CN115859760A - Method and device for simulating flow field turbulence - Google Patents
Method and device for simulating flow field turbulence Download PDFInfo
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Abstract
The application discloses a method and a device for simulating flow field turbulence. The method for simulating the flow field turbulence comprises the following steps: acquiring collected actual turbulence data, wherein the actual turbulence data comprises upper-layer turbulence data and lower-layer turbulence data; simulating the upper layer turbulence data by using large vortex simulation LES (linear expansion modeling) and obtaining a first simulation result; simulating the lower-layer turbulence data by using a Reynolds average method RANS, and obtaining a second simulation result; fitting the first simulation result and the second simulation result, and generating a final fitting result. According to the flow field turbulence simulation method and device, targeted turbulence simulation is adopted for different laminar flows, and finally, respective simulation results are fitted, so that under the condition that simulation accuracy is guaranteed, the calculated amount is reduced, and the simulation efficiency is improved.
Description
Technical Field
The application relates to the technical field of flow field modeling, in particular to a method and a device for simulating flow field turbulence.
Background
Turbulence is an irregular, multi-scale, structured flow, generally three-dimensional, unusual, with strong diffusivity and dissipation. From the physical structure, the turbulent flow is a flow formed by overlapping various vortexes with rotating structures in different scales, and the sizes of the vortexes and the direction distribution of a rotating shaft are random. The large-scale vortex is mainly determined by the boundary condition of the flow, the size of the large-scale vortex can be compared with the size of a flow field, the large-scale vortex exists mainly under the influence of inertia and is the reason for causing low-frequency pulsation; the small scale vortices are mainly determined by viscous forces, and their size may be only in the order of one-thousandth of the flow field scale, which is the cause of high frequency pulsations. The large scale vortex breaks to form a small scale vortex, and the smaller scale vortex breaks to form a smaller scale vortex. In a well developed turbulent region, the size of the fluid vortices can vary continuously over a fairly wide range. The large-scale vortices constantly gain energy from the main flow, and the energy is gradually transferred to the small-scale vortices through interaction between the vortices. Finally, due to the action of fluid viscosity, the small-scale vortex disappears continuously, and the mechanical energy is converted into the heat energy of the fluid. Meanwhile, new vortex is generated continuously due to the action of the boundary, the disturbance and the action of the velocity gradient, and turbulent motion is developed and continued.
At present, flow field turbulence simulation methods mainly include two major types, namely direct numerical simulation and indirect numerical simulation. Wherein the indirect numerical simulation further comprises a large eddy simulation LES and a Reynolds average method RANS. However, direct numerical simulation relies on experiments to obtain empirical data, which is not only expensive and long in cycle, but also impossible to implement in a completely similar laboratory for some practical engineering problems. The calculation amount of the large vortex simulation LES is also large, and the simulation efficiency is low. The closed model of the rana average method has poor universality, i.e., a unified closed model cannot be used for simulating all complex flows.
Disclosure of Invention
The object of the present application is to solve at least to some extent one of the above mentioned technical problems.
Therefore, a first objective of the present application is to provide a method for simulating a flow field turbulence, which can reduce the calculation amount and improve the simulation efficiency while ensuring the simulation accuracy.
A second object of the present application is to provide a simulation device of flow field turbulence.
A third object of the present application is to propose a computer device.
A fourth object of the present application is to propose a computer-readable storage medium.
In order to achieve the above object, an embodiment of the first aspect of the present application provides a method for simulating turbulent flow of a flow field, including:
acquiring acquired actual turbulence data, the actual turbulence data comprising upper layer turbulence data and lower layer turbulence data;
simulating the upper layer turbulence data by using large vortex simulation LES (linear expansion modeling) and obtaining a first simulation result;
simulating the lower-layer turbulence data by using a Reynolds average method RANS, and obtaining a second simulation result;
fitting the first simulation result and the second simulation result, and generating a final fitting result.
Optionally, simulating the upper turbulence data by using a large vortex simulation LES, and obtaining a first simulation result, including:
decomposing the upper layer turbulence data into large-scale eddy motion data and small-scale eddy motion data by using a filter function;
directly computationally simulating the large scale eddy motion data by a differential equation of motion, wherein the differential equation of motion is
Wherein it is present>
S ij P is the fluid density and gamma is the molecular viscosity system for the tensor elongationCounting;
adding the influence of the small-scale eddy motion data on the large-scale eddy motion data as a stress term to the motion differential equation to generate a filtered momentum equation, wherein the filtered momentum equation is
Wherein it is present>
In order to be a viscous stress,
is a viscosity coefficient>
The mixed length of the sub-lattices is represented by kappa, d, V and V, wherein kappa is a Karman constant, d is the distance from the wall surface meshes to the wall surface, and V is the volume of the control body;
and calculating and simulating the small-scale eddy motion data by using the filtered momentum equation.
Optionally, simulating the turbulence data of the lower layer by using a reynolds average method RANS, and obtaining a second simulation result, including:
respectively establishing a standard k-epsilon model, an RNG k-epsilon model and a standard k-omega model corresponding to the lower-layer turbulence data by using a Reynolds average method RANS;
simulating the underlying turbulence data based on the standard k-epsilon model, the RNG k-epsilon model, and the standard k-omega model.
Optionally, the k-epsilon model is expressed by a turbulent pulsating energy k equation and a turbulent dissipation rate epsilon equation, where the turbulent pulsating energy k equation is:
the turbulent dissipation ratio epsilon equation is as follows:
wherein it is present>
In order to be able to generate turbulent kinetic energy,
for turbulent dissipation ratio, is selected>
Is the coefficient of vortex viscosity, C μ Is an empirical constant.
Optionally, the RNG k-epsilon model is expressed by a first formula and a second formula, where the first formula:
the formula II is as follows:
η＝S k /ε。
optionally, the standard k- ω model is represented by a formula three and a formula four, where the formula three:
the formula four is as follows:
according to the flow field turbulence simulation method, the specific turbulence simulation is adopted for different laminar flows, and the respective simulation results are fitted, so that the calculated amount is reduced and the simulation efficiency is improved under the condition that the simulation accuracy is ensured.
In order to achieve the above object, a second aspect of the present application provides a simulation apparatus for flow field turbulence, including:
an acquisition module to acquire acquired actual turbulence data, the actual turbulence data comprising upper layer turbulence data and lower layer turbulence data;
the first simulation module is used for simulating the upper layer turbulence data by using a large vortex simulation LES and obtaining a first simulation result;
the second simulation module is used for simulating the lower-layer turbulence data by using a Reynolds average method RANS and obtaining a second simulation result;
and the fitting module is used for fitting the first simulation result and the second simulation result and generating a final fitting result.
Optionally, the first analog module is configured to:
decomposing the upper layer turbulence data into large-scale eddy motion data and small-scale eddy motion data by using a filter function;
directly computationally simulating the large scale eddy motion data by a differential equation of motion, wherein the differential equation of motion is
Wherein it is present>
S ij Is the tensile rate tensor, ρ is the fluid density, γ is the molecular viscosity coefficient;
adding the influence of the small-scale eddy motion data on the large-scale eddy motion data as a stress term to the motion differential equation to generate a filtered momentum equation, wherein the filtered momentum equation is
Wherein it is present>
In order to be a viscous stress,
is a viscosity coefficient>
Is the mixed length of the sub-lattices, kappa is the Karman constant, d is the distance from the wall surface meshes to the wall surface, and V is the volume of the control body;
and calculating and simulating the small-scale eddy motion data by using the filtered momentum equation.
Optionally, the second simulation module is configured to:
respectively establishing a standard k-epsilon model, an RNG k-epsilon model and a standard k-omega model corresponding to the lower-layer turbulence data by using a Reynolds average method RANS;
simulating the underlying turbulence data based on the standard k-epsilon model, the RNG k-epsilon model, and the standard k-omega model.
Optionally, the k-epsilon model is expressed by a turbulent pulsating energy k equation and a turbulent dissipation rate epsilon equation, and the turbulent pulsating energy k equation is as follows:
the turbulent dissipation ratio epsilon equation is as follows:
wherein it is present>
In order to be able to generate turbulent kinetic energy,
for turbulent dissipation ratio, is selected>
Is the vortex viscosity coefficient, C μ Is an empirical constant.
Optionally, the RNG k-epsilon model is expressed by a first formula and a second formula, where the first formula:
the formula two is as follows:
wherein the content of the first and second substances,
η＝S k /ε。
optionally, the standard k- ω model is represented by a formula three and a formula four, where the formula three:
the formula four is as follows:
the flow field turbulence simulation device provided by the embodiment of the application adopts targeted turbulence simulation for different laminar flows respectively, and finally fits respective simulation results, so that the calculated amount is reduced and the simulation efficiency is improved under the condition of ensuring the simulation accuracy.
In order to achieve the above object, a third aspect of the present application provides a computer device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and when the processor executes the computer program, the processor implements the simulation method of flow field turbulence as described in the first aspect of the present application.
In order to achieve the above object, a non-transitory computer-readable storage medium is further provided in an embodiment of the fourth aspect of the present application, where a computer program is stored on the non-transitory computer-readable storage medium, and when executed by a processor, the computer program implements the method for simulating flow field turbulence according to the embodiment of the first aspect.
Additional aspects and advantages of the present application will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the present application.
Drawings
FIG. 1 is a flow chart of a method of simulating flow field turbulence according to one embodiment of the present application;
fig. 2 is a schematic structural diagram of a simulation apparatus for flow field turbulence according to an embodiment of the present application.
Detailed Description
It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
The present invention is described in further detail below with reference to specific examples, which are not to be construed as limiting the scope of the invention as claimed.
A simulation method and apparatus of flow field turbulence of an embodiment of the present application is described below with reference to the accompanying drawings.
FIG. 1 is a flow chart of a method of simulating flow field turbulence according to one embodiment of the present application, as shown in FIG. 1, the method comprising the steps of:
s1, acquiring the collected actual turbulence data.
The actual turbulence data includes upper layer turbulence data and lower layer turbulence data.
In one embodiment of the present application, the cut-off point for the upper and lower layer turbulence data may be determined based on a machine learning algorithm. Because the turbulence characteristics at the upper layer are different from those at the lower layer, different simulation methods are needed to be adopted for respective simulation, so that the simulation accuracy is improved. A threshold value of a demarcation point may be preset, upper turbulence data and lower turbulence data may be input by a machine learning algorithm, and the threshold value of the demarcation point may be iteratively optimized and updated, so as to output an optimal demarcation point threshold value, for example, taking 2 meters as the demarcation point, which belongs to an upper layer at a distance of more than 2m from the earth's surface, and which belongs to a lower layer at a distance of less than 2m from the earth's surface.
And S2, simulating the upper layer turbulence data by using a large eddy simulation LES, and obtaining a first simulation result.
In one embodiment of the present application, the upper layer turbulence data may be decomposed into large scale eddy motion data and small scale eddy motion data using a filter function.
For large scale vortex motion data: the large scale eddy motion data can be modeled by direct calculation through a differential equation of motion.
Wherein the differential equation of motion is
Wherein the content of the first and second substances,
S ij for the tensile tensor, ρ is the fluid density and γ is the molecular viscosity coefficient.
For small scale vortex motion data: the effect of the small scale eddy motion data on the large scale eddy motion data may be added as a stress term to the differential equation of motion to generate a filtered momentum equation.
The filtered momentum equation is
Wherein, the first and the second end of the pipe are connected with each other,
is viscosity stress, based on>
For the filtered tension tensor to be the one,
is a viscosity coefficient>
Is the sub-lattice mixing length, κ is the karman constant, d is the wall mesh to wall distance, and V is the control volume.
After the filtered momentum equation is obtained, the filtered momentum equation can be used for calculating and simulating the small-scale eddy motion data.
And finally fitting the simulation result of the large-scale eddy motion data and the fitting result of the small-scale eddy motion data to obtain a first fitting result.
And S3, simulating the lower-layer turbulence data by using a Reynolds average method RANS, and obtaining a second simulation result.
Specifically, a standard k-epsilon model, an RNG k-epsilon model and a standard k-omega model corresponding to the lower-layer turbulence data can be respectively established by using a Reynolds average method RANS, and then the lower-layer turbulence data can be simulated based on the standard k-epsilon model, the RNG k-epsilon model and the standard k-omega model.
The k-epsilon model is expressed by a turbulent pulsating energy k equation and a turbulent dissipation rate epsilon equation, wherein the turbulent pulsating energy k equation is as follows:
the turbulent dissipation ratio epsilon equation is as follows:
wherein it is present>
In order to be able to generate turbulent kinetic energy,
for turbulent dissipation ratio, is selected>
Is the coefficient of vortex viscosity, C μ Is an empirical constant.
The RNG k-epsilon model is represented by formula one and formula two, the formula one:
the formula II is as follows:
wherein the content of the first and second substances,
η＝S k /ε，η 0 ＝4.38，β＝0.012。
the standard k- ω model is represented by formula three and formula four, wherein formula three:
the formula four is as follows:
because the lower-layer turbulence data has uncertainty, turbulence with different characteristics needs to be simulated by pertinently selecting a proper model, the lower-layer turbulence data can be simulated by utilizing a standard k-epsilon model, the RNG k-epsilon model and the standard k-omega model respectively, and then a model with the optimal simulation effect is selected for simulation. Further, the same machine learning algorithm can be used for simulating the same turbulence data by respectively adopting three models, and then the optimal simulation result is obtained by iterative updating. Finally, the best model is selected for simulation.
And S4, fitting the first simulation result and the second simulation result, and generating a final fitting result.
Fitting is carried out on a first simulation result obtained by simulating the upper layer turbulence data and a second simulation result obtained by simulating the lower layer turbulence data, and then a final flow field turbulence simulation result is generated.
According to the flow field turbulence simulation method, targeted turbulence simulation is adopted for different laminar flows, and finally, respective simulation results are fitted, so that the calculation amount is reduced and the simulation efficiency is improved under the condition that the simulation accuracy is guaranteed.
In order to realize the embodiment, the application also provides a simulation device for the flow field turbulence.
Fig. 2 is a schematic structural diagram of a simulation apparatus for flow field turbulence according to an embodiment of the present application.
As shown in fig. 2, the apparatus comprises an acquisition module 21, a first simulation module 22, a second simulation module 23 and a fitting module 24.
An acquisition module 21 for acquiring acquired actual turbulence data, said actual turbulence data comprising upper layer turbulence data and lower layer turbulence data.
The first simulation module 22 is configured to simulate the upper turbulence data by using a large vortex simulation LES, and obtain a first simulation result.
The first analog module 22 is configured to:
decomposing the upper layer turbulence data into large-scale eddy motion data and small-scale eddy motion data by using a filter function;
directly computationally simulating the large scale eddy motion data by a differential equation of motion, wherein the differential equation of motion is
Wherein it is present>
S ij Is the tensile rate tensor, ρ is the fluid density, γ is the molecular viscosity coefficient;
adding the influence of the small-scale eddy motion data on the large-scale eddy motion data as a stress term to the motion differential equation to generate a filtered momentum equation, wherein the filtered momentum equation is
Wherein +>
In order to have a viscous stress, it is preferable that,
is a viscosity coefficient>
The mixed length of the sub-lattices is represented by kappa, d, V and V, wherein kappa is a Karman constant, d is the distance from the wall surface meshes to the wall surface, and V is the volume of the control body;
and calculating and simulating the small-scale eddy motion data by using the filtered momentum equation.
And the second simulation module 23 is configured to simulate the turbulence data of the lower layer by using a reynolds average method RANS, and obtain a second simulation result.
The second simulation module 23 is configured to:
respectively establishing a standard k-epsilon model, an RNG k-epsilon model and a standard k-omega model corresponding to the lower-layer turbulence data by using a Reynolds average method RANS;
simulating the underlying turbulence data based on the standard k-epsilon model, the RNG k-epsilon model, and the standard k-omega model.
The k-epsilon model is expressed by a turbulent pulsating energy k equation and a turbulent dissipation rate epsilon equation, and the turbulent pulsating energy k equation is as follows:
the turbulent dissipation ratio epsilon equation is as follows:
wherein it is present>
In order to be able to generate turbulent kinetic energy,
for turbulent dissipation ratio>
Is the coefficient of vortex viscosity, C μ Is an empirical constant. />
The RNG k-epsilon model is expressed by a formula one and a formula two, wherein the formula one is as follows:
the formula two is as follows:
wherein, the first and the second end of the pipe are connected with each other,
η＝S k /ε，η 0 ＝4.38，β＝0.012。
the standard k- ω model is represented by formula three and formula four, wherein formula three:
the formula four is as follows:
a fitting module 24, configured to fit the first simulation result and the second simulation result, and generate a final fitting result.
It should be understood that the description of the simulation apparatus of the flow field turbulence is consistent with that of the corresponding simulation method of the flow field turbulence, and therefore, the description of this embodiment is omitted.
The flow field turbulence simulation device provided by the embodiment of the application adopts targeted turbulence simulation for different laminar flows respectively, and finally fits respective simulation results, so that the calculated amount is reduced and the simulation efficiency is improved under the condition of ensuring the simulation accuracy.
In order to implement the above embodiments, the present application also provides a computer device.
The computer device comprises a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the method for simulating flow field turbulence as embodied in the first aspect when the computer program is executed by the processor.
To implement the above embodiments, the present application also proposes a non-transitory computer-readable storage medium.
The non-transitory computer-readable storage medium has stored thereon a computer program which, when executed by a processor, implements a method of simulating flow field turbulence as an embodiment of the first aspect.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Further, the computer readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present application may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following techniques, which are known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It should be noted that in the description of the present specification, reference to the description of the term "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Claims (12)
1. A method of simulating flow field turbulence, comprising:
acquiring acquired actual turbulence data, the actual turbulence data comprising upper layer turbulence data and lower layer turbulence data;
simulating the upper layer turbulence data by using large vortex simulation LES (linear expansion modeling) and obtaining a first simulation result;
simulating the lower-layer turbulence data by using a Reynolds average method RANS, and obtaining a second simulation result;
and fitting the first simulation result and the second simulation result, and generating a final fitting result.
2. The method of claim 1, wherein simulating the upper turbulence data with a large vortex simulation LES and obtaining a first simulation result comprises:
decomposing the upper layer turbulence data into large-scale eddy motion data and small-scale eddy motion data by using a filter function;
directly computationally simulating the large scale eddy motion data by a differential motion equation, wherein the differential motion equation is
Wherein it is present>
S ij P is the elongation tensor, the fluid density, and γ is the molecular viscosity coefficient;
adding the influence of the small-scale eddy motion data on the large-scale eddy motion data as a stress term to the motion differential equation to generate a filtered momentum equation, wherein the filtered momentum equation is
Wherein it is present>
In order to be a viscous stress,
is a viscosity coefficient>
The mixed length of the sub-lattices is represented by kappa, d, V and V, wherein kappa is a Karman constant, d is the distance from the wall surface meshes to the wall surface, and V is the volume of the control body;
and calculating and simulating the small-scale eddy motion data by using the filtered momentum equation.
3. The method of claim 1, wherein simulating the subsurface turbulence data using reynolds average, RANS, and obtaining a second simulation result comprises:
respectively establishing a standard k-epsilon model corresponding to the lower-layer turbulence data by using a Reynolds average method RANS;
simulating the underlying turbulence data based on the standard k-epsilon model, the RNG k-epsilon model, and the standard k-omega model.
4. The method of claim 3, wherein the k-epsilon model is represented by a turbulent pulse energy k-equation and a turbulent dissipation ratio epsilon-equation, the turbulent pulse energy k-equation being:
the turbulent dissipation ratio epsilon equation is as follows: />
Wherein it is present>
Is turbulent kinetic energy, and is taken out>
For turbulent dissipation ratio, is selected>
Is the coefficient of vortex viscosity, C μ Is an empirical constant.
5. The method of claim 3, wherein the RNG k-epsilon model is represented by formula one and formula two, the formula one:
the formula II is as follows: />
η＝S k /ε。
6. The method of claim 3, wherein the standard k- ω model is represented by equation three and equation four, wherein equation three:
the formula four is as follows:
7. a simulation apparatus for flow field turbulence, comprising:
an acquisition module to acquire acquired actual turbulence data, the actual turbulence data comprising upper layer turbulence data and lower layer turbulence data;
the first simulation module is used for simulating the upper layer turbulence data by using a large vortex simulation LES and obtaining a first simulation result;
the second simulation module is used for simulating the lower-layer turbulence data by using a Reynolds average method RANS and obtaining a second simulation result;
and the fitting module is used for fitting the first simulation result and the second simulation result and generating a final fitting result.
8. The apparatus of claim 7, wherein the first analog module is to:
decomposing the upper layer turbulence data into large-scale eddy motion data and small-scale eddy motion data by using a filter function;
directly computationally simulating the large scale eddy motion data by a differential equation of motion, wherein the differential equation of motion is
Wherein it is present>
S ij P is the elongation tensor, the fluid density, and γ is the molecular viscosity coefficient;
adding the influence of the small-scale eddy motion data on the large-scale eddy motion data as a stress term to the motion differential equation to generate a filtered momentum equation, wherein the filtered momentum equation is
Wherein it is present>
For viscous stress, in>
Is a viscosity coefficient->
The mixed length of the sub-lattices is represented by kappa, d, V and V, wherein kappa is a Karman constant, d is the distance from the wall surface meshes to the wall surface, and V is the volume of the control body;
and calculating and simulating the small-scale eddy motion data by using the filtered momentum equation.
9. The apparatus of claim 7, wherein the second analog module is to:
respectively establishing a standard k-epsilon model, an RNG k-epsilon model and a standard k-omega model corresponding to the lower-layer turbulence data by using a Reynolds average method RANS;
simulating the underlying turbulence data based on the standard k-epsilon model, the RNG k-epsilon model, and the standard k-omega model.
10. The apparatus of claim 9, wherein the k-epsilon model is represented by a turbulent pulse energy k-equation and a turbulent dissipation rate epsilon-equation, the turbulent pulse energy k-equation being:
the turbulent dissipation ratio epsilon equation is as follows: />
Wherein +>
In the form of turbulent energy>
For turbulent dissipation ratio>
Is the vortex viscosity coefficient, C μ Is an empirical constant.
11. The apparatus of claim 9, wherein the RNG k-epsilon model is represented by formula one and formula two, the formula one:
the formula two is as follows:
wherein it is present>
η＝S k /ε。
12. The apparatus of claim 9, wherein the standard k- ω model is represented by equation three and equation four, the equation three：
The formula four is as follows:
/>
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372320A (en) * | 2016-08-31 | 2017-02-01 | 金斯科 | Method for performing large eddy simulation on highway tunnel turbulence by using sub-filtering scale model |
| CN112084727A (en) * | 2020-10-26 | 2020-12-15 | 中国人民解放军国防科技大学 | Transition prediction method based on neural network |
| CN112131800A (en) * | 2020-07-20 | 2020-12-25 | 中国科学院力学研究所 | Novel large vortex simulation method and device based on energy flow similarity |
| CN113468679A (en) * | 2021-09-06 | 2021-10-01 | 中国空气动力研究与发展中心计算空气动力研究所 | Turbulent flow length scale calculation method based on S-A model |
| CN114139465A (en) * | 2021-10-25 | 2022-03-04 | 中国空气动力研究与发展中心计算空气动力研究所 | Construction method of detached vortex simulation model |
-
2022
- 2022-04-29 CN CN202210467238.XA patent/CN115859760A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106372320A (en) * | 2016-08-31 | 2017-02-01 | 金斯科 | Method for performing large eddy simulation on highway tunnel turbulence by using sub-filtering scale model |
| CN112131800A (en) * | 2020-07-20 | 2020-12-25 | 中国科学院力学研究所 | Novel large vortex simulation method and device based on energy flow similarity |
| CN112084727A (en) * | 2020-10-26 | 2020-12-15 | 中国人民解放军国防科技大学 | Transition prediction method based on neural network |
| CN113468679A (en) * | 2021-09-06 | 2021-10-01 | 中国空气动力研究与发展中心计算空气动力研究所 | Turbulent flow length scale calculation method based on S-A model |
| CN114139465A (en) * | 2021-10-25 | 2022-03-04 | 中国空气动力研究与发展中心计算空气动力研究所 | Construction method of detached vortex simulation model |
Non-Patent Citations (10)
| Title |
|---|
| JESSE THE 等: "A critical review on the simulations of wind turbine aerodynamics focusing on hybrid RANS-LES methods", 《ELSEVIER》, vol. 1, pages 257 - 289 * |
| VEISI AMIN ALLAH 等: "Large Eddy Simulation of flow around a single and two in-line horizontal-axis wind turbines", 《ELSEVIER》, pages 533 - 544 * |
| 吴晶峰;宁方飞;: "后台阶流动的Hybrid RANS/LES模拟", 北京航空航天大学学报, no. 06, pages 701 - 704 * |
| 常书平;王永生;庞之洋;: "用基于SST模型的DES方法数值模拟圆柱绕流", 舰船科学技术, no. 02, pages 30 - 33 * |
| 常书平;王永生;庞之洋;丁江明;: "用基于SST模型的DES方法数值模拟圆柱绕流", 中国海洋平台, no. 06, pages 13 - 17 * |
| 李斌;吴颂平;: "基于湍流尺度的混合RANS/LES模型", 北京航空航天大学学报, no. 07, pages 755 - 758 * |
| 李泽伟 等: "基于ELES方法的T型三通管掺混特性研究", 《计算机仿真》, vol. 37, no. 5, pages 173 - 179 * |
| 王福军;: "流体机械旋转湍流计算模型研究进展", 农业机械学报, no. 02, pages 1 - 14 * |
| 许春晓: "壁湍流相干结构和减阻控制机理", 力学进展, vol. 1, no. 00, pages 111 - 140 * |
| 陈都;司海青;马晓晖;: "脱体涡模拟方法研究及其应用", 航空计算技术, no. 01, pages 9 - 13 * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117574029A (en) * | 2024-01-19 | 2024-02-20 | 中国空气动力研究与发展中心计算空气动力研究所 | Compatible method of high-resolution Reynolds stress and Reynolds average Navier-Stokes equation solver |
| CN117574029B (en) * | 2024-01-19 | 2024-04-26 | 中国空气动力研究与发展中心计算空气动力研究所 | Compatible method of high-resolution Reynolds stress and Reynolds average Navier-Stokes equation solver |
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