An Efficient Prediction Method for Fuel Regeneration Cooling Characteristics Based on a Coarse-Grained Model

Abstract

This invention discloses an efficient prediction method for fuel regenerative cooling characteristics based on a coarse-grained model, belonging to the field of aircraft thermal protection technology. This method aims to solve the problems of massive computational load and excessive time consumption in traditional CFD simulations. Its core steps include: firstly, coarse-grained spatial discretization of the regenerative cooling channel to obtain a small number of discrete mesh elements; then, constructing a fluid-structure coupled heat transfer network model and establishing a model for calculating the heat flux of each element; subsequently, integrating these to form a multi-field coupled numerical solution model; and finally, obtaining the system characteristics through unsteady-state iterative solutions. The significant advantage of this invention is that by drastically reducing the number of mesh elements and using physical correlations to replace the solution of complex control equations, the computational time is significantly reduced while maintaining prediction accuracy, achieving an order-of-magnitude improvement in efficiency. This provides an efficient tool for the rapid design and optimization of regenerative cooling systems.

Description

Technical Field

[0001] This invention relates to the field of thermal protection and thermal management technology for aircraft propulsion systems, and more specifically, to an efficient method for predicting fuel regeneration cooling characteristics based on a coarse-grained model. Background Technology

[0002] Current high-speed flight technology is developing towards longer endurance, higher Mach speeds, and reusability, posing new requirements and challenges for the thermal safety protection of aircraft propulsion systems. Regenerative cooling technology, which uses the hydrocarbon fuel carried by the aircraft itself as a cooling medium, can effectively reduce the combustion chamber wall temperature, improve fuel combustion stability, and maintain the safe operation of the aircraft for extended periods. It has broad application prospects in the fields of liquid rocket propulsion and air-breathing hypersonic vehicles.

[0003] Regenerative cooling systems generally operate above the fuel critical pressure. The cooling microchannels involve multiple complex heat transfer and flow states, including overpressure liquid heat transfer, supercritical fluid heat transfer, and fuel pyrolysis heat transfer. Furthermore, the three heat transfer processes between the high-temperature combustion gas, the combustion chamber wall, and the hydrocarbon fuel in the cooling channel are highly coupled, greatly increasing the complexity of regenerative cooling technology research. Currently, the research methods for fuel regenerative cooling characteristics are mainly divided into two categories: (1) traditional experimental research methods and (2) CFD numerical simulation methods.

[0004] Traditional experimental studies rely on high heat flux loading platforms to simulate the thermal load environment of the combustion chamber walls and analyze the heat transfer, flow, and pyrolysis reaction patterns of fuel within microscale channels. However, due to experimental conditions and extreme operating conditions, these studies are time-consuming, costly, and cannot accurately obtain key information such as the convective heat transfer coefficient on the inner surface of the flow channel and the evolution of the working fluid temperature distribution. Furthermore, thermoacoustic instability within the cooling microchannels reduces experimental repeatability, further affecting the reliability of the research, and making it difficult to directly apply the results to the quantitative analysis of regenerative cooling systems. Therefore, the experimental method has the following drawbacks: it relies on high heat flux loading platforms, resulting in high costs and long cycles; it is difficult to accurately measure the local convective heat transfer coefficient and temperature distribution inside the flow channel under extreme conditions; and it suffers from insufficient repeatability and reliability due to thermoacoustic instability.

[0005] CFD numerical simulation methods, to some extent, compensate for the shortcomings of experimental research. By coupling and solving governing equations such as the mass equation, momentum equation (NS equation), energy equation, reaction kinetics equation, and component transport equation, it obtains detailed information on the physical and chemical fields during fuel regeneration cooling, providing guidance for the regular analysis of regeneration cooling characteristics. However, in actual engineering, regeneration cooling structures are complex, with extremely fine and long flow channels, which greatly increases the time and manpower costs of numerical modeling, mesh generation, and solution calculation (the number of meshes for a single channel is generally over 3 million, and the total time for completing a single-condition calculation using a high-performance computing cluster is generally over 2.5 days). This cannot meet the needs of large-scale, batch engineering calculations and seriously restricts the iterative optimization process of regeneration cooling technology. Therefore, CFD numerical simulation methods have the following drawbacks: Although they can obtain detailed physical and chemical field information, for regeneration cooling systems with complex structures and long flow channels, numerical modeling requires generating millions or even tens of millions of meshes and solving the NS equation, energy equation, component transport equation, and reaction kinetics equation, resulting in a huge computational load. Single-process analysis on high-performance computing clusters often takes tens of hours, which cannot meet the needs of large-scale, batch rapid analysis and iterative optimization in engineering design. Summary of the Invention

[0006] The purpose of this invention is to provide an efficient method for predicting fuel regeneration cooling characteristics that can both ensure prediction accuracy and significantly improve computational efficiency.

[0007] To achieve the above-mentioned objectives, this invention provides an efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model, the method comprising:

[0008] Step S1: Geometrically segment a single cooling channel in the regenerative cooling structure of the aircraft's fuel regeneration cooling system to obtain the channel target unit; coarsely discretize the channel target unit to obtain multiple discrete mesh units, and extract the geometric information and material properties of each discrete mesh unit.

[0009] Step S2: Based on the spatial position and contact relationship between each discrete grid unit, and combined with the geometric information, determine the heat transfer logic between the discrete grid units, and construct the fluid-solid coupled heat transfer network of the regenerative cooling structure; the fluid-solid coupled heat transfer network is used to characterize the coupled energy transfer process involving solid heat conduction, fluid-solid convection and fuel cracking reaction from the inner wall of the combustion chamber to the outer wall.

[0010] Step S3: Establish a heat transfer flux calculation model for each discrete grid element in the fluid-structure interaction heat transfer network; the material-related physical property parameters in the heat transfer flux calculation model are determined based on the material properties;

[0011] Step S4: Integrate the fluid-solid coupled heat transfer network with the heat transfer flux calculation model, and combine the operating condition parameters to establish a multi-field coupled numerical solution model for the heat transfer-flow-pyrolysis reaction of the fuel regeneration cooling system.

[0012] Step S5: Set the initial state parameters and calculation time step of each discrete grid element in the multi-field coupled numerical solution model of heat transfer-flow-pyrolysis reaction, and perform unsteady-state iterative solution on the multi-field coupled numerical solution model of heat transfer-flow-pyrolysis reaction to obtain the heat transfer, flow and pyrolysis reaction characteristics of the fuel regeneration cooling system under time-varying conditions.

[0013] This invention aims to overcome the problems of high cost, long cycle time, and low computational efficiency of existing experimental and CFD simulation methods, and provides an efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model. By simplifying model complexity and avoiding the solution of complex control equations, it achieves rapid prediction and analysis of the heat transfer-flow-pyrolysis reaction characteristics of regeneration cooling systems while ensuring engineering-acceptable accuracy, providing an efficient tool for the thermal protection design and optimization of propulsion systems.

[0014] The core of the efficient prediction method proposed in this invention lies in constructing a low-dimensional fluid-structure coupled heat transfer network using a coarse-grained spatial discretization strategy, and directly coupling it with a high-precision physicochemical correlation model for rapid solution. The method first extracts a representative (geometrically symmetric, physically equivalent) single cooling channel from the regenerative cooling structure as the target element, and coarsely discretizes it to obtain a significantly reduced number of discrete mesh elements. Based on these elements, a fluid-structure coupled heat transfer network is constructed, and an element-level heat flux calculation model is established. By integrating the network structure and the calculation model, a complete system-level multi-field coupled numerical solution model is formed, and the system characteristics are finally obtained through unsteady-state iterative solution. This reduces the complexity of the three-dimensional continuous field problem to a network problem composed of a small number of discrete elements and their connections, fundamentally reducing the degrees of freedom in the solution. It solves the fundamental problems of large mesh size, complex solution equations, and long computation time in traditional CFD methods. It transforms numerical simulation from three-dimensional field solution to a combination of one-dimensional network transfer and zero-dimensional element calculation, laying a structural foundation for efficient computation.

[0015] Preferably, the coarse-grained spatial discretization of the flow channel target unit specifically includes:

[0016] Along the cross-sectional direction of the cooling channel, each solid domain is divided into several layers of discrete grid elements, while the fluid domain is not divided along the cross-sectional direction of the cooling channel.

[0017] Along the axial direction of the cooling channel, each solid domain and fluid domain is divided into several discrete grid cells.

[0018] In this design, the flow and heat transfer within the fluid domain are described holistically without the need to analyze internal details; axial segmentation is used to characterize the evolution of parameters along the flow direction. This approach solves the problem of massive computational cells caused by uniform and fine meshes. Necessary resolution is maintained in the critical direction (axial direction), while significant simplification is achieved in secondary directions (cross-sections), reducing the total mesh count to the hundreds of millimeter range. Compared to the millions of meshes in CFD, the computational dimensionality decreases exponentially.

[0019] Preferably, the geometric information includes: the geometric dimensions of the discrete mesh cells, the equivalent thermal conduction distance between adjacent discrete mesh cells, and the contact area between adjacent discrete mesh cells.

[0020] The extracted geometric information (size, contact area, heat transfer distance) is directly used to construct network connections and calculate various heat fluxes; the extracted material properties (such as thermal conductivity) serve as key physical property parameters input to the heat flux calculation model. The structure and material properties of physical space are transformed into topological parameters and calculation coefficients of the network model. This solves the problem of the correlation between model parameters and physical reality, ensuring that the simplified model still has clear physical meaning and a foundation for accuracy. It enables the abstract heat transfer network and calculation model to accurately reflect the properties of specific physical structures, guaranteeing the reliability of the prediction results.

[0021] Preferably, the fluid-structure interaction heat transfer network of the regenerative cooling structure is constructed as follows:

[0022] Based on the axial partitioning of the cooling channel and the fuel flow direction, the fuel inlet parameters are used as the input parameters of the first fluid unit, and the output parameters of the previous fluid unit are used as the input parameters of the next fluid unit. This process is repeated until the parameter transfer relationship of all sections is defined, thus constructing a fluid-solid coupling heat transfer network for the regenerative cooling structure.

[0023] The construction and solution strategy of the fluid-structure interaction heat transfer network follows the principle of partitioned recursion and segment-by-segment iteration. That is, calculations are performed sequentially along the flow direction, using the outlet parameters of the upstream segment as the inlet conditions of the downstream segment. Utilizing the physical characteristics of unidirectional flow of the cooling medium and segment-by-segment transfer of thermal parameters, the global coupling problem is decomposed into a series of sequential, locally coupled subproblems. This solves the convergence difficulties and computational burden of directly solving large nonlinear equation systems globally. A stable iterative solution process is achieved, significantly reducing the computational scale of a single iteration.

[0024] Preferably, the calculation formulas in the heat flux calculation model of each discrete grid element in the fluid-structure interaction heat transfer network include:

[0025] External input heat flux of each discrete grid cell Calculation method:

[0026] ;

[0027] in, For external input heat flux density, The heat flux input area of ​​the discrete mesh element;

[0028] Solid heat flux between discrete grid cells Calculation method:

[0029] ;

[0030] in, Let be the thermal conductivity of the discrete mesh element in the solid domain. The thermally conductive contact area between adjacent discrete grid cells. The temperature difference between adjacent discrete grid cells in the solid domain. This represents the equivalent heat transfer distance between adjacent discrete grid cells. The negative sign indicates that the heat transfer direction points in the direction of decreasing temperature.

[0031] Convective heat transfer flux between discrete grid cells in the solid domain and discrete grid cells in the fluid domain The calculation method is as follows:

[0032] ;

[0033] in, Let be the convective heat transfer area of ​​discrete grid cells in adjacent solid domains and discrete grid cells in fluid domains. This represents the temperature difference between discrete grid cells in adjacent solid domains and discrete grid cells in fluid domains. The negative sign indicates that the heat transfer direction points towards the direction of decreasing temperature. The convective heat transfer coefficient of the contact surface;

[0034] Fuel cracking heat source within discrete grid cells of the fluid domain The pyrolysis rate β is based on the discrete grid cell temperature T. f This was obtained by consulting the hydrocarbon fuel cracking energy-cracking rate distribution map;

[0035] Thermal increment of each discrete grid cell The calculation method is as follows:

[0036] ;

[0037] in, The density of the medium within the discrete grid cell. For discrete mesh cell volume, For the specific heat of the medium within a discrete grid cell, The rate of temperature change of discrete grid cells per unit time;

[0038] Heat flux output to the outside of each discrete grid cell The calculation method is as follows:

[0039] ;

[0040] in, For external output heat flux density, The heat flux output area of ​​the discrete grid cell.

[0041] The heat transfer flux calculation model integrates calculation formulas for all key energy transfer and conversion mechanisms, including conduction, convection, cracking reactions, heat capacity, and boundary heat flux. It employs simplified but physically complete lumped parameter models or empirical correlations to replace complex differential governing equations in describing various heat transfer processes. This solves the numerical complexity problem caused by directly solving momentum equations and energy differential equations. Even at the expense of local fine flow structure information, it can still accurately capture the mainstream heat transfer effect and overall energy balance, resulting in a qualitative leap in calculation speed.

[0042] Preferably, the convective heat transfer coefficient of the contact surface is calculated as follows:

[0043] ;

[0044] in, Let be the thermal conductivity of the fuel working fluid within a discrete grid cell of the fluid domain, and D be the hydraulic diameter of the discrete grid cell of the fluid domain. is the dimensionless surface heat transfer coefficient.

[0045] Preferably, the numerical solution model for the multi-field coupled heat transfer-flow-pyrolysis reaction of the fuel regeneration cooling system adopts an iterative solution strategy that proceeds segment by segment and time step by time step, specifically including:

[0046] Within each time step, the heat transfer, flow, and pyrolysis parameters of each discrete grid cell are iteratively calculated sequentially until the residual is less than a set threshold.

[0047] Use the current segment's exit parameters as the next segment's entry parameters until all segments have been calculated.

[0048] Update to the next time step and repeat the above process until all time steps have been calculated.

[0049] Preferably, the initial state parameters include:

[0050] Initial input heat flux Initial output heat flux Initial temperature of discrete grid cells in the fluid domain Initial flow Initial pyrolysis heat source Initial pyrolysis rate Initial back pressure Initial temperature of discrete grid cells in the solid domain Initial temperature of discrete grid cells in the solid domain based on Obtained through adjustments.

[0051] Preferably, the target flow channel unit is obtained in the following way:

[0052] A single cooling channel that is geometrically symmetrical and whose heat transfer characteristics are equivalent to the overall heat transfer characteristics of the regenerative cooling structure is extracted from the regenerative cooling structure and used as the target channel unit.

[0053] Preferably, the fuel regeneration cooling system is a fuel regeneration cooling system that uses hydrocarbon fuel as the cooling medium.

[0054] One or more technical solutions provided by this invention have at least the following technical effects or advantages:

[0055] Computational efficiency is improved by orders of magnitude: By using coarse-grained discretization, the number of computational grids is significantly reduced compared to the traditional CFD method, resulting in a substantial reduction in computation time and a significant improvement in efficiency.

[0056] While ensuring engineering accuracy, the cost is greatly reduced: Compared with expensive experimental research and time-consuming CFD simulation, the method of this invention achieves prediction accuracy close to that of high-precision CFD simulation results with lower computational resource consumption, meeting the accuracy requirements of engineering design and iterative optimization.

[0057] The modeling and solution process is greatly simplified: it avoids the fine mesh generation of complex geometry and the solution of computational fluid dynamics control equations, reduces the high requirements of the method on the user's professional background and computing resources, and is easier to deploy and apply quickly in engineering practice.

[0058] Suitable for dynamic process analysis and batch calculation: The established unsteady solution model can effectively simulate the transient characteristics of the system, and combined with its high efficiency, it can easily complete batch analysis and sensitivity studies of multiple working conditions and multiple parameters, providing strong support for system optimization design. Attached Figure Description

[0059] The accompanying drawings, which are provided to further illustrate embodiments of the invention and constitute a part of this invention, are not intended to limit the scope of the invention.

[0060] Figure 1 This is a flowchart illustrating the efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model in this invention.

[0061] Figure 2This is a schematic diagram of the fuel regeneration cooling heat transfer process;

[0062] Figure 3 This is a discrete schematic diagram of the cooling channel unit corresponding to the spatial discrete partitioning;

[0063] Figure 4 This is a discrete schematic diagram of the cooling channel unit corresponding to the discrete partitioning of the cross section. Figure 4 The numbers in the table correspond to the numbers of the discrete grid cells;

[0064] Figure 5 This is a schematic diagram of the heat transfer network of the flow channel unit;

[0065] Figure 6 This is a schematic diagram of the model solution process;

[0066] Figure 7 This is a schematic diagram of the regenerative cooling channel structure;

[0067] Figure 8 This is a schematic diagram of a discrete mesh element along the axial tangential direction;

[0068] Figure 9 This is a schematic diagram of discrete mesh elements divided by a cross section. Figure 9 The numbers in the table correspond to the numbers of the discrete grid cells;

[0069] Figure 10 This is a schematic diagram of a discrete unit heat transfer network;

[0070] Figure 11 This is a schematic diagram comparing and verifying the calculation results of the coarse-grained model. Detailed Implementation

[0071] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, where there is no conflict, the embodiments of the present invention and the features thereof can be combined with each other.

[0072] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0073] Those skilled in the art should understand that, in the disclosure of this invention, the terms "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, the above terms should not be construed as limiting this invention.

[0074] It is understood that the term "a" should be understood as "at least one" or "one or more", that is, in one embodiment, the number of an element can be one, while in another embodiment, the number of the element can be multiple, and the term "a" should not be understood as a limitation on the number.

[0075] Please refer to Figure 1 , Figure 1 This is a flowchart illustrating an efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model. The specific implementation steps provided by this invention are as follows:

[0076] Step 1 - Representative Unit Extraction and Coarse-grained Discretization: The regenerative cooling structure of the aircraft's fuel regeneration cooling system is characterized by multiple sets of fuel cooling microchannels formed by interlaced and spaced ribs in the combustion chamber / outer wall (e.g., Figure 2 (As shown in the example). Based on the geometric features of the regenerative cooling structure, a single cooling channel is extracted as the target element in the model file. Microscopic details of the channel structure are ignored. The continuous physical domain is discretized using a coarsened mesh (i.e., coarse-grained discretization), and the structural information of each discrete mesh element is statistically analyzed, as follows:

[0077] Single cooling channel segmentation principle: In order to ensure that the obtained single cooling channel has geometric similarity and physical equivalence with the regenerative cooling structure, the cooling channel segmentation should meet the following requirements: the target channel unit has spatial geometric symmetry characteristics, and the heat transfer characteristics of the channel unit are equivalent to the overall heat transfer characteristics of the regenerative cooling structure.

[0078] The principle of coarse-grained discretization of the target element in the flow channel: To reduce the solution complexity of the fluid / solid heat transfer process in the cooling system, based on the characteristics of the flow channel structure, each solid domain is divided into several layers (e.g., 2-4 layers, the specific number of layers can be adjusted according to actual needs, and this embodiment of the invention does not impose any corresponding limitations) of discrete grid elements along the flow channel cross-section direction. However, the fluid domain is not divided as a whole to simplify the internal heat transfer calculation of the fluid. Along the axial direction of the flow channel, each solid domain and the fluid domain are divided into several segments (e.g., 10-20 segments, the specific number of segments can be adjusted according to actual needs, and this embodiment of the invention does not impose any corresponding limitations) of discrete grid elements to ensure the spatiotemporal uniformity of the fluid / solid coupling heat transfer calculation along the flow direction of the fuel working fluid. The grid density can be reasonably adjusted. The denser the grid division, the higher the calculation accuracy, but the calculation time will also increase accordingly.

[0079] The geometric information of discrete mesh elements includes: element material properties, element geometric parameters, equivalent thermal conduction distance between adjacent elements, and contact area between adjacent elements.

[0080] Step 2 - Constructing a fluid-solid coupled heat transfer network: Determine the heat transfer logic relationship between each discrete grid unit and construct a regenerative cooling structure fluid / solid coupled heat transfer network.

[0081] The heat transfer process in a regenerative cooling channel generally includes: heat flow input q to the combustion chamber wall. in Heat conduction in the solid domain (lining, fins, outer wall); convective heat transfer between the solid and fluid domains; fuel cracking heat transfer in the fluid domain; heat output q from the outer wall surface to the environment. out ,like Figure 2 As shown in the example. Refer to step 1 to complete the discretization of the flow channel units (e.g., Figures 3-4 As shown in the example), and based on the heat transfer relationships between each unit, construct the corresponding heat transfer network (such as...). Figure 5 As shown in the example, the method for constructing the heat transfer network can be found in the following literature (Yang Shiming, Tao Wenquan. Heat Transfer. 4th Edition. Beijing: Higher Education Press. 2006). The embodiments of this invention will not be described in detail. Specifically, based on the characteristics of the axial partitioning of the flow channel, the fuel flow direction, and the heat transfer law in step 1, the fuel inlet parameters are used as the input parameters of the fluid unit in zone I, and the output parameters of the fluid unit in zone I are used as the input parameters of the fluid unit in zone II, and so on, until the final segment discrete unit parameter definition is completed.

[0082] Step 3 - Establishing a heat transfer flux calculation model: Based on the mathematical relationships of heat transfer, flow, and fuel pyrolysis reaction, derive the heat transfer flux of each discrete grid cell in the heat transfer network shown in Step 2. And related parameters, to complete the parameterization closed loop of the fluid / solid coupling heat transfer network, as detailed below:

[0083] External input heat flux of each discrete unit The following relationship exists:

[0084] ;

[0085] in, For external input heat flux density, The heat flux input area of ​​the discrete unit.

[0086] Solid heat flux between discrete grid cells According to Fourier's law of thermal conductivity:

[0087] ;

[0088] For the sake of simplicity, the discrete mesh element of the solid domain is simply referred to as the solid element, and the discrete mesh element of the fluid domain is simply referred to as the fluid element.

[0089] in, Thermal conductivity of a solid element (which can be determined based on the element temperature) (obtained by querying the physical property database) This refers to the thermally conductive contact area between adjacent units. The temperature difference between adjacent solid units. This represents the equivalent heat transfer distance between adjacent units. The negative sign indicates that the heat transfer direction points towards the direction of decreasing temperature.

[0090] Convective heat transfer flux between solid and fluid units According to Newton's law of cooling:

[0091] ;

[0092] in, This represents the convective heat transfer area between adjacent solid and fluid elements. This represents the temperature difference between adjacent solid and fluid units. The negative sign indicates that the direction of heat transfer is towards the direction of decreasing temperature. The convective heat transfer coefficient of the contact surface can be derived from relevant parameter formulas:

[0093] ;

[0094] in, Let be the thermal conductivity of the working fluid within the fluid unit, and D be the hydraulic diameter of the fluid unit. The dimensionless surface heat transfer coefficient can be expressed as the flow Reynolds number. Prandtl number of fuel working fluid Medium density within discrete grid cells Specific heat of medium within discrete grid cells , buoyancy force number Functions with equal parameters The above parameters can be based on the mass flow rate m, the flow area S of the fluid unit, and the temperature of the fluid unit. Fluid unit pressure Temperature of adjacent solid units The parameters are calculated. The specific heat transfer-flow function correlation is obtained. The calculation methods for related parameters can be found in the following literature (Pioro, IL, HF Khartabil, and RB Duffey, Heat transfer to supercritical fluids flowing in channels—empirical correlations (survey). Nuclear Engineering and Design, 2004. 230(1): 69-91), or based on the CFD numerical simulation results, the least squares method can be used to fit the data. The fitting method can be found in the following literature (Wang Hui. Study on flow and heat transfer characteristics in the near-critical pressure zone of the regenerative cooling channel of a scramjet engine. 2017, National University of Defense Technology: Changsha). The embodiments of this invention will not be described in detail.

[0095] Fuel cracking heat source Q within the fluid unit H The pyrolysis rate β can be determined based on the temperature of the discrete grid cells. The energy-cracking rate distribution of hydrocarbon fuels can be obtained by consulting the following literature (Wang, Y., et al., Molecular-level modeling investigation of n-decane pyrolysis at high temperature. Journal of Analytical and Applied Pyrolysis, 2017. 128: 412-422), or by fitting and constructing the model based on the CFD numerical simulation results and referring to the methods described in the above literature. The embodiments of this invention will not be elaborated upon accordingly.

[0096] Thermal increment of each discrete grid cell The following relationship exists:

[0097] ;

[0098] in, The density of the medium within the discrete grid cell. For discrete mesh cell volume, For the specific heat of the medium within a discrete grid cell, The rate of temperature change of discrete grid cells per unit time;

[0099] Heat flux output to the outside of each discrete grid cell The following relationship exists:

[0100] ;

[0101] in, For external output heat flux density, The heat flux output area of ​​the discrete grid cell.

[0102] Step 4 - Integrating a multi-field coupled numerical solution model: Based on the operating condition parameters, establish a multi-field coupled numerical solution model for the heat transfer, flow, and cracking reaction characteristics of the fuel regeneration cooling system.

[0103] Based on the methods in steps 2 and 3, a multi-field coupled numerical model of heat transfer, flow, and pyrolysis reaction in the fuel regeneration cooling system is constructed. Specific methodological details can be found in the following literature (Li Xian. MATLAB / Simulink System Simulation. 2nd Edition. Beijing: Tsinghua University Press. 2023). This embodiment of the invention will not elaborate further. The model solution approach is as follows: Figure 6 As shown: Based on the initial t At time 0, the operating parameters are used to initialize the parameters of the discrete mesh elements; this is combined with the calculation time step. and the first time step t 1 ( t 0+ Operating parameters, iterative solution t Heat transfer parameters (heat flux) of each discrete grid element in region I within time step 1 Q Ⅰ ( t 1) Temperature T Ⅰ ( t 1) etc.), flow parameters (flow velocity) v Ⅰ ( t 1) Reynolds number Re Ⅰ ( t 1) etc.), pyrolysis reaction parameters (pyrolysis heat source) Q H,Ⅰ ( t 1) and pyrolysis rate β Ⅰ ( t 1) When the residual is less than the set threshold, the outlet parameters of the flow channel in zone I are used as the inlet parameters of the flow in zone II, and the discrete grid cells of the next section are iteratively solved. This process is repeated until the last section is completed. m Iterative solution for the region; based on t The calculation results of each discrete grid element at time step 1, combined with t 2 time steps (t 1+ Running the operating parameters, the iterative calculations of heat transfer, flow, and pyrolysis reaction characteristics of each discrete grid element in each section are completed following the steps described above. This process is repeated until the final time step is completed. t n Iterative solution of discrete grid cells in each section.

[0104] Step 5 - Initialization and Iterative Solution: Based on the numerical solution model built in Step 4, set the initial values ​​of each discrete grid element in the model and the computation time step. Then, the iterative solution process begins.

[0105] Initial input heat flow of the model Initial output heat flux The initial operating conditions at time t0 are set as input parameters; the initial temperature of each fluid element in the model is... Initial flow Initial pyrolysis heat source Initial pyrolysis rate Based on the initial Calculation and setting of inlet parameters for the flow channel at specific times; initial temperature of each solid element in the model. At the initial temperature of the fluid Slight increases or decreases based on the quantity value (e.g.: This is to avoid numerical discretization.

[0106] Model computation time step The calculation can be set according to the calculation requirements. After each time step of iterative solution is completed, the calculation results of discrete grid cells in each segment are statistically analyzed. After all time steps of calculation are completed, the time-varying laws of heat transfer, flow and cracking reaction characteristics of the fuel regeneration cooling system can be obtained.

[0107] The fuel regenerative cooling structure is a physical structural system specifically designed to implement regenerative cooling technology, located inside or between the inner and outer walls of the combustion chamber (or rocket engine thrust chamber). Essentially, it is a highly integrated fuel channel network that combines cooling and energy recovery functions. Its specific structure and principles are as follows:

[0108] Basic structure: This structure typically consists of multiple parallel, circumferential, or spirally distributed micro-cooling channels. These channels can be machined into the lining of the combustion chamber wall or formed by spacer ribs between the inner and outer wall surfaces.

[0109] Location: Inside the outer shell or in the wall interlayer of high-temperature components such as the combustion chamber or nozzle section.

[0110] Core function: As a pre-set flow channel before fuel enters the combustion chamber for combustion, its core function is to use fuel as a coolant to absorb the heat transferred from the high-temperature combustion gas to the combustion chamber wall, thereby providing active thermal protection for the combustion chamber.

[0111] Working Process and Principle: Before entering the combustion chamber for combustion, fuel is pumped into this network of cooling channels. As it flows through the channels, the fuel absorbs heat from the walls through forced convection, increasing its own temperature. For endothermic hydrocarbon fuels, an endothermic cracking reaction occurs when the temperature reaches a certain level, providing additional chemical heat sink. The high-temperature fuel, having absorbed heat, is then injected into the combustion chamber for combustion.

[0112] To address the problems of high computational load, high cost, long time consumption, and low efficiency in existing methods for studying fuel regeneration cooling characteristics, this invention proposes a new method that constructs a fluid / solid coupled heat transfer network for the regeneration cooling structure by coarsely discretizing the model and coupling it with a high-precision mathematical correlation model of heat transfer-flow-pyrolysis reaction. This allows for rapid unsteady-state iterative solution calculations, thereby reducing the number of meshes and the difficulty of modeling, avoiding the solution of complex and time-consuming control equations, and ultimately achieving efficient prediction of fuel regeneration cooling characteristics.

[0113] Specifically, the fluid-solid coupled heat transfer network is used to characterize the energy transfer path in the regenerative cooling process. The energy transfer path begins with the heat flow input from the wall of the combustion chamber, which is dispersed in the solid domain by heat conduction and then transferred to the cooling fuel through convection between the solid domain and the fluid domain. The fuel cracking reaction in the fluid domain absorbs heat as an internal heat source, and the unused heat is finally output through the outer wall.

[0114] This method is based on the fundamental laws and engineering correlations of heat transfer, fluid mechanics and chemical reaction kinetics to establish a heat flux calculation model for each discrete grid element in a fluid-structure coupled heat transfer network. The material-related physical property parameters in the heat flux calculation model are determined based on the material properties extracted in step S1, and together with the element state parameters updated in the iterative solution, they constitute a complete model parameter set, thereby realizing the parameterized closed loop of the fluid-structure coupled heat transfer network.

[0115] The regenerative cooling structure refers to a structure in which spacer ribs are provided between the inner and outer walls of the combustion chamber to form multiple sets of fuel cooling microchannels.

[0116] Example 2;

[0117] Based on Embodiment 1, Embodiment 2 of the present invention provides a specific implementation example of a certain type of fuel regeneration cooling system.

[0118] The working fluid is n-decane, and the fuel inlet temperature is... Single-channel inlet traffic Heat flow input to the inner wall surface Heat flow output from the outer wall surface Initial back pressure The technical effects of the present invention will be illustrated using an unsteady total computation time of 180s as an example.

[0119] See the schematic diagram of the regenerative cooling channel structure. Figure 7 .

[0120] The cooling channel is coarsely discretized spatially, with 20 segments along the channel axis. A schematic diagram of the discretized mesh element is shown below. Figures 8-9 .

[0121] According to the method of the present invention, a discrete unit heat transfer network is drawn (see...). Figure 10 Furthermore, a coupled numerical solution model for the heat transfer, flow, and cracking reaction characteristics of the fuel regeneration cooling system was established.

[0122] Based on the solution parameters under operating conditions, set the initial values ​​for each discrete element: initial input heat flux. Initial output heat flow Initial temperature of the fluid unit Initial flow rate of fluid unit The initial pyrolysis heat source of the fluid unit Initial pyrolysis rate of fluid unit Initial back pressure of the fluid unit initial temperature of solid unit Set the solution time step .

[0123] Using the above numerical solution model and initial parameter values, iterative solutions were performed. After completing 180s of unsteady-state calculations, the model solution was finalized.

[0124] Regarding the same case mentioned above, Figure 11 The traditional CFD numerical simulation and the coarse-grained model proposed in this invention were compared, and the calculated fuel temperature in the cooling channel after stabilization was obtained. The axial distribution results of the fuel cracking rate β. The average error of the fuel temperature obtained by the two methods. Average error of fuel pyrolysis rate The CFD numerical model, with a total grid of 770,933, used a high-performance CPU (Xeon 6420R, 48 cores) and took 41.2 hours (approximately 1.7 days) to reach stable solution. The coarse-grained model, with a total grid of 700, used a standard CPU (Core i5-1135G7, 4 cores) and took only 220 seconds (approximately 3.7 minutes) to complete the solution. Compared to traditional CFD numerical simulation, the efficient prediction method based on the coarse-grained model proposed in this invention effectively reduces computational costs and improves computational efficiency, reducing computation time from the order of 40 hours to less than 4 minutes. Simultaneously, the computational accuracy is close to that of CFD numerical simulation (error less than 5%), meeting engineering design requirements and demonstrating the significant advantages of this invention in the efficient analysis of fuel regeneration cooling characteristics.

[0125] The key technical point of this invention is that in the prediction of fuel regeneration cooling characteristics, a coarse-grained spatial discretization method is adopted to ignore the microscopic details of the flow channel structure, effectively reducing the number of model meshes and the difficulty of numerical modeling. It also directly couples the heat transfer-flow-pyrolysis reaction mathematical correlation model, avoiding the complex and time-consuming iterative solution of a series of control equations such as momentum and energy in conventional CFD numerical solutions, significantly reducing the model's computational load and solution time, thereby obtaining the fuel regeneration cooling characteristics and evolution law more efficiently.

[0126] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0127] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A highly efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model, characterized in that, The method includes: Step S1: Geometrically segment a single cooling channel in the regenerative cooling structure of the aircraft's fuel regeneration cooling system to obtain the channel target unit; coarsely discretize the channel target unit to obtain multiple discrete mesh units, and extract the geometric information and material properties of each discrete mesh unit. Step S2: Based on the spatial position and contact relationship between each discrete grid unit, and combined with the geometric information, determine the heat transfer logic between the discrete grid units, and construct the fluid-solid coupled heat transfer network of the regenerative cooling structure; the fluid-solid coupled heat transfer network is used to characterize the coupled energy transfer process involving solid heat conduction, fluid-solid convection and fuel cracking reaction from the inner wall of the combustion chamber to the outer wall. Step S3: Establish a heat transfer flux calculation model for each discrete grid element in the fluid-structure interaction heat transfer network; the material-related physical property parameters in the heat transfer flux calculation model are determined based on the material properties; Step S4: Integrate the fluid-solid coupled heat transfer network with the heat transfer flux calculation model, and combine the operating condition parameters to establish a multi-field coupled numerical solution model for the heat transfer-flow-pyrolysis reaction of the fuel regeneration cooling system. Step S5: Set the initial state parameters and calculation time step of each discrete grid element in the multi-field coupled numerical solution model of heat transfer-flow-pyrolysis reaction, and perform unsteady-state iterative solution on the multi-field coupled numerical solution model of heat transfer-flow-pyrolysis reaction to obtain the heat transfer, flow and pyrolysis reaction characteristics of the fuel regeneration cooling system under time-varying conditions.

2. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The coarse-grained spatial discretization of the target flow channel unit specifically includes: Along the cross-sectional direction of the cooling channel, each solid domain is divided into several layers of discrete grid elements, while the fluid domain is not divided along the cross-sectional direction of the cooling channel. Along the axial direction of the cooling channel, each solid domain and fluid domain is divided into several discrete grid cells.

3. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The geometric information includes: the geometric dimensions of the discrete mesh cells, the equivalent thermal conduction distance between adjacent discrete mesh cells, and the contact area between adjacent discrete mesh cells.

4. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The fluid-structure coupled heat transfer network of the regenerative cooling structure is constructed as follows: Based on the axial partitioning of the cooling channel and the fuel flow direction, the fuel inlet parameters are used as the input parameters of the first fluid unit, and the output parameters of the previous fluid unit are used as the input parameters of the next fluid unit. This process is repeated until the parameter transfer relationship of all sections is defined, thus constructing a fluid-solid coupling heat transfer network for the regenerative cooling structure.

5. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The calculation formulas in the heat flux calculation model of each discrete grid element in the fluid-structure coupled heat transfer network include: External input heat flux Q of each discrete grid cell in Calculation method: ； in, For the external input heat flux density, A in The heat flux input area of ​​the discrete mesh element; Solid heat flux Q between discrete grid cells cond Calculation method: ； in, Let be the thermal conductivity of the discrete mesh element in the solid domain. The thermally conductive contact area between adjacent discrete grid cells. The temperature difference between adjacent discrete grid cells in the solid domain. This represents the equivalent heat transfer distance between adjacent discrete grid cells. The negative sign indicates that the heat transfer direction points in the direction of decreasing temperature. Convective heat transfer flux between discrete grid cells in the solid domain and discrete grid cells in the fluid domain The calculation method is as follows: ； in, Let be the convective heat transfer area of ​​discrete grid cells in adjacent solid domains and discrete grid cells in fluid domains. This represents the temperature difference between discrete grid cells in adjacent solid domains and discrete grid cells in fluid domains. The negative sign indicates that the heat transfer direction points towards the direction of decreasing temperature. The convective heat transfer coefficient of the contact surface; Fuel cracking heat source within discrete grid cells of the fluid domain The pyrolysis rate β is based on the temperature of the discrete grid cells. This was obtained by consulting the hydrocarbon fuel cracking energy-cracking rate distribution map; The thermal increment Q of each discrete grid element a The calculation method is as follows: ； in, The density of the medium within the discrete grid cell. For discrete mesh cell volume, For the specific heat of the medium within a discrete grid cell, The rate of temperature change of discrete grid cells per unit time; Heat flux output to the outside of each discrete grid cell The calculation method is as follows: ； in, For the external output heat flux density, A out The heat flux output area of ​​the discrete grid cell.

6. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 5, characterized in that, The convective heat transfer coefficient of the contact surface is calculated as follows: ； in, Let be the thermal conductivity of the fuel working fluid within a discrete grid cell of the fluid domain, and D be the hydraulic diameter of the discrete grid cell of the fluid domain. is the dimensionless surface heat transfer coefficient.

7. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The numerical solution model for the multi-field coupled heat transfer-flow-pyrolysis reaction of the fuel regeneration cooling system adopts an iterative solution strategy that proceeds segment by segment and time step by time step, specifically including: Within each time step, the heat transfer, flow, and pyrolysis parameters of each discrete grid cell are iteratively calculated sequentially until the residual is less than a set threshold. Use the current segment's exit parameters as the next segment's entry parameters until all segments have been calculated. Update to the next time step and repeat the above process until all time steps have been calculated.

8. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The initial state parameters include: Initial input heat flux Initial output heat flux Initial temperature of discrete grid cells in the fluid domain Initial flow Initial pyrolysis heat source Initial pyrolysis rate Initial back pressure Initial temperature of discrete grid cells in the solid domain Initial temperature of discrete grid cells in the solid domain based on Obtained through adjustments.

9. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The target unit of the flow channel is obtained as follows: A single cooling channel that is geometrically symmetrical and whose heat transfer characteristics are equivalent to the overall heat transfer characteristics of the regenerative cooling structure is extracted from the regenerative cooling structure and used as the target channel unit.

10. The efficient prediction method for fuel regeneration cooling characteristics based on a coarse-grained model according to claim 1, characterized in that, The fuel regeneration cooling system is a fuel regeneration cooling system that uses hydrocarbon fuel as the cooling medium.

Images