137 results about "Order reduction" patented technology

The present application relates to the field of nuclear reactor simulation. Disclosed are a fast simulation method and system for multi-physical field coupling and a storage medium. The method comprises: at intervals of a first preset interval time, collecting state information of each physical field of a nuclear reactor in a transient process, so as to obtain physical field snapshots of a plurality of physical fields, the transient process being simulated by a reactor simulation model corresponding to the nuclear reactor; stitching the physical field snapshots of the plurality of physical fields, so as to obtain a fully-concatenated physical field; constructing a matrix on the basis of the fully-concatenated physical field; performing order reduction processing on the matrix, so as to construct a timing model corresponding to the fully-concatenated physical field; and decomposing the timing model corresponding to the fully-concatenated physical field, so as to obtain timing models corresponding to the plurality of physical fields. The fast simulation method for multi-physical field coupling of the present application can reduce the inconsistency between different physical fields while increasing the computation speed.

A rapid multiphysics inversion method and apparatus for a power device, a device and a storage medium, relating to the technical field of power device inversion. The method comprises: using simulation software to construct a multiphysics simulation model of a power device; constructing a data set on the basis of a predefined input parameter set and output parameter set; by means of using a preset data order reduction method, performing order reduction on the data set, so as to obtain an order-reduced data set and an order reduction matrix; by means of using a preset inversion method, fitting the relationship between the order-reduced data set and the input parameter set, so as to obtain an inversion coefficient matrix; and on the basis of the order reduction matrix, the inversion coefficient matrix and an input parameter set to be inverted, performing rapid inversion on the multiphysics simulation model, so as to obtain a multiphysics inversion result of the power device. The present invention can obviously reduce the amount of calculation while satisfying calculation precision, achieves high calculation efficiency, and can adapt to the requirements for online monitoring and real-time simulation of power devices.

The invention relates to the technical field of power supply thermal management control, and discloses a high-voltage variable-frequency power supply thermal management control method for improving energy efficiency, and the method comprises the following steps: carrying out the order reduction processing in combination with a solar high-temperature thermal power generation system, and obtaining a low-dimensional model; carrying out data fusion by using Gaussian process regression and a Kalman filtering algorithm, and reconstructing temperature field information of the system; establishing a multi-objective optimization function, and dynamically adjusting a weight optimization objective; generating a control parameter sequence by using an improved particle swarm optimization algorithm, and adjusting a control decision in real time through iterative optimization; the optimized control parameters are input into a multi-rate hierarchical control system for division execution; and detecting a fault type and switching to a corresponding fault-tolerant control mode. According to the method, a multi-physical field coupling model and an intrinsic orthogonal decomposition technology are combined, an accurate coupling model of an electromagnetic field, a temperature field and a flow field in the high-voltage variable-frequency power generation system is established, through order reduction processing, the dimension of the model is reduced, the calculation complexity is remarkably reduced, and the calculation efficiency is improved.

The embodiment of the invention provides a small-disturbance stability optimization method and device for a single-network converter grid-connected system. The method comprises the following steps: constructing a small-signal model representing dynamic behaviors of the system based on control parameters and network parameters of the single-network converter grid-connected system; performing preprocessing operation on the small signal model to obtain a small signal model only comprising an active synchronization control link; performing order reduction processing on the small signal model to obtain a corresponding equivalent second-order model; determining the dynamic influence of active outer loop control, voltage inner loop control and short-circuit comparison on the stability of the current system according to the equivalent second-order model; adjusting a current system control parameter according to the dynamic influence to meet a small disturbance stability requirement; according to the method, the control parameters of the single-network converter can be optimized based on the small signal model, so that the small disturbance stability of the system is improved, and an optimization strategy is provided for stable operation of the network converter.

The invention belongs to the technical field of reduced-order model development, and particularly relates to a POD-SVD random forest reduced-order method and system for digital twin engines. Adjusting a plurality of simulation data generated by the digital twin engine under different working condition boundary conditions; extracting a physical quantity of the region of interest; combining and forming an original matrix required by PODSVD decomposition; decomposing an original matrix into three matrixes by using a PODSVD algorithm, and converting the three matrixes into a modal coefficient matrix, a modal matrix and an energy matrix through a calculation formula; training a random forest algorithm; inputting a boundary condition parameter corresponding to a working condition needing to be predicted into the trained random forest model, and generating a prediction modal coefficient; calculating and restoring a physical quantity needing to be predicted; and adding grid information for the obtained reduced physical quantity, and generating a. Dat file for post-processing. The method is used for breaking through the limitation of tradeoff between cost and model precision in traditional researches such as test / CFD, and meanwhile, the model precision and the evaluation speed are improved.

The invention provides a labor insurance article order dynamic scheduling and inventory management method based on the industrial internet, and relates to the technical field of logistics scheduling, and the method comprises the steps: obtaining the order demand information of each enterprise in an industrial park, and the real-time inventory data and historical delivery records of suppliers, and dynamically constructing a probability density function of the delivery capability of the suppliers, and a gravitation model is combined to calculate the adaptation degree between the supplier and the order demand, so that an accurate order distribution scheme is generated. In this way, order allocation rationality and efficiency can be improved, and order delay and resource waste are reduced. Besides, through an improved isochronous circle division method and a dynamic space-time combination optimization algorithm, clustering analysis and scheduling optimization are performed on the distribution path, so that the timeliness of distribution and the rationality of the path can be remarkably improved, the logistics cost is reduced, and the overall logistics management efficiency of the industrial park is improved.

The invention discloses an intelligent monitoring system for multi-source data fusion of a biomass intelligent power plant, relates to the technical field of intelligent monitoring, and solves the problems proposed in the background technology. The biomass multi-source data acquisition module is used for acquiring biomass fuel characteristic data, equipment operation data, environment-friendly emission data and raw material supply chain data in real time; the data preprocessing and order reduction module is used for carrying out denoising, normalization and coupling order reduction processing on the biomass fuel characteristic data and the equipment operation data; the multi-source data dynamic fusion module is used for carrying out weighted fusion on the multi-source data according to the fuel heat value, the water content and the environmental protection weight; the biomass combustion optimization diagnosis module is used for constructing a deep learning model based on the fusion data and outputting combustion efficiency optimization parameters and a fault diagnosis result; according to the method, multi-dimensional data of fuel characteristics, equipment operation, environmental protection emission and the like are integrated, through links of preprocessing, order reduction, fusion and the like, an information island phenomenon is eliminated, and scientificity and accuracy of decision making are improved.

The invention discloses an order early-warning method and system based on index configuration, and the method comprises the steps: carrying out the structural processing of early-warning demands, determining the type of an early-warning index to which each early-warning demand belongs, and generating an early-warning index according to the early-warning demands after the structural processing; configuring an early warning rule according to the early warning index and the early warning rule template; extracting an order corresponding to the trigger event, and determining a target order corresponding to each early warning rule; performing early warning judgment on the order data of the target order to obtain an early warning index result; and if the early warning index result is that the order data of the target order meets the trigger logic in the early warning index, performing order early warning on the target order, generating an early warning record, and sending an early warning notification. According to the method and the corresponding system, the early warning delay of a large number of orders can be reduced, the query pressure of an order database is reduced, the data consistency of hundred million-level orders is ensured, and the early warning efficiency of hundred million-level risk orders is effectively improved.

The invention discloses a singular perturbation order reduction method matched with a network construction type wind power plant. The method comprises the following steps: firstly, establishing a small signal model matched with each module of a network construction type wind power plant grid-connected system; constructing a full-order small signal model, solving a broadband oscillation mode, and calculating damping ratios of all modes to obtain an extremely weak damping mode; dividing fast and slow state variables based on the modal participation degree and Hankel singular value weight, and establishing a criterion to supplement the slow state variables based on a singular perturbation method to form a slow state variable set; and determining the order after order reduction, and establishing an order reduction model of the matching network construction type wind power plant grid-connected system in a singular perturbation form. According to the method, the problem that a matched network construction type wind power plant order reduction system of an existing order reduction scheme cannot replace a full-order system to efficiently analyze broadband oscillation can be solved, the order number of the matched network construction type wind power plant can be reduced, and the method has efficient and powerful broadband oscillation analysis capacity while the dynamic characteristics are reserved.

The invention is suitable for the technical field of automobiles, and provides an automobile passenger compartment modeling method based on neural network model order reduction, and the method comprises the steps: building a three-dimensional passenger compartment and dummy model, combining with a human body thermal comfort evaluation index to obtain data, carrying out the steady-state order reduction through a neural network, and verifying the precision through a decision coefficient and an error index. And carrying out transient order reduction by adopting a long-short-term memory network optimized by a genetic algorithm to finally obtain a one-dimensional model. According to the method, the problems that traditional three-dimensional modeling is time-consuming in simulation and the one-dimensional modeling precision is insufficient are solved, the temperature and wind speed determination coefficient of each position of the established model reaches about 0.94, the model can be coupled with an air conditioning system, the simulation operation time is shortened, and support is provided for design of an automobile thermal management control strategy. The method comprises the steps of model establishment, steady-state and transient order reduction, working condition analysis, model verification and the like, and has the advantages of precision and efficiency.

The invention relates to the technical field of computational fluid mechanics and biological microfluidic design, in particular to a model order reduction method for rapid optimization of a circulating tumor cell (CTC) sorting structure, which comprises the following steps of: constructing a three-dimensional full-order computational fluid mechanics model by collecting channel geometric structure parameters and fluid working condition parameters, and generating a training data set; organizing a flow field snapshot into a matrix form, extracting a dominant mode, constructing a low-dimensional modal space by taking the dominant mode as a base vector, establishing a low-dimensional ordinary differential model through Galerkin projection, and training a parameter-modal coefficient mapping relation by adopting a deep neural network to form a complete reduced-order model; and finally, constructing a multi-objective optimization problem based on the reduced-order model, carrying out optimization iteration by adopting an evolutionary algorithm, and returning an optimization result to the full-order model for verification, so that rapid optimization design of the CTC sorting structure is realized, and an effective solution is provided for intelligent design of a biomedical microfluidic device.

The invention discloses a wind power plant broadband oscillation suppression method and system based on amplitude-phase characteristic remodeling. The method comprises the following steps: respectively establishing a frequency domain impedance model of each wind turbine generator set and performing dynamic order reduction, analyzing amplitude-frequency and phase-frequency characteristics of wind power plant multi-machine parallel coupling based on the order reduction model, determining an interaction rule of a module value and a phase angle, designing additional damping control and active damping control, and determining a wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic of the wind power plant multi-machine parallel coupling amplitude-frequency and phase-frequency characteristic. A weak position of a system is positioned based on an s-domain node admittance matrix method, a function is set by taking improvement of amplitude-phase characteristics as a target and optimizing and setting control parameters for a target wind turbine generator corresponding to the position, the optimized parameters are applied to the target generator, and effective suppression of broadband oscillation is realized. According to the application, the broadband oscillation problem caused by high-proportion new energy grid connection can be systematically solved by synergistically remodeling the frequency domain amplitude-phase characteristics of the wind power plant.

This invention relates to the field of contract reconciliation management technology, specifically disclosing a contract settlement and automatic reconciliation system and method that deeply integrates business and financial data. After acquiring business performance data sequences and financial settlement data sequences, the system maps them into a two-dimensional time-series directed graph. Upon receiving an abnormal settlement trigger instruction, it traces back along the flow of funds and penetrates forward along the business-finance mapping to capture affected node clusters. Then, based on the comparison results between the accounting period identifier and the current closing time, it generates inter-period reversal action instructions and original order reduction action instructions, constructs alternative reversal path chains, and imports them into a virtual sandbox memory for verification. When the updated amount value is lower than a preset lower threshold, it triggers backtracking and reorganization until a legal reversal path scheme is selected. Finally, it calculates a physical disturbance score by combining the total number of nodes and the total number of inter-period actions, selects the scheme with the lowest score, and outputs it to the accounting platform to complete the synchronous update of business-finance reconciliation.

The present invention discloses a method for dynamic aeroelastic calculation based on a structural order reduction model. The method comprises: constructing a three-dimensional model of the component to be analyzed and performing modal analysis to obtain the various modes and structural vibration shapes of the component structure; simplifying the three-dimensional model of the component to obtain the component's aerodynamic shape; generating a flow field grid for aerodynamic calculation based on the component's aerodynamic shape; using a radial basis function neural network to interpolate the flow field grid based on the various modes and structural vibration shapes of the component structure to obtain interpolated results of the structural vibration shapes; based on the interpolated results of the structural vibration shapes, using computational fluid dynamics methods to first calculate unsteady aerodynamic forces, and then using structural model order reduction methods to calculate transient displacements to obtain aeroelastic response results. The method of the present invention can effectively improve the accuracy and efficiency of aeroelastic calculations.

The application provides a precise simplification method and system of a passive parasitic parameter network. The method comprises the following steps: obtaining a passive parameter parasitic network of a to-be-processed circuit; obtaining device information of all parasitic parameters; labeling node names of active device ports according to an active device naming rule; if a current parasitic parameter crosses multiple active device ports, deleting the current parasitic parameter; if a branch where the current parasitic parameter is located is an open circuit, deleting the branch and the parasitic parameters thereon; after traversing all parasitic parameters, obtaining a precise passive parasitic parameter network; and performing order reduction processing on the precise passive parasitic parameter network to obtain a simplified passive parasitic parameter network. The method precisely processes the passive parasitic parameter network, excludes active device ports and invalid ports, so that the passive parasitic parameter network can be correctly processed during simplification and order reduction processing, and the efficiency and accuracy of the order reduction and simplification processing are improved due to the exclusion of invalid paths.

This application discloses a method, apparatus, equipment, medium, and product for predicting damage evolution based on dynamic order reduction, relating to the field of aero-engine health management and monitoring. The method includes: conducting finite element fatigue / creep-fatigue simulation tests on structural components covering stress states under various operating conditions, and extracting the time-physics field matrix from the finite element fatigue simulation results under each operating condition; dynamically reducing the order of the time-physics field matrix to reduce its dimension, obtaining a reduced-order matrix; constructing a reduced-order model; restoring the equivalent stress field distribution based on the reduced-order model to obtain the reduced-order physical field; predicting the damage distribution evolution and fatigue life under fatigue or creep-fatigue loading based on the reduced-order physical field, and making corrections based on the fatigue life prediction model. This application can improve the efficiency and universality of life prediction.

The invention discloses a vulnerability analysis method of a multi-time-scale micro-grid under FDI attack, which comprises the following steps: decomposing system dynamics of a direct current micro-grid into a fast boundary layer subsystem and a slow order reduction subsystem by using a singular perturbation theory, respectively deducing stability conditions of each subsystem under a false data injection attack condition, and analyzing the vulnerability of the multi-time-scale micro-grid under the false data injection attack condition; and the input state stability of the low-speed subsystem under the FDI attack is proved. The worst deviation of the system state under the attack condition is calculated by constructing a zootope reachable set analysis framework, and the maximum allowable attack amplitude capable of ensuring safe operation of the system is quantified. According to the method, through numerical simulation and hardware experiment verification, a novel attack surface introduced by multi-time scale characteristics can be effectively revealed, and theoretical support and guidance are provided for safe and stable control of the DC micro-grid.

The application belongs to the technical field of simulation analysis, and particularly relates to a switch cabinet condensation margin evaluation method based on simulation analysis and order reduction reconstruction, steps of which comprise: establishing a simulation analysis model of the switch cabinet, adding a physical field and performing physical field coupling and mesh partitioning; solving the simulation analysis model to obtain the temperature and humidity distribution inside the switch cabinet; optimizing the simulation analysis model; replacing electromagnetic heat with loss data as a heat source input into a non-isothermal flow field; constructing a physical field snapshot library for order reduction model training; processing the physical field snapshot library based on a POD method to construct an order reduction model; training a Kriging model to reconstruct the temperature and humidity field of the switch cabinet; constructing a condensation margin evaluation cloud map; in the actual operation process of the switch cabinet, calculating an actual dynamic dew point temperature to generate an actual operation condensation margin cloud map, and completing online real-time evaluation. The application can realize efficient and accurate reconstruction of the temperature and humidity field inside the switch cabinet and efficient evaluation of the condensation margin of the switch cabinet.

The invention provides an online identification and input design method and device for a wireless power transmission system, and the method comprises the steps: carrying out the online estimation of a transfer function parameter of a target system, and building an initial small signal model; constructing a full-order model corresponding to the target system, and performing order adaptive order reduction processing; determining a loss function of the target system by taking minimization of disturbance of the excitation signal to the target system as a target; on the basis of defining the loss function, constructing an optimization problem of the target system under disturbance constraint, and performing optimal excitation input design; and constructing a robust controller of the target system by adopting a robust control method in combination with the initial small signal model. According to the method, the system modal change can be tracked in real time under the closed-loop working condition, the modeling precision and the real-time performance are improved, and the problem that the identification precision is poor in the prior art is solved.

The invention discloses a doubly-fed motor loss and temperature digital twin model establishment method and system based on finite element simulation, and relates to the field of motor loss simulation, and the method comprises the steps: constructing a finite element simulation model of a doubly-fed motor; carrying out electromagnetic loss calculation based on the finite element simulation model; performing temperature loss calculation based on the finite element simulation model; and based on a result of the electromagnetic loss calculation and a result of the temperature loss calculation, constructing a digital twin model of the loss and the temperature of the doubly-fed motor. According to the method, a finite element simulation model of a motor is established, loss and temperature distribution data under limited working conditions are used for establishing a data set, a reduction method is used for extracting space information of a loss field and a temperature field, flow field characteristics of a three-dimensional flow field are described through a few modes and mode coefficient combinations, and the flow field characteristics of the three-dimensional flow field are analyzed. Meanwhile, the invention provides a simulation order reduction method in which the POD-KAN algorithm is combined with the ARBF algorithm, so as to achieve the rapid prediction of the physical quantity to be researched of the equipment.

The invention discloses a river and lake hydrodynamic model reduced-order inversion initialization method for intelligent water conservancy, and the method comprises the steps: dividing a computational domain into a plurality of sub-domains according to hydraulic characteristics, building a physical constraint equation represented by reduced-order description parameters, and determining an independent to-be-inverted parameter set through the analysis of the degree of freedom; estimating an initial state of a part of sub-domains by using observation information or a priori value at a starting moment; determining an inversion window according to the hydraulic disturbance propagation time; constructing an agent model, and performing joint inversion on the to-be-inverted parameters and the boundary process line parameters to minimize the cost function; and driving the proxy model to generate a global initial state by using the optimal parameter obtained by inversion, mapping the global initial state to a basic model grid, and taking the global initial state as an initial condition after free evolution and boundary smoothing processing. According to the method, actual measurement data are fused through order reduction-inversion, global initial field construction can be rapidly and accurately completed, and the requirement of an intelligent water conservancy system for cold start of a hydrodynamic model is met.

The invention discloses a digital twinning reduced-order model abnormal value restoration method and device based on point cloud recognition and a digital twinning model of a traction system, and the method comprises the steps: screening point cloud sets of different working condition data, and obtaining a point cloud set of abnormal working condition data; carrying out abnormal value detection on the physical field parameters, and defining the physical field parameters exceeding the range of the fence as first abnormal values; the serial numbers of the point cloud sets of the abnormal working condition data are reordered, downsampling is carried out on the ordered point cloud sets of different voxel units, and a downsampled point cloud set composed of the first points of the different voxel units is obtained; establishing a first abnormal domain based on the boundary of the first voxel unit to which the first abnormal value belongs, and obtaining an adjacent abnormal domain; obtaining a second voxel unit based on the first voxel unit and a voxel unit in an adjacent abnormal domain; obtaining a point cloud abnormal value repairing area, and repairing the abnormal value in the second voxel unit to obtain a repaired point cloud set; and generating a corresponding order reduction model.

The invention discloses a transient multi-physics field reduced-order reconstruction method and device driven by a time sequence working condition, and relates to the technical field of digital twinning and calculation simulation acceleration. The method comprises the following steps: performing Latin hypercube sampling on monitoring working condition parameters to obtain a working condition parameter combination covering a sample space; establishing a high-fidelity finite element model to obtain geometric topology information; establishing a working condition parameter amplitude curve, performing batch finite element calculation in combination with the high-fidelity finite element model to obtain transient multi-physics field response data, and constructing a graph data structure; the training graph neural network auto-encoder comprises an encoder and a decoder; and training a time sequence neural network, establishing a mapping relation from time sequence working condition parameters to low-dimensional latent variables, and realizing rapid prediction and reconstruction from the time sequence working condition parameters to transient multi-physical field whole-field distribution in combination with a graph neural network decoder. According to the method, online rapid prediction of the transient physical field of the high-temperature turbine component can be realized, and the method has the advantages of high prediction precision, fast calculation response, strong cross-working-condition generalization capability and the like.

The invention provides a dynamic system order reduction method suitable for improving dynamic mode decomposition of a wind power signal, and belongs to the technical field of wind power signals, and the method comprises the steps: obtaining a wind power time sequence signal, carrying out the preprocessing, constructing a time delay data matrix, carrying out the dynamic mode decomposition of the time delay data matrix, and obtaining a wind power signal. Obtaining a system order reduction model comprising a modal matrix, an amplitude matrix and a dynamic evolution matrix; arranging the modal matrix by using an improved discrete time coefficient weighting criterion, and obtaining a reconstructed low-order linear model by using an inversion formula and a strategy of increasing a reserved modal number; performing inverse time delay operation and inverse normalization processing on the reconstructed low-order linear model to obtain one-dimensional signal data, and completing original dynamic system order reduction; according to the method, the problems that an existing wind power signal data driving model cannot give consideration to interpretability and accuracy, and errors caused by an original dynamic mode decomposition method in the aspects of mode selection and mode number determination are large are solved.

This invention relates to the field of intelligent equipment technology, and particularly to a time-optimal speed planning method and application with anti-envelope linear constraints. The method involves solving for control point deviations using the anti-envelope interval as a constraint condition, and then superimposing these deviations with the initial control points to obtain combined control points. An analytical model of the control points with linear constraint factors is constructed and converted into linear equations. Iterative solutions are used to obtain the linear constraint factors and corresponding linear control points. These linear control points are compared and adjusted with the initial control points and the control points corresponding to the maximum feed rate to obtain the final control points. Based on these final control points, an optimal feed rate curve satisfying all constraints is generated. By compensating for order reduction errors through anti-envelope calculations, and combining analytical solutions of linear constraint factors with iterative correction of control points, the method fully exploits constraint margins, achieving time-optimal, more uniform speed, and higher processing quality for large curvature sections while satisfying trajectory error and motor dynamic performance constraints.

The invention provides a motor electromagnetic-thermal coupling field inversion method based on a missing field POD, and the method comprises the following steps: carrying out the electromagnetic-steady-state thermal field finite element simulation calculation of a plurality of operation conditions of a motor through employing a load current and a cogging angle as parameters, and respectively constructing a magnetic field POD reduced-order model and a temperature field POD reduced-order model; a temperature sensor is arranged on the outer surface of the stator, the temperature is measured, and motor temperature distribution is obtained through inversion with a missing field POD method; based on the temperature distribution, the load current of the motor is determined through SVR; and calling a magnetic field POD order reduction model based on the load current and the cogging angle parameter, calculating the magnetic field distribution of the motor, and realizing inversion of the electromagnetic-thermal coupling field of the motor. The method can consider the influence of the material parameter change caused by the temperature on the electromagnetic field distribution, and enables the motor electromagnetic characteristics obtained through inversion to be more accurate.

The invention belongs to the technical field of electric power system time delay analysis, and relates to a Lagguerre-Taylor hybrid order reduction method for a multi-time-delay electric power system, which comprises the following steps of: S1, modeling the multi-time-delay electric power system and defining an order reduction problem; s2, a projection matrix is constructed, wherein a Laguerre function and Taylor are expanded and mixed; the method specifically comprises the steps that a projection matrix V1 is constructed based on scale Laguerre function expansion, a projection matrix V2 is constructed based on Taylor expansion, and a high-order block Krylov subspace orthogonal basis is generated based on a high-order block Arnoldi execution process; s3, constructing a Lagguerre-Taylor hybrid projection method of the multi-time-delay power system, and constructing an order reduction system; s4, analyzing the stability of the reduced-order system; according to the method, the reduced-order framework based on the projection matrix is constructed, the time-delay term algebraic structure of an original system is completely reserved, and the defect that the time-delay structure is damaged in an existing method is overcome.

The application discloses a radial magnetic bearing displacement vibration suppression method based on a phase shift reduced-order generalized integrator, and comprises the following steps: firstly, a magnetic bearing system model containing rotor mass imbalance is established; then, a synchronous displacement vibration suppression algorithm based on a phase shift reduced-order generalized integrator is designed, the orthogonal relationship of X / Y radial displacement error signals of the magnetic bearing is utilized, and the simultaneous extraction and compensation of double-channel synchronous components are realized; finally, the system stability is analyzed, and the phase shift angle parameters are designed in sections; the phase shift angle is introduced to adjust the phase without changing the amplitude characteristics at the synchronous frequency, so that the system stability is expanded. The application adopts the reduced-order generalized integrator structure, has less state quantity and lower calculation amount, and is suitable for high-speed real-time implementation of DSP / FPGA; the phase shift angle is introduced for phase advance adjustment, so that the stable working range of the algorithm can be effectively expanded; the orthogonal characteristics of the displacement signals are utilized to realize the unified implementation of X / Y double-channel synchronous compensation, and the structure is more simple.

This invention proposes a method and system for reducing the order of FPSO models by integrating time-recurrent neural networks and fuzzy logic, belonging to the field of marine engineering structural analysis and intelligent modeling technology. Addressing the problem of large degrees of freedom and low computational efficiency in FPSO finite element models, this invention extracts key degrees of freedom from the high-dimensional finite element model to construct low-dimensional feature vectors. It then uses a fuzzy logic system to perform regularized modeling of the dominant mechanical properties of the structure, achieving initial order reduction. Furthermore, a time-recurrent neural network is introduced to compensate for high-order residual effects that are difficult to accurately characterize using the fuzzy logic model, ultimately resulting in a low-dimensional, high-fidelity reduced-order model. The proposed method significantly reduces the degree of freedom and parameter dimension of the finite element model while maintaining the main mechanical properties of the structure, effectively reducing computational complexity and improving the model's applicability and practical engineering value in applications such as rapid finite element analysis, online structural response prediction, and engineering decision support.

The invention discloses a multistage time stepping lossy model numerical simulation method based on a wave equation. The method comprises the following steps: modeling a model with an ultrathin lossy structure; grid division is carried out on the model, and spatial discretization is carried out on a solution domain; deriving a second-order wave equation containing a lossy medium, introducing a model boundary condition, carrying out a Galerkin test, and converting the second-order wave equation into a lossy global matrix partial differential equation in combination with spatial discretization; all unknown quantities of the partial differential equation are written as a Taylor expansion, the partial differential equation is substituted into the Taylor expansion, and order reduction processing of high-order partial derivative terms is carried out; solving an unknown quantity in the taylor expansion after the order reduction processing by adopting a multi-stage stepping scheme; giving a multi-stage time stepping stability condition; and the speed-up ratio is obtained through theoretical analysis, and test verification is carried out. According to the method, not only can a flexible spatial discretization method be adopted, but also high-precision and stable numerical solution can be realized, particularly, the method has relatively high calculation efficiency for an ultrathin lossy structure, and the calculation cost is obviously reduced.