A numerical calculation method for simulating the combustion of iron powder particles

Abstract

The present invention discloses a numerical calculation method for simulating the combustion of iron powder group particles, which belongs to the field of numerical simulation calculation of iron powder combustion. The present invention can perform numerical simulation on the combustion of single particles and iron powder groups under microgravity conditions, quantitatively analyze the influence of differential diffusion on the flame temperature of single iron powder particles and iron powder groups, and consider the influence of iron powder combustion characteristics and flame propagation speed under different iron powder and oxygen concentration conditions. The invention has certain guiding significance for the study of lean-burn iron powder flames with low propagation speeds and the subsequent application of iron powder fuel. The microgravity environment can eliminate the influence of convection and buoyancy, making the combustion process more stable and predictable, and helping to conduct in-depth research on the basic mechanism of iron powder combustion, including combustion rate, oxidation process and phase change.

Description

Technical Field

[0001] The present invention belongs to the field of numerical simulation calculation of iron powder combustion, and particularly relates to a numerical calculation method for simulating the combustion of iron powder group particles. Background Art

[0002] As a reactive metal, iron holds promise for driving the transition to low-carbon energy. In recent years, researchers have conducted numerous experiments and simulations on iron powder combustion. Experimental studies of iron powder combustion have primarily been conducted on ground-based single iron powder particles. However, to minimize the effects of particle settling and natural convection on the flame velocity of lean iron, the particle diameter is limited to less than 10 μm. This, however, can easily lead to thermal diffusion instabilities. Consequently, some researchers have conducted iron powder combustion experiments under microgravity conditions. However, these experiments are expensive and require stringent technical requirements. Regarding numerical calculations, previous work has primarily focused on numerical simulations of single-particle and particle group combustion under gravity. A drawback of this type of research is that it fails to account for the effects of particle dynamics on flame propagation due to gravity and buoyancy. Furthermore, previous work has not considered the impact of differential diffusion on the combustion process of iron powder groups. Summary of the Invention

[0003] In response to the problems raised in the background technology, the present invention provides a numerical calculation method for simulating the combustion of iron powder group particles. This calculation method is applicable to the combustion of single iron particles as well as the combustion of iron powder groups. It can handle the combustion of iron powder groups under different oxygen concentrations and differential diffusion conditions in a microgravity environment.

[0004] To achieve the above object, the technical solution adopted by the present invention is:

[0005] A numerical calculation method for simulating the combustion of iron powder particles includes the following steps:

[0006] Step 1: Build a mathematical model for multiphase turbulent combustion to determine the size distribution, shape, and initial temperature of the iron powder particles. Select a reaction mechanism based on the chemical reaction characteristics of the iron powder combustion and available computing resources. Use the heat and mass transfer calculation results of the iron powder particles as source terms in the gas equations to establish and solve the solid-gas coupling system.

[0007] Step 2: Based on the meshing situation, geometric model characteristics, initial conditions and boundary conditions, the multiphase turbulent combustion mathematical model established in step 1 is numerically discretized to obtain a set of algebraic equations regarding velocity, density, composition, temperature and pressure, and the algebraic equations are subjected to nonlinear processing;

[0008] In step three, the time is gradually advanced by numerical methods. In each time step, the nonlinear equations in step two are iteratively solved. The velocity and pressure fields are gradually corrected through an outer loop, and the velocity field is corrected with the updated pressure field to ensure convergence. The turbulence model is corrected. Through the above three steps, the numerical simulation process of the combustion of the iron powder particle group is realized.

[0009] Furthermore, the size distribution and shape of the iron powder in step 1 can be determined according to relevant experimental parameters.

[0010] Furthermore, the chemical reaction mechanism in step 1 should take into account the oxidation reaction kinetics on the particle surface.

[0011] Furthermore, in the above step 2, the finite volume method is used to discretize the spatial derivatives of velocity, composition, temperature, pressure and density through the central difference scheme or the upwind scheme on the basis of the computational domain grid division, and then the explicit, implicit or semi-implicit time discretization scheme is introduced to obtain the algebraic equation defined at the center point of the grid.

[0012] Furthermore, the boundary conditions in step 2 need to consider the coupling relationship between particles and gas, including a drag model and a heat transfer model.

[0013] Furthermore, in step three, an implicit method is used to advance time, and a PIMPLE algorithm is used in the solution process.

[0014] Furthermore, the control equation involved in step three includes two parts: gas phase and particle phase, and the parameters of each component are obtained by directly solving the control equation of the component.

[0015] Beneficial effects:

[0016] 1. It can be used to address the effects of differential diffusion and microgravity on the combustion of individual and group iron powders, eliminating the effects of convection and buoyancy, and more accurately describing the mass, heat, and momentum exchange between iron powder particles and the surrounding gas. The simulation results can help develop more stable combustors.

[0017] 2. By varying the iron powder concentration and oxygen concentration, the combustion of iron powder under lean and rich burn conditions can be simulated, contributing to the development of more environmentally friendly combustion technologies. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 This is a simulation calculation method for the combustion of iron powder particles of the present invention.

[0019] Figure 2 3 is a comparison diagram of the iron powder particle position distribution and flame front in the simulation and experiment in the embodiment of the present invention.

[0020] Figure 3This is a comparison chart of the flame speed prediction value and experimental value at different iron powder concentrations in the embodiment of the present invention.

[0021] Figure 4 1 is a graph showing the change of particle temperature and normalized Damköhler number under different environments in an embodiment of the present invention.

[0022] Figure 5 The figures are a gas temperature contour map superimposed with iron particles, an average curve map of oxygen mass fraction, and a distribution map of iron oxide mass fraction under different conditions in an embodiment of the present invention.

[0023] Figure 6 This is a time evolution diagram of the 850K temperature contour line and a propagation change diagram of the negative curvature area under 40% oxidant conditions in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The present invention is described in detail below with reference to the accompanying drawings and specific embodiments. However, the following embodiments are intended only to illustrate the present invention; the scope of protection of the present invention encompasses the entire contents of the claims. Furthermore, through the description of the following embodiments, those skilled in the art will be able to fully implement the entire contents of the claims. This invention couples pressure and velocity correction methods within the framework of the finite volume method, considers the governing equations for both the particle and gas phases, and achieves a numerical solution for the reaction flow, applicable to the numerical calculation of iron powder combustion in microgravity.

[0025] like Figure 1 As shown, a numerical calculation method for simulating the combustion of iron powder particles of the present invention specifically includes the following steps:

[0026] Step 1: Build a mathematical model for multiphase turbulent combustion, determine the size distribution, shape, and initial temperature of the iron powder particles, and select an appropriate reaction mechanism based on the chemical reaction of the iron powder combustion. Simultaneously, the heat and mass transfer calculation results of the iron powder particles need to be used as source terms in the gas equations to establish and solve the solid-gas coupling system.

[0027] Step 2: Based on the initial conditions, grid division and boundary conditions, the multiphase turbulent combustion mathematical model established in step 1 is discretized to obtain a set of algebraic equations for velocity, density, composition, temperature and pressure, and the algebraic equations are processed nonlinearly.

[0028] In step three, the time is gradually advanced by numerical methods. In each time step, the nonlinear equations in step two are iteratively solved. The velocity and pressure fields are gradually corrected through an outer loop, and the velocity field is corrected with the updated pressure field to ensure convergence. The turbulence model is corrected. Through the above three steps, the numerical simulation process of the combustion of the iron powder particle group is realized.

[0029] Specifically, if Figure 1 As shown in Figure 2, the multiphase turbulent combustion mathematical model established in step 1 includes the following equations:

[0030] (1) The Navier-Stokes equations describe the change in momentum of iron powder particles during their motion, taking into account factors such as the inertia, viscosity, pressure gradient, and external forces of the reaction flow.

[0031] (2) Component equation: The component equation is based on the principles of conservation of mass and conservation of composition, and describes the mass transport process of oxides generated by the reaction of iron powder with oxygen during combustion. In this embodiment, the oxide is assumed to be iron oxide.

[0032] (3) Temperature equation: The solution of the temperature equation needs to be combined with the momentum equation and the component equation. Based on the law of conservation of energy, it describes the heat transfer and temperature change process during the combustion process, and can predict the temperature distribution during the combustion process of iron powder.

[0033] (IV) Pressure Equation: The pressure equation consists of four parts: the time derivative term, the convection term, the diffusion term, and the source term. It is a transport equation for the pressure variable. This invention considers density changes as well as the pressure gradient term, the viscous stress term, and the source term of the momentum equation when solving the pressure equation. The pressure equation is discretized and solved iteratively until convergence conditions are met.

[0034] (5) Mass equation: The mass equation is used to describe the conservation characteristics of fluid mass. The mass equation is discretized using the finite volume method and solved using an iterative method.

[0035] In step two, the finite volume method is used to divide the computational domain into a finite number of small control volumes. Conservation laws are applied to each control volume. The component, pressure, temperature, and momentum equations established in step one are volume-integrated within each grid, and then time-integrated within the time step. By using different spatial and temporal discretization schemes, the algebraic equation defined at the center of the grid is obtained. The same operation is then performed on all grids to obtain the algebraic equation system for each equation in the entire computational domain. The algebraic equation system is nonlinearized and iteratively solved to obtain the corresponding variable field.

[0036] like Figure 1 As shown in Figure 3, in step 3, the multiphase turbulent combustion model is coupled to the pressure and velocity correction method to solve the variable fields in the combustion process of the iron powder group particles. The specific solution steps are as follows:

[0037] Step (1) Determine the time step and initialize the values ​​of the velocity field and pressure field. The time step is determined based on the CFL (Courant-Friedrichs-Lewy) condition or is dynamically adjusted during the calculation process to ensure calculation stability.

[0038] Step (2) solving the velocity field and pressure field that satisfy mass conservation through the pressure-velocity coupling algorithm;

[0039] Step (3) Solve the momentum equations of the discretized particle phase and gas phase, considering the effects of convection, diffusion, pressure gradient, etc. on the velocity field;

[0040] Step (4) Based on the corrected velocity field and pressure field, the discretized energy equation and component equation are solved to obtain the distribution of the temperature field and component field;

[0041] Step (5) updates the pressure field data using the discretized pressure equation, and corrects the velocity field according to the new pressure field, completing a pressure correction cycle; and determines the residual of the mass conservation equation. If the residual of the mass conservation equation is less than the set value, the loop is exited and the next step is entered;

[0042] Step (6) repeats the above steps (3) to (5) until the specified number of iterations or time step is reached;

[0043] Step (7) updates the density value by solving the ideal gas state equation;

[0044] Step (8) determines whether the set end time has been reached, otherwise repeat steps (1) to (7).

[0045] In the above process, the algebraic equations must be nonlinearized before solving them. The complex nonlinear equations are solved by establishing a diagonal matrix, manipulating nonlinear terms, applying relaxation factors, and constructing pressure correction equations. Furthermore, the effects of differential diffusion must be considered when solving the component equations, and the interaction between particles and the surrounding gas must be considered during heat and mass transfer. The most recently calculated temperature, density, and component mass fractions are used in the estimated correction iterations of the pressure and velocity correction methods. When solving the equations during the pressure and velocity correction process, the interaction between the particle and gas phases, as well as the effects of differential diffusion, must be considered.

[0046] Example

[0047] Taking the numerical calculation of the combustion of iron powder particle groups under micro-weightlessness conditions as an example, the present invention is described in detail and comprehensively. This method is not limited to micro-weightlessness conditions, but is applicable to all iron powder particle group combustion scenarios. The embodiment of the present invention is mainly divided into the following steps: Step 1, description of the mathematical model and selection of relevant parameters; Step 2, determination of the calculation domain, initial conditions and boundary conditions; Step 3, description of the grid division and calculation process; Step 4, prediction and analysis of the flame temperature field, velocity field and component field based on the numerical calculation method.

[0048] Step 1: First, describe the mathematical model and select relevant parameters:

[0049] The numerical model used in this simulation is a solver suitable for multiphase turbulent combustion. In this solver, the governing equations for the mass, momentum, composition, and energy of the gas phase are solved. The governing equations for the composition and energy of the gas phase are in the form:

[0050] Component equation: ,

[0051] Energy equation: ,

[0052] in, : gas phase density; : Component The quality score of : Euler time; : Gas phase velocity at the first Components in each direction; : No. Cartesian coordinates of directions; : Mass diffusion coefficient, assuming the Lewis number is 1, that is ,in: : thermal conductivity, : specific heat capacity of gas phase; : thermal diffusivity, and : Bidirectional coupling source term. The particle phase governing equation is:

[0053] Velocity equation: ,

[0054] Chemical reaction rate equation: ,

[0055] in, : No. The particle speed in each direction, : Drag coefficient calculated based on the solid sphere assumption, : Particle density, calculated as ,in, : particle mass, : the relative velocity between gas and particles, : No. The gravitational acceleration in all directions. : total surface area of ​​particles; : oxygen mass fraction; : surface area of ​​reactive particles; : surface reaction rate (calculated from single-step reaction and first-order Arrhenius reaction), ,in : pre-index coefficient, : Activation energy, assuming that the oxidation product of iron is FeO, we can get =7.5·10 6 m / s, =1.2·10 8 J / kmol. : The diffusion coefficient of oxygen from the gas side to the particle surface can be calculated as , : Sherwood number, : Particle diameter, calculated from the mass and density of iron and iron oxide, : The diffusion coefficient of oxygen in the thin layer on the particle surface can be calculated by the following formula:

[0056] ,

[0057] Where, : thermal conductivity of gas on the surface of iron powder; : Specific heat capacity of the gas phase on the surface of iron powder; : Lewis number of oxygen, which is set to 0.63 based on the mixture-averaged diffusion model in an oxidizing environment. The relative importance of oxygen diffusion rate and surface reaction rate can be expressed by the normalized Damköhler number (Damköhler number). hlernumber) definition:

[0058] = ,

[0059] From the above formula, we can see that when When the number is close to 1, the overall reaction rate is controlled by the oxygen diffusion rate; when When the number is close to 0, the overall reaction rate is controlled by the surface chemical reaction rate.

[0060] Heat is transferred between the iron powder particles and the surrounding gas through convection, the heat release rate of the particles, and energy transfer due to mass transfer. The instantaneous temperature of the iron powder particles can be calculated by the following formula:

[0061] ,

[0062] in, : specific heat capacity of the particle phase; : particle mass; : Nusselt number, calculated by the Ranz-Marshall model; Prandtl number is set to 0.6. The relaxation time of the particle is given by Calculated, where : particle density, : Gas phase dynamic viscosity. Combustion enthalpy According to the previous experimental data, the stoichiometric ratio is set to 4844kJ / kg. : The ratio of the change in mass of unburned particles to the change in mass of burned particles, the last item : Due to the energy change caused by interphase mass transfer, the total enthalpy of oxygen can be calculated as, ,in : reference temperature, : Enthalpy of formation of oxygen at reference temperature.

[0063] In this embodiment, the combustion characteristics of the iron powder group are defined as the particle combustion time and the characteristic time of heat transfer between particles in space In order to quantitatively analyze the relative importance of these two times, a discrete space parameter is introduced. .

[0064] ,

[0065] in, : gas phase thermal diffusivity; : The average spatial distance between particles in the iron powder group. When the spatial discrete parameter When it is much larger than 1, that is, the particle combustion time dominates, the flame speed and its dependence on thermochemical quantities can be approximated by the corresponding single-phase flame model; when the heat transfer between spatial particles becomes a limiting factor, it is more appropriate to use a discrete model.

[0066] Step 2: Determination and description of the computational domain, initial conditions, and boundary conditions:

[0067] This step studies the combustion characteristics of iron powder particles under different volume fraction conditions (20% O2 / 80% Xe (xenon) and 40% O2 / 60% Xe). The average particle size of the iron powder in the simulation is about 28μm. Calculated from the thermal conductivity, density and specific heat capacity of the gas mixture, when the oxygen volume fraction is 20%, ; When the oxygen volume fraction is 40%, When the particle size distribution is the same, the average distance between particles is only related to the fuel mass concentration. This example calculates the discrete parameters of oxygen with different volume fractions at three mass concentrations: The value of .

[0068] Table 1. Discrete parameters at different iron powder concentrations and oxygen environments

[0069]

[0070] The simulation calculation area is a rectangular region 80 mm long and 30 mm wide. Iron powder particles of varying mass concentrations are randomly distributed within the calculation area. Particle calculations are based on the PSI-CELL (particle-source-in-cell) model, treating particles as point sources. The simulation corresponds to the moment in the experiment when the tube is completely filled with iron powder. At this point, the iron particles and gas reach a quiescent state, effectively allowing observation of the flame propagation characteristics within the iron powder cluster. At the beginning of the simulation, all iron powder particles within the x < 1 mm region are preheated to 2000 K to ignite unburned iron powder particles within the x > 1 mm region, forming an iron powder flame that propagates in the x direction.

[0071] Step 3. Description of the numerical simulation calculation process of iron powder group particle combustion under microgravity conditions:

[0072] First, the computational domain is physically discretized, with an average spacing of 10 grid points between each particle to solve the interparticle heat transfer problem. The composition equation, temperature equation, pressure equation, mass equation, and momentum equation are then numerically discretized across all grids using the finite volume method. Spatial discretization employs the second-order central difference method, while temporal discretization employs the Euler difference algorithm. Finally, the resulting algebraic equations for velocity, density, composition, temperature, and pressure at each grid center are nonlinearized.

[0073] Next, the nonlinear equations are solved according to the solution method of the pressure-velocity correction coupled multiphase turbulent combustion model. The specific steps are as follows:

[0074] Step 1: Solve the velocity field and pressure field that satisfy mass conservation through the pressure-velocity coupling algorithm;

[0075] Step 2: Solve the momentum equations of the particle phase and the gas phase, taking into account the effects of convection, diffusion, pressure gradient, etc. on the velocity field, and obtain the corrected velocity value;

[0076] Step 3: Based on the corrected velocity field and pressure field, considering the influence of differential diffusion and oxygen concentration, solve the discretized energy equation and composition equation to obtain the distribution of temperature field and composition field;

[0077] Step 4: Use the discretized pressure equation to update the pressure field data. Substitute the calculated pressure value into the velocity equation and solve it to obtain the velocity correction value. Also, determine the relationship between the residual of the mass conservation equation and the set value. When the residual is less than the set value, proceed to the next step.

[0078] Step 5: Loop steps 2 to 4 until the specified number of iterations or time step is reached;

[0079] Step 6: Update the density value by solving the ideal gas state equation;

[0080] Step 7: Determine whether the set end time has been reached, otherwise repeat steps 1 to 6.

[0081] Step 4: Predict and analyze the temperature field, velocity field, and component field of the iron powder particle group combustion flame:

[0082] The simulation results using this patented method are basically consistent with the results of the sounding rocket experiment. The comparison diagram of the overall flame structure is attached. Figure 2 . Figure 2 (a): , , Temperature contours (T=850K) and particle position distribution under 40%O2 / 60%Xe conditions; Figure 2 (b): , , Oxygen mass fraction contours and particle temperature distribution under 40%O2 / 60%Xe conditions; Figure 2 (c): High-speed camera recording of the experiment under the same iron powder concentration and oxygen conditions. As can be seen from the figure, when the distance between the particles is relatively small, the flame burns violently, while the iron powder particles close to the wall are extinguished due to the lower temperature. The comparison of flame speed under different oxygen concentration conditions shows that when the oxygen volume fraction is 20%, the dispersion coefficient is roughly in the range of 0.08-0.2; when the oxygen volume fraction is 40%, the dispersion coefficient is in the range of 0.03-0.07. See the attached for details. Figure 3 .

[0083] For the combustion of a single iron powder particle, the design of the simulation calculation area is similar to the experiment, that is, a cylinder with a length of 300 mm and an inner diameter of 12 mm. and Two situations.

[0084] 1. The combustion of a single iron particle is mainly controlled by diffusion. This is because different Lewis numbers (considering the influence of differential diffusion) The change is small; when differential diffusion is considered, the predicted particle temperature increases by 320K and 350K for the 20% O2 and 40% O2 cases, respectively. Figure 4 , Figure 4 (a): Curve of particle temperature changing with time under different oxygen concentrations, Figure 4 (b): Normalized Damköhler number under different oxygen concentration conditions ( ) change curve.

[0085] 2. The flame structure depends on the local concentration of iron powder under lean combustion conditions. is 0.63, that is, the flame propagation speed when differential diffusion is considered is 4.7 mm / s faster than when differential diffusion is not considered; see the attached Figure 5 , Figure 5 (a): Gas temperature contour distribution of iron particles superimposed by iron oxide mass fraction when Le = 0.63, Figure 5 (b): Gas temperature contour distribution of iron particles superimposed by iron oxide mass fraction when Le = 1.0, Figure 5 (c): , , the change curves of gas temperature and oxygen mass fraction under the two conditions of Le=0.63 and Le=1.0 when the oxygen content is 40%, Figure 5 (d): , , the iron oxide mass fraction change curve under the two conditions of Le=0.63 and Le=1.0 when the oxygen content is 40%.

[0086] 3. Before t=0.6s, the effect of differential diffusion on the flame structure is small; but after t=0.8s, the flame structures in the two cases are very different. This is because the flames in the negative curvature region will propagate in different directions when differential diffusion is considered. ), the negative curvature flame front propagates in the x direction, and when (considering differential diffusion), the flame front with negative curvature propagates in the lateral direction, forming a sharp area, such as the Figure 6 As shown by the green arrow in the figure. Afterwards, the flame fronts in the sharp negative curvature area merged to form a positive curvature flame front. This phenomenon still exists under high iron powder concentration. See the attached figure for details. Figure 6 , Figure 6 (a): , Time evolution of the 850K temperature contour under 40% O2 conditions (t = 0.2s to t = 1.0s), Figure 6 (b): , Time evolution of the 850K temperature contour under 40% O2 conditions (t = 0.2s to t = 1.0s). The solid line represents the case of Le = 0.63, and the dashed line represents the case of Le = 1.0.

[0087] The foregoing is merely a list of specific embodiments of the present application, intended to enable those skilled in the art to understand or implement the present application. 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 present application. Therefore, the present application is not limited to the embodiments shown herein, but is intended to conform to the broadest scope consistent with the principles and novel features of the present application.

Claims

1. A numerical calculation method for simulating the combustion of iron powder particles, characterized in that: The steps include: Step 1: Build a mathematical model for multiphase turbulent combustion to determine the size distribution, shape, and initial temperature of the iron powder particles. Select a reaction mechanism based on the chemical reaction characteristics of the iron powder combustion and available computing resources. Use the heat and mass transfer calculation results of the iron powder particles as source terms in the gas equations to establish and solve the solid-gas coupling system. Step 2: Based on the meshing situation, geometric model characteristics, initial conditions and boundary conditions, the multiphase turbulent combustion mathematical model established in step 1 is numerically discretized to obtain a set of algebraic equations regarding velocity, density, composition, temperature and pressure, and the algebraic equations are subjected to nonlinear processing; Step three: Using a numerical discretization method, the time is gradually advanced. Within each time step, the nonlinear equations in step two are iteratively solved. The velocity and pressure fields are gradually updated through an outer loop, and the velocity field is corrected with the updated pressure field to ensure convergence. The turbulence model is also modified. Through the above three steps, the numerical simulation process of the iron powder particle group combustion is realized. In step three, the computational domain is first physically discretized, with grid points set between the average spacing of each particle to solve the heat transfer problem between particles. Then, the composition equation, temperature equation, pressure equation, mass equation, and momentum equation are numerically discretized on all grids using the finite volume method. The spatial discretization uses the second-order central difference method, and the temporal discretization uses the Euler difference algorithm. Finally, the algebraic equations for velocity, density, composition, temperature, and pressure at each grid center point are nonlinearized. The multiphase turbulent combustion model is coupled to the pressure and velocity correction method to solve the variable fields in the iron powder particle group combustion process. The solution steps are as follows: Step 1: Determine the time step and initialize the values ​​of the velocity field and pressure field. The time step is determined based on the CFL condition or is dynamically adjusted during the calculation process to ensure calculation stability. Step 2: Solve the velocity field and pressure field that satisfy mass conservation by using the pressure-velocity coupling algorithm; Step 3: Solve the momentum equations of the discretized particle and gas phases, taking into account the effects of convection, diffusion, pressure gradient, etc. on the velocity field; Step 4: Based on the corrected velocity field and pressure field, solve the discretized energy equation and component equation to obtain the distribution of the temperature field and component field; Step 5: Use the discretized pressure equation to update the pressure field data, and correct the velocity field according to the new pressure field to complete a pressure correction cycle; and judge the residual of the mass conservation equation. If the residual of the mass conservation equation is less than the set value, exit the loop and enter the next step; Step 6: Repeat steps 3 to 5 until the specified number of iterations or time step is reached. Step 7: Update the density value by solving the ideal gas state equation; Step 8: Determine whether the set end time has been reached, otherwise repeat steps 1 to 7.

2. A numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: The mathematical model of multiphase turbulent combustion established in step 1 includes the following equations: Navier-Stokes equations: These describe the momentum changes of iron powder particles during their motion, taking into account the inertia, viscosity, pressure gradient, and external forces of the reaction flow. Component equation: Based on the principles of conservation of mass and composition, it describes the mass transport process of iron powder reacting with oxygen to form oxides during combustion; Temperature equation: The solution of the temperature equation is combined with the momentum equation and the component equation. Based on the law of conservation of energy, it describes the heat transfer and temperature change process during the combustion process and is used to predict the temperature distribution during the combustion of iron powder. Pressure equation: It consists of four parts: time derivative term, convection term, diffusion term, and source term. It is a transport equation for the pressure variable. The pressure equation is discretized and solved iteratively during the solution process until the convergence condition is met. Mass equation: The mass equation is used to describe the conservation characteristics of fluid mass. The mass equation is discretized using the finite volume method and solved using an iterative method.

3. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: In step 2, the finite volume method is used to divide the computational domain into a finite number of small control volumes. The conservation law is applied to each control volume. The component equation, pressure equation, temperature equation, mass equation, and momentum equation established in step 1 are volume integrated within each grid, and then time integrated within the time step. By adopting different spatial discretization formats and time discretization formats, the algebraic equation defined at the center point of the grid is obtained. The same operation is then performed on all grids to obtain the algebraic equation group of each equation in the entire computational domain. The algebraic equation group is nonlinearized and solved iteratively to obtain the corresponding variable field.

4. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: The size distribution and shape of the iron powder in step 1 are determined based on relevant parameters of a comparative experiment.

5. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: The chemical reaction mechanism in step 1 should take into account the oxidation reaction kinetics on the surface of the iron powder particles.

6. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: In step 2, the finite volume method is used to discretize the spatial derivatives of velocity, composition, temperature, pressure and density based on the grid division of the computational domain through the central difference format or the upwind format, and then an explicit, implicit or semi-implicit time discretization format is introduced to obtain the algebraic equation defined at the center point of the grid.

7. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: The boundary conditions in step 2 consider the coupling relationship between particles and gas, including a drag model and a heat transfer model.

8. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: In step three, the implicit method is used to advance time, and the PIMPLE algorithm is used in the solution process.

9. The numerical calculation method for simulating the combustion of iron powder particles according to claim 1, characterized in that: The control equations involved in step three include gas phase and particle phase. The parameters of each component are obtained by directly solving the control equations of the components.

Images