Numerical analysis method for optimizing gas turbine heat transfer performance based on mass flow weighting

Abstract

The method for optimizing the heat transfer performance of a gas turbine based on mass flow weighting belongs to the field of computational fluid dynamics. The present invention solves the technical defects in the prior art that the flow field changes of the cooling system are difficult to accurately evaluate and the results are unreliable, and provides an optimized geometric model and analysis method. The model includes a rectangular flow channel and at least two rows of staggered spoiler columns. The flow channel inlet is set as the flow velocity and temperature boundary of the cooling gas, the outlet is a pressure boundary, the wall has no slip and maintains a constant heat flux. At the same time, two 2D interception surfaces are set at the inlet and outlet positions to capture the local average temperature of the cooling fluid. The analysis method is based on this model, captures temperature data and calculates the temperature difference, evaluates the heat transfer effect of different wall surfaces, shear stress and the resistance characteristics of the fluid in the spoiler columns and the flow channel, and is suitable for gas turbine cooling system performance evaluation.

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), especially in 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 with relatively simple geometric structures and relatively simple flow processes.

[0003] In existing research, gas turbine internal cooling typically relies on structures such as spoiler columns to enhance heat transfer. For example, some studies have attempted to improve heat transfer performance by adding spoiler columns within the flow channel and changing the flow path of the cooling gas. These measures can effectively increase the turbulence of the fluid, thereby improving 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 multiple factors, including the complexity of the geometry, the tortuosity of the cooling flow channel, and the arrangement of the spoiler columns. These factors can cause irregular backflow and flow separation in certain areas of the flow field, thereby affecting the overall heat transfer effect.

[0004] While 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 geometries and internal cooling performance, lacking in-depth analysis of details. Many existing studies rely on basic formulas to calculate temperature differences, 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 are unable to effectively perceive flow changes in complex flow fields, making it difficult to detect potential heat transfer unevenness problems within gas turbines 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] To address the technical deficiencies in the prior art of evaluating the performance of the internal cooling system of a gas turbine, such as the inability to accurately reflect the flow field changes of the cooling fluid and the unreliable evaluation results, the present invention provides the following technical solutions:

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

[0008] A rectangular flow channel having at least two rows of spoiler columns;

[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 to 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 above model and includes:

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

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

[0015] Steps for comprehensively evaluating the heat transfer effects and shear stress changes in different wall areas;

[0016] Procedure for evaluating the drag characteristics of cooling gas flowing through spoiler pins and a tortuous flow channel.

[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 according to the 2D cross-section 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 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 above 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 effects and shear stress changes in different wall areas;

[0025] This module evaluates the drag characteristics of cooling gas flowing through spoiler pillars and tortuous channels.

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

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

[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] Using the mass flow-weighted massFlowAve() formula to calculate the local average temperature effectively improves the accuracy of temperature calculations and reduces the temperature difference calculation errors caused by complex flow within the flow channel. Compared to traditional methods, this solution can better capture the temperature changes of the cooling fluid in complex geometric structures, significantly improving the accuracy of heat transfer performance assessment.

[0031] By setting 2D cross-section planes (such as Plane1 and Plane2) at key locations in the flow channel, we can accurately capture the inlet and outlet temperatures of the cooling fluid, better reflecting the fluid temperature changes during the cooling process. Compared to the existing method of directly using the average temperature difference between the flow channel inlet and outlet, this approach can significantly reduce errors caused by complex flow fields and improve data reliability.

[0032] The Nusselt number calculation method based on area-weighted average comprehensively considers the heat transfer effects of various wall surfaces (such as the wall bottom, wall top, and pin fins), providing a more comprehensive evaluation of heat transfer performance. Compared to traditional evaluation methods that directly calculate temperature differences, this method can more comprehensively reflect the heat transfer performance of the entire geometric model, showing significant advantages in complex cooling structures.

[0033] The friction coefficient, f, is calculated using a mass flow-weighted method, effectively reflecting the changes in resistance of the cooling gas as it passes through spoilers or bends in the flow channel. Compared with traditional friction coefficient calculation methods, this method better describes the impact of complex flows on the friction coefficient, thereby improving the accuracy of the assessment of the cooling channel resistance characteristics.

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

[0035] It is suitable for use in the evaluation of the performance of the internal cooling system of gas turbines. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0037] Among them, 1 represents Plane 1, 2 represents Pin fin, 3 represents WALLBOTTOM, 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 transfer performance of a gas turbine based on mass flow weighting. The model includes:

[0041] A rectangular flow channel having at least two rows of spoiler columns;

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

[0048] Steps for comprehensively evaluating the heat transfer effects and shear stress changes in different wall areas;

[0049] Procedure for evaluating the drag characteristics of cooling gas flowing through spoiler pins and a tortuous flow channel.

[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 performance of a cooling system with complex geometric structures by establishing a geometric model, setting boundary conditions, performing numerical simulation, and performing post-processing analysis. The specific implementation is as follows:

[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 consists of a rectangular channel with multiple rows of spoilers to simulate the actual cooling structure at the trailing edge of a gas turbine. The spoilers are cylindrical in shape, ranging in number from 10 to 50, and arranged in a staggered pattern to maximize turbulence and cooling. This staggered arrangement effectively increases the turbulence of the cooling gas, thereby enhancing heat transfer performance. Furthermore, the width-to-height ratio of the rectangular channel is set to 5:6 to optimize the cooling gas flow path and ensure sufficient flow and heat transfer in all areas of the cooling fluid.

[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 flow velocity at the inlet is between 4 and 30 m / s, and the temperature range is 300 to 600 Kelvin (K) to ensure 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 flow stability. The wall boundary conditions are set to no slip and constant heat flux, with a heat flux value of 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 simulations, meshing plays a crucial role in the accuracy of simulation results. A structured mesh is used to partition the entire geometric model to accommodate complex geometries. Furthermore, mesh refinement is employed around spoiler columns and in areas with significant fluid flow variations to capture subtle changes in the flow 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] The geometric model was numerically solved using CFD software. The Reynolds-averaged Navier-Stokes (RANS) equations were used to simulate the cooling fluid flow characteristics, and the Realizable k-ε turbulence model was selected to simulate the turbulent flow characteristics in the flow channel to improve computational accuracy. During the solution process, the massFlowAve() formula, weighted by mass flow, was used to capture the local average temperature in the flow channel to reduce computational errors introduced by the complex flow field. The SIMPLE algorithm and steady-state solver were used in the simulation to ensure convergence and computational stability.

[0063] 5. Post-processing analysis

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

[0065] During the post-processing phase, simulation data is analyzed in depth using tools such as CFD-POST. First, 2D cross-section surfaces are set at the inlet and outlet of the flow channel (located at 5 / 36 of the inlet and 7 / 36 of the outlet, 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, we can generate contour plots and cloud maps to visually display the flow characteristics of the cooling fluid and the heat transfer effect around the spoiler. Simultaneously, streamline plots can be used to analyze the cooling gas flow path and evaluate the impact of the spoiler design on the fluid heat transfer performance.

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

[0068] Brief Description: Calculates the global 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 operating 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 a 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. It can more comprehensively reflect the flow and heat transfer of the cooling fluid in the flow channel, making it 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] Implementation 5: This implementation further limits the numerical analysis method for optimizing the heat transfer performance of a gas turbine based on mass flow weighting provided in Implementation 2. The overall Nusselt number Nu is calculated 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] Implementation method six. 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. 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 column and the tortuous flow channel.

[0082] Embodiment 7: This embodiment 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 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 effects and stress conditions of different wall areas;

[0086] This module evaluates the drag characteristics of cooling gas flowing through spoiler pillars 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] Implementation method 9: This implementation method provides a computer, including a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method provided in implementation method 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 11: Combination Figure 1 This embodiment further describes the above technical solution in detail through specific examples, specifically:

[0091] like Figure 1 Shown is a complex geometric model of a rectangular flow channel containing spoiler pins. This model is a schematic cross-section of the internal cooling structure at the trailing edge of a gas turbine. When the cooling fluid flows from the left into Plane 11, a 2D cross-section is set to capture the local average temperature at the inlet. Passing through the array of spoiler pins 2, a highly complex flow field occurs. The use of spoiler pins 2 is currently the predominant method for heat transfer at the trailing edge of gas turbines. After impacting the spoiler pins, some of the cooling gas continues forward along the pins, while some rises and descends along the pins, impacting the walls of Wall Top 4 and Wall Bottom 3. Finally, as the gas passes through Plane 25, a 2D cross-section is also set to capture the local average temperature at the outlet. This cross-section selection and placement effectively captures the effect of backflow on the heat transfer within the L-section heat exchange area. The calculated data and contour map provide a more intuitive representation of the heat transfer performance.

[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 complex flow channel and the obstruction of the turbulent 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 applicable to 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] massFlowAve(Temperature)@Plane1: represents the mass flow-weighted average temperature of the cooling fluid at Plane 1. Plane 1 is typically located at the 5 / 36 position of the flow channel inlet.

[0096] massFlowAve(Temperature)@Plane2: Indicates the mass flow-weighted average temperature of the cooling fluid at Plane2. Plane2 is typically set at the 7 / 36 position along the flow path.

[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 horizontal coordinate point of the model, and L is the total length of the fluid flow change under study. Therefore, X / L represents 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. 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. The absolute value is then subtracted from the actual heated wall temperature to obtain the difference.

[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 formula for calculating 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 flowing through the flow channel on the internal cooling mechanism based on the position of each cross-section divided in the image. The overall Nusselt number calculated using this formula is more consistent 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 represents the problem of calculating the area average value of the heat transfer index (Nussel 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 of the whole 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 the spoiler, and the numerator is the sum of the product of the average area of ​​the heat transfer index (Nussel number) of each region and the average value of the surface. all It can comprehensively consider the heat transfer performance of each area and more accurately reflect the overall heat transfer effect.

[0110] The same idea as formula (2) is used to calculate the total shear stress.

[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 stress 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 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, the quotient is divided by the sum of the areas corresponding to each region, which is the required 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 treated by combining the original basic formula with the massFlowAve() formula and the 2D cross-sections Plane 1 and Plane 2 to develop a new formula. This formula better describes the stagnation of cooling gas when passing through spoilers or curved flow paths, making it more suitable for measuring the change in friction resistance in such geometric assemblies.

[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] massFlowAve(Pressure)@Plane 1 - massFlowAve(Pressure)@Plane 2 represents the pressure difference between the average mass flow rate at the flow channel inlet (Plane 1) and outlet (Plane 2). This pressure difference is the primary driving force for fluid 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:

[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] The friction coefficient f can be calculated from this formula, representing the pressure loss of the cooling gas in the flow channel due to fluid resistance. This friction coefficient is closely related to the length and hydraulic diameter of the flow channel, the velocity and density of the fluid, and the pressure difference within the flow channel.

[0138] The physical significance of the friction coefficient f lies in describing the resistance characteristics of the cooling gas flowing through the flow channel. It can be used to evaluate the impact of flow channel designs with different structural designs on fluid flow, thus providing a basis for design optimization. For example, by varying the flow channel length, the inclination angle of the spoiler, or other geometric parameters, the friction coefficient can be minimized to reduce drag losses and optimize the energy efficiency of the cooling system.

[0139] This implementation utilizes the massFlowAve() formula, a weighted mass flow rate formula applied to each point in the CFD-POST post-processing software, as a basis. This innovative modification of the existing basic formula allows for objective reflection of flow field variations and heat transfer within the flow channel, significantly improving the accuracy and reliability of numerical simulations of gas turbine heat transfer performance. This method is universally applicable, allowing the use of parameters such as Plane1 and WALL TOP, as described in this formula, to other complex models. It effectively evaluates the internal cooling performance of complex geometries. By setting 2D planes at key flow channel locations and employing a mass flow rate weighted formula, data for key parameters such as the Nusselt number (Nu), friction factor (f), thermal efficiency (TP), and wall shear stress (WSS) can be more efficiently obtained. Compared to traditional methods, the evaluation method designed in this implementation not only improves the reliability and accuracy of trailing edge internal cooling structure design but also significantly enhances the persuasiveness of post-processing data results, providing a powerful tool for optimizing the design and performance improvement of heat transfer 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. This formula 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 , using formula (2) 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, 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 curved flow channel. Through this embodiment, the present embodiment can more accurately obtain data on key parameters such as the 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 calculation method for the design of cooling mechanisms, but also significantly enhances the reliability of post-processing data results, providing a powerful tool for the optimized design and performance improvement of heat transfer equipment such as gas turbines.

[0143] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A numerical analysis method for optimizing gas turbine heat transfer performance based on mass flow weighting, characterized in that: The method is based on the model implemented as follows: A rectangular flow channel having at least two rows of spoiler columns; 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 to no-slip and constant heat flux conditions; The step of setting at least two 2D cross-sectional planes for capturing the local average temperature of the cooling fluid, wherein the 2D cross-sectional planes are respectively set at positions near the inlet and outlet of the flow channel, i.e., Plane1 and Plane2, and the local average temperature of the inlet of the cooling fluid is captured at the position of Plane1, and the local average temperature of the outlet is captured at the position of Plane2; Among them, the massFlowAve() formula weighted by mass flow is used to capture the local average temperature in the flow channel; According to the 2D cross-section, the local average temperature of the cooling fluid is captured separately; Analytical methods include: Steps to capture the local average temperature in the flow channel; The steps of capturing the local average temperature of the cooling fluid and the actual temperature of the heated wall respectively and calculating the temperature difference; Specifically: Temperature difference ΔT : = Temperature -(massFlowAve( Temperature )@Plane1+ X / L *(massFlowAve( Temperature )@Plane2-massFlowAve( Temperature )@Plane1)) Among them, massFlowAve( Temperature )@Plane1: represents the mass flow-weighted average temperature of the cooling fluid at Plane1; Plane1 is usually set at the 5 / 36 position of the flow channel inlet; massFlowAve( Temperature )@Plane2: represents the mass flow-weighted average temperature of the cooling fluid at Plane2; Plane2 is usually set at the 7 / 36 position of the flow channel; X / L : 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, L is the total length of the fluid flow variation under study, so X / L Indicates the relative position change of the current position in the flow channel, ranging from 0 to 1; massFlowAve( Temperature )@Plane1+ X / L *(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 the inlet section 1, the variable within the L section range is calculated. X The difference between the temperature at section 2 and the temperature at section 1 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; 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. Steps for comprehensively evaluating the heat transfer effects and shear stress changes in different wall areas; Procedure for evaluating the drag characteristics of cooling gas flowing through spoiler posts and a tortuous flow channel.

2. The method for optimizing the heat transfer performance of a gas turbine based on mass flow weighting according to claim 1, characterized in that: The overall Nusselt number Nuall and wall shear stress WSSall are calculated by area weighting to comprehensively evaluate the heat transfer effect and shear stress changes in different wall areas.

3. The method for optimizing the heat transfer performance of a gas turbine based on mass flow weighting according to claim 1, 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 and the tortuous flow channel.

4. A numerical analysis device for optimizing gas turbine heat transfer performance based on mass flow weighting, characterized in that: The device is implemented based on a model constructed as follows: A rectangular flow channel having at least two rows of spoiler columns; 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 to no-slip and constant heat flux conditions; The step of setting at least two 2D cross-sectional planes for capturing the local average temperature of the cooling fluid, wherein the 2D cross-sectional planes are respectively set at positions near the inlet and outlet of the flow channel, i.e., Plane1 and Plane2, and the local average temperature of the inlet of the cooling fluid is captured at the position of Plane1, and the local average temperature of the outlet is captured at the position of Plane2; Among them, the massFlowAve() formula weighted by mass flow is used to capture the local average temperature in the flow channel; According to the 2D cross-section, the local average temperature of the cooling fluid is captured separately; The analysis device includes: a module that captures the local average temperature in the flow channel; A module that captures the local average temperature of the cooling fluid and the actual temperature of the heated wall, and calculates the temperature difference; Specifically: Temperature difference ΔT : = Temperature -(massFlowAve( Temperature )@Plane1+ X / L *(massFlowAve( Temperature )@Plane2-massFlowAve( Temperature )@Plane1)) Among them, massFlowAve( Temperature )@Plane1: represents the mass flow-weighted average temperature of the cooling fluid at Plane1; Plane1 is usually set at the 5 / 36 position of the flow channel inlet; massFlowAve( Temperature )@Plane2: represents the mass flow-weighted average temperature of the cooling fluid at Plane2; Plane2 is usually set at the 7 / 36 position of the flow channel; X / L : 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, L is the total length of the fluid flow variation under study, so X / L Indicates the relative position change of the current position in the flow channel, ranging from 0 to 1; massFlowAve( Temperature )@Plane1+ X / L *(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 the inlet section 1, the variable within the L section range is calculated. X The difference between the temperature at section 2 and the temperature at section 1 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; 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. A module for comprehensively evaluating the heat transfer effects and shear stress changes in different wall areas; This module evaluates the drag characteristics of cooling gas flowing through spoiler pillars and tortuous channels.

5. A computer storage medium for storing a computer program, characterized in that: When the computer reads the computing program, the computer executes the method according to claim 1 .

6. 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 according to claim 1 .

7. Computer program product comprising a computer program, characterized in that When the computer program is executed, the method according to claim 1 is implemented.

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