High-precision simulation inflow condition generation method for reactor thermal fluid

Abstract

The invention discloses a high-precision simulation inflow condition generation method for reactor thermal fluid, and belongs to the technical field of computational fluid mechanics. According to the method, experimental data in a turbulence database and completely developed turbulence structure data obtained through two high-precision simulation methods of DNS and LES are utilized, and turbulence data under different working conditions and different physical conditions are selected according to calculation requirements and model design when numerical simulation is performed on reactor fluid, so that the numerical simulation of the reactor fluid is realized. And matching the data into the calculated flow field grid by using an interpolation method to generate an inflow condition with a turbulent flow structure. The method aims to generate an inflow condition which can be used for high-precision simulation of reactor thermal fluid, has physical properties and is completely developed, of a turbulence structure according to turbulence data selected by a user, the simulation calculation requirement of the user is met, the user does not need to carry out additional adjustment design at an entrance, and time and calculation cost are saved.

Description

Technical Field

[0001] The invention relates to the technical field of computational fluid dynamics, and in particular to a method for generating high-precision simulation inflow conditions for reactor thermal fluids. Background Art

[0002] How to accurately describe and simulate the complex flow characteristics inside a nuclear reactor is a key issue in the field of nuclear energy engineering. As an important facility for energy generation, the internal fluid dynamics process of a nuclear reactor directly affects the cooling effect of fuel elements, the performance and safety of heat exchangers, etc. The inflow conditions of the fluid play a key role, which directly affects the formation of turbulent structures, the efficiency of energy transmission, and the stability of the system. Computational fluid dynamics (CFD) is an important method for simulating physical processes such as reactor fluid flow and heat transfer. It simulates and predicts the fluid flow process by solving the Navier-Stokes equations. The inflow conditions define the initial state of the fluid flow problem and are one of the necessary conditions to ensure that the Navier-Stokes equations have a unique solution. Accurate inflow conditions can ensure the stability and convergence of the numerical solution and avoid instability or divergence caused by initial errors. Therefore, it is crucial to deeply study the inflow conditions of reactor thermal fluids.

[0003] Different thermal fluid simulation methods have different sensitivities to inflow conditions. The Reynolds-averaged Navier-Stokes (RANS) method commonly used in engineering models all turbulent fluctuations, ignores the impact of fluctuations, and has low requirements for boundary conditions; the Large-eddy Simulation (LES) method only simulates large-scale eddies, models smaller-scale eddies, and is more sensitive to inflow conditions; Direct Numerical Simulation (DNS) directly simulates turbulence of all scales, and subtle disturbances can continue to propagate over time and space, making it more sensitive to inflow conditions. In order to improve the accuracy of reactor-oriented thermal fluid simulation methods, high-quality, physically realistic inflow conditions are essential because they affect the flow of downstream fluids, which in turn affects the accuracy of the entire simulation. If the fluctuations of the inflow turbulence are not realistic and reasonable enough, the turbulent fluctuations may dissipate before reaching the calculation area, or it may take a long distance to form realistic turbulence.

[0004] At present, the commonly used methods for setting the inflow conditions of reactor thermal fluids include: specifying a steady flow rate or velocity field, using the data obtained from laboratory measurements as the inflow conditions, and using the internal flow field distribution obtained from the pre-conducted numerical simulation as the inflow conditions. However, due to the geometric complexity, dimensional precision, large scale and high-fidelity numerical calculation requirements of the core structure, the existing methods have the following shortcomings: 1) The method of specifying a steady flow rate or velocity field completely ignores turbulent fluctuations, cannot accurately simulate the dynamic changes of the fluid over time under different working conditions or operation stages, and cannot capture the local non-uniformity caused by factors such as geometric shape, component layout, and local heat transfer. This method is only suitable for computational simulations that require simplified processing of boundary condition changes; 2) Directly using the data obtained from laboratory measurements as the inflow conditions, there is a problem that the fluid conditions in the laboratory may not completely match the conditions in the nuclear reactor, such as the size of the model is different; 3) The method of pre-conducting numerical simulations, when facing large-scale reactors, fine grids, and high-precision computational simulations, different geometric models and boundary conditions must be simulated in advance, which will significantly increase the simulation time and computational cost.

[0005] In order to improve the calculation accuracy, some scholars proposed to use the existing data and calculation results to generate inflow conditions with turbulence intensity and fluctuations. For example, Dhamankar et al. (Dhamankar, NS, GABlaisdell, and A.S.Lyrintzis. Overview of Turbulent Inflow Boundary Conditions for Large-Eddy Simulations [J]. AIAA Journal 56 (4): 1317–1334. doi: 10.2514 / 1.J055528.) and Tabor et al. (GR Tabor, MHBaba-Ahmadi. Inlet conditions for large eddy simulation: A review [J]. Computers & Fluids. 2010: 553-567.) concluded that turbulent databases can provide detailed and accurate turbulent inflow conditions, have wide applicability, and have high accuracy and reliability in calculation results. Combined with JUSchluter et al. (Schluter, Jorg & Moin, Parviz & Pitsch. Large-Eddy Simulation Inflow Conditions for Coupling with Reynolds-Averaged Flow Solvers [J] Aiaa Journal - AIAA. 2004 42 (3) 478-484.10.2514 / 1.3488.) analyzed the application of the turbulence database to LES inflow conditions and found that its calculation results were consistent with the experimental data.

[0006] Based on the deficiencies of the prior art and combined with the existing turbulence database research, the present invention proposes a method for generating inflow conditions for high-precision simulation of reactor thermal fluids. Summary of the invention

[0007] The purpose of the present invention is to propose a method for generating high-precision simulation inflow conditions for reactor thermal fluids to solve the problems raised in the background technology. The present invention stores experimental data and other simulation calculation results using RANS, LES and DNS methods, including flow field information, grid point coordinates, velocity components in each direction and solved passive scalar information, in a database in advance. When performing numerical simulation, according to calculation requirements, the corresponding turbulence data file is selected and read, and it is matched to the flow field grid position to be solved by interpolation method to generate turbulent inflow conditions.

[0008] In order to achieve the above object, the present invention adopts the following technical solutions:

[0009] A method for generating high-precision simulation inflow conditions for reactor thermal fluids, comprising the following steps:

[0010] S1. Construct a turbulence database, wherein the database includes a data source module, a data storage module, a data reading module and a data application module;

[0011] S2. Obtain fluid mechanics experimental data, and use RANS, LES and DNS methods to obtain simulation calculation results, and store the obtained experimental data and simulation results in the data source module;

[0012] S3, dividing the simulation results of the experimental data set obtained in S2 into flow field information files and turbulence data files and storing them in the data storage module, unifying the type and length of the data during storage to improve storage efficiency and reading speed;

[0013] S4, based on the data reading module, uses multi-threading and parallel I / O technology to read data and improve reading efficiency;

[0014] S5. After the data is read, before applying the data to the flow field, prepare the network of the flow field to be simulated, set the boundary conditions, and judge whether the turbulence data matches the grid or spatial resolution of the flow field based on the data application module. If they match, directly enter S6; if not, based on the adaptive interpolation algorithm of geometric classification, according to the geometric dimension of the model, the structured and unstructured grid, apply the turbulence data to different flow field models, and then enter S6;

[0015] S6. Use the input data to perform simulation calculations;

[0016] S7. After the simulation calculation is completed, the calculation results will be compared with the experimental data or known theoretical results to evaluate the accuracy and applicability of the simulation results, and the simulation parameters or data application methods will be adjusted according to the verification results.

[0017] Preferably, the flow field information file in S3 is used to store flow field and turbulence information, including turbulence energy spectrum, relevant time scale, turbulence intensity, type of calculation process and flow field information; the turbulence data file is used to store data sets, including point coordinates, velocity component values ​​in various directions and passive scalar values.

[0018] Preferably, the method of reading data using multithreading and parallel I / O technology in S4 specifically includes the following contents:

[0019] S4.1, data segmentation: divide the large file into multiple small blocks, each block contains a certain number of flow field data points;

[0020] S4.2, multi-threaded reading: Use the standard thread library to create multiple threads, each thread is responsible for reading one or more data blocks;

[0021] S4.3, data buffering: Each thread stores the read data block into a shared buffer, using a lock mechanism to ensure thread safety;

[0022] S4.4, Data parsing: The read data block is parsed into specific flow field point data in the buffer, including coordinates, velocity components and passive scalars.

[0023] Preferably, the adaptive interpolation algorithm based on geometric classification described in S5 applies turbulence data to different flow field models according to the geometric dimensions of the model and the structured and unstructured nature of the grid, and specifically includes the following contents:

[0024] If the model is one-dimensional, it means that the flow field modeling is simple, and linear interpolation is used to apply the data;

[0025] If the model is two-dimensional, the network used in the flow field model is divided into structured and unstructured. When the grid constructed by the flow field is a structured grid, the bilinear interpolation method is used for physical quantity interpolation; if the constructed grid is an unstructured grid, the WENO-based high-accuracy interpolation method (Weighted Essentially Non-Oscillatory-based high-accuracy interpolation) is used for interpolation;

[0026] If the model is three-dimensional, radial basis function interpolation is used to interpolate the turbulence data.

[0027] Compared with the prior art, the present invention provides a method for generating high-precision simulation inflow conditions for reactor thermal fluids, which has the following beneficial effects:

[0028] (1) Saving time costs and computing resources: In view of the numerical simulation requirements of large-scale reactors, high-precision grids, and small time steps, the present invention uses a pre-generated turbulence database, eliminating the need to repeat large-scale simulation calculations and directly extracting inflow conditions from the database, which significantly reduces computing time and resource consumption.

[0029] (2) Providing a realistic turbulence structure: The turbulence database is based on a large amount of experimental data and high-precision numerical simulation results. The present invention can accurately describe turbulence characteristics, such as turbulence energy spectrum, relevant time scale, turbulence intensity, etc., to ensure the authenticity and accuracy of inflow conditions.

[0030] (3) Applicable to high-precision simulation of reactor thermal fluids: The data in the turbulence database are derived from experimental data or high-precision numerical simulations. The present invention can provide relatively realistic and accurate turbulence characteristics, has certain verification and reliability, and can generate high-quality inflow conditions for high-precision simulation of reactor thermal fluids.

[0031] (4) Wide applicability: The turbulence database constructed by the present invention can cover a variety of fluid types and different operating conditions, such as fluid types with different flow rates, temperatures, pressures, etc., including steady-state conditions, transient conditions, and accident conditions. The database can adapt to various nuclear reactor designs and operating conditions and has strong versatility and adaptability. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Figure 1 The overall principle diagram of a method for generating high-precision simulation inflow conditions for reactor thermal fluids proposed in Example 1 of the present invention mainly includes: saving experimental data or simulation results obtained by RANS, LES and DNS methods into a turbulent database, selecting appropriate turbulent data from the database during the main simulation, and applying the data to the grid points using the interpolation method, that is, generating inflow conditions with turbulent structures;

[0033] Figure 2 The schematic diagram is a schematic diagram of the four component modules of the turbulence database constructed in Example 1 of the present invention, including: a data source module, a data storage module, a data reading module and a data application module;

[0034] Figure 3 This is a specific method flow chart of a method for generating high-precision simulation inflow conditions for reactor thermal fluids proposed in Example 1 of the present invention. DETAILED DESCRIPTION

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0036] Embodiment 1:

[0037] In order to better adapt to the three-dimensional geometric structure of complex spirals, a hybrid meshing method is often used, that is, combining multiple mesh forms such as tetrahedron, hexahedron and triangular prism; in addition, when studying the tangent plane flow characteristics of the geometric structure of complex spirals, a two-dimensional hybrid meshing method is also often used, that is, combining the mesh forms of triangles and quadrilaterals, and dividing according to the specific characteristics of each part, in order to achieve the best simulation effect. In order to make the hybrid mesh of tetrahedron (hereinafter referred to as tetra), hexahedron (hereinafter referred to as hex) and triangular prism (hereinafter referred to as wedge) in three dimensions and triangle (hereinafter referred to as tri) and quadrilateral (hereinafter referred to as quad) in two dimensions to perform spectral element method DNS, and solve the problems that the conversion process cannot be automated, is time-consuming and has high memory requirements, the present invention proposes a method for generating high-precision simulation inflow conditions for reactor thermal fluids, which effectively converts the hybrid mesh of the complex flow field of the spiral structure into a high-quality hexahedral mesh through parallel processing and optimization algorithms, thereby overcoming the automation difficulties and high resource consumption problems in traditional methods. This technology can not only handle complex mixed grids commonly found in complex flow fields with spiral structures, but also significantly improve conversion efficiency and reduce computing resource requirements while maintaining grid consistency, providing an efficient and reliable grid generation solution for DNS based on the spectral element method.

[0038] A method for generating high-precision simulation inflow conditions for reactor thermal fluids proposed by the present invention is described below in conjunction with the accompanying drawings and specific examples, which specifically include the following contents.

[0039] Embodiment 1:

[0040] See also Figure 1-2 The present invention proposes a method for generating high-precision simulation flow conditions for reactor thermal fluids, comprising: Figure 2 It consists of 4 modules shown.

[0041] Module 1. Data source: To ensure that the generated inflow conditions are physically reasonable, the data in the turbulence database are derived from experimental data and other simulation calculation results. The experimental data mainly come from fluid mechanics experiments in strictly controlled laboratory environments. These experiments use advanced PIV (particle image velocimetry) and LDV (laser Doppler velocimetry) and other technologies to obtain high-precision velocity field and turbulence characteristics data. Other high-precision simulation calculation results refer to the use of RANS, LES and DNS methods, using high-quality and fine grids to simulate and calculate reactor thermal fluids to obtain high-fidelity results. In order to improve the versatility of the turbulence database in the field of high-precision simulation of reactor thermal fluids, the database stores turbulence data under different operating conditions, including steady-state conditions, transient conditions and accident conditions, and provides different fluid types, such as different flow rates, temperatures, pressures, etc.

[0042] Module 2. Data storage: Strict naming conventions and data formats are used when storing turbulence data. The file name of each data file contains key flow field information, such as operating condition type, flow velocity, temperature and pressure, etc., for easy retrieval and use. At the same time, each data file contains a .md file in the directory where the data file is located, which records the flow field information of the data file in detail, including turbulence energy spectrum, relevant time scale, turbulence intensity, type of calculated flow field, fluid information, etc. The data file stores the coordinates, velocity components (u, v, w) and passive scalar values ​​(such as temperature, concentration, etc.) of each point in the flow field. These data are stored in binary format to improve storage efficiency and reading speed. Each data file is compressed and verified to ensure the integrity and accuracy of the data.

[0043] Module 3. Data reading: The data in the turbulence database comes from laboratory data or high-precision LES and DNS calculations, and the amount of stored data is large. In order to read this data efficiently, multi-threading and parallel I / O technology are used. The data reading process is divided into several steps: data segmentation, multi-threaded reading, data buffering, and data parsing.

[0044] Module 4. Data application: Before applying the data in the turbulence database to the flow field, it is necessary to prepare the grid of the flow field to be simulated and set the boundary conditions. After selecting and reading the turbulence data according to the simulated model and calculation conditions, apply it to the flow field grid. When the data does not match the grid of the simulation calculation in space, the turbulence data is applied to different flow field models through an adaptive interpolation algorithm based on geometric classification according to the geometric dimensions of the model and the structured and unstructured nature of the grid, making the turbulence database widely applicable. The non-uniformity of the grid and the details of the turbulent structure are considered in the interpolation process to ensure the physical rationality and calculation accuracy of the interpolation results.

[0045] Based on the above content, the present invention further specifically describes the proposed method for generating high-precision simulation inflow conditions for reactor thermal fluids:

[0046] When storing turbulence data, relevant information of the flow field and turbulence numerical files need to be stored in each data folder. The file names contain key information of the flow field, such as operating condition type, flow velocity, temperature and pressure. The file types and their descriptions are as follows:

[0047]

[0048] When storing data in a unified format, the data type and length are used to facilitate simulation calculations.

[0049] The specific method flow is as follows Figure 3 The specific contents are as follows:

[0050] (1) Understanding of data information

[0051] Before using turbulence data, users need to understand the working conditions, fluid condition information and stored content of each data for later use. In addition, users also need to understand the data types and specific contents contained in the turbulence database, such as turbulence energy spectrum, relevant time scale, turbulence intensity, etc. These data describe the spatial and temporal characteristics of turbulence, thereby helping users to select the most appropriate turbulence data according to their simulation calculation needs.

[0052] (2) Simulation calculation preparation

[0053] Before performing flow field simulation, users need to prepare the mesh and initial condition settings required for the simulation, including defining the geometry of the computational domain, the initial flow field state, and the boundary condition settings, and select the model used for the simulation, such as LES or DNS.

[0054] (3) Select appropriate turbulence data

[0055] The user selects the turbulence characteristic data that meets the conditions and is physically reasonable according to the defined geometric model and calculation requirements.

[0056] (4) Reading data

[0057] After selecting the turbulence data, read the file according to the file name. Since the turbulence data contains a lot of information, in order to improve reading efficiency and reduce time, multi-threading and parallel I / O are used to read the data. Specifically, the data reading process is divided into the following steps:

[0058] Data chunking: Divide a large file into multiple small chunks, each containing a certain number of flow field data points.

[0059] Multi-threaded reading: Use the standard thread library to create multiple threads, each thread is responsible for reading one or more data blocks.

[0060] Data buffering: Each thread stores the read data block in a shared buffer and uses a lock mechanism (such as a mutex lock) to ensure thread safety.

[0061] Data parsing: The read data blocks are parsed into specific flow field point data in the buffer, including coordinates, velocity components, and passive scalars.

[0062] The parallel I / O read method is shown in the pseudo code below.

[0063]

[0064] Each processor first calculates the specific index of the turbulence data file it is responsible for reading, and then each processor locates and reads the data blocks in the corresponding data file according to these indexes. After reading the data, each processor parses it into flow field point data and preprocesses the parsed local data, including data compression and packaging, to optimize the subsequent data collection process. Then, non-blocking parallel I / O operations are used to collect them to the designated main processor (P0), and P0 unpacks the data to form complete flow field data.

[0065] (5) Data grid interpolation matching

[0066] When applying the data to a simulated flow field, one must consider whether the turbulence data matches the grid or spatial resolution of the flow field.

[0067] If the selected data matches the grid and spatial resolution of the flow field, the data can be directly applied to the flow field for calculation.

[0068] If the data in the turbulence database does not match the grid or spatial resolution used in the actual simulation, the data provided by the turbulence database needs to be inserted into the corresponding position in the simulation grid by interpolation. In order to make the turbulence database more universal and applicable, the present invention proposes an adaptive interpolation algorithm based on geometric classification. The pseudo code of the algorithm is shown below.

[0069]

[0070] Following are the input parameters of the algorithm and their description:

[0071]

[0072] Furthermore, the output of the algorithm is the interpolated mesh data.

[0073] The design idea of ​​the algorithm is: according to the input parameters, that is, the geometric dimensions of the model to be solved and whether the grid is structured, the turbulence data is applied to the corresponding position of the grid using the interpolation method.

[0074] If the model is one-dimensional, it means that the flow field modeling is relatively simple, and the linear interpolation method is used.

[0075] Interpolation) to apply the data.

[0076] If the model is two-dimensional, the grid used in the flow field model can be structured or substructured. When the flow field is constructed with a structured grid, due to the regularity of the structured grid, a relatively simple bilinear interpolation method is used for physical quantity interpolation; if the grid is unstructured, the high-precision interpolation method based on WENO (Weighted Essentially Non-Oscillatory-based high-accuracy interpolation) is used for interpolation.

[0077] When the model is three-dimensional, the flow field uses a finer unstructured grid to build the model to improve the accuracy of the calculation. Therefore, the radial basis function interpolation method is used when inserting turbulence data to meet the needs of high-precision simulation calculations.

[0078] (6) Perform simulation calculations

[0079] After inserting the turbulence data into the flow field grid, the actual simulation calculation begins.

[0080] (7) Result verification and analysis

[0081] After the simulation is completed, the calculation results will be compared with experimental data or known theoretical results to evaluate the accuracy and applicability of the simulation results, and the simulation parameters or data application methods will be adjusted according to the verification results. If the simulation results are consistent with the actual situation, further flow field analysis and optimization can be carried out to further understand and improve the accuracy and reliability of flow field simulation.

[0082] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. For ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles described in the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for generating high-precision simulation inflow conditions for reactor thermal fluids, characterized in that: The following steps are involved: S1. Construct a turbulence database, wherein the database includes a data source module, a data storage module, a data reading module and a data application module; S2. Obtain fluid mechanics experimental data, and use RANS, LES and DNS methods to obtain simulation calculation results, and store the obtained experimental data and simulation results in the data source module; S3, dividing the simulation results of the experimental data set obtained in S2 into flow field information files and turbulence data files and storing them in the data storage module, unifying the type and length of the data during storage to improve storage efficiency and reading speed; S4, based on the data reading module, uses multi-threading and parallel I / O technology to read data and improve reading efficiency; S5. After the data is read, before applying the data to the flow field, prepare the network of the flow field to be simulated, set the boundary conditions, and judge whether the turbulence data matches the grid or spatial resolution of the flow field based on the data application module. If they match, directly enter S6; if not, based on the adaptive interpolation algorithm of geometric classification, according to the geometric dimension of the model, the structured and unstructured grid, apply the turbulence data to different flow field models, and then enter S6; S6. Use the input data to perform simulation calculations; S7. After the simulation calculation is completed, the calculation results will be compared with the experimental data or known theoretical results to evaluate the accuracy and applicability of the simulation results, and the simulation parameters or data application methods will be adjusted according to the verification results.

2. A method for generating high-precision simulation inflow conditions for reactor thermal fluid according to claim 1, characterized in that: The flow field information file in S3 is used to store flow field and turbulence information, including turbulence energy spectrum, relevant time scale, turbulence intensity, type of calculation process and flow field information; the turbulence data file is used to store data sets, including point coordinates, velocity component values ​​in various directions and passive scalar values.

3. The method for generating high-precision simulation inflow conditions for reactor thermal fluid according to claim 1, characterized in that: S4 uses multithreading and parallel I / O technology to read data, specifically including the following: S4.1, data segmentation: divide the large file into multiple small blocks, each block contains a certain number of flow field data points; S4.2, multi-threaded reading: Use the standard thread library to create multiple threads, each thread is responsible for reading one or more data blocks; S4.3, data buffering: Each thread stores the read data block into a shared buffer, using a lock mechanism to ensure thread safety; S4.4, Data parsing: The read data block is parsed into specific flow field point data in the buffer, including coordinates, velocity components and passive scalars.

4. The method for generating high-precision simulation inflow conditions for reactor thermal fluid according to claim 1, characterized in that: The adaptive interpolation algorithm based on geometric classification described in S5 applies turbulence data to different flow field models according to the geometric dimensions of the model and the structured and unstructured nature of the grid, including the following: If the model is one-dimensional, it means that the flow field modeling is simple and the data is applied using linear interpolation; If the model is two-dimensional, the network used in the flow field model is divided into structured and unstructured. When the grid constructed by the flow field is a structured grid, the bilinear interpolation method is used to interpolate the physical quantity; if the constructed grid is an unstructured grid, the high-precision interpolation method based on WENO is used for interpolation; If the model is three-dimensional, the turbulence data is interpolated using radial basis functions.

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