Gas turbine heat exchange performance optimization numerical analysis method based on mass flow weighting

Abstract

The invention discloses a gas turbine heat exchange performance optimization method based on mass flow weighting, and belongs to the field of computational fluid mechanics. The technical defects that in the prior art, the flow field change of a cooling system is difficult to accurately evaluate, and the result is unreliable are overcome, and the optimized geometric model and the analysis method are provided. The model comprises a rectangular flow channel and at least two rows of staggered turbulent flow columns, an inlet of the flow channel is set as a flow velocity and temperature boundary of cooling gas, an outlet of the flow channel is a pressure boundary, and the wall surface does not slip and keeps constant heat flux. Meanwhile, two 2D interception faces are arranged at the inlet and the outlet and used for capturing the local average temperature of the cooling fluid. The analysis method is based on the model, captures temperature data, calculates a temperature difference value, evaluates heat exchange effects and shear stress of different wall surfaces and resistance characteristics of fluid in a spoiler column and a flow channel, and is suitable for performance evaluation of a gas turbine cooling system.

Description

Technical Field

[0001] It involves the field of computational fluid dynamics (CFD), especially in the numerical simulation and post-processing analysis of gas turbine heat transfer performance. Background Art

[0002] In the field of computational fluid dynamics (CFD) technology, especially for the study of gas turbine heat transfer performance, a large number of studies have focused on evaluating the performance of gas turbine internal cooling systems through numerical simulation and experimental means. For example, common methods include the calculation of convective heat transfer coefficient (Nu), friction factor (f), thermal efficiency (TP), and wall shear stress (WSS) to optimize the design and operating efficiency of gas turbines. Mainstream post-processing software on the market, such as CFD-POST, provides conventional post-processing functions that can process data on fluid flow, heat transfer, etc. However, the functions of these tools are mainly concentrated in the calculation of basic heat transfer processes, relying on simple algorithms and formulas, and are suitable for some situations where the geometric structure is relatively simple and the flow process is relatively simple.

[0003] In existing research, gas turbine internal cooling usually relies on structures such as spoiler columns to enhance the heat transfer effect. For example, some studies have attempted to improve the heat transfer performance by adding spoiler columns in the flow channel and changing the flow path of the cooling gas. These measures can effectively increase the turbulence of the fluid, thereby improving the convective heat transfer efficiency. However, due to the complex internal structure of the gas turbine, the flow of the cooling fluid is often affected by many factors, including the complexity of the geometry, the tortuosity of the cooling flow channel, and the arrangement of spoiler columns. These factors may cause irregular reflux and flow separation in some areas of the flow field, thereby affecting the overall heat transfer effect.

[0004] Although existing CFD software can handle simple fluid calculations, existing technologies have certain limitations when evaluating the heat transfer performance of complex equipment such as gas turbines. For example, traditional post-processing methods often fail to provide accurate evaluation results when dealing with complex geometric structures and internal cooling performance, and lack in-depth analysis of details. Many existing studies rely on basic formulas to calculate temperature differences, while ignoring the impact of local complex flows on overall heat transfer performance, thereby introducing large errors in the calculation results. In addition, existing monitoring methods cannot effectively perceive flow changes in complex flow fields, resulting in potential uneven heat transfer problems inside gas turbines that are difficult to detect in a timely manner.

[0005] Therefore, the existing technology has the following main problems when evaluating the performance of the internal cooling system of the gas turbine: insufficient comprehensive evaluation capability for complex geometric structures and inability to accurately reflect the flow field changes of the cooling fluid. Summary of the invention

[0006] In order to solve the technical defects in the prior art that the flow field changes of the cooling fluid cannot be accurately reflected and the evaluation results are not reliable enough in the existing work of evaluating the performance of the internal cooling system of the gas turbine, the technical solution provided by the present invention is:

[0007] A geometric model for optimizing heat transfer performance of a gas turbine based on mass flow weighting. The model includes:

[0008] A rectangular flow channel in which at least two rows of spoiler columns are arranged;

[0009] The spoiler columns are arranged in a staggered manner;

[0010] The inlet of the rectangular flow channel is set as the flow velocity inlet and temperature boundary of the cooling gas, the outlet is set as the pressure boundary, and the wall surface is set as the no-slip and constant heat flux conditions;

[0011] The step of setting at least two 2D cross-sectional surfaces for capturing the local average temperature of the cooling fluid, wherein the 2D cross-sectional surfaces are respectively set near the inlet and outlet of the flow channel.

[0012] Based on the same inventive concept, the present invention also provides a numerical analysis method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting, the method is implemented based on the model, and includes:

[0013] Steps to capture the local average temperature in the flow channel;

[0014] The steps of respectively capturing the local average temperature of the cooling fluid and calculating the temperature difference;

[0015] Steps to comprehensively evaluate the heat transfer effect and shear stress changes in different wall areas;

[0016] Procedure for evaluating the drag characteristics of cooling gas as it flows through spoiler pins and tortuous channels.

[0017] Furthermore, a preferred embodiment is provided, which uses a mass flow weighted massFlowAve() formula to capture the local average temperature in the flow channel.

[0018] Furthermore, a preferred embodiment is provided to capture the local average temperature of the cooling fluid respectively according to the 2D cross-sectional surface.

[0019] Furthermore, a preferred embodiment is provided to calculate the overall Nusselt number Nu by area weighting. all and wall shear stress WSS all , in order to comprehensively evaluate the heat transfer effects and stress conditions in different wall areas.

[0020] Furthermore, a preferred embodiment is provided, in which the friction coefficient f is calculated based on a mass flow weighting method to evaluate the resistance characteristics of the cooling gas when it flows in the spoiler posts and the tortuous flow channel.

[0021] Based on the same inventive concept, the present invention also provides a numerical analysis device for optimizing the heat exchange performance of a gas turbine based on mass flow weighting. The device is implemented based on the model and includes:

[0022] A module that captures the local average temperature in the flow channel;

[0023] A module for capturing the local average temperature of the cooling fluid and calculating the temperature difference;

[0024] A module for comprehensively evaluating the heat transfer effect and shear stress changes in different wall areas;

[0025] A module for evaluating the drag characteristics of cooling gas flowing through spoiler pins and tortuous channels.

[0026] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program, and when the computer reads the computer program, the computer executes the described method.

[0027] Based on the same inventive concept, the present invention also provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method described.

[0028] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0029] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0030] The massFlowAve() formula weighted by mass flow is used to calculate the local average temperature, which effectively improves the accuracy of temperature calculation and reduces the temperature difference calculation error caused by complex flow in the flow channel. Compared with traditional methods, this solution can better capture the temperature changes of cooling fluid in complex geometric structures and significantly improve the evaluation accuracy of heat transfer performance.

[0031] By setting 2D interception planes (such as Plane1 and Plane2) at key locations of the flow channel, the inlet and outlet temperatures of the cooling fluid can be accurately captured, thereby better reflecting the temperature changes of the fluid during the cooling process. Compared with the method of directly using the average temperature difference between the inlet and outlet of the flow channel in the prior art, this method can significantly reduce the errors caused by complex flow fields and improve the reliability of the data.

[0032] The Nusselt number calculation method based on area-weighted average can comprehensively consider the heat transfer effect of each wall surface (such as WALLBOTTOM, WALL TOP and Pin fin), thereby providing a more comprehensive evaluation of heat transfer performance. Compared with the traditional direct temperature difference evaluation method, this method can more comprehensively reflect the heat transfer performance of the entire geometric model, especially in complex cooling structures.

[0033] The friction coefficient f is calculated by mass flow weighting method, which effectively reflects the change of resistance of cooling gas when it passes through spoiler column or bend area in the flow channel. Compared with the traditional friction coefficient calculation method, this method can better describe the influence of complex flow on friction coefficient, thereby improving the accuracy of evaluating the resistance characteristics of cooling flow channel.

[0034] By comprehensively calculating the wall shear stress WSS all , which can more accurately reflect the interaction force between the cooling fluid and the wall, thereby evaluating the stress on the wall. Compared with the traditional local shear stress evaluation method, this calculation method can better reflect the shear stress change state of the entire wall and improve the accuracy of the safety and reliability evaluation of the cooling system.

[0035] It is suitable for evaluating the performance of gas turbine internal cooling systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the geometric model for optimizing the heat transfer performance of a gas turbine based on mass flow weighting;

[0037] Among them, 1 represents Plane 1, 2 represents Pin fin, 3 represents WALL BOTTOM, 4 represents WALL TOP, and 5 represents Plane 2.

[0038] Among them, Plane 1 is section 1, Pin fin is the spoiler column, WALLBOTTOM is the lower wall, WALL TOP is the upper wall, and Plane 2 is section 2. DETAILED DESCRIPTION

[0039] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0040] Embodiment 1: This embodiment provides a geometric model for optimizing heat exchange performance of a gas turbine based on mass flow weighting, and the model includes:

[0041] A rectangular flow channel in which at least two rows of spoiler columns are arranged;

[0042] The spoiler columns are arranged in a staggered manner;

[0043] The inlet of the rectangular flow channel is set as the mass flow rate and temperature boundary of the cooling gas, the outlet is set as the pressure boundary, and the wall surface is set as the no-slip and constant heat flux conditions;

[0044] The step of providing at least two 2D cross-sectional surfaces for capturing the local average temperature of the cooling fluid, wherein the 2D cross-sectional surfaces are provided near the inlet and outlet locations of the flow channel.

[0045] Embodiment 2: This embodiment provides a numerical analysis method for optimizing heat exchange performance of a gas turbine based on mass flow weighting. The method is implemented based on the model provided in embodiment 1 and includes:

[0046] Steps to capture the local average temperature in the flow channel;

[0047] The steps of respectively capturing the local average temperature of the cooling fluid and calculating the temperature difference;

[0048] Steps to comprehensively evaluate the heat transfer effect and shear stress changes in different wall areas;

[0049] Procedure for evaluating the drag characteristics of cooling gas as it flows through spoiler pins and tortuous channels.

[0050] This embodiment provides a method for evaluating the heat transfer performance of a gas turbine internal cooling system based on computational fluid dynamics (CFD) technology. This method can accurately evaluate the heat transfer effect of a cooling system with a complex geometric structure by establishing a geometric model, setting boundary conditions, performing numerical simulation, and performing post-processing analysis. The specific implementation is carried out in the following steps:

[0051] 1. Geometric model establishment

[0052] Brief description: First, the geometric model of the internal cooling system at the trailing edge of the gas turbine is established.

[0053] The geometric model includes a rectangular flow channel and multiple rows of spoiler columns to simulate the actual cooling structure of the trailing edge of a gas turbine. The spoiler columns are cylindrical in shape, with a number between 10 and 50, and are arranged in a staggered manner to maximize turbulence and cooling effects. The staggered arrangement of the spoiler columns can effectively increase the turbulence effect of the cooling gas, thereby enhancing the heat transfer performance. In addition, the width-to-height ratio of the rectangular flow channel is set to 5:6 to optimize the flow path of the cooling gas and ensure that the cooling fluid can fully flow and exchange heat in each area.

[0054] 2. Boundary condition setting

[0055] Brief Description: Set inlet, outlet and wall boundary conditions for the geometric model.

[0056] The inlet boundary conditions of the model include the mass flow rate and temperature of the cooling gas. The average velocity at the inlet is between 4 and 30 m / s, and the temperature range is between 300 and 600 Kelvin (K), ensuring that the simulation results are consistent with the actual operating conditions of the gas turbine. The outlet is set as a pressure boundary to maintain the stability of the flow. The wall boundary conditions are set to no slip and constant heat flux, and the heat flux value is 1000 W / m 2 , used to simulate the heat exchange of the cooling system under high temperature conditions. The wall material is made of metal with excellent thermal conductivity to improve the heat exchange efficiency.

[0057] 3. Grid division

[0058] Brief description: Mesh the geometric model.

[0059] In numerical simulation, the division of meshes plays a vital role in the accuracy of simulation results. Structured meshes are used to divide the entire geometric model to adapt to complex geometric shapes. At the same time, mesh encryption is used around spoiler columns and in areas where fluid flow changes significantly to capture subtle changes in the fluid and ensure the accuracy and reliability of the calculation results.

[0060] 4. Numerical simulation solution

[0061] Brief description: Perform numerical simulation of cooling system.

[0062] CFD software is used to numerically solve the geometric model. The Reynolds-averaged Navier-Stokes (RANS) equations are used to simulate the flow characteristics of the cooling fluid, and the Realizable k-ε turbulence model is selected to simulate the turbulent characteristics in the flow channel to improve the accuracy of the calculation. During the solution process, the massFlowAve() formula weighted by mass flow is used to capture the local average temperature in the flow channel to reduce the calculation error introduced by the complex flow field. The SIMPLE algorithm and steady-state solver are used in the simulation to ensure convergence and calculation stability.

[0063] 5. Post-processing analysis

[0064] Brief description: Post-processing analysis of numerical simulation results.

[0065] In the post-processing stage, the simulation data is deeply analyzed through post-processing tools such as CFD-POST. First, 2D interception surfaces are set at the inlet and outlet of the flow channel (located at 5 / 36 of the inlet section and 7 / 36 of the outlet section of the flow channel, respectively) to capture the local average temperature of the cooling fluid for calculating the temperature difference.

[0066] By analyzing the fluid temperature, velocity and pressure distribution, contour maps and cloud maps can be generated to intuitively display the flow characteristics of the cooling fluid and the heat transfer effect around the spoiler column. At the same time, the flow path of the cooling gas is analyzed through the streamline diagram to evaluate the impact of the spoiler column design on the fluid heat transfer performance.

[0067] 6. Nusselt number and wall shear stress calculation

[0068] Description: Calculates the overall Nusselt number and wall shear stress.

[0069] Based on the post-processing results, the overall Nusselt number Nu is calculated using an area-weighted approach. all , in order to comprehensively consider the heat transfer effects of different areas in the flow channel.

[0070] By comprehensively considering the heat transfer conditions of different wall surfaces, the overall heat transfer performance of the geometric model can be more comprehensively evaluated. The heat transfer capacity of the cooling structure under different working conditions can be evaluated.

[0071] 7. Calculation of friction coefficient

[0072] Brief description: The friction coefficient is calculated based on the mass flow weighting method.

[0073] The friction coefficient f of the cooling gas in the flow channel is calculated using the mass flow weighted method to evaluate the resistance characteristics of the cooling gas when passing through the spoiler and the tortuous flow channel.

[0074] By calculating the friction coefficient, the resistance loss of cooling gas in complex structures can be analyzed, providing reliable data support for optimizing flow channel design.

[0075] 8. Results Analysis and Summary

[0076] Brief description: Comprehensive analysis and summary of various calculation results.

[0077] The temperature difference ΔT and the overall Nusselt number Nu calculated by the above steps are all , wall shear stress WSS all The heat transfer performance of the internal cooling system of the gas turbine is comprehensively evaluated by using key parameters such as the friction coefficient f. The results show that the mass flow weighting method can significantly improve the calculation accuracy of temperature, shear stress and friction coefficient in complex geometric structures, and can more comprehensively reflect the flow and heat transfer effect of the cooling fluid in the flow channel, which is suitable for the design and optimization of highly complex cooling systems such as gas turbines.

[0078] Implementation method three: This implementation method further limits the numerical analysis method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting provided in implementation method two, and adopts the mass flow weighted massFlowAve() formula to capture the local average temperature in the flow channel.

[0079] Embodiment 4: This embodiment further limits the numerical analysis method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting provided in Embodiment 2, and captures the local average temperature of the cooling fluid according to the 2D cross-section surface.

[0080] Embodiment 5: This embodiment further limits the numerical analysis method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting provided in Embodiment 2, and calculates the overall Nusselt number Nu by area weighting. all and wall shear stress WSS all , in order to comprehensively evaluate the heat transfer effects and stress conditions in different wall areas.

[0081] Embodiment 6. This embodiment is a further limitation of the numerical analysis method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting provided in embodiment 2. The friction coefficient f is calculated based on the mass flow weighting method to evaluate the resistance characteristics of the cooling gas when it flows in the spoiler columns and the tortuous flow channels.

[0082] Embodiment 7: This embodiment provides a numerical analysis device for optimizing heat exchange performance of a gas turbine based on mass flow weighting. The device is implemented based on the model provided in embodiment 1, and includes:

[0083] A module that captures the local average temperature in the flow channel;

[0084] A module for capturing the local average temperature of the cooling fluid and calculating the temperature difference;

[0085] A module for comprehensively evaluating the heat transfer effect and stress conditions of different wall areas;

[0086] A module for evaluating the drag characteristics of cooling gas flowing through spoiler pins and tortuous channels.

[0087] Embodiment 8: This embodiment provides a computer storage medium for storing a computer program. When the computer reads the computer program, the computer executes the method provided in embodiment 2.

[0088] Embodiment 9: This embodiment provides a computer, including a processor and a storage medium. When the processor reads a computer program stored in the storage medium, the computer executes the method provided in embodiment 2.

[0089] Embodiment 10: This embodiment provides a computer program product, which is a computer program. When the computer program is executed, the method provided in embodiment 2 is implemented.

[0090] Implementation Method XI: Combination Figure 1 This embodiment is described in detail by using specific examples to further describe the technical solution provided above. Specifically:

[0091] like Figure 1 The figure shows a complex geometric model of a rectangular flow channel containing spoiler columns. This model is a schematic diagram of the internal cooling structure at the trailing edge of the gas turbine. When the cooling fluid flows from the left to the Plane 11 position, a 2D interception surface is set here to capture the local average temperature at the inlet. When passing through the spoiler column 2 array, a very complex flow field change will occur. The setting of spoiler column 2 is currently the main method adopted for heat exchange at the trailing edge of the gas turbine. After the fluid hits the spoiler column, part of the cooling gas will continue to move forward along the column, and part of the gas will climb up and down along the spoiler column, thereby hitting the wall surface of WALLTOP4 and WALL BOTTOM3. Finally, when the gas flows through Plane25, a 2D interception surface is also set to capture the local average temperature at the outlet. The selection and setting method of this section can effectively capture the influence of backflow on the heat exchange in the L section heat exchange area, and the calculated data and cloud map can also more intuitively represent the quality of heat exchange capacity.

[0092] In the general process of temperature difference change, ΔT is generally calculated by the average temperature difference between the outlet and the inlet. Since the cooling gas in the flow channel is often affected by the tortuous and complex flow channel and the obstruction of the spoiler column, backflow often occurs. Simply using the numerical difference as a standard to measure the temperature change, the data value obtained with large errors is often not referenceable. To address this pain point, the present invention selects the massFlowAve() formula suitable for the weighted mass flow rate of each point on a specific 2D plane position as the basis, and uses the difference between the hot wall temperature and the overall local average temperature to replace the conventional temperature difference ΔT. The formula is shown in (1):

[0093] ΔT=Temperature-(massFlowAve(Temperature)@Plane1+X / L[mm]*(massFlowAve(Temperature)@Plane2-massFlowAve(Temperature)@Plane1)) (1)

[0095] Wherein, massFlowAve(Temperature)@Plane1: represents the mass flow weighted average temperature of the cooling fluid at the section Plane1. Plane1 is usually set at the 5 / 36 position of the flow channel inlet.

[0096] massFlowAve(Temperature)@Plane2: represents the mass flow weighted average temperature of the cooling fluid at plane 2. Plane 2 is usually set at the 7 / 36 position of the flow channel.

[0097] X / L[mm]: indicates the position change in the horizontal direction of the flow channel relative to the length L. X is the difference change relative to the model's horizontal coordinate point distance, and L is the total length of the fluid flow change under study, so X / L indicates the relative position change of the current position in the flow channel (range between 0 and 1)

[0098] massFlowAve(Temperature)@Plane1+X / L[mm]*(massFlowAve(Temperature)@Plane2-massFlowAve(Temperature)@Plane1): This part represents the local overall average temperature value in the flow channel based on linear interpolation calculation. By capturing the mass flow average temperature at section 1 of the inlet section, the difference between the temperature of variable X at section 2 and the temperature at section 1 within the L-segment range is calculated, and this difference is added to the mass flow average temperature at section 1 as the local overall average temperature, and then the difference is subtracted from the actual heated wall temperature to obtain the absolute value.

[0099] Temperature: The actual temperature of the heating wall. The difference ΔT is the difference between the wall temperature of the mainstream inlet and outlet sections and the overall average temperature of the cooling gas.

[0100] Based on the ΔT described in formula (1), a calculation formula for the overall Nusselt number Nu, which measures the overall heat transfer efficiency of the geometric model, is given. This formula carefully considers the impact cooling effect of the cooling gas on the internal cooling mechanism after flowing through the flow channel according to the position of each cross section divided in the picture. The overall Nusselt number calculated using this formula is more in line with the actual heat transfer situation.

[0101] Nu all =(areaAve(VariableNu)@WALL BOTTOM*area()@WALL

[0102] BOTTOM+areaAve(VariableNu)@WALLTOP*area()@WALLTOP+areaAve(VariableNu)@pinfin*area()@pinfin) / (area()@WALL BOTTOM+area()@WALLTOP+area()@pinfin) (2)

[0104] Among them, Nu all Represents the overall Nusselt number, which can represent the overall heat transfer effect of flow channels with different shapes.

[0105] areaAve(VariableNu)@WALL BOTTOM means calculating the area average value of the heat transfer index (Nusselt number) on the bottom wall.

[0106] area()@WALL BOTTOM represents the area of ​​the bottom wall.

[0107] areaAve(VariableNu)@WALLTOP and area()@WALLTOP similarly represent the area average of the Nusselt number of the top wall and the average area of ​​the top wall.

[0108] areaAve(VariableNu)@pinfin*area()@pinfin and area()@pinfin represent the average Nusselt value of the area on the spoiler column and the average area corresponding to the spoiler column.

[0109] The whole formula calculates the heat transfer effect on the overall geometric model by taking the arithmetic average of the bottom, top wall and spoiler. The denominator area in the formula is the sum of the area of ​​the wall and spoiler, and the numerator is the sum of the product of the average area of ​​the heat transfer index (Nusselt number) of each region and the average area of ​​the surface. all It can comprehensively consider the heat exchange performance of each area and more accurately reflect the overall heat exchange effect.

[0110] The calculation of the total shear stress is also carried out in the same way as in formula (2).

[0111] WSS all =(areaAve(Wall Shear)@WALL BOTTOM*area()@WALL BOTTOM+areaAve(Wall

[0112] Shear)@WALLTOP*area()@WALLTOP+areaAve(Wall

[0113] Shear)@pinfin*area()@pinfin) / (area()@WALL BOTTOM+area()@WALLTOP+area()@pinfin) (3)

[0115] WSS all : Represents the total wall shear stress in the geometric model and is suitable for analyzing flow under different wall force conditions.

[0116] areaAve(WallShear)@WALL BOTTOM: represents the area average of the shear stress on the bottom wall (WALL BOTTOM).

[0117] area()@WALL BOTTOM: indicates the area of ​​the bottom wall.

[0118] areaAve(WallShear)@WALL TOP and area()@WALLTOP: represent the surface average of the shear stress on the top wall and the area of ​​the corresponding wall, respectively.

[0119] areaAve(WallShear)@pinfin*area()@pinfin and area()@pinfin: represent the surface average shear stress on the surface of the spoiler column (pinfin) and the area of ​​the corresponding spoiler column.

[0120] Assume that the average wall shear stress value of each region is multiplied by the area of ​​the corresponding region to obtain the total shear stress value of the entire region. Then divide it by the sum of the areas corresponding to each region, and you get the desired WSS. all .

[0121] area()@WALL BOTTOM+area()@WALLTOP+area()@pinfin: represents the sum of the areas of the bottom wall, top wall and spoiler column.

[0122] The friction coefficient is processed by combining the original basic formula with the massFlowAve() formula and 2D cross sections Plane 1 and Plane 2 to propose a new formula. This formula can better describe the stagnation of cooling gas when passing through spoiler columns or curved flow channels, and is more suitable for measuring the change in friction resistance of the geometric assembly.

[0123] f=(massFlowAve(Pressure)@Plane 1-massFlowAve(Pressure)@Plane

[0124] 2) / 0.5 / (((massFlowAve(Velocity)@Plane 1+massFlowAve(Velocity)@Plane2) / 2)

[0125] ^2) / ((massFlowAve(Density)@Plane 1+massFlowAve(Density)@Plane 2) / 2)*Dh[mm] / L[mm] (4)

[0127] Among them, massFlowAve(Pressure)@Plane 1-massFlowAve(Pressure)@Plane 2: represents the pressure difference of the average mass flow rate between the flow channel inlet (Plane 1) and outlet (Plane 2). This pressure difference is the main driving force for the fluid to flow in the flow channel, and is also an important measure of friction loss.

[0128] 0.5: Coefficient used to calculate kinetic energy.

[0129] massFlowAve(Velocity)@Plane 1 and massFlowAve(Velocity)@Plane 2: represent the mass flow-weighted average velocity at the flow channel inlet and outlet, respectively.

[0130] (massFlowAve(Density)@Plane 1+massFlowAve(Density)@Plane 2) / 2: Calculates the average of the density at the inlet and outlet sections.

[0131] ^2: indicates square.

[0132] Dh[mm]: represents the hydraulic diameter of the flow channel in millimeters. The hydraulic diameter is used to describe the effective flow channel size in complex geometric structures.

[0133] L[mm]: indicates the length of the flow channel in millimeters.

[0134] The overall structure of the formula is:

[0135] The numerator represents the pressure difference in the flow channel and is the driving term related to the resistance.

[0136] The denominator calculates the average kinetic energy of the fluid, which is used to measure the flow intensity of the fluid in the flow channel.

[0137] Through the whole formula, the friction coefficient f can be calculated, which is used to represent the pressure loss of cooling gas in the flow channel due to fluid resistance. The friction coefficient is closely related to the length of the flow channel, hydraulic diameter, fluid velocity, density and pressure difference in the flow channel.

[0138] The physical meaning of the friction coefficient f is to describe the resistance characteristics of the cooling gas when it flows in the flow channel. It can be used to evaluate the impact of flow channels with different structural designs on fluid flow, thereby providing a basis for optimal design. For example, by changing the length of the flow channel, the inclination angle design of the spoiler column or other geometric parameters, the friction coefficient can be minimized to reduce resistance loss and optimize the energy efficiency of the cooling system.

[0139] This implementation is based on the massFlowAve() formula suitable for the mass flow weighting of each point at this position in the CFD-POST post-processing software, and makes innovative changes to the basic formula in the existing research, which can objectively reflect the flow field changes in the flow channel and the quality of the heat exchange, and significantly improve the accuracy and reliability of the numerical simulation of the heat exchange performance of the gas turbine. This method has a certain degree of universality, and the parameters such as Plane1 and WALL TOP wall involved in this formula can be extended to other complex models, and can effectively evaluate the internal cooling effect of complex geometric structures. By setting a 2D plane at the key position of the flow channel and using the mass flow weighting formula, it can more effectively obtain the data of key parameters such as Nusselt number (Nu), friction factor (f), thermal efficiency (TP) and wall shear stress (WSS). Compared with the traditional method, the evaluation method designed in this implementation not only improves the reliability and accuracy of the design of the internal cooling structure of the trailing edge, but also significantly enhances the persuasiveness of the post-processing data results, and provides a powerful tool for the optimization design and performance improvement of heat exchange equipment such as gas turbines.

[0140] In the specific implementation work, this embodiment considers an internal cooling structure at the trailing edge of a gas turbine, which includes a rectangular flow channel and multiple rows of spoiler columns. In order to evaluate the heat transfer performance of the structure, this embodiment sets two 2D cross-sections, namely Plane1 and Plane2, at the inlet and outlet positions of the flow channel, respectively. At the Plane1 position, this embodiment captures the local average inlet temperature of the cooling fluid; and at the Plane 2 position downstream of the spoiler column array, this embodiment captures the local average outlet temperature. Through these two cross sections, this embodiment can effectively capture the impact of backflow on the main heat exchange area and calculate more accurate heat transfer efficiency data.

[0141] Specifically, this embodiment uses the mass flow weighted massFlowAve() formula as the basis and uses linear interpolation to calculate the temperature difference ΔT, which takes into account the difference between the hot wall temperature and the local overall average temperature, as shown in formula (1). Based on this ΔT value, this embodiment further calculates the overall Nusselt number Nu all , formula (2) is used to comprehensively consider the overall Nusselt number in the flow channel. Similarly, this embodiment uses formula (3) to calculate the total wall shear stress WSS all .

[0142] Finally, in order to evaluate the friction coefficient, this embodiment uses formula (4) to describe the stagnation effect of the cooling gas when passing through the spoiler column or the curved flow channel. Through this embodiment, this embodiment can more accurately obtain the data of key parameters such as Nusselt number (Nu), friction factor (f), thermal efficiency (TP) and wall shear stress (WSS), thereby significantly improving the accuracy and reliability of the numerical simulation of gas turbine heat transfer performance. This embodiment not only provides a reliable measurement and calculation method for the design of the cooling mechanism, but also significantly enhances the reliability of the post-processing data results, providing a powerful tool for the optimization design and performance improvement of heat exchange equipment such as gas turbines.

[0143] The technical solution provided by the present invention is further described in detail above through several specific implementation modes in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific implementation modes described above are not intended to be used as limitations on the present invention. Any reasonable modification and improvement of the present invention, combination of implementation modes and equivalent substitution within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A geometric model for optimizing heat transfer performance of a gas turbine based on mass flow weighting, characterized in that the model include: A rectangular flow channel in which at least two rows of spoiler columns are arranged; The spoiler columns are arranged in a staggered manner; The inlet of the rectangular flow channel is set as the flow velocity inlet and temperature boundary of the cooling gas, the outlet is set as the pressure boundary, and the wall surface is set as the no-slip and constant heat flux conditions; The step of setting at least two 2D cross-sectional surfaces for capturing the local average temperature of the cooling fluid, wherein the 2D cross-sectional surfaces are respectively set at positions near the inlet and outlet of the flow channel.

2. A numerical analysis method for optimizing heat transfer performance of a gas turbine based on mass flow weighting, characterized in that: The method is implemented based on the model described in claim 1, comprising: Steps to capture the local average temperature in the flow channel; The steps of respectively capturing the local average temperature of the cooling fluid and calculating the temperature difference; Steps to comprehensively evaluate the heat transfer effect and shear stress changes in different wall areas; Procedure for evaluating the drag characteristics of cooling gas flowing through spoiler pins and tortuous channels.

3. The method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting according to claim 2, characterized in that: The massFlowAve() formula is weighted by mass flow rate to capture the local average temperature in the flow channel.

4. The method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting according to claim 2, characterized in that: The local average temperature of the cooling fluid is captured separately according to the 2D cut surface.

5. The method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting according to claim 2, characterized in that: Calculate the overall Nusselt number Nu by area weighting all and wall shear stress WSS all , in order to comprehensively evaluate the heat transfer effect and shear stress changes in different wall areas.

6. The method for optimizing the heat exchange performance of a gas turbine based on mass flow weighting according to claim 2, characterized in that: The friction coefficient f is calculated based on the mass flow weighting method to evaluate the resistance characteristics of the cooling gas when it flows in the spoiler pillars and tortuous flow channels.

7. The numerical analysis device for optimizing the heat transfer performance of a gas turbine based on mass flow weighting is characterized in that: The device is implemented based on the model described in claim 1, and includes: A module that captures the local average temperature in the flow channel; A module for capturing the local average temperature of the cooling fluid and calculating the temperature difference; A module for comprehensively evaluating the heat transfer effect and shear stress changes in different wall areas; A module for evaluating the drag characteristics of cooling gas flowing through spoiler pins and tortuous channels.

8. A computer storage medium for storing a computer program, characterized in that: When the computer reads the computer program, the computer executes the method of claim 2.

9. A computer, comprising a processor and a storage medium, characterized in that: When the processor reads the computer program stored in the storage medium, the computer executes the method of claim 2 .

10. A computer program product, being a computer program, characterized in that When the computer program is executed, the method according to claim 2 is implemented.

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