A method for detecting the performance of a porous media burner

Abstract

A kind of porous medium combustor performance detection method, including the following steps: establishing multi-scale combustion model, for describing the complex physical process inside porous medium combustor, including microscale modeling, mesoscale modeling, macro scale modeling, complete multi-scale coupling and optimization;Obtain structural parameters and operating conditions;Numerical simulation and simulation;Performance evaluation and comparative analysis.

Description

Technical Field

[0001] This invention belongs to the field of porous media burner technology, and specifically relates to a method for testing the performance of porous media burners. Background Technology

[0002] Premixed combustion in a porous media burner is a complex heat exchange process involving chemical reactions and the interaction of three heat exchange modes: conduction, convection, and thermal radiation.

[0003] A porous media burner is a new type of combustion device that uses porous materials (such as ceramics, metal foam, or cordierite) as the combustion carrier. Its core principle is to achieve full mixing and uniform combustion of fuel and oxidant through porous media, thereby reducing the instability and pollutant emissions of traditional flame combustion.

[0004] Key features of porous media burners include:

[0005] Premixed combustion: Fuel and air are fully mixed before entering the combustion zone;

[0006] High-efficiency heat transfer: During combustion, porous media can achieve a combination of radiation, convection and conduction.

[0007] Low emissions: Uniform combustion temperature distribution effectively reduces the generation of pollutants such as NOx and CO;

[0008] Stable combustion: The porous medium provides a stable flame propagation path, which can maintain combustion even under low calorific value fuel conditions;

[0009] Porous media burners, through their internal porous structure, allow for thorough premixing of fuel and air before combustion, resulting in a more uniform and efficient combustion process. Compared to traditional burners, porous media burners offer higher combustion efficiency, lower pollutant emissions, and better heat transfer characteristics. However, the performance of porous media burners is influenced by various factors, including the physical properties of the porous material (such as thermal conductivity, porosity, and permeability), the burner's structural design (such as shape, size, and pore distribution), and operating conditions (such as fuel type, flow rate, and excess air coefficient).

[0010] Porous media burners, as an advanced combustion technology, achieve thorough premixing of fuel and air and efficient combustion through their complex internal porous structure, effectively improving combustion efficiency and reducing pollutant emissions. They have shown broad application potential and prospects in industrial heating, distributed energy systems, hot water supply, and environmental protection. However, accurate and comprehensive performance testing and evaluation of porous media burners remains a challenge.

[0011] Performance testing of porous media burners primarily relies on experimental methods, such as combustion efficiency testing, pollutant emission testing, and thermal efficiency testing. However, these methods suffer from high costs, long processing times, and large data dispersion, making it difficult to comprehensively and accurately evaluate the burner's overall performance. Furthermore, experimental testing methods are limited by testing conditions and equipment, hindering in-depth studies of burner performance under different operating conditions. Therefore, there is an urgent need for an efficient and accurate performance testing method to achieve a comprehensive evaluation of porous media burner performance. Summary of the Invention

[0012] The purpose of this invention is to provide a method for testing the performance of a porous media burner, characterized by the following steps:

[0013] A multi-scale combustion model was established to describe the complex physical processes inside a porous media burner, including microscale modeling, mesoscale modeling, and macroscale modeling, and to complete multi-scale coupling and optimization.

[0014] Obtain structural parameters and operating conditions;

[0015] Numerical simulation and modeling;

[0016] Performance evaluation and comparative analysis.

[0017] The technical solution provided in this application also has the following technical features:

[0018] Preferably, in one embodiment of this application, microscale modeling includes the following steps: reconstructing the microstructure of porous media, establishing a physical model, completing numerical solutions, and outputting results;

[0019] Reconstructing the microstructure of porous media: Obtaining the actual microstructure of porous media using techniques such as CT scanning and microscopic imaging;

[0020] The image is processed by software to generate a three-dimensional porous medium model;

[0021] If measured data is lacking, a random generation algorithm can be used to generate the simulation structure;

[0022] Establish the flow equations, heat transfer equations, and chemical reaction equations for the physical model:

[0023] Flow equations: describe the flow of gas within pores using the incompressible Navier-Stokes equations;

[0024] Heat transfer equation: considering heat conduction and convection between the solid skeleton and the fluid;

[0025] Chemical reaction equations: The reaction rate is described using a surface reaction kinetic model;

[0026] Numerical solution was completed: The direct numerical simulation method was used, and calculations were performed at the pore level using CFD software;

[0027] Output results: local flow field, heat transfer coefficient, surface reaction rate.

[0028] Preferably, in one embodiment of this application, mesoscale modeling includes the following steps: equivalent parameterization, establishing chemical reaction equations, and numerical solution;

[0029] Equivalent parameterization: The microscopic simulation results are extracted into equivalent parameters, and the Darcy-Brinkman-Forchheimer model is used to describe the flow in porous media.

[0030]

[0031] in:

[0032] K: Penetration rate;

[0033] C f : Coefficient of inertia;

[0034] Establish chemical reaction equations: use local average reaction rates, combined with volume source terms to represent heat of reaction and mass production;

[0035] Heat transfer: Considering heat exchange between solid and gas, a two-temperature model is used:

[0036]

[0037] T f Fluid temperature;

[0038] T s Solid temperature;

[0039] h s : Interface heat transfer coefficient;

[0040] Numerical solution: CFD calculations are performed on a mesoscale grid, and the output includes temperature distribution, flow field, and reaction rate.

[0041] Preferably, in one embodiment of this application, macroscopic scale modeling includes the following steps: geometric modeling, establishing macroscopic governing equations, determining boundary conditions, and numerical solution:

[0042] Geometric modeling: Construct an overall geometric model of the burner, setting the inlet, outlet, and porous media regions; use the equivalent continuous media assumption in the porous region to simplify calculations based on effective parameters;

[0043] Establish macroscopic governing equations, including flow equations, chemical reaction equations, and heat transfer equations;

[0044] Flow equation: Navier-Stokes equations;

[0045] Chemical reaction equations: A simplified reaction mechanism is used to describe the formation of pollutants;

[0046] Heat transfer equation: combining heat conduction and convection in porous media regions and free-flow regions;

[0047] Define boundary conditions: input fuel flow rate, air flow rate, and preheating temperature; use a pressure outlet boundary to ensure flow convergence.

[0048] Numerical solution: The overall flow field is calculated using CFD tools, and the model parameters are adjusted using experimental data to improve accuracy.

[0049] Preferably, in one embodiment of this application, multi-scale coupling and optimization includes parameter transfer, dynamic coupling, verification and optimization, and optimization of porous media parameters;

[0050] Parameter transfer: Transferring surface reaction rate and local heat transfer coefficient at the microscale to the mesoscale model; transferring equivalent heat transfer coefficient and permeability at the mesoscale to the macroscale model;

[0051] Dynamic coupling: Achieving cross-scale dynamic coupling using multi-grid methods or nested grid techniques; improving simulation efficiency through data interaction;

[0052] Validation and optimization: Compare and validate the simulation results with experimental data;

[0053] Optimize porous media parameters and burner operating conditions.

[0054] Preferably, in one embodiment of this application, obtaining structural parameters and operating conditions includes the following steps: obtaining structural parameters, obtaining operating conditions, and data processing and verification;

[0055] Structural parameters include porosity, specific surface area, pore size distribution, and thermophysical parameters;

[0056] Operating conditions include fuel type and composition, fluid flow rate, operating temperature, operating pressure, air-fuel ratio, and excess air coefficient;

[0057] Data processing and verification include simulation analysis verification and experimental verification;

[0058] The acquired structural parameters and operating conditions are input into the modeling software to create a parameter table, which facilitates simulation analysis.

[0059] An experimental setup was constructed, and the test results were compared with those of the simulation to verify the reliability of the data.

[0060] Preferably, in one embodiment of this application, numerical simulation includes the following steps: data input and modeling, equation definition, and numerical calculation and solution;

[0061] Data input and modeling include the following steps: obtaining structural parameters and operating conditions to establish a three-dimensional geometric model and its mesh generation, and selecting the computational domain and boundary conditions;

[0062] A three-dimensional porous medium structure with a three-dimensional geometric model is established, and meshing is performed using CFD tools:

[0063] Microscopic region: Fine mesh (structured or unstructured) is used to describe the flow inside the pores;

[0064] Mesoscopic region: Local regional characteristics are defined using homogenization methods;

[0065] Macroscopic region: Based on pore distribution, it is simplified to an equivalent porous medium;

[0066] The definition of the equation includes establishing the governing equation, establishing the chemical reaction model, and establishing the radiation model;

[0067] Numerical computation and solution: Numerical solution is performed using commercial software or open-source tools.

[0068] Preferably, in one embodiment of this application, the performance evaluation and comparative analysis includes the following steps: calculating the performance indicators of the porous media burner based on the numerical simulation results.

[0069] Preferably, in one embodiment of this application, the porosity is obtained by experimental measurement or CT scanning.

[0070] Specific surface area is obtained through BET analysis or CT reconstruction.

[0071] The aperture distribution was obtained by mercury porosimetry or microscopic imaging.

[0072] The thermophysical parameters were obtained through experimental measurements.

[0073] Preferably, in one embodiment of this application, the performance indicators of the porous media burner include combustion efficiency, thermal radiation intensity, pollutant emissions, temperature distribution, combustion stability, pressure drop, and response time;

[0074] Combustion efficiency: refers to the ratio of the heat energy released by the fuel to the heat energy input, and is an important indicator for evaluating the performance of the burner;

[0075] Thermal radiation intensity: The thermal radiation intensity of the burner during the combustion process reflects the effective heat release capacity of the porous medium;

[0076] Pollutant emissions: including the concentrations of pollutants such as NOx, CO, and unburned hydrocarbons (UHC);

[0077] Temperature distribution: The uniformity and distribution of temperature in the combustion zone affect combustion efficiency and emission characteristics;

[0078] Combustion stability: Whether there is backfire or flameout during the combustion process; the ability of the burner to maintain stable combustion under different operating conditions (such as flow rate, temperature, and pressure);

[0079] Pressure drop: The pressure drop of combustion gases as they pass through a porous medium affects the system's energy consumption and combustion efficiency;

[0080] Response time: The speed at which the burner responds to changes in fuel load.

[0081] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Detailed Implementation

[0082] These embodiments are for illustrative purposes only and are not intended to limit the scope of the invention.

[0083] In the description of this invention, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," indicating orientation or positional relationships, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of this invention. Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0084] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0085] Furthermore, in the description of this invention, unless otherwise stated, "a plurality of" means two or more.

[0086] To address the shortcomings of existing technologies for testing the performance of porous media burners, this invention aims to provide an efficient and accurate testing method. This method can comprehensively consider the complex physical processes of porous media burners, including combustion reaction, flow characteristics, and radiative heat transfer, thereby achieving a comprehensive evaluation of burner performance.

[0087] The technical problems to be solved by this invention include:

[0088] (1) How to establish an accurate mathematical model of a porous media burner to describe its internal combustion reaction, flow characteristics and radiative heat transfer and other physical processes;

[0089] (2) How to obtain the structural parameters and operating conditions of the porous media burner as input data for numerical simulation;

[0090] (3) How to use a high-performance computing platform to perform numerical simulation in order to obtain key parameters such as temperature field, velocity field, and component concentration distribution inside the burner;

[0091] (4) How to calculate key performance indicators such as combustion efficiency, thermal efficiency, and pollutant emission concentration based on numerical simulation results, and compare and analyze them with preset performance indicators or industry standards to evaluate the overall performance of the burner.

[0092] The performance indicators of porous media burners generally include:

[0093] Combustion efficiency: refers to the ratio of the heat energy released by the fuel to the heat energy input, and is an important indicator for evaluating the performance of the burner.

[0094] Thermal radiation intensity: The thermal radiation intensity of the burner during the combustion process reflects the effective heat release capability of the porous medium.

[0095] Pollutant emissions: including the concentrations of pollutants such as NOx, CO, and unburned hydrocarbons (UHC).

[0096] Temperature distribution: The uniformity and distribution of temperature in the combustion zone affect combustion efficiency and emission characteristics.

[0097] Stability: Whether there is backfire or flameout during the combustion process.

[0098] Pressure drop: The pressure drop of combustion gases as they pass through a porous medium affects the system's energy consumption and combustion efficiency.

[0099] Response time: The speed at which the burner responds to changes in fuel load.

[0100] This invention proposes a performance testing method for porous media burners, which combines advanced mathematical modeling with multi-parameter simulation and real-time performance evaluation. The method includes the following steps:

[0101] I. Establishing a multi-scale combustion model

[0102] To accurately describe the complex physical processes inside a porous media burner, this invention employs a multi-scale modeling method, combining the microstructural characteristics of the porous media to establish a multi-scale combustion model that includes flow, heat transfer, and chemical reactions.

[0103] Specifically, this invention employs a volume averaging method to average the microstructure (such as pores, channels, and particles) within the porous medium, obtaining macroscopic equations for flow, heat transfer, and chemical reactions. Simultaneously, this invention also considers the influence of the porous medium on flow, heat transfer, and chemical reactions, such as changes in physical properties like permeability, porosity, and thermal conductivity. The overall energy-saving equation for the porous medium burner can be formulated as follows:

[0104]

[0105] in:

[0106] ρ is the density of the porous medium.

[0107] C_p is the specific heat capacity.

[0108] T is the local temperature.

[0109] k_eff is the effective thermal conductivity.

[0110] Q_conv represents convective heat transfer.

[0111] Q_rad represents the radiative heat transfer using a hybrid model of P1 and DOM.

[0112] Regarding radiative heat transfer, this invention employs an improved P-1 radiation model. The traditional P-1 radiation model assumes uniform radiation intensity distribution in all directions. However, in porous media burners, due to the presence of the porous structure, radiation intensity may vary significantly in different directions. Therefore, this invention improves the P-1 radiation model by introducing scattering effects and the attenuation effect of the porous medium on radiation, to more accurately describe the radiative heat transfer process inside the porous media burner.

[0113] 1.1 Establishing a multi-scale combustion model includes: microscale modeling, mesoscale modeling, and macroscale modeling;

[0114] Mesoscale modeling refers to the process of describing and modeling physical phenomena at an intermediate scale between the microscopic and macroscopic.

[0115] Microscale: Describes the fine structure and local phenomena within a single pore (such as flow, heat transfer, and chemical reactions within a single pore).

[0116] Macro scale: describes the overall performance of the entire burner (such as combustion efficiency and pollutant emissions).

[0117] Mesoscale: This scale lies between the two, studying flow, heat transfer, and chemical reactions within assemblies of multiple pores, focusing on regional characteristics rather than single pores or the overall system.

[0118] (1) Microscale modeling includes the following steps:

[0119] Reconstructing the microstructure of porous media: Obtaining the actual microstructure of porous media using techniques such as CT scanning and microscopic imaging;

[0120] Images are processed using software (such as ImageJ) to generate three-dimensional porous media models;

[0121] If measured data is lacking, a random generation algorithm (such as Voronoi diagram or Monte Carlo) can be used to generate the simulation structure.

[0122] Establish a physical model:

[0123] Establish flow equations: Describe the flow of gas within the pores using the incompressible Navier-Stokes equations.

[0124] Establish the heat transfer equation: consider heat conduction and convection between the solid skeleton and the fluid.

[0125] Establish chemical reaction equations: combine surface reaction kinetic models (such as the Arrhenius equation) to describe the reaction rate.

[0126] Complete the numerical solution:

[0127] Numerical simulation methods are used to perform calculations at the pore level using CFD software (such as ANSYS Fluent and OpenFOAM).

[0128] Output results: local flow field, heat transfer coefficient, surface reaction rate.

[0129] (2) Mesoscale modeling

[0130] Equivalent parameterization: Extracting microscopic simulation results into equivalent parameters (such as porosity, permeability, and specific surface area).

[0131] Describing flow in porous media using the Darcy-Brinkman-Forchheimer model:

[0132]

[0133] in:

[0134] K: Penetration rate

[0135] C f Inertia coefficient

[0136] Establish chemical reaction equations: use local average reaction rates, combined with volume source terms to represent the heat of reaction and mass production.

[0137] Heat transfer: Considering heat exchange between solid and gas, a two-temperature model is used:

[0138]

[0139] T f Fluid temperature

[0140] T s Solid temperature

[0141] h s Interfacial heat transfer coefficient

[0142] Numerical solution: CFD calculations are performed on a mesoscale grid, and the output includes temperature distribution, flow field, and reaction rate.

[0143] (3) Macroscale modeling

[0144] Geometric modeling: Construct the overall geometric model of the burner and set the inlet, outlet and porous media regions.

[0145] The equivalent continuous medium assumption is used in porous regions to simplify calculations based on effective parameters.

[0146] Establish the macroscopic governing equations:

[0147] Flow: Navier-Stokes equations (applicable to non-porous regions);

[0148] Chemical reactions: Simplified reaction mechanisms are used to describe the formation of pollutants (such as two-step or three-step mechanisms);

[0149] Heat transfer: combining conduction and convection in porous media regions and free-flow regions;

[0150] Define boundary conditions: input fuel flow rate, air flow rate, and preheating temperature; use pressure outlet boundary to ensure flow convergence.

[0151] Numerical solution: CFD tools (such as Fluent and OpenFOAM) are used to calculate the overall flow field; model parameters are adjusted based on experimental data to improve accuracy.

[0152] (4) Multiscale Coupling and Optimization

[0153] Parameter transfer: The surface reaction rate and local heat transfer coefficient at the microscale are transferred to the mesoscale model; the equivalent heat transfer coefficient and permeability at the mesoscale are transferred to the macroscale model.

[0154] Dynamic coupling: Achieve cross-scale dynamic coupling using multi-grid methods or nested grid techniques; improve simulation efficiency through data interaction (such as COMSOL and MATLAB linkage).

[0155] Validation and optimization: Compare and validate the simulation results with experimental data;

[0156] Optimize porous media parameters (porosity, specific surface area) and burner operating conditions (fuel flow rate, excess air coefficient).

[0157] 1.2 The software and tools used in the above steps are as follows:

[0158] Geometric modeling and mesh generation:

[0159] Software: SolidWorks, ANSYS SpaceClaim, Gmsh.

[0160] Microstructure simulation:

[0161] Software: COMSOL Multiphysics, OpenFOAM.

[0162] Macroscopic simulation and coupling:

[0163] Software: ANSYS Fluent, OpenFOAM, Star-CCM+.

[0164] Data Analysis and Optimization:

[0165] Software: MATLAB, Python

[0166] Through the above steps and methods, the established multi-scale combustion model can effectively describe the complex physical processes inside the porous media burner, enabling optimized design and performance improvement.

[0167] II. Obtaining Structural Parameters and Operating Conditions

[0168] To conduct numerical simulations, it is necessary to obtain the structural parameters and operating conditions of the porous media burner. Structural parameters include the burner's geometric dimensions (such as diameter, length, and porosity), material properties (such as thermal conductivity and density), and specific characteristics of the porous structure (such as pore distribution and particle size). Operating conditions include fuel type, flow rate, excess air coefficient, and burner inlet temperature. These parameters and conditions will serve as input data for the numerical simulation, used to model the combustion reactions, flow characteristics, and radiative heat transfer processes within the porous media burner.

[0169] To perform accurate numerical simulations, it is necessary to collect the structural parameters (such as porosity, specific surface area, and pore size distribution) and operating conditions (such as fuel type, flow rate, temperature, and pressure) of the porous media burner. The following are the detailed implementation steps:

[0170] Obtaining structural parameters and operating conditions includes the following steps: obtaining structural parameters and obtaining operating conditions;

[0171] 2.1 Obtaining Structural Parameters

[0172] 1. Porosity

[0173] Definition: Porosity is the proportion of pore volume to total volume in a porous medium.

[0174] Experimental measurement method:

[0175] The ratio of pore volume to total volume is measured using either the water injection method or the gas displacement method.

[0176]

[0177] Equipment used: Specific surface area and porosity analyzer (such as BET analyzer);

[0178] CT scan method:

[0179] The three-dimensional structure of porous media was scanned using X-ray CT.

[0180] Calculate the pore volume ratio using image processing software (such as ImageJ).

[0181] 2. Specific surface area

[0182] Definition: The total surface area per unit volume of a porous medium;

[0183] BET analysis: The specific surface area of ​​a sample is measured using gas adsorption (such as nitrogen adsorption).

[0184] Equipment: Surface area analyzer;

[0185] CT reconstruction method: Extract the porous medium skeleton from CT scan images and calculate the surface area;

[0186] 3. Aperture distribution

[0187] Definition: The distribution characteristics of pore size in pores, usually described by average pore size and distribution curve;

[0188] Mercury intrusion porosimetry: This method utilizes the principle of mercury permeating porous media under different pressures to measure pore size distribution.

[0189] Equipment: Mercury porosimetry pore size analyzer;

[0190] Microscopic imaging method: Using a scanning electron microscope (SEM) to acquire images of the pore structure;

[0191] The aperture distribution is obtained by analyzing the image;

[0192] Laser particle size analysis: This method analyzes the particle and pore size characteristics within pores using laser scattering.

[0193] 4. Thermal properties

[0194] Definition: The thermal conductivity, heat capacity, and other properties of porous media;

[0195] Experimental measurements: Thermal conductivity measurement: using the transient planar heat source method (HotDisk method) or laser flash method;

[0196] Heat capacity measurement: Differential scanning calorimeter (DSC) was used;

[0197] Computational method: Based on the composition and geometric properties of materials, calculations are performed using empirical formulas or numerical simulations;

[0198] 2.2 Obtaining Operating Conditions

[0199] 1. Fuel types and components

[0200] Experimental analysis: Gas chromatography (GC) was used to analyze the chemical composition of the fuel (such as the content of methane, ethane, hydrogen, etc.).

[0201] Manufacturer data: Obtain fuel properties (calorific value, combustion reactivity, etc.) from fuel suppliers.

[0202] 2. Fluid flow rate

[0203] Flow meter measurement: Install a mass flow meter or volumetric flow meter (such as a turbine flow meter or thermal mass flow meter) at the burner inlet.

[0204] Calculation method: Based on the system design, calculate the flow rate using the flow velocity and cross-sectional area: Q = v·A

[0205] 3. Operating temperature

[0206] Thermocouple measurement: Thermocouples (such as type K and type S) are placed at the burner inlet, outlet and inside the porous medium.

[0207] Infrared temperature measurement: Using an infrared thermal imager to detect temperature distribution.

[0208] 4. Operating pressure

[0209] Pressure sensor measurement: Install pressure sensors at the burner inlet, outlet and key locations to record operating pressure.

[0210] Design conditions: Obtain the theoretical pressure range based on the burner design documents.

[0211] 5. Air-fuel ratio and excess air coefficient

[0212] Definition: Air-fuel ratio represents the mass ratio of air to fuel, and excess air coefficient is the ratio of actual air volume to theoretical air volume;

[0213] Flow meter method: Measure the fuel and air flow rates separately, and calculate the air-fuel ratio.

[0214]

[0215] Oxygen content measurement: The oxygen concentration in the combustion exhaust gas is measured, and the excess air coefficient is deduced from this.

[0216] 2.3 Data Processing and Validation

[0217] Data integration: Input the acquired structural parameters and operating conditions into modeling software (such as ANSYS Fluent, COMSOL Multiphysics);

[0218] Establish a parameter table to facilitate simulation analysis;

[0219] Experimental verification: Build an experimental setup (such as a small burner prototype), test and compare the results with the simulation results to verify the reliability of the data;

[0220] Sensitivity analysis: Analyze the impact of key parameters (such as porosity and air-fuel ratio) on burner performance;

[0221] Optimize the range of parameter values.

[0222] III. Numerical Simulation and Modeling

[0223] A high-performance computing platform was used to perform three-dimensional numerical simulations by combining a multi-scale combustion model with structural parameters and operating conditions. During the numerical simulation, reasonable boundary and initial conditions needed to be set to ensure the accuracy and reliability of the simulation results. Boundary conditions included flow, temperature, and radiation conditions at the burner inlet and outlet; initial conditions included temperature and component concentration distributions inside the burner. Through numerical simulation, key parameters such as the temperature field, velocity field, and component concentration distribution inside the porous media burner, as well as performance indicators such as combustion efficiency, thermal efficiency, and pollutant emission concentrations, could be obtained. The simulation solved the energy, mass, and momentum conservation equations for premixed combustion. The radiative heat flux was calculated using the mixing model as follows:

[0224]

[0225] Where: D is the diffusion coefficient.

[0226] G is the radiation energy density controlled by the discrete coordinate method.

[0227] Numerical simulation and modeling include the following steps: data input and modeling, equation definition, and numerical computation and solution.

[0228] 3.1 Data input and modeling include the following steps: obtaining structural parameters and operating conditions, establishing a three-dimensional geometric model and its mesh generation, and selecting the computational domain and boundary conditions;

[0229] Obtain structural parameters and operating conditions: Use experimental or analytical methods to obtain structural parameters of the porous medium, such as porosity, specific surface area, pore size distribution, and thermal conductivity. Define operating conditions: fuel flow rate, air-fuel ratio, inlet pressure, and temperature, etc.

[0230] Establish a three-dimensional geometric model:

[0231] Use CAD software (such as SolidWorks) or directly import 3D porous media structures from CT scans;

[0232] Mesh generation is performed using CFD tools (such as ANSYS Fluent and OpenFOAM).

[0233] Microscopic region: Fine mesh (structured or unstructured) is used to describe the flow inside the pores;

[0234] Mesoscopic region: Local regional characteristics are defined using homogenization methods;

[0235] Macroscopic region: Based on the pore distribution, it is simplified to an equivalent porous medium.

[0236] Select the computational domain and boundary conditions:

[0237] Burner inlet: Define velocity boundary conditions or flow rate;

[0238] Burner outlet: Set pressure boundary conditions (usually ambient pressure);

[0239] Wall: Defines the thermal insulation or heat transfer boundary conditions.

[0240] 3.2 The definition of equations includes establishing governing equations, establishing chemical reaction models, and establishing radiation models;

[0241] The governing equations include the mass conservation equation, the momentum conservation equation, and the energy conservation equation;

[0242] Chemical reaction models include premixed combustion models and chemical kinetic sub-models;

[0243] Radiation models, using hybrid radiation models (such as the P1 model, the Discrete Coordinates (DOM) method) to calculate radiative heat flux.

[0244]

[0245] Where κ is the absorption coefficient and G is the radiation flux.

[0246] 3.3 Numerical Solving: Numerical solutions are performed using commercial software (such as ANSYS Fluent, COMSOL Multiphysics) or open-source tools (such as OpenFOAM). Deployment is carried out on a high-performance computing platform, supporting multi-core parallel computing.

[0247] The numerical solution process includes mesh generation and optimization, initial condition setting, discretization method, iterative solution, result output and analysis;

[0248] 3.3.1 Mesh Generation and Optimization: Refine the mesh in the combustion reaction zone; use local mesh refinement techniques in porous media regions;

[0249] 3.3.2 Initial Condition Setting: Set the initial fluid velocity, pressure, and temperature distribution;

[0250] Initial concentration of chemical substances: such as the initial distribution of fuel and oxygen;

[0251] 3.3.3 Discretization method: Discretize partial differential equations using the finite volume method (FVM);

[0252] Convection terms: Use a high-resolution format (such as MUSCL);

[0253] Time term: Use explicit or implicit methods (such as Euler implicit);

[0254] 3.3.4 Iterative solution:

[0255] Choose between a steady-state or transient solver;

[0256] Adjust the relaxation factor to ensure iteration convergence;

[0257] Regularly monitor residuals (such as mass, momentum, and energy residuals);

[0258] 3.3.5 Results Output and Analysis:

[0259] Extracting temperature field, flow field, and chemical reaction distribution;

[0260] Analyze the results using visualization tools such as ParaView and Tecplot.

[0261] In summary, numerical simulation and modeling offer advantages over experimental measurement.

[0262] High efficiency and controllability: Simulating multi-field coupling phenomena: Numerical simulations can simultaneously study flow, heat transfer and chemical reactions on a single platform.

[0263] No complex equipment required: saves experimental costs and avoids dangerous operations such as high temperature and high pressure.

[0264] Refined Analysis: Global Distribution Information: Provides the temperature, flow, and concentration field distributions across the entire computational domain, whereas experiments typically only obtain local data from measurement points.

[0265] Microscopic to macroscopic unity: Through multi-scale models, performance from the pore level to the overall burner can be analyzed.

[0266] Flexibility: Parametric research: Structural parameters or operating conditions can be quickly adjusted to conduct sensitivity analysis.

[0267] Extreme conditions: operating conditions that are difficult to achieve in simulation experiments (such as extremely high temperatures and rapid dynamic changes).

[0268] Environmental protection and safety: Reduce the number of tests: Reduce fuel and energy consumption.

[0269] High safety: Avoids safety risks caused by unstable combustion during testing.

[0270] IV. Performance Evaluation and Comparative Analysis

[0271] Based on the numerical simulation results, the key performance indicators of the porous media burner, such as combustion efficiency, are calculated and given by the following formula:

[0272] η = E_output / E_input × 100%

[0273] The pressure drop of a porous media burner is given by the following formula:

[0274] ΔP=f L / D(ρv^2) / 2

[0275] Where: f is the coefficient of friction;

[0276] L is the length of the porous section;

[0277] D is the diameter;

[0278] v is velocity.

[0279] In addition, there are related indicators such as thermal efficiency and pollutant emission concentration. These indicators will be used to evaluate the overall performance of the burner. Simultaneously, the calculated performance indicators will be compared with preset performance indicators or industry standards to determine whether the burner's performance meets the standards or has room for improvement. During the comparative analysis, key parameters such as temperature field, velocity field, and component concentration distribution in the simulation results can be used to further analyze the internal combustion reaction, flow characteristics, and radiative heat transfer processes of the burner, providing strong support for burner design optimization.

[0280] Compared with existing technologies, the efficient detection and comprehensive evaluation method for porous media burner performance proposed in this invention has the following significant effects and advantages:

[0281] 1. High-precision simulation

[0282] By introducing multi-scale modeling methods and an improved P-1 radiation model, this invention can more accurately describe the complex physical processes inside porous media burners, including combustion reactions, flow characteristics, and radiative heat transfer. Compared with traditional experimental testing methods, numerical simulation methods have higher accuracy and reliability, and can more accurately reflect the actual conditions inside the burner.

[0283] 2. Comprehensive assessment

[0284] This invention comprehensively considers multiple performance indicators of porous media burners, including combustion efficiency, thermal efficiency, and pollutant emission concentration, achieving a comprehensive evaluation of burner performance. By comparing and analyzing the differences and relationships between different performance indicators, a deeper understanding of the internal physical processes and performance characteristics of the burner can be obtained, providing strong support for burner design optimization.

[0285] 3. High efficiency and convenience

[0286] Compared with traditional experimental testing methods, the numerical simulation method provided by this invention has advantages such as low cost, short cycle time, and good data continuity. Through numerical simulation, key parameters and performance indicators such as the temperature field, velocity field, and component concentration distribution inside the burner can be quickly obtained, greatly improving the efficiency of performance testing and evaluation. Furthermore, the numerical simulation method is highly flexible and easy to adjust input parameters, making it adaptable to the performance testing needs of porous media burners under different types and operating conditions.

[0287] 4. Guidance on Design Optimization

[0288] Based on key parameters and performance indicators such as temperature field, velocity field, and component concentration distribution obtained from numerical simulations, the design of porous media burners can be optimized. For example, by adjusting parameters such as burner geometry, material properties, or operating conditions, the internal combustion reaction, flow characteristics, and radiative heat transfer processes can be improved, thereby enhancing burner performance and efficiency. Furthermore, numerical simulation methods can be used to predict burner performance under different operating conditions, providing strong support for the market promotion and application of burners.

[0289] 1. Data Acquisition and Preprocessing

[0290] First, the porous ceramic burner underwent geometric dimensional measurements and material property tests. Geometric dimensions included the burner's diameter, length, and porosity; material properties included thermal conductivity, density, and coefficient of thermal expansion. Simultaneously, the burner's operating conditions, such as natural gas flow rate, air flow rate, and burner inlet temperature, were recorded. These data will serve as input for the numerical simulation.

[0291] 2. Establishment of a multi-scale combustion model

[0292] Based on the volume averaging method and considering the microstructural characteristics of porous ceramic burners, a multi-scale combustion model incorporating flow, heat transfer, and chemical reactions is established. The model considers the influence of porous media on flow, heat transfer, and chemical reactions, such as variations in physical properties like permeability, porosity, and thermal conductivity. Simultaneously, an improved P-1 radiation model is employed to describe the radiative heat transfer process during combustion, more accurately reflecting the radiative heat transfer characteristics within the porous ceramic burner.

[0293] 3 Numerical Simulation and Modeling

[0294] Using high-performance computing platforms (such as ANSYS Fluent and other CFD software), a multi-scale combustion model is combined with structural parameters and operating conditions to perform three-dimensional numerical simulations. During the numerical simulation, reasonable boundary and initial conditions are set to ensure the accuracy and reliability of the simulation results. Through numerical simulation, key parameters such as the temperature field, velocity field, and component concentration distribution inside the porous ceramic burner can be obtained.

[0295] 4. Performance Evaluation and Comparative Analysis

[0296] Based on numerical simulation results, key performance indicators of porous ceramic burners, such as combustion efficiency, thermal efficiency, and pollutant emission concentration, are calculated. The calculated performance indicators are compared with preset performance indicators or industry standards to determine whether the burner's performance meets the standards or has room for improvement. Simultaneously, based on key parameters such as temperature field, velocity field, and component concentration distribution from the simulation results, the internal combustion reaction, flow characteristics, and radiative heat transfer processes of the burner are further analyzed, providing strong support for burner design optimization.

[0297] 5. Design Optimization and Verification

[0298] Based on numerical simulation and performance evaluation results, the design of porous ceramic burners is optimized. For example, parameters such as burner geometry, material properties, or operating conditions can be adjusted to improve internal combustion reactions, flow characteristics, and radiative heat transfer. The optimized burner is shown below.

[0299] Specifically, in one embodiment of this application, the following steps 1-4 are performed:

[0300] 1. Establish a multi-scale combustion model

[0301] Microscale: Simulate the pore structure and local reaction processes of porous media units to determine chemical reaction rates and heat transfer parameters.

[0302] Mesoscale: Simulates gas flow, heat conduction, and radiation in local regions of porous media, and calculates the flow field and temperature field distribution.

[0303] Macroscale: Simulates the overall thermodynamic behavior of the burner, including fuel supply, combustion efficiency, and exhaust emissions.

[0304] 2. Obtain structural parameters and operating conditions

[0305] Structural parameters:

[0306] Porosity: 80%.

[0307] Pore ​​diameter: 0.5 mm.

[0308] Thermal conductivity of the material: 2 W / (m·K).

[0309] Operating conditions:

[0310] Fuel: Methane, flow rate 5 L / min.

[0311] Air flow rate: 60L / min.

[0312] Excess air coefficient: 1.2.

[0313] Numerical simulation and modeling

[0314] 3. Simulate three-dimensional flow field, temperature field and concentration field.

[0315] The radiative heat flux was calculated using a hybrid model.

[0316] The energy, mass, and momentum conservation equations were solved using a high-performance computing platform.

[0317] 4. Performance Evaluation and Comparative Analysis

[0318] The accuracy of the model was verified using experimental data.

[0319] Evaluate performance indicators such as combustion efficiency, pollutant emissions, and temperature uniformity.

[0320] The following data (1-3) were obtained:

[0321] 1. Combustion efficiency measurement

[0322]

[0323] Comparative analysis: Multi-scale modeling and simulation more accurately optimized the burner design, improving thermal efficiency by 4 percentage points.

[0324] 2. Temperature Uniformity Analysis

[0325]

[0326] Comparative analysis: This method significantly improves temperature uniformity and avoids local overheating by optimizing structural parameters and operating conditions.

[0327] 3. Pollutant emissions

[0328] Types of pollutants Traditional experimental measurement (ppm) This method uses simulation to predict (ppm) CO 50 20 NOx 25 10

[0329] Comparative analysis: Simulation optimization reduced CO emissions by 83% and NOx emissions by 60%, achieving cleaner combustion.

[0330] Explanation of experimental data comparison:

[0331] Traditional method:

[0332] Data obtained through direct experimental measurements are subject to local errors and have limited optimization capabilities.

[0333] Relying on physical experiments with burners is time-consuming and costly.

[0334] This method:

[0335] By using multi-scale model simulation, burner performance can be accurately predicted.

[0336] By combining structural parameters with operating conditions optimization, pollutant emissions are reduced and thermal efficiency is improved.

[0337] Numerical simulation can replace some experimental measurements, reducing development costs.

[0338] Economic comparison

[0339] project Traditional experimental methods This method (modeling and simulation) Development time (days) 60 30 Testing cost (ten thousand yuan) 20 10 Optimize the number of iterations 2 5

[0340] Comparative analysis: This method significantly shortens development time, reduces testing costs, and increases the number of optimization iterations, ensuring a comprehensive improvement in performance.

[0341] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and substitutions can be made without departing from the technical principles of the present invention, and these improvements and substitutions should also be considered within the scope of protection of the present invention.

Claims

1. A method for testing the performance of a porous media burner, characterized in that, Includes the following steps: A multi-scale combustion model was established to describe the complex physical processes inside a porous media burner, including microscale modeling, mesoscale modeling, and macroscale modeling, and to complete multi-scale coupling and optimization. Obtain structural parameters and operating conditions; Numerical simulation and modeling; Performance evaluation and comparative analysis.

2. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, Microscale modeling includes the following steps: reconstructing the microstructure of porous media, establishing a physical model, completing numerical solutions, and outputting results. Reconstructing the microstructure of porous media: Obtaining the actual microstructure of porous media using CT scanning or microscopic imaging techniques; The image is processed by software to generate a three-dimensional porous medium model; If measured data is lacking, a random generation algorithm is used to generate the simulation structure; Establish the flow equations, heat transfer equations, and chemical reaction equations for the physical model: Flow equations: describe the flow of gas within pores using the incompressible Navier-Stokes equations; Heat transfer equation: considering heat conduction and convection between the solid skeleton and the fluid; Chemical reaction equations: The reaction rate is described using a surface reaction kinetic model; Numerical solution was completed: The direct numerical simulation method was used, and calculations were performed at the pore level using CFD software; Output results: local flow field, heat transfer coefficient, surface reaction rate.

3. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, Mesoscale modeling includes the following steps: equivalent parameterization, establishing chemical reaction equations, and numerical solution. Equivalent parameterization: The microscopic simulation results are extracted into equivalent parameters, and the Darcy-Brinkman-Forchheimer model is used to describe the flow in porous media. in: K: Penetration rate; C f : Coefficient of inertia; Establish chemical reaction equations: use local average reaction rates, combined with volume source terms to represent heat of reaction and mass production; Heat transfer: Considering heat exchange between solid and gas, a two-temperature model is used: T f Fluid temperature; T s Solid temperature; h s : Interface heat transfer coefficient; Numerical solution: CFD calculations are performed on a mesoscale grid, and the output includes temperature distribution, flow field, and reaction rate.

4. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, Macroscale modeling includes the following steps: geometric modeling, establishing macroscopic governing equations, determining boundary conditions, and numerical solution. Geometric modeling: Construct an overall geometric model of the burner, setting the inlet, outlet, and porous media regions; use the equivalent continuous media assumption in the porous region to simplify calculations based on effective parameters; Establish macroscopic governing equations, including flow equations, chemical reaction equations, and heat transfer equations; Flow equation: Navier-Stokes equations; Chemical reaction equations: A simplified reaction mechanism is used to describe the formation of pollutants; Heat transfer equation: combining heat conduction and convection in porous media regions and free-flow regions; Define boundary conditions: input fuel flow rate, air flow rate, and preheating temperature; use a pressure outlet boundary to ensure flow convergence. Numerical solution: The overall flow field is calculated using CFD tools, and the model parameters are adjusted using experimental data to improve accuracy.

5. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, Multiscale coupling and optimization includes parameter transfer, dynamic coupling, verification and optimization, and optimization of porous media parameters; Parameter transfer: Transferring surface reaction rate and local heat transfer coefficient at the microscale to the mesoscale model; transferring equivalent heat transfer coefficient and permeability at the mesoscale to the macroscale model; Dynamic coupling: Achieving cross-scale dynamic coupling using multi-grid methods or nested grid techniques; improving simulation efficiency through data interaction; Validation and optimization: Compare and validate the simulation results with experimental data; Optimize porous media parameters and burner operating conditions.

6. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, The process of obtaining structural parameters and operating conditions includes the following steps: obtaining structural parameters, obtaining operating conditions, and data processing and verification. Structural parameters include porosity, specific surface area, pore size distribution, and thermophysical parameters; Operating conditions include fuel type and composition, fluid flow rate, operating temperature, operating pressure, air-fuel ratio, and excess air coefficient; Data processing and verification include simulation analysis verification and experimental verification; The acquired structural parameters and operating conditions are input into the modeling software to create a parameter table, which facilitates simulation analysis. An experimental setup was constructed, and the test results were compared with those of the simulation to verify the reliability of the data.

7. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, Numerical simulation and modeling include the following steps: data input and modeling, equation definition, and numerical computation and solution. Data input and modeling include the following steps: obtaining structural parameters and operating conditions to establish a three-dimensional geometric model and its mesh generation, and selecting the computational domain and boundary conditions; A three-dimensional porous medium structure with a three-dimensional geometric model is established, and meshing is performed using CFD tools: Microscopic region: Fine mesh (structured or unstructured) is used to describe the flow inside the pores; Mesoscopic region: Local regional characteristics are defined using homogenization methods; Macroscopic region: Based on pore distribution, it is simplified to an equivalent porous medium; The definition of the equation includes establishing the governing equation, establishing the chemical reaction model, and establishing the radiation model; Numerical computation and solution: Numerical solution is performed using commercial software or open-source tools.

8. The method for testing the performance of a porous media burner as described in claim 1, characterized in that, The performance evaluation and comparative analysis includes the following steps: Based on the numerical simulation results, calculate the performance indicators of the porous media burner.

9. The method for testing the performance of a porous media burner as described in claim 6, characterized in that, Porosity is obtained through experimental measurement or CT scanning. Specific surface area is obtained through BET analysis or CT reconstruction. The aperture distribution was obtained by mercury porosimetry or microscopic imaging. The thermophysical parameters were obtained through experimental measurements.

10. The method for testing the performance of a porous media burner as described in claim 8, characterized in that, The performance indicators of porous media burners include combustion efficiency, thermal radiation intensity, pollutant emissions, temperature distribution, combustion stability, pressure drop, and response time.

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