194 results about "Orthogonal decomposition" patented technology

The invention discloses a non-Newtonian fluid dynamic characteristic intelligent simulation and integrated measurement and control method, which comprises the following steps: sequentially reading basic data such as equipment temperature, pressure intensity, vibration, shear rate, stress, strain and the like, and carrying out anomaly detection and dynamic weight local polynomial regression cleaning by adopting a Gaussian mixture probability density model; carrying out standardization processing through a self-adaptive standardization model; performing multi-scale feature extraction and correlation analysis on the standardized data, and establishing a mapping relation between the rheological parameters and the physical quantities; carrying out multivariable coupling analysis and carrying out turbulence correction by adopting an improved intrinsic orthogonal decomposition method; and performing simulation calculation based on a non-Newtonian fluid analysis model, and analyzing and evaluating model performance and optimizing model parameters through multi-dimensional error analysis. According to the method, the dynamic characteristics of the non-Newtonian fluid under the complex working conditions can be accurately described, and the simulation precision and the control effect are improved.

The invention provides a compressor flow field reduced-order modeling method and device based on POD and deep learning, and relates to the technical field of compressor flow field modeling, and the method comprises the steps: obtaining three-dimensional transient flow field data of a compressor flow field region under a plurality of working condition parameters, and constructing a flow field data set containing a plurality of time snapshots based on the three-dimensional transient flow field data; obtaining target flow field snapshot data of the compressor based on the flow field data set; performing intrinsic orthogonal decomposition on the target flow field snapshot data, and obtaining a dimension-reduced time coefficient matrix based on the decomposed data; and constructing a training set based on the working condition parameters and the dimensionality-reduced time coefficient matrix, and training a pre-constructed neural network prediction model by using the training set to obtain a target prediction model. According to the compressor flow field reduced-order modeling method, the target prediction model integrated with the flow physical law can be trained and constructed, and the prediction task under the new working condition can be completed by adopting the target prediction model.

The invention relates to a thermal coupling over-reduced order prediction method and system for a multi-component assembled nonlinear system. The method comprises the following steps: collecting multi-scale physical field data; constructing an intrinsic orthogonal decomposition basis function space of a temperature field and a stress field, and establishing a double-field coupling constraint equation; constructing contact thermal resistance parameterized proxy models of a cylinder contact area, a bolt area and a free deformation area by adopting a domain discrete empirical interpolation method; constructing a parametric intrinsic mode tensor network to obtain a decline model which is used for realizing real-time reconstruction of a mode basis function through acquired tensor slices in an online stage; performing dynamic inversion based on a modal basis function reconstruction result, and outputting a predicted transient displacement field, a predicted temperature gradient field and a predicted contact stress field; and obtaining real-time parameters, and calculating a residual error with a corresponding prediction result so as to dynamically update the primary function and interpolation point distribution. Compared with the prior art, the real-time prediction of the transient thermal coupling of the multi-component contact system is realized on the premise of ensuring the precision.

The invention discloses a micro reactor transient multi-physical field rapid prediction method based on a reduced-order model, and the method comprises the steps: taking a reactor core parameter change and a time change in a transient process as all discrete parameter points in a parameter space, and building a response matrix of the reactor core power, the reactor core reactivity, the reactor core temperature and the matrix expansion rate, which serve as target information; reducing the order of the response matrix into a low-dimensional matrix by utilizing intrinsic orthogonal decomposition (POD); in a new parameter point input in real time in an online stage, a radial basis function is used to establish a relationship between the parameter point and a response vector, and rapid prediction is performed through a low-dimensional matrix, so that a result with relatively high calculation precision is obtained at low calculation cost. According to the method, transient multi-physics field coupling simulation is carried out on the reactor starting process of the micro reactor based on an intrinsic orthogonal decomposition radial basis function (POD-RBF) method, so that the calculation efficiency is remarkably improved, and meanwhile, relatively high prediction precision is ensured. A traditional high-fidelity simulation method usually faces high calculation cost when multi-physics coupling calculation is carried out, and the calculation amount is remarkably reduced on the premise that the precision is guaranteed.

The invention relates to the technical field of refrigeration house energy-saving control and digital twinning, in particular to a refrigeration house fan energy-saving operation method and system, and the method achieves the whole-field temperature deduction of a sensor-free area by constructing a CFD reference model and simulating and predicting the three-dimensional transient airflow and temperature distribution in a refrigeration house. Model parameters are calibrated through measured data, and the order of the model is reduced by adopting intrinsic orthogonal decomposition and Galerkin projection, so that the calculation amount is remarkably reduced, and online optimization is supported. And in combination with a data assimilation algorithm, the prediction result of the reduced-order model is continuously corrected, and the adaptability to environment change and system aging is improved. The optimization control stage takes minimization of the total energy consumption of a fan as a target, solves an optimal fan control sequence under the hard constraint that the whole-field temperature does not exceed the cargo safety upper limit and does not exceed the cargo safety lower limit, and adopts a rolling time domain mode for execution to realize collaborative optimization of safety and energy conservation.

The invention provides a multiphase filtering sampling method suitable for satellite communication. The method comprises the following steps: receiving an original modulation signal and converting the original modulation signal into a digital signal sequence; extracting a signal feature vector and generating a processing configuration parameter; performing orthogonal decomposition to generate I / Q signal pairs and performing oversampling; initializing a multi-phase filter and generating an optimal filter coefficient; carrying out phase calibration and filtering; determining an interpolation coefficient and executing multi-stage integer interpolation and mixed precision fractional interpolation; configuring a dynamic cache system; phase reordering and synthesis are executed; spectral analysis is performed and optimization parameters are generated. According to the invention, high-precision signal processing is realized, the adaptive capability and the efficient resource utilization characteristic are realized, and the performance of a satellite communication system is remarkably improved.

The invention relates to a trajectory prediction model robustness enhancement method based on dynamic subspace projection decomposition. Comprising the following steps: firstly, extracting hidden layer semantic features containing historical tracks and map topology through a multi-modal feature encoder; secondly, constructing a dynamic routing mechanism based on scene self-adaption, and calculating projection weights of input features on a plurality of expert subspaces; then, executing truncation projection operation based on orthogonal decomposition, retaining core semantics located in a low-dimensional space, and filtering out adversarial disturbance located in an orthogonal complementary space; and finally, introducing a feature consistency constraint training mechanism, taking the reconstructed features of the clean sample as anchor points, and compulsively aligning the purified features of the confrontation sample with the anchor points. Compared with the prior art, the method has the advantages that the robustness of the model in white box gradient attack, black box query attack and physical semantic deception scenes is remarkably improved through feature purification of a physical level and structured consistency constraint, and the prediction reliability of the automatic driving system is ensured.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention provides an MRI (Magnetic Resonance Imaging) reconstruction method of a subspace diffusion model based on orthogonal decomposition. The MRI reconstruction method comprises the following steps: forming an open source brain data set; a subspace diffusion model based on orthogonal decomposition is constructed, and k-space data is decomposed into subspaces for diffusion, so that data distribution is better captured, and robustness is kept in a noise environment; designing an iterative solution algorithm for extracting prior information in the model; and applying an iterative solution algorithm to the model to test the test data set, and outputting a final restored image. According to the method, the learning of effective information in complex distribution is promoted, the inherent high-dimensional extrapolation challenge is solved, and the calculation cost related to a diffusion model is reduced. Comprehensive experiments on different data sets clearly show that the method is superior to other methods in the aspects of reconstruction speed and quality.

The invention discloses a monitoring equipment fault rapid positioning method based on causal diagram reasoning, and the method comprises the following steps: building a thermal diffusion model composed of equipment nodes and links through collecting the topological structure and operation data of a monitoring system; normalizing and fusing the operation deviation information into a node abnormal energy value, and establishing an abnormal energy propagation equation; when an alarm is triggered, collecting an energy change sequence of each node to generate a space-time energy distribution matrix, and reversely solving a thermal diffusion equation to calculate a disturbance source item; performing clustering backtracking on disturbance source items, determining fault root cause nodes, and comprehensively evaluating confidence; and when multi-source abnormity exists, thermal field orthogonal decomposition is executed, and model parameters are adaptively updated according to field results. According to the method, abnormal propagation of monitoring equipment is modeled into a thermal diffusion process, and reverse solution and adaptive calibration are combined, so that rapid, explainable and high-precision positioning of fault root causes in a complex monitoring network is realized.

The invention provides a method for testing flow-induced noise of a pipeline. The method comprises the following steps: S1, determining measurement positions at an upstream flow channel and a downstream flow channel of a measured element, carrying out normalization processing on measured signals, and calculating the pressure power spectrum density of the inner wall surface of a pipe; s2, calculating a wall surface pressure fluctuation time domain cross correlation coefficient, and analyzing correlation characteristics between wall surface pressure pulsation; s3, spectrum intrinsic orthogonal decomposition is carried out on the pressure pulsation field, and space-time decoupling is achieved through time domain-frequency domain conversion; s4, extracting a dominant flow-induced noise mode, constructing an empirical frequency-wave number spectrum, and decomposing fluctuating energy distribution into contributions of different frequency and wave number combinations; s5, analyzing the decoupled flow-induced noise characteristic result, and identifying the amplitude-frequency characteristic of the flow-induced noise; according to the invention, real-time measurement of pressure pulsation in the pipeline can be realized, decoupling analysis of flow field pulsation and sound pressure pulsation can be carried out on the measured signal, and characteristics and a generation mechanism of pipeline flow-induced noise can be identified.

The invention discloses a flow field prediction-oriented full-process automatic joint reduced-order modeling method, which comprises the following steps of: specifying a target physical field parameter space, and randomly generating a sample space according to a Latin hypercube sampling method; constructing a full-process automatic simulation tool chain, driving target physical field numerical calculation and generating a training data set; carrying out singular value decomposition-based intrinsic orthogonal decomposition on the output physical field data, and only retaining first r main feature components to construct a reduced-order data set; constructing a multi-input multi-output full-connection feedforward neural network, and modeling and training a nonlinear mapping relation between input parameters and reduced-order features; new working condition parameters are input, reduced-order features are predicted through the trained neural network, distribution of a target physical field is reconstructed according to a singular value decomposition reduction matrix, and more flexible and reliable technical support is provided for reducing the training cost of a reduced-order model and improving simulation efficiency.

The invention discloses a mountainous area wind field downscaling optimization method based on high-precision simulation, and relates to the technical field of meteorological numerical simulation, and the method comprises the steps: obtaining the terrain elevation data and meteorological monitoring data of a target mountainous area, recognizing slope abrupt change points, constructing a spiral sampling path, calculating a slope change value, determining the distance between sampling points, and generating target sampling data; performing orthogonal decomposition on the sampled data to obtain a frequency component, calculating a surface fluctuation coefficient, and constructing a boundary disturbance equation to obtain surface stress distribution; calculating an airflow motion state based on surface stress distribution, solving a vorticity equation to obtain vorticity characteristics of a leeward area, and calculating airflow motion correction parameters; and carrying out downscaling iterative operation on the corrected parameters and the meteorological monitoring data to generate target area wind field data with hectometer-magnitude spatial resolution. The simulation precision of the local wind field under the complex terrain condition is improved.

The invention relates to the technical field of digital twinning, physical modeling and multi-source data fusion, and provides a digital twinning dynamic construction method based on multi-source data fusion and physical simulation. The method comprises the following steps: acquiring a multi-source heterogeneous data stream from a preset sensor array, a numerical simulation result and a historical database, identifying a key feature mode of a dominant physical process in the multi-source heterogeneous data stream, acquiring a key feature mode time-varying physical field evolution rule corresponding to the key feature mode by using a time sliding window and a forgetting mechanism, extracting a low-dimensional sparse characteristic parameter set reflecting dynamic behaviors from a high-dimensional observation space, and constructing a reduced-order proxy model by adopting Gaussian process regression, a neural network proxy model or an intrinsic orthogonal decomposition combined interpolation technology; and receiving a corresponding real-time observation data stream to establish a full-closed-loop feedback link from model prediction, high-fidelity solution verification to observation data correction in combination with the reduced-order proxy model so as to complete the construction of the digital twin.

The invention belongs to the technical field of oil-gas exploration seismic data processing, and particularly discloses a method, a system, equipment and a medium for improving seismic data resolution. The method comprises the following steps: giving an order, expressing a resolution improving operator in a Legendre polynomial orthogonal decomposition form, and obtaining a frequency domain resolution improving result mathematical expression expressed by a Legendre polynomial; a frequency band expected to be output is given, an expected frequency spectrum for improving the resolution ratio is designed, and a mathematical expression of Legendre polynomial coefficients is obtained through a least square method; calculating the amplitude spectrum of the seismic data, substituting the amplitude spectrum into the mathematical expression of the Legendre polynomial coefficient, and obtaining each coefficient value; various coefficient values are substituted into a mathematical expression of a frequency domain resolution improving result and are inversely transformed into a time domain to obtain a final result. According to the method, a high-resolution broadband seismic data result can be obtained, seismic wavelets do not need to be estimated, parameter setting is few, and quality control is convenient.

The invention discloses a summer rainfall sub-season prediction method and system fused with multi-scale deep learning, and the method comprises the steps: collecting multi-source weather forecast data and observation data, and carrying out the empirical orthogonal decomposition of the observation data, and obtaining a rainfall main mode and a mode sequence; performing multi-scale signal extraction on the observation data, and performing correlation analysis on the observation data and the modal sequence to obtain respective weight fields; constructing and training a deep learning model fusing a multi-pole attention mechanism and time sequence decomposition; inputting the forecast data into the trained model to carry out transfer learning, and optimizing the model; and substituting forecast data of preset time into the trained model to generate a high-quality summer rainfall sub-season forecast product. According to the method, the synergistic effect of sea, land and gas and the interaction of multi-scale signals are fully considered, the model is constructed based on an artificial intelligence method and a numerical model forecasting product, the sub-season forecasting skill of summer rainfall is effectively improved, and the method plays an important role in disaster prevention and reduction.

The invention relates to the technical field of multi-degree-of-freedom motion control and self-stabilization platforms, in particular to an error compensation control method for a self-stabilization holder under multi-branch-chain redundancy collaboration, which comprises the following steps of: acquiring a pose and a disturbance signal based on branch chain physical topology, constructing a disturbance propagation path, and identifying a main error source branch chain and a secondary error branch chain; performing orthogonal decomposition on the error component of the main error source branch chain, and mapping the error of each secondary error branch chain to a unified error coordinate system; extracting time domain features of the direction and amplitude error, and generating a corresponding compensation signal; distributing a branch chain weight according to the disturbance intensity ratio, and constructing a redundant branch chain cooperative control instruction; synchronously driving each branch chain to execute compensation, and verifying whether the attitude error is converged or not; and if not, correcting the error identification parameter and the disturbance model, and feeding back to the control cycle. According to the invention, multi-branch chain high-dynamic coordination and error self-learning compensation can be realized, and the attitude stability and anti-disturbance capability of the holder are improved.

The invention discloses an analysis method, system and device for an internal flow structure of a flow field. The analysis method comprises the following steps: acquiring characteristic quantity information of the flow field; converting the flow field characteristic quantity information into an atlas changing along with frequency, and obtaining discrete peak frequency in flow field pulsation from the atlas; obtaining a nonlinear relationship between discrete peak frequencies, and confirming a first characteristic frequency corresponding to the unsteady flow; processing the characteristic quantity information of the flow field by using an intrinsic orthogonal decomposition method, obtaining energy distribution and a flow structure of the flow field, and determining a dominant frequency and a corresponding dominant unsteady flow structure in combination with the first characteristic frequency; and performing frequency domain transformation on the characteristic quantity information of the flow field to obtain a two-dimensional distribution cloud picture corresponding to the dominant frequency, obtaining space distribution of the dominant frequency in the flow field, and combining energy distribution of the flow field to obtain an area where the dominant unsteady flow structure is located. According to the method, through multi-angle analysis, the dominant unsteady flow structure is clearly determined, and the characteristic frequency and the action area of the dominant unsteady flow structure are clarified.

The invention discloses a truss type load-bearing structure time domain dynamics topological optimization method and system based on an intrinsic orthogonal decomposition method, and relates to the technical field of truss type load-bearing structure topological optimization. The method is provided for solving the problems of high calculation power and the like caused by large-scale space-time response iteration of original transient problems and intensive calculation of dual problem sensitivity analysis in the prior art. According to the method, a local geometric control strategy for the cross interference problem of rod piece units is introduced, and a transient dynamics full-order topological optimization model of the truss structure is established through an equal geometric stiffness diffusion method. Based on an intrinsic orthogonal decomposition method, a transient dynamics reduced-order model of a truss structure is constructed, a reduced-order model base is updated in real time through an incremental singular value decomposition method, and then efficient solving of a transient dynamics equation and an adjoint equation in sensitivity analysis is achieved. And updating an iterative design variable by adopting a penalty function method, and seeking the optimal layout design of the truss structure. The invention provides a time-domain dynamics topological optimization method of the truss type load-bearing structure based on an intrinsic orthogonal decomposition method, and the method can be used for efficiently solving the dynamics layout optimization problem of the truss structure under the action of a transient load.

The invention discloses a transient flow field solving reduced-order modeling method based on orthogonal decomposition, and belongs to the technical field of flow field calculation. Firstly, CFD simulation calculation of a transient flow field is carried out, and a flow field simulation data set is constructed; and preprocessing the flow field simulation data set to generate a transient flow field data set for reduced-order modeling. According to the transient flow field data set, an orthogonal substrate is obtained based on an orthogonal decomposition method; normalizing the orthogonal substrate in a two-norm form; projecting the transient flow field data set to a space formed by the space substrate to obtain a projection coefficient; and recombining the projection coefficient, and solving a time base and a parameter coefficient based on an orthogonal decomposition method. And constructing a training model to train a mapping relationship between the input parameters and the base parameter coefficients. And finally, superposing the parameter coefficient, the time base and the space base of the to-be-solved flow field to obtain the distribution condition of the to-be-solved flow field in a certain time range. The method has the stability and the high efficiency of reduced-order modeling calculation, and the transient flow field can be rapidly solved.

The invention relates to the technical field of fluid mechanics modeling and aerodynamic acoustic analysis, in particular to a spectral orthogonal decomposition-based separated flow wall surface pressure pulsation dominant structure modeling method, which comprises the following steps of: acquiring a whole flow field and / or wall surface unsteady pressure data of a separated flow under a preset working condition; performing spectral orthogonal decomposition on the unsteady pressure data, performing frequency domain decoupling on multi-scale features, and extracting feature values and spatial dominant feature modes under each feature frequency; constructing a complex wave packet physical parameterized model, fitting the spatial dominant feature modals, and compressing the spatial dominant feature modals into sparse physical parameter vectors after nonlinear regression solution; and reconstructing a wall surface pressure pulsation dominant component based on the sparse physical parameter vector and the characteristic value, and carrying out at least one of flow mechanism analysis and pneumatic order reduction modeling according to the dominant component. Therefore, the problems of low reconstruction precision and the like caused by the fact that asymmetric evolution and variable acceleration convection of a large-scale structure in separation flow wall surface pressure pulsation cannot be accurately represented in a frequency domain in related technologies are solved.

The invention discloses a citrus lossless identification method and system based on near infrared spectrum characteristic decoupling, and the method comprises the steps: obtaining near infrared spectrum data of a citrus sample, and coding the near infrared spectrum data to a submerged space to obtain spectrum characteristic representation; mapping the pooled features to a discrete citrus mode codebook by using a vector quantizer, and extracting mode features; performing multi-scale convolution operation on the spectral feature representation, and extracting fine-grained spectral features; performing orthogonal decomposition on the fine-grained spectral features into personalized features and invalid features through orthogonal decomposition; and according to the extracted pattern features and personalized features, citrus category identification is realized. The citrus lossless identification system integrates and realizes spectral feature representation, quantization, feature extraction and category identification functions. According to the method, the mode features and the personalized features of the citrus are extracted, the accuracy rate reaches 85.24% in 80 types of citrus identification tasks, and the method has a wide application prospect.

The invention discloses a grid-connected inverter fusion control method based on voltage and current source time sharing, and relates to the technical field of electric power automation, and the method comprises the steps: carrying out the two-phase rotating coordinate system conversion of power grid coupling data, and generating a voltage-current joint component; performing power error analysis on the voltage-current joint component, outputting a power grid state vector, performing modal decomposition on the power grid state vector, obtaining a dominant oscillation mode matrix, performing Lyapunov exponent calculation on the dominant oscillation mode matrix, forming a stability pre-judgment mark, and inputting the stability pre-judgment mark into a time-sharing fusion decision network model to obtain a time-sharing fusion decision network model. The characteristic coupling layer carries out cross physical quantity correlation, and the stability evaluation layer carries out stability boundary calculation and outputs a voltage-current source control signal. Through the intrinsic orthogonal decomposition algorithm and the time-sharing fusion decision network model, the response speed and the fusion control capability of the grid-connected inverter are improved.

The invention relates to the technical field of wind power plant power control, in particular to a wind power plant dynamic power distribution optimization method based on projection dimensionality reduction. The method comprises the following steps: collecting a high-dimensional original operation data stream of a wind power plant, and preprocessing to output a high-dimensional state vector; extracting a dominant mode matrix through an intrinsic orthogonal decomposition algorithm, and projecting a high-dimensional state vector to a low-dimensional space; future modal coefficient evolution is predicted through an autoregression prediction model, and a low-dimensional rolling optimization problem is constructed and solved through a sequential quadratic programming algorithm; reconstructing the optimal low-dimensional modal coefficient vector into a target power instruction of each fan, and issuing and executing the target power instruction; and updating the dominant mode matrix through an incremental singular value decomposition algorithm and correcting parameters of the autoregressive prediction model. According to the method, projection dimensionality reduction from high-dimensional data to a low-dimensional space is realized through intrinsic orthogonal decomposition, and the problem of low optimization solution efficiency caused by high data dimensionality of a traditional method is solved.

The invention relates to the technical field of circuit load state monitoring, and discloses a directional drilling pipeline surface coating damage characteristic analysis method based on data inversion, which comprises the following steps: injecting a dual-frequency composite excitation signal into a pipeline transmission loop; locking a current phase, collecting a magnetic flux gradient vector and performing orthogonal decomposition; according to the method, impedance distortion caused by medium heterogeneity is eliminated through the pilot frequency response difference, the problem of misjudgment caused by the fact that traditional detection depends on medium isomorphism hypothesis is solved, and the detection accuracy is improved. And accurate identification of transmission load leakage impedance abnormity is realized.

The invention discloses a maneuvering target tracking method and system based on orthogonal decomposition and parameter adaptation. The method comprises the following steps: acquiring an initial state of a target; a self-adaptive interactive multi-model filtering framework is provided, complex three-dimensional space motion of a target is orthogonally decomposed into a horizontal plane and two perpendicular planes which are perpendicular to each other, and complete description and tracking are carried out on any space maneuver; and designing an online parameter adaptive mechanism, intelligently adjusting the smooth strength of the quasi-uniform B spline according to the real-time maneuvering mode probability output by the IMM filter, robustly identifying the turning angular velocity in each plane through a multi-section geometric average strategy based on the optimal smooth trajectory, and dynamically feeding back the identification result to the corresponding collaborative turning model. According to the method, a complex high-dimensional nonlinear tracking problem is converted into a plurality of parallel and adaptive low-dimensional problems for solving, and finally, high-precision, fast-convergence and strong-robustness tracking of the high-maneuvering target is realized.

The invention belongs to the technical field of hydraulic engineering digital twinning, and provides an arch dam static response rapid calculation method which comprises the following steps: sampling and simulating based on a finite element model in an offline stage to obtain a displacement field snapshot matrix, extracting a dominant mode through intrinsic orthogonal decomposition to construct a reduced-order subspace, and calculating a mode coefficient; and training a radial basis function neural network to establish a nonlinear mapping relation by taking the working condition parameters as input and the modal coefficient as output, and finally, inputting the target working condition parameters into the trained network to predict the modal coefficient in an online stage, and quickly reconstructing a complete displacement field response through linear combination with the POD modal. The method achieves the quick and accurate prediction of the static physical field of the arch dam, and remarkably improves the calculation efficiency of the displacement field of the arch dam.

The invention discloses a phase-locked loop-free power grid phase optimization method and system based on an extended state observer, and the method comprises the steps: solving an LESO discrete state equation, and obtaining a disturbance compensation voltage outputted by the LESO, converting the disturbance compensation voltage in the three-phase static abc coordinate system into a target disturbance compensation voltage of a two-phase static alpha beta coordinate system according to constant amplitude Clarke conversion; obtaining a positive-sequence component in the target disturbance compensation voltage according to a second-stage generalized integrator, and constructing a closed-loop bandwidth-adjustable orthogonal signal generator according to the positive-sequence component; and calculating a phase optimization reference signal according to an orthogonal decomposition result of the orthogonal signal generator, positioning the phase optimization reference signal in a finite search domain according to a partition search bisection iteration method, and determining an optimal phase angle through cost function evaluation. The problems that a traditional phase-locked loop control method is too slow in dynamic response and difficult to adapt to extreme working conditions are solved, and real-time accurate tracking of phases is achieved.

The invention relates to the technical field of thermal management, in particular to a liquid cooling plate flow resistance characteristic evaluation method and device, a medium and equipment. According to the method, the calculation load is remarkably reduced by converting the flow data into the reduced-order model by utilizing the intrinsic orthogonal decomposition. This can achieve faster analysis without sacrificing accuracy, so that the design can be evaluated and optimized in real time. Moreover, different from the traditional method which may depend on a simplified model or empirical correlation, the method based on the intrinsic orthogonal decomposition can capture a main flow mode, namely a dominant mode, which causes resistance. These modes reveal critical flow characteristics, such as high shear stress, vortex formation and flow separation, which are crucial for understanding the root cause of flow resistance. The capability of quantifying and isolating the important flow structure can deeper understand how the specific flow behavior affects the resistance, so that guidance is provided for the optimization design of the geometrical shape of the liquid cooling plate.

The invention provides a cavitation flow field reconstruction method based on sparse perception and an unsupervised learning method, and the method comprises the steps: firstly, carrying out the numerical simulation of cavitation flow around a specific type of hydrofoil, considering the unique characteristics of a cavitation flow velocity field, executing the intrinsic orthogonal decomposition, and representing the intrinsic orthogonal decomposition as a representation form of the superposition of an average velocity field and a pulsation velocity field; a fluid velocity field modal library is constructed on the basis; and then an unsupervised clustering algorithm and sparse data sampling are executed to extract data samples, and fluid velocity field reconstruction is completed by using the velocity field modal library and a small number of data samples. Through verification and comparison of reconstruction effects under different sparse sampling strategies, high-fidelity reconstruction of a complex cavitation flow structure can be finally realized, and compared with the prior art, accuracy and robustness are remarkably improved.