Simulation test method and system for water conservancy and hydropower equipment

Abstract

The invention discloses a water conservancy and hydropower equipment simulation test method which is characterized by comprising the following steps: S1, twin construction and data acquisition, S2, twin dynamic evolution, S3, model construction and state prediction, S4, multi-field coupling simulation test, and S5, result analysis and strategy feedback. A water conservancy and hydropower equipment simulation test system is characterized by comprising a digital twinborn body module, an artificial intelligence auxiliary simulation module, a multi-physics field coupling modeling module and a result analysis and feedback module. According to the method, through high-precision modeling and multi-source data fusion, in combination with adaptive filtering and a deep reinforcement learning algorithm, the error between a digital twinborn body and physical equipment is smaller than 2%, the dynamic evolution period is shortened to 3 minutes, the running state of the equipment can be reproduced in real time, a precise data base is provided for fault early warning, the on-site inspection frequency is reduced, and the labor cost is reduced by 30% or above.

Description

Technical Field

[0001] This invention relates to the field of simulation testing technology, and in particular to a simulation testing method and system for water conservancy and hydropower equipment. Background Technology

[0002] The water conservancy and hydropower equipment simulation and testing system is an intelligent system that integrates technologies such as digital twins, artificial intelligence, and multiphysics coupling modeling. It is used to simulate equipment operating conditions, predict faults, and optimize designs.

[0003] In the field of water conservancy and hydropower, equipment operates in complex environments with extremely high reliability requirements, posing numerous challenges to traditional simulation testing methods. Existing technologies struggle to accurately reflect the multi-field coupling effects of equipment under actual operating conditions, leading to blind spots in design optimization. Furthermore, equipment condition monitoring relies on manual inspections and experience-based judgment, lacking real-time performance and failing to provide early warnings of potential faults. Moreover, the application of digital twin technology in water conservancy equipment largely remains at the static modeling level, lacking dynamic response capabilities to real-time operational data and hindering accurate mapping between the twin and the physical equipment. With the development of intelligent sensing, deep learning, and multi-physics simulation technologies, combining dynamic data-driven digital twin evolution, AI-assisted prediction, and multi-field coupling modeling to construct an intelligent simulation testing system covering the entire equipment lifecycle has become a key technological bottleneck for improving the operational safety and efficiency of water conservancy and hydropower equipment. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by proposing a simulation testing method and system for water conservancy and hydropower equipment.

[0005] To achieve the above objectives, the present invention adopts the following technical solution:

[0006] A simulation testing method for water conservancy and hydropower equipment includes the following steps: S1, twin construction and data acquisition; S2, twin dynamic evolution; S3, model construction and state prediction; S4, multi-field coupled simulation testing; S5, result analysis and strategy feedback.

[0007] S1. Digital Twin Construction and Data Acquisition: Construct an initial digital twin based on equipment design parameters and working environment, and collect equipment operation data in real time through sensors;

[0008] S2. Dynamic evolution of the digital twin: Preprocess the collected data, remove noise and outliers, map the processed data to the digital twin, and update its state parameters using machine learning algorithms to achieve dynamic evolution;

[0009] S3. Model Building and Status Prediction: Store real-time and historical data, extract key features, and use machine learning algorithms to build simulation models to predict the operating status of equipment.

[0010] S4. Multi-field coupling simulation test: Select at least two physical fields, establish a multi-physics coupling model using finite element analysis or computational fluid dynamics, and conduct simulation tests in combination with the simulation model;

[0011] S5. Results Analysis and Strategy Feedback: Analyze test results using risk assessment and performance optimization algorithms, generate optimization strategies, and feed them back to the actual equipment operation and management system.

[0012] Preferably, when preprocessing real-time data, the filtering algorithm uses Kalman filtering or mean filtering, and the statistical method uses box plot method or Z-score method to identify and remove outliers.

[0013] Preferably, when constructing the simulation model, the neural network algorithm uses a multilayer perceptron, a convolutional neural network, or a recurrent neural network, and the support vector machine algorithm uses a linear kernel, a polynomial kernel, or a radial basis function for model training.

[0014] Preferably, when establishing the multiphysics coupling model, if the fluid dynamics field and the solid dynamics field are coupled, the effects of fluid pressure and viscous force on the solid structure and the influence of solid deformation on the fluid flow boundary are considered; if the fluid dynamics field and the thermodynamics field are coupled, the influence of convective heat transfer and heat transfer of fluid flow on fluid properties is considered; if the solid dynamics field and the thermodynamics field are coupled, the influence of thermal expansion and thermal stress on the solid structure is considered.

[0015] Preferably, when optimizing the generation equipment strategy, the risk assessment algorithm adopts fault tree analysis, event tree analysis or Bayesian network analysis, and the performance optimization algorithm adopts genetic algorithm, particle swarm algorithm or simulated annealing algorithm.

[0016] A simulation and testing system for water conservancy and hydropower equipment, characterized in that it includes: a digital twin module, an artificial intelligence-assisted simulation module, a multiphysics coupling modeling module, and a result analysis and feedback module;

[0017] The digital twin module includes modeling, data acquisition, data processing, and model updating units, used to build, acquire, process, and update the state of the digital twin.

[0018] The AI-assisted simulation module includes data storage, feature extraction, model building, and prediction units, used to store data, extract features, build models, and predict equipment status.

[0019] The multiphysics coupling modeling module includes units for physics selection, model building, and simulation testing, used to select physics, build coupling models, and perform tests.

[0020] The results analysis and feedback module includes risk analysis, performance analysis, strategy generation, and feedback execution units, which are used to analyze risks and performance, generate strategies, and provide feedback.

[0021] The modeling unit of the digital twin module uses 3D modeling software to construct the initial digital twin, and the sensors of the data acquisition unit transmit data through industrial Ethernet, CAN bus or wireless communication.

[0022] The data storage unit of the AI-assisted simulation module uses a relational database or a non-relational database to store data, and the feature extraction unit and model building unit use a machine learning framework to implement their functions.

[0023] The model building unit of the multiphysics coupling modeling module uses professional finite element analysis software or computational fluid dynamics software to build a multiphysics coupling model, and the simulation test unit runs the test on the software platform and records the results.

[0024] The present invention has the following beneficial effects:

[0025] 1. By using high-precision modeling and multi-source data fusion, combined with adaptive filtering and deep reinforcement learning algorithms, the error between the digital twin and the physical equipment is less than 2%, the dynamic evolution cycle is shortened to 3 minutes, the equipment operating status can be reproduced in real time, providing an accurate data basis for fault early warning, reducing the frequency of on-site inspections, and reducing labor costs by more than 30%.

[0026] 2. By leveraging multimodal deep learning networks to extract multidimensional features and combining federated learning to protect data privacy, a fusion model is constructed to achieve a 97% prediction accuracy rate, with fault warnings occurring 15 days in advance. It can identify early faults that are difficult to detect using traditional methods, and the edge-cloud collaborative architecture enables millisecond-level online prediction, improving early warning response efficiency by 50%.

[0027] 3. Based on multi-field coupling algorithms to simulate physical field interactions under complex working conditions, combined with intelligent optimization strategies, equipment efficiency is improved by 8% and fatigue life is extended by 30%. Accurate analysis of multi-field coupling effects avoids design blind spots, reduces the number of physical prototype iterations, shortens the R&D cycle by 40%, and reduces material costs by 20%.

[0028] 4. Through the closed-loop linkage of digital twins, AI simulation, multi-field coupling, and result analysis modules, an intelligent solution covering the entire equipment lifecycle is formed. The data-driven decision-making model improves the accuracy of risk assessment to 92%, and the optimization strategy is automatically fed back to the operating system, achieving an overall operating efficiency improvement of 15%-20% and a 60% reduction in unplanned downtime. Attached Figure Description

[0029] Figure 1This is a schematic diagram illustrating the steps of a simulation testing method for water conservancy and hydropower equipment proposed in this invention.

[0030] Figure 2 This is a schematic diagram of a simulation and testing system for water conservancy and hydropower equipment proposed in this invention.

[0031] Figure 3 This is a schematic diagram of the structure of a digital twin module of a water conservancy and hydropower equipment simulation and testing system proposed in this invention;

[0032] Figure 4 This is a schematic diagram of the structure of an artificial intelligence-assisted simulation module in a water conservancy and hydropower equipment simulation and testing system proposed in this invention;

[0033] Figure 5 This is a schematic diagram of the structure of a multiphysics coupling modeling module in a simulation and testing system for water conservancy and hydropower equipment proposed in this invention.

[0034] Figure 6 This is a schematic diagram of the result analysis and feedback module of a simulation testing system for water conservancy and hydropower equipment proposed in this invention. Detailed Implementation

[0035] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0036] Example 1: Twin Construction and Dynamic Data Acquisition

[0037] Comprehensive High-Precision Digital Twin Construction: For the turbine-generator unit of a large-scale water conservancy project, the initial digital twin construction was carried out using CreoParametric 3D modeling software. In addition to considering the mechanical structure, fluid channels, microstructure of key components, electromagnetic properties, and thermal conductivity, installation process information was also incorporated. For example, in modeling the generator windings, the impact of the winding method, insulation layer thickness, and material on electrical performance and heat dissipation was accurately simulated. For the turbine blades, the distribution of internal micro-defects resulting from the casting process was considered, and the simulation of blade performance was further refined through microstructure modeling.

[0038] Multi-sensor fusion data acquisition: A variety of high-precision sensors are used for data acquisition. In key parts of the turbine, in addition to fiber optic vibration sensors (FBG-V-100, accuracy ±0.05mm / s), pressure sensors (PT-500, accuracy ±0.2%FS), and infrared thermal imaging sensors (IR-T-200, accuracy ±0.3℃), strain gauge sensors (ST-200, accuracy ±0.1%) are added to monitor stress changes in the blades and shaft. Sensor data transmission uses a combination of industrial Ethernet and 5G wireless communication. For data with extremely high real-time requirements, such as vibration data, industrial Ethernet is prioritized; while for data from more dispersed sensors, such as some temperature sensor data, 5G wireless communication is used. The data acquisition server uses an Advantech IPC-610L industrial computer to meet the needs of high-speed data storage and processing.

[0039] Data preprocessing and twin update:

[0040] Intelligent composite data preprocessing: The acquired data is first subjected to noise removal using an adaptive Kalman filter algorithm. This algorithm incorporates fuzzy logic control, enabling it to more intelligently adjust the filtering parameters based on the dynamic characteristics of the data. The core formula of the Kalman filter is as follows.

[0041] Prediction steps:

[0042] Covariance prediction:

[0043] Update steps:

[0044] Kalman gain:

[0045] Status Update:

[0046] Covariance update: P k|k =(I-KkHk)P k|k-1

[0047] in, Here, F is the state estimate, B is the control input matrix, u is the control input, P is the covariance matrix, Q is the process noise covariance, H is the observation matrix, z is the observation value, R is the observation noise covariance, K is the Kalman gain, and I is the identity matrix.

[0048] Simultaneously, the Isolation Forest algorithm, the Local Outlier Factor (LOF) algorithm, and the Z-score method are used for triple outlier identification. The Z-score calculation formula is: Where x is the value of the data point, μ is the mean of the dataset, and σ is the standard deviation of the dataset. For stress data, the Isolation Forest algorithm is first used for preliminary screening to identify potential outliers. Then, the LOF algorithm is used to further identify data points with local density anomalies. Finally, the Z-score method is used for precise judgment. For outlier data, not only are they marked and recorded, but a source tracing procedure is also automatically initiated to find sensor malfunctions or equipment abnormalities that may have caused the anomalies.

[0049] Deep reinforcement learning adaptive update: Processed data is mapped to a digital twin, and the state parameters of the twin are updated using the Deep Deterministic Policy Gradient (DDPG) algorithm combined with expert knowledge rules. The DDPG algorithm is based on the policy network μ(s|θ) μ ), and the value network Q(s,a|θ) Q The policy network is used to learn actions a = μ(s|θ). u The value network is used to evaluate the value Q(s,a|θ) of state-action pairs. Q ).

[0050] Its update formula is as follows:

[0051] Value network update:

[0052] Target value calculation: y i =ri+γQ'(s i +1, μ'(s i +1θ μ' )|θ Q' )

[0053] Loss function:

[0054] Update policy gradient:

[0055]

[0056] Where r is the reward, γ is the discount factor, N is the batch size, and θ is the value of the reward. μ and θ Q respectively

[0057] These are the parameters of the policy network and the value network, θ μ and These are the parameters of the target network. When a sudden increase in the vibration amplitude of the turbine is detected, the algorithm, in addition to automatically adjusting parameters such as blade stiffness and damping, will also determine whether it may be due to an abnormal water flow impact angle based on expert knowledge rules, and adjust the water flow simulation parameters accordingly. A dynamic evolution is completed every 3 minutes. By establishing a real-time error evaluation model, the simulation results of the digital twin are continuously compared with the operating data of the actual equipment to ensure that the error between the twin and the physical equipment is <2%.

[0058] Digital twin module

[0059] The visual intelligent modeling unit, in addition to supporting CAD model import and automatic modeling, also features intelligent recommendation and visual interactive modeling capabilities. After the user inputs the basic parameters of the hydro-generator unit, the system intelligently recommends parameter settings and structural layouts for the initial twin based on historical successful cases and industry best practices. Simultaneously, within the visual modeling interface, users can parametrically modify and optimize the model through gestures, voice commands, and other methods. For example, users can adjust the blade curvature using voice commands, and the system will display the modified model's effect and changes in relevant performance indicators in real time.

[0060] The data processing and source tracing unit integrates an adaptive Kalman filter algorithm library, an isolated forest-LOF-Z-score anomaly detection module, and a data source tracing module. This unit monitors the data processing process in real time, recording the processing status of each data point in detail. It automatically generates detailed data cleaning reports, including the effectiveness of noise removal, the number and distribution of outliers, and the source tracing results of outliers. Simultaneously, it provides data visualization and analysis tools, allowing users to intuitively view the data processing process and results.

[0061] The intelligent model update unit incorporates the DDPG training engine and an expert knowledge rule base, employing a distributed computing architecture combined with cloud computing resources to accelerate the training process. This architecture consists of multiple local computing nodes and cloud servers. Local computing nodes handle real-time data and some computational tasks, while cloud servers provide powerful computing resources to support the training and updating of complex models. It features model self-evaluation and adaptive adjustment capabilities, establishing a model performance evaluation index system to periodically assess the simulation results of the digital twin. When errors exceed a certain range, it automatically adjusts training parameters, update strategies, and expert knowledge rules to ensure the accuracy and timeliness of the twin's state updates.

[0062] Comparison table of data between Example 1 and the comparative example:

[0063] Comparison Dimensions Comparative Example Example Data differences Twin error 10％-15％ ＜2％ Reduce by 80%+ Inspection frequency (daily) 2 times 0.5 times Reduce by 75% Fault location time 2-4 hours <15 minutes shorten by 90%+

[0064] Example 2:

[0065] Data storage and feature extraction

[0066] Distributed heterogeneous data storage architecture: This architecture utilizes the distributed time-series database TimescaleDB combined with object storage Ceph and graph database Neo4j to store real-time data (sampling frequency increased to 1000Hz) and historical data (storage period extended to 10 years). TimescaleDB stores real-time time-series data such as vibration, pressure, and temperature data. Ceph stores large amounts of historical data and related files, such as equipment maintenance records and fault reports. Neo4j stores data on the relationships between various components of the equipment, such as the electromagnetic coupling between generator stators and rotors, and the fluid-solid coupling between turbine blades and volutes. The data storage system employs multi-replica storage, automatic failover, and data backup and recovery mechanisms to ensure high availability, reliability, and recoverability of the data. Simultaneously, it provides data compression and encryption functions, using the LZ4 compression algorithm to compress data with a compression ratio exceeding 50%; and using the AES encryption algorithm to encrypt data, ensuring data security during transmission and storage.

[0067] Multimodal deep fusion feature extraction: Feature extraction is performed using a multimodal deep learning network, combining Convolutional Neural Networks (CNN), Long Short-Term Memory Networks (LSTM), Graph Neural Networks (GNN), and an attention mechanism. For vibration signals, CNN is used to extract their frequency domain features, and an attention mechanism is used to highlight important spectral features. The calculation formula for the convolutional layer in the CNN is as follows:

[0068] in It is the j-th feature map in the l-th layer. It is the i-th feature map in the (l-1)-th layer. It is a convolution kernel. This represents the bias, and * indicates a convolution operation. For temperature data, LSTM is used to extract its temporal features, and an attention mechanism is combined to capture key time points of temperature changes. The calculation formula for an LSTM unit is as follows:

[0069] Input gate: i t =σ(W ii x t +W hi h t-1 +b i )

[0070] Forgotten Gate: f t =σ(W if x t +W hf h t-1 +b f )

[0071] Cell status update:

[0072] Cell state:

[0073] Output gate: o t =σ(W io x t +W ho h t-1 +b o )

[0074] Hidden state: h t =o t ⊙tanh(C t )

[0075] Where, x t It is the input, h t It is in a hidden state. Here, W represents the cell state, b is the weight matrix, b is the bias vector, σ is the sigmoid function, tanh is the hyperbolic tangent function, and ⊙ represents element-wise multiplication. For the relationships between the various components of the device, a GNN is used to model the connections, and an attention mechanism is used to emphasize important connecting edges and nodes. Simultaneously, features from different modalities are fused, and comprehensive features are further extracted through fully connected layers.

[0076] Model building and state prediction

[0077] A multi-model prediction system is constructed: a comprehensive prediction model integrating deep residual networks (ResNet), convolutional neural networks (CNN), long short-term memory networks (LSTM), and multilayer perceptrons (MLP) based on attention mechanisms is built. Input features include 50-dimensional parameters such as vibration amplitude, pressure fluctuation, temperature gradient, component correlation, and equipment operating time. Outputs include equipment health status level (normal / warning / fault), fault type (e.g., bearing wear, blade crack, winding short circuit), and fault probability. Model training employs a combination of transfer learning, federated learning, and ensemble learning. First, the model is pre-trained using public datasets to learn general feature representations. Then, it is fine-tuned using internal enterprise data to adapt the model to the specific operating characteristics of the equipment. During federated learning, data from each participant is trained locally, with only the model's gradient information uploaded to a central server for aggregation, protecting data privacy. Finally, ensemble learning is used to fuse multiple trained models, improving the model's prediction accuracy and stability. The model achieves a prediction accuracy of over 97% and can provide early warnings of potential faults such as bearing wear and blade cracks up to 15 days in advance.

[0078] Artificial intelligence-assisted simulation module

[0079] Intelligent Data Storage and Management Unit: The distributed database cluster employs multi-replica storage, automatic failover, and data backup and recovery mechanisms to ensure high availability, reliability, and recoverability of data. It also provides data compression and encryption functions to reduce storage space usage and ensure data security. It features intelligent data retrieval and analysis capabilities, allowing users to quickly find the data they need through keyword search and conditional filtering. Furthermore, the system automatically classifies and labels data, facilitating subsequent feature extraction and model training.

[0080] Multimodal Feature Extraction and Fusion Unit: Integrating both TensorFlow and PyTorch frameworks, it supports flexible construction and training of multimodal deep learning networks. It features feature selection and dimensionality reduction capabilities, automatically selecting important features through methods such as correlation analysis, principal component analysis, and feature importance ranking to reduce feature dimensionality and improve model training efficiency and accuracy. Simultaneously, it provides feature visualization tools, allowing users to intuitively view the distribution and importance of features across different modalities.

[0081] Intelligent Prediction and Early Warning Unit: Employing an edge-cloud collaborative computing architecture, this unit deploys some model inference tasks to edge computing nodes, such as industrial-grade edge computing gateways (Huawei AR500 series) installed at the equipment site, achieving millisecond-level online prediction. Simultaneously, the model is continuously optimized and updated via a cloud platform. Early warning information is communicated to relevant personnel in real time through various means, including SMS, email, app push notifications, and audible and visual alarms. When the model predicts a potential equipment failure risk, the system automatically generates a detailed fault diagnosis report and handling suggestions, and arranges maintenance personnel and materials through an intelligent scheduling system.

[0082] Comparison table of data between Example 2 and the comparative example:

[0083]

[0084]

[0085] Example 3:

[0086] Multi-field coupling model establishment

[0087] Refined multi-field coupled scenario modeling: Taking a water turbine runner as an example, coupled fluid dynamics fields (water flow range 200-1000 m³ / h). 3 The simulation model comprises a multiphysics field (considering the evolution of the material's microstructure and fatigue crack propagation), a solid mechanical field (considering convective heat transfer, heat conduction, and radiative heat transfer between the fluid and solid), and an electromagnetic field (considering the electromagnetic induction and eddy current losses of the generator). A coupled model was established using the professional multiphysics simulation software COMSOL Multiphysics and ANSYS Workbench.

[0088] High-precision multiphysics simulation: Large eddy simulation (LES) combined with the wall function method is used in the fluid domain to more accurately simulate turbulent flow. It considers fluid compressibility, non-Newtonian properties, cavitation, and multiphase flow characteristics. For high-velocity and complex flow conditions, it can more accurately calculate fluid pressure, velocity, temperature, and cavitation distribution. The fluid continuity equation is: The momentum equation is: Where ρ is the fluid density, v is the fluid velocity, p is the pressure, τ is the viscous stress tensor, and g is the gravitational acceleration. Nonlinear finite element analysis is employed in the solid domain, considering the material's elastoplastic deformation, fatigue damage accumulation, microstructure evolution, and crack propagation. For the blades of a water turbine runner, the stress distribution, deformation, fatigue life, and crack propagation path under different operating conditions are simulated. The nonlinear constitutive relations, microstructure evolution model, and crack propagation criteria of the material are considered. The equilibrium equations in solid mechanics are: Where σ is the stress tensor and f is the body force. In the thermodynamic field, the heat conduction equation, convection heat transfer equation, and radiation heat transfer equation are coupled for solution, considering the heat exchange between the fluid and the solid and the influence of internal heat sources. The heat conduction equation is: Where c p Here, k is the specific heat capacity, k is the thermal conductivity, T is the temperature, and q is the internal heat source. In the electromagnetic field, Maxwell's equations and circuit equations are coupled for solution, considering the generator's electromagnetic induction, eddy current losses, and magnetic field distribution. Faraday's law of electromagnetic induction in Maxwell's equations is: Ampere's law is Where E is the electric field strength, B is the magnetic induction intensity, H is the magnetic field strength, J is the current density, and D is the electric displacement vector.

[0089] A precise multi-field coupling algorithm: The coupling interface utilizes a fluid-structure-thermo-electromagnetic multi-field coupling algorithm to accurately transfer physical quantities such as pressure, temperature, displacement, and electromagnetic fields. The coupling state is updated every 30 steps to ensure the convergence and accuracy of the coupled model. A combination of partitioned coupling and global coupling is employed to partition the coupling regions of different physical fields, improving computational efficiency. Simultaneously, global coupling ensures the physical consistency of the entire model.

[0090] Simulation Test and Result Analysis

[0091] Multi-condition, full-factor simulation testing: Simulation tests are conducted under various operating conditions (such as different flow rates, different speeds, different temperatures, and different electromagnetic loads) to record parameters such as stress distribution, deformation, temperature field distribution, fluid flow characteristics, electromagnetic field distribution, and fatigue life of the impeller. Monte Carlo simulation, Latin hypercube sampling, and response surface methodology are used to analyze the model's uncertainties and evaluate the reliability of the simulation results. Considering uncertainties in material properties, errors in boundary conditions, and deviations in manufacturing processes, simulations are performed through multiple random samplings to obtain the probability distribution and confidence intervals of the simulation results.

[0092] Multi-objective optimization design strategy: Based on test results, a multi-objective optimization strategy combining genetic algorithm, simulated annealing algorithm, and particle swarm optimization algorithm is used to optimize the blade shape, material selection, and winding design of the turbine runner. The optimization objectives are to improve runner efficiency, extend fatigue life, reduce electromagnetic loss, and reduce cavitation. The optimal design scheme is searched using a multi-objective optimization algorithm. After simulation verification, the runner efficiency is improved by 8%, fatigue life is extended by 30%, electromagnetic loss is reduced by 15%, and cavitation is significantly reduced.

[0093] Multiphysics Coupled Modeling Module

[0094] The intelligent physics selection and configuration unit provides a rich library of physics fields, supporting user-defined combinations and coupling methods. It also offers multi-field coupling templates for typical hydraulic and hydropower equipment, such as fluid-solid-thermal-electromagnetic coupling templates for pumps, sluices, and generators, facilitating rapid modeling. Furthermore, it features an intelligent recommendation function that automatically suggests suitable combinations and coupling methods based on user-input equipment information and operating conditions.

[0095] Collaborative Modeling and Mesh Optimization Unit: Integrates interfaces with multiple professional software such as COMSOL and ANSYS, supporting multi-scale, multi-physics collaborative modeling. It features adaptive mesh generation and optimization, automatically adjusting mesh density based on the model's geometry, physical properties, and computational accuracy requirements. Employing an octree mesh generation algorithm and mesh refinement technology, it uses finer meshes in key areas such as the blade surfaces, edges, cavitation regions, and stress concentration areas of the turbine runner to accurately capture changes in the physical field.

[0096] High-performance simulation testing and results analysis unit: Employs a high-performance computing cluster to accelerate the solution process and supports large-scale parallel computing. Test results are automatically generated into 3D visualization animations, 2D graphs, and detailed data reports, facilitating result analysis and decision-making for users. It also features data mining and machine learning analysis capabilities, enabling the discovery of potential patterns and problems through the mining and analysis of simulation result data, providing decision support for the optimized design and operation management of equipment.

[0097] Comparison table of data between Example 3 and the comparative example:

[0098] Comparison type Comparative Example Example Data differences Design iteration count 8-10 times 3-4 times Reduce by 60% Equipment efficiency improvement 2％-3％ 8％ Increase by 266%+ Material utilization rate 65％-70％ 85％+ Increase by 21%+

[0099] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A simulation testing method for water conservancy and hydropower equipment, characterized in that, Includes the following steps: S1. Twin construction and data acquisition; S2. Twin dynamic evolution; S3. Model construction and state prediction; S4. Multi-field coupling simulation test; S5. Result analysis and strategy feedback: S1. Digital Twin Construction and Data Acquisition: Construct an initial digital twin based on equipment design parameters and working environment, and collect equipment operation data in real time through sensors; S2. Dynamic evolution of the digital twin: Preprocess the collected data, remove noise and outliers, map the processed data to the digital twin, and update its state parameters using machine learning algorithms to achieve dynamic evolution; S3. Model Building and Status Prediction: Store real-time and historical data, extract key features, and use machine learning algorithms to build simulation models to predict the operating status of equipment. S4. Multi-field coupling simulation test: Select at least two physical fields, establish a multi-physics coupling model using finite element analysis or computational fluid dynamics, and conduct simulation tests in combination with the simulation model; S5. Results Analysis and Strategy Feedback: Analyze test results using risk assessment and performance optimization algorithms, generate optimization strategies, and feed them back to the actual equipment operation and management system.

2. The simulation testing method for water conservancy and hydropower equipment according to claim 1, characterized in that, When preprocessing real-time data, the filtering algorithm uses Kalman filtering or mean filtering, and the statistical method uses box plot method or Z-score method to identify and remove outliers.

3. The simulation testing method for water conservancy and hydropower equipment according to claim 1, characterized in that, When constructing the simulation model, the neural network algorithm uses a multilayer perceptron, convolutional neural network, or recurrent neural network, and the support vector machine algorithm uses a linear kernel, polynomial kernel, or radial basis function for model training.

4. The simulation testing method for water conservancy and hydropower equipment according to claim 1, characterized in that, When establishing a multiphysics coupling model, if the fluid dynamics field and the solid dynamics field are coupled, the effects of fluid pressure and viscous force on the solid structure and the influence of solid deformation on the fluid flow boundary are considered; if the fluid dynamics field and the thermodynamics field are coupled, the influence of convective heat transfer and heat transfer of fluid flow on fluid properties are considered; if the solid dynamics field and the thermodynamics field are coupled, the influence of thermal expansion and thermal stress on the solid structure is considered.

5. The simulation testing method for water conservancy and hydropower equipment according to claim 1, characterized in that, When formulating the optimization strategy for generating equipment, the risk assessment algorithm uses fault tree analysis, event tree analysis, or Bayesian network analysis, and the performance optimization algorithm uses genetic algorithm, particle swarm optimization algorithm, or simulated annealing algorithm.

6. A simulation testing system for water conservancy and hydropower equipment, characterized in that, include: Digital twin module, AI-assisted simulation module, multiphysics coupling modeling module, and results analysis and feedback module; The digital twin module includes modeling, data acquisition, data processing, and model updating units, used to build, acquire, process, and update the state of the digital twin. The AI-assisted simulation module includes data storage, feature extraction, model building, and prediction units, used to store data, extract features, build models, and predict equipment status. The multiphysics coupling modeling module includes units for physics selection, model building, and simulation testing, used to select physics, build coupling models, and perform tests. The results analysis and feedback module includes risk analysis, performance analysis, strategy generation, and feedback execution units, which are used to analyze risks and performance, generate strategies, and provide feedback.

7. The water conservancy and hydropower equipment simulation and testing system according to claim 6, characterized in that, The modeling unit of the digital twin module uses 3D modeling software to construct the initial digital twin, and the sensors of the data acquisition unit transmit data through industrial Ethernet, CAN bus or wireless communication.

8. The simulation and testing system for water conservancy and hydropower equipment according to claim 6, characterized in that, The data storage unit of the AI-assisted simulation module uses a relational database or a non-relational database to store data, and the feature extraction unit and model building unit use a machine learning framework to implement their functions.

9. The simulation and testing system for water conservancy and hydropower equipment according to claim 6, characterized in that, The model building unit of the multiphysics coupling modeling module uses professional finite element analysis software or computational fluid dynamics software to build a multiphysics coupling model, and the simulation test unit runs the test on the software platform and records the results.

Images