9 results about "Reduced order" patented technology

The application provides a method and system for offshore wind turbine integrated dynamic feature reservation. The full state time series data of the offshore wind turbine multi-physical field coupling system is obtained, the modal parameters of the system are extracted by using a dynamic modal decomposition method, the participation factor matrix is calculated, the participation degree of each state quantity and the corresponding physical link to each mode is quantified, the dominant state quantity of the extracted mode and the corresponding physical link are located, the dominant state set is constructed, the time scale distribution of each mode is quantified, the time scale distribution characteristic atlas of different physical links of the overall system is formed, the fast time scale, analysis time scale and slow time scale boundaries in the atlas are divided according to different research needs, and the offshore wind turbine integrated reduced order model is constructed based on the singular perturbation theory. The application can effectively reserve the dynamic features of the offshore wind turbine multi-field coupling characteristics, and provides a precondition and quantitative basis for physical-based model reduction modeling.

A polyhedral reduced-order model (ROM) generator is provided for use by an optimization controller in a heating, ventilation, and air conditioning system. The physical model generator includes: interface circuitry for receiving a training dataset via a network connected to an analog computer; a memory for storing the polyhedral ROM for predicting the dynamics of airflow in a room, the training dataset, and instructions for calculating parameters of the polyhedral ROM; and a processor for calculating the parameters of the polyhedral ROM. The computation includes computing a global projection operation from a higher-dimensional state to a reduced-order state, computing a global lifting operation from the reduced-order state to a higher-dimensional state, constructing a local reduced-order model of the reduced-order state dynamics for each physical parameter value in the training dataset, and generating the polyhedral ROM by combining a weighted average of the local reduced-order model with the projection and lifting between the reduced-order state and the full state.

The application discloses a kind of carbon fiber composite material performance optimization method and system based on multi-physical field reduced order model, and it is related to carbon fiber composite material design field.Method includes: generating sample point in design variable space and carrying out high-fidelity simulation, extract full-field response data to construct snapshot matrix;Eigenvalue orthogonal decomposition is carried out to snapshot matrix to extract leading mode and constitute reduced order base, construct deep neural network to establish the nonlinear mapping of design parameter to reduced order coordinate, train network using the total loss function of data fitting loss and physical residual loss weighted summation;Reduced order base and neural network are encapsulated as fast predictor;Predictor is integrated into optimization algorithm, and the process of recommending candidate point, predicting performance, updating historical data is carried out, and the optimal design scheme is output.The application shortens single analysis time from hour level to second level by reduced order technique, and physical information constraint ensures model reliability, to realize the efficient and high-precision optimization design of composite structure.

A system for monitoring corrosion-induced degradation of electronic devices. A modeling processor generates a model of the physical characteristics of an electronic device. The modeling processor generates and validates a complex model of the localized effect of corrosion on the electronic device. The modeling processor then generates a reduced order model based on the complex model. A corrosion monitor processor receives sensor measurements within a cabinet associated with the electronic device. The corrosion monitor processor executes the reduced order model based on the physical characteristics of the electronic device and the sensor measurements to generate a predicted corrosion rate. The corrosion monitor generates an expected lifetime of the electronic device based on the corrosion rates and provides alerts based on expected remaining lifetime.

The present application relates to a micron-level coating process modeling and precision control method based on digital twinning, comprising the following steps: S1: adopting a hybrid modeling strategy of physical information neural network and intrinsic orthogonal decomposition, constructing a multi-layer composite substrate proxy model; S2: using Monte Carlo Dropout and active learning strategy, quantifying the uncertainty of key parameters of the multi-layer composite substrate proxy model, and obtaining a reduced order model; S3: based on the reduced order model and online sensor data, constructing a dynamic adaptive edge twin, and outputting a predicted film thickness field; S4: according to the predicted film thickness field and the measured film thickness, based on the micro-scale error source sparse decomposition of the graph neural network, the error source probability vector is obtained; S5: taking the error source probability vector as the basis for feedforward compensation, using the feedforward-feedback composite compensation based on robust model predictive control. The present application effectively improves the efficiency and reliability of micron-level coating process control.

The application discloses a kind of lithium ion battery distributed thermal process sensor fault estimation method, comprising: using two-dimensional partial differential equation to describe rectangular lithium ion battery distributed thermodynamic equation;Distributed thermal model of large size lithium ion battery is constructed;Using Chebyshev-Galerkin method, distributed thermodynamic equation and distributed thermal model are decomposed into reduced order model described by standard state space equation in time domain;State variable in reduced order model is constructed using sensor measurement output and a Hurwitz matrix, and enhanced reduced order model is constructed;Enhanced adaptive observer and error state space equation are constructed using enhanced reduced order model;Based on error state space equation, the fault strength of temperature sensor that fails is estimated using fast adaptive algorithm.The application can effectively estimate sensor fault, including time-invariant and time-varying fault, and single sensor and multiple sensor fault.

The application belongs to the technical field of fluid mechanics numerical simulation and unsteady flow field prediction based on reduced-order modeling, and particularly relates to an unsteady flow field reduced-order prediction method with geometric prior weighting and related devices. The unsteady flow field reduced-order prediction method with geometric prior weighting comprises the following steps: constructing a spatial distance function based on the geometric structure information; constructing a geometric prior weight function based on the spatial distance function; constructing a weighted inner product space based on the geometric prior weight function, and performing weighted orthogonal modal decomposition on unsteady flow field snapshot data to obtain weighted spatial modes and corresponding reduced-order modal coefficients; establishing a modal coefficient prediction model, predicting the modal coefficients at a test time based on the reduced-order modal coefficients to obtain predicted modal coefficients; and performing flow field inverse transformation reconstruction based on the predicted modal coefficients and the weighted spatial modes to obtain an unsteady flow field prediction result at a target time.

The application relates to a flow state online monitoring system and method based on an FPGA integrated reduced-order model, which comprises a sensing parameter measurement module, an FPGA development board and a visual display module connected in sequence, the FPGA development board is provided with a data receiving and filtering module, a reduced-order algorithm integration module and an output signal conversion module, the sensing parameter measurement module is used for collecting flow state physical parameter signals in real time; the data receiving and filtering module is used for filtering the flow state physical parameter signals; the reduced-order algorithm integration module is used for combining the flow state physical parameters with the reduced-order model, obtaining the physical field parameters of the full three-dimensional space flow state by solving the reduced-order model; and the output signal conversion module is used for converting the physical field parameters of the full three-dimensional space flow state into corresponding visualized data and transmitting the visualized data to the visual display module for monitoring result display. Compared with the prior art, the application can effectively improve the timeliness and accuracy of full three-dimensional space flow state monitoring.