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CN-121997802-A - Solving method for sparse gas-solid two-phase jet flow interference flow field

CN121997802ACN 121997802 ACN121997802 ACN 121997802ACN-121997802-A

Abstract

The invention provides a solving method for sparse gas-solid two-phase jet flow interference flow fields, which comprises the steps of S1 determining gas phase initial conditions of the jet flow interference flow fields, S2 calculating pure gas phase jet flow interference flow fields to obtain distribution of the gas phase flow fields, S3 determining initial injection conditions of particles, S4 injecting a first particle group, accurately calculating motion tracks of the first particle group in the gas phase flow fields along with time by adopting a Lagrangian method until the first particle group escapes from the gas phase flow fields, S5 injecting a subsequent particle group, obtaining motion information of the subsequent particle group by directly calling track data obtained by the step S4, S6 calculating the distribution of all particles in the gas phase flow fields, calculating the momentum and energy effects of the distribution of the particles on the gas phase flow fields, accounting for a gas phase control equation source item, S7 repeating the steps S2 to S6, and iteratively solving until the source item converges. The method has high calculation efficiency and is suitable for quick simulation of the gas-solid two-phase jet flow interference flow field.

Inventors

  • LIU YAOFENG
  • MA JIKUI
  • LIU YUWEI
  • CAO NING
  • ZHANG JIAYUE

Assignees

  • 中国航天空气动力技术研究院

Dates

Publication Date
20260508
Application Date
20251225

Claims (10)

  1. 1. S1, determining gas phase initial conditions of the jet flow interference flow field, wherein the gas phase initial conditions comprise flow field geometry and gas flow parameters; S2, calculating a pure gas-phase jet flow interference flow field to obtain the distribution of the gas-phase flow field; 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 Lagrangian method until the first particle swarm escapes the gas-phase flow field; S5, injecting a subsequent particle swarm, and obtaining motion information of the subsequent particle swarm by directly calling the track data obtained by the calculation in the step S4; s6, counting the distribution of all particles in the gas-phase flow field, calculating the momentum and energy action of the distribution of the particles on the gas-phase flow field, and accounting in a gas-phase control equation source term; And S7, repeating the steps S2 to S6, and iteratively solving until the source term converges.
  2. 2. The method of claim 1, wherein the gas flow parameters in step S1 include velocity, pressure and temperature.
  3. 3. The method according to claim 1 or 2, characterized in that in step S1, gas phase initial conditions in the jet disturbance flow field are determined based on actual incoming flow conditions and jet conditions, wherein the actual incoming flow conditions comprise mach number and altitude.
  4. 4. A method according to any one of claims 1 to 3, wherein in step S2 the gas phase flow field distribution is obtained by solving a three-dimensional compressible Navier-Stokes equation, the gas phase flow field distribution comprising a distribution of velocity, pressure and temperature.
  5. 5. The method according to any one of claims 1 to 4, wherein the initial injection conditions of the particles in step S3 include particle size and injection speed; determining initial injection conditions of particles specifically includes: The particles are injected at the inlet of the spray pipe in a surface source mode, the initial speed of the particles is equal to the local gas phase speed, the total number n of the particles is calculated according to the mass fraction of the particles, the particle group is evenly distributed to m injection points, and each injection point represents n/m particles by one particle cloud.
  6. 6. The method according to any one of claims 1 to 5, wherein in step S4, a motion profile of each particle cloud in the first particle population is accurately calculated and stored, the motion profile comprising time-varying position, velocity and temperature information.
  7. 7. The method according to any one of claims 1 to 6, wherein in step S5, the position, velocity and temperature information of the subsequently injected particle swarm is obtained from the calculated trajectory data by direct interpolation or mapping, based on the movement time of the subsequently injected particle swarm in the flow field.
  8. 8. A method according to any one of claims 1 to 7, wherein the method is suitable for gas-solid two-phase flow simulation in a solid rocket ramjet or turbojet.
  9. 9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the method of any one of claims 1 to 8 when executing the computer program.
  10. 10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the method according to any one of claims 1 to 8.

Description

Solving method for sparse gas-solid two-phase jet flow interference flow field Technical Field The invention relates to the technical field of gas-solid two-phase flow numerical simulation, in particular to a rapid solving method for a sparse gas-solid two-phase jet flow interference flow field. Background In the technical fields of aviation, energy power and the like, the accurate simulation of the gas-solid two-phase jet flow interference flow field has important research significance and engineering value. For example, the operation of turbojet engines in a dust environment, the combustion and transport of metal particles in solid rocket ramjet engines, and the like, involve complex movement and interaction of solid particles in high-velocity gas-phase flow fields. Currently, for numerical simulation of such gas-solid two-phase flow problems, the euler-lagrangian framework is generally employed. In this framework, the gas phase is considered as a continuous medium and is described by solving the Navier-Stokes equation, whereas discrete solid particles are taken as discrete phases, whose trajectories are tracked by solving the Lagrange equation of motion of the particles. However, this conventional approach has significant limitations in the face of sparse but large particle-population containing jets interfering with the flow field. In order to obtain statistically stable particle distribution and influence on the gas-phase flow field, massive particles need to be injected into the flow field and tracked and calculated one by one. Because the motion trail of each particle or particle group needs to be independently and accurately solved, the stress and heat transfer process of each particle or particle group generates huge calculation load, and the calculation efficiency is low. In particular, when dealing with large scale particle simulation with the same or similar initial conditions, existing methods perform repetitive trajectory calculations for each particle swarm, which is essentially a waste of computational resources. The high calculation cost severely restricts the scale and speed of simulation, and places too high requirements on calculation hardware, so that the real-time requirements of rapid analysis and optimization in engineering design are difficult to meet. Therefore, a novel solving method capable of greatly improving the calculation efficiency of the sparse gas-solid two-phase jet flow interference flow field (especially the steady interference flow field) on the premise of ensuring the simulation precision is urgently needed in the field. In view of this, the present invention has been made. Disclosure of Invention The invention aims to provide a solving method for sparse gas-solid two-phase jet flow interference flow field, which solves at least one problem in the background technology. S1, determining gas phase initial conditions of a jet flow interference flow field, wherein the gas phase initial conditions comprise flow field geometry and gas flow parameters; S2, calculating a pure gas-phase jet flow interference flow field to obtain the distribution of the gas-phase flow field; 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 Lagrangian method until the first particle swarm escapes the gas-phase flow field; S5, injecting a subsequent particle swarm, and obtaining motion information of the subsequent particle swarm by directly calling the track data obtained by the calculation in the step S4; s6, counting the distribution of all particles in the gas-phase flow field, calculating the momentum and energy action of the distribution of the particles on the gas-phase flow field, and accounting in a gas-phase control equation source term; And S7, repeating the steps S2 to S6, and iteratively solving until the source term converges. According to some embodiments, the gas flow parameters in step S1 include velocity, pressure and temperature. According to some embodiments, in step S1, a gas phase initial condition in the jet disturbance flow field is determined according to an actual incoming flow condition and a jet condition, wherein the actual incoming flow condition includes a mach number and a height. According to some embodiments, in step S2, the gas phase flow field distribution is obtained by solving a three-dimensional compressible Navier-Stokes equation, the gas phase flow field distribution including a distribution of velocity, pressure and temperature. According to some embodiments, the initial injection conditions of the particles in the step S3 comprise particle size and injection speed, and the initial injection conditions of the particles are determined specifically by injecting the particles in a surface source mode at the inlet of a spray pipe, wherein the initial speed is equal to the local gas p