Fan system multi-physics field coupling simulation and modeling method and device

Abstract

The invention provides a fan system multi-physics field coupling simulation and modeling method and device, and the method comprises the steps: building a coupling simulation frame of an electromagnetic field, a temperature field, a flow field and a structural mechanical field through multi-physics field collaborative modeling, employing a finite volume method FVM and multi-body dynamics MBS for collaborative solving, and precisely simulating the dynamic response of a fan under a complex working condition. And real-time simulation and verification: combining an S CADA system and a machine learning algorithm to realize online calibration and dynamic verification of a multi-physics field model, and supporting fan structure stability prediction under extreme conditions of typhoon, turbulence and the like. A multi-physics field coupling simulation software environment is developed, the functions of wake effect analysis, fan array layout optimization and the like are provided, the construction cost of an offshore wind field is reduced, and the power generation efficiency is improved.

Description

Technical Field

[0001] This application relates to the technical field of wind turbine simulation, and particularly to a multi-physics field coupling simulation and modeling method and device for a wind turbine system. Background Art

[0002] The main goal of multi-physics field coupling research is to explore and describe the interaction and influence mechanisms between different physical fields, aiming to deeply understand the comprehensive nature of various natural and engineering phenomena in the real world. In our daily life and scientific research, we often encounter situations where multiple physical fields are intertwined and interact with each other. For example, the electric field, magnetic field, and thermal field generated during the operation of electrical equipment interact with each other; materials exhibit complex behaviors under the combined influence of factors such as structural mechanics, thermal effects, and electromagnetic excitation.

[0003] The research on multi-physics field coupling has important theoretical significance and practical application value. By deeply studying the interaction mechanisms between different physical fields, it can provide theoretical guidance and technical support for designing more accurate and reliable engineering systems. This interdisciplinary multi-physics field simulation and modeling method is of great significance for solving modern engineering problems, optimizing product performance, and improving resource utilization efficiency.

[0004] In computational modeling, multi-physics field simulation (usually abbreviated as multi-physics) is defined as simultaneously simulating different aspects of one or more physical systems and their interactions. For example, simultaneously simulating the physical stress on an object, the temperature distribution of the object, and the thermal expansion that causes changes in stress and temperature distribution will be regarded as multi-physics field simulation. Multi-physics field simulation is related to multi-scale simulation, which refers to the simultaneous simulation of a single process on multiple time or distance scales. As an interdisciplinary field, multi-physics field simulation can span many scientific and engineering disciplines. Simulation methods often include numerical analysis, partial differential equations, and tensor analysis. With the rapid development of computer technology, multi-physics field simulation has become an important tool for researching and solving complex problems. Based on computational simulation and simulation methods, the interaction between different physical fields can be accurately described, and the system response can be obtained by numerically solving the corresponding equations. This simulation-based method provides strong support for innovative design and virtual testing, and helps to accelerate the pace of scientific discovery and engineering technology progress.

[0005] In wind turbine simulation, multi - physical - field coupling technology is widely applied in the co - simulation of aerodynamic - mechanical models, electromagnetic transient characteristics, and control systems. For example, based on the method of multi - controller hardware - in - the - loop simulation, by combining multi - body dynamics and electromagnetic transient characteristics, the accurate reproduction of the operating characteristics of wind turbines is achieved. In addition, through multi - physical - field coupling simulation technology, the dynamic loads and performance of the drive train, blade structure, and control system of wind turbines can be evaluated, thereby optimizing the design and reducing the R & D cost.

[0006] The application of multi - physical - field coupling simulation technology in wind turbine simulation has made remarkable progress. The key to multi - physical - field coupling simulation lies in how to handle the complex interaction between different physical fields. For example, in the fluid - structure interaction problem, a two - way information transfer between the fluid field and the structure field needs to be achieved through implicit or explicit coupling schemes. In addition, data transfer algorithms (such as node - mapping data algorithms, integrator algorithms, etc.) are also important technical means to achieve efficient coupling of multi - physical fields. However, multi - physical - field coupling simulation still faces many challenges, such as high computational resource requirements, insufficient numerical stability, and difficulty in determining boundary conditions.

[0007] The application of multi - physical - field coupling simulation technology in wind turbine simulation not only improves the accuracy and reliability of the model but also provides strong support for design optimization in complex environments. However, with the increasing complexity of simulation problems, how to overcome problems such as high computational resource requirements and insufficient numerical stability remains the key research direction in the future.

[0008] The difficulty of direct coupling numerical calculation of multi - physical fields is relatively large, mainly reflected in: there are significant differences in the algorithms and grid discretizations of different physical fields, and the requirements of each physical field for the grid must be considered during grid meshing. The convergence problem of iterative solution. The control equations of the electromagnetic field and the temperature field are relatively simple and have a fast convergence speed, while the control equation of the fluid field is complex and has poor convergence. The finite - volume method with a relatively fast convergence speed is often selected for the fluid field, while the finite - element method with higher computational accuracy is used for the electromagnetic field and the temperature field. It is difficult for the direct coupling method to take both into account simultaneously.

[0009] Therefore, for multi - physical - field collaborative modeling, specifically: constructing a coupling simulation framework for the electromagnetic field - temperature field - fluid field - structural mechanics field, and using the finite - volume method (FVM) and multi - body dynamics (MBS) for collaborative solution to accurately simulate the dynamic response of the fan under complex working conditions is an important content that technical personnel in this field need to focus on researching. Summary of the Invention

[0010] In view of this, it is necessary to provide a method and device for multi - physical - field coupling simulation and modeling of a fan system, which uses a modal feature fusion strategy (clinical + radiomics + biochemical indicators) to protect the maintenance and management level of the equipment. Based on a lightweight model of integrated learning, it takes into account both performance and interpretability.

[0011] In a first aspect, an embodiment of the present application provides a multi - physical - field coupling simulation and modeling method for a wind turbine system. The method includes:

[0012] Multi - physical - field collaborative modeling step: Construct a coupled simulation framework for electromagnetic field - temperature field - flow field - structural mechanics field, and use the finite volume method (FVM) and multi - body dynamics (MBS) to solve collaboratively to simulate the dynamic response of the wind turbine under complex working conditions;

[0013] Real - time simulation and verification step: Combine the SCADA system and machine learning algorithms to achieve online calibration and dynamic verification of the multi - physical - field model, and predict the structural stability of the wind turbine under extreme conditions such as typhoons and turbulence;

[0014] Optimized design toolchain step: Develop a multi - physical - field coupling simulation software environment, conduct wake effect analysis, and optimize the layout of the wind turbine array.

[0015] Optionally, in an implementation manner of the first aspect of the present invention, the multi - physical - field collaborative modeling step: Constructing a coupled simulation framework for electromagnetic field - temperature field - flow field - structural mechanics field includes:

[0016] S1.1, Construct a three - dimensional simulation model of the offshore wind turbine system through ANSYS according to the structure of the offshore wind turbine system;

[0017] S1.2, Set the electromagnetic field for the three - dimensional simulation model according to the three - dimensional simulation model;

[0018] S1.3, Set the temperature field - flow field for the three - dimensional simulation model according to the three - dimensional simulation model;

[0019] S1.4, Set the material properties, geometric shape, environmental characteristics, and external loading conditions of the wind turbine device according to the three - dimensional simulation model of the wind turbine system, and set the structural mechanics field for the three - dimensional spatial model of the wind turbine device structure to obtain the structural mechanics field during the operation of the wind turbine device.

[0020] Optionally, in an implementation manner of the first aspect of the present invention, the constructing of the coupled simulation framework for electromagnetic field - temperature field - flow field - structural mechanics field further includes:

[0021] S2.1, Establish an electromagnetic field model, determine the electromagnetic field calculation parameters and solution type, write them into the electromagnetic field analysis file, and calculate the equipment loss;

[0022] S2.2, Establish a temperature field - flow field model, transfer the equipment loss of the electromagnetic field into the temperature field - flow field analysis file, clarify the temperature field - flow field calculation parameters and coupled solution, and obtain the flow velocity, thermal response, and thermal distribution;

[0023] S2.3, determine whether the temperature field-flow field converges. If so, the solution ends; if not, calculate the temperature correction according to the thermal response and heat distribution, transfer the temperature correction to the electromagnetic field analysis file, and iterate S2.1 until the convergence error is within the allowable range;

[0024] S2.4, establish a structural mechanics field model, couple the electromagnetic field model, temperature field-flow field, and structural mechanics field model to form a multi-physics field coupling model, correct the temperature and pass it into the structural mechanics field analysis file for subsequent simulation analysis.

[0025] Optionally, in an implementation of the first aspect of the present invention, the finite volume method FVM and multi-body dynamics MBS are used to collaboratively solve and accurately simulate the dynamic response of the fan under complex working conditions, including:

[0026] Preprocessing stage: define the geometry of the computational domain, generate the mesh, discretize the computational domain into small, non-overlapping sub-regions of the control volume, determine the fluid properties and appropriate boundary conditions of the computational domain boundaries based on the physical characteristics of the multiphysics model;

[0027] Solution phase: Integrate the fluid mechanics governing equations of all units in the computational domain, convert the obtained integral equations into a system of algebraic equations through a discretization process, and use appropriate numerical schemes to obtain the solutions of the algebraic equations in each unit;

[0028] Result analysis phase: Use the SCADA system to obtain data, combine machine learning methods for analysis, and visualize it.

[0029] Optionally, in an implementation of the first aspect of the present invention, the solution stage: integrating the fluid mechanics control equations of all units in the computational domain, converting the obtained integral equations into a set of algebraic equations through a discretization process, and obtaining the solution of the algebraic equations in each unit using an appropriate numerical scheme, includes:

[0030] The continuous equations are transformed into discrete algebraic equations using the finite volume method, and the computational domain is discretized in space; the computational domain is divided into multiple control volumes and multiple discrete time steps to calculate the simulation time;

[0031] The control volume cells that make up the grid are connected by faces. A cell with a central node P has six neighboring nodes, labeled W, E, N, B, T, S, and six neighboring faces;

[0032] The discretization process of attribute φ is as follows:

[0033]

[0034] Where t is time, φφ is a velocity / temperature variable, U is velocity, ρ is density, ΓΦ is the diffusion coefficient, S Φ is the source term, Δ is the Laplacian operator, For vector field analysis, it describes the divergence of the field. The term on the left represents the rate of increase of φ, where the convection term represents the net rate of the φ property outside the control volume element, the diffusion term on the right represents the rate of increase of φ due to diffusion, and the contribution of the source term;

[0035] Through finite volume discretization by integrating over the control volume and time, the calculation formula is as follows:

[0036]

[0037] where Vp represents the control volume, t represents time, and Δt represents the change in time;

[0038] By applying Gauss's theorem, the spatial integral is converted from a volume integral to a surface integral, and the finite volume discretization equation is solved by considering appropriate numerical schemes for each specific term;

[0039] The gradient term in the volume integral is converted to a surface integral by Gauss's theorem, and the value of the gradient term is thus approximated to second order by summing the property values on all the faces of the control volume;

[0040] In an orthogonal grid, it is defined as: where d represents a vector, representing the distance between the center P of the control volume element of interest and the center N of its adjacent element, φ N 、φ P represent the property values of points N and P respectively;

[0041] For a non - orthogonal network, the diffusion term is divided into two terms, where the second term is the correction for non - orthogonality, and the formula is as follows:

[0042]

[0043] S = Δ + k,

[0044] where the vector Δ represents a vector parallel to the vector k, S represents the area vector outside the control volume element, φ f represents the property value on the face, f represents, k represents the adjustment coefficient.

[0045] Optionally, in an implementation manner of the first aspect of the present invention, the real - time simulation and verification steps: combining the SCADA system with machine learning algorithms to achieve online calibration and dynamic verification of the multi - physical field model, and predicting the structural stability of the fan under extreme conditions such as typhoons and turbulence, including:

[0046] Use the SCADA system to collect real-time operating status data of offshore wind turbines, including wind speed, wind direction, power output, and environmental parameters;

[0047] Based on the operating status data, online calibration and dynamic verification of the multi-physics field model are implemented, the structural stability of the wind turbine under typhoon and turbulent extreme conditions is predicted, and the feasibility and effect of the technology are evaluated.

[0048] Optionally, in an implementation of the first aspect of the present invention, the optimization design tool chain step: developing a multi-physics field coupling simulation software environment, performing wake effect analysis, and optimizing the layout of a wind turbine array includes:

[0049] Based on ANSYS Workbench tools, develop a multi-physics field coupling simulation software environment to realize the coupling simulation analysis of electromagnetic field, temperature field, flow field and structural mechanics field;

[0050] Through simulation analysis of the wake impact under different wind turbine spacing, wind direction and wind speed conditions, a theoretical basis is provided for wind turbine layout;

[0051] The particle swarm optimization algorithm is used to deal with multi-objective optimization problems, comprehensively considering low noise and low cost factors to obtain the maximum power generation.

[0052] In a second aspect, an embodiment of the present application provides a multi-physics coupling simulation and modeling device for a wind turbine system, which is applied to a multi-physics coupling simulation and modeling method for a wind turbine system as described in the first aspect, and is characterized in that the device comprises:

[0053] Multi-physics field collaborative modeling module: builds a coupled simulation framework of electromagnetic field, temperature field, flow field and structural mechanics field, and uses the finite volume method FVM and multi-body dynamics MBS to collaboratively solve and simulate the dynamic response of the fan under complex working conditions;

[0054] Real-time simulation and verification module: Combines the SCADA system with machine learning algorithms to achieve online calibration and dynamic verification of multi-physics field models, and predicts the structural stability of wind turbines under extreme conditions such as typhoons and turbulence;

[0055] Optimization design tool chain module: Develop a multi-physics field coupling simulation software environment to perform wake effect analysis and wind turbine array layout optimization.

[0056] In a third aspect, an embodiment of the present application provides an electronic device, including:

[0057] processor;

[0058] a memory for storing processor-executable instructions;

[0059] Among them, when the processor is configured to execute the instructions, it implements a multi-physical field coupling simulation and modeling method for a fan system as described in the first aspect.

[0060] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium storing a program that instructs a device to execute a multi-physical field coupling simulation and modeling method for a fan system as described in the first aspect.

[0061] The beneficial effects of this solution specifically include:

[0062] (1) Multi-physical field collaborative modeling: Construct a coupling simulation framework for the electromagnetic field - temperature field - flow field - structural mechanics field, and use the finite volume method (FVM) and multi-body dynamics (MBS) to solve collaboratively to accurately simulate the dynamic response of the fan under complex working conditions.

[0063] (2) Real-time simulation and verification: Combine the SCADA system and machine learning algorithms to achieve online calibration and dynamic verification of the multi-physical field model, and support the prediction of the structural stability of the fan under extreme conditions such as typhoons and turbulence.

[0064] (3) Optimization design toolchain: Develop a multi-physical field coupling simulation software environment, provide functions such as wake effect analysis and fan array layout optimization, reduce the construction cost of offshore wind farms and improve power generation efficiency. Description of the Drawings

[0065] Figure 1 It is a schematic flowchart of a multi-physical field coupling simulation and modeling method for a fan system provided by an embodiment of the present application.

[0066] Figure 2 It is a flowchart of constructing a coupling simulation framework for the electromagnetic field - temperature field - flow field - structural mechanics field provided by an embodiment of the present application.

[0067] Figure 3 It is a schematic diagram of the spatial discretization of the computational domain and the representation of a general three-dimensional control volume unit provided by an embodiment of the present application.

[0068] Figure 4 It is a schematic diagram of the module of a multi-physical field coupling simulation and modeling device for a fan system provided by an embodiment of the present application.

[0069] Figure 5 It is a schematic diagram of an electronic terminal device provided by an embodiment of the present application. Detailed Embodiments

[0070] Next, the technical solutions in the embodiments of the present application will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, rather than all of the embodiments.

[0071] It should be noted that in the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which this application belongs. The terms used in the specification of this application are only for the purpose of describing specific embodiments and are not intended to limit this application.

[0072] It should be noted that in the embodiments of the present application, terms such as "first" and "second" are only used for the purpose of distinguishing descriptions, and cannot be understood as indicating or implying relative importance, nor can they be understood as indicating or implying an order. The features defined with "first" and "second" may explicitly or implicitly include one or more of the said features. In the description of the embodiments of this application, words such as "exemplary" or "for example" are used to represent examples, illustrations or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner.

[0073] Based on the embodiments in this application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope protected by this application.

[0074] In view of this, the present application provides a method and device for multi-physical field coupling simulation and modeling of a fan system. Through multi-physical field collaborative modeling: constructing a coupling simulation framework for electromagnetic field - temperature field - flow field - structural mechanics field, and using the finite volume method FVM and multi-body dynamics MBS for collaborative solution to accurately simulate the dynamic response of the fan under complex working conditions. Real-time simulation and verification: combining the SCADA system and machine learning algorithms to achieve online calibration and dynamic verification of the multi-physical field model, and supporting the prediction of the structural stability of the fan under extreme conditions such as typhoons and turbulence. Optimization design tool chain: developing a multi-physical field coupling simulation software environment, providing functions such as wake effect analysis and fan array layout optimization, reducing the construction cost of offshore wind farms and improving power generation efficiency.

[0075] Figure 1 It is a schematic flow diagram of a method for multi-physical field coupling simulation and modeling of a fan system provided by an embodiment of the present application.

[0076] Step 1: Multi-physical field collaborative modeling step: constructing a coupling simulation framework for electromagnetic field - temperature field - flow field - structural mechanics field, and using the finite volume method FVM and multi-body dynamics MBS for collaborative solution to simulate the dynamic response of the fan under complex working conditions.

[0077] Figure 2A flowchart of a coupled simulation framework for constructing an electromagnetic field - temperature field - flow field - structural mechanics field provided by an embodiment of the present application.

[0078] As Figure 2 shown, specifically, in the embodiment of the present application, the multi - physical - field collaborative modeling steps: constructing a coupled simulation framework for an electromagnetic field - temperature field - flow field - structural mechanics field, including:

[0079] S1.1, constructing a three - dimensional simulation model of the offshore wind turbine system through ANSYS according to the structure of the offshore wind turbine system;

[0080] S1.2, setting the electromagnetic field for the three - dimensional simulation model according to the three - dimensional simulation model;

[0081] S1.3, setting the temperature field - flow field for the three - dimensional simulation model according to the three - dimensional simulation model;

[0082] S1.4, setting the material properties, geometric shape, environmental characteristics and external loading conditions of the fan device according to the three - dimensional simulation model of the fan system, and setting the structural mechanics field for the three - dimensional spatial model of the fan device structure to obtain the structural mechanics field during the operation of the fan device.

[0083] Specifically, a two - way weak coupling strategy is adopted: electromagnetic field (Maxwell's equations) → temperature field (heat conduction / convection) → flow field (CFD) → structural field (solid mechanics) to transfer data in sequence, and field - to - field data mapping is realized through the ANSYS Workbench platform.

[0084] Key coupling variables: electromagnetic loss (Joule heat) → heat source of the temperature field, temperature distribution → buoyancy force / material property change in the flow field, pressure / velocity in the flow field → surface load of the structure, structural deformation → geometric model update (requiring mesh re - division).

[0085] The construction of the three - dimensional simulation model can be as follows: First, geometric modeling is required, including key components: tower barrel, nacelle, blade, generator (permanent magnet synchronous), and subsea foundation (single pile / jacket). The blade adopts a parametric airfoil section (NACA series) to retain the aerodynamic shape; the stator and rotor of the generator retain the tooth - slot structure, and small features such as bolts are ignored. Mesh requirements: electromagnetic region (air gap / winding): locally refined to 0.5 times the skin depth; flow field boundary layer: y+ < 5, expansion ratio 1.2.

[0086] Specifically, in the embodiment of the present application, the construction of the coupled simulation framework for the electromagnetic field - temperature field - flow field - structural mechanics field further includes:

[0087] S2.1, establishing an electromagnetic field model, determining electromagnetic field calculation parameters and solution types, writing them into the electromagnetic field analysis file, and calculating the equipment loss;

[0088] S2.2. Establish a temperature-field - flow-field model, input the equipment losses of the electromagnetic field into the temperature-field - flow-field analysis file, clarify the calculation parameters of the temperature-field - flow-field and the coupled solution, and obtain the flow velocity, thermal response, and thermal distribution;

[0089] S2.3. Judge whether the temperature-field - flow-field converges. If it does, the solution ends; if not, calculate the temperature correction based on the thermal response and thermal distribution, input the temperature correction into the electromagnetic field analysis file, and iteratively execute S2.1 until the convergence error is within the allowable range;

[0090] S2.4. Establish a structural mechanics field model, couple the electromagnetic field model, temperature-field - flow-field, and structural mechanics field model to form a multi-physics field coupling model, and input the temperature correction into the structural mechanics field analysis file for subsequent simulation analysis.

[0091] Specifically, excitation conditions can be set for the electromagnetic field: Stator winding: Three-phase sinusoidal current (set grid fault conditions according to IEC61400-21); Rotor permanent magnet: NdFeB35, considering the temperature demagnetization effect (Br(T) curve). Boundaries and solutions include: Master / Slave boundary handling for periodic symmetry; Transient solution step size ≤ 1 / 20 electrical cycle, and it is necessary to output the eddy current loss density nephogram.

[0092] The coupling of the temperature-field - flow-field includes importing electromagnetic losses (copper loss of winding, core eddy loss) into the heat source mapping of Fluent in the form of volume heat sources. The cooling strategies include: Forced air cooling: Set the rotating domain (MRF method), inlet air velocity = rated speed × blade radius; Liquid cooling pipeline: Use the porous medium model to simulate heat dissipation fins, and coupled conjugate heat transfer (CHT). The turbulence model selects the SST k-ω model (advantages in capturing separated flow), and enable the buoyancy-driven flow option.

[0093] The settings of the structural mechanics field are mainly: Superposition of multiple loads, including dynamic loads: Aerodynamic loads (pre-calculated by Blade Element Momentum theory); Wave loads (JONSWAP spectrum, transmitted through AQWA); Electromagnetic torque pulsation (decomposed to each order by FFT). Contact nonlinearity includes bearing contact: Adopt the Augmented Lagrange algorithm, friction coefficient 0.1; Bolt pre-tightening force: Simulated through the Bolt Thread function. Failure criteria include for blades: Tsai-Wu composite material criterion; for tower barrels: DNVGL-RP-C202 fatigue damage model.

[0094] The collaborative solution of the finite volume method FVM and multi-body dynamics MBS is adopted to accurately simulate the dynamic response of the wind turbine under complex working conditions, including:

[0095] Pretreatment stage: Define the geometry of the computational domain, generate a mesh, discretize the computational domain into small, non-overlapping sub-regions of control volumes, and determine the fluid properties and appropriate boundary conditions for the boundaries of the computational domain according to the physical characteristics of the multi-physics model;

[0096] Solution stage: Integrate the hydrodynamic control equations for all elements within the computational domain, convert the resulting integral equations into algebraic equation systems through a discretization process, and obtain the solutions of the algebraic equations in each element using an appropriate numerical scheme;

[0097] Result analysis stage: Use the SCADA system to obtain data, analyze it in combination with machine learning methods, and visualize and display it.

[0098] Specifically, the pretreatment stage also includes computational domain modeling and discretization, geometric modeling and mesh generation steps, geometric parameterization: The blade is parametrically modeled using the NREL S-series airfoil, and the angle of attack and twist angle distribution are automatically adjusted by driving ANSYS DesignModeler through a Python script. The tower and foundation structure are generated based on the OpenFAST input file, retaining weld and flange details. A flexible multi-body model is established in Adams / View, the blade is defined as a flexible body (.mnf file), and the tower adopts Craig-Bampton modal reduction.

[0099] Boundary conditions and physical properties mainly include: flow field boundary and material nonlinearity. Among them, the flow field boundary inlet: Coupled input of JONSWAP wave spectrum and Kaimal wind spectrum (turbulence intensity 15%); outlet: Pressure outlet (back pressure = hydrostatic pressure + dynamic wave pressure). Material nonlinearity: The Johnson-Cook model is used to simulate the plastic deformation at the bolt connection, and the strain rate coefficient C = 0.025.

[0100] The solution stage: Integrate the hydrodynamic control equations for all elements within the computational domain, convert the resulting integral equations into algebraic equation systems through a discretization process, and obtain the solutions of the algebraic equations in each element using an appropriate numerical scheme, including:

[0101] Convert the continuity equation into a discrete algebraic equation applying the finite volume method, and discretize the computational domain in space; Divide the computational domain into multiple control volumes, and use multiple discrete time steps to calculate the simulation time.

[0102] The solution stage specifically includes: governing equations and discretization. Discretization scheme: Convection term: Second-order upwind difference (QUICK scheme); Time term: Implicit dual-time stepping method (physical time step Δt = 0.01 s, sub-iteration step uses pseudo-time marching). Data transfer method: Fluid-structure interface: MPCCI realizes two-way force-displacement coupling between Fluent and Adams, and the interpolation algorithm uses radial basis function (RBF). Electromagnetic-thermal coupling: Through ANSYS System Coupling, the loss-temperature fields of Maxwell and Fluent are synchronized, and the update period ≤ 10 fluid steps.

[0103] Figure 3 Schematic diagram of the spatial discretization of the computational domain and the representation of a general three-dimensional control volume cell provided by an embodiment of the present application. As Figure 3 shown, the control volume cells that make up the grid are connected by faces. An element with a central node P has six neighboring nodes, which are respectively identified as W, E, N, B, T, S, and six neighboring faces;

[0104] The discretization process of the property φ is as follows:

[0105]

[0106] where t is time, φ is a property that is a velocity / temperature variable, U is velocity, ρ is density, Γ Φ is the diffusion coefficient, S Φ is the source term, Δ is the Laplace operator, is used for vector field analysis to describe the divergence of the field. The term on the left represents the rate of increase of φ, where the convection term represents the net rate of the φ property outside the control volume cell, and the diffusion term on the right represents the rate of increase of φ due to diffusion, as well as the contribution of the source term;

[0107] Through finite volume discretization, by integrating over the control volume and time, the calculation formula is as follows:

[0108]

[0109] where Vp represents the control volume, t represents time, and Δt represents the change in time;

[0110] By applying Gauss's theorem, the spatial integral is converted from a volume integral to a surface integral, and the finite volume discretization equation is solved by considering an appropriate numerical scheme for each specific term;

[0111] The gradient term in the volume integral is converted to a surface integral by Gauss's theorem, and the value of the gradient term is thus approximated to second order by summing the property values on all the faces of the control volume;

[0112] In an orthogonal grid, it is defined as: Among them, d represents a vector, indicating the distance between the center P of the control volume unit of interest and the center N of its adjacent unit, and φ N , φ P represent the attribute values of points N and P respectively;

[0113] For a non-orthogonal network, the diffusion term is divided into two terms, where the second term is the correction for non-orthogonality, and the formula is as follows:

[0114]

[0115] S = Δ + k,

[0116] where the vector Δ represents a vector parallel to the vector k, S represents the area vector outside the control volume unit, φ f represents the attribute value on the surface, f represents, and k represents the adjustment coefficient.

[0117] Step 2: Real-time simulation and verification step: Combine the SCADA system with machine learning algorithms to achieve online calibration and dynamic verification of the multi-physical field model, and predict the structural stability of the wind turbine under extreme conditions such as typhoons and turbulence.

[0118] Specifically, in the embodiment of the present application, the real-time simulation and verification step: Combine the SCADA system with machine learning algorithms to achieve online calibration and dynamic verification of the multi-physical field model, and predict the structural stability of the wind turbine under extreme conditions such as typhoons and turbulence, including:

[0119] Use the SCADA system to collect the operation status data of the offshore wind turbine in real time, including wind speed, wind direction, power output, and environmental parameters;

[0120] Based on the operation status data, achieve online calibration and dynamic verification of the multi-physical field model, predict the structural stability of the wind turbine under extreme conditions such as typhoons and turbulence, and evaluate the feasibility and effectiveness of the technology.

[0121] In the embodiment of the present application, the real-time simulation and verification step realizes the dynamic calibration of the multi-physical field model and the prediction of the wind turbine stability under extreme working conditions by integrating the real-time data acquisition of the SCADA system and machine learning algorithms.

[0122] The real-time data acquisition of the SCADA system includes: Data source: The SCADA system deployed on the offshore wind turbine collects the following multi-dimensional data in real time: Environmental parameters: wind speed, wind direction, environmental temperature, air pressure, wave height, sea current speed. Unit status: generator speed, power output, pitch angle, yaw angle, tower vibration frequency, bearing temperature. Structural response: blade strain, tower displacement, foundation inclination (supplemented by embedded sensors).

[0123] Data preprocessing: The sliding window algorithm is used to denoise the original data (such as wavelet transform to eliminate high-frequency interference), and the synchronization of multi-source data is ensured through time alignment.

[0124] Online calibration of the multi-physical field model can be assisted by machine learning: Use the LSTM network to predict model parameter deviations (such as aerodynamic damping coefficient, structural stiffness decay), and update the model parameters through online backpropagation. For example: When SCADA detects abnormal vibration, the LSTM outputs a correction coefficient to adjust the material fatigue parameters in FEM. Data assimilation technology: Use the Ensemble Kalman Filter (EnKF) to fuse real-time observation data and model output to reduce uncertainty.

[0125] Dynamic verification under extreme conditions mainly includes typhoon / turbulence simulation: Generate extreme wind fields based on the historical typhoon path database (such as JMA data), combine CFD to simulate the turbulent pulsation component, and inject it into the model as a boundary condition. Stability prediction: Short-term prediction (second level): Use a random forest classifier to determine whether the current state triggers a preset threshold (such as tower top displacement > safety limit). Long-term prediction (minute level): Based on the physical model + GAN (Generative Adversarial Network) to predict the structural cumulative damage (such as blade crack propagation rate).

[0126] Verification indicators: Quantitative: The MAE (Mean Absolute Error) between the model output and SCADA data < 5%, and the resonance frequency prediction error < 2%. Qualitative: Display the tower stress nephogram and the failure probability heat map through the digital twin visualization platform.

[0127] Feasibility assessment includes technical feasibility: Measured data shows that online calibration improves the load prediction accuracy of the model under typhoon conditions by 40% (compared with the uncalibrated model). The machine learning algorithm reduces the abnormal detection response time from 30s to 3s. Economic verification: Reduce unplanned downtime through predictive maintenance, and estimate that the annual operation and maintenance cost of a single unit is reduced by 15%.

[0128] Step 3: Optimize the design tool chain steps: Develop a multi-physical field coupling simulation software environment, conduct wake effect analysis, and optimize the layout of the wind turbine array.

[0129] Specifically, in the embodiment of the present application, the steps of optimizing the design tool chain: Develop a multi-physical field coupling simulation software environment, conduct wake effect analysis, and optimize the layout of the wind turbine array, including:

[0130] Based on the ANSYS Workbench tool, develop a multi-physical field coupling simulation software environment to achieve the coupled simulation analysis of the electromagnetic field - temperature field - flow field - structural mechanics field;

[0131] Through simulation, analyze the wake effects under different fan spacings, wind directions, and wind speeds, providing a theoretical basis for fan layout;

[0132] Use the particle swarm optimization algorithm to handle multi-objective optimization problems. Considering factors such as low noise and low cost comprehensively, maximize the power generation.

[0133] In the embodiment of this application, the steps of the optimization design tool chain realize the quantitative analysis of the fan wake effect and the global optimization of the array layout by constructing a multi-physics field coupling simulation environment and an intelligent optimization algorithm. It mainly includes the development of a multi-physics field coupling simulation software environment, wake effect simulation and data analysis, multi-objective optimization of the fan array, verification, and output steps.

[0134] Among them, the platform architecture for the development of the multi-physics field coupling simulation software environment is to build a modular simulation process based on ANSYS Workbench and integrate the following solvers: Flow field analysis: Fluent (RANS equation + turbulence model such as SST k-ω) simulates the wake vortex structure. Structural mechanics: Mechanical APDL calculates the dynamic load of the blade and the tower mode. Temperature field: Steady-state thermal analysis evaluates the influence of the heat generation of the gearbox on the structural deformation. Electromagnetic field: Maxwell simulates the electromagnetic loss of the generator and the coupling with the temperature field. Coupling mechanism: Unidirectional coupling: The flow field pressure distribution is mapped as the structural load (through the System Coupling module). Bidirectional coupling: Iterative solution of the temperature field - electromagnetic field (the convergence condition is set as the residual < 1e-5).

[0135] The wake effect simulation and data analysis include parametric modeling: Define the fan spacing (3D - 10D, D is the impeller diameter), wind direction deviation angle (0° - 45°), and wind speed (cut-in - cut-out wind speed) as input variables. Use DesignXplorer to automatically generate a DOE (design of experiments) matrix, reducing the number of simulation times by 70%. Wake feature extraction: Quantify the velocity deficit rate and the increase in turbulence intensity in the wake area through CFD post-processing. C wake = a·e -b(x / D) , where x is the downstream distance, and a and b are fitting coefficients.

[0136] In the multi-objective optimization of the fan array, the objective function of the optimization model is: Maximize the annual energy production (AEP): where P i is the power of the i-th fan, and V eff is the effective wind speed considering the wake. Minimize noise pollution: where d ij is the fan spacing, and c is the sound attenuation coefficient. Minimize the cable cost: Constraints: Safety distance ≥ 3D (to avoid tower shadow effect), turbulence intensity ≤ 15% (IEC 61400-1 standard). Optimization algorithm: Improved particle swarm optimization (PSO): Adaptive inertia weight: Constraint processing: The penalty function method is used to convert constraint violations into penalty terms in the objective function.

[0137] Verification and output steps: Typhoon conditions: Coupled fluid-structure simulation to verify the layout's ability to resist extreme winds (such as the wake superposition effect causing the blade root bending moment to ↑12%). Turbulent conditions: Compare array efficiency before and after optimization (after optimization, the wake loss dropped from 18% to 9%). Output results: Wind turbine coordinate matrix (GeoJSON format) and yaw strategy configuration file. Cost-energy sensitivity analysis report (Monte Carlo method to assess uncertainty).

[0138] Figure 4 A schematic diagram of a multi-physics coupling simulation and modeling device module for a fan system provided in one embodiment of the present application. Figure 4 A method and device for multi-physics coupling simulation and modeling of a wind turbine system is shown, the system includes a multi-physics collaborative modeling module 11, a real-time simulation and verification module 12, and an optimization design tool chain module 13 connected in sequence, wherein:

[0139] It can be understood that the multi-physics field collaborative modeling module 11 is used to construct a coupled simulation framework of electromagnetic field-temperature field-flow field-structural mechanics field, and adopts the finite volume method FVM and multi-body dynamics MBS to collaboratively solve and simulate the dynamic response of the fan under complex working conditions.

[0140] It can be understood that the real-time simulation and verification module 12 is used to combine the SCADA system with the machine learning algorithm to realize the online calibration and dynamic verification of the multi-physics field model and predict the structural stability of the wind turbine under typhoon and turbulent extreme conditions.

[0141] It can be understood that the optimization design tool chain module 13 is used to develop a multi-physics field coupling simulation software environment to perform wake effect analysis and wind turbine array layout optimization.

[0142] See also Figure 5 , Figure 5 This is an electronic terminal device 500 provided in an embodiment of the present application. Figure 5The electronic terminal device 500 shown at least includes the following parts: one or more processors 501, one or more input devices 502, one or more output devices 503, and one or more memories 504. The above-mentioned processors 501, input devices 502, output devices 503, and memories 504 communicate with each other through a communication bus 505. The memory 504 is used to store computer programs, and the computer programs include program instructions. The processor 501 is used to execute the program instructions stored in the memory 504. Among them, the processor 501 is configured to call program instructions to perform the functions of each module / unit in the above-mentioned device embodiments, for example Figure 1 the function of the module shown.

[0143] In the embodiment of the present application, a computer-readable storage medium includes instructions, and the instructions direct the device to execute the system as described in the first aspect. For example, the instructions direct the device to execute as Figure 1 a method and device for multi-physical field coupling simulation and modeling of a fan system shown in the steps.

[0144] It should be understood that in the embodiment of the present invention, the so-called processor 501 may be a central processing unit (CPU), and this processor may also be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), off-the-shelf programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or this processor may also be any conventional processor, etc. It should be noted that a part of the electronic device 500 in the above implementation manner is also implemented by a computer. In this case, the program for implementing this control function can be recorded on a computer-readable recording medium, and is implemented by reading the program recorded on this recording medium into the computer and executing it.

[0145] The input device 502 may include a touchpad, a fingerprint sensor (for collecting the fingerprint information and the direction information of the fingerprint of the user), a microphone, etc., and the output device 503 may include a display (such as an LCD), a speaker, etc.

[0146] It should be noted that the "computer" mentioned here refers to the computer built into the electronic device 500, which is a computer using hardware including an OS, peripheral devices, etc. In addition, the "computer-readable recording medium" refers to removable media such as floppy disks, magneto-optical disks, ROMs, CD-ROMs, and storage devices such as hard disks built into the computer.

[0147] Moreover, the "computer-readable recording medium" may include: a medium that stores a program dynamically for a short period of time, such as a communication line in the case of transmitting a program via a network such as the Internet or a communication line such as a telephone line; and a medium that stores a program for a fixed period of time, such as a volatile memory inside a computer of a server or a client in this case. In addition, the above program may be a program for implementing a part of the above functions, and may also be a program that can implement the above functions by combining with a program already recorded in a computer.

[0148] In addition, the electronic device 500 in the above embodiment can also be implemented as an aggregate (device group) composed of multiple devices. Each device constituting the device group may have some or all of the functions or function blocks of the electronic device 500 in the above embodiment. As the device group, it is sufficient to have all the functions or function blocks of the electronic device 500.

[0149] Those of ordinary skill in the art of this technology should recognize that the above embodiments are only used to illustrate the present application, rather than to limit the present application. As long as it is within the scope of the essential spirit of the present application, appropriate changes and variations made to the above embodiments fall within the scope of protection required by the present application.

Claims

1. A multi-physical field coupling simulation and modeling method for a fan system, characterized in that The method includes: Multi-physical field collaborative modeling step: Construct a coupled simulation framework for electromagnetic field - temperature field - fluid flow field - structural mechanics field, and use the finite volume method (FVM) and multi-body dynamics (MBS) to solve collaboratively to simulate the dynamic response of the wind turbine under complex working conditions; Real-time simulation and verification step: Combine the SCADA system and machine learning algorithms to achieve online calibration and dynamic verification of the multi-physical field model, and predict the structural stability of the wind turbine under extreme conditions such as typhoons and turbulence; Optimized design toolchain step: Develop a multi-physical field coupled simulation software environment to conduct wake effect analysis and optimize the layout of the wind turbine array.

2. A multi-physical field coupling simulation and modeling method for a fan system according to claim 1, characterized in that, The multi-physical field collaborative modeling step: Construct a coupled simulation framework for electromagnetic field - temperature field - fluid flow field - structural mechanics field, including: S1.1, Construct a three-dimensional simulation model of the offshore wind turbine system through ANSYS according to the structure of the offshore wind turbine system; S1.2, Set the electromagnetic field for the three-dimensional simulation model according to the three-dimensional simulation model; S1.3, Set the temperature field - fluid flow field for the three-dimensional simulation model according to the three-dimensional simulation model; S1.4, Set the material properties, geometric shape, environmental characteristics, and external loading conditions of the wind turbine device according to the three-dimensional simulation model of the wind turbine system, and set the structural mechanics field for the three-dimensional spatial model of the wind turbine device structure to obtain the structural mechanics field during the operation process of the wind turbine device.

3. A multi-physical field coupling simulation and modeling method for a fan system according to claim 2, characterized in that, The construction of the coupled simulation framework for electromagnetic field - temperature field - fluid flow field - structural mechanics field further includes: S2.1, Establish an electromagnetic field model, determine the electromagnetic field calculation parameters and solution type, write them into the electromagnetic field analysis file, and calculate the equipment loss; S2.2, Establish a temperature field - fluid flow field model, transfer the equipment loss of the electromagnetic field into the temperature field - fluid flow field analysis file, clarify the temperature field - fluid flow field calculation parameters and coupled solution, and obtain the flow velocity, thermal response, and thermal distribution; S2.3, Determine whether the temperature field - fluid flow field converges. If so, the solution ends; if not, calculate the temperature correction according to the thermal response and thermal distribution, transfer the temperature correction into the electromagnetic field analysis file, and iteratively execute S2.1 until the convergence error is within the allowable range; S2.4, Establish a structural mechanics field model, couple the electromagnetic field model, temperature field - fluid flow field, and structural mechanics field models to form a multi-physical field coupled model, and transfer the temperature correction into the structural mechanics field analysis file for subsequent simulation analysis.

4. A multi-physical field coupling simulation and modeling method for a fan system according to claim 3, characterized in that, The use of the finite volume method (FVM) and multi-body dynamics (MBS) to solve collaboratively to accurately simulate the dynamic response of the wind turbine under complex working conditions includes: Preprocessing stage: Define the geometric shape of the computational domain, generate grids, discretize the computational domain into small, non-overlapping sub-regions of control volumes, and determine the fluid properties and appropriate boundary conditions for the boundaries of the computational domain according to the physical characteristics of the multi-physical field model; Solution stage: Integrate the hydrodynamic control equations for all cells in the computational domain, convert the obtained integral equations into algebraic equations through the discretization process, and use an appropriate numerical scheme to obtain the solutions of the algebraic equations in each cell; Result analysis phase: Use the SCADA system to obtain data, combine machine learning methods for analysis, and visualize it.

5. A multi-physical field coupling simulation and modeling method for a fan system according to claim 4, characterized in that, The solution stage is: integrating the fluid mechanics governing equations of all units in the computational domain, converting the obtained integral equations into a set of algebraic equations through a discretization process, and obtaining the solutions of the algebraic equations in each unit using an appropriate numerical scheme, including: The continuous equations are transformed into discrete algebraic equations using the finite volume method, and the computational domain is discretized in space; the computational domain is divided into multiple control volumes and multiple discrete time steps to calculate the simulation time; The control volume cells that make up the grid are connected by faces. A cell with a central node P has six neighboring nodes, labeled W, E, N, B, T, S, and six neighboring faces; The discretization process of attribute φ is as follows: where t is time, φ is a variable with the property of velocity / temperature, U is velocity, ρ is density, Γ φ is the diffusion coefficient, S Φ is the source term, Δ is the Laplacian operator, for vector field analysis, describes the divergence of the field, the term on the left represents the rate of increase of φ, where the convection term represents the net rate of the φ property outside the control volume element, the diffusion term on the right represents the rate of increase of φ due to diffusion, and the contribution of the source term; The finite volume discretization is calculated by integrating the control volume and time as follows: Where Vp represents the control volume, t represents the time, and Δt represents the change in time; By applying Gauss’ theorem, the spatial integral is converted from a volume integral to a surface integral, and the finite volume discretized equations are solved by considering appropriate numerical schemes for each specific term; The gradient term in the volume integral is converted to a surface integral by Gauss’s theorem, and the value of the gradient term is therefore approximated with second-order accuracy by summing the property values over all surfaces of the control volume; In the orthogonal grid, it is defined that: where d represents a vector, which is the distance between the center P of the control volume cell of interest and the center N of its adjacent cell, and φ N , φ P represent the attribute values of points N and P respectively; For non-orthogonal networks, the diffusion term is divided into two terms, where the second term is a correction for non-orthogonality, as follows: S = Δ + k, where the vector Δ represents a vector parallel to the vector k, S represents the area vector outside the control volume element, φ f represents the property value on the surface, f represents, and k represents the adjustment coefficient.

6. A multi-physical field coupling simulation and modeling method for a fan system according to claim 5, characterized in that, The real-time simulation and verification steps: combining the SCADA system with the machine learning algorithm to realize the online calibration and dynamic verification of the multi-physics field model, and predict the structural stability of the wind turbine under typhoon and turbulent extreme conditions, including: Use the SCADA system to collect real-time operating status data of offshore wind turbines, including wind speed, wind direction, power output, and environmental parameters; Based on the operating status data, online calibration and dynamic verification of the multi-physics field model are implemented, the structural stability of the wind turbine under typhoon and turbulent extreme conditions is predicted, and the feasibility and effect of the technology are evaluated.

7. A multi-physical field coupling simulation and modeling method for a fan system according to claim 6, characterized in that The optimization design tool chain steps are: developing a multi-physics field coupling simulation software environment, performing wake effect analysis, and optimizing the layout of wind turbine arrays, including: Based on the ANS YS Workbench tool, develop a multi-physics field coupling simulation software environment to realize the coupling simulation analysis of electromagnetic field, temperature field, flow field and structural mechanics field; Through simulation analysis of the wake impact under different wind turbine spacing, wind direction and wind speed conditions, a theoretical basis is provided for wind turbine layout; The particle swarm optimization algorithm is used to deal with multi-objective optimization problems, comprehensively considering low noise and low cost factors to obtain the maximum power generation.

8. A multi-physical field coupling simulation and modeling device for a fan system, which is applied to a multi-physical field coupling simulation and modeling method for a fan system according to any one of claims 1 to 7, characterized in that The device comprises: Multi-physics field collaborative modeling module: builds a coupled simulation framework of electromagnetic field, temperature field, flow field and structural mechanics field, and uses the finite volume method FVM and multi-body dynamics MBS to collaboratively solve and simulate the dynamic response of the fan under complex working conditions; Real-time simulation and verification module: Combining the SCADA system with machine learning algorithms to achieve online calibration and dynamic verification of multi-physical field models, and predict the structural stability of wind turbines under extreme conditions such as typhoons and turbulence; Optimized design toolchain module: Develop a multi-physical field coupled simulation software environment to conduct wake effect analysis and optimize the layout of wind turbine arrays.

9. An electronic device, characterized in that, Including: A processor; A memory for storing executable instructions of the processor; Wherein, the processor is configured to implement a multi-physical field coupled simulation and modeling method for a wind turbine system as described in any one of claims 1 to 7 when executing the instructions.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a program, and the program instructs the device to execute a multi-physical field coupled simulation and modeling method for a wind turbine system as described in any one of claims 1 to 7.

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