Hybrid power collaborative optimization system of rocket engine

Abstract

The invention discloses a hybrid power collaborative optimization system of a rocket engine. The hybrid power collaborative optimization system comprises a data acquisition and processing module, a dynamic modeling and optimization module, a collaborative control execution module and a closed-loop feedback unit module. The data acquisition and processing module acquires and preprocesses working state parameters of a power source through a sensor group; the dynamic modeling and optimization module constructs a multi-physical field coupling theoretical model, and after data correction, a cooperative control strategy is generated by using a hybrid particle swarm-genetic algorithm and other multi-objective optimization algorithms; the cooperative control execution module adjusts an output parameter according to a strategy, and triggers a redundancy mode when the output parameter exceeds a threshold value; and the closed-loop feedback unit compares and analyzes real-time data, and starts iterative optimization if a target is deviated. The system realizes power source nonlinear cooperative control, sets multiple constraint conditions to guarantee safety, improves engine fuel efficiency and thrust stability, and reduces thermal load.

Description

Technical Field

[0001] The present invention relates to the technical field of hybrid power coordination of rocket engines, and in particular to a hybrid power coordination optimization system of rocket engines. Background Art

[0002] In the field of rocket engines, the increasing complexity and diversity of space missions are driving increasingly stringent performance requirements for rocket engine propulsion systems. Deep space exploration missions require engines with high specific impulse to reduce fuel load and extend flight distances. Satellite launches place extremely high demands on thrust stability, as even the slightest thrust fluctuation can affect the accuracy of a satellite's orbital insertion.

[0003] Rocket engines using hybrid power lack an accurate and effective collaborative optimization system, and the power sources cannot cooperate efficiently. Existing data acquisition and processing methods are difficult to fully and accurately obtain the engine operating status parameters. Existing models cannot accurately reflect the actual operating status and are insufficient in simulating complex multi-physical field coupling phenomena. Summary of the Invention

[0004] The object of the present invention is to provide a hybrid power collaborative optimization system for a rocket engine to solve the problems in the prior art raised in the above background technology.

[0005] To achieve the above-mentioned object, the present invention provides the following technical solution: a hybrid power collaborative optimization system for a rocket engine, comprising:

[0006] S1, data acquisition and processing module, configured as follows:

[0007] S101. Using a sensor group, collect operating state parameters of at least two power sources in a rocket engine in real time, including fuel flow rate, oxidizer flow rate, combustion chamber pressure, temperature field distribution, thrust value, and vibration frequency;

[0008] S102, preprocessing the collected raw data, including noise filtering, data normalization and outlier removal, to generate a standardized data set;

[0009] S2, dynamic modeling and optimization module, configured as follows:

[0010] S201. Construct a theoretical dynamic model of a hybrid power system based on the principles of thermodynamics, fluid mechanics, and chemical reaction kinetics.

[0011] S202, using the standardized data set to modify parameters of the theoretical model to generate a real-time dynamic model that reflects the actual operating state;

[0012] S203. Searching and generating a coordinated control strategy for the power source using a multi-objective optimization algorithm with the optimization objectives of minimizing total fuel consumption, maximizing specific impulse, improving thrust stability, and reducing thermal load; the multi-objective optimization algorithm may include a combination of a genetic algorithm, a particle swarm algorithm, or a simulated annealing algorithm;

[0013] S3, collaborative control execution module, configured as follows:

[0014] S301, dynamically adjusting the output parameters of each power source according to the coordinated control strategy, including the fuel-oxidant mixture ratio, the injector injection angle, and the turbopump power distribution;

[0015] S302: When it is detected that the temperature field distribution or the vibration frequency exceeds the safety threshold, the auxiliary power module is triggered to switch to the redundant working mode and the output power of the main power module is simultaneously reduced;

[0016] S4, closed-loop feedback unit, configured as:

[0017] S401. Continuously collect real-time operating data of the hybrid power system and compare and analyze it with the preset collaborative optimization target;

[0018] S402: If the actual operating parameters deviate from the optimization target, re-execute the dynamic modeling and optimization module and the collaborative control execution module to form an iterative optimization loop based on real-time data;

[0019] Among them, the multi-objective optimization algorithm realizes nonlinear collaborative control of the power source in timing by coupling the dynamic equations of the main power module and the auxiliary power module, and the fuel mixture ratio adjustment and the nozzle expansion ratio adjustment have a linkage relationship based on the combustion chamber pressure threshold.

[0020] Furthermore, the sensor group of the data acquisition and processing module includes:

[0021] Fuel flow sensor and oxidizer flow sensor for real-time monitoring of propellant flow;

[0022] Combustion chamber pressure sensor and temperature sensor array, used to monitor the pressure distribution and temperature field in the combustion chamber;

[0023] Thrust measurement sensor and vibration accelerometer, used to collect engine thrust value and vibration frequency data;

[0024] The preprocessing step includes using wavelet transform or Kalman filtering algorithm to remove noise interference, and normalizing the data by using Z-score standardization method.

[0025] Furthermore, the theoretical dynamic model of the dynamic modeling and optimization module is a multi-physics field coupling model, including:

[0026] The chemical reaction kinetic equation in the combustion chamber is:

[0027]

[0028] in, is the molar generation rate of component i, ν ij is the stoichiometric coefficient of reaction j, r j is the rate of reaction j;

[0029] Fluid dynamics equations:

[0030]

[0031] Where ρ is density, v is flow velocity, p is pressure, μ is viscosity, and f is volume force;

[0032] Thermodynamic equation:

[0033]

[0034] in, and are the heat input and output, ΔH i is the enthalpy change of component i.

[0035] Furthermore, the multi-objective optimization algorithm is a hybrid particle swarm-genetic algorithm, which specifically includes:

[0036] The inertia weight ω of the particle swarm algorithm is dynamically adjusted with the number of iterations t:

[0037]

[0038] Among them, ω max =0.9,ω min =0.4, T is the maximum number of iterations;

[0039] Crossover probability P of genetic algorithm c and mutation probability P m Dynamic adjustment based on population diversity:

[0040]

[0041] Furthermore, the dynamic adjustment parameters of the collaborative control execution module include:

[0042] The mixing ratio of fuel to oxidant φ is adjustable within the range of 0.8≤φ≤1.2;

[0043] The injection angle θ of the injector is adjustable within the range of 5°≤θ≤45°;

[0044] The speed n of the turbo pump is adjustable within the range of 5000 ≤ n ≤ 15000 rpm;

[0045] The linkage relationship between the mixing ratio φ and the nozzle expansion ratio ∈ satisfies:

[0046]

[0047] When the combustion chamber pressure P combustion ≥P threshold When , k = 0.15, otherwise k = 0.

[0048] Furthermore, the comparative analysis method of the closed-loop feedback unit includes:

[0049] Calculate the actual thrust value F actual and target thrust value F target The relative error is:

[0050]

[0051] When Error F >5% or when the fuel consumption rate exceeds the preset benchmark value by 10%, the re-optimization process is triggered;

[0052] The vibration frequency of N = 10 consecutive sampling points is averaged and filtered by the sliding window method. If the filtered frequency exceeds the safety threshold f max , it is determined to be an abnormal operating condition.

[0053] Furthermore, the constraints of the multi-objective optimization model in S2 include:

[0054] Specific Impulse I sp The lower bound constraint is:

[0055] I sp ≥I sp,min =300

[0056] Thrust stability constraint: thrust fluctuation amplitude ΔF ≤ 5% F nominal ; ΔF is the difference between the real-time thrust value and the rated thrust F nominal Maximum deviation thermal load constraint: combustion chamber wall temperature T wall ≤T max =3000K; where T wall The temperature is measured in real time by a distributed thermocouple array (at least 16 measurement points). max Determination of heat resistance limit based on nickel-based high-temperature alloys;

[0057] Fuel consumption rate constraint:

[0058]

[0059] in, is the fuel consumption rate, is the fuel consumption rate benchmark value under design conditions;

[0060] Furthermore, the redundant working mode includes:

[0061] When the turbopump of the main power module fails, the auxiliary power module's backup turbopump is triggered to start and the fuel supply to the main power module is reduced to 70% of the rated value;

[0062] When the electric propulsion system detects abnormal current fluctuations, it switches to the redundant electric thrusters and simultaneously reduces the oxidizer flow rate of the main power module to 85% of the rated value;

[0063] The response time of redundant mode switching does not exceed 200ms.

[0064] Furthermore, the combustion chamber pressure threshold P of the linkage relationship threshold Determine this by following these steps:

[0065] During the ground test, the propellant mixture ratio was gradually increased to the critical value, and the combustion chamber pressure P was recorded. critical ;

[0066] P threshold Set to 0.9·P critical , ensuring that the system operates within a safe range;

[0067] During the flight, the pressure sensor is used to monitor P in real time. combustion , when P combustion ≥P threshold , the linkage adjustment mechanism is triggered.

[0068] Furthermore, the synergistic efficiency coefficient E eff The calculation formula is:

[0069]

[0070] Among them, T wall is the wall temperature, f vib is the vibration frequency, T max and f max To preset safety threshold;

[0071] When E eff When ≥0.9, the system is judged to be in the optimal collaborative state.

[0072] Compared with the existing technology, the present invention has the following beneficial effects: by equipping a sensor group composed of multiple sensors, it can comprehensively collect the operating state parameters of at least two power sources in a rocket engine, covering key information such as fuel flow, oxidizer flow, combustion chamber pressure, temperature field distribution, thrust value and vibration frequency. The multi-physics field coupling theoretical dynamic model constructed based on the principles of thermodynamics, fluid mechanics and chemical reaction kinetics can accurately describe the inherent physical mechanism of the hybrid power system. The parameters of the theoretical model are corrected using a standardized data set, and the generated real-time dynamic model can more accurately reflect the actual operating status. By adopting multi-objective optimization algorithms such as hybrid particle swarm-genetic algorithm, by coupling the dynamic equations of the main power module and the auxiliary power module, nonlinear coordinated control of the power source in time sequence is achieved, effectively avoiding falling into local optimal solutions. The multi-objective optimization model sets a series of strict constraints such as the lower limit constraint of specific impulse, thrust stability constraint, thermal load constraint and fuel consumption rate constraint to ensure the safe operation of the engine under the premise of meeting various performance indicators. BRIEF DESCRIPTION OF THE DRAWINGS

[0073] Figure 1 Schematic diagram of the system flow of the present invention. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] Specific implementation method 1: Please refer to Figure 1 The present invention provides a technical solution: a hybrid power collaborative optimization system for a rocket engine, comprising:

[0076] S1, data acquisition and processing module, configured as follows:

[0077] S101. Using a sensor group, collect operating state parameters of at least two power sources in a rocket engine in real time, including fuel flow rate, oxidizer flow rate, combustion chamber pressure, temperature field distribution, thrust value, and vibration frequency;

[0078] S102, preprocessing the collected raw data, including noise filtering, data normalization and outlier removal, to generate a standardized data set;

[0079] The sensor group of the data acquisition and processing module includes:

[0080] Fuel flow sensor and oxidizer flow sensor for real-time monitoring of propellant flow;

[0081] Combustion chamber pressure sensor and temperature sensor array for monitoring pressure distribution and temperature field in the combustion chamber;

[0082] Thrust measurement sensor and vibration accelerometer for collecting engine thrust value and vibration frequency data;

[0083] The preprocessing step includes removing noise interference using wavelet transform or Kalman filter algorithm, and normalizing the data by Z-score standardization method.

[0084] For the measurement of key parameters, additional sensors are used. When the main sensor fails, the backup sensor can immediately switch seamlessly to work, ensuring the continuity of data acquisition and avoiding system misjudgment or control failure due to data loss. At the same time, the protective shell of the sensor is optimized, using high-strength, high-temperature-resistant materials with electromagnetic shielding function to prevent high temperature, high pressure and strong electromagnetic interference during engine operation from affecting the sensor signal, ensuring the accuracy of the collected data.

[0085] For core parameters such as fuel flow and combustion chamber pressure, three-redundancy sensor configuration is used to eliminate single-point fault data through majority voting algorithm, improving data confidence.

[0086] Before each engine start, the sensor is calibrated by the built-in standard source for zero and range, and the calibration error needs to meet: flow sensor ≤ ± 0.5% FS, pressure sensor ≤ ± 1% FS.

[0087] Increased Lida Criterion (3σ Rule) and Time Sequence Correlation Check: Data deviating from the historical mean by 3 times the standard deviation for 3 consecutive sampling points and having no reasonable coupling relationship with adjacent parameters are judged as abnormal values.

[0088] S2, dynamic modeling and optimization module, configured as:

[0089] S201, based on the principles of thermodynamics, fluid mechanics and chemical reaction kinetics, a theoretical dynamic model of the hybrid power system is constructed;

[0090] S202, using the standardized data set to correct the parameters of the theoretical model, generating a real-time dynamic model reflecting the actual operating state;

[0091] S203, through a multi-objective optimization algorithm, the optimization objectives are to minimize total fuel consumption, maximize specific impulse, improve thrust stability and reduce thermal load, search and generate a collaborative control strategy for the power source; the multi-objective optimization algorithm includes a combination of genetic algorithm, particle swarm algorithm or simulated annealing algorithm;

[0092] The multi-objective optimization algorithm is a hybrid particle swarm-genetic algorithm, which specifically includes:

[0093] The inertia weight ω of the particle swarm algorithm is dynamically adjusted with the number of iterations t:

[0094]

[0095] Among them, ω max =0.9,ω min =0.4, T is the maximum number of iterations;

[0096] Crossover probability P of genetic algorithm c and mutation probability P m Dynamic adjustment based on population diversity:

[0097]

[0098] The constraints of the multi-objective optimization model include:

[0099] Specific Impulse I sp The lower bound constraint is:

[0100] I sp ≥I sp,min =300

[0101] Thrust stability constraint: thrust fluctuation amplitude ΔF ≤ 5% F nominal ; ΔF is the difference between the real-time thrust value and the rated thrust F nominal The maximum deviation

[0102] Thermal load constraint: combustion chamber wall temperature T wall ≤T max =3000K; where T wall The temperature is measured in real time by a distributed thermocouple array (at least 16 measurement points). max Determination of heat resistance limit based on nickel-based high-temperature alloys;

[0103] Fuel consumption rate constraint:

[0104]

[0105] in, is the fuel consumption rate, It is the reference value of fuel consumption rate under design conditions.

[0106] The theoretical dynamic model of the dynamic modeling and optimization module is a multi-physics field coupling model, including:

[0107] The chemical reaction kinetic equation in the combustion chamber is:

[0108]

[0109] in, is the molar generation rate of component i, ν ij is the stoichiometric coefficient of reaction j, r j is the rate of reaction j;

[0110] Fluid dynamics equations:

[0111]

[0112] Where ρ is density, v is flow velocity, p is pressure, μ is viscosity, and f is volume force;

[0113] Thermodynamic equation:

[0114]

[0115] in, and are the heat input and output, ΔH i is the enthalpy change of component i.

[0116] Considering the influence of combustion chamber wall vibration on fuel atomization and mixing process, the correction term of vibration displacement δ(t) on the equivalent flow area of ​​injector is introduced:

[0117] A inj (t) = A0.[1+α.δ(t)]

[0118] Where α is the vibration coupling coefficient, which is determined by modal analysis tests.

[0119] Establish the combustion chamber wall temperature T wall Conjugate heat transfer equations with coolant flow and temperature, and real-time calculation accuracy of corrected thermal load constraints.

[0120] Inequality constraints in multi-objective optimization (I sp ≥300), using the adaptive penalty function method:

[0121]

[0122] where λ j Dynamically adjust with the number of iterations, initially focusing on feasibility (λ j Increase), late focus optimality (λ j decrease).

[0123] S3, collaborative control execution module, configured as follows:

[0124] S301, dynamically adjusting the output parameters of each power source according to the coordinated control strategy, including the fuel-oxidant mixture ratio, the injector injection angle, and the turbopump power distribution;

[0125] S302: When it is detected that the temperature field distribution or the vibration frequency exceeds the safety threshold, the auxiliary power module is triggered to switch to the redundant working mode and the output power of the main power module is simultaneously reduced;

[0126] The dynamic adjustment parameters of the collaborative control execution module include:

[0127] The mixing ratio of fuel to oxidant φ is adjustable within the range of 0.8≤φ≤1.2;

[0128] The injection angle θ of the injector is adjustable within the range of 5°≤θ≤45°;

[0129] The speed n of the turbo pump is adjustable within the range of 5000 ≤ n ≤ 15000 rpm;

[0130] The linkage relationship between the mixing ratio φ and the nozzle expansion ratio ∈ satisfies:

[0131]

[0132] When the combustion chamber pressure P combustion ≥P threshold When , k = 0.15, otherwise k = 0.

[0133] When a thermocouple at a measuring point fails, a Kriging interpolation model is constructed using the temperature data of adjacent measuring points to reconstruct the temperature of the failure point in real time, ensuring uninterrupted thermal load calculation.

[0134] The standby turbo pump enters the preheating state (speed increases to 30% of the rated value) 50ms before the main pump fails, shortening the response time from 200ms to 120ms, and reducing the thrust fluctuation amplitude during the switching process to ≤3%F nominal .

[0135] Redundant working modes include:

[0136] When the turbopump of the main power module fails, the auxiliary power module's backup turbopump is triggered to start and the fuel supply to the main power module is reduced to 70% of the rated value;

[0137] When the electric propulsion system detects abnormal current fluctuations, it switches to the redundant electric thrusters and simultaneously reduces the oxidizer flow rate of the main power module to 85% of the rated value;

[0138] The response time of redundant mode switching does not exceed 200ms.

[0139] S4, closed-loop feedback unit, configured as:

[0140] S401. Continuously collect real-time operating data of the hybrid power system and compare and analyze it with the preset collaborative optimization target;

[0141] S402: If the actual operating parameters deviate from the optimization target, re-execute the dynamic modeling and optimization module and the collaborative control execution module to form an iterative optimization loop based on real-time data;

[0142] Among them, the multi-objective optimization algorithm realizes nonlinear collaborative control of the power source in timing by coupling the dynamic equations of the main power module and the auxiliary power module, and the fuel mixture ratio adjustment and the nozzle expansion ratio adjustment have a linkage relationship based on the combustion chamber pressure threshold.

[0143] The comparative analysis methods of closed-loop feedback units include:

[0144] Calculate the actual thrust value F actual and target thrust value F target The relative error is:

[0145]

[0146] When Error F >5% or when the fuel consumption rate exceeds the preset benchmark value by 10%, the re-optimization process is triggered;

[0147] The vibration frequency of N = 10 consecutive sampling points is averaged and filtered by the sliding window method. If the filtered frequency exceeds the safety threshold f max , it is determined to be an abnormal operating condition.

[0148] Combustion chamber pressure threshold P of linkage relationship threshold Determine this by following these steps:

[0149] During the ground test, the propellant mixture ratio was gradually increased to the critical value, and the combustion chamber pressure P was recorded. critical ;

[0150] P threshold Set to 0.9·P critical , ensuring that the system operates within a safe range;

[0151] During the flight, the pressure sensor is used to monitor P in real time. combustion , when P combustion ≥P threshold , the linkage adjustment mechanism is triggered.

[0152] Furthermore, the synergistic efficiency coefficient E eff The calculation formula is:

[0153]

[0154] Among them, T wall is the wall temperature, f vib is the vibration frequency, T max and f maxTo preset safety threshold;

[0155] When E eff When ≥0.9, the system is judged to be in the optimal collaborative state.

[0156] Formula derivation and theoretical basis

[0157] Formula design goal: synergistic efficiency coefficient E eff It aims to comprehensively evaluate the thrust performance, fuel efficiency, thermal load safety and vibration stability of the rocket engine hybrid system, and to achieve quantitative evaluation of the overall synergistic state of the system through multi-parameter weighted calculation.

[0158] Theoretical basis:

[0159] The formula is derived based on the following engineering principles:

[0160] The product of thrust and specific impulse (numerator): reflects the energy output efficiency of the power system, that is, the effective thrust generated per unit fuel consumption;

[0161] Fuel consumption rate (first item in the denominator): reflects fuel economy;

[0162] Normalized ratio of temperature to vibration (two terms after the denominator): by taking the actual wall temperature T wall and the vibration frequency f vib With the safety threshold T max and f max The ratio of quantifies the safety margin of system operation.

[0163] Parameter definition and measurement method

[0164] The physical meaning, measurement method and unit of each parameter in the formula must be defined one by one in the manual:

[0165] F total : Total thrust (unit: N), collected in real time by the thrust measurement sensor, defined as the superposition of the thrust of the chemical propellant and the auxiliary power module.

[0166] I sp : Specific impulse (unit: s), calculated based on the propellant mass flow rate and thrust, the formula is;

[0167]

[0168] Where g0 is the standard acceleration of gravity (9.81m / s).

[0169] Fuel consumption rate (unit: kg / s), directly measured by the fuel flow sensor.

[0170] T wall With Tmax :

[0171] T wall is the combustion chamber wall temperature (unit: K), measured by the temperature sensor array;

[0172] T max It is the maximum temperature allowed by the material (3000K), determined according to the material properties of the engine.

[0173] f vib With f max :

[0174] f vib is the vibration frequency (unit: Hz), measured by accelerometer and obtained by Fourier transform analysis;

[0175] f max is the safe vibration frequency threshold (100 Hz), determined based on structural dynamics simulation.

[0176] In ground tests,

[0177] F total =100,000N, I sp =310s,

[0178] T wall =2800K=280,f vib =85Hz,

[0179] T max =3000K, f max =100Hz=3000.

[0180] Substitute into the formula to calculate:

[0181]

[0182] By comparing the E eff Value, verify that the system is in E eff The optimal synergy state is ≥0.99.

[0183] Actual sensors and actuators are connected to the digital twin model to simulate extreme working conditions such as high altitude low air pressure and strong vibration, verify the robustness of redundant mode switching, and require that the false trigger rate in 1,000 tests is ≤0.1%.

Claims

1. A hybrid power collaborative optimization system for a rocket engine, characterized in that: include: S1, data acquisition and processing module, configured as follows: S101. Using a sensor group, collect operating state parameters of at least two power sources in a rocket engine in real time, including fuel flow rate, oxidizer flow rate, combustion chamber pressure, temperature field distribution, thrust value, and vibration frequency; S102, preprocessing the collected raw data, including noise filtering, data normalization and outlier removal, to generate a standardized data set; S2, dynamic modeling and optimization module, configured as follows: S201. Construct a theoretical dynamic model of a hybrid power system based on the principles of thermodynamics, fluid mechanics, and chemical reaction kinetics. S202, using the standardized data set to modify parameters of the theoretical model to generate a real-time dynamic model that reflects the actual operating state; S203. Searching and generating a coordinated control strategy for the power source using a multi-objective optimization algorithm with the optimization objectives of minimizing total fuel consumption, maximizing specific impulse, improving thrust stability, and reducing thermal load; the multi-objective optimization algorithm may include a combination of a genetic algorithm, a particle swarm algorithm, or a simulated annealing algorithm; S3, collaborative control execution module, configured as follows: S301, dynamically adjusting the output parameters of each power source according to the coordinated control strategy, including the fuel-oxidant mixture ratio, the injector injection angle, and the turbopump power distribution; S302: When it is detected that the temperature field distribution or the vibration frequency exceeds the safety threshold, the auxiliary power module is triggered to switch to the redundant working mode and the output power of the main power module is simultaneously reduced; S4, closed-loop feedback unit, configured as: S401. Continuously collect real-time operating data of the hybrid power system and compare and analyze it with the preset collaborative optimization target; S402. If the actual operating parameters deviate from the optimization target, the dynamic modeling and optimization module and the collaborative control execution module are re-executed to form an iterative optimization loop based on real-time data; wherein, the multi-objective optimization algorithm realizes nonlinear collaborative control of the power source in timing by coupling the dynamic equations of the main power module and the auxiliary power module, and the fuel mixture ratio adjustment and the nozzle expansion ratio adjustment have a linkage relationship based on the combustion chamber pressure threshold.

2. The hybrid power collaborative optimization system for a rocket engine according to claim 1, characterized in that: The sensor group of the data acquisition and processing module includes: Fuel flow sensor and oxidizer flow sensor for real-time monitoring of propellant flow; Combustion chamber pressure sensor and temperature sensor array, used to monitor the pressure distribution and temperature field in the combustion chamber; Thrust measurement sensor and vibration accelerometer, used to collect engine thrust value and vibration frequency data; The preprocessing step includes using wavelet transform or Kalman filtering algorithm to remove noise interference, and normalizing the data by using Z-score standardization method.

3. The hybrid power collaborative optimization system for a rocket engine according to claim 2, characterized in that: The theoretical dynamic model of the dynamic modeling and optimization module is a multi-physics field coupling model, including: The chemical reaction kinetic equation in the combustion chamber is: in, is the molar generation rate of component i, ν ij is the stoichiometric coefficient of reaction j, r j is the rate of reaction j; Fluid dynamics equations: Where ρ is density, v is flow velocity, p is pressure, μ is viscosity, and f is volume force; Thermodynamic equation: in, and are the heat input and output, ΔH i is the enthalpy change of component i.

4. The hybrid power collaborative optimization system for a rocket engine according to claim 3, characterized in that: The multi-objective optimization algorithm is a hybrid particle swarm-genetic algorithm, which specifically includes: The inertia weight ω of the particle swarm algorithm is dynamically adjusted with the number of iterations t: Among them, ω max =0.9,ω min =0.4, T is the maximum number of iterations; Crossover probability P of genetic algorithm c and mutation probability P m Dynamic adjustment based on population diversity:

5. The hybrid power collaborative optimization system for a rocket engine according to claim 4, characterized in that: The dynamic adjustment parameters of the collaborative control execution module include: The mixing ratio of fuel to oxidant φ is adjustable within the range of 0.8≤φ≤1.2; The injection angle θ of the injector is adjustable within the range of 5°≤θ≤45°; The speed n of the turbo pump is adjustable within the range of 5000 ≤ n ≤ 15000 rpm; The linkage relationship between the mixing ratio φ and the nozzle expansion ratio ∈ satisfies: When the combustion chamber pressure P combustion ≥P threshold When , k = 0.15, otherwise k = 0.

6. The hybrid power collaborative optimization system for a rocket engine according to claim 5, characterized in that: The comparative analysis method of the closed-loop feedback unit includes: Calculate the actual thrust value F actual and target thrust value F target The relative error is: When Error F >5% or when the fuel consumption rate exceeds the preset benchmark value by 10%, the re-optimization process is triggered; The vibration frequency of N = 10 consecutive sampling points is averaged and filtered by the sliding window method. If the filtered frequency exceeds the safety threshold f max , it is determined to be an abnormal operating condition.

7. The hybrid power collaborative optimization system for a rocket engine according to claim 6, characterized in that: The model constraints of the multi-objective optimization in S2 include: Specific Impulse I sp The lower bound constraint is: I sp ≥I sp,min =300 Thrust stability constraint: thrust fluctuation amplitude ΔF ≤ 5% F nominal ; ΔF is the difference between the real-time thrust value and the rated thrust F nominal Maximum deviation thermal load constraint: combustion chamber wall temperature T wall ≤T max =3000K; where T wall The temperature is measured in real time by a distributed thermocouple array (at least 16 measurement points). max Determination of heat resistance limit based on nickel-based high-temperature alloys; Fuel consumption rate constraint: in, is the fuel consumption rate, It is the reference value of fuel consumption rate under design conditions.

8. The hybrid power collaborative optimization system for a rocket engine according to claim 7, characterized in that: The redundant working mode includes: When the turbopump of the main power module fails, the auxiliary power module's backup turbopump is triggered to start and the fuel supply to the main power module is reduced to 70% of the rated value; When the electric propulsion system detects abnormal current fluctuations, it switches to the redundant electric thrusters and simultaneously reduces the oxidizer flow rate of the main power module to 85% of the rated value; The response time of redundant mode switching does not exceed 200ms.

9. The hybrid power collaborative optimization system for a rocket engine according to claim 8, characterized in that: The combustion chamber pressure threshold P of the linkage relationship threshold Determine this by following these steps: During the ground test, the propellant mixture ratio was gradually increased to the critical value, and the combustion chamber pressure P was recorded. critical ; P threshold Set to 0.9·P critical , ensuring that the system operates within a safe range; During the flight, the pressure sensor is used to monitor P in real time. combustion , when P combustion ≥P threshold When the linkage adjustment mechanism is triggered.

10. The hybrid power collaborative optimization system for a rocket engine according to claim 9, characterized in that: The synergistic efficiency coefficient E eff The calculation formula is: Among them, T wall is the wall temperature, f vib is the vibration frequency, T mcx and f max To preset safety threshold; When E eff When ≥0.9, the system is judged to be in the optimal collaborative state.

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