CN111310316A - Vehicle model selection method based on high-precision simulation of far-field noise of high-speed train - Google Patents
Vehicle model selection method based on high-precision simulation of far-field noise of high-speed train Download PDFInfo
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Abstract
The invention relates to a model selection method based on high-precision simulation of far-field noise of a high-speed train, which comprises the following steps of: s1) constructing a geometric model; s2) dividing grids according with the flow field characteristics of the high-speed train by cutting the grids; s3) establishing a far-field noise simulation model; s4) performing steady-state flow calculation and transient flow calculation according to the noise simulation model; s5) setting a sound source surface and collecting noise source data; s6) carrying out simulation calculation of the far-field noise of the high-speed train to obtain the far-field noise of a plurality of noise receiving points.
Description
Technical Field
The invention relates to a train far-field noise simulation method, in particular to a model selection method based on high-precision simulation of far-field noise of a high-speed train.
Background
The high-speed train high-speed operation brings strong pneumatic noise, and generates strong noise interference to the riding comfort in the train and the environment outside the train. How warriors indicate that the problems of noise and vibration can restrict the bottleneck of further speed increase of high-speed trains in China. Pneumatic noise generated by a high-speed train is transmitted outside the train, so that great interference is brought to life and work of residents along the line, and the pneumatic noise is one of environmental pollution which is strictly controlled by national and foreign laws and regulations. As the construction of high-speed railways has advanced from four longitudinal directions, four transverse directions to eight longitudinal directions, eight transverse directions and development along the high-speed railway line, the control of far-field noise of high-speed trains becomes more and more important. How to simulate the far-field noise quickly and accurately becomes a problem which needs to be solved urgently at present. Traditional pneumatic noise simulation by direct numerical simulation is theoretically possible, but requires enormous computational resources, which are currently not allowed by computational power and time. The acoustic analogy-based method is the only method currently used for aerodynamic noise simulation of high-speed trains, and the method is basically feasible not only from theory but also from practical operation. However, from the present disclosure, the overall simulation accuracy is not high. Most simulation results lack test verification, and extremely individual simulation results with test verification have large magnitude difference and obvious frequency spectrum difference. There are various reasons that the model and the grid are unreasonable to construct, and the calculation setting and the evaluation are unreasonable.
Patent CN108009344A discloses a method for train far-field aerodynamic noise simulation, which includes constructing various three-train marshalling train models according to relevant parameters of a train, and constructing an aerodynamic noise source file and a network file from the models, so as to realize train whole-train far-field aerodynamic noise simulation. According to the patent disclosure, the method is based on a FLUENT platform, a tetrahedral grid is adopted in a region close to a train, a hexahedral grid is adopted in a region far away from the train, and pentahedron is adopted for transition between the tetrahedral grid and the hexahedral grid. The steady state uses a standard k-epsilon turbulence model, and the transient state uses a large vortex simulation and an earlier and simpler vortex-viscous sub-lattice model. The given sound pressure level frequency spectrum curve has huge fluctuation, the middle and low frequency band is about 20dB, the high frequency band is about 40dB, and the difference from the real situation is larger. In a word, the method has poor far-field noise simulation precision and no test result support, and is more suitable for a simpler high-speed train model and obviously not suitable for a complex high-speed train model and high-precision requirements due to the high requirement of the FLUENT platform on the grid quality.
When the current high-speed train is selected or optimized, the shape is changed slightly, and the changes cause little far-field noise change. With a clearly different head shape, it is possible that far field noise differs by less than 2 dB. Optimization of certain components, such as pod shape optimization or changing the truck skirt, may differ by less than 1.5dB in far field noise. The prior art and the method obviously cannot realize the type selection or optimization, and a method with higher precision is urgently needed.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, improve a simulation model and a turbulence model, and provide a model selection method based on high-precision simulation of far-field noise of a high-speed train, which is suitable for meeting the requirement of high-precision model selection and improving the performance of the high-speed train.
The purpose of the invention can be realized by the following technical scheme:
a vehicle model selection method based on high-precision simulation of far-field noise of a high-speed train completes the optimization and model selection of vehicle models according to the simulation test result of the far-field noise of trains of different vehicle models, and the simulation test of the far-field noise of the train comprises the following steps:
s1) constructing a geometric model;
s2) dividing grids according with the flow field characteristics of the high-speed train by cutting the grids;
s3) establishing a far-field noise simulation model;
s4) performing steady-state flow calculation and transient flow calculation according to the noise simulation model;
s5) setting a sound source surface and collecting noise source data;
s6) performing simulation calculation of the far-field noise of the high-speed train to obtain the far-field noise of a plurality of noise receiving points.
Further, the geometric model include high-speed train model, track, road bed and virtual wind-tunnel, consider road bed and track when the simulation model is established for the first time, on the one hand can more truly reproduce the train in the actual line condition, on the other hand can establish the model unanimous with the wind-tunnel test, the high-speed train model including connect gradually form the head car, middle car and the tail car of automobile body and set up bogie, carriage junction and the pantograph on the automobile body, the middle car be in the straight section of high-speed train model, head car and tail car respectively including nose point, the curve section and the straight section of transition in proper order.
Further, the step S2) specifically includes:
s201) creating boundary layer grids simulating the surface of the high-speed train;
s202) dividing a face grid and a body grid in a flow large separation area;
s203) adopting a mode of gradually increasing from the vehicle body to the outside to create a spatial volume grid;
s204) creating a grid encryption zone.
By adopting a cutting grid dividing technology with smaller numerical errors, the method realizes smaller grid size and lower numerical dissipation, and avoids unreasonable attenuation of flow field pulsation.
Furthermore, the large flow separation area comprises curve sections of a head car and a tail car, a bogie, a pantograph and a carriage joint, and the spatial body grids comprise a track body grid, a roadbed body grid and a virtual wind tunnel body grid.
Still more preferably, the area of the surface mesh in the flow large separation area is determined according to the size of each component and is less than 3 mm.
Still further preferably, the surface grid of the track is smaller than 3mm, the surface grid of the roadbed is smaller than 6mm, and the surface grid of the virtual wind tunnel is smaller than 48 mm.
Further, the step S3) specifically includes:
s301) setting boundary conditions;
s302) setting a turbulence model for steady-state simulation and a turbulence model for transient simulation;
s303) setting a time value format and a space value format, and giving a time step length and an iteration step number.
Further preferably, the boundary conditions include a virtual wind tunnel inlet, a virtual wind tunnel outlet, two sides of a virtual wind tunnel, a virtual wind tunnel top surface, a virtual wind tunnel ground, a roadbed, a rail and a train surface;
the virtual wind tunnel inlet is set according to the test wind speed and the incoming flow turbulence degree, or set according to the train speed and the corresponding turbulence degree;
the virtual wind tunnel outlet is set as a pressure outlet with free outflow or relative pressure of 0 Pa;
the two sides of the virtual wind tunnel and the top surface of the virtual wind tunnel are set as symmetrical boundaries so as to avoid the generation of a counter pressure gradient in a test section caused by the growth of the boundary layer;
the virtual wind tunnel ground, the roadbed, the track and the train surface are set to be non-slip wall surfaces, and the roughness is set to be smaller.
Further preferably, the steady-state simulated turbulence model is k- ω sst (shear stress), and the transient simulated turbulence model is WLES (Large impact Simulation + Wall adaptation local impact visualization). Compared with the traditional model, the WALE (wall adaptive Local Eddy sensitivity) model is insensitive to coefficient values, does not require any damping on the near wall surface, and improves the prediction accuracy near the wall surface. Under the steady-state simulation and the transient simulation, the time and space numerical formats are both at least a second-order format.
Further, the step S5) specifically includes:
s501) setting an internal surface created on or near the surface of the vehicle body as a sound source surface;
s502) collecting noise source data of set time after the flow field enters dynamic stability, wherein the set time t is longer than the flow time, and the flow time is the ratio of the vehicle length to the vehicle speed.
Further, the far-field noise is simulated through an FW-H (Ffowcs Williams & Hawkings) equation to obtain the average total sound pressure level and the corresponding sound pressure level frequency spectrum of all noise receiving points.
The plurality of noise receiving points are arranged at the vehicle height which is at least six times away from the vehicle body to form a noise receiving point array;
in the height direction, the noise receiving lattice columns are a plurality of rows of noise receiving points with different heights;
in the flow direction, the first noise receiving point in the noise receiving point array is over against the nose tip of the head car, the last noise receiving point is over against the carriage junction between the middle car and the tail car or the nose tip of the tail car, and other noise receiving points are arranged between the first noise receiving point and the last noise receiving point at equal intervals.
And step S6), comparing the tested and simulated spectrum curves of the same noise receiving point and the error between the simulated value and the tested value of the total sound pressure level of all the noise receiving points through a far-field noise wind tunnel test, and indicating that the simulation method has high precision.
Compared with the prior art, the invention has the following advantages:
1) according to the method, the roadbed and the track are considered for the first time when the simulation model is established, so that the actual line condition of the train can be more truly reproduced on one hand, and a model consistent with a wind tunnel test can be established on the other hand;
2) the invention adopts a cutting grid dividing technology with smaller numerical error, realizes smaller grid size and lower numerical dissipation, and effectively avoids unreasonable attenuation of flow field pulsation compared with the traditional method of dividing tetrahedral grids near the vehicle body, dividing hexahedral grids far away from the vehicle body and dividing pentahedral grids between the tetrahedral grids and the hexahedral grids;
3) according to the method, a WALE model insensitive to coefficient values is adopted, an early and simpler sub-lattice model is abandoned, the model does not require any damping on the near wall surface, and the prediction precision near the wall surface is greatly improved;
4) the prior related technology lacks the support of the wind tunnel test result, and the defect of prediction cannot be known, so that the multiple iterative improvement is not carried out, and the simulation method provided by the invention obtains the verification of the wind tunnel test, which shows that the precision is high;
5) the simulation result precision in the invention is far higher than that of the existing method, so that the method can be used for selecting different schemes with small difference, and the optimal model selection of the train model under the modern high-precision requirement is realized.
Drawings
FIG. 1 is a schematic flow chart of a simulation method of the present invention;
FIG. 2 is a far field noise simulation model of a high speed train;
FIG. 3 is a schematic diagram of a grid encryption zone;
FIG. 4 is a diagram of far field noise reception points;
FIG. 5 is a comparison of the sound pressure level spectra at measurement point 4 and measurement point 22, where FIG. 5a is a comparison of the sound pressure level spectra at measurement point 4 facing the first bogie of the head car and FIG. 5b is a comparison of the sound pressure level spectra at measurement point 22 facing the pantograph.
101, a head car, 102, a middle car, 103, a tail car, 104, a connecting part of the head car and the middle car, 105, a connecting part of the middle car and the tail car, 106, a trailer bogie, 107, a power bogie, 108, a pantograph, 109, a guide cover, 110, a track, 111, a roadbed, 112, a virtual wind tunnel, 201, a first encryption region, 202, a second encryption region, 203, a third encryption region, 204, a fourth encryption region, 205, a fifth encryption region, 206, a sixth encryption region, 207, a seventh encryption region, 208 and an eighth encryption region.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be obtained by a person skilled in the art without any inventive step based on the embodiments of the present invention, shall fall within the scope of protection of the present invention.
Examples
A model selection method based on high-precision simulation of far-field noise of a high-speed train completes the optimized model selection of the train model according to the simulation test result of the far-field noise of trains of different train models, wherein the simulation method comprises the steps of constructing a reasonable geometric model; dividing grids which accord with the flow field characteristics of the high-speed train; setting a proper boundary condition; an effective turbulence model and a numerical format are adopted; and determining a proper far-field noise evaluation standard and the like. The simulation method provided by the invention can be suitable for all high-speed train models, and is proved to have high precision through test verification, and can realize accurate simulation of far-field noise, thereby providing powerful guarantee for model selection and accurate sequencing of an optimization scheme.
As shown in fig. 1, the simulation method includes the following steps:
s1) constructing a geometric model;
s2) dividing grids according with the flow field characteristics of the high-speed train by cutting the grids;
s3) establishing a far-field noise simulation model;
s4) performing steady-state flow calculation and transient flow calculation according to the noise simulation model;
s5) setting a sound source surface and collecting noise source data;
s6) performing simulation calculation of the far-field noise of the high-speed train to obtain the far-field noise of a plurality of noise receiving points.
Wherein, the main key steps are as follows:
1) and constructing a reasonable geometric model. The current high-speed train model for wind tunnel test is mainly a three-train marshalling model with a 1:8 scaling ratio, which is limited by the size of wind tunnel test sections at home and abroad. The constructed geometric model comprises a 1:8 scaling high-speed train model, a track, a roadbed and a virtual wind tunnel. The high-speed train comprises a head train, a middle train, a tail train, a bogie, a carriage joint and a pantograph, wherein the head train, the middle train and the tail train are sequentially connected to form a train body, the bogie, the carriage joint and the pantograph are arranged on the train body, the middle train is located in a straight section of a high-speed train model, the head train and the tail train respectively comprise a nose tip, a curve section and a straight section which are sequentially transited, and the geometric model comprises key areas such as a head type, the bogie, the carriage joint and the pantograph. The track and the roadbed are both standard tracks and standard roadbeds in China. Creating a virtual wind tunnel reference standard TB/T3503.4-2018;
2) and dividing grids which accord with the flow field characteristics of the high-speed train. Firstly, establishing a grid capable of better simulating a surface boundary layer of a high-speed train, then dividing a small surface grid and a small body grid in a large flow separation area such as a curve section of a head-tail train, a bogie, a pantograph and a carriage joint, and finally adopting a mode of gradually increasing from a train body to the outside for the space body grid;
3) appropriate boundary conditions are set. The virtual wind tunnel inlet is set according to the test wind speed and the incoming flow turbulence degree, or set according to the actual speed and the corresponding turbulence degree of the train. The virtual wind tunnel outlet assumes a wake sufficiently developed to be set as a free outflow or a pressure outlet with a relative pressure of 0 Pa. Symmetrical boundaries are adopted on two sides of the virtual wind tunnel and the top surface of the virtual wind tunnel so as to avoid the condition that the boundary layer grows to cause the adverse pressure gradient generated in the test section. The virtual wind tunnel ground, the roadbed, the track and the train surface all adopt non-slip wall surfaces, and the roughness is set to be smaller.
4) An efficient turbulence model and numerical format are employed. In order to obtain a high-precision far-field noise simulation result, a k-w SST turbulence model is adopted for steady-state simulation, and a WLES turbulence model developed in recent years is adopted for transient simulation. The time and space numerical format is at least a second order format, whether steady state or transient simulation. An implicit format should be used and given a small time step and a sufficient number of iteration steps.
5) A reasonable sound source plane is set and sufficiently long noise source data is collected. Far field noise simulation is performed by using the FW-H equation, and only the surface of the vehicle body or an internal surface near the surface of the vehicle body can be selected as a sound source surface. After the flow field enters the dynamic stability, collecting noise source data of at least more than one time of flow time (t > L/U, L is the vehicle length, and U is the vehicle speed).
6) And determining a proper far-field noise evaluation standard. In order to meet the compact sound source requirement, a plurality of noise receiving points should be created at least six times outside the vehicle height away from the vehicle body. In the flowing direction, the first noise receiving point can be over against the nose tip of the head car, the last noise receiving point can be over against the carriage junction between the middle car and the tail car or the nose tip of the tail car, and a plurality of noise receiving points can be arranged at equal intervals between the two noise receiving points. In the height direction, a plurality of rows of noise-receiving points of different heights should be arranged to evaluate the effect of ground reflection. The evaluation of the far-field noise is not suitable for evaluating the total sound pressure level of a single point, but the evaluation is carried out by mainly adopting the average total sound pressure level of all points and assisting the corresponding sound pressure level frequency spectrum.
The specific implementation of the simulation method and the test provided in this embodiment is as follows:
as shown in fig. 2, the far-field noise simulation model of the high-speed train in the present embodiment includes a head car 101, a middle car 102, a tail car 103, a car junction 104 between the head car and the middle car, a car junction 105 between the middle car and the tail car, a trailer bogie 106, a power bogie 107, a pantograph 108, a wind deflector 109, a rail 110, a roadbed 111, and a virtual wind tunnel 112. The high-speed train comprises a head train 101, a middle train 102 and a tail train 103 which are sequentially connected to form a train body of the high-speed train, wherein the middle train 102 is located at a straight section of a high-speed train model, the head train 101 and the tail train 103 respectively comprise a nose tip, a curve section and a straight section which are sequentially transited, a power bogie 107 is arranged at the curve section of the head train 101, trailer bogies 106 are respectively arranged at the tail of the head train 101 and the front of the middle train 102, the trailer bogies 106 are respectively arranged at the rear part of the middle train 102 and the front part of the tail train 103, and a connecting part 105 of the middle train and the tail train is provided with a pantograph 108 and a flow.
For a 1:8 scale three consist high speed train model, a grid is first created that conforms to its flow characteristics. The size of the plane grid of the straight section of the car body is less than 5mm, the plane grid of the curve section of the head car 101 and the tail car 103 is less than 3mm, and the plane grid of the nose tip of the head car 101 and the tail car 103 is less than 2 mm. The surface grids of the obstacle deflector at the bottom of the vehicle body, the bogie cabin, the junction 104 of the head vehicle and the middle vehicle and the junction 105 of the middle vehicle and the tail vehicle are less than 3 mm. The trailer bogie 106, the power bogie 107 and the pantograph 108 create surface meshes with different sizes according to the sizes of parts of the trailer bogie 106, the surface mesh of a larger part is required to be smaller than 3mm, the surface mesh of a smaller part is required to be smaller than 2mm, and parts below 1mm can be directly deleted. The surface grid of the track 110 should be less than 3mm and the surface grid of the roadbed 111 should be less than 6 mm. The face grid of virtual wind tunnel 112 should be less than 48 mm. Finally, as shown in fig. 3, grid encryption regions are respectively created in the curved sections of the head car 101 and the tail car 103, the areas of the trailer bogie 106 and the power bogie 107, the area of the pantograph 108, the area 104 of the carriage connection between the head car and the intermediate car, and the area 105 of the carriage connection between the intermediate car and the tail car, wherein the first encryption region 201 is a grid encryption region of the virtual wind tunnel 112 far away from the car body and is 24mm in size, the second encryption region 202 is a grid encryption region of the virtual wind tunnel 112 near the car body and is 12mm in size, the third encryption region 203 is a grid encryption region of the curved section of the head car and is 6mm in size, the fourth encryption region 204 is a grid encryption region of the obstacle eliminator of the head car and is 1.5mm in size, the fifth encryption region 205 is a grid encryption region of the power bogie 107 and is 3mm in size, the sixth encryption region 206 is a grid encryption region of the trailer bogie 106 and is 6mm in size, the seventh encryption region 207 is a grid encryption region of the connection between the carriage connection 104 between the head car and the intermediate car 105, the size is 6mm, and the eighth encryption region 208 is a grid encryption region of the pantograph 108, and the size is 6 mm.
And then respectively adopting a k-omega SST turbulence model to perform steady-state flow calculation, and adopting a WLES turbulence model to perform transient flow calculation. Wherein, the setting of each boundary condition is as follows: the given speed of the virtual wind tunnel inlet is 300km/h, the relative pressure of the virtual wind tunnel outlet is 0Pa, and the turbulence degree and the viscosity rate of the virtual wind tunnel inlet are 1% and 10% respectively; the top and two sides of the virtual wind tunnel are symmetrical boundaries; the virtual wind tunnel ground, the roadbed, the track and the train surface are all non-slip wall surfaces. The numerical format, time step size and iteration step number are set as follows: the steady-state simulation uses a second-order windward format to iterate 3000 steps, and iteration is determined to reach convergence by monitoring residual error, aerodynamic force and key point speed. The time value format and the space value format of the transient simulation both adopt a second-order format, and the calculation scheme shown in table 1 is adopted in consideration of calculation accuracy and calculation efficiency.
TABLE 1 time step and iteration step settings
In order to verify the effect of the simulation method of the present invention, three rows of 30 noise receiving points in total were arranged outside the section 7.5m from the vehicle body, as shown in fig. 4. The heights of the measuring points are 0.4m, 0.8m and 1.2m away from the ground respectively, the distance between the measuring points in the horizontal direction is 0.8m, the first measuring point is over against the position of the nose tip of the head type, and the far-field noise evaluation of the high-speed train is carried out through the magnitude of the average total sound pressure level of 30 noise receiving points and the sound pressure level spectrum.
Fig. 5a shows a test and simulation sound pressure level spectrum of the measuring point 4 facing the first bogie of the head car, and fig. 5b shows a test and simulation sound pressure level spectrum of the measuring point 22 facing the pantograph, the total of the test and simulation frequency spectrum curves is relatively close, and most frequencies have small difference, which indicates that the simulation method has high precision. In addition, as shown in tables 2 and 3, when the vehicle speed is 300km/h and the pantograph is in the pantograph-raised state, the simulated values of the total sound pressure level of 30 far-field measuring points are very close to the test values: the errors of 15 measuring points are less than 1dB (A), and the errors of the other 15 measuring points are 1-3 dB (A). The simulated values and the experimental values of the average total sound pressure level of the 30 measuring points in the far field are also very close to each other for different models and different pantograph states: the ascending and descending arches of model A differ by 0.7dB (A), while the ascending and descending arches of model B differ by 0.6dB (A) and 0.9dB (A), respectively. The models A and B are sorted according to ascending arch or descending arch, and consistent results are obtained. These comparisons all show that the simulation method of the present invention has high accuracy.
Table 2300 km/h speed, in pantograph-ascending state, total sound pressure level contrast/dB (A) of 30 measuring points
TABLE 3300 km/h speed, average total sound pressure level contrast/dB (A) for 30 points under different models and pantograph conditions
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A vehicle model selection method based on high-precision simulation of far-field noise of a high-speed train completes the optimized model selection of vehicle models according to the simulation results of the far-field noise of trains of different vehicle models, and is characterized in that the simulation of the far-field noise of the train comprises the following steps:
s1) constructing a geometric model;
s2) dividing grids according with the flow field characteristics of the high-speed train by cutting the grids;
s3) establishing a far-field noise simulation model;
s4) performing steady-state flow calculation and transient flow calculation according to the noise simulation model;
s5) setting a sound source surface and collecting noise source data;
s6) performing simulation calculation of the far-field noise of the high-speed train to obtain the far-field noise of a plurality of noise receiving points.
2. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 1, wherein the geometric model comprises a high-speed train model, a track, a roadbed and a virtual wind tunnel, the high-speed train model comprises a head train, a middle train and a tail train which are sequentially connected to form a train body, and a bogie, a carriage joint and a pantograph which are arranged on the train body, the middle train is in a straight section of the high-speed train model, and the head train and the tail train respectively comprise a nose tip, a curve section and a straight section which are sequentially transited.
3. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 2, wherein said step S2) specifically comprises:
s201) creating boundary layer grids simulating the surface of the high-speed train;
s202) dividing a face grid and a body grid in a flow large separation area;
s203) adopting a mode of gradually increasing from the vehicle body to the outside to create a spatial volume grid;
s204) creating a grid encryption zone.
4. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 3, wherein the large flow separation area comprises curve sections of a head train and a tail train, a bogie, a pantograph and a carriage junction, and the spatial volume grids comprise a track volume grid, a road volume grid and a virtual wind tunnel volume grid.
5. The model selection method based on the high-precision simulation of the far-field noise of the high-speed train as claimed in claim 4, wherein the surface grids in the large flow separation area are determined according to the sizes of all the parts and are all smaller than 3 mm.
6. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 4, wherein the surface grid of the track is smaller than 3mm, the surface grid of the roadbed is smaller than 6mm, and the surface grid of the virtual wind tunnel is smaller than 48 mm.
7. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 1, wherein said step S3) specifically comprises:
s301) setting boundary conditions;
s302) setting a turbulence model for steady-state simulation and a turbulence model for transient simulation;
s303) setting a time value format and a space value format, and giving a time step length and an iteration step number.
8. The model selection method based on high-precision simulation of far-field noise of the high-speed train as claimed in claim 7, wherein the turbulence model of the steady-state simulation is k- ω SST, the turbulence model of the transient simulation is WLES, and the time and space numerical formats are both in at least a second order format under the steady-state simulation and the transient simulation.
9. The model selection method based on the high-precision simulation of the far-field noise of the high-speed train is characterized in that the boundary conditions comprise a virtual wind tunnel inlet, a virtual wind tunnel outlet, two sides of a virtual wind tunnel, a virtual wind tunnel top surface, a virtual wind tunnel ground, a roadbed, a track and a train surface;
the virtual wind tunnel inlet is set according to the test wind speed and the incoming flow turbulence degree, or set according to the train speed and the corresponding turbulence degree;
the virtual wind tunnel outlet is set as a pressure outlet with free outflow or relative pressure of 0 Pa;
the two sides of the virtual wind tunnel and the top surface of the virtual wind tunnel are set as symmetrical boundaries;
the virtual wind tunnel ground, the roadbed, the track and the train surface are set to be non-slip wall surfaces.
10. The model selection method based on high-precision simulation of the far-field noise of the high-speed train as claimed in claim 1, wherein the far-field noise is simulated by an FW-H equation to obtain an average total sound pressure level and a corresponding sound pressure level spectrum of all noise receiving points;
the plurality of noise receiving points are arranged at the vehicle height which is at least six times away from the vehicle body to form a noise receiving point array;
in the height direction, the noise receiving lattice columns are a plurality of rows of noise receiving points with different heights;
in the flow direction, the first noise receiving point in the noise receiving point array is over against the nose tip of the head car, the last noise receiving point is over against the carriage junction between the middle car and the tail car or the nose tip of the tail car, and other noise receiving points are arranged between the first noise receiving point and the last noise receiving point at equal intervals.
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| CN111824193A (en) * | 2020-07-29 | 2020-10-27 | 西南交通大学 | High-speed train wake passive control method |
| CN113340614A (en) * | 2021-06-10 | 2021-09-03 | 中国第一汽车股份有限公司 | Wind noise index determination platform, wind noise simulation target determination method and device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN111824193A (en) * | 2020-07-29 | 2020-10-27 | 西南交通大学 | High-speed train wake passive control method |
| CN111824193B (en) * | 2020-07-29 | 2021-10-26 | 西南交通大学 | High-speed train wake passive control method |
| CN113722811A (en) * | 2021-05-17 | 2021-11-30 | 中国空气动力研究与发展中心计算空气动力研究所 | Method for estimating relation between pressure wave amplitude and vehicle speed ratio during train open line intersection |
| CN113722811B (en) * | 2021-05-17 | 2023-11-28 | 中国空气动力研究与发展中心计算空气动力研究所 | Method for estimating relation between pressure wave amplitude and vehicle speed ratio during train open-line intersection |
| CN113340614A (en) * | 2021-06-10 | 2021-09-03 | 中国第一汽车股份有限公司 | Wind noise index determination platform, wind noise simulation target determination method and device |
| CN115610464A (en) * | 2022-12-07 | 2023-01-17 | 成都流体动力创新中心 | High-speed train lift wing connecting rod air guide sleeve and design method thereof |
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