Turbulence Schmidt number dynamic regulation and control method based on cold air concentration

Abstract

The invention discloses a turbulence Schmidt number dynamic regulation and control method based on cold air concentration, and the method achieves the real-time adjustment of the turbulence Schmidt number through building a relation model between the turbulence Schmidt number and the cold air concentration, thereby predicting the distribution condition of the cooling efficiency of the downstream of a film hole more accurately, enhancing the reliability of a numerical simulation result, and improving the reliability of a model. And a more accurate reference is provided for the design of the turbine air film hole.

Description

Technical Field

[0001] This invention belongs to the field of fluid mechanics technology, specifically relating to a dynamic control method for turbulent Schmidt number based on cold gas concentration. Background Technology

[0002] The turbine cooling system of an aero-engine operates under high temperature and high pressure conditions, and its performance directly affects the engine's efficiency and lifespan. Effective distribution of cool air within the turbine is a key factor in ensuring its safe operation. The turbulent Schmidt number (Sc) is crucial. t The turbulent Schmitt number, a dimensionless parameter describing the diffusion characteristics of matter in turbulent flow, reflects the ratio of momentum diffusivity to mass diffusivity. In turbine cooling systems, the value of the turbulent Schmitt number significantly influences the diffusion behavior of the cooled air. However, in existing technologies, the turbulent Schmitt number is typically taken as a fixed value, making it difficult to accurately reflect the dynamic changes of the cooled air under actual operating conditions. This fixed value leads to insufficient precision in cooling system design, limiting improvements in cooling efficiency and extending turbine lifespan. Summary of the Invention

[0003] To overcome the shortcomings of existing technologies, this invention provides a dynamic control method for turbulent Schmitt number based on cold gas concentration. By establishing a relationship model between turbulent Schmitt number and cold gas concentration, the turbulent Schmitt number can be adjusted in real time, thereby more accurately predicting the cooling efficiency distribution downstream of the film cooling orifice, enhancing the reliability of numerical simulation results, and providing a more accurate reference for turbine film cooling orifice design.

[0004] The technical solution adopted by this invention to solve its technical problem is as follows:

[0005] Step 1: Establish a model relating turbulent Schmidt number to cold gas concentration;

[0006] Step 2: Dynamic turbulent Schmidt number control mechanism;

[0007] Preferably, step 1 specifically comprises:

[0008] Define the physical properties of the cold air, and determine the mathematical relationship between the turbulent Schmidt number and the concentration of cold air components through experimental measurement or numerical simulation, thus establishing the turbulent Schmidt number Sc. t With cold air concentration C A The quadratic function relation:

[0009]

[0010] In the formula, C A ∈[0,1] represents the dimensionless concentration of the cold air, Sc t Let be the turbulent Schmitt number.

[0011] Preferably, step 2 specifically comprises:

[0012] In the numerical simulation of turbine cooling systems, the momentum ratio I is introduced as a flow characteristic parameter, which is defined as the ratio of the momentum of the coolant to that of the mainstream fluid:

[0013]

[0014] Where, ρ c and ρ m These are the densities of the coolant and the mainstream, respectively, U c and U m For the corresponding flow rate;

[0015] Combining momentum ratio and dynamic Sc t The model corrects the diffusion term in the turbulent transport equation, as shown below:

[0016]

[0017] In the formula, Y represents the mass fraction of the cooling air, μ t For turbulent viscosity, Sc t The above quadratic function is used for dynamic calculation; t represents time, ρ represents the fluid density, and u represents the fluid velocity vector. This represents the gradient operator, used to represent the spatial rate of change of a field quantity;

[0018] Turbulent viscosity μ t Calculated using the k-ε turbulence model:

[0019]

[0020] Among them, C μ =0.09 is the model constant, k is the turbulent kinetic energy, and ε is the turbulent dissipation rate.

[0021] A computer program that causes a computer to execute the above-described dynamic control method for turbulent Schmitt number.

[0022] An electronic device includes a processor and a memory; the memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to enable the electronic device to perform the above-described dynamic control method for turbulent Schmitt number.

[0023] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described dynamic control method for turbulent Schmitt number.

[0024] A chip includes a processor for retrieving and running a computer program from a memory, causing a device equipped with the chip to perform the aforementioned dynamic control method for turbulent Schmitt number.

[0025] A computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the above-described dynamic control method for turbulent Schmitt number.

[0026] The beneficial effects of this invention are as follows:

[0027] This invention addresses the shortcomings of existing technologies by providing a dynamic control method for the turbulent Schmitt number based on cold gas concentration. By establishing a relationship model between the turbulent Schmitt number and the cold gas concentration, real-time adjustment of the turbulent Schmitt number is achieved, thereby enabling more accurate prediction of the cooling efficiency distribution downstream of the film cooling orifice. This improvement enhances the reliability of numerical simulation results and provides a more accurate reference for turbine film cooling orifice design. Furthermore, this invention can also be applied to other high-performance engineering equipment, such as gas turbines and nuclear reactor cooling systems, possessing broad market application prospects and promotional value. Attached Figure Description

[0028] Figure 1 This is a graph showing the relationship between the turbulent Schmidt number and the cold gas concentration in an embodiment of the present invention;

[0029] Figure 2 This is a comparison chart of the optimized results and the original results of the embodiments of the present invention. Detailed Implementation

[0030] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0031] This invention aims to provide a dynamic control method for turbulent Schmidt number based on cold gas concentration. By establishing a relationship model between turbulent Schmidt number and cold gas concentration, the method enables real-time adjustment of turbulent Schmidt number, thereby improving the prediction accuracy of downstream cooling efficiency of film cooling orifices and the reliability of numerical simulation results.

[0032] 1. Establishment of a model relating turbulent Schmidt number to cold gas concentration;

[0033] This invention relates to a numerical simulation method for hot-end components of aero-engine turbines, defining common simulation conditions. The physical properties of the cold air used in this invention include, but are not limited to, density and viscosity. These properties are applicable to the simulation of most non-extreme operating conditions and can accurately reflect the hydrodynamic characteristics of the cold air under typical conditions. In a very few extreme conditions, such as ultra-high-speed turbulence, it may be necessary to adjust or replace the corresponding physical parameters according to the actual situation to ensure the accuracy of the numerical results.

[0034] Through experimental measurements or numerical simulations, the mathematical relationship between the turbulent Schmidt number and the concentration of cold gas components was determined, and the turbulent Schmidt number (Sc) was established. t ) and cold air concentration (C A The quadratic function relation is:

[0035]

[0036] 2. Dynamic turbulent Schmidt number control mechanism;

[0037] During the design of the turbine cooling system, the turbulent Schmidt number is calculated in real time based on the changes in the component concentration of the cooling gas under different operating conditions. The dynamic turbulent Schmidt number model is applied to the fluid dynamics simulation of the cooling system to accurately simulate the flow behavior of the cooling gas inside the turbine.

[0038] In the numerical simulation of the turbine cooling system, the momentum ratio (I) is introduced as a flow characteristic parameter, which is defined as the ratio of the momentum of the cooling air to that of the mainstream fluid:

[0039]

[0040] Where, ρ c and ρ m These are the densities of the cold air and the mainstream air, respectively, U c and U m For the corresponding flow velocity. Combining momentum ratio and dynamic Sc t Model, correcting the diffusion term in the turbulent transport equation:

[0041]

[0042] In the formula, Y represents the mass fraction of the cooling air, μ t For turbulent viscosity, Sc t The above quadratic function is dynamically calculated.

[0043] This invention relates to a computer program that enables a computer to implement a method for dynamically controlling the turbulent Schmidt number, thereby generating the turbulent Schmidt number Sc. t With cold air concentration C AThe functional relationship between them is established. This functional relationship can be implemented on a fluid dynamics calculation platform, thereby enabling the platform to effectively execute the dynamic control method of the turbulent Schmitt number.

[0044] Example:

[0045] Set the turbulent Schmidt number (Sc) t ) and cold air concentration (C A () is a quadratic function relation. Figure 1 The graph shows that the quadratic function first decreases and then increases within the range of 0 to 1 for the cold air concentration. The turbulent Schmidt number reaches its maximum value of 0.5 at cold air concentrations of 0 and 1.0, and its minimum value of 0.2 at a cold air concentration of 0.5.

[0046] Sc t =1.2(C A -0.5) 2 +0.2

[0047] Using this formula, combined with real-time calculation of cold air concentration, Sc t The values ​​were then substituted into the fluid dynamics simulation. The momentum ratio I was set to 0.97 (based on a typical value under experimental conditions).

[0048] In this embodiment, the differences and advantages of the proposed method compared with traditional methods in predicting the cooling efficiency distribution of the flow field are further illustrated by analyzing and verifying the cooling flow field under the condition of momentum ratio I = 0.97. Figure 2 As shown in the figure, dimensionless coordinates x / d and y / d are used to characterize the spatial distribution of the downstream and spanwise directions of the film gas holes (where d is the diameter of the film gas holes), and the diffusion and mixing of the cold gas in the flow field are represented by the colored area of ​​the cooling efficiency η.

[0049] First, the experimental results clearly show that when cold air diffuses downstream from the film pores, the η value in the near-field region is relatively high, gradually decreasing with increasing axial distance, accompanied by a certain degree of lateral diffusion. This measured distribution provides an objective benchmark for subsequent comparison with numerical simulation results.

[0050] Fixed turbulent Schmitt number Sc t A comparison of the numerical prediction results of the traditional model with a parameter value of 0.7 with the experimental results shows that, under this fixed parameterization method, the spatial distribution and attenuation characteristics of the high cooling efficiency region are not fully reproduced. For example, the traditional model often leads to an overly concentrated or insufficiently diffused distribution of cold gas, thus failing to comprehensively and accurately describe the transport and mixing behavior of cold gas in the actual flow field.

[0051] In contrast, this embodiment significantly improves the consistency between numerical simulation results and experimental data through targeted optimization of the turbulent Schmitt number. The optimized numerical prediction results are in good agreement with the measured data in terms of the distribution range of cooling efficiency, peak decay trend, and spatial diffusion characteristics, demonstrating more accurate and reliable prediction capabilities.

[0052] This embodiment demonstrates that the parameter optimization method proposed in this invention can effectively improve the accuracy and adaptability of numerical simulations of complex cooling flow fields, providing more reliable technical support for subsequent engineering design and optimization. Through the application of this method, not only can the working performance and reliability of the cooling system be improved under actual working conditions, but it also lays a solid foundation for the research and application of related flow problems.

[0053] Turbulent Schmidt number Sc t It is a dimensionless parameter in fluid mechanics that describes the diffusion characteristics of turbulent flow; it is defined as the turbulent momentum diffusion coefficient ν. t With turbulent mass diffusion coefficient D t The ratio:

[0054]

[0055] The significance of each item:

[0056] (1) Turbulent Motion Diffusion Coefficient ν t ;

[0057] This represents the momentum transfer efficiency caused by turbulence, reflecting the contribution of turbulent vortices to momentum diffusion. It is usually calculated using turbulence models (such as the k-ε model or the k-ω model).

[0058] (2) Turbulent mass diffusion coefficient D t ;

[0059] This represents the mass transfer efficiency caused by turbulence, describing the contribution of turbulent vortices to mass diffusion. It is similar to the turbulent momentum diffusion coefficient, but specifically addresses mass transfer.

[0060] In this invention, the cold gas diffusion coefficient D t Calculated using the Wilke-Chang equation:

[0061]

[0062] In the formula, T is the temperature, η is the viscosity of the mixed gas, V is the molar volume of the solute, and φ is the correlation factor.

[0063] (3) Turbulent Schmidt number Sc t ;

[0064] Used to describe the relative efficiency of momentum and mass transfer in turbulent flow. When Sct When ≈1, the efficiency of momentum transfer and mass transfer is equivalent; when Sc t When Sc > 1, momentum transfer efficiency is higher than mass transfer efficiency; when Sc t When the mass transfer efficiency is less than 1, the mass transfer efficiency is higher than the momentum transfer efficiency.

[0065] The innovative aspects of this invention are as follows:

[0066] 1. Dynamic Control of Turbulent Schmidt Number: This work is the first to propose a dynamic correlation between the turbulent Schmidt number and the concentration of cooling gas, overcoming the limitations of traditional fixed turbulent Schmidt number models and significantly improving the accuracy of cooling system simulations. Simultaneously, it is the first to propose the Sc... t =f(C A The explicit mathematical expression of ) breaks through the limitations of the traditional fixed value assumption.

[0067] 2. Dynamic Control Method: A dynamic control method is proposed based on the dynamic turbulent Schmidt number model. The dynamic Schmidt number Sc... t With momentum ratio I and turbulent viscosity μ t By combining these methods, a high-precision simulation model of the cooling flow field is constructed. This approach effectively corrects the diffusion term in the turbulent transport equation, further enhancing the reliability of the numerical simulation results and thus providing a more accurate basis for predicting the distribution of cold gas.

Claims

1. A method for dynamic control of turbulent Schmidt number based on cold gas concentration, characterized in that, Includes the following steps: Step 1: Establish a model relating turbulent Schmidt number to cold gas concentration; Step 2: Dynamic turbulent Schmidt number control mechanism.

2. The method for dynamic control of turbulent Schmidt number based on cold gas concentration according to claim 1, characterized in that, Step 1 specifically involves: Define the physical properties of the cold air, and determine the mathematical relationship between the turbulent Schmidt number and the concentration of cold air components through experimental measurements or numerical simulations, thus establishing the turbulent Schmidt number Sc. t With cold air concentration C A The quadratic function relation: In the formula, C A ∈[0,1] represents the dimensionless concentration of the cold air, Sc t Let be the turbulent Schmitt number.

3. The method for dynamic control of turbulent Schmidt number based on cold gas concentration according to claim 2, characterized in that, Step 2 specifically involves: In the numerical simulation of turbine cooling systems, the momentum ratio I is introduced as a flow characteristic parameter, which is defined as the ratio of the momentum of the coolant to that of the mainstream fluid: Where, ρ c and ρ m These are the densities of the coolant and the mainstream, respectively, U c and U m For the corresponding flow rate; Combining momentum ratio and dynamic Sc t The model corrects the diffusion term in the turbulent transport equation, as shown below: In the formula, Y represents the mass fraction of the cooling air, μ t For turbulent viscosity, Sc t The above quadratic function is used for dynamic calculation; t represents time, ρ represents the fluid density, and u represents the fluid velocity vector. This represents the gradient operator, used to represent the spatial rate of change of a field quantity; Turbulent viscosity μ t Calculated using the k-ε turbulence model: Among them, C μ =0.09 is the model constant, k is the turbulent kinetic energy, and ε is the turbulent dissipation rate.

4. A computer program, characterized in that, The computer program causes the computer to perform the method as described in any one of claims 1 to 3.

5. An electronic device, characterized in that, include: Processor and memory; The memory is used to store a computer program, and the processor is used to execute the computer program stored in the memory to cause the electronic device to perform the method as described in any one of claims 1 to 3.

6. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 3.

7. A chip, characterized in that, include: A processor for retrieving and running a computer program from memory, causing a device on which the chip is mounted to perform the method as described in any one of claims 1 to 3.

8. A computer program product, characterized in that, The computer program product includes a computer storage medium storing a computer program, the computer program including instructions executable by at least one processor, which, when executed by the at least one processor, implement the method as described in any one of claims 1 to 3.

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