Rocket pressurized delivery system heat exchange parameter identification method and device

Abstract

This invention provides a method and apparatus for identifying heat transfer parameters in a rocket pressurization and delivery system, comprising: establishing a nonlinear mathematical model of a closed-loop pressurization and delivery system considering heat transfer and mass transfer; determining the sensitivity of key pressurization performance to heat transfer parameters at all locations using the range method based on orthogonal experiments, and selecting target features to be identified from the nonlinear mathematical model; determining an objective function, and optimizing the identification results obtained from the objective function using a chaotic adaptive particle swarm optimization algorithm to obtain optimized identification results; verifying, checking, and analyzing the effectiveness of the optimized identification results to determine the heat transfer parameters. This invention can identify heat transfer terms that have a significant impact on pressurization performance from numerous heat transfer terms, and identify the thermal conductivity and convective heat transfer correlation of these heat transfer terms, which can significantly reduce the design value of pressurization gas consumption, reduce the cost of experimental and design iterations, and has good application prospects and engineering promotion value.

Description

Technical Field

[0001] This invention relates to the field of launch vehicle technology, and more specifically, to a method and apparatus for identifying heat exchange parameters in a rocket pressurization and delivery system. Background Technology

[0002] Rocket upper stages and spacecraft are characterized by low thrust, small overload variations, and narrow engine inlet pressure ranges, and often employ closed pressurization systems for tank pressurization.

[0003] Closed-loop pressurization and delivery systems are structurally complex, with heat and mass transfer phenomena frequently occurring between the pressurizing gas and propellant. Under complex mission profiles, these factors can significantly impact pressurization performance and cannot be ignored during forward design. For example, in a certain currently operational launch vehicle, the pressure in the pressurizing cylinders at the time of secondary startup rises by more than 10% compared to the pressure at the end of the first flight, while the pressure in the purging cylinders rises by more than 30%. This is due to the thermal backflow of the cryogenic cylinder walls by the ambient-temperature rocket body structure during gliding. Modern spacecraft mission profiles are complex, with long single-mission durations. Ignoring heat and mass transfer phenomena during the design process could lead to excessive redundancy or safety hazards. Analyzing the impact of these heat and mass transfer processes on key pressurization parameters and identifying them in conjunction with flight test data from mature models can provide valuable guidance for the feasibility study of new models, helping to reduce potential overweight risks and excessive redundancy in design.

[0004] Currently, simulations of pressurized delivery systems for newly developed models often rely on adiabatic assumptions. This results in calculations of booster flow rate, cylinder gas consumption, and air cushion volume that are significantly higher than flight test results. This leads to overweight components, reduced effective design load, and increased costs for iterative verification. Furthermore, pressurized delivery systems are complex, with numerous heat transfer terms. There are no effective measurement methods for these terms in existing models, and research on the heat transfer characteristics of pressurized delivery systems is scarce both domestically and internationally. Moreover, the task of identifying the thermal parameters of pressurized delivery systems is offline and highly nonlinear, making it difficult to solve using traditional gradient descent methods. Summary of the Invention

[0005] To address the shortcomings of existing technologies, the purpose of this invention is to provide a method and apparatus for identifying heat exchange parameters in a rocket pressurization and delivery system.

[0006] In a first aspect, embodiments of this application provide a method for identifying heat transfer parameters in a rocket pressurization and delivery system, including:

[0007] Step 1: Establish a nonlinear mathematical model of the closed-loop pressurized conveying system that considers heat exchange and mass transfer;

[0008] Step 2: Determine the sensitivity of key pressurization performance to heat transfer parameters at all locations using the range method based on orthogonal experiments, and screen out target features to be identified from the nonlinear mathematical model;

[0009] Step 3: Determine the objective function and use the chaotic adaptive particle swarm optimization algorithm to optimize the identification results obtained from the objective function, thereby obtaining the optimized identification results;

[0010] Step 4: Verify, check, and analyze the effectiveness of the optimized identification results to determine the heat exchange parameters.

[0011] Optionally, step 1 includes:

[0012] Step 1.1: Determine the temperature characteristics of the variable-volume or fixed-volume gas chamber containing heat exchange terms. The calculation formula for the temperature characteristics is as follows:

[0013]

[0014] p u V u =Z·m u RT u (2)

[0015] Where, p u V u T u m u These are the pressure, volume, temperature, and mass of the air cushion, respectively; T i C represents the temperature of the inflowing gas. p C v Here, Q represents the isobaric specific heat capacity and isovolumetric specific heat capacity of the pressurized gas; Q is the heat transfer term; Z = f(T,P) is the compressibility factor of helium; and m... s Let R be the mass of the pressurized gas, t be time, and R be the gas constant.

[0016] Step 1.2: Determine the flow characteristics of the gas throttling coil. The calculation formula for the flow characteristics is as follows:

[0017]

[0018] Among them, C d P is the flow coefficient of gas passing through the throttling orifice; A is the physical area of ​​the throttling orifice; P i P o For the pressure before and after the throttling coil, P cr The critical pressure ratio of the working fluid; k is the gas adiabatic index; m is the gas mass after passing through the throttling coil; C x T is the valve opening coefficient. s Let be the absolute temperature of the gas passing through the throttling coil, and Z be the compressibility factor of the gas.

[0019] Step 1.3: Determine the general expression for the heat transfer model as follows:

[0020]

[0021] For natural convection, we have the following formula:

[0022]

[0023] For forced convection, the following formula holds:

[0024]

[0025] Where h is the heat transfer coefficient; A is the heat transfer area; ΔT is the temperature difference; k f L is the thermal conductivity of the pressurized gas. s ρ is the characteristic length; f n is the gas density; g is the acceleration due to gravity; n is the gas density. x β is the overload factor. f μ is the coefficient of gas expansion under pressure. f For the viscosity of the pressurized gas, c pf For the isobaric specific heat capacity of the pressurized gas, v g Where is the gas flow rate; C and n are constants related to the heat transfer conditions;

[0026] For heat transfer through the gas cylinder wall, the general expression of the heat transfer model is modified as follows:

[0027]

[0028] Where K p The overall thermal conductivity of the gas cylinder;

[0029] Step 1.4: Determine the dynamic characteristic expression of the positive unloading pressure reducing valve as follows:

[0030]

[0031]

[0032]

[0033] The formula for calculating the damping term is as follows:

[0034]

[0035]

[0036] Where M is the valve core mass, c and k are the damping and spring stiffness respectively; x is the valve core stroke, α is the valve disc cone angle, and A ocr For the throttling area, q in qout u in u out F represents the gas flow rate and velocity before and after throttling; s For static friction, F c v is the Coulomb friction force, v is the valve core velocity, v s Stribeck velocity; η is the coefficient of viscous friction; x0 is the initial position of the valve core; A H P represents the area affected by the high pressure. H For the high-pressure chamber pressure, A L P is the effective area of ​​the low-pressure cavity. L For the low-pressure chamber pressure, F D For pneumatic force, D is the valve port diameter, L is the characteristic length, and F is the characteristic length. f F is the frictional resistance, F is the spring force acting on the valve core, and s(v) is the Stribeck frictional damping.

[0037] Optionally, step 2 includes:

[0038] Step 2.1: Construct a 7-factor, 3-level orthogonal experimental table, where the rated heat flux is selected according to the reference value, and the three levels are 80%, 100%, and 120% of the reference value, respectively. Then, call the system simulation model according to the orthogonal experimental table to obtain 18 sets of three-index simulation results.

[0039] Step 2.2: Use range analysis to determine the sensitivity indices. The range is defined as follows: Assume the average value of the index corresponding to the i-th level of the j-th factor is denoted as... The formula for calculating the range of factor j for this indicator is as follows:

[0040]

[0041] Step 2.3: Select the heat transfer terms represented by the three factors with the largest range. For any selected heat transfer term, if its type is thermal conduction, then select the thermal conductivity as the variable to be identified; if it is convective heat transfer, then select C and n in formulas (5) and (6) as the variable to be identified.

[0042] Optionally, the objective function in step 3 is the root mean square error J with a weight decay mechanism, calculated as follows:

[0043]

[0044]

[0045] in, Let L = 3, and let N = 3, representing the simulated value and observed value at the i-th time point of the l-th measurement, respectively. The corresponding measurement items are gas cylinder pressure, fuel tank pressure, and oxygen tank pressure, respectively. N represents the total number of times the signal device actuates at the time corresponding to each measurement value. λ is the attenuation coefficient, which aims to reduce the uncertainty introduced by the pressure drift of the pressure signal device actuation on the identification results. λ is set to 0.95.

[0046] Optionally, step 4 includes:

[0047] Step 4.1: Based on the three heat transfer terms selected by the range method, determine the particle dimension D and search range, and determine the basic parameters of the particle swarm optimization algorithm. The basic parameters include: number of particles N, social cognition coefficients c1 and c2, and inertia weight boundary ω. max ω min And the maximum number of iterations T, and set the initial position and initial velocity of the particle swarm using a uniform distribution within the search range;

[0048] Step 4.2: In each iteration, substitute the particle position into formula (13) to calculate the historical best position x of each particle's fitness. pbest,k and the historical best position x of the group gbest,k The system updates the velocity and position of each particle, incrementing the iteration count by 1 after each update. The update calculation formula is as follows:

[0049] v i,k+1 =ω·v i,k +c1γ1(x pbest,k -x i,k )+c2γ2(x gbest,k -x i,k (15)

[0050] x i,k+1 =x i,k +v i,k (16)

[0051] Among them, v i,k x i,k Let ω be the velocity vector and position vector of the i-th particle in the k-th iteration, c1 and c2 be the cognitive coefficients, and γ1 and γ2 be random numbers between [0,1].

[0052] Based on the dispersion of particle swarm fitness, the inertia weight is decayed, with each particle having an inertia weight ω. i The calculation formula is as follows:

[0053]

[0054] Among them, f i Let i be the fitness of the i-th particle. f is the average fitness of all particles. min This represents the minimum fitness value in the population.

[0055] Step 4.3: The improved fitness variance is used as the criterion for precocious convergence. The calculation formula is as follows:

[0056]

[0057] Where F is the fitness variance, and C is the adjustment coefficient, used to suppress the influence of a small number of particles deviating far from the local optimum when premature convergence occurs, which causes the variance to be affected. The formula for calculating C is as follows:

[0058]

[0059] The perturbation quantities of each particle are z = [z1, z2, ..., z D The elements are arranged into an N×D matrix Z, where Z should balance randomness and stability. The Logistic mapping is selected to update Z, and the calculation formula is as follows:

[0060] z i,j,k+1 =μz i,j,k (1-z i,j,k (20)

[0061] Among them, z i,j Let μ be the value of the element in the i-th row and j-th column of Z during the k-th iteration, and μ be a control variable.

[0062] When the maximum number of iterations is reached, the global optimal position and the corresponding fitness value are output and substituted into the simulation model for verification.

[0063] Secondly, embodiments of this application provide a heat exchange parameter identification device for a rocket pressurization and delivery system, comprising:

[0064] The model building module is used to build a nonlinear mathematical model of a closed-loop pressurized transport system that takes into account heat exchange and mass transfer.

[0065] The target feature screening module is used to determine the sensitivity of key pressurization performance to heat transfer parameters at all locations by using the range method based on orthogonal experiments, and to screen out target features from the nonlinear mathematical model.

[0066] The identification result optimization module is used to determine the objective function and optimize the identification result obtained by the objective function using the chaotic adaptive particle swarm algorithm to obtain the optimized identification result.

[0067] The verification module is used to verify, check, and analyze the effectiveness of the optimized identification results, and to determine the heat exchange parameters.

[0068] Thirdly, embodiments of this application provide a heat exchange parameter identification device for a rocket pressurization and delivery system, comprising: a processor and a memory, wherein the memory stores executable program instructions, and when the processor calls the program instructions in the memory, the processor is used to:

[0069] Perform the steps of the method for identifying heat exchange parameters of a rocket pressurization and delivery system as described in any one of the first aspects.

[0070] Fourthly, embodiments of this application provide a computer-readable storage medium for storing a program, which, when executed, implements the steps of the method for identifying heat exchange parameters of a rocket pressurization and delivery system as described in any one of the first aspects.

[0071] Compared with the prior art, the present invention has the following beneficial effects:

[0072] This application provides a method and apparatus for identifying heat transfer parameters in a rocket pressurization and delivery system. This method can identify heat transfer parameters that significantly impact pressurization performance from a large number of heat transfer parameters, and identify the thermal conductivity and convective heat transfer correlations of these parameters. It can significantly reduce the design value of pressurization gas consumption, reduce the cost of testing and design iterations, and has excellent application prospects and engineering promotion value. Attached Figure Description

[0073] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are merely embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort. Other features, objects, and advantages of the present invention will become more apparent by reading the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0074] Figure 1 A flowchart illustrating a method for identifying heat exchange parameters in a rocket pressurization and delivery system, provided as an embodiment of this application;

[0075] Figure 2 This is a schematic diagram illustrating the thermodynamic parameter transfer principle of the closed-loop dual-path redundant pressurized conveying system in this application embodiment;

[0076] Figure 3 This is a schematic diagram showing the range analysis results of the first action time of the gas cylinder pressure signal device on seven heat transfer coefficients in an embodiment of this application.

[0077] Figure 4 This is a schematic diagram showing the range analysis results of the first action time of the fuel tank pressure signaler on seven heat transfer coefficients in an embodiment of this application.

[0078] Figure 5 This is a schematic diagram showing the range analysis results of the first action time of the oxygen tank pressure signaler on seven heat transfer coefficients in an embodiment of this application.

[0079] Figure 6 This is a flowchart illustrating the improved particle swarm optimization algorithm for identifying heat transfer parameters in a pressurized delivery system, as described in this application.

[0080] Figure 7 This is a comparison chart of the simulation results and measured data after the gas cylinder pressure was identified in the embodiments of this application;

[0081] Figure 8 This is a comparison chart of the simulation results and measured data after identifying the fuel and oxygen tank pressures in the embodiments of this application;

[0082] Figure 9 This is a comparison chart showing the changes in simulation values ​​after the identification results in this embodiment are applied to similar newly developed models. Detailed Implementation

[0083] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0084] It should be noted that when a component is said to be "fixed" to another component, it can be directly on the other component or it can be in a middle component. When a component is said to be "connected" to another component, it can be directly connected to the other component or it may be in a middle component.

[0085] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the specification of this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0086] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention described herein can be implemented, for example, in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0087] The technical solutions of the present invention and how they solve the above-mentioned technical problems are described in detail below with specific embodiments. These specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0088] Figure 1 This is a flowchart illustrating a method for identifying heat exchange parameters in a rocket pressurization and delivery system, as provided in an embodiment of this application. Figure 1 As shown, the method in this embodiment may include:

[0089] Step S101: Establish a nonlinear mathematical model of a closed-loop pressurized conveying system that considers heat exchange and mass transfer.

[0090] In this embodiment, step S101 may include:

[0091] Step S1011: Determine the temperature characteristics of the variable-volume or fixed-volume gas chamber containing heat exchange terms. The calculation formula for the temperature characteristics is as follows:

[0092]

[0093] p u V u =Z·m u RT u (2)

[0094] Where, p u V u T u m u These are the pressure, volume, temperature, and mass of the air cushion, respectively; T i C represents the temperature of the inflowing gas. p C v Here, Q represents the isobaric specific heat capacity and isovolumetric specific heat capacity of the pressurized gas; Q is the heat transfer term; Z = f(T,P) is the compressibility factor of helium; and m... sLet R be the mass of the pressurized gas, t be time, and R be the gas constant.

[0095] Step S1012: Determine the flow characteristics of the gas throttling coil. The calculation formula for the flow characteristics is as follows:

[0096]

[0097] Among them, C d P is the flow coefficient of gas passing through the throttling orifice; A is the physical area of ​​the throttling orifice; P i P o For the pressure before and after the throttling coil, P cr The critical pressure ratio of the working fluid; k is the gas adiabatic index; m is the gas mass after passing through the throttling coil; C x T is the valve opening coefficient. s Let be the absolute temperature of the gas passing through the throttling coil, and Z be the compressibility factor of the gas.

[0098] Step S1013: Determine the general expression for the heat transfer model as follows:

[0099]

[0100] For natural convection, we have the following formula:

[0101]

[0102] For forced convection, the following formula holds:

[0103]

[0104] Where h is the heat transfer coefficient; A is the heat transfer area; ΔT is the temperature difference; k f L is the thermal conductivity of the pressurized gas. s ρ is the characteristic length; f n is the gas density; g is the acceleration due to gravity; n is the gas density. x β is the overload factor. f μ is the coefficient of gas expansion under pressure. f For the viscosity of the pressurized gas, c pf For the isobaric specific heat capacity of the pressurized gas, v g Where is the gas flow rate; C and n are constants related to the heat transfer conditions;

[0105] For heat transfer through the gas cylinder wall, the general expression of the heat transfer model is modified as follows:

[0106]

[0107] Where K p The overall thermal conductivity of the gas cylinder;

[0108] Step S1014: Determine the dynamic characteristic expression of the positive unloading pressure reducing valve as follows:

[0109]

[0110]

[0111]

[0112] The formula for calculating the damping term is as follows:

[0113]

[0114]

[0115] Where M is the valve core mass, c and k are the damping and spring stiffness respectively; x is the valve core stroke, α is the valve disc cone angle, and A ocr For the throttling area, q in q out u in u out F represents the gas flow rate and velocity before and after throttling; s For static friction, F c v is the Coulomb friction force, v is the valve core velocity, v s Stribeck velocity; η is the coefficient of viscous friction; x0 is the initial position of the valve core; A H P represents the area affected by the high pressure. H For the high-pressure chamber pressure, A L P is the effective area of ​​the low-pressure cavity. L For the low-pressure chamber pressure, F D For pneumatic force, D is the valve port diameter, L is the characteristic length, and F is the characteristic length. f F is the frictional resistance, F is the spring force acting on the valve core, and s(v) is the Stribeck frictional resistance.

[0116] Step S102: Determine the sensitivity of key boosting performance to heat transfer parameters at all locations using the range method based on orthogonal experiments, and screen out target features to be identified from the nonlinear mathematical model.

[0117] In this embodiment, step S102 includes:

[0118] Step S1021: Construct a 7-factor, 3-level orthogonal experimental table, where the rated heat flux is selected according to the reference value, and the three levels are 80%, 100%, and 120% of the reference value, respectively. Then, call the system simulation model according to the orthogonal experimental table to obtain 18 sets of three-index simulation results.

[0119] Step S1022: Determine the sensitivity indices using range analysis, where the range is defined as follows: Assume the average value of the index corresponding to the i-th level of the j-th factor is denoted as... The formula for calculating the range of factor j for this indicator is as follows:

[0120]

[0121] Step S1023: Select the heat transfer terms represented by the three factors with the largest range. For any selected heat transfer term, if its type is thermal conduction, then select the thermal conductivity as the variable to be identified; if it is convective heat transfer, then select C and n in formulas (5) and (6) as the variable to be identified.

[0122] Step S103: Determine the objective function and use the chaotic adaptive particle swarm algorithm to optimize the identification result obtained from the objective function, so as to obtain the optimized identification result.

[0123] In this embodiment, the objective function is the root mean square error J with a weight decay mechanism, and the calculation formula is as follows:

[0124]

[0125]

[0126] in, Let L = 3, and let N = 3, representing the simulated value and observed value at the i-th time point of the l-th measurement, respectively. The corresponding measurement items are gas cylinder pressure, fuel tank pressure, and oxygen tank pressure, respectively. N represents the total number of times the signal device actuates at the time corresponding to each measurement value. λ is the attenuation coefficient, which aims to reduce the uncertainty introduced by the pressure drift of the pressure signal device actuation on the identification results. λ is set to 0.95.

[0127] Step S104: Verify, check, and analyze the effectiveness of the optimized identification results to determine the heat exchange parameters.

[0128] Step S104 in this embodiment may include:

[0129] Step S1041: Based on the three heat transfer terms selected by the range method, determine the particle dimension D and search range, and determine the basic parameters of the particle swarm optimization algorithm. The basic parameters include: number of particles N, social cognition coefficients c1 and c2, and inertia weight boundary ω. max ω min And the maximum number of iterations T, and set the initial position and initial velocity of the particle swarm using a uniform distribution within the search range;

[0130] Step S1042: In each iteration, substitute the particle position into formula (13) to calculate the historical best position x of each particle's fitness. pbest,kand the historical best position x of the group gbest,k The system updates the velocity and position of each particle, incrementing the iteration count by 1 after each update. The update calculation formula is as follows:

[0131] v i,k+1 =ω·v i,k +c1γ1(x pbest,k -x i,k )+c2γ2(x gbest,k -x i,k (15)

[0132] x i,k+1 =x i,k +v i,k (16)

[0133] Among them, v i,k x i,k Let ω be the velocity vector and position vector of the i-th particle in the k-th iteration, c1 and c2 be the cognitive coefficients, and γ1 and γ2 be random numbers between [0,1].

[0134] Based on the dispersion of particle swarm fitness, the inertia weight is decayed, with each particle having an inertia weight ω. i The calculation formula is as follows:

[0135]

[0136] Among them, f i Let i be the fitness of the i-th particle. f is the average fitness of all particles. min This represents the minimum fitness value in the population.

[0137] Step S1043: The improved fitness variance is used as the criterion for precocious convergence. The calculation formula is as follows:

[0138]

[0139] Where F is the fitness variance, and C is the adjustment coefficient, used to suppress the influence of a small number of particles deviating far from the local optimum when premature convergence occurs, which causes the variance to be affected. The formula for calculating C is as follows:

[0140]

[0141] The perturbation quantities of each particle are z = [z1, z2, ..., z D The elements are arranged into an N×D matrix Z, where Z should balance randomness and stability. The Logistic mapping is selected to update Z, and the calculation formula is as follows:

[0142] z i,j,k+1=μz i,j,k (1-z i,j,k (20)

[0143] Among them, z i,j Let μ be the value of the element in the i-th row and j-th column of Z during the k-th iteration, and μ be a control variable.

[0144] When the maximum number of iterations is reached, the global optimal position and the corresponding fitness value are output and substituted into the simulation model for verification.

[0145] Figure 2 This is a schematic diagram illustrating the thermodynamic parameter transfer principle of the closed-loop dual-path redundant pressurized delivery system in this application embodiment, as shown below. Figure 2 As shown, the parameters to be identified were determined based on the sensitivity analysis results of key pressurization performance. The range analysis chart of the first activation time of the cylinder final pressure and fuel tank pressure signal devices versus the first activation time of the oxygen tank pressure signal device for the seven heat transfer coefficients is shown below. Figures 3-5 Comprehensive analysis revealed that the heat transfer parameters located at three points—the outer wall of the gas cylinder and the combustion and oxygen transect pipes—were selected. The corresponding parameter to be identified is [K]. p ,c r ,c y ,n r ,n y The heat exchange parameters and search range are shown in Table 1.

[0146] Table 1

[0147]

[0148] The identification results are shown in Table 2. Substituting the identification results into the simulation model, simulated values ​​of the pressure in the gas cylinder and oxygen tank were obtained and compared with the measured values. The consistency was good, as shown in Table 2. Figure 7 , 8 As shown; furthermore, when the identification results are introduced into a system-level simulation of a similar newly developed model, the final pressure and temperature of the gas cylinder show a significant increase compared to the adiabatic assumption, such as... Figure 8 As shown, the design volume of the gas cylinder is reduced by 32L, and the design weight is reduced by 11.6kg compared to the adiabatic assumptions, significantly reducing redundant design caused by the lack of prior information on heat transfer. This demonstrates that the heat transfer parameter identification method proposed in this invention has good identification accuracy and can reduce the cost of design iterations, providing important guidance for model development and possessing significant engineering application value.

[0149] Table 2

[0150] Parameter name <![CDATA[K p ]]> <![CDATA[c r ]]> <![CDATA[c y ]]> <![CDATA[n r ]]> <![CDATA[n y ]]> Identification results 424.41 0.133 0.759 0.213 0.731

[0151] In this embodiment, a nonlinear mathematical model of the booster delivery system was established. Based on this, sensitivity analysis was conducted using orthogonal experiments to screen out sensitive heat transfer parameters. Finally, an improved particle swarm optimization (PSO) algorithm was used to identify these parameters. Feature screening based on sensitivity analysis can remove irrelevant features that have little impact on booster performance, avoiding high-dimensional searches and excessive computational loads. The PSO algorithm, which introduces a chaotic adaptive weighting strategy, can avoid getting trapped in local optima during the optimization process, obtaining non-physical identification results. After applying the identification results to the simulation of the newly developed model, compared with the previous adiabatic assumptions, the new simulation model can better guide the design, reduce unnecessary redundancy, and lower the manpower and cost of design iterations, demonstrating good engineering application value.

[0152] This application also provides a device for identifying heat exchange parameters in a rocket pressurization and delivery system, including:

[0153] The model building module is used to build a nonlinear mathematical model of a closed-loop pressurized transport system that takes into account heat exchange and mass transfer.

[0154] The target feature screening module is used to determine the sensitivity of key pressurization performance to heat transfer parameters at all locations by using the range method based on orthogonal experiments, and to screen out target features from the nonlinear mathematical model.

[0155] The identification result optimization module is used to determine the objective function and optimize the identification result obtained by the objective function using the chaotic adaptive particle swarm algorithm to obtain the optimized identification result.

[0156] The verification module is used to verify, check, and analyze the effectiveness of the optimized identification results, and to determine the heat exchange parameters.

[0157] The apparatus in this embodiment can perform the above-described... Figure 1 The specific implementation details and technical effects of the methods shown will not be elaborated here.

[0158] This application also provides a program product including a computer program stored in a readable storage medium. At least one processor of the server can read the computer program from the readable storage medium, and the at least one processor executes the computer program to cause the server to implement any of the methods described in the embodiments of the present invention.

[0159] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media capable of storing program code, such as read-only memory (ROM), random access memory (RAM), magnetic disk, or optical disk.

[0160] However, the program product of the present invention is not limited thereto. In this document, the readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus or device.

[0161] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of readable storage media include: electrical connections having one or more wires, portable disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0162] Computer-readable storage media may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium may also be any readable medium other than a readable storage medium that can transmit, propagate, or transfer a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0163] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0164] The various embodiments described in this specification are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0165] The specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various modifications or variations within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. A method for identifying heat transfer parameters in a rocket pressurization and delivery system, characterized in that, include: Step 1: Establish a nonlinear mathematical model of the closed-loop pressurized conveying system that considers heat exchange and mass transfer; Step 2: Determine the sensitivity of key pressurization performance to heat transfer parameters at all locations using the range method based on orthogonal experiments, and screen out target features to be identified from the nonlinear mathematical model; Step 3: Determine the objective function and use the chaotic adaptive particle swarm optimization algorithm to optimize the identification results obtained from the objective function, thereby obtaining the optimized identification results; Step 4: Verify, check, and analyze the effectiveness of the optimized identification results to determine the heat transfer parameters; Step 1 includes: Step 1.1: Determine the temperature characteristics of the variable-volume or fixed-volume gas chamber containing heat exchange terms. The calculation formula for the temperature characteristics is as follows: （1） （2） in, , , , These are the pressure, volume, temperature, and mass of the air cushion, respectively. The temperature of the inflowing gas. , The specific heat capacity at constant pressure and the specific heat capacity at constant volume of the pressurized gas; For heat transfer, Z is the compressibility factor of helium. Let R be the mass of the pressurized gas, t be time, and R be the gas constant. Step 1.2: Determine the flow characteristics of the gas throttling coil. The calculation formula for the flow characteristics is as follows: （3） in, The flow coefficient of gas passing through the throttling orifice; The physical area of ​​the throttling orifice; , For the pressure before and after the throttle coil, The critical pressure ratio of the working fluid; Here, is the gas adiabatic index, and m is the mass of the gas passing through the throttling coil. This is the valve opening coefficient. The absolute temperature of the gas passing through the throttling coil; Step 1.3: Determine the general expression for the heat transfer model as follows: （4） For natural convection, we have the following formula: （5） For forced convection, the following formula holds: （6） in, Heat transfer coefficient; For heat exchange area; For temperature difference; The thermal conductivity of the pressurized gas; The characteristic length; The density of the gas; It is the acceleration due to gravity. Overload factor, The coefficient of expansion of the pressurized gas. To increase the viscosity of the pressurized gas, For the isobaric specific heat capacity of the pressurized gas, This refers to the gas flow rate; and These are constants related to heat transfer. For heat transfer through the gas cylinder wall, the general expression of the heat transfer model is modified as follows: in The overall thermal conductivity of the gas cylinder; Step 1.4: Determine the dynamic characteristic expression of the positive unloading pressure reducing valve as follows: （7） （8） （9） The formula for calculating the damping term is as follows: （10） （11） Where M is the valve core mass, c and k are the damping and spring stiffness respectively; x is the valve core stroke, α is the valve disc cone angle, and A ocr For the throttling area, q in q out u in u out F represents the gas flow rate and velocity before and after throttling; s For static friction, F c v is the Coulomb friction force, v is the valve core velocity, v s Stribeck velocity; η is the coefficient of viscous friction. This is the initial position of the valve core. The area affected by high pressure. This refers to the pressure in the high-pressure chamber. The effective area of ​​the low-pressure cavity. For low-pressure chamber pressure, The force is pneumatic, and D is the valve port diameter. Where F is the frictional resistance, and F is the spring force acting on the valve core. For Stribeck friction damping.

2. The method for identifying heat exchange parameters of a rocket pressurization and delivery system according to claim 1, characterized in that, Step 2 includes: Step 2.1: Construct a 7-factor, 3-level orthogonal experimental table, where the rated heat flux is selected according to the reference value, and the three levels are 80%, 100%, and 120% of the reference value, respectively. Then, call the system simulation model according to the orthogonal experimental table to obtain 18 sets of three-index simulation results. Step 2.2: Use range analysis to determine the sensitivity indices. The range is defined as follows: Assume the average value of the index corresponding to the i-th level of the j-th factor is denoted as... The formula for calculating the range of factor j for this indicator is as follows: （12） Step 2.3: Select the heat transfer terms represented by the three factors with the largest ranges. For any selected heat transfer term, if its type is thermal conduction, then select the thermal conductivity as the variable to be identified; if it is convective heat transfer, then select the value from formulas (5) and (6). and As the quantity to be identified.

3. The method for identifying heat exchange parameters of a rocket pressurization and delivery system according to claim 1, characterized in that, The objective function in step 3 is the root mean square error J with a weight decay mechanism, and the calculation formula is as follows: ， （13） in, , These are the simulated and observed values ​​corresponding to the first time point of the l-th measurement, respectively, S=3, and the corresponding measurement items are gas cylinder pressure, fuel tank pressure, and oxygen tank pressure; N1, N2, N n These represent the total number of times the signaler activated at each time corresponding to each measurement value; The attenuation coefficient is used to reduce the uncertainty introduced by the pressure drift of the pressure signaler's action pressure on the identification results. Take 0.

95.

4. The method for identifying heat exchange parameters of a rocket pressurization and delivery system according to claim 3, characterized in that, Step 4 includes: Step 4.1: Based on the three heat transfer terms selected by the range method, determine the particle dimension D and search range, and determine the basic parameters of the particle swarm optimization algorithm. The basic parameters include: number of particles N, social cognition coefficients c1 and c2, and inertia weight boundary. , And the maximum number of iterations, and set the initial position and initial velocity of the particle swarm using a uniform distribution within the search range; Step 4.2: In each iteration, substitute the particle position into formula (13) to calculate the historical best position of each particle's fitness. and the historical best position of the group The system updates the velocity and position of each particle, incrementing the iteration count by 1 after each update. The update calculation formula is as follows: （15） （16） in, , Let i be the velocity vector and position vector of the i-th particle in the k-th iteration. For inertial weights, , A random number between [0, 1]; Based on the dispersion of particle swarm fitness, the inertia weight is decayed, and the inertia weight of each particle is... The calculation formula is as follows: （17） in, Let i be the fitness of the i-th particle. The average fitness of all particles. This represents the minimum fitness value in the population. Step 4.3: The improved fitness variance is used as the criterion for precocious convergence. The calculation formula is as follows: （18） Where F is the fitness variance, and C is the adjustment coefficient, used to suppress the influence of a small number of particles deviating far from the local optimum when premature convergence occurs, which causes the variance to be affected. The formula for calculating C is as follows: （19） The perturbation amount of each particle Arrange the data into an N×D matrix Z, where Z should balance randomness and stability. Use the Logistic mapping to update Z, calculated as follows: （20） Among them, z i,j,k Let Z be the value of the element in the i-th row and j-th column at the k-th iteration. For control variables; When the maximum number of iterations is reached, the global optimal position and the corresponding fitness value are output and substituted into the simulation model for verification.

5. A heat exchange parameter identification device for a rocket pressurization and delivery system, characterized in that, The apparatus for implementing the method for identifying heat exchange parameters of a rocket pressurization and delivery system as described in any one of claims 1 to 4 includes: The model building module is used to build a nonlinear mathematical model of a closed-loop pressurized transport system that takes into account heat exchange and mass transfer. The target feature screening module is used to determine the sensitivity of key pressurization performance to heat transfer parameters at all locations by using the range method based on orthogonal experiments, and to screen out target features from the nonlinear mathematical model. The identification result optimization module is used to determine the objective function and optimize the identification result obtained by the objective function using the chaotic adaptive particle swarm algorithm to obtain the optimized identification result. The verification module is used to verify, check, and analyze the effectiveness of the optimized identification results, and to determine the heat exchange parameters.

6. A device for identifying heat exchange parameters in a rocket pressurization and delivery system, characterized in that, include: A processor and a memory, wherein the memory stores executable program instructions, and when the processor invokes the program instructions in the memory, the processor is used to: The steps of the method for identifying heat exchange parameters of a rocket pressurization and delivery system as described in any one of claims 1 to 4.

7. A computer-readable storage medium for storing a program, characterized in that, When the program is executed, it implements the steps of the method for identifying heat exchange parameters of a rocket pressurization and delivery system as described in any one of claims 1 to 4.

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