Improved large vortex simulation vortex generation method

Abstract

The invention discloses an improved large-vortex simulation vortex generation method, belongs to the technical field of turbulence simulation, and solves the problems of insufficient vortex randomness control, significant inlet turbulence statistical error and large computing resource consumption in the existing synthetic vortex method. The method comprises the following steps: generating randomly distributed synthetic vortexes at an inlet based on target turbulence characteristics, processing a velocity field by using a dynamic normalization factor to meet a statistic requirement, and finally determining the number of vortexes according to a specific ratio and enabling the vortexes to be randomly distributed. According to the improved large-vortex simulation vortex generation method, traditional limitation is broken through, vortexes can be placed at will, statistical accuracy is improved, small-scale turbulence coverage is optimized, calculation efficiency is improved by 1-2 orders of magnitude, cost is reduced, the method can be achieved based on OpenFOAM software, the method is suitable for various scenes, and powerful support is provided for multiple fields.

Description

Technical Field

[0001] The present invention relates to the technical field of turbulence simulation, and in particular to an improved large eddy simulation vortex generation method. Background Art

[0002] With the rapid development of computer science and technology, the application field of computational fluid dynamics (CFD) is becoming increasingly extensive, and its role in the design and development of aircraft engines is becoming more and more significant. Today, CFD software has become an important tool for evaluating and optimizing new design schemes. Through simulation and numerical verification, engineers can simulate complex fluid behaviors in a virtual environment, thereby greatly improving the accuracy and efficiency of the design. Among the many CFD numerical simulation methods, large eddy simulation (LES) is highly favored due to its unique advantages. The LES method directly simulates large-scale vortices and models small-scale vortices, thereby effectively reducing computational complexity and resource consumption while ensuring computational accuracy.

[0003] In the aviation and automotive industries, engineers have shown great interest in large eddy simulation (LES), mainly because LES can provide an unsteady turbulent flow field, which is crucial for calculating the aeroacoustic noise generated by vehicles or wings. However, in practice, LES calculations are usually only applied to specific regions of interest, such as the trailing edge of a wing or the rearview mirror of a car, because these areas have a significant impact on the noise and flow characteristics.

[0004] To achieve this, engineers often use an approach called "embedded LES." In this approach, the LES domain is embedded within a larger RANS (Reynolds-averaged Navier-Stokes) simulation domain. RANS technology is used to simulate the entire geometry, providing stable flow conditions, while LES focuses on specific, high-resolution regions to capture unsteady turbulence effects.

[0005] Although the embedded LES approach is feasible in theory, it still faces some challenges in practice:

[0006] 1. Transition from RANS to LES:

[0007] The flow conditions need to transition smoothly between the RANS and LES domains. RANS provides stable average flow conditions, while LES requires mature unsteady turbulent conditions. A key challenge is how to generate a mature LES solution from a stable RANS solution in the shortest possible distance.

[0008] 2. Calculate the cost:

[0009] The computational cost of LES calculations is much higher than that of RANS. Therefore, limiting the size of the LES embedding domain is key to reducing the overall computational cost. How to keep the LES domain large enough while minimizing its range is a difficult problem that needs to be solved.

[0010] 3. Boundary conditions:

[0011] At the interface between the RANS domain and the LES domain, how to set appropriate boundary conditions to ensure effective transfer of flow information is also an important issue. Improper boundary conditions may lead to flow distortion or computational instability.

[0012] To solve this problem, a variety of methods for generating turbulence at the LES inlet have been proposed. Currently, the methods used to generate inlet turbulence mainly include: vortex method, spectral synthesizer, synthetic eddy method, etc.

[0013] Among them, the vortex generation method is a turbulence generation method based on the classical view that turbulence is the superposition of vortices. The idea behind it is to focus directly on prescribing coherent structures rather than directly reverting to spectral methods. This method is easy to implement, runs fast, and is applicable to any geometry and any type of flow. The generated data shows very good physical properties, but it also has some limitations when generating turbulent inlet conditions, such as the randomness and statistical characteristics of the vortex are difficult to accurately control. At the same time, the vortices generated using this method are actually non-uniform, which will cause the generated results to have certain errors compared to expectations. Summary of the Invention

[0014] The present invention proposes an improved large eddy simulation vortex generation method. By introducing a dynamic normalization factor to optimize the vortex statistical characteristics, allowing vortex generation at any spatial position and redefining the relationship between vortex number and density, it solves the problems of insufficient control of vortex randomness, significant inlet turbulence statistical errors and high computing resource consumption in existing synthetic vortex methods.

[0015] An improved large eddy simulation vortex generation method, the method comprising the following steps:

[0016] Step 1: Based on the statistical characteristics of the target turbulence, a synthetic vortex with random spatial distribution is generated at the inlet of the computational domain;

[0017] Step 2: normalizing the velocity field of the synthetic vortex by a dynamic normalization factor so that the synthetic velocity field satisfies preset first-order and second-order statistics;

[0018] Step 3: Determine the number of active vortices based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, and allow the synthetic vortex to be dynamically distributed at any position in the inlet space to optimize small-scale turbulence coverage and computational efficiency.

[0019] Furthermore, in step 2, the velocity field of the synthetic vortex after normalization is:

[0020]

[0021] Statistical properties as well as Independent of the spatial distribution and length scale of the eddies.

[0022] Furthermore, in step 2, the dynamic normalization factor is determined by taking the running average of the eddy current concentration, so that the synthetic velocity field satisfies the unit variance condition and accurately recovers the target first-order and second-order statistics.

[0023] Furthermore, in step 3, the calculation formula for the eddy current number is:

[0024]

[0025] Among them A inlet is the inlet area, A eddy is the average effective cross-sectional area of the vortex.

[0026] Furthermore, the improved large eddy simulation vortex generation method is implemented based on OpenFOAM software, and a TurbulentInletSEM inlet condition library is established for simulation calculation.

[0027] A storage medium having a computer program stored thereon, characterized in that when the computer program is executed by a processor, the improved large eddy simulation vortex generation method mentioned above is implemented.

[0028] A computer device, characterized in that it includes: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned improved large eddy simulation vortex generation method.

[0029] Beneficial effects of the present invention:

[0030] 1. It allows vortices to be placed arbitrarily in the inlet space, which solves the limitation of traditional SEM that vortices can only be generated in a three-dimensional Cartesian box, and ensures that all vortices are active at all time steps.

[0031] 2. The original SEM suffers from non-uniformity in the vortex length scale, which prevents the accurate recovery of the intended turbulence statistics. This invention introduces a new normalization procedure to ensure the accurate recovery of the target statistics. This improvement significantly improves the statistical accuracy of the simulation, bringing the results closer to actual physical phenomena, thus providing more reliable data support for engineering design and scientific research.

[0032] 3. The original SEM requires significant computational resources to generate turbulent boundary conditions, especially at high Reynolds numbers and with complex geometries. This invention generalizes the vortex placement strategy to allow arbitrary vortex placement, thereby reducing the number of required vortices while maintaining the target vortex density. This improvement results in a computational efficiency improvement of approximately one to two orders of magnitude, enabling large-scale LES simulations to be completed in a shorter time, reducing computational costs and improving research efficiency.

[0033] 4. The improvements of this invention are not only applicable to the generation of turbulent inlet boundary conditions in LES but can also be extended to other SEM-based methods, such as volume source terms and dynamic forcing algorithms. This means that the advantages of this invention can be applied to a wider range of CFD applications, including but not limited to aerospace, automotive engineering, energy, and environmental science. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 Generate eddy current distribution map for initial SEM;

[0035] Figure 2 Generate eddy current distribution maps for improved SEM. DETAILED DESCRIPTION

[0036] 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. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0037] An improved large eddy simulation vortex generation method, the method comprising the following steps:

[0038] Step 1: Based on the statistical characteristics of the target turbulence, a synthetic vortex with random spatial distribution is generated at the inlet of the computational domain;

[0039] Step 2: normalizing the velocity field of the synthetic vortex by a dynamic normalization factor so that the synthetic velocity field satisfies preset first-order and second-order statistics;

[0040] Step 3: Determine the number of active vortices based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, and allow the synthetic vortex to be dynamically distributed at any position in the inlet space to optimize small-scale turbulence coverage and computational efficiency.

[0041] Specifically, the improved large eddy simulation vortex generation method described in the present invention generates synthetic vortices with random spatial distribution based on the target turbulence statistical characteristics at the entrance of the computational domain, breaking through the limitations of traditional methods. Traditional SEM can only generate vortices in a three-dimensional Cartesian box, while the present invention allows vortices to be placed arbitrarily in the entrance space, which makes the vortex distribution more flexible and can more comprehensively simulate actual turbulent conditions. This improvement ensures that all vortices are active at each time step, greatly improving the authenticity and effectiveness of the simulation. After the synthetic vortex is generated, its velocity field is normalized by a dynamic normalization factor, which is the key technical innovation of the present invention. The original SEM has a non-uniformity problem in the setting of the vortex length scale, which makes it difficult to accurately restore the preset turbulence statistics. The new normalization procedure introduced in the present invention determines the dynamic normalization factor by taking the running average of the vortex concentration, so that the synthetic velocity field meets the preset first-order and second-order statistics, and can also meet the unit variance condition, effectively solving this problem. This improvement significantly improves the statistical accuracy of the simulation, allowing the simulation results to more accurately reflect the actual physical phenomena, and providing more reliable data support for engineering design and scientific research. Determining the number of active vortices based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, and allowing the synthetic vortex to be dynamically distributed at any position in the inlet space, is the core means of improving the computational efficiency of the present invention. The original SEM consumes a lot of computing resources when generating turbulent boundary conditions, especially in the case of high Reynolds numbers and complex geometries. By optimizing the vortex placement strategy, the present invention reduces the number of required vortices while maintaining the target "vortex density", performs better in terms of small-scale turbulence coverage, and achieves a computational efficiency improvement of about 1-2 orders of magnitude. This enables large-scale LES simulations to be completed in a shorter time, significantly reduces computational costs, and improves research efficiency. In addition, the improvement of the present invention has a wide range of applicability. It is not only applicable to the generation of turbulent inlet boundary conditions in LES, but can also be extended to other SEM-based methods, such as volume source terms and dynamic forcing algorithms. This means that its advantages can be applied to many CFD application scenarios such as aerospace, automotive engineering, energy and environmental science, greatly expanding the application scope of this technology and bringing new technical support and development opportunities to related research and engineering practices in different fields.

[0042] Furthermore, in step 2, the velocity field of the synthetic vortex after normalization is:

[0043]

[0044] Statistical properties as well as Independent of the spatial distribution and length scale of the eddies.

[0045] Specifically, when generating synthetic vortices, a random perturbation function is introduced to adjust the initial parameters of the vortices. The introduction of the random perturbation function to adjust the initial parameters of the vortices makes the generation of synthetic vortices more consistent with the complex and changeable characteristics of actual turbulence. In actual turbulent phenomena, the initial state of the vortex is affected by a variety of uncertain factors and presents a high degree of randomness. The initial parameters of the vortices generated by traditional methods are relatively fixed, making it difficult to accurately simulate this real situation. The present invention uses a random perturbation function to allow the initial parameters of each synthetic vortex to randomly vary within a certain range, greatly enhancing the randomness and diversity of the synthetic vortex, thereby making the generated turbulence closer to the actual situation and improving the authenticity and reliability of the simulation. This random adjustment of the initial parameters of the vortex helps to improve the uniformity and stability of the synthetic velocity field. In large eddy simulation, the uniformity and stability of the velocity field are crucial to the accuracy of the simulation results. The introduction of the random perturbation function avoids local anomalies in the velocity field caused by the overly regular initial parameters of the vortex, making the velocity field more evenly distributed in the entire calculation domain and with smaller fluctuations. This not only improves the accuracy of the simulation results, but also reduces the calculation errors and convergence problems that may be caused by the instability of the velocity field, and ensures the smooth progress of the simulation process. Since the generated synthetic vortex is more consistent with the actual turbulence situation, and the synthetic velocity field is more uniform and stable, the present invention can more accurately capture the characteristics of small-scale turbulence in subsequent calculations. Small-scale turbulence plays a key role in many practical engineering problems, and accurately simulating small-scale turbulence is crucial to understanding and solving these problems. The present invention improves the resolution and simulation capabilities of small-scale turbulence by enhancing the randomness of the synthetic vortex and optimizing the velocity field, providing more valuable reference data for related engineering fields. This improvement is also conducive to reducing computing costs. A more accurate simulation means that when achieving the same accuracy requirements, there is no need to perform too many calculation iterations or use too high a computational grid density as in traditional methods. By optimizing the generation of synthetic vortices and the characteristics of the velocity field, unnecessary calculations are reduced, thereby effectively improving computing efficiency and reducing the consumption of computing resources while ensuring the quality of the simulation.

[0046] Furthermore, in step 2, the dynamic normalization factor is determined by taking the running average of the eddy current concentration, so that the synthetic velocity field satisfies the unit variance condition and accurately recovers the target first-order and second-order statistics.

[0047] Specifically, in the present invention, the dynamic normalization factor is determined by taking the running average of the eddy concentration. This unique approach effectively solves the problem that the traditional method is difficult to accurately control the statistical characteristics of the vortex when generating turbulent inlet conditions. In traditional eddy generation methods, due to the lack of scientific setting of the normalization factor, the randomness and statistical characteristics of the generated eddies are biased, and the preset turbulence statistics cannot be accurately restored. The present invention determines the dynamic normalization factor by accurately calculating the running average of the eddy concentration, so that the synthetic velocity field can meet the unit variance condition while meeting the preset first-order and second-order statistics. This means that the simulation results can more accurately reflect the statistical laws of actual turbulence, greatly improving the statistical accuracy of the simulation. In actual engineering applications, such as aeroengine design, automotive aerodynamics research and other fields, more accurate turbulence simulation data can provide engineers with a more reliable basis, helping them to more deeply understand the fluid flow characteristics, thereby optimizing product design and improving product performance. This way of determining the dynamic normalization factor makes the statistical properties of the synthetic eddy velocity field independent of the spatial distribution and length scale of the vortex. This characteristic is of great significance. It overcomes the interference of the spatial distribution and length scale of vortices on statistical properties in traditional methods, ensuring stable and accurate statistical results regardless of the distribution of vortices at the computational domain entrance or how their length scale varies. In complex engineering scenarios, the geometry and boundary conditions of the flow field are ever-changing, and traditional methods often struggle to ensure simulation accuracy and stability in these complex situations. However, this characteristic of the present invention makes simulation results unaffected by these factors, providing greater adaptability and versatility, and capable of stably outputting reliable simulation data in various application scenarios. By accurately recovering the target first-order and second-order statistics, the present invention provides a solid data foundation for subsequent numerical simulation and analysis. In large eddy simulation, these statistics are key parameters for studying turbulent characteristics and predicting flow behavior. Accurate statistical data can help researchers further study the physical mechanisms of turbulence, such as turbulent energy transfer and vortex interactions. At the same time, in engineering design, optimization based on these accurate statistical data can effectively improve product performance and reliability. For example, in the aviation field, accurate turbulence simulation can help design more efficient wings and engine air inlets, reduce drag, and improve fuel efficiency; in the energy field, for the design of wind turbine blades, accurate turbulence simulation can help improve the efficiency of wind energy capture and reduce costs. In addition, this method of determining the dynamic normalization factor also indirectly improves computational efficiency. Because the statistical properties of the synthetic velocity field are more stable and accurate, a large number of iterative calculations are not required to correct statistical errors during the simulation process, reducing the waste of computing resources. At the same time, stable statistical properties also help to speed up the convergence of the calculation, allowing the entire simulation process to be completed in a shorter time.

[0048] Furthermore, in step 3, the calculation formula for the eddy current number is:

[0049]

[0050] Among them A inlet is the inlet area, A eddy is the average effective cross-sectional area of the vortex.

[0051] Specifically, the number of vortices is determined based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, which provides a scientific and reasonable basis for the simulation process. Compared with the traditional method, this calculation method fully considers the three-dimensional characteristics of the vortex, so that the determined number of vortices is more in line with the actual flow field conditions. In actual fluid flow, vortices do not exist in isolation, and their distribution is closely related to the geometric shape and boundary conditions of the flow field. The traditional method of setting the number of vortices is often relatively simple and does not fully consider these complex factors, which easily leads to a large deviation between the simulation results and the actual situation. The calculation formula of the present invention comprehensively considers the inlet area and the effective cross-sectional area of the vortex itself, and can more accurately reflect the number of vortices required under different working conditions, thereby optimizing the small-scale turbulence coverage effect. For example, in the complex flow field environment inside an aircraft engine, accurate vortex number setting can more accurately simulate small-scale turbulence phenomena, providing strong support for the optimization of engine performance. This way of determining the number of vortices significantly improves the calculation efficiency while ensuring the simulation accuracy. The number of vortices obtained through precise calculation can not only ensure that the small-scale turbulent area is fully covered and the key information in the flow field is accurately captured, but also avoid the waste of computing resources caused by the excessive generation of unnecessary vortices. In the case of high Reynolds numbers and complex geometric shapes, traditional methods require a large amount of computing resources to generate turbulent boundary conditions, and the computational efficiency is low. The present invention reduces the required number of vortices while maintaining the target "vortex density" by reasonably determining the number of vortices, so that large-scale LES simulations can be completed in a shorter time. This not only reduces the computing cost, but also improves research efficiency, allowing engineers and researchers to evaluate and optimize multiple design schemes in a shorter time, accelerating the product development process. In practical applications, the calculation formula enhances the reliability and repeatability of the simulation results. Because it is based on clear physical parameters and mathematical relationships, different researchers can obtain consistent vortex number settings when using the method of the present invention, as long as they are given the same inlet area and average effective cross-sectional area of the vortex, thereby ensuring the consistency and comparability of the simulation results.

[0052] Furthermore, the improved large eddy simulation vortex generation method is implemented based on OpenFOAM software, and a TurbulentInletSEM inlet condition library is established for simulation calculation.

[0053] Specifically, OpenFOAM software is a widely used and open source computational fluid dynamics software platform with a rich algorithm library and powerful computing power, which provides a solid foundation for implementing the method of the present invention. Development based on OpenFOAM software can make full use of its existing mature technology and extensive user community resources. This means that the method of the present invention can quickly build a simulation environment with the help of the advantages of OpenFOAM, reducing the large amount of time and energy required for development from scratch. At the same time, the open source nature of OpenFOAM enables researchers to have an in-depth understanding of the code details, flexibly adjust and optimize the algorithm according to specific needs, and further improve the accuracy and adaptability of the simulation to meet the special requirements of large eddy simulation in different engineering scenarios. The establishment of the TurbulentInletSEM inlet condition library provides a convenient and efficient calling method for simulation calculations. When performing large eddy simulation, the accurate setting of the inlet conditions is crucial to the simulation results.

[0054] The TurbulentInletSEM inlet condition library encapsulates the improved vortex generation method of the present invention. Users can simply call it and apply the method in simulation calculations to generate turbulent inlet conditions that meet the requirements, avoiding tedious repetitive programming work. This not only greatly improves the work efficiency of researchers, but also lowers the entry threshold for use, allowing more engineers without deep programming skills to easily use the technology of the present invention for simulation analysis. In addition, the establishment of this inlet condition library enables the method of the present invention to better cooperate with other functional modules of the OpenFOAM software, realizing the integration and automation of the simulation process, further improving the fluency and stability of the simulation calculation. This implementation method based on specific software and the inlet condition library is conducive to the promotion and application of the technology of the present invention. In the engineering field, many research teams and companies are already using OpenFOAM software for related research and design work. The present invention is implemented based on OpenFOAM, allowing these users to easily integrate the new vortex generation method into existing workflows without having to relearn and adapt to a new software platform. At the same time, the sharing and dissemination of the inlet condition library is also easier, and researchers can quickly reuse the library in different projects, accelerating the diffusion of the technology.

[0055] A storage medium stores a computer program, which, when executed by a processor, implements the improved large eddy simulation vortex generation method.

[0056] Specifically, the storage medium, as a carrier of the computer program, achieves the solidification and preservation of the technology, greatly enhancing the transferability of the technology of the present invention. Traditional technology dissemination relies heavily on text descriptions and oral teaching, in which technical details are easily lost or misunderstood. However, by writing the method of the present invention into a computer program and storing it on a storage medium, a series of complex technical steps and algorithmic logic, from eddy current generation to normalization processing, are completely preserved. Whether the current researchers involved in R&D review the research process later or new researchers join the project, they can accurately obtain the core technical content of the present invention by reading the program on the storage medium, avoiding information decay during the technology transfer process and ensuring the long-term maintenance of the integrity of the technology. In terms of application expansion, the presence of the storage medium makes the method of the present invention easily applicable across different computing devices and R&D environments. As long as the corresponding processor and operating environment are available, the computer program in the storage medium can be executed on various computer devices. This breaks the hardware and environmental limitations of technology application. Whether it is conducting large-scale complex simulations on the high-performance computing cluster of a scientific research institution or conducting rapid verification of small projects on an engineer's personal workstation, the technology of the present invention can be conveniently applied. At the same time, across diverse industries, such as aerospace, automotive engineering, and energy, any work involving LES can access and apply this method through storage media, broadening the technology's scope of application and promoting cross-disciplinary technical exchange and collaboration. From the perspective of optimizing R&D processes, computer programs on storage media facilitate the reuse and improvement of technology. In actual R&D work, simulation methods often require multiple trials and optimizations. Storage media allows researchers to quickly access previously written programs and modify and improve them without having to rewrite the code each time, saving significant manpower and time. Furthermore, during collaborative R&D, storage media serves as a shared resource, facilitating the sharing of code and research results among team members. This promotes communication and collaboration, improves R&D efficiency, accelerates project progress, and drives the continuous development and innovation of LES technology.

[0057] A computer device comprises: a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the above-mentioned improved large eddy simulation vortex generation method.

[0058] Specifically, the computer device of the present invention provides a stable and efficient operating environment for the improved large eddy simulation (LES) vortex generation method. The processor, as the core computing unit, possesses powerful data processing capabilities, enabling rapid processing of large amounts of data related to vortex generation, velocity field normalization, and vortex number determination based on inlet area and average effective cross-sectional area. When generating synthetic vortices based on target turbulence statistics, the processor operates at high speed to accurately simulate random spatial distributions. When normalizing the synthetic vortex velocity field to meet preset first- and second-order statistics, the processor rapidly performs complex mathematical operations, ensuring the accuracy and timeliness of the results. This efficient computing capability addresses the slow computational speed and inefficiency typically associated with traditional computing devices when handling such complex simulation tasks, enabling the entire simulation process to be completed in a relatively short time, meeting the demand for rapid simulation results in practical engineering applications and scientific research. Memory plays an indispensable role in this computer device, providing stable storage and access support for various types of data during the simulation process. During simulation execution, it stores a large amount of information, including the initial parameters required to generate the synthetic vortexes, intermediate data during the calculation process, and the final simulation results. These data are the basis for the smooth progress of the simulation. The stable storage performance of the memory ensures the integrity and accuracy of the data, avoiding the impact of data loss or errors on the simulation results. At the same time, the fast data reading capability enables the processor to obtain the required data in a timely manner, further improving the fluency and efficiency of the entire simulation process, and ensuring the efficient operation of a series of operations from eddy current generation to the final simulation result output. The simulation method program of the present invention is integrated into the computer device to achieve a deep fusion of hardware and software. This fusion greatly reduces the threshold for using the technology. For engineers and scientific researchers, there is no need to spend a lot of energy to build a complex computing environment and configure software parameters. They can easily use the variable dimension simulation method of the present invention by operating the computer device. This not only improves R&D efficiency, but also enables more people to participate in related research and engineering design work, promoting the widespread application and further innovation of technology. In the fields of aerospace, automotive engineering, etc., engineers can use the computer device to quickly simulate turbulence conditions under different working conditions, provide strong support for product design, shorten product R&D cycle, reduce R&D costs, promote the continuous advancement of related industry technologies, and enhance the competitiveness of the entire industry.

[0059] The following is a specific embodiment of the present invention:

[0060] The present invention provides an improved vortex generation method (SEM) for generating inlet turbulence in large eddy simulation (LES). The method improves the control accuracy of the randomness and statistical characteristics of the vortex by introducing a new vortex generation algorithm.

[0061] The original SEM method generates vortices by defining a fixed number of synthetic vortices with tight support, which are generated in a virtual Cartesian box at the inlet. The velocity field formed by these vortices is formulated as follows:

[0062]

[0063] However, this method will lead to uneven eddy currents, which will introduce errors when reconstructing the Reynolds stress, and such a velocity field cannot meet the unit variance condition of the initial velocity field.

[0064] The improved SEM method of the present invention proposes a universal normalization factor, which can be found by taking a running average of the eddy current concentration. The improved eddy current velocity field becomes:

[0065]

[0066] This modification makes it easier to obtain the relevant statistical properties of the eddy current. Compared with the original SEM, the statistical properties as well as It will be independent of the spatial distribution and length scale of the vortices, and the accuracy of the calculation results will be higher.

[0067] For the location of eddy currents, in traditional SEM methods, the recommended number of eddy currents N is set as:

[0068]

[0069] Where V eddy is the volume with the smallest volume among all vortices. This method is relatively simple when defining the size, but in many practical cases, the number of vortices is large, and using this definition method will significantly increase the computational cost.

[0070] The improved SEM method also optimizes the number of eddy currents, and its formula is:

[0071] Among them A inlet is the inlet area, A eddy is the average effective cross-sectional area of the vortex. This definition takes into account the three-dimensional characteristics of the vortex, and can make all vortices active, and has better coverage in small-scale areas.

[0072] Figure 1 and Figure 2 The improved SEM method and the original SEM method generate distribution maps of a fixed number of randomly placed eddies.

[0073] By comparison, it can be found that the original SEM method of locating eddies results in insufficient coverage of areas with smaller length scales, while the eddies generated by the improved SEM are all concentrated near the dotted lines, indicating that all eddies are active and have better coverage of small length scale areas. In actual calculations, the computational cost will also be relatively reduced.

[0074] To verify the effectiveness of the present invention in numerical simulations, the improved SEM method was written into code and compiled based on the OpenFOAM software. An OpenFOAM library for generating inlet turbulence conditions was established, and a new inlet condition named TurbulentInletSEM was defined for use in simulation calculations.

[0075] The results of testing the new method show that it is able to recover the first- and second-order statistics of the target at the inlet regardless of the vortex distribution and uniformity, and can place vortices at arbitrary positions in space, which greatly increases the possibility of improving the efficiency of the SEM method.

[0076] In the vortex generation link, the present invention generates randomly distributed synthetic vortices at the entrance of the computational domain based on the statistical characteristics of the target turbulence, breaking through traditional limitations and being able to be placed at any position, ensuring that the vortices are active at each time step and highly restoring the actual turbulent state. The synthetic vortex velocity field is normalized by a dynamic normalization factor, which is determined by the running average value of the vortex concentration. This not only makes the synthetic velocity field meet the preset first-order and second-order statistics and unit variance conditions, but also allows the statistical properties to break away from the influence of the spatial distribution and length scale of the vortex, significantly improving the statistical accuracy of the simulation and providing reliable data for engineering design and scientific research. The number of active vortices is determined based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, optimizing the small-scale turbulence coverage, reducing the number of vortices while maintaining the vortex density, bringing about an improvement in computational efficiency of about 1-2 orders of magnitude, reducing computational costs, and being suitable for large-scale LES simulations. In addition, this method is implemented based on the OpenFOAM software and establishes a TurbulentInletSEM inlet condition library, leveraging the advantages of the software to reduce development costs, facilitate calling, and facilitate technology promotion. The coordination of storage media and computer equipment enables the inheritance of technology and cross-environmental application. The deep integration of hardware and software lowers the threshold for use, promotes technological innovation and industry development, and has a wide range of applications, covering aerospace, automotive engineering and other fields.

[0077] While the specific embodiments of the present invention have been described in detail above, these are intended to be exemplary only, and the present invention is not limited thereto. Any equivalent modifications or substitutions to the present invention that would be apparent to those skilled in the art are also within the scope of the present invention. Therefore, any equivalent modifications or substitutions made without departing from the spirit and scope of the present invention are intended to be encompassed within the scope of the present invention.

Claims

1. An improved large eddy simulation vortex generation method, characterized in that: The method comprises the following steps: Step 1: Based on the statistical characteristics of the target turbulence, a synthetic vortex with random spatial distribution is generated at the inlet of the computational domain; Step 2: normalizing the velocity field of the synthetic vortex by a dynamic normalization factor so that the synthetic velocity field satisfies preset first-order and second-order statistics; Step 3: Determine the number of active vortices based on the ratio of the inlet area to the average effective cross-sectional area of the vortex, and allow the synthetic vortex to be dynamically distributed at any position in the inlet space to optimize small-scale turbulence coverage and computational efficiency.

2. The improved large eddy simulation vortex generation method according to claim 1, characterized in that: In step 2, the velocity field of the synthetic vortex after normalization is: Statistical properties as well as Independent of the spatial distribution and length scale of the eddies.

3. The improved large eddy simulation vortex generation method according to claim 2, characterized in that: In step 2, the dynamic normalization factor is determined by taking the running average of the eddy current concentration, so that the synthetic velocity field satisfies the unit variance condition and accurately recovers the target first-order and second-order statistics.

4. The improved large eddy simulation vortex generation method according to claim 3, characterized in that: In step 3, the eddy current number is calculated as: Among them A inlet is the inlet area, A eddy is the average effective cross-sectional area of the vortex.

5. The improved large eddy simulation vortex generation method according to claim 4, characterized in that: The improved large eddy simulation vortex generation method is implemented based on OpenFOAM software, and a TurbulentInletSEM inlet condition library is established for simulation calculation.

6. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the improved large eddy simulation vortex generation method according to any one of claims 1 to 5 is implemented.

7. A computer device, characterized in that: include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the improved large eddy simulation vortex generation method according to any one of claims 1 to 5.

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