A method for simplifying the mechanism of combustion chemical reaction of adn-based energetic liquid propellant

Abstract

The application discloses a mechanism simplification method of ADN-based energetic liquid propellant combustion chemical reaction, a rhoRTFoam solver is developed and verified, and a simplified mechanism of ADN-based energetic liquid propellant combustion chemical reaction is obtained through analysis. In the rhoRTFoam solver, adaptive grid encryption technology and dynamic load balancing technology are also introduced, so that the calculation speed is improved, the load is uniformly distributed, the program running efficiency is improved, and the method is verified by experiments. The application can provide theoretical guidance for engineering practice of ADN-based engines.

Description

Technical Field

[0001] This application belongs to the field of liquid propellant technology, and in particular relates to a simplified method for the mechanism of combustion chemical reaction of ADN-based energetic liquid propellant. Background Technology

[0002] In recent years, traditional hydrazine-based liquid propellants, which dominate aerospace applications, have become unsuitable for the power source requirements of microsatellites and missile-like weapons due to their low specific impulse, flammability, explosiveness, and high toxicity. Therefore, finding a propellant that can meet the demands of controllable thrust and reliable safety in propulsion systems, while also possessing high specific impulse and environmental friendliness, has become a key research focus for controllable propulsion sources for spacecraft. Against this backdrop, ammonium dinitramide (ADN)-based liquid propellants have attracted significant attention. ADN-based liquid propellants are composed of ADN as the oxidant, methanol as the fuel, and water. ADN-based liquid propellants can overcome the shortcomings of traditional hydrazine-based propellants, possessing the potential for rapid response and low-cost development of propulsion systems, while meeting the requirements of high specific impulse and environmental friendliness in aerospace applications. They are expected to provide technical support for the controllable propulsion and rapid maneuverability of microsatellites and weapons, thus contributing to improving the controllability and rapid maneuverability of microsatellites and weapons.

[0003] The Beijing Institute of Control Science and Technology team has conducted extensive exploratory basic research on ADN-based high-performance propulsion systems. Their ADN-based thruster successfully underwent in-orbit verification testing on the Shijian-17 satellite launched in 2016. However, problems such as heat re-immersion and thrust instability were discovered during the development and testing of low-thrust ADN-based thrusters. Because the ADN-based propellant within the thruster involves multiple physical processes, including flow, decomposition, combustion, and their coupling, and because the chemical reaction steps in ADN-based propellant are numerous, the reaction rates are rapid, and the reactants and products are diverse, the combustion chemical reaction of ADN-based propellants has not yet yielded an ideal simplified result. Summary of the Invention

[0004] This application provides a simplified mechanism for the combustion chemical reaction of ADN-based propellants, which can generate an ideal simplified path, and the computation speed using this simplified mechanism is significantly improved compared to using a detailed reaction mechanism.

[0005] The simplified mechanism of the combustion chemical reaction of the ADN-based energetic liquid propellant includes:

[0006] S1: Develop a real-time solver for compressible flow (rhoRTFoam solver) based on the compressible flow numerical simulation program OpenFOAM;

[0007] S2: Verify the feasibility of the rhoRTFoam solver in handling compressible reactive flow problems;

[0008] S3: Evaluate the consistency between the detailed and simplified responses of the homogeneous ignition problem of ADN-based propellants using the rhoRTFoam solver.

[0009] Optionally, the rhoRTFoam solver uses the finite volume method and employs a strong splitting strategy to solve the flow process and chemical reaction process separately, enabling it to solve multi-component problems that consider detailed chemical reactions.

[0010] Optionally, the rhoRTFoam solver uses a Riemann solver based on HLLC pressure correction to solve the convection term and a Mixture-averaged multi-component transport model to solve the transport term.

[0011] Optionally, the Seulex ode solver is used to solve the splitting of the chemical reaction process, employing the explicit Euler method to realize the evolution of physical quantities over time. The Seulex ode solver is a solver that uses the Seulex algorithm to solve ordinary differential equations.

[0012] Optionally, the rhoRTFoam solver employs an adaptive mesh refinement module to spatially refine the computational domain at a two-dimensional level. Introducing adaptive mesh refinement (AMR) technology can improve computational speed.

[0013] Optionally, the rhoRTFoam solver introduces dynamic load balancing technology to repartition the solution part of the chemical reaction ODE equation based on adaptive grid, so as to ensure that the load is evenly distributed among the computing cores as much as possible, thereby improving the program running efficiency.

[0014] Optionally, the verification involves comparing the numerical results simulated using the rhoRTFoam solver with the calculation results from the ASURF program.

[0015] Optionally, in step S2, the object of verification includes at least one of the following: gas diffusion process, shock wave and expansion wave propagation process, and overdrive detonation wave propagation process.

[0016] Optionally, the gas diffusion process is described as a one-dimensional multi-component diffusion problem.

[0017] Optionally, the propagation process of the shock wave and the expansion wave can be described as a one-dimensional shock tube problem and a two-dimensional double Mach reflection problem.

[0018] Optionally, the verification of the propagation process of the overdrive detonation wave includes: calculating the velocity and peak pressure of the detonation wave when the CJ state is reached based on CJ theory, and comparing the two with the calculation results of the rhoRTFoam solver.

[0019] Optionally, the verification of the propagation process of the overdrive detonation wave also includes: comparing the pressure distribution curves obtained at different times using the rhoRTFoam solver and the ASURF program respectively.

[0020] Optionally, step S3 includes: constructing a correlation function using the HDMR method to simulate the zero-dimensional homogeneous ignition process of different fuels, and analyzing and comparing the result with the result of fully solving the rigid differential equation; wherein the input variables of the function include at least: the temperature T at time t, and the mass fraction Y of ADN. ADN The mass fraction of methanol Y CH3OH water mass fraction Y H2O The output variables of the function include at least: the temperature T* at the next time step t+Δt, and the temperature Y at the next time step. ADN Y CH3OH Y H2O The mass fraction of and the mass fraction of other components.

[0021] Optionally, reaction pathway analysis can be used to simplify the combustion chemical reaction mechanism of the ADN-based propellant.

[0022] Optionally, the reaction pathway analysis method includes: determining the correlation coefficients between the components, and describing the importance of the reaction pathway by generating flux share and consuming flux share;

[0023] The procedure iteratively checks the relationship between other components and the selected component.

[0024] By removing minor components and their associated reactions based on a given correlation threshold, a simplified mechanism can be obtained.

[0025] The beneficial effects that this application can produce include:

[0026] 1) The simplified mechanism of the combustion chemical reaction of ADN-based energetic liquid propellant provided in this application can generate a simplified pathway for ADN decomposition and combustion, containing a simplified mechanism of 18 components and 39 elementary reactions. Compared with the existing detailed chemical reaction mechanism of ADN fuel (composed of three components: NH4N(NO2)2, CH3OH, and H2O, with the mass fractions of each component being NH4N(NO2)2:CH3OH:H2O = 0.634:0.112:0.254), which contains 48 components and 242 elementary reactions, the reaction mechanism is significantly simplified. Furthermore, this application demonstrates the rationality of the proposed possible reaction pathway for ADN decomposition and combustion.

[0027] 2) The calculation speed of chemical reaction is significantly improved by using the simplified mechanism generated by the method of this application, which can be 3 to 6 times faster than using the detailed reaction mechanism.

[0028] 3) The research content and results of this application help to deepen the understanding of the physical nature of ADN thermal decomposition and combustion reaction, and have important theoretical guiding significance for the engineering practice of ADN-based engines. Attached Figure Description

[0029] Figure 1 This is a simplified flowchart illustrating the mechanism of the combustion chemical reaction of an ADN-based energetic liquid propellant in one embodiment of this application.

[0030] Figure 2 This is a result verification diagram for a one-dimensional multi-component diffusion problem in one embodiment of this application;

[0031] Figure 3 This is a result verification diagram for a one-dimensional shock tube problem in one embodiment of this application;

[0032] Figures 4(a)-4(c) This is an adaptive grid distribution diagram for numerical simulation of a two-dimensional double Mach reflection problem in one embodiment of this application;

[0033] Figure 5 This is a verification diagram of the numerical simulation results for one-dimensional detonation propagation in one embodiment of this application;

[0034] Figure 6 This is a comparison diagram of the detailed and simplified reactions of a homogeneous ignition model of ADN-based propellant in one embodiment of this application. Detailed Implementation

[0035] The present application is described in detail below with reference to the embodiments, but the present application is not limited to these embodiments.

[0036] This application provides a simplified method for understanding the mechanism of combustion chemical reactions in ADN-based energetic liquid propellants, the method comprising:

[0037] S1: Development of the rhoRTFoam solver based on the compressible flow numerical simulation program OpenFoam;

[0038] S2: Verify the feasibility of the rhoRTFoam solver in handling compressible reactive flow problems;

[0039] S3: Evaluate the consistency between the detailed and simplified responses of the homogeneous ignition problem of ADN-based propellants using the rhoRTFoam solver.

[0040] Please see Figure 1It illustrates the process steps of the method in one implementation.

[0041] (1) Development of rhoRTFoam solver under OpenFOAM framework for numerical simulation of compressible flow.

[0042] For the conservation law equations, the rhoRTFoam solver employs the finite volume method and utilizes a strongsplitting strategy to solve the flow process and chemical reaction process separately, supporting the solution of multi-component problems that consider detailed chemical reactions.

[0043] In terms of calculation format, the rhoRTFoam solver uses a Riemann solver based on HLLC pressure correction to solve the convection term and a Mixture-averaged multi-component transport model to solve the transport term.

[0044] For solving chemical reaction processes, the Seulex Ode solver is used, and the explicit Euler method is used to realize the evolution of physical quantities over time.

[0045] Because the computational complexity of compressible reactive flow problems is relatively concentrated in space and requires high mesh refinement, the rhoRTFoam solver introduces adaptive mesh refinement (AMR) technology to improve computational speed. Its adaptive mesh refinement module can spatially refine the computational domain at a two-dimensional level, overcoming the limitations of traditional mesh refinement methods. The built-in adaptive module has the drawback of encrypting two-dimensional problems in three-dimensional space.

[0046] Furthermore, precisely because the spatial distribution of computational load for compressible reactive flow problems is relatively concentrated, the computational load during parallel computing often concentrates on a few cores, resulting in an uneven load distribution and significantly impacting program efficiency. Simultaneously, for multi-component reactive flow problems, the computational load is concentrated in solving the ODE equations for the chemical reactions. Since the rhoRTFoam solver employs strong splitting to solve the flow and chemical reaction components separately, the solution for the chemical reaction component is only related to the local mesh. Therefore, the rhoRTFoam solver introduces a dynamic load balancing technique to repartition the ODE equation solution based on an adaptive mesh, ensuring that the load is distributed as evenly as possible across the computational cores, thereby improving program efficiency.

[0047] (2) Verify the rhoRTFoam solver.

[0048] The verification problems mainly include the one-dimensional multi-component diffusion problem, the one-dimensional shock tube problem, and the two-dimensional double Mach reflection problem. The verification results are as follows: Figure 2 As shown in -4.

[0049] The description of the initial field setting is as follows: In the region of 0 < x < 2 mm on the left side of the computational domain with a length of 50 cm, a high-temperature and high-pressure region with a pressure of 90 atm and a temperature of 3000 K is set for initiation, and the pressure in the remaining region is 1 atm and the temperature is 300 K; the computational domain is filled with H2 / air mixed gas with an equivalence ratio of 1. In this example, 25,000 uniform grids are used, and there are 8 - 9 grids in the induction zone. The pressure distribution curves in the computational domain at different times are compared with the calculation results of the ASURF program.

[0050] Figure 2 and Figure 3 It shows that the results predicted by the rhoRTFoam solver are almost exactly the same as those predicted by the ASURF program, which indicates that the rhoRTFoam solver developed in this project can accurately simulate the diffusion process and the propagation process of shock waves and expansion waves. Figure 4(a) is a schematic diagram of the parameter settings of the shock wave model to be verified. Among them, U1 = (7.15 - 4.130) is the current shock wave velocity, P1 = 116.5 is the current pressure magnitude, T1 = 20.39 is the current temperature, ρ1 = 8 is the current air flow density; U2 = (0 0 0) is the shock wave velocity at the next moment, P2 = 1 is the pressure magnitude at the next moment, T2 = 1 is the temperature at the next moment, and ρ2 = 1.4 is the air flow density at the next moment. Figures 4(b) and 4(c) are respectively the two-dimensional simulation result display diagram and the adaptive grid distribution of the numerical simulation for the two-dimensional double Mach reflection problem. It is not difficult to find that the adaptive grid encryption adopted in this application can effectively simulate the shock wave problem.

[0051] In one embodiment, the verification further includes: conducting numerical simulations on one-dimensional and two-dimensional detonation propagations and comparing them with the calculation results of the ASURF program.

[0052] Such as Figure 5 shown, the unburned gas in the high-temperature and high-pressure region quickly ignites and initiates to form an overdriven detonation wave. The peak pressure of the overdriven detonation wave gradually decays and reaches the CJ state at the position of x = 33 cm and propagates forward with this intensity unchanged. According to the CJ theory, the detonation wave velocity V CJ = 1977.1 m / s, the peak pressure of the detonation wave P CJ = 27.7 atm, and the calculation result of the rhoRTFoam solver is V = 2026.0 m / s, P VN = 27.3 atm, which is close to the theoretical value; at the same time, the pressure distribution curves at different times obtained by using rhoRTFoam basically coincide with the pressure distribution curves obtained by using ASURF. In summary, the ability of the rhoRTFoam program to handle compressible reactive flow problems has been verified.

[0053] (3) Evaluate the consistency between the detailed and simplified reactions of the homogeneous ignition problem of ADN-based propellants under the rhoRTFoam solver. Figure 6 The figure shows a comparison between the detailed and simplified reactions in the homogeneous ignition model of ADN-based propellants. As shown, the simplified chemical reaction calculations for the homogeneous ignition temperature changes of ADN-based propellants under four different initial temperature conditions all agree well with their corresponding detailed chemical reactions, indicating that the simplified chemical reaction can effectively simulate the detailed chemical reaction.

[0054] In one implementation, the evaluation includes: constructing a correlation function using the High Dimensional Model Representation (HDMR) method, simulating the zero-dimensional homogeneous ignition process of different fuels, and comparing the results with those obtained by fully solving rigid differential equations. The core of the HDMR method is to establish a functional relationship between input and output variables. When constructing the function, the input variables include: temperature T at time t, and the mass fraction Y of ADN. ADN The mass fraction of methanol Y CH3OH water mass fraction Y H2O The output variables are: the temperature T* at the next time step t+Δt, and Y. ADN Y CH3OH Y H2O The mass fraction of the substance and the mass fraction of other components, etc.

[0055] In one implementation, reaction pathway analysis is used to simplify the combustion chemical reaction mechanism of ADN-based propellants. First, the correlation coefficients between the various components are determined. Then, the importance of the reaction pathway is described by the generation flux share and consumption flux share. Next, the correlation procedure between other components and the selected component is checked iteratively. Finally, minor components and their related reactions are removed according to a given correlation threshold, thereby obtaining a simplified mechanism.

[0056] Reaction pathway analysis uses the production flux share and consumption share to represent the importance of a reaction pathway. Assuming component A is a pre-selected component, i.e., the component to be retained, the production flux P of component A... A and consumption flux C A It can be obtained through equations (1) and (2), as shown in the following formula.

[0057] P A =∑ i＝1,I max(v A,i ,w i ,0) (1)

[0058] C A =∑ i＝1,I max(-v A,i,w i ,0) (2)

[0059] Among them, v A,i w is the stoichiometric coefficient of component A in the i-th reaction. i It is the net reaction rate of the i-th reaction, and I is the total number of elementary reactions.

[0060] The correlation coefficient of component B with respect to component A is defined as follows:

[0061]

[0062]

[0063] Among them, P AB and C AB The correlation coefficients for the formation and consumption of component B relative to component A are given. This means that the value is 1 when B is the i-th reactant or product based on the reaction, and 0 otherwise.

[0064] To characterize the dependencies between components, the following correlation coefficient-flux share is defined:

[0065]

[0066]

[0067] The correlation coefficient of component B relative to component A is defined as:

[0068]

[0069] Where, r AB It reflects the degree of correlation between components A and B, and r′ is the correlation coefficient between components A and B through other components.

[0070] Based on the simplified mechanism of the combustion chemical reaction of ADN-based energetic liquid propellant in this application, the simplified path of ADN decomposition and combustion is finally obtained, as shown in Table 1, which includes a simplified mechanism of 18 components and 39 elementary reactions.

[0071] Table 1

[0072]

[0073]

[0074]

[0075] Theoretically, in the chemical reaction calculation section, the computational speed using simplified mechanisms can be 3 to 6 times faster than using detailed reaction mechanisms. The research content and results of this application contribute to a deeper understanding of the physical nature of ADN thermal decomposition and combustion reactions, and have important theoretical guiding significance for the engineering practice of ADN-based engines.

[0076] The above description is merely a few embodiments of this application and is not intended to limit this application in any way. Although this application discloses preferred embodiments as described above, it is not intended to limit this application. Any changes or modifications made by those skilled in the art without departing from the scope of the technical solution of this application using the disclosed technical content are equivalent to equivalent implementation cases and fall within the scope of the technical solution.

Claims

1. A simplified method for understanding the mechanism of combustion chemical reactions in ADN-based energetic liquid propellants, characterized in that, The method includes: S1: Based on the compressible flow numerical simulation program OpenFOAM, the rhoRTFoam solver was developed; the rhoRTFoam solver adopts an adaptive mesh densification module, which is used to spatially densify the computational domain at a two-dimensional level, and the rhoRTFoam solver adopts dynamic load balancing technology to repartition the solution part of the chemical reaction ODE equation based on the adaptive mesh. S2: Verify the feasibility of the rhoRTFoam solver in handling compressible reactive flow problems; the object of verification includes the propagation process of overdrive detonation waves, and the verification of the propagation process of overdrive detonation waves includes: calculating the velocity of the detonation wave and the peak value of the detonation wave pressure when the CJ state is reached based on CJ theory, and comparing the two with the calculation results of the rhoRTFoam solver; S3: Evaluate the consistency between the detailed and simplified reactions of the homogeneous ignition problem of ADN-based propellant under the rhoRTFoam solver; the composition of the ADN-based energetic liquid propellant is 63.4% NH4N(NO2)2, 11.2% CH3OH, and 25.4% H2O by mass fraction. The rhoRTFoam solver uses the finite volume method and a strong splitting mode strategy to split and solve the flow and chemical reaction processes. It also uses a Riemann solver based on HLLC pressure correction to solve the convection term and a Mixture-averaged multi-component transport model to solve the transport term. The Seulex ode solver is used to solve the splitting of the chemical reaction process, and the explicit Euler method is used to realize the evolution of physical quantities over time. The verification involves comparing the numerical results simulated using the rhoRTFoam solver with the calculation results from the ASURF program. Step S3 includes: constructing a correlation function using the HDMR method to simulate the zero-dimensional homogeneous ignition process of different fuels, and analyzing and comparing the results with those obtained by fully solving the rigid differential equation; wherein the input variables of the function include at least: the temperature T at time t, and the mass fraction Y of ADN. ADN The mass fraction of methanol Y CH3OH water mass fraction Y H2O The output variables of the function include at least: the temperature T* at the next time step t+Δt, and the temperature Y at the next time step. ADN Y CH3OH Y H2O The mass fraction of and the mass fraction of other components; The simplified method targets the key reaction chains contained in the following table for ADN propellants: 。 2. The simplified mechanism of the combustion chemical reaction of ADN-based energetic liquid propellant according to claim 1, characterized in that, In step S2, the objects being verified include at least one of the following: gas diffusion process, shock wave and expansion wave propagation process, and overdrive detonation wave propagation process.

3. The simplified mechanism of the combustion chemical reaction of ADN-based energetic liquid propellant according to claim 2, characterized in that, The gas diffusion process is described as a one-dimensional multi-component diffusion problem.

4. The simplified mechanism of the combustion chemical reaction of ADN-based energetic liquid propellant according to claim 2, characterized in that, The propagation process of the shock wave and the expansion wave is described as a one-dimensional shock tube problem and a two-dimensional double Mach reflection problem.

5. The simplified method for the mechanism of combustion chemical reaction of ADN-based energetic liquid propellant according to claim 2, characterized in that, The verification of the propagation process of the overdrive detonation wave also includes: comparing the pressure distribution curves obtained at different times using the rhoRTFoam solver and the ASURF program respectively.

6. The simplified method for the mechanism of combustion chemical reaction of ADN-based energetic liquid propellant according to claim 1, characterized in that, The combustion chemical reaction mechanism of the ADN-based propellant was simplified by using reaction pathway analysis.

7. The simplified method for the mechanism of combustion chemical reaction of ADN-based energetic liquid propellant according to claim 1, characterized in that, The reaction pathway analysis method includes: determining the correlation coefficients between various components, and describing the importance of the reaction pathway by generating flux share and consuming flux share; The procedure iteratively checks the relationship between other components and the selected component. By removing minor components and their associated reactions based on a given correlation threshold, a simplified mechanism can be obtained.

Images