Efficient physical simulation method

Abstract

The invention discloses a high-efficiency physical simulation method, which comprises the following steps of: introducing a physical simulation engine supporting simulation of various physical phenomena, enabling different physical phenomena to correspond to respective modules through the modular design of the physical simulation engine, and covering various fields of thermodynamics, dynamics and the like; complex tasks such as fluid dynamics, structural mechanics, heat conduction and the like can be quickly processed by utilizing GPU multi-core architecture for parallel computing. By dynamically distributing GPU resources and an intelligent load balancing algorithm, a plurality of simulation tasks can be operated at the same time, resource configuration is optimized, it is ensured that the calculation time of each task is controllable, real-time simulation in a complex scene is achieved, and the time requirements of scenes such as real-time monitoring and dynamic control are met. A simulation result is output in real time and can be compared with actual sensor data for verification, so that the result accuracy is ensured.

Description

Technical Field

[0001] The present invention belongs to the technical field of data processing, and in particular relates to an efficient physical simulation method. Background Art

[0002] Physics simulation uses computer technology and physical principles to simulate real-world physical phenomena, systems, or processes. By building mathematical models and applying numerical algorithms to solve physical equations, it recreates physical scenarios on a computer to study and predict the behavior of physical systems, playing a key role in many fields.

[0003] Physical phenomena in the real world are often intertwined. For example, in large industrial equipment, multiple physical processes such as heat conduction, fluid flow, electromagnetic effects, and structural mechanical response occur simultaneously. Traditional methods have difficulty in comprehensively and accurately simulating these complex multi-physics coupling phenomena.

[0004] Complex physical simulations typically involve extensive computational effort. Traditional methods are time-consuming and unable to meet real-time requirements. For example, in real-time monitoring and control scenarios, simulation results must be quickly obtained to guide decision-making. Summary of the Invention

[0005] The purpose of the present invention is to provide an efficient physical simulation method to solve the problem in the prior art proposed in the background art that complex physical simulation usually involves a large amount of calculation, traditional calculation methods are time-consuming and cannot meet real-time requirements.

[0006] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0007] An efficient physical simulation method comprises the following steps:

[0008] Step S1: introducing a physical simulation engine to support the simulation of multiple physical phenomena; a high-performance physical simulation engine with a modular architecture supports the simulation of at least any one of thermodynamics, dynamics, fluid mechanics, electromagnetism, and structural mechanics;

[0009] Step S2: Improving simulation efficiency through GPU parallel computing, enabling real-time simulation in complex scenarios; utilizing the GPU's multi-core parallel computing architecture to parallelize the simulation tasks of physical phenomena, enabling real-time simulation in complex scenarios;

[0010] In step S3, the simulation results are output in real time and compared with the actual sensor data. Based on the comparison results, the model parameters or boundary conditions are automatically adjusted to correct the simulation results.

[0011] According to the above technical solution, the modular architecture of the physical simulation engine includes:

[0012] Thermodynamics module: This module processes heat conduction and radiation based on the finite element method (FEM), calculates temperature distribution through the heat conduction equation, and adjusts the geometric model based on the thermal expansion coefficient.

[0013] Fluid Mechanics Module: Uses the finite volume method (FVM) to simulate liquid or gas flows, supporting numerical solutions for turbulent and laminar flows.

[0014] Electromagnetic Module: Solve the electromagnetic field distribution based on Maxwell equations using the finite difference time domain method (FDTD) or finite element method (FEM);

[0015] Structural Mechanics Module: Performs stress and vibration analysis based on the finite element method (FEM), supporting stability assessment of complex structures.

[0016] According to the above technical solution, the modular architecture of the physical simulation engine includes: in step S2, GPU parallel computing is used to improve simulation efficiency. Specifically, the system dynamically allocates GPU computing resources according to the requirements of the simulation scenario, runs simulation tasks of multiple physical phenomena at the same time, and uses an intelligent load balancing algorithm to dynamically adjust resource allocation to ensure that the calculation time of each physical phenomenon is within a controllable range, thereby achieving near real-time simulation feedback.

[0017] According to the above technical solution, the intelligent load balancing algorithm is specifically as follows:

[0018] Predictive Scheduling: The intelligent load balancing algorithm first collects real-time computing demand data and resource utilization data by monitoring the load of each computing node and physical phenomenon in the simulation system;

[0019] Adaptive adjustment: Based on the current task execution efficiency, the algorithm adaptively adjusts the allocation ratio of computing resources to ensure that the system always maintains an efficient load state; based on the characteristics of different physical phenomena, the simulation engine will decompose the overall task into multiple subtasks; the intelligent load balancing algorithm analyzes the computational workload and priority of each subtask, and then allocates tasks to appropriate computing resources based on the real-time status of the computing nodes.

[0020] According to the above technical solution, the computing demand data and resource utilization data include the usage of CPU, GPU, memory, task queue length, and the computational complexity of each physical phenomenon.

[0021] According to the above technical solution, in step S3, by integrating actual sensors, the simulation results are compared with the actual sensor data of the physical object in real time to determine whether there is a deviation between the simulation results and the sensor data.

[0022] According to the above technical solution, if there is a deviation between the simulation result and the sensor data, the accuracy and reliability can be improved by adjusting the model parameters or boundary conditions and re-running the simulation. If there is no deviation, the simulation is continued until it is completed.

[0023] According to the above technical solution, after the simulation is completed, the simulation data and comparison results are stored and a simulation report is generated. The simulation report includes the simulation scene setting, physical phenomenon description, visualization chart of the results, and comparison analysis results with sensor data.

[0024] Compared with the prior art, the present invention has the following beneficial effects:

[0025] The physical simulation engine introduced in this invention can support the simulation of various physical phenomena such as thermodynamics, dynamics, fluid mechanics, electromagnetism, and structural mechanics. With the help of modular design, different physical phenomena have corresponding simulation modules, and the multi-core architecture of the GPU is used for parallel computing. This can significantly improve the processing speed for complex tasks such as fluid dynamics multi-particle system simulation, structural mechanics meshing and solution, and large-scale heat conduction data processing. By dynamically allocating GPU computing resources and intelligent load balancing algorithms, multiple physical simulation tasks can be run simultaneously, optimizing resource allocation, ensuring that the computing time of each task is controllable, and realizing real-time simulation in complex scenarios, meeting the time requirements of scenarios such as real-time monitoring and dynamic control. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 This is a flow chart of the simulation method of the present invention. DETAILED DESCRIPTION

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0028] Example 1

[0029] like Figure 1 As shown, an efficient physical simulation method includes the following steps:

[0030] Step S1: introducing a physical simulation engine to support the simulation of multiple physical phenomena; a high-performance physical simulation engine with a modular architecture supports the simulation of at least any one of thermodynamics, dynamics, fluid mechanics, electromagnetism, and structural mechanics;

[0031] Step S2: Improving simulation efficiency through GPU parallel computing, enabling real-time simulation in complex scenarios; utilizing the GPU's multi-core parallel computing architecture to parallelize the simulation tasks of physical phenomena, enabling real-time simulation in complex scenarios;

[0032] In step S3, the simulation results are output in real time and compared with the actual sensor data. Based on the comparison results, the model parameters or boundary conditions are automatically adjusted to correct the simulation results.

[0033] The physical simulation engine introduced in this invention can support the simulation of various physical phenomena such as thermodynamics, dynamics, fluid mechanics, electromagnetism, and structural mechanics. With the help of modular design, different physical phenomena have corresponding simulation modules, and the multi-core architecture of the GPU is used for parallel computing. This can significantly improve the processing speed for complex tasks such as fluid dynamics multi-particle system simulation, structural mechanics meshing and solution, and large-scale heat conduction data processing. By dynamically allocating GPU computing resources and intelligent load balancing algorithms, multiple physical simulation tasks can be run simultaneously, optimizing resource allocation, ensuring that the computing time of each task is controllable, and realizing real-time simulation in complex scenarios, meeting the time requirements of scenarios such as real-time monitoring and dynamic control.

[0034] Example 2

[0035] This embodiment provides a specific implementation method, an efficient physical simulation method suitable for simulating complex physical phenomena, utilizing GPU parallel computing to improve simulation efficiency and achieve real-time simulation, while also verifying and correcting the results through sensor data comparison to ensure the accuracy of the simulation results.

[0036] The simulation method includes the following steps: Step 1: Introducing a physical simulation engine to support the simulation of multiple physical phenomena.

[0037] Introducing a high-performance physics simulation engine that supports the simulation of the following physical phenomena:

[0038] Thermodynamics: Simulates heat conduction and heat radiation processes, suitable for temperature field analysis of heat exchange equipment and materials.

[0039] Dynamics: Realizes motion simulation of objects, including elastic deformation, collision and other phenomena, and is applied to mechanical system design.

[0040] Fluid Mechanics: Supports simulation of liquid and gas flows, and is suitable for scenarios such as pipeline flow and ventilation systems.

[0041] Electromagnetism: Simulating the propagation of electromagnetic fields and magnetic field effects, which are applied to the design of electrical equipment and communication systems.

[0042] Structural mechanics: used for stress analysis and vibration analysis to ensure the stability and safety of structures.

[0043] Modular design: corresponding modules are set up for different physical phenomena. For example, the thermodynamics module handles thermal conductivity, heat capacity and complex temperature boundary conditions; the fluid mechanics module uses the finite volume method (FVM) to handle turbulent and laminar flow simulations; the electromagnetics module uses the numerical solution method of Maxwell equations to simulate electromagnetic fields; the structural mechanics module uses the finite element method (FEM) for stress and vibration analysis.

[0044] Take the geometry adjustment of the Thermodynamics Module (FEM method) and the Electromagnetism Module as examples:

[0045] When simulating physical phenomena (such as thermodynamics), geometric adjustments can be achieved using the finite element method (FEM), which is used to handle the deformation of materials when subjected to heat or stress. The FEM divides the entire physical domain into multiple small cells, and the temperature distribution of each cell is calculated according to the heat conduction equation.

[0046] Unit division: Divide the geometric structure into multiple finite elements, and set the temperature field or material parameters such as thermal conductivity and heat capacity in each unit.

[0047] Temperature field solution: Based on the heat conduction equation, calculate the temperature change of the material:

[0048]

[0049] Where: ρ is the density of the material; C is the specific heat capacity; k is the thermal conductivity; T is the temperature; and q is the heat source term.

[0050] Through finite element discretization, this partial differential equation is transformed into an algebraic equation, and the temperature distribution is updated at each time step.

[0051] Geometry Adjustment: Based on material temperature changes, the material's geometric deformation is calculated using the thermal expansion coefficient and the geometry model is adjusted. Temperature changes in each element cause stress and strain, which are simulated using FEM to adjust the overall geometry model.

[0052] Electromagnetism Module: Uses the numerical solution method of Maxwell's equations to simulate the interaction between magnetic fields and currents.

[0053] Maxwell's equations are the fundamental equations that describe electromagnetic interactions, encompassing the dynamics of electric and magnetic fields. The electromagnetics module within the simulation engine uses numerical methods to solve these equations. Common numerical methods include the finite difference method (FDTD) and the finite element method (FEM).

[0054] The basic form of the Maxwell equations is:

[0055] Gauss's law (electric field): The divergence of the electric field is equal to the charge density.

[0056]

[0057] Where, Divergence operator is used to describe the divergence or convergence characteristics of a vector field at a certain point.

[0058] E: Electric field strength vector, representing the strength and direction of the electric field.

[0059] ρ: Charge density, which describes the density of charge distribution in space.

[0060] Gauss's law (magnetic field): The divergence of the magnetic field is zero, indicating that there are no magnetic monopoles.

[0061]

[0062] Faraday's law of induction: A changing magnetic field generates an electric field.

[0063]

[0064] Ampere's law (modified): Changing electric fields and current densities produce magnetic fields.

[0065]

[0066] Where, The curl operator represents the rotation characteristics of a vector field.

[0067] B: Magnetic induction intensity vector, which describes the strength and direction of the magnetic field.

[0068] μ0: vacuum magnetic permeability, a constant that characterizes the ability of vacuum to conduct magnetic fields, with a value of approximately 4π×10 -7 H / m.

[0069] J: Current density vector, describing the distribution of current in space.

[0070] ∈0: Vacuum dielectric constant, which characterizes the response of vacuum to electric field, and its value is about 8.854×10 -12 F / m.

[0071] The partial derivative of the electric field intensity with respect to time t reflects the rate of change of the electric field with time.

[0072] Meshing: The simulation space is discretized into small grid cells, and the changes in the electric field E and magnetic field B are calculated in each cell. The electric field and magnetic field are usually stored in different locations of the grid (such as the electric field at the grid nodes and the magnetic field at the grid edges) to accommodate differential solutions.

[0073] Spatial discretization: Use finite difference or finite element methods to spatially discretize the Maxwell equations. Common methods include:

[0074] Finite Difference Time Domain (FDTD) method: It converts partial differential equations into difference equations by discretizing them in time and space. It is suitable for solving transient electromagnetic problems.

[0075] Finite element method (FEM): Divide the problem into several small units and use local approximation to solve Maxwell equations. It is suitable for electromagnetic field problems with complex geometric structures and boundary conditions.

[0076] Time marching: The electric and magnetic fields are updated by explicit or implicit numerical integration methods, with the time step size usually determined by stability conditions (such as the CFL condition). Changes in the electric field in the equations affect the magnetic field and vice versa, so both fields need to be updated at each time step.

[0077] Boundary condition setting: In numerical solutions, boundary conditions are very important for accurate simulation of electromagnetic fields. Typical boundary conditions include:

[0078] Conductor boundary: The electric field is perpendicular to the conductor boundary and the magnetic field is parallel.

[0079] Absorbing boundary condition (PML): used to deal with open space, absorb incident electromagnetic waves and prevent them from being reflected back to the calculation area.

[0080] Structural Mechanics Module: Use the Finite Element Method (FEM) to perform stress and vibration analysis of complex structures.

[0081] High-precision physical simulation: Through precise numerical algorithms (such as the finite element method and the finite volume method), the simulation engine can accurately describe physical laws, improving the realism and reliability of the simulation. For nonlinear problems or coupled phenomena, adaptive meshing and multi-physics coupling methods are used.

[0082] Step 2: Improve simulation efficiency through GPU parallel computing, enabling real-time simulation in complex scenarios. GPU accelerated parallel computing specifically includes:

[0083] The GPU's multi-core architecture significantly improves simulation efficiency through parallel processing, making it particularly suitable for:

[0084] Simulation of multi-particle systems for fluid dynamics: for example, particle tracking in liquids.

[0085] Meshing and solving of structural mechanics: stress analysis of large and complex structures.

[0086] Large-scale data processing in heat conduction: such as high-resolution temperature field calculation.

[0087] Highly concurrent task processing: The system dynamically allocates GPU computing resources based on the simulation scenario requirements, allowing simultaneous execution of multiple physical phenomenon simulations. For example, in complex device simulations, the GPU can process thermodynamics and stress analysis in parallel, ensuring interaction and feedback between physical phenomena.

[0088] Real-time optimization: Use intelligent load balancing algorithms to dynamically adjust computing resource allocation to ensure that the calculation time of each physical phenomenon is within a controllable range, thereby achieving near real-time simulation feedback.

[0089] Real-time optimization: By using intelligent load balancing algorithms, we ensure that computing resources are dynamically allocated to different physical phenomena in complex simulation environments to ensure real-time performance across the entire system. The goal is to maintain near-real-time simulation feedback, avoiding computational bottlenecks or wasted resources.

[0090] Load Monitoring and Analysis: The intelligent load balancing algorithm first monitors the load of each computing node and physical phenomenon in the simulation system, collecting real-time computing demand and resource utilization. This data includes CPU, GPU, and memory usage, task queue length, and the computational complexity of each physical phenomenon (such as fluid dynamics and thermodynamics).

[0091] Task decomposition and allocation: The simulation engine decomposes the overall task into multiple subtasks based on the characteristics of different physical phenomena (such as fluid, thermal, and electromagnetic). An intelligent load balancing algorithm analyzes the computational load and priority of each subtask and then allocates tasks to appropriate computing resources based on the real-time status of computing nodes.

[0092] High-priority tasks: Tasks that require high simulation real-time performance (such as real-time rendering and dynamic physics simulation) will be given priority to allocate more computing resources to ensure the shortest feedback time for these tasks.

[0093] Low-priority tasks: For tasks that do not require immediate feedback (such as data storage and offline analysis), the system can delay processing or allocate fewer resources to reduce real-time pressure.

[0094] Dynamically Adjusting Resource Allocation: During simulations, an intelligent load balancing algorithm continuously monitors the status of each node and dynamically adjusts computing resources based on task performance. For example, if a node becomes overloaded, the algorithm will shift some tasks to less busy nodes. Conversely, when tasks complete or the system load decreases, resources can be reallocated to higher-priority tasks.

[0095] Real-time feedback optimization: Through dynamic adjustments, the system effectively reduces computational bottlenecks, balances the load across nodes, and prevents individual tasks from slowing down the overall simulation. Intelligent load balancing algorithms iteratively optimize based on real-time feedback, ensuring that the response time of the entire simulation system remains within set thresholds and delivering near-real-time simulation feedback.

[0096] Algorithm implementation: The algorithm predicts upcoming computing needs based on historical data and current load and adjusts resource allocation in advance.

[0097] Adaptive adjustment: Based on the current task execution efficiency, the algorithm adaptively adjusts the allocation ratio of computing resources so that the system always maintains an efficient load state.

[0098] Through this intelligent load balancing mechanism, the simulation system can effectively manage complex computing tasks under limited resources and achieve near real-time feedback effects.

[0099] Support for large-scale simulation scenarios: The GPU parallel computing architecture supports simulation of large-scale scenarios, such as simultaneous calculations on hundreds of thousands or even millions of simulation units. It is suitable for simulation of industrial equipment, building structures, and even city-level systems.

[0100] Step 3: The simulation results are output in real time, supporting comparison and verification with actual sensor data.

[0101] Real-time simulation result output: The simulation system can output results in real time during the simulation process, such as stress distribution diagrams, temperature fields, fluid flow trajectories, etc., which users can view dynamically through the interactive interface.

[0102] Sensor data integration: The system integrates sensor data and compares simulation results with actual sensor data of physical objects (such as temperature, pressure, displacement, etc.) in real time to ensure the accuracy of simulation results.

[0103] Simulation result verification and feedback mechanism: If there is a deviation between the simulation results and the sensor data, the system automatically adjusts the model parameters or boundary conditions and reruns the simulation to improve accuracy and reliability.

[0104] Data storage and report generation: All simulation data and comparison results are automatically stored, and the system can generate simulation reports, including simulation scenario settings, physical phenomenon descriptions, visualization charts of results, and comparative analysis results with sensor data.

[0105] Through the above design and implementation methods, this efficient physical simulation system can simulate a variety of physical phenomena in complex scenarios, realize real-time simulation, and ensure the accuracy and reliability of simulation results through the integration and comparison of sensor data.

[0106] In this method, simulation results are output in real time and compared with actual sensor data. If any deviation occurs, the system automatically adjusts model parameters or boundary conditions and re-simulates. This feedback correction mechanism effectively ensures the consistency of simulation results with actual physical conditions, improves their accuracy and reliability, and enhances the applicability of this method in fields requiring high precision, such as aerospace and precision manufacturing.

[0107] Due to its powerful simulation capabilities, efficient computational speed, and reliable results, this method has broad application prospects in a wide range of fields, including industrial design, scientific research, construction engineering, and urban planning. It can be used to optimize performance in advance in industrial equipment design, simulate difficult-to-observe physical processes in scientific research, assess structural safety in construction engineering, and simulate wind conditions and heat island effects in urban planning, driving innovation and optimized decision-making in various fields.

[0108] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0109] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. An efficient physical simulation method, characterized by: The following steps are involved: Step S1: introducing a physical simulation engine to support the simulation of multiple physical phenomena; a high-performance physical simulation engine with a modular architecture supports the simulation of at least any one of thermodynamics, dynamics, fluid mechanics, electromagnetism, and structural mechanics; Step S2: Improving simulation efficiency through GPU parallel computing, enabling real-time simulation in complex scenarios; utilizing the GPU's multi-core parallel computing architecture to parallelize the simulation tasks of physical phenomena, enabling real-time simulation in complex scenarios; In step S3, the simulation results are output in real time and compared with the actual sensor data. Based on the comparison results, the model parameters or boundary conditions are automatically adjusted to correct the simulation results.

2. An efficient physical simulation method according to claim 1, characterized in that: The modular architecture of the physics simulation engine includes: Thermodynamics module: This module processes heat conduction and radiation based on the finite element method (FEM), calculates temperature distribution through the heat conduction equation, and adjusts the geometric model based on the thermal expansion coefficient. Fluid Mechanics Module: Uses the finite volume method (FVM) to simulate liquid or gas flows, supporting numerical solutions for turbulent and laminar flows. Electromagnetic Module: Solve the electromagnetic field distribution based on Maxwell equations using the finite difference time domain method (FDTD) or finite element method (FEM); Structural Mechanics Module: Performs stress and vibration analysis based on the finite element method (FEM), supporting stability assessment of complex structures.

3. The efficient physical simulation method according to claim 1, characterized in that: The modular architecture of the physical simulation engine includes: In step S2, GPU parallel computing is used to improve simulation efficiency. Specifically, the system dynamically allocates GPU computing resources according to the requirements of the simulation scenario, runs simulation tasks of multiple physical phenomena at the same time, and uses an intelligent load balancing algorithm to dynamically adjust resource allocation to ensure that the calculation time of each physical phenomenon is within a controllable range, thereby achieving near real-time simulation feedback.

4. The efficient physical simulation method according to claim 3, characterized in that: The specific intelligent load balancing algorithm is: Predictive Scheduling: The intelligent load balancing algorithm first collects real-time computing demand data and resource utilization data by monitoring the load of each computing node and physical phenomenon in the simulation system; Adaptive adjustment: Based on the current task execution efficiency, the algorithm adaptively adjusts the allocation ratio of computing resources to ensure that the system always maintains an efficient load state; according to the characteristics of different physical phenomena, the simulation engine will decompose the overall task into multiple subtasks; The intelligent load balancing algorithm analyzes the computational workload and priority of each subtask, and then allocates the task to the appropriate computing resources based on the real-time status of the computing nodes.

5. The efficient physical simulation method according to claim 4, characterized in that: Computational demand data and resource utilization data include CPU, GPU, memory usage, task queue length, and the computational complexity of each physical phenomenon.

6. The efficient physical simulation method according to claim 5, characterized in that: In step S3, by integrating actual sensors, the simulation results are compared with the actual sensor data of the physical object in real time to determine whether there is a deviation between the simulation results and the sensor data.

7. The efficient physical simulation method according to claim 6, characterized in that: If the simulation results deviate from the sensor data, adjust the model parameters or boundary conditions and rerun the simulation to improve accuracy and reliability. If there is no deviation, continue running until the simulation is complete.

8. The efficient physical simulation method according to claim 7, characterized in that: After the simulation is completed, the simulation data and comparison results are stored and a simulation report is generated. The simulation report includes the simulation scene settings, physical phenomenon descriptions, visualization charts of the results, and comparative analysis results with sensor data.

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