Mixed particle bed filling state detection method, electronic device and medium

Abstract

This invention discloses a method, electronic device, and medium for detecting the filling state of a hybrid particle bed, relating to the field of hybrid particle bed filling technology for solid-state breeder blankets in fusion reactors. The method includes: constructing a parameter system to characterize the internal filling state of the hybrid particle bed; converting candidate filling state parameters into corresponding equivalent parameters of candidate porous media; establishing a flow field geometric model of the hybrid particle bed, and introducing the equivalent parameters of candidate porous media into the flow field geometric model for simulation calculation to obtain simulation response data; acquiring actual response data of the hybrid particle bed under actual operating conditions, and performing matching analysis between the simulation response data and the actual response data to determine the target filling state parameters; quantitatively evaluating the filling effect of the hybrid particle bed based on the target filling state parameters, and outputting the evaluation result of the filling effect. This method enables the reverse identification of the internal filling state and quantitative evaluation of the filling effect without damaging the structure of the hybrid particle bed.

Description

Technical Field

[0001] This invention relates to the field of hybrid particle bed filling technology for solid breeder blankets in fusion reactors, and in particular to a method, electronic device, and medium for detecting the filling status of a hybrid particle bed. Background Technology

[0002] In fusion reactor solid-state blanket engineering applications, mixed particle beds are typically formed by packing particles of different materials and sizes. The internal packing state directly affects heat and mass transfer performance, flow resistance characteristics, and structural stability. Because the particle system is susceptible to the combined influence of various factors during packing, such as particle size distribution, material density differences, particle breakage, dust entrainment, and packing process conditions, the actual internal structure of the particle bed often exhibits significant uncertainty and non-uniformity. This leads to phenomena such as fluctuations in packing ratio, localized segregation, and changes in particle contact states.

[0003] In relevant engineering practices, the assessment of particle bed filling quality mainly relies on external information such as filling process control parameters, total filling mass, height measurement, or local observation results. While these methods can reflect whether the filling process meets technological requirements to some extent, they typically only provide macroscopic or indirect characterization and are insufficient to reflect the true internal filling structure of the particle bed. In particular, they struggle to quantitatively identify key parameters such as filling rate, pore structure characteristics, and the contact between different material particles. These problems are even more pronounced in engineering scenarios where filling space is limited or the filling process cannot be disassembled.

[0004] From the perspective of detection methods, while local sampling, disassembly analysis, or tomographic scanning can obtain some information about the internal structure, they generally suffer from high costs, long cycles, and interference or even damage to the structure, making them unsuitable for routine testing needs in engineering. On the other hand, pressure drop measurement methods based on gas flow processes are relatively simple to operate and can reflect the overall flow resistance characteristics, thus having a certain application basis in engineering. However, these methods mostly focus on the pressure drop measurement itself or the study of flow characteristics, making it difficult to reflect the internal filling structure of the particle bed through externally measurable response information.

[0005] Furthermore, the internal structure of mixed particle beds is complex, and its pore structure and flow characteristics vary significantly under different filling states. Currently, there is a lack of a universal method to establish a stable correlation between internal structural characteristics and external measurable responses, as well as a technical approach to reliably determine the filling effect under engineering conditions. Therefore, how to effectively characterize the internal filling state of mixed particle beds without damaging the structure and to achieve quantitative evaluation of the filling effect remains a pressing problem in the relevant technical field. Summary of the Invention

[0006] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the purpose of this invention is to provide a method, electronic device, and medium for detecting the filling state of a mixed particle bed, so as to achieve quantitative identification of the internal filling state of the mixed particle bed and reliable evaluation of the filling effect.

[0007] To achieve the above objectives, a first aspect of the present invention provides a method for detecting the filling state of a mixed particle bed, comprising: Based on the geometric structure information and basic particle properties of the mixed particle bed, a parameter system is constructed to characterize the internal filling state of the mixed particle bed, and multiple sets of candidate filling state parameters are formed. The candidate filling state parameters are converted into corresponding candidate porous media equivalent parameters through the porous media equivalence relationship; A flow field geometric model of the mixed particle bed is established, and the equivalent parameters of the candidate porous medium are introduced into the flow field geometric model. Simulation calculations are performed under preset flow boundary conditions to obtain simulation response data corresponding to different candidate filling states, and a mapping relationship between the candidate filling state parameters and the simulation response data is constructed. Obtain the actual response data of the mixed particle bed under actual working conditions, and match and analyze the simulation response data and actual response data in the mapping relationship to determine the target filling state parameters corresponding to the actual response data; The filling effect of the mixed particle bed is quantitatively evaluated based on the target filling state parameters, and the evaluation results of the filling effect are output.

[0008] In addition, the method of the above embodiments of the present invention may also have the following additional technical features: According to one embodiment of the present invention, the parameter system for characterizing the internal filling state of the mixed particle bed includes a filling rate and a dissimilar particle contact rate; wherein, the filling rate characterizes the proportion of the particle volume to the effective filling space volume; and the dissimilar particle contact rate characterizes the degree of contact between particles of different materials.

[0009] According to one embodiment of the present invention, forming multiple sets of candidate filling state parameters includes: Set the value range for the filling rate and the value range for the contact rate of dissimilar particles respectively; The range of filling rate and the range of contact rate of dissimilar particles are processed according to a preset step size or a preset combination rule, and multiple sets of candidate filling state parameters are generated by cross-combination.

[0010] According to an embodiment of the present invention, converting the candidate filling state parameters into corresponding candidate porous medium equivalent parameters through porous medium equivalence relations includes: The void ratio is determined based on the fill ratio in the candidate fill state parameters; Based on the filling rate and dissimilar particle contact rate in the candidate filling state parameters, as well as the equivalent particle size of the mixed particle bed, the permeability and inertial drag parameters are calculated by introducing a correction term related to the dissimilar particle contact rate.

[0011] According to one embodiment of the present invention, the simulation calculation process includes: In the flow field geometry model, a porous medium region is defined, and the fluid resistance within the porous medium region is set to be composed of a viscous resistance term and an inertial resistance term determined based on the equivalent parameters of the candidate porous medium. The simulation response data corresponding to each set of candidate filling states is obtained by solving multiple preset flow conditions formed under the preset flow boundary conditions.

[0012] According to one embodiment of the present invention, obtaining the actual response data of the mixed particle bed under actual working conditions includes: Pressure drop experiments were conducted under one or more of the preset flow conditions, and actual response data for multiple conditions were obtained based on the measured pressure difference between the two ends of the mixed particle bed.

[0013] According to one embodiment of the present invention, the matching analysis includes: Matching analysis is performed using actual response data under a single operating condition and simulated response data under the corresponding operating condition, or matching analysis is performed using actual response data under multiple operating conditions and simulated response data; wherein, the matching analysis is achieved through at least one of the following methods: The actual response data is numerically compared and matched with the simulated response data; An optimized inversion algorithm is adopted, with the filling state parameters as variables, and the goal is to minimize the deviation between the simulated response data and the actual response data for iterative solution.

[0014] According to one embodiment of the present invention, the method further includes: The mixed particle bed is divided into multiple interconnected porous media sub-regions along the filling height direction; By matching and analyzing the simulation response data and actual response data corresponding to each sub-region, the target filling state parameters corresponding to each sub-region are determined. The filling uniformity of the mixed particle bed is quantitatively evaluated based on the differences in target filling state parameters between each sub-region.

[0015] To achieve the above objectives, a second aspect of the present invention provides an electronic device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the above-described method for detecting the filling state of a mixed particle bed.

[0016] To achieve the above objectives, a third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed, implements the steps of the above-described method for detecting the filling state of a mixed particle bed.

[0017] The method, electronic device, and medium for detecting the filling state of a mixed particle bed according to embodiments of the present invention construct a filling state parameter system based on the geometric structure information and particle property parameters of the mixed particle bed. Combining the equivalent relationship of porous media and a flow simulation model, a mapping relationship between the filling state parameters and response data is established. Based on this, through matching analysis of actual response data and simulation results, the internal filling state parameters of the mixed particle bed are identified by reverse deduction. This allows for quantitative evaluation of the particle bed filling effect without damaging the structure. This method transforms internal structural features that are difficult to measure directly into information that can be obtained through external responses, improving the quantifiability and engineering applicability of the detection results, and contributing to improving the accuracy and consistency of mixed particle bed filling quality assessment. Attached Figure Description

[0018] Figure 1 This is a flowchart illustrating a method for detecting the filling state of a mixed particle bed in one embodiment; Figure 2 This is a schematic diagram of the simulation calculation process in one embodiment; Figure 3 This is a schematic diagram of the process for quantitatively evaluating the uniformity of filling in one embodiment. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0020] The implementation details of the technical solutions of the embodiments of the present invention are described in detail below.

[0021] In one embodiment, such as Figure 1 The diagram illustrates a process flow chart for detecting the filling state of a mixed particle bed, which may include the following steps: Step S101: Based on the geometric structure information and basic particle properties of the mixed particle bed, a parameter system is constructed to characterize the internal filling state of the mixed particle bed, and multiple sets of candidate filling state parameters are formed.

[0022] The process involves acquiring the geometric structure information and basic particle properties of the filling region containing the mixed particle bed to be tested. The geometric structure information includes the spatial boundaries of the filling region, the effective filling space, and the volume occupied by internal non-filling components. The basic particle properties include parameters such as particle size, volume fraction, density, particle size distribution, and surface condition for different types of particles. In practical applications, the geometric structure information and basic particle properties serve as unified input conditions, providing a consistent physical basis for subsequent analysis.

[0023] Based on the above information, the filling effect of the mixed particle bed is characterized by parameters that reflect its internal structural characteristics. This transforms the internal filling state, which was originally difficult to observe directly, into a quantifiable parameter form, thereby enabling the parameterized expression of the internal filling state of the mixed particle bed. Different filling states can be uniformly described by a set of parameters, thus constructing a parameter system for characterizing the internal filling state of the mixed particle bed.

[0024] Based on the parameter system, different filling states that may occur in the mixed particle bed are combined to form multiple sets of candidate filling state parameters, which are used to reflect the possible changes in the internal structure of the mixed particle bed under different filling conditions or disturbance factors.

[0025] In one embodiment, the parameter system used to characterize the internal filling state of the mixed particle bed includes the filling rate and the contact rate of dissimilar particles. The two parameters jointly characterize the internal structural state of the mixed particle bed from two aspects: macroscopic volume distribution characteristics and microscopic particle contact structure characteristics.

[0026] The filling rate describes the proportion of particle volume occupied in the effective filling space, reflecting the overall density of the mixed particle bed and characterizing the compactness of particle packing and the overall porosity. The dissimilar particle contact rate describes the degree of contact between particles of different materials, reflecting the uniformity of mixing of different types of particles in space and the characteristics of interface contact distribution, thereby indirectly characterizing the local structural connectivity and transmission path characteristics.

[0027] By combining the above parameters, the internal structure of the mixed particle bed can be uniformly characterized, so that the macroscopic filling level and the microscopic mixing structure can be synergistically described under the same parameter system. This makes different filling states distinguishable in the parameter space and provides a structural basis for subsequent porous media parameter conversion and flow response analysis.

[0028] In one embodiment, the process of forming multiple sets of candidate filling state parameters includes: Set separate ranges for the filling rate and the contact rate of dissimilar particles. The ranges can be determined based on the particle size composition, volume fraction ratio, and actual filling process conditions to ensure that the parameter space covers the range of possible filling states.

[0029] Subsequently, the value ranges are processed according to preset step sizes or preset combination rules. Specifically, when using preset step sizes, the value ranges for the filling rate and the contact rate of dissimilar particles are discretized, resulting in multiple discrete value points. When using preset combination rules, based on the physical relationships or empirical distribution characteristics between different parameters, non-uniform values ​​or conditional constraints are applied to the filling rate and the contact rate of dissimilar particles to reduce invalid combinations and improve the representativeness of parameter combinations.

[0030] Based on this, the processed filling rate value and the contact rate value of different particles are cross-combined to generate multiple sets of candidate filling state parameters, so that the candidate filling state parameters can not only cover different possible filling states, but also reflect the differential distribution characteristics between different structural states.

[0031] Step S102: The candidate filling state parameters are converted into the corresponding candidate porous medium equivalent parameters through the porous medium equivalent relationship.

[0032] For multiple sets of candidate filling state parameters, based on the concept of porous media equivalence, the complex particle packing structure inside the mixed particle bed is equivalently characterized and transformed into corresponding candidate porous media equivalent parameters, enabling it to participate in flow analysis as a continuous medium. These candidate porous media equivalent parameters are a set of equivalent physical parameters characterizing the flow resistance and fluid throughput of the particle bed, used to replace discrete particle structures in macroscopic flow calculations.

[0033] Specifically, by establishing the conversion relationship between filling state parameters and equivalent parameters of porous media, the structural state corresponding to discrete particle systems is mapped into an equivalent parameter expression form suitable for flow calculation, thereby enabling different candidate filling states to be compared and analyzed under a unified physical framework.

[0034] In this process, the conversion is carried out under physical constraints consistent with the geometry and flow boundary conditions of the actual filling area, so as to ensure that the subsequent simulation analysis results are comparable with the actual working conditions, thereby providing a consistent parameter basis for subsequent matching analysis based on response data.

[0035] In one embodiment, the equivalent parameters of the candidate porous media include porosity, permeability, and inertial drag parameters. Porosity characterizes the proportion of void volume to effective packing volume in the mixed particle bed, reflecting the percentage of space through which fluid can pass. Permeability characterizes the fluid's seepage capacity in the particle bed, reflecting the degree to which the particle structure impedes fluid passage. The inertial drag parameter characterizes the additional flow resistance caused by inertial effects under higher flow rate conditions.

[0036] The porosity is determined based on the fill rate in the candidate fill state parameters, and the porosity and fill rate satisfy the following relationship:

[0037] In the above formula, Indicates porosity. This indicates the fill rate.

[0038] When determining the permeability and inertial drag parameters, based on the equivalent relationship in the fixed-bed flow resistance theory and considering the multi-size and multi-material structural characteristics of mixed particle beds, an equivalent particle size is introduced to uniformly characterize the influence of particle size on different particle sizes. Furthermore, a correction term related to the contact rate between dissimilar particles is used to compensate for the differences in inter-particle contact structure, ensuring that the influence of dissimilar contact states on local flow channel connectivity, flow tortuosity, and overall resistance level is reflected in the equivalent parameters. Specifically, the permeability and inertial drag parameters can be expressed as follows:

[0039]

[0040] In the above formula, This indicates the penetration rate, expressed in square meters. This represents the inertial drag parameter, expressed in meters. Indicates the contact rate with dissimilar particles The relevant penetration rate correction factor; Indicates the contact rate with dissimilar particles The relevant inertial drag correction factor; The equivalent particle size of the mixed particle bed is expressed in meters.

[0041] In practical applications, the equivalent particle size is used to represent a mixed system composed of particles of different sizes as a particle system with a uniform characteristic scale in flow resistance calculations. Its value can be determined based on the particle size and volume fraction combination of various particles, thus reflecting the comprehensive influence of multi-size structures on the flow channel scale. The correction coefficient can be obtained through theoretical analysis, numerical simulation, or experimental calibration, so that the equivalent parameter can reflect the actual flow characteristics of the mixed particle bed under different filling conditions.

[0042] Step S103: Establish the flow field geometric model of the mixed particle bed, introduce the equivalent parameters of the candidate porous medium into the flow field geometric model, perform simulation calculations under the preset flow boundary conditions, obtain the simulation response data corresponding to different candidate filling states, and construct the mapping relationship between the candidate filling state parameters and the simulation response data.

[0043] The flow field geometry model is used to characterize the spatial structure of fluid flow and its boundary constraints within the filling region of a mixed particle bed. It is a geometric abstraction of the actual filling structure in the dimension of flow analysis. In practical applications, the spatial boundaries, effective filling area, and volume occupied by internal non-filling components of the mixed particle bed under test can be modeled according to the actual structure of the filling region, thus forming the flow field geometry model.

[0044] Equivalent parameters of candidate porous media corresponding to different filling states are introduced into the flow field geometry model, enabling different filling structures to participate in flow analysis within a unified continuous medium framework. Simultaneously, flow boundary conditions consistent with actual operating conditions are set in the flow field geometry model, including inlet conditions, outlet conditions, and fluid property parameters, allowing the simulation analysis to reflect the flow characteristics under actual operating conditions.

[0045] Under the above conditions, simulation calculations were performed on multiple candidate filling states to obtain the simulation response data corresponding to each candidate filling state under different operating conditions. In practical applications, since the flow resistance of fluid in porous media changes with the filling structure parameters, and this flow resistance can be directly reflected macroscopically in the form of pressure drop, the simulation response data can specifically be the pressure drop result or its variation law when the fluid flows through the mixed particle bed.

[0046] Based on the simulation response data, a mapping relationship between candidate filling state parameters and simulation response data is established. The mapping relationship can be specifically represented in the form of a pressure drop database, a set of pressure drop response curves, a parameter correspondence table, or a response surface model, etc., to describe the correlation between the filling rate, the contact rate of dissimilar particles and their corresponding porous media parameters and the pressure drop response, and to provide a basis for subsequent matching analysis.

[0047] In one embodiment, Figure 2 The flowchart of the simulation calculation is shown, which may include the following steps: Step S201: Define a porous medium region in the flow field geometry model, and set the fluid resistance in the porous medium region to be composed of viscous resistance and inertial resistance terms determined based on the equivalent parameters of the candidate porous medium.

[0048] In the flow field geometry model, the region containing the mixed particle bed is defined as a porous medium region. This region represents the flow area with a porous structure formed by the accumulation of a large number of particles. The fluid resistance within this region is defined as consisting of both viscous and inertial resistance terms, thus providing an equivalent characterization of the flow resistance in the particle bed. The viscous resistance term describes the linear resistance dominated by fluid viscosity under low-velocity conditions, and its magnitude is related to fluid viscosity and permeability. The inertial resistance term describes the nonlinear additional resistance caused by fluid inertia under higher flow rates, and its magnitude is related to fluid density and inertial resistance parameters. Specifically, the pressure drop relationship within the porous medium region satisfies the following expression:

[0049] In the above formula, The pressure drop across the porous medium region is expressed in Pascals. The characteristic length in the direction of flow, in meters; The fluid dynamic viscosity is expressed in Pascals per second. Permeability, in square meters; Apparent velocity, measured in meters per second; This is an inertial drag parameter, expressed in meters. This refers to the fluid density, expressed in kilograms per cubic meter.

[0050] The above modeling method replaces the individual modeling of discrete particles with the porous medium equivalent method, which significantly reduces the computational complexity while maintaining the overall flow resistance characteristics, making large-scale calculations for multiple candidate filling states feasible.

[0051] Step S202: Solve the multiple preset flow conditions formed under the preset flow boundary conditions to obtain the simulation response data corresponding to each set of candidate filling states.

[0052] Multiple preset flow conditions can be formed under preset flow boundary conditions. Specifically, by changing operating parameters such as inlet flow rate, inlet velocity, fluid temperature or pressure, multiple flow states with different characteristics can be constructed under the same geometric model and physical property conditions to cover different operating conditions that may occur in actual operation.

[0053] The simulation response data for each candidate filling state is obtained by solving the problem under various flow conditions. The simulation response data can be represented as pressure drop results or their variation characteristics under different flow conditions. By performing calculations under multiple flow conditions, multi-condition pressure drop characteristics corresponding to the same candidate filling state can be formed, thereby improving the distinguishability between different filling states and enhancing the stability and accuracy of the subsequent reverse identification process.

[0054] Step S104: Obtain the actual response data of the mixed particle bed under actual working conditions, and perform matching analysis between the simulation response data and the actual response data in the mapping relationship to determine the target filling state parameters corresponding to the actual response data.

[0055] Actual response data is acquired after the mixed particle bed is filled and under predetermined operating or testing conditions. It reflects the macroscopic response characteristics of the actual filled structure under fluid action. After acquiring the actual response data, it is compared with the simulated response data in the mapping relationship. By analyzing the degree of difference between the two, the candidate filling state parameter that most closely approximates the actual response is determined and used as the target filling state parameter. The target filling state parameter includes at least the filling rate and the contact rate between dissimilar particles, as well as the corresponding equivalent parameters of the porous medium.

[0056] Through the above matching analysis process, the macroscopic response information obtained from actual measurements can be mapped to the corresponding filling state parameter space, realizing the reverse identification from the external measurable response to the internal filling structure parameters, thereby obtaining the target filling state parameters that can characterize the actual filling state of the mixed particle bed. These target filling state parameters can be further used for quantitative evaluation of the filling effect and subsequent analysis.

[0057] In one embodiment, obtaining actual response data of the mixed particle bed under actual operating conditions includes: conducting pressure drop experiments under one or more preset flow conditions, and obtaining actual response data for multiple operating conditions based on the measured pressure difference between the two ends of the mixed particle bed.

[0058] After the mixed particle bed is filled and reaches the predetermined test state, a pressure drop experiment is carried out according to the flow boundary conditions consistent with the simulation analysis. The inlet pressure, outlet pressure and pressure difference are measured, and the flow rate, temperature and fluid state parameters are recorded to obtain the actual pressure drop response data of the mixed particle bed under different operating conditions.

[0059] By conducting experimental measurements under multiple flow conditions, pressure drop response curves reflecting the flow resistance characteristics of the pebble bed under different flow conditions can be generated, thereby improving the completeness and representativeness of the actual response data. In practical applications, data processing can also be performed on multiple measurement results, including averaging, outlier removal, and consistency verification, to improve the stability and reliability of the data.

[0060] In one embodiment, the matching analysis includes matching analysis under a single operating condition and matching analysis under multiple operating conditions. Specifically, when using the single-operating-condition matching method, the actual response data under a certain flow condition is compared with the simulated response data under the corresponding operating condition. By analyzing the differences between the two, the candidate filling state that is closest to the actual response is determined, thereby achieving rapid identification of the filling state parameters.

[0061] When using a multi-condition matching method, the actual response data under multiple operating conditions are jointly analyzed with the corresponding simulation response data. By integrating the response characteristics under different operating conditions, the ability to distinguish between different candidate filling states is improved, thereby reducing the identification uncertainty caused by similar response results under a single operating condition.

[0062] In the specific implementation process, the actual response data and the simulation response data can be numerically compared and matched. For example, the pressure drop difference or error index under each working condition can be calculated, and candidate filling state parameters can be screened based on the error magnitude pattern.

[0063] In another implementation, an optimization inversion algorithm can be used, with the filling state parameters as variables, and the goal of minimizing the deviation between the simulated response data and the actual response data is to perform an iterative solution. During this process, a matching error function can be constructed to uniformly measure the response deviation under multiple operating conditions; its expression is:

[0064] In the above formula, For the matching error function; The number of working conditions participating in the matching; For the first The weighting coefficients for each working condition are dimensionless. For the first Actual response data under various operating conditions, in Pascals; For the first Simulation response data corresponding to candidate fill rate and heterogeneous contact rate under various working conditions, in Pascals; Fill rate; This represents the heterogeneous contact rate.

[0065] By traversing and searching or iteratively optimizing the candidate filling state parameters, the parameter combination that minimizes the matching error function is determined as the target filling state parameter, thereby realizing the reverse identification of the internal filling structure parameters from the actual response.

[0066] Furthermore, matching analysis is not limited to matching a single numerical point. It can also construct pressure drop curves based on multi-condition response data for curve feature matching, or construct response surface models based on mapping relationships and perform interpolation searches, thereby further improving matching accuracy and stability.

[0067] Step S105: Quantitatively evaluate the filling effect of the mixed particle bed based on the target filling state parameters, and output the evaluation results of the filling effect.

[0068] Based on the obtained target filling state parameters and the matching deviation between the actual response data and the simulation response data, the filling state of the mixed particle bed is comprehensively analyzed, and the filling effect is quantitatively characterized from multiple aspects such as density, particle mixing state and flow resistance characteristics.

[0069] During the evaluation process, the target filling state parameters can be compared with the design requirements or acceptance criteria to output the evaluation results of whether the filling effect of the mixed particle bed meets the predetermined requirements. When the target filling state parameters fall within the preset range and the matching error is within the allowable range, the filling effect can be determined to meet the requirements; when there is a deviation, it indicates that the filling state is inconsistent.

[0070] When the evaluation results show that the actual filling effect deviates from the target state, the filling process can be analyzed based on the evaluation results. For example, it can identify problems such as uneven filling, particle distribution segregation, or insufficient contact state, and adjust the filling parameters, compaction conditions, or mixing methods accordingly to improve the subsequent filling process.

[0071] Through the above quantitative evaluation process, the complex internal filling structure can be transformed into a judgmentable evaluation result, thereby improving the objectivity and operability of the mixed particle bed filling quality assessment.

[0072] In one embodiment, Figure 3 A flowchart illustrating the quantitative evaluation of filling uniformity is shown, which may include the following steps: Step S301: Divide the mixed particle bed into multiple interconnected porous media sub-regions along the filling height direction.

[0073] The mixed particle bed is segmented along the filling height direction, forming multiple sub-regions arranged sequentially in the flow direction. Each sub-region can be divided based on the stratification records or filling units during the actual filling process, so that the division results can reflect the structural distribution characteristics during the actual filling process.

[0074] After the division is completed, each sub-region is regarded as a porous medium region connected in series, thereby realizing the hierarchical characterization of the internal structure of the overall mixed particle bed.

[0075] Step S302: By matching and analyzing the simulation response data and actual response data corresponding to each sub-region, the target filling state parameters corresponding to each sub-region are determined.

[0076] For each sub-region, the candidate filling state parameters corresponding to each sub-region are calculated and processed based on the flow field geometric model to obtain the simulation response data of each sub-region under different candidate filling states. The simulation response data includes the overall pressure drop simulation response and the local pressure difference simulation response corresponding to each sub-region.

[0077] Based on this, the simulation response data corresponding to each sub-region is matched and analyzed with the actual response data obtained by actual measurement (including the actual response of overall pressure drop and the actual response of local pressure difference in each layer). By comparing the joint constraints of the overall pressure drop response and the segmented local pressure difference response, the target filling state parameters corresponding to each sub-region are determined.

[0078] This method enables the reverse identification of differences in the filling structure at different height positions within a mixed particle bed.

[0079] Step S303: Quantitatively evaluate the filling uniformity of the mixed particle bed based on the differences in target filling state parameters between each sub-region.

[0080] After obtaining the target filling state parameters for each sub-region, the differences in parameters between different sub-regions are analyzed. By comparing the differences in target filling state parameters between sub-regions, specifically including comparing the differences in filling rate and heterogeneous particle contact rate between any two adjacent sub-regions, or comparing the deviation between the overall average level and local parameters, the spatial distribution consistency of filling rate and heterogeneous particle contact rate is evaluated, thereby achieving a quantitative characterization of the uniformity of the mixed particle bed filling. When the parameter differences between different sub-regions exceed a preset threshold, it is determined that there is uneven filling or local segregation.

[0081] Furthermore, the above differences can be quantitatively determined based on a preset uniformity judgment criterion. When the difference in filling rate or the difference in contact rate between different particles between any adjacent sub-regions exceeds the corresponding threshold, it is determined that there is uneven filling or local segregation. When the deviation of a certain sub-region from the overall average level exceeds a preset range, the sub-region is determined to be an abnormal segment.

[0082] To illustrate how the present invention enables rapid identification of the overall filling state of a mixed particle bed under a single flow condition, an application example is provided.

[0083] This application example is applicable to mixed particle beds with relatively regular structures where the detection target is mainly determined by the overall filling state.

[0084] First, the basic parameters of the mixed particle bed to be tested are obtained, including the geometric dimensions of the filling area, the effective filling volume, the volume occupied by the internal non-filling components, and the actual particle size range, true density, volume fraction, and sphericity of the two types of particles. Simultaneously, the operating conditions of the purge gas are obtained. The pressure drop test operating conditions include at least the fluid type, inlet conditions, outlet conditions, temperature conditions, and flow rate conditions. The same set of boundary conditions is used in subsequent simulation calculations and pressure drop experiments to ensure consistency between simulation and experimental results.

[0085] Based on this, several candidate filling states are constructed according to a preset range. Each candidate filling state parameter includes at least two parameters: filling rate and foreign particle contact rate. Each candidate state corresponds to a unique combination of filling rate and foreign particle contact rate.

[0086] Subsequently, porous media parameters are converted for each group of candidate filling states, including calculating the corresponding porosity, permeability and inertial drag parameters, thereby forming the corresponding candidate porous media equivalent parameters for subsequent flow analysis.

[0087] After completing the parameter conversion, a flow field geometric model consistent with the actual mixed sphere bed was established, and the sphere bed region was defined as a porous medium region. The corresponding porosity, permeability and inertial drag parameters were input into the model. At the same time, fluid properties and boundary conditions consistent with the experiment were set, and simulation calculations were performed on each group of candidate filling states under a single working condition to obtain the corresponding simulation response results. A mapping relationship table between candidate filling state parameters and simulation response data was formed.

[0088] After completing the simulation calculations, a pressure drop experiment was conducted on the mixed particle bed that had been filled, sealed, and reached the predetermined test state. Under steady-state conditions, the inlet pressure, outlet pressure, pressure difference, flow rate, and temperature were recorded. Three consecutive tests were performed under the same operating conditions, and the arithmetic mean of the three results was taken as the actual response result. If any single measurement deviated from the average value by more than 5%, that measurement result was determined to be an outlier and discarded. A follow-up measurement was then performed to obtain stable and reliable actual response data.

[0089] The actual response data is compared item by item with the mapping table, and the candidate filling state parameter with the smallest absolute deviation is selected as the back-inference result (i.e., the target filling state parameter). When the minimum deviation is no more than 10%, the back-inference result is considered valid; when the minimum deviation is greater than 10%, it is determined that the current simulation database cannot effectively cover the actual filling state, or that the experimental boundary conditions are inconsistent with the simulation boundary conditions, and re-verification is required.

[0090] After obtaining the back-calculated filling rate and dissimilar particle contact rate, these are compared and analyzed with the pre-set design target range to determine the overall filling effect of the mixed particle bed. Specifically, when the back-calculated filling rate meets the design requirements and the dissimilar particle contact rate is within the predetermined allowable range, the overall filling effect of the mixed particle bed is determined to meet the requirements; when any parameter deviates from the corresponding target range, the filling effect is determined to have a deviation.

[0091] If the filling effect is found to be deviated, the above test results can be fed back to the filling process to guide subsequent compaction treatment, mixing method adjustment or refilling operation, thereby achieving closed-loop optimization of filling quality.

[0092] To illustrate how the present invention improves the accuracy and distinguishability of filling state recognition under multiple flow conditions, a second application embodiment is provided.

[0093] This application example is applicable to situations where different internal structural states may produce similar pressure drops under a single operating condition, and it is difficult to accurately distinguish them based on a single pressure drop value alone.

[0094] First, the geometric structure information, particle parameters, and pressure drop experimental parameters of the mixed particle bed are obtained in the same way as in the first application embodiment. Based on this, multiple pressure drop experimental conditions are pre-set, covering low flow rate, intermediate flow rate, and high flow rate regions. Simultaneously, the same sequence of conditions is used in subsequent simulation calculations and experimental tests to ensure comparability between the data from multiple conditions.

[0095] Subsequently, multiple sets of candidate filling state parameters were established according to the preset range. Each set of candidate filling state parameters included the filling rate and the contact rate of different particles. When necessary, the mixing uniformity index corresponding to each set of candidate state parameters was recorded as an auxiliary analysis parameter.

[0096] For each set of candidate filling state parameters, the equivalent parameters of porous media are converted sequentially to obtain the corresponding porosity, permeability and inertial resistance parameters. The method is the same as in the first application embodiment, so as to obtain a set of candidate porous media equivalent parameters corresponding to each set of candidate state parameters.

[0097] Based on this, a porous medium flow field model is established, and simulations are performed on each set of candidate filling state parameters under multiple preset flow conditions to obtain a corresponding set of pressure drop results. The pressure drop data of all candidate states under all conditions are then organized to form a pressure drop database, thereby constructing a mapping relationship between candidate filling state parameters and multi-condition simulation response data.

[0098] In the experimental phase, pressure drop tests were conducted on the actually filled mixed particle bed according to a predetermined sequence of operating conditions. The pressure drop value was recorded after each operating condition reached steady state. Three repeated tests were performed under each operating condition, and the results were averaged. If any single measurement under a certain operating condition deviated from the average value of that condition by more than 5%, the measurement was considered an outlier and discarded. A follow-up test was then performed to obtain complete multi-condition actual response data.

[0099] Subsequently, the actual response data obtained from the experiment under multiple operating conditions were compared item by item with the simulation response data corresponding to each candidate filling state parameter in the database. A matching error function was constructed to uniformly measure the matching deviation of each candidate state. Based on this, the candidate filling state parameter with the smallest total matching error was selected as the back-inference result (i.e., the target filling state parameter). When the total matching deviation corresponding to the optimal candidate state parameter is no greater than 10%, the back-inference result is deemed valid; when the difference in the total matching error between the optimal and second-best candidate state parameters is no greater than 3%, the two are deemed to have insufficient distinguishability, and a secondary screening is required, combining the total filling mass, final filling height, layered filling records, or vibration process records.

[0100] After the back-analysis of the filling state parameters is completed, the obtained target filling state parameters are compared with the design target requirements to comprehensively evaluate the filling quality of the mixed particle bed. Specifically, when the back-analysis results meet the design target requirements, the filling quality of the mixed particle bed is deemed qualified; when the back-analysis results deviate from the design target but are still within the preset allowable range, it is deemed an acceptable deviation, and the direction for optimizing the filling process can be proposed accordingly; when the total matching deviation is greater than 10% or the back-analyzed filling state parameters significantly exceed the target range, the filling quality is deemed unqualified.

[0101] If the filling quality does not meet the requirements, the filling process can be further reviewed, and a decision can be made based on the evaluation results as to whether process adjustments or refilling are necessary to improve the consistency and stability of subsequent filling effects.

[0102] To achieve quantitative detection and stratification identification of the uniformity of mixed particle bed packing along the height direction, a third application example is provided: The third application example is applicable to mixed particle beds that are filled using a layer-by-layer filling process, and is used to evaluate their filling uniformity along the height direction.

[0103] First, based on the filling process records, the mixed particle bed is divided into several filling units along its height. Each filling unit corresponds to a segment in the actual filling process, and each filling unit is treated as an independent porous media sub-region. This layered division ensures that each sub-region corresponds to the segment information in the specific filling process, thereby guaranteeing that subsequent identification results can be correlated with the actual process.

[0104] After completing the stratification, candidate filling state parameters are set for each sub-region. These parameters include at least the filling rate and the contact rate between different types of particles. The parameters can be the same or different between sub-regions. By combining the parameters of different layers, a multi-group stratified candidate filling state is constructed. Through this combination method, a set of candidate states covering the distribution characteristics of different stratified structures can be formed.

[0105] Subsequently, for the candidate filling state parameters of each sub-region, the porous media parameters are converted to obtain the porosity, permeability and inertial resistance parameters corresponding to each sub-region, thus forming the equivalent parameters of the candidate porous media with layered parameterization.

[0106] Based on this, a stratified flow field geometric model was established. In this model, the mixed particle bed region was divided into multiple porous media sub-regions connected in series along the height direction, and each sub-region was assigned corresponding porosity, permeability, and inertial drag parameters. Under multiple preset pressure drop experimental conditions, simulations were performed on different stratified candidate filling states to obtain the corresponding overall pressure drop response and the local pressure difference response data of each sub-region, thereby constructing a stratified simulation response database.

[0107] During the actual testing process, pressure drop experiments were conducted on the filled mixed particle bed. In addition to recording the overall inlet and outlet pressure drop, local pressure difference data between different height sections were also acquired. At least three repeated measurements were performed under each operating condition, and the results were averaged. If any measured value deviated from the average value by more than 5%, the measured value was deemed an outlier and discarded, and a retest was conducted to obtain reliable actual response data for the overall pressure drop and segmented pressure differences.

[0108] Subsequently, the overall pressure drop and segmented pressure difference data obtained from the experiment were compared with the layered simulation response database obtained from the simulation. Through joint matching analysis of the overall response and the layered response, the candidate filling state combination that is closest to the actual response was determined, thereby deriving the target filling state parameters corresponding to each sub-region. Specifically, by calculating the comprehensive matching deviation of the overall pressure drop and the local pressure difference of each layer, the candidate combination with the smallest total deviation was selected as the target filling state parameters. When the total matching deviation is no greater than 10%, the layered identification result is deemed valid; when the total matching deviation is greater than 10%, it is determined that the database coverage or the consistency between the experimental and simulation boundary conditions needs to be verified.

[0109] After obtaining the target filling state parameters for each sub-region, the parameter differences between different sub-regions are analyzed. When the difference in filling rate between any two adjacent sub-regions is no greater than 2%, and the difference in contact rate between dissimilar particles is no greater than 0.05, the filling uniformity of the ball bed along the height direction is determined to be good. When the difference in filling rate between any two adjacent sub-regions is greater than 2%, or the difference in contact rate between dissimilar particles is greater than 0.05, stratification differences or local segregation are determined to exist. When the deviation of a certain sub-region from the overall average filling rate exceeds 3%, the sub-region is determined to be an abnormal filling layer.

[0110] Furthermore, the identified abnormal layers can be correlated with the filling process records for analysis. If the abnormal layers exhibit issues such as uneven material distribution, insufficient vibration, abnormal mixing time, unsatisfactory surface flatness, or excessive layer height deviation during the filling process, then these layers are further identified as weak areas in the process, thus pinpointing potential process problems and providing a basis for optimizing the subsequent filling process.

[0111] The above technical solution enables the reverse identification of the internal filling structure state of a mixed particle bed based on a small amount of measurable macroscopic flow response data, without requiring individual modeling or direct observation of discrete particles. By constructing a parameter system centered on filling rate and heterogeneous particle contact rate, and combining it with the porous media equivalent method, the complex particle packing structure is transformed into an equivalent parameter form that can participate in continuous media flow analysis. This allows for simulation calculations and comparative analyses of different filling states within a unified physical framework. Furthermore, by establishing a mapping relationship between candidate filling states and simulation response data, and combining this with matching analysis of response data under actual operating conditions, accurate mapping from external measurable flow characteristics to internal structural parameters can be achieved, improving the accuracy and stability of filling state identification. Simultaneously, quantitative evaluation of the identified target filling state parameters allows for objective judgment of the filling effect and provides a basis for optimizing the filling process, thereby significantly improving computational efficiency and engineering applicability while ensuring analytical accuracy.

[0112] In one embodiment, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement a method for detecting the filling state of a mixed particle bed.

[0113] In one embodiment, a computer storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements a method for detecting the filling state of a mixed particle bed.

[0114] In the description of this specification, references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0115] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0116] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for detecting the filling state of a mixed particle bed, characterized in that, include: Based on the geometric structure information and basic particle properties of the mixed particle bed, a parameter system is constructed to characterize the internal filling state of the mixed particle bed, and multiple sets of candidate filling state parameters are formed. The candidate filling state parameters are converted into corresponding candidate porous media equivalent parameters through the porous media equivalence relationship; A flow field geometric model of the mixed particle bed is established, and the equivalent parameters of the candidate porous medium are introduced into the flow field geometric model. Simulation calculations are performed under preset flow boundary conditions to obtain simulation response data corresponding to different candidate filling states, and a mapping relationship between the candidate filling state parameters and the simulation response data is constructed. Obtain the actual response data of the mixed particle bed under actual working conditions, and match and analyze the simulation response data and actual response data in the mapping relationship to determine the target filling state parameters corresponding to the actual response data; The filling effect of the mixed particle bed is quantitatively evaluated based on the target filling state parameters, and the evaluation results of the filling effect are output.

2. The method for detecting the filling state of a mixed particle bed according to claim 1, characterized in that, The parameter system used to characterize the internal filling state of the mixed particle bed includes the filling rate and the contact rate between different particles; wherein, the filling rate characterizes the proportion of the particle volume to the effective filling space volume; and the contact rate between different particles characterizes the degree of contact between particles of different materials.

3. The method for detecting the filling state of a mixed particle bed according to claim 2, characterized in that, The formation of multiple sets of candidate filling state parameters includes: Set the value range for the filling rate and the value range for the contact rate of dissimilar particles respectively; The range of filling rate and the range of contact rate of dissimilar particles are processed according to a preset step size or a preset combination rule, and multiple sets of candidate filling state parameters are generated by cross-combination.

4. The method for detecting the filling state of a mixed particle bed according to claim 2, characterized in that, The step of converting the candidate filling state parameters into corresponding candidate porous medium equivalent parameters through the porous medium equivalent relationship includes: The void ratio is determined based on the fill ratio in the candidate fill state parameters; Based on the filling rate and dissimilar particle contact rate in the candidate filling state parameters, as well as the equivalent particle size of the mixed particle bed, the permeability and inertial drag parameters are calculated by introducing a correction term related to the dissimilar particle contact rate.

5. The method for detecting the filling state of a mixed particle bed according to claim 1, characterized in that, The simulation calculation process includes: In the flow field geometry model, a porous medium region is defined, and the fluid resistance within the porous medium region is set to be composed of a viscous resistance term and an inertial resistance term determined based on the equivalent parameters of the candidate porous medium. The simulation response data corresponding to each set of candidate filling states is obtained by solving multiple preset flow conditions formed under the preset flow boundary conditions.

6. The method for detecting the filling state of a mixed particle bed according to claim 5, characterized in that, Obtaining the actual response data of the mixed particle bed under actual working conditions includes: Pressure drop experiments were conducted under one or more of the preset flow conditions, and actual response data for multiple conditions were obtained based on the measured pressure difference between the two ends of the mixed particle bed.

7. The method for detecting the filling state of a mixed particle bed according to claim 6, characterized in that, The matching analysis includes: Matching analysis is performed using actual response data under a single operating condition and simulated response data under the corresponding operating condition, or matching analysis is performed using actual response data under multiple operating conditions and simulated response data; wherein, the matching analysis is achieved through at least one of the following methods: The actual response data is numerically compared and matched with the simulated response data; An optimized inversion algorithm is adopted, with the filling state parameters as variables, and the goal is to minimize the deviation between the simulated response data and the actual response data for iterative solution.

8. The method for detecting the filling state of a mixed particle bed according to claim 1, characterized in that, The method further includes: The mixed particle bed is divided into multiple interconnected porous media sub-regions along the filling height direction; By matching and analyzing the simulation response data and actual response data corresponding to each sub-region, the target filling state parameters corresponding to each sub-region are determined. The filling uniformity of the mixed particle bed is quantitatively evaluated based on the differences in target filling state parameters between each sub-region.

9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method for detecting the filling state of a mixed particle bed according to any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method for detecting the filling state of the mixed particle bed according to any one of claims 1 to 8.

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