and effectively relieving the variable dimension disaster problem caused by geometric uncertainty; and then, by taking the low-dimensional fundamental mode weight as input, directly embedding the key physical characteristic, namely the blade tip leakage flow rate ratio, into a deep neural network hidden layer and taking the gas compressor stability margin variable quantity as output, constructing a physical constraint and data driving collaborative optimization framework, and remarkably improving the prediction precision of the pneumatic stability of the gas compressor under the small sample condition. According to the method, a quantification tool with high precision and physical credibility is provided for solving the problem of performance degradation caused by geometric deviation of the CAES multi-stage axial flow compressor under the non-design working condition, and key technical support is provided for achieving robustness design of the CAES multi-stage axial flow compressor.

The invention relates to the technical field of vehicle simulation design and automatic control, and discloses a self-balancing two-wheeled vehicle simulation design method based on a digital twin model, which comprises the following steps: S1, constructing and calibrating the digital twin model, and calculating to obtain a model credibility inverse index for quantifying the uncertainty of the digital twin model; s2, adaptively adjusting and optimizing algorithm parameters according to the credibility inverse index so as to search a robust design space meeting constraints; s3, implementing bidirectional evolution of the model and the optimization target, namely updating the optimization target according to the difference between the model and actually measured data, and expanding the model function according to the newly added target; and S4, outputting a robust design space and recommending a scheme, and performing final verification by using the evolved digital twinborn model. According to the self-balancing two-wheeled vehicle simulation design method, a digital twinning optimization closed loop capable of sensing the credibility of the self-balancing two-wheeled vehicle and self-evolving is constructed, so that the efficiency and the adaptability of the self-balancing two-wheeled vehicle simulation design and the robustness and the reliability of a final scheme are improved.

The invention discloses an uncertainty analysis method and system for turbine blade tip gas thermal performance. The method comprises the steps that firstly, key structure parameters of a turbine blade tip are determined to serve as uncertainty variables, and a multi-precision sample set is constructed; then, a multi-fidelity deep neural network model is constructed by adopting a prediction sub-network and a correction sub-network which are cascaded, and a mapping correction relation from low precision to high precision is established through staged training; carrying out gas-heat performance distribution analysis by utilizing the trained blade tip gas-heat performance prediction network model, and quantifying the influence degree of each uncertainty variable on performance fluctuation in combination with a Sobol global sensitivity analysis method; and finally, screening significant parameters according to the influence degree sequence, and determining an optimal value combination of the significant parameters to form a design scheme for enabling the turbine blade tip gas-heat performance to approach to global optimization. According to the method, the dimension limitation of high-dimensional uncertainty analysis is effectively broken through, the calculation efficiency and the prediction precision are remarkably improved, and reliable technical support is provided for robust design of the turbine blade top.

The invention provides a PID (Proportion Integration Differentiation) controller robust design method and system of an inertia and delay fractional order object. The method comprises the following steps: describing a controlled object by adopting an inertia and delay fractional order transfer function; a proportional-integral-differential controller is adopted to describe the controller; a crossing frequency and a phase margin are given, and a proportional gain, an integral gain and a differential gain of a proportional-integral-differential controller are uniquely solved based on three constraint equations of the given crossing frequency, the given phase margin and the condition that the slope of an open-loop phase of a closed-loop system at the crossing frequency is zero (also known as phase flatness); if a null set is solved, the values of the crossing frequency and the phase margin need to be given again, and solving is carried out; according to the method, the proportional-integral-differential controller parameters can be directly solved on the basis of three given constraint equations such as the crossing frequency, the phase margin and the phase flatness, it is guaranteed that the proportional-integral-differential controller has high robustness, and the method has practical application value.

The invention discloses a robust design method of an air reconfigurable intelligent surface communication system, electronic equipment and a storage medium. Comprising the steps of system modeling, auxiliary variable calculation, reference model initialization, optimization loop judgment, power optimization, passive beam forming optimization, unmanned aerial vehicle position optimization, auxiliary variable updating, optimization loop judgment returning and optimization result outputting. On the premise that a platform hardware damage model and channel dynamic change are jointly considered, reliable guarantee of communication link performance in a non-ideal hardware condition and a complex and changeable environment is achieved through end-to-end compression estimation and a robust optimization framework; the problem that an existing Aerial RIS auxiliary communication system is difficult to consider hardware non-ideality and multi-target performance constraint in a high dynamic scene can be solved, reliable guarantee of communication link performance in non-ideal hardware conditions and complex and changeable environments is achieved, and the method is suitable for complex propagation environments facing ports and channels.

The application discloses a robust design method of surface-mounted permanent magnet synchronous motor considering manufacturing tolerance, and the method is as follows: step 1, a parameterized model and a data set of the surface-mounted permanent magnet synchronous motor are established; step 2, a multi-output proxy model based on integrated learning is constructed; step 3, a manufacturing tolerance model is constructed; step 4, a robust genetic algorithm considering manufacturing tolerance is executed; and step 5, evolution optimization and verification are performed. Through construction of a high-precision mapping model of structure parameters-performance indexes, and embedding tolerance analysis based on Monte Carlo sampling in the individual evaluation link of the genetic algorithm, the performance robustness optimization of the motor design scheme in the manufacturing error environment is realized. Compared with the method of optimization only under the nominal size, the application can significantly reduce the performance discreteness of batch products, improve the tolerance to manufacturing deviation and the qualified rate while maintaining the motor efficiency and output torque level, and is suitable for structural design and parameter optimization of various permanent magnet motors.

The invention provides a power battery liquid cooling plate robustness optimization method and system fused with uncertainty modeling. The method comprises the following steps: 1) constructing a high-precision numerical simulation model and planning a proxy model training sample set; 2) constructing a liquid cooling plate size-performance mapping agent model; 3) constructing a probability distribution model of the key geometric parameters; 4) in combination with Monte Carlo random sampling and an agent model, quantifying statistical characteristics of each performance index under manufacturing disturbance; 5) taking the standard deviation and mean value minimization of the performance response as an optimization target, and constructing a multi-target robust optimization model; and 6) solving the Pareto optimal solution set by adopting a multi-objective evolutionary algorithm, and outputting the optimal nominal design parameter meeting the engineering preference and the corresponding key performance fluctuation level.According to the method, a robust design scheme which is most insensitive to manufacturing disturbance is realized in a feasible design domain, and the performance consistency of liquid cooling plate batch production is remarkably improved.

The application discloses a kind of nanocrystalline high-frequency transformer robust design method considering magnetic core uncertainty, belong to high-frequency transformer technical field, S1, based on multi-physical field, initial design of nanocrystalline high-frequency transformer is carried out;S2, the optimization target of nanocrystalline high-frequency transformer is defined and the corresponding robust optimization design model is constructed;S3, after determining the robust optimization design model, representative sample points are generated using orthogonal experimental design;S4, support vector regression surrogate model is constructed;S5, the uncertainty of design is evaluated by Monte Carlo analysis;S6, the Pareto front is obtained by non-dominated genetic algorithm-III, and then multi-objective optimization is carried out.The application uses the above-mentioned nanocrystalline high-frequency transformer robust design method considering magnetic core uncertainty, reduces energy loss, optimizes heat management and improves structural stability, meets the demand of new energy, electric vehicle and other fields.

This invention relates to the field of turbomachinery performance evaluation technology, and discloses a method for quantitatively evaluating the performance uncertainty of turbomachinery blade geometric deviations. The method includes parametrically modeling geometric deviation characteristics and sampling to construct a sample; generating a three-dimensional blade simulation model based on the sample by performing deviation modeling on the original design blade profile; performing aerodynamic simulation calculations on the simulation model; extracting aerodynamic indicators using a condition control strategy to construct a mapping database; training a surrogate model using the database to establish mapping relationships; and finally using the surrogate model to perform aerodynamic performance inference to complete the evaluation. Furthermore, interpretable machine learning methods are combined to calculate contribution to identify geometric features highly sensitive to aerodynamic performance. This invention overcomes the limitation of two-dimensional evaluation in failing to consider radial flow effects by reconstructing a three-dimensional simulation model, and eliminates flow deviation operating errors by employing a condition control strategy, providing a quantitative decision-making basis for robust design, machining tolerance setting, and cleaning and maintenance schemes for turbomachinery.