Solving method for sparse gas-solid two-phase jet flow interference flow field

Abstract

The invention provides a solution method for a sparse gas-solid two-phase jet flow disturbing flow field. The solution method comprises the following steps: S1, determining a gas phase initial condition of the jet flow disturbing flow field; s2, a pure gas phase jet flow interference flow field is calculated, and distribution of a gas phase flow field is obtained; s3, determining initial injection conditions of particles; s4, injecting a first particle swarm, and accurately calculating the motion trail of the first particle swarm in the gas phase flow field along with time by adopting a Lagrange method until the first particle swarm escapes from the gas phase flow field; s5, injecting subsequent particle swarms, and obtaining motion information of the subsequent particle swarms by directly calling the trajectory data calculated in the step S4; s6, counting distribution of all particles in the gas phase flow field, calculating momentum and energy effects of the distribution of the particles on the gas phase flow field, and counting into a gas phase control equation source item; and S7, repeating the steps S2 to S6, and carrying out iterative solution until the source item converges. The method is high in calculation efficiency and suitable for rapid simulation of the gas-solid two-phase jet flow interference flow field.

Description

Technical Field

[0001] This invention relates to the field of numerical simulation technology for gas-solid two-phase flow, and in particular to a fast solution method for the disturbed flow field of a rarefied gas-solid two-phase jet. Background Technology

[0002] In the fields of aerospace, energy and power engineering, accurate simulation of gas-solid two-phase jet interference flow fields is of significant research importance and engineering value. For example, the working process of turbojet engines in dusty environments and the combustion and transport of metal particles in solid rocket ramjet engines all involve the complex motion and interaction of solid particles in high-speed gas-phase flow fields.

[0003] Currently, numerical simulations of such gas-solid two-phase flow problems typically employ the Euler-Lagrange framework. Within this framework, the gas phase is treated as a continuous medium and described by solving the Navier-Stokes equations, while discrete solid particles are treated as a discrete phase, and their trajectories are traced by solving the Lagrange equations of particle motion. However, this traditional method has significant limitations when dealing with sparse but large-scale jet-like flow fields. To obtain a statistically stable particle distribution and its influence on the gas-phase flow field, a massive number of particles need to be injected into the flow field and individually traced and calculated. Since the trajectory of each particle or particle group requires independent and accurate solution of its stress and heat transfer processes, this process generates a huge computational load, leading to low computational efficiency.

[0004] Especially when dealing with large-scale particle simulations with the same or similar initial conditions, existing methods perform repetitive trajectory calculations for each particle group, which is essentially a waste of computational resources. This high computational cost severely restricts the scale and speed of simulations, places excessive demands on computing hardware, and makes it difficult to meet the real-time requirements for rapid analysis and optimization in engineering design.

[0005] Therefore, there is an urgent need in this field for a novel solution method that can significantly improve the computational efficiency of rarefied gas-solid two-phase jet interference flow fields (especially steady interference flow fields) while ensuring simulation accuracy.

[0006] In view of this, the present invention is proposed. Summary of the Invention

[0007] The purpose of this invention is to provide a solution method for the interference flow field of rarefied gas-solid two-phase jets, which solves at least one of the problems mentioned in the background art.

[0008] In a first aspect, the present invention provides a method for solving the interference flow field of a rarefied gas-solid two-phase jet, comprising the following steps: S1. Determining the initial gas phase conditions of the jet interference flow field, wherein the initial gas phase conditions include flow field geometry and gas flow parameters; S2. Calculate the interference flow field of the pure gas phase jet to obtain the distribution of the gas phase flow field; S3. Determine the initial injection conditions for the particles; S4. Inject the first particle group and use the Lagrange method to accurately calculate the trajectory of the first particle group in the gas phase flow field over time until the first particle group escapes the gas phase flow field. S5. Inject subsequent particle groups by directly calling the trajectory data calculated in step S4 to obtain the motion information of subsequent particle groups; S6. Statistically determine the distribution of all particles in the gas phase flow field, and calculate the momentum and energy effects of these particle distributions on the gas phase flow field, and include them in the source terms of the gas phase control equation. S7. Repeat steps S2 to S6, iteratively solving until the source term converges.

[0009] According to some embodiments, the gas flow parameters in step S1 include velocity, pressure, and temperature.

[0010] According to some embodiments, in step S1, the initial gas phase conditions in the jet interference flow field are determined based on the actual incoming flow conditions and the jet flow conditions, wherein the actual incoming flow conditions include Mach number and height.

[0011] According to some embodiments, in step S2, the gas phase flow field distribution is obtained by solving the three-dimensional compressible Navier-Stokes equations, and the gas phase flow field distribution includes the distribution of velocity, pressure and temperature.

[0012] According to some embodiments, the initial injection conditions of particles in step S3 include particle size and injection velocity; determining the initial injection conditions of particles specifically includes: the particles are injected in the form of a surface source at the nozzle inlet, and their initial velocity is equal to the local gas phase velocity; the total number of particles n is calculated based on the mass fraction of the particles, and the particle group is evenly distributed to m injection points; each injection point is represented by a particle cloud representing n / m particles.

[0013] According to some embodiments, in step S4, the motion trajectory of each particle cloud in the first particle group is accurately calculated and stored, and the motion trajectory includes position, speed and temperature information that change over time.

[0014] According to some embodiments, in step S5, the position, velocity and temperature information of the subsequently injected particle group are obtained by direct interpolation or mapping from the calculated trajectory data based on the motion time of the particle group in the flow field.

[0015] According to some embodiments, the method is applicable to the simulation of gas-solid two-phase flow in solid rocket ramjet engines or turbojet engines.

[0016] A second aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0017] A third aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0018] The present invention has at least the following beneficial effects: (1) Improved computational efficiency: By reusing trajectories, the computation time is greatly reduced, making it suitable for large-scale particle simulation.

[0019] (2) Reduced hardware requirements: Significantly reduces reliance on computing hardware, meeting the real-time requirements of engineering projects. Attached Figure Description

[0020] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0021] Figure 1 This is a flowchart of a method for rapidly solving the interference flow field of a rarefied gas-solid two-phase jet in an embodiment of the present invention; Figure 2 This describes the particle distribution during the first injection in this embodiment of the invention. Figure 3 This is a particle distribution diagram of the interference flow field of the flat plate jet obtained in an embodiment of the present invention. Detailed Implementation

[0022] It should be noted that the following detailed description is illustrative and intended to provide further explanation of the invention. Unless otherwise specified, 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 invention pertains.

[0023] It should be noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of exemplary embodiments according to the invention. As used herein, the singular form includes the plural form as well, unless the context clearly indicates otherwise. Furthermore, it should be understood that when the terms "comprising" and / or "including" are used in this specification, they indicate the presence of features, steps, operations, devices, components, and / or combinations thereof.

[0024] It should be noted that if the text uses terms such as "first" or "second", these terms are only used to distinguish similar objects and should not be interpreted as indicating or implying their relative importance, order of precedence, or implicitly indicating the number of technical features indicated. It should be understood that the data in the descriptions of "first" and "second" can be interchanged where appropriate.

[0025] Throughout the accompanying drawings, identical elements are represented by the same or similar reference numerals. Conventional structures or configurations may be omitted where they might cause confusion in understanding the invention. Furthermore, the shapes, dimensions, and positional relationships of the components in the drawings do not reflect actual size, scale, or actual positional relationships. Additionally, any reference symbols placed within parentheses in this invention should not be construed as limiting the scope of the invention.

[0026] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] Figure 1 This is a flowchart of a method for rapidly solving the interference flow field of a rarefied gas-solid two-phase jet in an embodiment of the present invention.

[0028] This invention proposes a solution method for the interference flow field of rarefied gas-solid two-phase jets, such as... Figure 1 As shown, the method includes the following steps: Step S1. Determine the initial gas phase conditions of the jet interference flow field, which include flow field geometry and gas flow parameters.

[0029] The gas flow parameters in this step can include parameters such as velocity, pressure, and temperature.

[0030] Furthermore, specifically, the initial gas phase conditions in the jet interference flow field are determined based on actual incoming flow conditions and jet conditions, where actual incoming flow conditions include Mach number and altitude. Using real flight conditions such as incoming Mach number and altitude as input ensures that the determined initial conditions directly correspond to the actual engineering scenario, making the simulation results more valuable for application.

[0031] Step S2. Calculate the interference flow field of the pure gas phase jet to obtain the distribution of the gas phase flow field.

[0032] The pure gas-phase jet interference flow field here refers to the steady-state flow field obtained solely from the gas dynamics governing equations and boundary conditions, without considering the presence and influence of the particulate phase. This flow field serves as the initial reference flow field for subsequent particle trajectory calculations and two-phase coupled iterations.

[0033] In step S2, the gas phase flow field distribution can be obtained by solving the three-dimensional compressible Navier-Stokes equations. The gas phase flow field distribution includes the distribution of velocity, pressure and temperature.

[0034] The steps for establishing the coordinate system are as follows: Use a three-dimensional Cartesian coordinate system, with the x-axis along the model flow direction, the y-axis along the normal direction, and the z-axis along the circumferential direction. The origin O is selected as the midpoint of the model's leading edge. The three-dimensional compressible Navier-Stokes equations are as follows: Formula I in, As a conserved variable, , , Let x, y, and z be the inviscid flux vectors in the three directions, respectively. , , These are the viscous flux vectors in the x, y, and z directions, respectively. t For time.

[0035] Step S3. Determine the initial injection conditions for the particles.

[0036] The initial injection conditions for particles in step S3 may include particle size and injection rate.

[0037] Determining the initial injection conditions for the particles may specifically include: The particles are injected at the nozzle inlet in the form of a surface source, with an initial velocity equal to the local gas phase velocity; the total number of particles n is calculated based on the mass fraction of the particles, and the particle group is evenly distributed to m injection points; each injection point is represented by a particle cloud representing n / m particles.

[0038] The above-described particle injection model balances accuracy and efficiency. By setting "area source injection" and "particle cloud," and while being physically reasonable (with velocity synchronized with airflow), it simplifies a massive number of particles into a finite number of representative points for tracking. This is a key simplification strategy for making large-scale simulations computationally feasible.

[0039] Step S4. Inject the first particle group and use the Lagrangian method to accurately calculate the trajectory of the first particle group in the gas phase flow field over time until the first particle group escapes the gas phase flow field. The Lagrangian equation for solid particles can be established using the following set of equations II: Formula II In the formula: v s These represent the mass, specific heat capacity, and velocity of the particulate phase, respectively; v, Do not consider local gas speed, temperature, and particle temperature; F represents the particle surface area, h represents the particle enthalpy, and F d The drag coefficient of the gas phase represents the drag force exerted on the particulate phase. The trajectory of the solid particles can be obtained by integrating the velocity.

[0040] In step S4, the motion trajectory of each particle cloud in the first particle swarm is accurately calculated and stored. The motion trajectory includes information on position, velocity, and temperature that changes over time. This limitation clearly defines the stored content and lays the foundation for reuse. Storing complete trajectory information containing position, velocity, and temperature constructs a detailed "motion profile," providing a unique data source for directly calling these data in subsequent steps and avoiding redundant calculations.

[0041] Step S5. Inject subsequent particle groups by directly calling the trajectory data calculated in step S4 to obtain the motion information of subsequent particle groups.

[0042] In step S5, based on the motion time of the subsequently injected particle swarm in the flow field, its position, velocity, and temperature information are obtained directly from the calculated trajectory data through interpolation or mapping. This defines the reuse method, achieving a significant efficiency leap. By using time mapping for interpolation or direct data retrieval, the complex solution of differential equations is transformed into a simple data reading operation, resulting in an order-of-magnitude improvement in computational efficiency.

[0043] Step S6. Statistically determine the distribution of all particles in the gas phase flow field, and calculate the momentum and energy effects of these particle distributions on the gas phase flow field, incorporating them into the source terms of the gas phase control equation.

[0044] The source term equation is as follows:

[0045] F px F py F pz Q p These represent the force and heat of interaction between the particles and the gas, respectively, and their expressions are shown below.

[0046] in:

[0047]

[0048] These represent the total force and heat exerted by the particles on the gas within a specific grid.

[0049] For details on the calculation of the source term equation, please refer to the following literature: Research on particle simulation method of solid particle effect in three-dimensional jet flow field, Journal of Astronautics, 2022, 23(5).

[0050] Step S7. Repeat steps S2 to S6, iteratively solving until the gas-phase equation containing the source term converges. The convergence criterion can be the same as that for the pure gas-phase equation.

[0051] Specifically, the source term is substituted into the three-dimensional compressible Navier-Stokes equations to solve the Navier-Stokes equations containing the source term. Steps S2 to S6 are repeated until the source term converges.

[0052] This invention discloses a rapid solution method for the disturbed flow field of a rarefied gas-solid two-phase jet. By only tracking the trajectory of the injected particle swarm in the first step and reusing its data, subsequent particle swarms are predicted, significantly improving the computational efficiency of the steady flow field disturbed by the gas-solid two-phase jet, while maintaining simulation accuracy. This invention has high computational efficiency and is suitable for the rapid simulation of the disturbed flow field of a gas-solid two-phase jet.

[0053] This invention achieves accurate initial calculation and storage of particle trajectories as baseline data in a steady gas-phase flow field; subsequent particle motion is obtained by directly accessing this data, enabling trajectory reuse. Accuracy is ensured by iteratively coupling particle interactions (source terms) with the gas-phase flow field. This method achieves an order-of-magnitude improvement in computational efficiency, with extremely low computational costs for subsequent particle calculations, while strictly maintaining the physical accuracy of sparse two-phase flow simulations, making it highly practical for engineering applications.

[0054] The method described above in this invention can be applied to the simulation of gas-solid two-phase flow in solid rocket ramjet engines or turbojet engines, and has significant engineering application prospects.

[0055] Another aspect of the present invention provides a computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the method described above.

[0056] Another aspect of the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0057] The technical solution of the present invention will be described in detail below through specific embodiments, but the present invention is not limited to the following embodiments.

[0058] This embodiment uses the method of the present invention to numerically simulate a typical flat plate jet interference flow field to verify the effectiveness and computational efficiency of the method.

[0059] 1. Calculation Model and Condition Settings Geometric model: A flat plate model with nozzles is used. The jet stream is injected vertically into the incoming flow from the nozzles on the flat plate, forming a complex disturbance flow field structure.

[0060] Gas phase inflow conditions: Mach number for free-flowing streams: Ma = 5.0 (high-speed conditions) Jet Mach Number M j =3.0 The ratio of the total pressure of the jet to the static pressure of the free flow, P j / P ∞ =23.7 The inflow boundary layer thickness at the leading edge of the flat plate is δ=20 mm. Nozzle exit diameter d = 12.35 mm The above conditions together define a high-speed jet-disrupted steady flow field with strong shock waves, shear layers, and separation zones.

[0061] Particle phase parameters: Particulate material: Aluminum oxide (Al2O3), a typical combustion product of solid rocket propellants.

[0062] Material properties: density 4004 kg / m³ 3 Specific heat capacity: 1380 J / (kg·K).

[0063] Particle diameter: 1 μm (typical small particle size).

[0064] Number of entry particle beam orbits: 1600. This is the same as the number of injection points m mentioned in step S3, representing 1600 "particle cloud" injection points uniformly arranged on the nozzle cross-section.

[0065] Particle mass fraction: 0.3. This means the ratio of the mass flow rate of the particle stream to that of the gas jet is 0.3, falling under the category of rarefied two-phase flow. The total number of particles is obtained by multiplying the particle mass flow rate by 0.3. The particle mass flow rate is equal to the number of particles injected per unit time multiplied by the particle mass; the number of particles injected per unit time is the number injected per unit time multiplied by the time step.

[0066] For details on calculating the gas phase mass flow rate, please refer to the following literature: Gas Jet Dynamics, edited by Zhao Chengqing and Jiang Yi, Beijing Institute of Technology Press, 1998.

[0067] 2. Calculation process According to the flowchart of the present invention ( Figure 1Perform the steps shown in the diagram: Determine initial conditions and calculate pure gas phase flow field (i.e., without any particles): Based on the above incoming flow and jet conditions, by solving the three-dimensional compressible Navier-Stokes equations, the steady gas phase flow field without particle interference is first obtained, including shock wave location, separation zone, pressure distribution, etc.

[0068] Set up and inject the first-generation particle swarm: Calculate the total number of physical particles, n, based on a particle mass fraction of 0.3. Set m=1600 injection points at the center of the computational grid on the nozzle cross-section. Initialize a particle cloud at each injection point, with an initial velocity consistent with the local gas phase velocity. Each particle cloud represents n / 1600 physical particles.

[0069] Accurate tracking and recording of trajectories: For the aforementioned 1600 particle clouds (i.e., the "first-generation particle swarm"), the Lagrangian method is used to accurately calculate their motion trajectories, velocity, and temperature changes in the gas phase flow field obtained in the first step, until all particle clouds leave the computational domain. This process fully considers the physical processes such as drag force and heat transfer experienced by the particles. Figure 2 The instantaneous distribution of the first injected particle swarm calculated in this step is shown, reflecting the initial diffusion of particles in the disturbed flow field.

[0070] Trajectory reuse and coupled iteration: In subsequent iterations, for newly injected particle swarms, their motion equations are not resolved; instead, the trajectory data calculated and stored in the first step is directly called. Based on the particle injection time, the particle position and velocity information are mapped from this database. The distribution of all active particles in the flow field is statistically analyzed, and their momentum and energy source terms with respect to the gas phase are calculated and fed back to the gas phase governing equations.

[0071] Iteration to convergence: Repeat the process of "updating the gas phase flow field containing source terms → reusing the trajectory to obtain new particle states → calculating new source terms". When the change in the source terms of the interaction between the particle phase and the gas phase is less than the set convergence criterion, the calculation is considered to have converged, and a self-consistent gas-solid two-phase coupling solution is obtained.

[0072] 3. Results and Validation Figure 3 The particle distribution in the gas-solid two-phase flow field, which has reached a convergent state, is shown by the method of this invention.

[0073] From the perspective of physical effectiveness, Figure 3The particle distribution clearly reflects the typical characteristics of a jet-disrupted flow field: particles are accelerated and transported within the jet shear layer, with higher concentrations in the high-pressure region following the jet shock and bow shock, and some particles entering the separation zone in front of the flat plate due to backflow. This distribution conforms to the physical expectations of this type of flow, proving that the method of this invention, after introducing trajectory reuse, can still accurately capture the mainstream physical laws of the sparse particle phase in complex supersonic flow fields.

[0074] Verifying the core advantage (efficiency) of the invention, in this embodiment, the calculation time for particle trajectories in subsequent iterations is reduced by more than 95% compared to the first accurate tracking (theoretically close to the time required for data retrieval), significantly improving the overall computational efficiency of the two-phase flow coupling solution, while ensuring the accuracy of the simulation through the final coupling convergence.

[0075] In summary, this embodiment, through simulation of a flat plate jet interference case with a clear theoretical background and engineering significance, fully verifies the effectiveness and superiority of the "first accurate tracking + subsequent trajectory reuse" method of the present invention, which can significantly improve the calculation efficiency of the steady flow field of rarefied gas-solid two-phase jet interference while maintaining simulation accuracy.

[0076] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for solving the disturbed flow field of a rarefied gas-solid two-phase jet, characterized in that, Includes the following steps: S1. Determine the initial gas phase conditions of the jet interference flow field, wherein the initial gas phase conditions include flow field geometry and gas flow parameters; S2. Calculate the interference flow field of the pure gas phase jet to obtain the distribution of the gas phase flow field; S3. Determine the initial injection conditions for the particles; S4. Inject the first particle group and use the Lagrange method to accurately calculate the trajectory of the first particle group in the gas phase flow field over time until the first particle group escapes the gas phase flow field. S5. Inject subsequent particle groups by directly calling the trajectory data calculated in step S4 to obtain the motion information of subsequent particle groups; S6. Statistically determine the distribution of all particles in the gas phase flow field, and calculate the momentum and energy effects of these particle distributions on the gas phase flow field, and include them in the source terms of the gas phase control equation. S7. Repeat steps S2 to S6, iteratively solving until the source term converges.

2. The method according to claim 1, characterized in that, The gas flow parameters in step S1 include velocity, pressure, and temperature.

3. The method according to claim 1 or 2, characterized in that, In step S1, the initial gas phase conditions in the jet interference flow field are determined based on the actual incoming flow conditions and jet conditions, wherein the actual incoming flow conditions include Mach number and height.

4. The method according to any one of claims 1 to 3, characterized in that, In step S2, the gas phase flow field distribution is obtained by solving the three-dimensional compressible Navier-Stokes equations, and the gas phase flow field distribution includes the distribution of velocity, pressure and temperature.

5. The method according to any one of claims 1 to 4, characterized in that, The initial injection conditions for the particles in step S3 include particle size and injection rate; Determining the initial injection conditions for the particles specifically includes: The particles are injected at the nozzle inlet in the form of a surface source, with an initial velocity equal to the local gas phase velocity; the total number of particles n is calculated based on the mass fraction of the particles, and the particle group is evenly distributed to m injection points; each injection point is represented by a particle cloud representing n / m particles.

6. The method according to any one of claims 1 to 5, characterized in that, In step S4, the motion trajectory of each particle cloud in the first particle group is accurately calculated and stored. The motion trajectory includes position, velocity and temperature information that change over time.

7. The method according to any one of claims 1 to 6, characterized in that, In step S5, based on the motion time of the subsequently injected particle group in the flow field, its position, velocity and temperature information are obtained by direct interpolation or mapping from the calculated trajectory data.

8. The method according to any one of claims 1 to 7, characterized in that, The method is applicable to the simulation of gas-solid two-phase flow in solid rocket ramjet engines or turbojet engines.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the method as described in any one of claims 1 to 8.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the method as described in any one of claims 1 to 8.

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