Parameter Calibration Method Research Articles

Bridging Macro–Microscopic Parameters: Correspondence and Calibration Approaches

Clarifying the correlation between macro–microscopic granular parameters and establishing effective parameter calibration methods for determining granular microparameters is of great importance for numerical simulations of granular flows. Currently, there is a gap in the systematic study of small particles, using sandy soil as an example, and the development of rational and effective calibration methods. Through inter-particle friction experiments, particle–wall friction experiments, uniaxial compression experiments and numerical simulations, we studied the relationship between macroscopic and microscopic parameters. Based on these studies, appropriate empirical formulas were proposed, and based on the correlation of macroscopic and microscopic parameters, a method for calibrating microscopic parameters was presented. This approach uses empirical formulas for macroscopic and microscopic parameters in combination with the constraints imposed by the granular stiffness coefficient to calibrate granular parameters. This study provides a systematic process framework for the calibration of microscopic parameters of small particles, standardizing the parameter calibration process and improving both efficiency and quality.

Research on the Calibration Method of the Bonding Parameters of the EDEM Simulation Model for Asphalt Mixtures

To enhance the accuracy and reliability of the discrete element simulation software EDEM 2023 for pavement asphalt mixture simulation, three representative coarse aggregate particles were modeled in 3D using the SolidWorks 2018 software and imported into the EDEM 2023 software for particle filling. The Hertz–Mindlin with bonding contact model was used to construct the EDEM simulation model of asphalt mixtures, and the quadratic regression model of asphalt mixtures’ splitting tensile strength and four bonding parameters, namely, normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress, was found by the response surface methodology. The results show that the relationship between the significance magnitude of the four bonding parameters on the splitting tensile strength of the asphalt mixture simulation model is as follows: critical normal stress > shear stiffness per unit area > normal stiffness per unit area > critical shear stress. The calibration results of the bonding parameters were used for simulation verification, and the relative error between the simulation and actual splitting tensile strength was only −2.48%. The feasibility of this bonding parameter calibration method is demonstrated, and it can lay a foundation for EDEM to simulate the performance of asphalt mixtures on pavements with high-precision simulation.

A hybrid predictive model with an error-trigger adjusting method of thermal load in super-high buildings

The precise prediction of thermal load has consistently garnered attention owing to its significant impact on energy conservation in buildings. The methodologies employed primarily concentrate on modeling within steady-state conditions, utilizing time-series data or mechanism model on fixed parameters. However, given the pronounced time-varying and multifaceted disturbance characteristics associated with building loads, current approaches exhibit constrained efficacy in addressing abrupt fluctuations in demand load and managing data noise. This limitation consequently undermines the accuracy of predictions. This paper proposes a novel hybrid model and an error-trigger adjusting strategy for predicting the thermal load in super-high buildings. The model is constructed by combining a thermodynamic model and an error cancellation model. The former, derived from an examination of the variations of material and energy in buildings, is proposed in the form of an approximate resistance-capacitance structure. The latter is developed using a wavelet threshold denoising technique, in conjunction with a convolutional neural network and a long short-term memory network. A self-adaptive state transition algorithm has been proposed, which relies on dynamically adjusting factors within the feasible region to optimize the selection of unknown parameters in the thermodynamic model. To enhance the flexibility of the hybrid model in effectively respond to the intricacies and fluctuations within the thermal conditions of buildings, an error-trigger adaptive updating strategy and a parameter calibration method based on sensitivity analysis are established. The real-world application results demonstrate the effectiveness of the presented hybrid model and the adjusting strategy.

Autonomous Landing Strategy for Micro-UAV with Mirrored Field-of-View Expansion.

Positioning and autonomous landing are key technologies for implementing autonomous flight missions across various fields in unmanned aerial vehicle (UAV) systems. This research proposes a visual positioning method based on mirrored field-of-view expansion, providing a visual-based autonomous landing strategy for quadrotor micro-UAVs (MAVs). The forward-facing camera of the MAV obtains a top view through a view transformation lens while retaining the original forward view. Subsequently, the MAV camera captures the ground landing markers in real-time, and the pose of the MAV camera relative to the landing marker is obtained through a virtual-real image conversion technique and the R-PnP pose estimation algorithm. Then, using a camera-IMU external parameter calibration method, the pose transformation relationship between the UAV camera and the MAV body IMU is determined, thereby obtaining the position of the landing marker's center point relative to the MAV's body coordinate system. Finally, the ground station sends guidance commands to the UAV based on the position information to execute the autonomous landing task. The indoor and outdoor landing experiments with the DJI Tello MAV demonstrate that the proposed forward-facing camera mirrored field-of-view expansion method and landing marker detection and guidance algorithm successfully enable autonomous landing with an average accuracy of 0.06 m. The results show that this strategy meets the high-precision landing requirements of MAVs.

Calibration of discrete meta-parameters of bamboo flour based on magnitude analysis and BP neural network.

In the research and development of technology and equipment for bamboo products deep processing, such as filling, drying, and medicinal use of bamboo flour (BF), the poor compaction and fluidity of BF materials entails the need for accurate discrete element model (DEM) and BF parameters to provide a reference for the simulation of BF processing operationsand the development of related equipment. The average particle size of the 5 types of BFs ranges from 0.136 mm to 0.293 mm, and the small particle size of BF particles causes to the number of BF particles in bamboo processing equipment to reach tens of millions or even billions. When conventional methods are used for simulation, ordinary computers cannot provide the required computing power. To address the aforementioned challenges, this paper proposes a calibration method for the discrete element contact parameters of BFs based on dimensional analysis and a back propagation (BP) neural network. Using particle scaling theory and dimensional analysis methods, the average particle size of the BF was increased to 1 mm, and the main discrete element contact parameters of the five types of BF to be tested were used as input layers. The injection method and sidewall collapse method were used to obtain the angle of repose (AR) as the output layer. Fifty groups were randomly selected using MATLAB for EDEM simulation, and the simulation results were trained using the BP neural network algorithm; an ideal neural network model was obtained, the discrete element parameters of different BFs were predicted, and physical experiments were performed to verify two types of AR and mold hole compression under calibrated parameters. The relative error between the simulated AR obtained through calibration parameters and the physical experimental values is less than 2.3%. Through BF parameter validity verification, the simulated maximum compression displacement and compression ratio after stabilization were 34.81 mm and 0.477, which were close to the actual experimental results of 34.77 mm and 0.461, respectively, verifying the accuracy of the neural network prediction model. The research results provide a reference for the simulation of BF processing operations and the development of related equipment.

Three-dimensional deformation monitoring of internal nodes of large-span suspended dome structure using videogrammetry under camera instability

Large-span suspended domes often serve as landmarks for cities or countries. This paper proposes a videogrammetric method for monitoring the 3D deformation of internal key nodes in such structures, even under conditions of camera instability. Utilizing circular targets strategically placed on the measurement area, a dynamic parameter calibration method is developed to estimate the instantaneous position and orientation of cameras relative to the world coordinate system, minimizing camera motion-induced errors. Combined with surface targets on the rigid structure and local coordinate frame transformation, the 3D displacement response of structural internal nodes can be precisely measured using the proposed tracking method. The effectiveness and accuracy of the proposed methods were validated through exhaustive experiments, improving positioning accuracy for the structural internal nodes from 47.24 mm to 1.62 mm after dynamic parameter correction. This methodology offers a low-cost, easily implemented, and non-destructive vision-based solution for the long-term monitoring of large-span suspended domes.

Accurate simulation of extreme rainfall–flood events via an improved distributed hydrological model

Recently, the high incidence of extreme rainfall and flood events worldwide has severely harmed regional economies and societies. Therefore, flood simulations and forecasts, which can provide key technical support for regional flood control and disaster reduction, are urgently needed. The Liuxihe model, as a fully distributed, physically based hydrological model, was improved in this study to simulate extreme rainfall flood events in the Beijiang River Basin. This basin is a famous rainstorm centre in Guangxi Province, China. In this work, the Liuxihe model is improved in two aspects: first, its structure and runoff generation and confluence algorithm are improved, and second, the parameter calibration method is optimised. These two adjustments improve the flood simulation performance of the model and reduce the uncertainty of the simulation results. The results revealed that the flood simulated by the improved Liuxihe model was strongly consistent with the measured values, and the index values of the Nash coefficient, correlation coefficient, process relative error, and flood peak flow error performed very well in the scheme evaluation; in particular, the mean process relative error, flood peak error, and peak time difference decreased by 62%, 63%, and 80%, respectively, after model improvement. The error indicators of the simulation were within the allowable error range from the Standard for Hydrological Information and Hydrological Forecasting (GB/T-22482–2008), which meets the accuracy requirements of flood forecasting in the local hydrological department and can be used as a practical operational plan for flood forecasting. These satisfactory flood simulation results showed that the model and algorithm were improved; thus, the improved Liuxihe model can provide important theoretical guidance for regional flood forecasting and flood disaster mitigation.

Industrial robot calibration optimization method based on R-optimal criterion and improved IOOPS algorithm

Abstract In this paper, a novel kinematic parameter calibration method based on the R-optimal criterion and the improved iterative one-by-one pose search (IOOPS) algorithm is proposed to improve the end-effector positioning accuracy of industrial robots. First, a novel industrial robot error calibration model based on the product of exponential model and generalized error model is established, and the ideal pose transformation matrix from the robot base coordinate system to the laser tracker measurement coordinate system is obtained using the least-squares method, thereby obtaining the initial parameter vector of the robot calibration system. Second, various joint angle values at different positions within the robot’s workspace and the corresponding end-effector coordinate data, namely calibration sampling point data, are collected. Subsequently, the improved IOOPS algorithm combined with the observability index O R proposed by the R-optimal criterion is used to select the optimized calibration sampling points. Finally, the optimized sampling point data are combined with the Levenberg–Marquardt algorithm to optimize the parameters and obtain the optimal kinematic parameters. The experimental results show that the average end-effector error of the robot decreases from 0.5822 to 0.2492 mm after calibration using this method, demonstrating the effectiveness of the optimized sampling points in the calibration process achieved through the improved algorithm and the new observability index.

Prediction of rotational bending fatigue life of bearing steel based on damage mechanics

Based on the theory of continuous damage mechanics, this paper proposes a fatigue damage evolution model and numerical calculation method considering the influence of vacuum chemical heat treatment process, and studies the rotational bending fatigue damage analysis and life prediction method of M50NiL bearing steel. Firstly, the constitutive model, fatigue damage evolution equation and material parameter calibration method in the theoretical model are given. Secondly, based on the ABAQUS platform, the damage mechanics-finite element numerical calculation method of fatigue damage analysis considering the influence of hardened layer is realized by writing the UMAT subroutine; then, the rotational bending fatigue test of M50NiL bearing steel treated by vacuum chemical heat treatment of quenching and tempering, carburizing and carbonitriding is carried out, and the influence of heat treatment process on fatigue performance is analyzed; finally, based on the proposed fatigue damage model and numerical calculation method, the rotational bending fatigue life of M50NiL bearing steel is predicted, and the results are compared with the experimental results to verify the correctness of the proposed method.

Reliable simulation analysis for high-temperature inrush water hazard based on the digital twin model of tunnel geological environment

In complex mountainous terrains, tunnel construction faces unique challenges from high-temperature water inrush hazards, a systemic risk arising from the interplay of stress, seepage, and temperature fields. Traditional simulation methods, focusing on isolated disaster scenarios, fall short in addressing the multifaceted nature of these risks due to geological ambiguity and data incompleteness. Digital twin technology presents an effective solution to these challenges; however, its core challenge lies in how to utilize digital twin technology for data-model-co-driven simulation analysis of coupled multi-physical fields in situations of incomplete data. This paper introduces a digital twin paradigm for the simulation analysis of water inrush, which significantly enhances efficiency and accuracy through the integration of advanced machine learning and finite element analysis techniques. Specifically, this is achieved by combining a high-precision geological modeling method based on Gaussian Processes (GP) with a parameter calibration method through Gaussian Process-Differential Evolution (GP-DE) back-analysis. Firstly, a voxel structure is utilized to integrate the multi-field attribute features of the tunnel environment. Secondly, through the integration of multi-source advanced geological prediction data, we construct a dynamic digital twin model of the tunnel environment leveraging machine learning techniques. To overcome the issue of low modeling accuracy, the GP is employed, enhancing the exploitation of latent information within multi-source geophysical data. Lastly, we utilize the GP-DE back-analysis method to calibrate the parameters of the tunnel environment, thereby enhancing the precision and reliability of water inrush simulations. The method has been validated through application to a section of an ultra-high-temperature water inrush tunnel in China, featuring a burial depth of 230 meters. The accuracy of the method is corroborated by the monitoring data from the tunnel, supporting dynamic optimization design and safety prevention measures during construction.

Prediction of rotational bending fatigue life of bearing steel based on damage mechanics

Based on the theory of continuous damage mechanics, this paper proposes a fatigue damage evolution model and numerical calculation method considering the influence of vacuum chemical heat treatment process, and studies the rotational bending fatigue damage analysis and life prediction method of M50NiL bearing steel. Firstly, the constitutive model, fatigue damage evolution equation and material parameter calibration method in the theoretical model are given. Secondly, based on the ABAQUS platform, the damage mechanics-finite element numerical calculation method of fatigue damage analysis considering the influence of hardened layer is realized by writing the UMAT subroutine; then, the rotational bending fatigue test of M50NiL bearing steel treated by vacuum chemical heat treatment of quenching and tempering, carburizing and carbonitriding is carried out, and the influence of heat treatment process on fatigue performance is analyzed; finally, based on the proposed fatigue damage model and numerical calculation method, the rotational bending fatigue life of M50NiL bearing steel is predicted, and the results are compared with the experimental results to verify the correctness of the proposed method.

Calibration of Discrete Element Method Parameters for a High-Fidelity Lunar Regolith Simulant Considering the Effects of Realistic Particle Shape

The Discrete Element Method (DEM) is an important tool for investigating the geotechnical properties of lunar regolith. The accuracy of DEM simulations largely depends on precise particle modeling and the appropriate selection of mesoscopic parameters. To enhance the reliability and accuracy of the DEM in lunar regolith studies, this paper utilized the high-fidelity IRSM-1 lunar regolith simulant to construct a DEM model with realistic particle shapes and conducted an angle of repose (AoR) simulation test. The optimal DEM parameters were calibrated using a combination of the Plackett–Burman test, steepest ascent test, and Box–Behnken design. The results indicate that the sliding friction coefficient, rolling friction coefficient, and surface energy significantly influence the simulation AoR. By optimizing against the measured AoR using a second-order regression model, the optimal parameter values were determined to be 0.633, 0.401, and 0.2, respectively. Under these optimal parameters, the error between the simulation and experimental AoR was 2.1%. Finally, the calibrated mesoscopic parameters were validated through a lifting cylinder test, showing an error of 6.3% between the simulation and experimental results. The high similarity in the shape of the AoR further confirms the accuracy and reliability of the parameter calibration method. This study provides a valuable reference for future DEM-based research on the mechanical and engineering properties of lunar regolith.

A Kriging-based method for calibrating the bonded-particle model parameters of iron ore

In order to successfully simulate the crushing process of iron ore in a gyratory crusher, it is crucial to precisely model the iron ore based on the bonded-particle model, this requires reasonably calibrated micromechanical parameters in the model. In this study, a calibration method for iron ore BPM bonding parameters was proposed. Firstly the combination of the design of experimental method and discrete element simulation tests was used to investigate the effects of normal stiffness per unit area, shear stiffness per unit area, critical normal stress, and critical shear stress on the compressive strength and critical strain properties of the BPM. Furthermore, a fitted mathematical model between micro-mechanical parameters and macro-mechanical property parameters of iron ore was established. According to this, a bonding parameter calibration method based on Kriging surrogate model for the iron ore bonded-particle model is proposed. Finally, the micromechanical parameters of the calibrated actual iron ore bonded-particle model were verified by simulation test, which confirmed the accuracy and reliability of the proposed method and provided a new idea for the subsequent related research.

The irtQ R package: a user-friendly tool for item response theorybased test data analysis and calibration.

Computerized adaptive testing (CAT) has become a widely adopted test design for high-stakes licensing and certification exams, particularly in the health professions in the United States, due to its ability to tailor test difficulty in real time, reducing testing time while providing precise ability estimates. A key component of CAT is item response theory (IRT), which facilitates the dynamic selection of items based on examinees' ability levels during a test. Accurate estimation of item and ability parameters is essential for successful CAT implementation, necessitating convenient and reliable software to ensure precise parameter estimation. This paper introduces the irtQ R package (http://CRAN.R-project.org/), which simplifies IRTbased analysis and item calibration under unidimensional IRT models. While it does not directly simulate CAT, it provides essential tools to support CAT development, including parameter estimation using marginal maximum likelihood estimation via the expectation-maximization algorithm, pretest item calibration through fixed item parameter calibration and fixed ability parameter calibration methods, and examinee ability estimation. The package also enables users to compute item and test characteristic curves and information functions necessary for evaluating the psychometric properties of a test. This paper illustrates the key features of the irtQ package through examples using simulated datasets, demonstrating its utility in IRT applications such as test data analysis and ability scoring. By providing a user-friendly environment for IRT analysis, irtQ significantly enhances the capacity for efficient adaptive testing research and operations. Finally, the paper highlights additional core functionalities of irtQ, emphasizing its broader applicability to the development and operation of IRT-based assessments.

Creating cell-specific computational models of stem cell-derived cardiomyocytes using optical experiments.

Human induced pluripotent stem cell-derived cardiomyocytes (iPSC-CMs) have gained traction as a powerful model in cardiac disease and therapeutics research, since iPSCs are self-renewing and can be derived from healthy and diseased patients without invasive surgery. However, current iPSC-CM differentiation methods produce cardiomyocytes with immature, fetal-like electrophysiological phenotypes, and the variety of maturation protocols in the literature results in phenotypic differences between labs. Heterogeneity of iPSC donor genetic backgrounds contributes to additional phenotypic variability. Several mathematical models of iPSC-CM electrophysiology have been developed to help to predict cell responses, but these models individually do not capture the phenotypic variability observed in iPSC-CMs. Here, we tackle these limitations by developing a computational pipeline to calibrate cell preparation-specific iPSC-CM electrophysiological parameters. We used the genetic algorithm (GA), a heuristic parameter calibration method, to tune ion channel parameters in a mathematical model of iPSC-CM physiology. To systematically optimize an experimental protocol that generates sufficient data for parameter calibration, we created in silico datasets by simulating various protocols applied to a population of models with known conductance variations, and then fitted parameters to those datasets. We found that calibrating to voltage and calcium transient data under 3 varied experimental conditions, including electrical pacing combined with ion channel blockade and changing buffer ion concentrations, improved model parameter estimates and model predictions of unseen channel block responses. This observation also held when the fitted data were normalized, suggesting that normalized fluorescence recordings, which are more accessible and higher throughput than patch clamp recordings, could sufficiently inform conductance parameters. Therefore, this computational pipeline can be applied to different iPSC-CM preparations to determine cell line-specific ion channel properties and understand the mechanisms behind variability in perturbation responses.

A FDEM study on the mechanical properties and failure behavior of soft-hard interbedded rocks considering the size effect

Soft–hard interbedded rocks are widely distributed at the Earth’s surface, and their mechanical properties and failure behavior directly affect the stability of local tunnel and slope engineering projects. Previous studies have rarely considered the influence of size effects on the mechanical properties and failure behavior of such rocks. Therefore, this paper used the combined finite–discrete element numerical method (FDEM) to study the mechanical properties and failure behavior of soft–hard interbedded rock samples considering the size effect. First, appropriate input parameters were calibrated and verified by using a new parameter calibration method. Second, the effects of the element size and loading rate were studied to obtain appropriate model parameters. Finally, the effects of the layer number, sample size, and height–diameter ratio of composite rock samples on their mechanical properties and failure behavior at different layer dip angles and layer thickness ratios were investigated. The results support the following findings: (1) For the composite rock samples with a layer thickness of 10 mm, reliable simulation results can be obtained by using a 2.2 mm element size, and the loading rate should not exceed 0.2 m/s in FDEM numerical modeling. (2) The number of layers in the sample should be at least 5, and when the height–diameter ratio is a constant 2.0, the height of the sample should not be less than 110 mm. (3) As the height–diameter ratio of the composite rock samples increases, both the compressive strength and elastic modulus decrease for all layer dip angles considered, but the rock failure mode changes for layer dip angles of 15–75°; in addition, the sample size effect is most significant for layer dip angles of 60° and 75°. (4) Taking horizontally layered composite rock samples as examples, both the compressive strength and elastic modulus of samples with different layer thickness ratios decrease with increasing height–diameter ratio and their failure modes also depend on the height–diameter ratio.

A study on parameter calibration of a general crop growth model considering non-foliar green organs

Accurate crop yield estimation is essential for ensuring national food security and sustainable development. The crop growth model is one of the primary methods for estimating production and has been effectively applied in simulating crop growth and development, environmental factor effects and yield formation processes. However, many related studies have focused on Gramineae crops, such as wheat, rice and maize, while there has been little research on rape and soybean yield estimation. Non-foliar green organs such as rape siliques and soybean pods make vital contributions to plant photosynthesis and influence crop output. The parameter calibration method based on the leaf area index (LAI) cannot satisfy the existing demand for high-precision yield estimation for crops with active non-foliar green organs. Therefore, a new method for crop growth model calibration was proposed, which considers non-foliar green organs and plant photosynthetic succession processes and combines them with those of leaves to construct a photosynthetic area index (PAI). With wheat, rape and soybean as the research objects, their yields were simulated via the proposed crop growth model calibration method. The results showed that using the proposed calibration method to calibrate World Food Study (WOFOST) model parameters on the basis of the PAI improved the yield estimation accuracy over that of the crop model calibration method based on the LAI. The determination coefficient (R2) of the total dry weight of storage organs (TWSO) simulation value increased from 0.73 (using the LAI) to more than 0.90 (using the PAI). The R2 values of TWSO at the rape calibration and verification points were 0.910 and 0.922, respectively, and the R2 values of TWSO at the soybean calibration and verification points were 0.741 and 0.926, respectively. The above results verified the feasibility and effectiveness of the proposed calibration method. Consequently, the application of the crop model calibration method proposed in this paper is important for accurately estimating the crop yield of plants with active non-foliar green organs, promoting the expansion of general crop models for different types of crops and achieving high-precision crop yield estimations.

Advanced cyclic constitutive model and parameter calibration method for austenitic stainless steel

Austenitic stainless steel is recognized for its potential in improving structural seismic resistance due to its exceptional ductility. This study seeks to analyse the material characteristics and cyclic constitutive model of austenitic stainless steel under cyclic loading using a combination of experiments and numerical studies. The primary goal is to enhance the precision of numerical simulations predicting the seismic performance of stainless steel and to minimize the costs associated with calibrating the parameters of the cyclic constitutive model. By conducting cyclic loading experiments on stainless steel with different plate thicknesses, the hysteresis curves under different loading protocols were obtained. The loading protocol significantly influences the cyclic hardening behaviour of stainless steel. To calibrate material parameters in the Chaboche model more effectively, a new parameter calibration method based on genetic algorithm optimization is proposed. Additionally, a method is proposed to calibrate the material parameters of the Hu model by utilizing the monotonic tensile stress-strain curve of austenitic stainless steel, which provides a new material model option for the numerical simulation of stainless steel seismic performance. The applicability of the Chaboche model and the Hu model in numerical simulations of stainless steel members is then verified and compared using existing seismic performance test results. The findings indicate that the Hu model offers more accurate numerical simulations of cyclic loading on austenitic stainless steel members compared to the Chaboche model.

On-orbit high-precision calibration for deep-coupled parameters of star sensor and gyroscope systems.

To enhance the dynamic performance and stability of spacecraft attitude measurement, a commonly adopted approach is to integrate a star sensor with an inertial gyroscope. While an integrated sensor system offers benefits in high-precision and high-dynamic scenarios, it also introduces errors in the extrinsic parameters of the camera and gyroscope, directly contributing to increased fixed error in attitude determination. Hence, real-time correction of integrated system parameters becomes crucial for enhancing the accuracy of attitude estimation. However, a deep coupling relationship exists between the camera's principal point and the extrinsic parameters of the two sensors, unified calibration of system parameters increases the computational time to decouple all the parameters. In response to this challenge, a stepwise calibration method for system parameters is proposed. Firstly, the calibration of camera intrinsic parameters leverages the principle of invariant interior star angles, which are independent of extrinsic parameters. Secondly, based on the observation vector error model of the integrated sensor, observation equations and state equations are constructed, followed by proposing a 4-position maneuver strategy based on observability analysis to achieve rapid decoupling of gyroscope bias, attitude error, and extrinsic parameters. Simulation results affirm the effectiveness of the proposed method, extrinsic parameter errors reaching the arc-second level.

Calibration method of discrete element parameters of crushed coal based on mechanical and engineering tests

Current discrete element parameter calibration methods primarily rely on mechanical tests to analyze particle properties and often overlook the machinery's interaction with the particles. The extensive variation in particle sizes of crushed coal poses challenges in accurately applying parameters derived from mechanical test calibrations to industrial simulations. DEM models of mechanical tests for coal were developed to examine how parameters influence coal's mechanical properties through factor analysis. Simplified engineering test models were developed based on mining equipment, with the equipment responses used as indicators to optimize mechanical test calibration parameters. On this basis, a calibration method of discrete elemental parameters of coal based on the crushing simulation of mining equipment was proposed. This method was validated through mechanical and engineering simplification tests, resulting in a mean error of <10 % in the time-varying response. The research findings enable calibration of discrete element parameters for crushed coal in industrial simulation.