A method for designing a cryogenic hydrogen ejector for a rocket engine

Abstract

The application discloses a low-temperature hydrogen ejector design method for a rocket engine and belongs to the technical field of ejectors. The method first carries out an experiment under a typical working condition of an existing ejector to obtain real data, constructs a multi-model coupling numerical simulation system by means of the data, solves the problem of "inaccurate calculation" caused by fluid idealization, and ensures the accuracy of the model; then, a multi-factor optimal analysis model is established to analyze the weight system and coupling relationship of parameters of the ejector, according to which the value range of each parameter is determined, and the "expensive calculation" problem caused by parameter diversification is solved; finally, the design of the ejector under the target working condition is completed in combination with the multi-model coupling numerical simulation and test verification. The application can accurately match the extraction demand of pre-cooled low-temperature hydrogen in the high-altitude simulation test of the rocket engine, ensures stable and efficient operation of the ejector under the specified working condition, significantly improves the accuracy, efficiency and robustness of the design of the ejector, and provides reliable technical support for the high-altitude simulation test of the rocket engine.

Description

Technical Field

[0001] This invention belongs to the field of ejector technology, specifically relating to a design method for a cryogenic hydrogen ejector for rocket engines. Background Technology

[0002] Upper-stage rocket hydrogen-oxygen engines use cryogenic liquid hydrogen and liquid oxygen as propellants. If the engine and piping temperatures are too high, the propellant is prone to vaporization, forming a gas-liquid two-phase mixture, which can cause pump cavitation, idling, stall, and other malfunctions. In severe cases, it can damage equipment or even prevent the engine from starting. Liquid hydrogen, due to its high calorific value and high physical heat sink, is an ideal working fluid for fuel heat exchange and precooling.

[0003] After heat exchange, the liquid hydrogen partially vaporizes. In ground tests of rocket engines, high-altitude simulation technology is needed to create a low-pressure vacuum environment on the ground to recreate its high-altitude working state. The ejector system is the most widely used high-altitude simulation technology. Its performance is affected by key structural parameters such as nozzle outlet size and mixing chamber length. Currently, there is an urgent need to develop an efficient cryogenic hydrogen ejector to extract hydrogen from the engine to create a vacuum environment and improve the authenticity and reliability of the test.

[0004] The flow field inside an ejector is complex, involving supersonic flow, shock waves, and gas-liquid two-phase flow. Scholars both domestically and internationally have conducted extensive numerical simulations and experimental studies on ejectors, covering aspects such as parameter optimization, structural design, and flow characteristics. However, existing research faces challenges such as large errors between numerical simulations and experiments, weak research on gas-liquid two-phase flow, the reliance on single-factor optimization without considering parameter coupling, and the high resource consumption of multi-parameter global optimization. These challenges restrict the performance improvement and engineering application of cryogenic hydrogen ejectors. Therefore, a design method for ejectors that can balance accuracy and efficiency is needed. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a design method for a cryogenic hydrogen ejector for rocket engines, which solves the problems of large numerical simulation errors, insufficient consideration of parameter coupling, and high resource consumption for global optimization in existing ejector designs, and achieves accurate and efficient design of cryogenic hydrogen ejectors to meet the extraction requirements of pre-cooled cryogenic hydrogen in high-altitude simulation tests of rocket engines.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] A design method for a cryogenic hydrogen ejector for a rocket engine, comprising:

[0008] Based on the existing ejector, multiple tests were conducted under typical operating conditions to obtain real data;

[0009] Based on the obtained data, a multi-model coupled numerical simulation system should be constructed. This system should be able to obtain the same results as the experiment under atypical working conditions with relatively accurate results.

[0010] Based on dimensionality reduction and theoretical calculation methods, weight and coupling relationship analysis is performed to establish a multi-factor optimal analysis model;

[0011] The range of values ​​for each parameter of the ejector is determined based on a multi-factor optimal analysis model to avoid blind attempts and increase computational costs.

[0012] Based on the range of parameters, multi-model coupled numerical simulations are performed under the target working conditions to find the optimal parameter combination. Combined with experimental verification, the ejector design is completed.

[0013] The phrase "conducting multiple tests under typical operating conditions based on existing ejectors to obtain real data" includes: selecting at least three existing ejectors with representative structures, conducting three sets of typical operating conditions (different initial temperatures and pressures for primary and secondary flows) tests in the rocket engine, and obtaining test data.

[0014] Flow involves processes such as phase change and multi-medium mixing. For example, the ejector in a rocket engine needs to use cryogenic nitrogen to eject hydrogen. The difference in boiling points between the two can easily lead to hydrogen liquefaction. Conventional numerical simulations that do not consider phase change cannot reflect the real situation. It is necessary to select a combination of component transport model, phase change model and turbulence model, and achieve multi-physics model coupling by adjusting the model parameters.

[0015] Before obtaining the range of values ​​for each parameter of the ejector, a weight analysis is required, including: obtaining the relationship between each parameter and the ejection ratio based on the theoretical calculation formula, determining the priority based on the magnitude of change, and combining expert experience to obtain a weight system to ensure that the influence of each parameter on the ejection efficiency can be accurately quantified.

[0016] Before obtaining the range of values ​​for each parameter of the ejector, a coupling relationship analysis needs to be performed, including: based on the priority of each parameter, first analyze the coupling relationship between the parameter with higher priority and other parameters, and analyze the dependence and degree of influence between different factors by plotting the curve of key structural dimensions-ejection ratio.

[0017] The range of values ​​for each parameter of the ejector needs to be determined, including: visually presenting the impact of the interaction between the two parameters on the performance by plotting a three-dimensional response surface (such as the "nozzle diameter - mixing chamber length - ejector coefficient" surface), and determining the smaller range of values ​​accordingly;

[0018] The phrase "conducting multi-model coupled numerical simulations under target operating conditions based on various parameter ranges, finding the optimal parameter combination, and completing the ejector design through experimental verification" includes: using the multi-model coupled numerical simulation system under target operating conditions, performing simulation calculations on at least 30 sets of parameter combinations within the parameter range, outputting performance indicators such as ejection coefficient and vacuum chamber pressure, and determining candidate parameter combinations accordingly; based on the obtained parameter combinations, fabricating ejector test pieces, conducting verification tests under target operating conditions consistent with the numerical simulation, collecting inlet and outlet pressures, flow rates, and vacuum chamber pressures, comparing the deviations between the experimental data and simulation results, and determining the optimal parameter combination under this operating condition.

[0019] Experimental data acquisition: Three existing ejectors were selected, and at least three sets of typical working condition tests were carried out on a low-temperature hydrogen environment simulation platform. Each working condition was repeated no less than five times. Inlet and outlet pressure, flow rate, temperature, ejector coefficient, and key cross-sectional parameters of the internal flow field were collected. After filtering and outlier removal, real data samples were formed.

[0020] Construction of a multi-model coupled numerical simulation system: Based on a real database, component transport models, phase change models and various turbulence models are selected and combined. By adjusting the model parameters, multi-physics model coupling is achieved, ensuring that the simulation results of this system under atypical working conditions have an error of less than 10% compared with the experimental data.

[0021] Establishment of a multi-factor optimal analysis model: Based on dimensionality reduction and theoretical calculation methods, weight analysis and coupling relationship identification are performed on key structural parameters of the ejector (throat diameter, mixing chamber size, nozzle layout, etc.) to clarify the priority of each parameter's influence on ejection performance and the interaction law.

[0022] Determining the range of parameter values: Based on the results of the multi-factor optimal analysis model, the reasonable range of values ​​for each key parameter is defined to avoid blind attempts, reduce computational costs, and provide a basis for subsequent selection of the optimal parameter combination.

[0023] Optimal parameter selection and experimental verification: Under the target operating conditions, multi-model coupled numerical simulations were performed on at least 30 parameter combinations within the parameter range. With the maximization of the ejector coefficient as the core objective, and combined with pressure loss and flow field stability constraints, three candidate parameter combinations were selected. Verification tests were carried out on fabricated test pieces, and the actual effects were compared to select the optimal ejector.

[0024] The beneficial effects of this invention are as follows:

[0025] 1. A multi-model coupled numerical simulation system is constructed based on real experimental data, which significantly improves simulation accuracy and solves the problem of large deviation between traditional models and actual working conditions;

[0026] 2. Theoretical calculation methods are used to analyze parameter priority and coupling relationship, so as to achieve multi-parameter collaborative global optimization and avoid the limitations of single-factor optimization;

[0027] 3. Define the parameter value range in advance to reduce unnecessary calculations and experiments, lower design costs, and improve design efficiency;

[0028] 4. Through multiple rounds of simulation and testing, the ejector is ensured to operate stably and efficiently under target conditions, providing reliable support for high-altitude simulation tests of rocket engines.

[0029] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0030] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0031] Fig. 1 This is a schematic flowchart of the structural design method for a cryogenic hydrogen ejector in a rocket engine according to an embodiment of the present invention.

[0032] Fig. 2 This is a three-dimensional structural diagram of the ejector according to an embodiment of the present invention;

[0033] Fig. 3 This is a schematic diagram of the design variables of the ejector in an embodiment of the present invention. Detailed Implementation

[0034] like Figs. 1-3 As shown, this invention discloses a design method for a cryogenic hydrogen ejector for a rocket engine.

[0035] like Fig. 1 As shown in the figure, the present invention provides a schematic flowchart of the structural design method for a cryogenic hydrogen ejector in a rocket engine.

[0036] This invention can precisely match the extraction requirements of pre-cooled cryogenic hydrogen in high-altitude simulation tests of rocket engines, ensuring the ejector operates stably and efficiently under specified conditions, significantly improving the accuracy, efficiency, and robustness of ejector design, and providing reliable technical support for high-altitude simulation tests of rocket engines. Specifically, the ejector structure design method includes the following steps:

[0037] Step 1: Select 3 existing ejectors and conduct at least 3 sets of typical working condition tests on a low-temperature hydrogen environment simulation platform. Each working condition is repeated at least 5 times. Collect inlet and outlet pressure, flow rate, temperature, ejector coefficient and key cross-sectional parameters of the internal flow field. After filtering and outlier removal, form real data samples.

[0038] Step 1.1: Select three existing ejectors with representative structural differences, corresponding to different throat diameters, mixing chamber aspect ratios, etc.

[0039] Step 1.2: Design at least three typical operating conditions for each ejector, i.e., set three characteristic values ​​Q1, Q2, and Q3 for the primary flow rate. Under each operating condition, first inject cryogenic hydrogen into the system. After the temperature and flow rate reach the set values ​​and stabilize for 2 minutes, start the ejector to conduct the test. During the test, simultaneously collect macroscopic performance parameters such as inlet and outlet pressure, instantaneous flow rate, medium temperature, ejection coefficient, and other internal flow field parameters. Repeat the test for each operating condition at least five times to avoid the impact of random errors from a single test on data reliability.

[0040] Step 1.3: Systematically process the collected raw data. First, identify and remove outlier data points to ensure data validity. Then, calculate the average value and standard deviation of multiple tests for each working condition to form standardized data. Finally, form a set of 9 sets of valid data frames to provide comprehensive and reliable benchmark data for the subsequent construction of a multi-model coupled numerical simulation system.

[0041] The original model of the ejector can be drawn using the 3D software Solidworks.

[0042] like Fig. 3 In this embodiment, the ejector design parameters are defined as follows: primary flow inlet diameter D1, primary flow pipeline length D2, primary flow chamber radius D3, primary flow chamber length D4, secondary flow pipeline length D5, secondary flow pipeline diameter D6, compression section length D7, nozzle length D8, nozzle diameter D9, nozzle spacing D10, mixing section length D11, diffuser section length D12, diffuser section outlet diameter D13, mixing section diameter D14, vacuum chamber length D15, and vacuum chamber diameter D16.

[0043] Step 2: Based on a high-fidelity real-world database, a multi-physics model coupled simulation system is constructed by integrating component transport models, phase transition models, and various turbulence models. The ejector studied in this invention contains two media: hydrogen and nitrogen. These two gases have significantly different physical properties and a wide temperature range, and exhibit mixing behavior caused by concentration gradients, velocity gradients, and turbulence during ejection. The component transport model can simultaneously consider the coupling effects of molecular diffusion and turbulent diffusion, thus realistically reflecting the mixing patterns of different gases in the mixing and diffusion sections of the ejector. After mixing low-temperature hydrogen (20–50 K) and nitrogen (≥77 K), the nitrogen undergoes condensation or even solidification due to the large temperature difference, making the phase transition phenomenon non-negligible. The phase transition model is used to describe the formation, disappearance, mass transfer, and energy exchange processes of the gas-liquid two-phase system. By finely adjusting key model parameters and optimizing the coupling adaptation logic between various physical fields, the deviation between the simulation results and experimental data under atypical operating conditions is controlled within 10%, ensuring simulation accuracy and engineering applicability.

[0044] Step 2.1: Based on the characteristics of the target operating condition, the component transport model is determined as the essential core model, which is used to accurately describe the mixed transport process of hydrogen and nitrogen.

[0045] Step 2.2: Define the range of basic thermodynamic parameters of key components: Nitrogen has a melting point of 63K and a boiling point of 77K under standard atmospheric pressure, limiting its initial temperature to ≥77K; Hydrogen has a boiling point of 20K, limiting its initial temperature to 20K~50K; Based on the above parameter analysis, it is determined that when hydrogen (20K~50K) and nitrogen (≥77K) are mixed, nitrogen will be cooled by hydrogen, and there is a possibility of liquefaction and solidification. The system may present a gas-liquid two-phase coexistence or a gas-liquid-solid three-phase coexistence state.

[0046] Step 2.3: Prioritize simulations of gas-liquid two-phase coexistence scenarios, selecting the equilibrium condensation model as the initial phase change model; select two typical turbulence models for comparison and verification, and construct two initial model combinations: Combination 1: component transport model + equilibrium condensation model + Realizable k-ε turbulence model; Combination 2: component transport model + equilibrium condensation model + SST k-ω turbulence model, and determine the optimal turbulence model.

[0047] Step 2.4: For each initial model combination, select at least three different operating conditions for simulation testing; compare the simulation results with the experimental data. If the deviation exceeds the acceptable range, try adjusting the initial nitrogen temperature first to increase its temperature to reduce the probability of liquefaction and solidification, and then repeat the simulation verification; if the initial nitrogen temperature cannot be adjusted, proceed to the next step to change the phase change model.

[0048] Step 2.5: Equilibrium Condensation Model vs. Non-Equilibrium Condensation Model: The equilibrium condensation model has low computational cost and is suitable for early-stage engineering design and performance prediction, but the results tend to be conservative. The non-equilibrium model can more accurately reflect actual operating conditions, especially droplet growth and phase change processes. The appropriate model should be selected for refined simulation as needed in the study. Discrete Phase Model (DPM): This requires customized development based on the gas-liquid two-phase flow characteristics inside the ejector. For modules not yet embedded in the software, user-defined subroutines should be used.

[0049] Step 2.6: Equation for calculating the pressure and temperature of nitrogen at the gas-liquid saturation line:

[0050]

[0051] It is the Helmholtz free energy, in units of R is the gas constant, taken as 8.31451. T is temperature, in Kelvin (K). = ,in =11.1839mol*dm -3 Let τ be the critical density of nitrogen. ,in =126.192K is the critical temperature of nitrogen. It is the dimensionless quantity of the Helmholtz free energy of an ideal gas. It is the dimensionless quantity of the remaining Helmholtz free energy. , , These are coefficients, without specific meaning, obtained through fitting a large amount of data.

[0052] The Lee model was used to simulate the mass transfer process between water vapor and liquid water:

[0053]

[0054] In the formula It is the volume fraction; Density is V; velocity is V. These represent the mass transfer rates due to evaporation and condensation, respectively; subscripts: v for gas phase, l for liquid phase. The gas-liquid mass transfer process is controlled by temperature.

[0055] If T l >T s (evaporation):

[0056]

[0057] If T l >T s (Condensation):

[0058]

[0059] In the formula, coeff is the relaxation coefficient.

[0060] Step 2.7: If the simulation results corresponding to the equilibrium condensation model have large deviations, first replace it with a non-equilibrium condensation model (this model considers the droplet condensation nucleation and growth process), constructing a new model combination: component transport model + non-equilibrium condensation model + optimal turbulence model; if the non-equilibrium condensation model still cannot meet the accuracy requirements, replace it with DPM, constructing a model combination: component transport model + DPM + optimal turbulence model; for each updated model combination, select at least three different operating conditions for verification. The above steps can obtain the model combination with the smallest deviation and optimal stability, which can be used as the final numerical simulation scheme for the target operating condition.

[0061] Step 3: Using theoretical calculation methods, priority analysis and coupling relationship mining are carried out on key structural parameters such as ejector throat diameter, mixing chamber size, and nozzle layout. This method clarifies the priority ranking of the influence of each parameter on ejection performance and reveals the interaction mechanism between parameters.

[0062] Step 3.1: Based on the actual working conditions of the target ejector and combined with existing research results, determine the key structural parameters to be optimized, including the nozzle throat diameter, the distance between the nozzle and the mixing chamber, the diameter of the mixing chamber, the length of the mixing chamber, and the length of the diffuser chamber;

[0063] Step 3.2: Based on Sokolov's theory, the correlation between key structural parameters and ejector performance is clarified, providing theoretical support for subsequent calculations and analyses. Taking the ejector nozzle throat area as an example, Sokolov provides an empirical formula: ,in , The throat area, high-pressure gas consumption, critical density of high-pressure gas, and critical velocity are respectively used to calculate the throat diameter.

[0064] Step 3.3: Use Matlab software to write a one-dimensional numerical calculation program, set multiple different ejection ratio conditions, and solve for the specific values ​​of nozzle throat diameter, nozzle-mixing chamber distance, mixing chamber diameter, mixing chamber length and diffuser chamber length under each condition.

[0065] Step 3.4: Taking the ejection ratio as the abscissa and each key structural dimension as the ordinate, respectively plot the change curves of "nozzle throat diameter - ejection ratio", "distance between nozzle and mixing chamber - ejection ratio", "mixing chamber diameter - ejection ratio", "mixing chamber length - ejection ratio", and "diffuser chamber length - ejection ratio"; analyze the change amplitude of each curve. The larger the change amplitude, the higher the sensitivity of the parameter to the ejection ratio. Based on this, determine the influence priority of each key structural parameter (the higher the sensitivity, the higher the priority).

[0066] Step 3.5: Adopt the response surface method (RSM), taking each key structural parameter as the independent variable and the ejector coefficient as the dependent variable, to construct a multiple regression model of parameters and performance; based on the regression model, plot typical three-dimensional response surfaces (such as "nozzle throat diameter - mixing chamber length - ejector coefficient", "mixing chamber diameter - distance between nozzle and mixing chamber - ejector coefficient", etc.), and visually present the influence law of the interaction of any two parameters on the ejection performance through the surface morphology.

[0067] Step 4: Combining the interaction relationship with the parameter priority order determined in Step 3.4, first focus on the optimization interval of high-priority parameters, and then sequentially determine the value ranges of other parameters.

[0068] Step 4.1: Combining the parameter influence priority clarified in Step 3.4 (sorted from high to low sensitivity, assuming the sorting is: nozzle diameter D9 > mixing section length D11 > mixing section diameter D14 > diffuser section length D12), following the principle of "optimizing high-priority parameters first and adapting and adjusting low-priority parameters", through the multi-parameter interaction constraint process, gradually determine the value ranges of each key structural parameter.

[0069] Step 4.2: For the high-priority parameter nozzle diameter D9, first take the initial calculated value of the nozzle diameter corresponding to the rated operating condition (target ejection ratio) of the ejector design (denoted as D90) as the benchmark, refer to the curve change law in Step 3.4, and select the interval where the ejection ratio does not exceed 10% of the rated value as the basic range, that is, D91 < D9 < D92 (where D91 is the critical value of the ejection ratio reaching the standard at the left segment of the curve, and D92 is the critical value of the ejection ratio reaching the standard at the right segment of the curve).

[0070] Step 4.3: Use the same method to obtain the basic value ranges of the remaining parameters.

[0071] Step 4.4: Based on priority ranking, focus first on the three-dimensional response surfaces of the high-priority parameter nozzle diameter D9 and the second-highest priority parameter mixing section length D11. Analyze the interaction between the two by observing the surface morphology (tilt, concavity, extreme value distribution): If the surface tilts significantly along the coupling direction of D9 and D11, it indicates a strong interaction effect between the two parameters. When D9 is a small value within the basic range [D91, D92], it needs to be paired with a large value within the basic range [D111, D112] to avoid insufficient entrainment of the ejector fluid; when D9 is a large value within the basic range, the value of D11 needs to be reduced accordingly to reduce flow field separation loss. Based on this interaction pattern, three feature points (maximum, minimum, and median values) are selected from the basic range of D9 [D91, D92]. The effective sub-interval of D11 (i.e., the ejection ratio is not less than 95% of the nominal value) is determined for each feature point. The intersection of the three sub-intervals is taken as the constraint range of D11, thus completing the coupling optimization of the second-highest priority parameter and the high-priority parameter.

[0072] Step 4.5: Subsequently, the three-dimensional response surfaces of the nozzle diameter D9 and the other low-priority parameters are analyzed in turn to determine the value range of the remaining parameters.

[0073] Step 5: Under the target operating conditions, conduct multi-physics model coupled numerical simulations for no fewer than 30 parameter combinations within the parameter range. With maximizing the ejector coefficient as the core optimization objective, select three candidate parameter combinations with excellent performance from the simulation results; then fabricate the corresponding test pieces and conduct verification tests. By comparing the actual effects of the tests, the optimal ejector scheme is finally determined.

[0074] Step 5.1: Determine the parameter combination generation rules: Based on the value range of the key structural parameters defined in Step 4, generate no fewer than 30 parameter combinations. During the sampling process, it is necessary to ensure that the parameter values ​​of each combination evenly cover the effective range of each parameter, while also taking into account the coupling relationship between parameters, and avoiding duplicate or invalid combinations.

[0075] Step 5.2: Perform geometric feasibility verification on the 30 generated parameter combinations, and eliminate unreasonable combinations such as structural interference and flow channel blockage caused by parameter matching; at the same time, in combination with engineering processing technology limitations, eliminate combinations that exceed the processing accuracy range.

[0076] Step 5.3: Using the optimal multiphysics model coupling system determined in Step 2, and based on the 3D geometric model drawn in Solidworks, import it into CFD software for mesh generation. The core flow channel region (nozzle outlet, mixing chamber, diffuser section) is processed with structured mesh refinement; the non-core region is processed with unstructured mesh. The overall mesh quality must ensure that the distortion rate is ≤5%, and pass the mesh independence verification.

[0077] Step 5.4: Set boundary conditions according to the target operating parameters. Set the primary flow inlet as a mass flow inlet, the secondary flow inlet as a pressure inlet, and the outlet as a pressure outlet; set the wall as an adiabatic no-slip boundary. Calculate the thermodynamic parameters of the phase change process using the nitrogen saturated pressure-temperature correlation formula from Step 2. Couple the evaporation-condensation mass transfer process using the Lee model, and calibrate the relaxation coefficient coeff to its optimal value based on the experimental data from Step 1.

[0078] Step 5.5: Start numerical simulations sequentially for each set of effective parameter combinations. During the calculation, implicit schemes are used to solve the governing equations, and the iterative convergence criterion is set to a residual of less than 1 × 10⁻⁻⁻⁶. 6 After the simulation is completed, core performance indicators are collected, including the ejector coefficient (the ratio of secondary flow rate to primary flow rate), vacuum chamber pressure (the target value must meet the requirements of high-altitude simulation), pressure loss (total pressure difference between inlet and outlet), phase change rate (nitrogen liquefaction ratio), etc., to construct a multi-dimensional simulation result database.

[0079] Step 5.6: With "maximizing the ejector coefficient" as the core objective, and setting the following constraints: ① Pressure loss ≤ design threshold (determined based on the allowable range of engine pipeline pressure loss); ② Vacuum chamber pressure ≤ target vacuum level; ③ Phase change rate ≤ 10% (to avoid excessive liquefaction leading to flow channel blockage), select parameter combinations that meet the conditions. The Analytic Hierarchy Process (AHP) is used to weight the selection results, with the ejector coefficient accounting for 75%, pressure loss for 20%, and phase change rate for 5%. All combinations are sorted in descending order based on the weighted scores, and the top 3 combinations are selected as candidate parameter combinations, labeled as Scheme A, Scheme B, and Scheme C, respectively.

[0080] Step 5.7: Fabrication Drawing Design: Based on the three sets of candidate parameter combinations, draw the 3D fabrication drawings for the ejector and fabricate the finished products. Install the three sets of candidate test pieces onto the test bench in sequence, and debug the system according to the target operating parameters. Adjust the temperature, pressure, and flow rate of the primary and secondary flows to the design values ​​through the control system. After stable operation for 2 minutes, start the test. Repeat the test for each set of test pieces no less than 5 times. During each test, collect core performance indicators consistent with the numerical simulation, including inlet and outlet pressure, flow rate, vacuum chamber pressure, ejector coefficient, and flow field visualization data (observe the flow state inside the mixing chamber through a high-speed camera). Filter and remove outliers from the collected test data, calculate the average value and standard deviation of each set of tests, and form a test result database. Compare the test data with the corresponding simulation results, analyze the sources of deviation, including ejector coefficient deviation, pressure loss deviation, flow field stability deviation, etc., and set the allowable deviation range to ≤10%.

[0081] Step 5.8: Determining the Optimal Solution: A comprehensive evaluation of the actual performance of the three candidate test pieces is conducted. The solution with the highest ejector coefficient and a deviation ≤10% is prioritized. If the ejector coefficients are close, the solution with the lowest pressure loss and optimal flow field stability is selected. Finally, one optimal parameter combination is determined as the final design scheme for the cryogenic hydrogen ejector under the target operating conditions.

[0082] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A design method for a cryogenic hydrogen ejector for a rocket engine, characterized in that, Includes the following steps: S1. Data Acquisition: Select various existing ejector structures and conduct tests under multiple typical working conditions to obtain real data samples; S2. Construct a multi-model coupled numerical simulation system: Based on the real data samples, construct a multi-model coupled numerical simulation system, and adjust the parameters of each model to control the error between the simulation results of the multi-model coupled numerical simulation system and the experimental data within a preset threshold. S3. Parameter Analysis: Based on theoretical calculation methods, weight analysis and coupling relationship analysis are performed on multiple key structural parameters of the ejector to establish a multi-factor optimal analysis model; S4. Range Definition: Based on the multi-factor optimal analysis model, determine the range of values ​​for each key structural parameter of the ejector. S5. Optimization and Verification: Under the target working conditions, a multi-model coupled numerical simulation system is used to perform numerical simulations of multiple parameter combinations based on the defined parameter value range. Candidate parameter combinations are selected with the maximization of the ejector coefficient as the core objective. Based on the candidate parameter combinations, corresponding test pieces are fabricated, and verification tests are conducted using the test pieces. The optimal parameter combination is obtained by comparing the actual effects of the verification tests, and the optimal ejector design scheme is completed based on the optimal parameter combination.

2. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 1, characterized in that: S1 specifically includes: selecting at least 3 ejectors with different throat diameters or mixing chamber aspect ratios, conducting at least 3 sets of operating condition tests under different initial temperatures and pressures in the primary and secondary flows, and repeating the test for each set of operating conditions no less than 5 times, collecting inlet and outlet pressures, flow rates, temperatures and ejector coefficients during the tests, and forming real data samples after filtering and outlier removal.

3. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 1, characterized in that: The multi-model coupled numerical simulation system described in S2 integrates a component transport model, a phase change model, and a turbulence model to describe the mixing of hydrogen and nitrogen. The phase change model is an equilibrium condensation model, a non-equilibrium condensation model, or a discrete phase model. When constructing the multi-model coupled numerical simulation system, the model combination with the smallest deviation is selected as the optimal model combination by comparing the deviation between the simulation results of different model combinations and the experimental data.

4. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 3, characterized in that: S2 specifically includes: S21. Initially select component transport models, multiple phase transition models, and turbulence models to combine them to form multiple candidate combined models; S22. Use multiple candidate combination models to simulate the typical working conditions in S1, compare the simulation results with the real experimental data, and if the error exceeds the set threshold, prioritize adjusting the parameters of each model or replacing the physical model, and repeat the simulation and comparison until the optimal model combination that can match the experimental data with an error less than the preset threshold under multiple typical working conditions is selected. This constitutes the multi-model coupled numerical simulation system, which is used to predict the performance of the ejector structure under various working conditions.

5. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 1, characterized in that: Key structural parameters in S3 include nozzle throat diameter, nozzle-mixing chamber distance, mixing chamber diameter, mixing chamber length, and diffuser chamber length. The weighting analysis specifically includes: obtaining the relationship between each key structural parameter and the ejection ratio based on theoretical calculation formulas; determining the priority of each key structural parameter based on its variation range; and obtaining a weighting system based on expert experience. The coupling analysis specifically includes: using Response Surface Methodology (RSM), with each key structural parameter as the independent variable and the ejector ejection coefficient as the dependent variable, constructing a multiple regression model of parameters and performance; based on the multiple regression model, plotting a three-dimensional response surface to present the influence of the interaction between any two parameters on ejection performance through the surface morphology; and establishing a multi-factor optimal analysis model based on weighting analysis and coupling analysis.

6. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 5, characterized in that: In S4, determining the value range of each key structural parameter specifically includes: based on a multi-factor optimal analysis model, determining the value range of each parameter according to the weight of each key structural parameter and the coupling relationship between the interaction of any two parameters and the influence of the ejection performance.

7. The design method for a cryogenic hydrogen ejector for a rocket engine according to claim 1, characterized in that: In S5, numerical simulations of multiple parameter combinations are performed based on a defined parameter range. Specifically, this includes: using a multi-model coupled numerical simulation system to simulate at least 30 parameter combinations within the parameter value range, outputting performance indicators, including the entrainer coefficient and the pressure inside the vacuum chamber; and selecting candidate parameter combinations, specifically: taking the maximization of the entrainer coefficient as the core objective, while simultaneously satisfying the constraints of pressure loss ≤ design threshold, pressure inside the vacuum chamber ≤ target vacuum level, and phase transition rate ≤ 10%. After selecting multiple parameter combinations that meet the constraints, a weighted score is performed using the analytic hierarchy process (AHP), where the entrainer coefficient accounts for 75% of the weight, pressure loss for 20%, and phase transition rate for 5%. Based on the weighted score results, the top three combinations are selected as candidate parameter combinations for experimental verification.

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