Parameters Of Simulation Model Research Articles

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Abstract Several computational models have been introduced in recent years to
yield comprehensive insights into microstructural evolution analyses. However, the
identification of the correct input parameters to a simulation that corresponds to a
certain experimental result is a major challenge on this length scale. To complement
simulation results with experimental data (and vice versa) is not trivial since, e.g.,
simulation model parameters might lack a physical understanding or uncertainties
in the experimental data are neglected. Computational costs are another challenge
mesoscale models always have to face, so comprehensive parameter studies can be
costly. In this paper, we introduce a surrogate model to circumvent continuum
dislocation dynamics simulation by a data-driven linkage between well-defined input
parameters and output data and vice versa. We present meaningful results for a
forward surrogate formulation that predicts simulation output based on the input
parameter space, as well as for the inverse approach that derives the input parameter
space based on simulation as well as experimental output quantities. This enables, e.g.,
a direct derivation of the input parameter space of a continuum dislocation dynamics
simulation based on experimentally provided stress-strain data.

HGS-PDAF (version 1.0): a modular data assimilation framework for an integrated surface and subsurface hydrological model

Abstract. This article describes a modular ensemble-based data assimilation (DA) system which is developed for an integrated surface–subsurface hydrological model. The software environment for DA is the Parallel Data Assimilation Framework (PDAF), which provides various assimilation algorithms like the ensemble Kalman filters, non-linear filters, 3D-Var and combinations among them. The integrated surface–subsurface hydrological model is HydroGeoSphere (HGS), a physically based modelling software for the simulation of surface and variably saturated subsurface flow, as well as heat and mass transport. The coupling and capabilities of the modular DA system are described and demonstrated using an idealised model of a geologically heterogeneous alluvial river–aquifer system with drinking water production via riverbank filtration. To demonstrate its modularity and adaptability, both single and multivariate assimilations of hydraulic head and soil moisture observations are demonstrated in combination with individual and joint updating of multiple simulated states (i.e. hydraulic heads and water saturation) and model parameters (i.e. hydraulic conductivity). With the integrated model and this modular DA framework, we have essentially developed the hydrologically and DA-wise robust toolbox for developing the basic model for operational management of coupled surface water–groundwater resources.

小区小麦联合收获机割台清理离散元仿真参数标定

The analysis of the clearing process of the cutting deck of a small plot wheat combine harvester requires the use of discrete element simulation methods. However, the current simulation test lacks the contact parameters such as wheat stalk and stalk-seed. In this paper, the wheat stalks and seeds at harvest time were taken as the research objects, and the calibration study of the discrete element simulation model parameters of stalks and stalk-seeds was carried out by means of mechanical test determination and EDEM software simulation. The stiffness coefficients of wheat stalks were determined by mechanical tests; the average values of wheat stalk stacking angle of 39.22° and wheat stalk-seed stacking angle of 44.41° were obtained by stacking angle tests. By the steepest climb test and binary regression test, the stalk normal stiffness coefficient was determined to be 5e+08N/m2 and tangential stiffness was determined to be 6.35e+08N/m2; the stalk-stalk collision recovery coefficient was obtained to be 0.551, static friction coefficient was obtained to be 0.797, and rolling friction coefficient was obtained to be 0.079 by the two-level analytical factorization test, the steepest climb test, and the three-factor response surface test. Based on this, the average value of wheat stalk-seed stacking angle was obtained to be 39.22° and the average value of wheat stalk-seed stacking angle was obtained to be 44.41° by the stacking angle test. On this basis, the coefficient of recovery of stalk-stalk collision was 0.434, the coefficient of static friction was 0.884, and the coefficient of rolling friction was 0.339 obtained by the three-factor response surface test. Three validation experiments were carried out by substituting the obtained parameters into the simulation test, and the error values were close to the error value %0.255 in the model, which proved that the experimental data were reliable.

FRF-based model updating of liquid-filled pipeline system considering tolerance intervals of test errors in the antiresonant frequencies

The frequency response function (FRF)-based model updating technique is an effective method to calibrate system parameters of the dynamic simulation model of pipeline systems. However, the existing FRF-based model updating methods is difficult to solve the problem of the test errors in the antiresonant frequencies, which significantly affects the accuracy of the updated results. Hence, this paper proposes a new FRF-based model updating method of the liquid-filled pipeline considering the tolerance intervals of test errors in the antiresonant frequencies. In this method, the effect of test errors on FRFs is quantified by employing the interval method, and then the tolerance intervals of test errors in the antiresonant frequencies can be identified from the interval FRFs. The obtained tolerance intervals are integrated into the objective function of the FRF-based model updating through the penalty function method, and the optimal system parameters can be obtained by using a global optimization algorithm. Compared with the existing FRF-based model updating methods, the proposed method can not only effectively reduce the effect of test errors in the measured FRFs on the updated results but also alleviate the ill-condition of model updating calculations, so that the accuracy and convergence of model updating calculations are improved simultaneously. Numerical and experimental validations of the proposed method are carried out by using an L-shaped water-filled pipeline system. The performances of the proposed method are compared with the typical FRF-based model updating method. The results demonstrate that the proposed method can obtain the updated results with higher accuracy and reliability than the typical method.

A robust nano-indentation modeling method for ion-irradiated FCC single crystals using strain-gradient crystal plasticity theory and particle swarm optimization algorithm

Addressing the challenge of identifying an appropriate set of material and irradiation parameters for accurate simulation models using crystal plasticity finite element method (CPFEM), this study proposes a novel two-stage method for nano-indentation modeling of ion-irradiated face-centered cubic (FCC) materials. It includes implementing the strain-gradient crystal plasticity (SGCP) theory with irradiation effects and the calibration of simulation parameters using the particle swarm optimization (PSO) algorithm with experimental data. The proposed method consists of two stages: establishing CPFEM without irradiation effects in stage 1 and modeling irradiation effects based on CPFEM in stage 2. Modeling the nano-indentation test of ion-irradiated stainless steel 304 (SS304) using real experimental data is conducted to evaluate the efficiency of the proposed method. The accuracy of the calibration method using PSO is verified through comparisons between simulation and experimental results for force-indentation depth and hardness-indentation depth relationships under both unirradiated and irradiated conditions. Moreover, effect of ion-irradiation on the mechanical behavior during the nano-indentation of single crystal SS304 is also examined to demonstrate that the proposed method is a powerful approach for nano-indentation modeling of ion-irradiated FCC single crystals using SGCP theory and the PSO algorithm.

Household energy vulnerability evaluation in southern Spain through parametric energy simulation models and socio-economic data

When proposing the most suitable strategies for retrofitting the residential stock it is essential to consider energy vulnerability, the current state of stock, users’ economic capacity and climate. This work analyses energy vulnerability on regional and neighbourhood scale, applied to the social housing stock in southern Spain. Three key vulnerability indicators are assessed through extensive public databases and validated parametric energy simulation models: 1) buildings energy performance, based on simulated indoor thermal comfort; 2) users’ socio-economic vulnerability, based on their income levels; and 3) climate influence, implementing future climate data into the simulations. The different energy vulnerability levels identified in the research are of great interest for retrofitting the existing stock as they help establish priority action guidelines. It is concluded that social housing in Andalusia currently exhibits a noticeable level of energy vulnerability in winter, with a greater severity of undercooling compared to overheating. In 2050, overheating in summer will significantly worsen, generally surpassing cooling. Despite of the Mediterranean climate, there are noticeable comfort differences between the cities analysed, with Cordoba and Granada being particularly relevant. Finally, a worse thermal performance in winter of the H-block typology was observed, which in summer occur for the linear block buildings.

Three-dimensional modeling of moisture transport in wood using near-infrared hyperspectral imaging and X-ray computed tomography in conjunction with finite element analysis

In past studies, finite element analysis (FEA) methods have been used to simulate the thermal and moisture coupling of wood. However, challenges remain in achieving high-quality three-dimensional (3D) simulations, mainly because of the heterogeneous and complex structure of wood and its difficult-to-detect internal structure, which makes modeling challenging, in addition to the lack of robust experimental techniques to validate simulation results. In this study, the FEA simulation model was refined by combining X-ray computed tomography (CT) and near-infrared hyperspectral imaging (NIR-HSI). CT was used to probe the 3D density of wood, and a novel FEA tetrahedral mesh was constructed based on the results. The NIR-HSI method visualizes the moisture distribution during adsorption and desorption inside the wood. This result is then used to adjust the parameters of the FEA simulation model and as a reference value to evaluate the simulation results. The visualization and simulation results fit well with the theoretical properties. The simulation results can more accurately reflect the spatial distribution and transfer trend of wood moisture at different points in time. Therefore, the CT and NIR-HSI-based 3D heat and moisture-coupled FEA model of wood proposed in this study can be used as a basis for optimizing drying parameters to provide high-quality wood.

Calibration and Testing of Parameters for the Discrete Element Simulation of Soil Particles in Paddy Fields

The parameters of the discrete element simulation model for rice field soils serve as valuable data references for investigating the dynamic characteristics of the walking wheel of high-speed precision seeding machinery in paddy fields. The research specifically targets clay loam soil from a paddy field in South China. Calibration of essential soil parameters was achieved using EDEM_2022 software (and subsequent versions) discrete element simulation software, employing the Edinburgh Elasto-Plastic Adhesion (EEPA) nonlinear elastic-plastic contact model. The tillage layer and plough sub-base layer underwent calibration through slump and uniaxial compression tests, respectively. Influential contact parameters affecting slump and axial pressure were identified through a Plackett–Burman test. The optimal contact parameter combinations for the discrete element model of the tillage layer and plough sub-base layer were determined via a quadratic rotational orthogonal test. The accuracy of the discrete element simulation model’s parameters for paddy field soils was further validated through a comparative analysis of the simulation test’s cone penetration and the field soil trench test. Results indicate that the Coefficient of Restitution, surface energy, Contact Plasticity Ratio, and Tensile Exp significantly influence slump (p < 0.05). Additionally, the Coefficient of Restitution, Contact Plasticity Ratio, coefficient of rolling friction, and Tangential Stiff Multiplier significantly impact axial pressure (p < 0.05). Optimal contact parameters for the plough layer were achieved with a particle recovery coefficient of 0.49, a surface energy of 18.52 J/m2, a plastic deformation ratio of 0.45, and a tensile strength of 3.74. For the plough subsoil layer, optimal contact parameters were a particle recovery coefficient of 0.47, a coefficient of interparticle kinetic friction of 0.32, a plastic deformation ratio of 0.49, and a tangential stiffness factor of 0.31. Results from the cone penetration test reveal no significant disparity in compactness between the actual experiment and the simulation test. The calibrated discrete element model’s contact parameters have been verified as accurate and reliable. The findings of this study offer valuable data references for understanding the dynamic characteristics of the walking wheel of the entire machinery in high-speed precision seeding in paddy fields.

Project for the development of the Eurasian transport framework in the countries of Central Asia

The paper puts the importance of an international transport corridor for the Republic of Kazakhstan. Two directions of railway communication within the latitudinal Central Eurasian Corridor have been identified. A technique has been developed for applying simulation modeling to identify the bottlenecks on the way of railway flows. Bottlenecks include the intersections of vehicular and pedestrian traffic with rail traffic. In such places, conflicting flows are formed, which may be accompanied by a merger of individual flows. Using the methods of structural analysis, synthesis, system analysis and step-by-step inclusion (or exclusion), the input parameters of simulation models of objects and processes have been established for the case study of Aktobe city in the Republic of Kazakhstan. A graphical model of the behavior of groups of objects for conflicting flows in bottlenecks has been developed, being the basis for the simulation model.

Study of Error Flow for Hydraulic System Simulation Models for Construction Machinery Based on the State-Space Approach

This study presents an error flow research method for simulation models of hydraulic systems in construction machinery based on the state-space approach, aiming to ensure the reliable application of digital twin models. Initially, a comprehensive analysis of errors in the simulation modeling of hydraulic systems in construction machinery was conducted, highlighting simulation model parameters as the primary error sources. Subsequently, a set of metrics for assessing the accuracy of simulation models was developed. Following this, an error flow analysis method for simulation models of hydraulic systems in construction machinery was explored based on the state space approach, delving into the sources, transmission, and accumulation of errors in the simulation modeling of valve-controlled cylinder systems. The research results unequivocally indicate that the spring stiffness, viscous damping coefficient, and hydraulic cylinder external leakage coefficient are critical parameters affecting the accuracy of valve-controlled cylinder system simulation models. Furthermore, it was observed that the simulation model of the control valve has a significantly greater impact on the errors in the valve-controlled cylinder system simulation model than the hydraulic cylinder model. In conclusion, the reliability of the error flow model was confirmed through simulation experiments, revealing a maximum relative error of only 3.73% between the error flow model and the results of the simulation experiments.

Identification of Johnson-Cook material model parameters for laser shock peening process simulation for AA2024, Ti–6Al–4V and Inconel 718

This paper addresses the identification of Johnson-Cook material model parameters for the simulation of high strain rate processes such as laser shock peening. A combined numerical and experimental approach is described for the identification of the material parameters for AA2024-T3, Ti–6Al–4V, and Inconel 718 alloys. Validation of the parameters was performed based on depth-resolved residual stress profile evaluation after both experimental and numerical laser shock peening application. However, it has been observed that the strain rate-dependent coefficient identified at low strain rates is insufficient for accurately representing the behavior of the Ti–6Al–4V alloy. Consequently, there is a need to identify and determine the appropriate parameter specifically tailored to the strain rates that are relevant to the intended application of the material. The outcomes of this study provide significant insights into the accurate identification of Johnson-Cook material model parameters for laser shock peening simulations. The findings emphasize the critical importance of considering the strain rate-dependent coefficient C in the Johnson-Cook material model to enhance the accuracy and precision of representing material behavior in simulation efforts.

A simulation modeling methodology considering random multiple shots for shot peening process

Abstract Shot peening (SP) process is a typical surface strengthening process for metal and metal matrix composites, which can significantly improve the fatigue life and strength. The traditional SP simulation model falls short as it only takes into account one or a few shots, proving insufficient for accurately simulating the entire impact process involving hundreds of shots. In this study, a random multiple shots simulation modeling methodology with hundreds of random shots is proposed to simulate the impact process of SP. In order to reduce the simulation error, the random function Rand of MATLAB is used to generate the shot distributions many times, and the shot distribution closest to the average number is selected and the three-dimension parametric explicit dynamics numerical simulation model is built using ABAQUS software. Orthogonal experiments are carried out to investigate the influences of shot diameter, incident impact velocity, and angle on the residual stress distribution, roughness, and specimen deformation. Results showed that the average relative errors of maximum residual compressive stress, roughness, and deformation of specimen between simulation model and experimental value are 30.99, 16.14, and 16.73%, respectively. The primary factors affecting residual stress and deformation is shot diameter, and the main factor affecting roughness is impact velocity.

Mamdani vs. Takagi-Sugeno Fuzzy Inference Systems in the Calibration of Continuous-Time Car-Following Models.

The transition to intelligent transportation systems (ITSs) is necessary to improve traffic flow in urban areas and reduce traffic congestion. Traffic modeling simplifies the understanding of the traffic paradigm and helps researchers to estimate traffic behavior and identify appropriate solutions for traffic control. One of the most used traffic models is the car-following model, which aims to control the movement of a vehicle based on the behavior of the vehicle ahead while ensuring collision avoidance. Differences between the simulated and observed model are present because the modeling process is affected by uncertainties. Furthermore, the measurement of traffic parameters also introduces uncertainties through measurement errors. To ensure that a simulation model fully replicates the observed model, it is necessary to have a calibration process that applies the appropriate compensation values to the simulation model parameters to reduce the differences compared to the observed model parameters. Fuzzy inference techniques proved their ability to solve uncertainties in continuous-time models. This article aims to provide a comparative analysis of the application of Mamdani and Takagi-Sugeno fuzzy inference systems (FISs) in the calibration of a continuous-time car-following model by proposing a methodology that allows for parallel data processing and the determination of the simulated model output resulting from the application of both fuzzy techniques. Evaluation of their impact on the follower vehicle considers the running distance and the dynamic safety distance based on the observed behavior of the leader vehicle. In this way, the identification of the appropriate compensation values to be applied to the input of the simulated model has a great impact on the development of autonomous driving solutions, where the real-time processing of sensor data has a crucial impact on establishing the car-following strategy while ensuring collision avoidance. This research performs a simulation experiment in Simulink (MATLAB R2023a, Natick, MA, USA: The MathWorks Inc.) and considers traffic data collected by inductive loops as parameters of the observed model. To emphasize the role of Mamdani and Takagi-Sugeno FISs, a noise injection is applied to the model parameters with the help of a band-limited white-noise Simulink block to simulate sensor measurement errors and errors introduced by the simulation process. A discussion based on performance evaluation follows the simulation experiment, and even though both techniques can be successfully applied in the calibration of the car-following models, the Takagi-Sugeno FIS provides more accurate compensation values, which leads to a closer behavior to the observed model.

Exploring an intelligent adaptation method of hydrological model parameters for flood simulations based on the light gradient-boosting machine

Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods.

Performance in generation: An automatic generalizable generative-design-based performance optimization framework for sustainable building design

Performance-based design has become increasingly important for designing environmentally friendly buildings. It is an interdisciplinary and time-consuming process that requires architects to possess not only knowledge of design, performance simulation and optimization, but also the ability to make decisions that effectively align with environmental goals while exploring a range of design schemes. To promote such kind of design, this study proposes an innovative generalizable generative-design-based performance optimization framework (GGPOF). The framework combines generative design with performance-based design techniques to enhance various environmental aspects of buildings, including thermal comfort, daylighting and solar radiation. To facilitate performance-based design and optimization, the GGPOF automatically generates building variants and parametric simulation models, quick iteration with simulation feedback, sensitivity analysis and multi-objective optimization. A case study with two design scenarios demonstrates the high efficiency of GGPOF in green building design. More specifically, GGPOF can automatically generate every optimized design solution in an average time of 1.72 min. The optimal designs also achieve remarkable improvements in the predicted mean vote index for thermal comfort and the daylight factor compliance rate with increases of up to 86.57% and 72.73%, respectively, compared to the original user-defined design. The innovations of this study are as follows: first, it proposes a systematic and comprehensive solution for optimizing building performance from the architects' design perspective; second, it develops an automatic generalizable performance optimization framework at the early design stage.

Predicting Friction of Tapered Roller Bearings with Detailed Multi-Body Simulation Models

In the presented work, a parametric multibody simulation model is presented that is capable of predicting the friction torque and kinematics of tapered roller bearings. For a highly accurate prediction of bearing friction, consideration of solid and lubricant friction is mandatory. For tapered roller bearings in particular, the friction in the contact between the rolling element and raceway is of importance. Friction forces in the contact between the rolling element end face and inner ring rib as well as roller cage pocket contacts are also considered in the model. A large number of tests were carried out to validate the model in terms of the simulated frictional torque. Influencing variables such as speed, axial load, radial load, and temperature were investigated. The simulation results show good agreement with the measured friction torque, which confirms that the model is well suited to predict frictional torques and therefore the kinematics of tapered roller bearings.

Calibration and Modeling of Parameters for Kale Root Stubble Simulation Based on the Discrete Unit Method

Today, the post-harvest root stubble treatment of kale in Hunan mostly uses manual pulling and centralized treatments, which are inefficient and labor-intensive. In this study, to realize the direct mechanical crushing of kale root stubble and return it to the field after harvesting, we established an accurate simulation model of kale root stubble by creating a model of the root stubble of kale and calibrating the parameters of the simulation. This study took Jingfeng No. 1 kale stubble as the research object and used EDEM2021.2 simulation software to study the parameters of the kale stubble-crushing simulation model. The peak shear force of the sheared kale root stubble was used as the test data, and the most significant factors affecting the shear force were screened out through the Plackett–Burman test for the Design-Expert design. In addition, the steepest climb test and Box–Behnken test were used to accurately assess the factor data to obtain the best simulation value, which was 861.02 N. The relative error between the simulated and measured values was 0.61%. Finally, an accurate simulation stubble model was established by combining the best simulation parameters with the measured stubble length and diameter. This model provides a theoretical basis and technical support for more in-depth research on stubble simulation and mechanized stubble return.

Construction of an Accurate Wheat-Grain Model Based on X-ray Tomography and Bonding Parameters by Discrete Element

There will be great damage in the process of harvesting, transporting, and storing after grain matures. The injury rate is as high as 8% to 12%. After damage, the germination rate of the grain becomes lower, the quality decreases, and it is easily infected with pests and molds. This study of the grain-crushing characteristics is of great significance to ensure grain quality, and an accurate crushing model is a prerequisite for effectively simulating crushing characteristics. This paper studies the shattering characteristics of wheat grains. Two-dimensional slices of wheat grain were obtained using X-ray tomography technology. Then, an accurate three-dimensional outer contour model of the wheat particle was constructed using image filtering and segmentation algorithms. The particle filling process was conducted using EDEM 2018 software to establish a wheat particle simulation model based on the Hertz–Mindlin with a Bonding contact model. Using the DOE experimental design method, single-factor experiments, Plackett–Burman experiments, steepest-climb experiments, and Box–Behnken were designed to study the fragmentation characteristics of wheat particles combined with parameter calibration and physical experiments. The test results show that the normal stiffness per unit area is 7.392 × 1010 N/m3, critical normal stress is 5.293 × 106 Pa, critical tangential stress is 5.001 × 106 Pa, and the relative error about 3%, which verifies the reliability of the simulation parameters in the discrete-element crushing model of wheat grain. This study focuses on two essential aspects: 1. establishing an accurate wheat-grain contour model; and 2. calibrating the bonding parameters of the discrete-element simulation model of wheat grain. The wheat grain discrete-element crushing model and the calibration of its bonding parameters are constructed to provide a foundation for the study of wheat-grain crushing characteristics. It is of great significance to study the situation of wheat grains and where cracks are produced. In this paper, an accurate model of the wheat-grain contour is established, and the bonding parameters of the discrete-element simulation model of the wheat grain are calibrated. The calibration of the model of the discrete elements of wheat-grain fragmentation and its bonding parameters will provide a basis for studying the crushing characteristics of wheat grain. Understanding the condition of wheat grains and the causes of cracks carries significant academic significance.

Optimal Discrete Element Parameters for Black Soil Based on Multi-Objective Total Evaluation Normalized-Response Surface Method

The lack of accurate black soil simulation model parameters in the design and optimization of soil remediation equipment has led to large errors in simulation results and simulation outcomes, which to some extent restricts the development of soil remediation equipment. Accurate discrete element parameters can improve the efficiency of soil remediation equipment. To improve the reliability of the discrete element contact parameters for black soil, a set of optimal discrete element contact parameters was found that could comprehensively represent a variety of particle sizes and minimize error. In this paper, the best discrete element contact parameters were selected by using a multi-indicator total evaluation normalization method combined with the response surface method, combined with black soil solid and simulated stacking tests. First, the physical parameters of the black soil and the accumulation angle were determined. Next, Plackett–Burman tests were carried out for each grain size in turn to obtain the contact parameters that had a significant effect on the black soil accumulation angle. The important parameters obtained for different particle sizes are all as follows: black soil–black soil static friction coefficient, black soil–black soil rolling friction coefficient, and black soil–stainless steel rolling friction coefficient. In conjunction with the Plackett–Burman test screening results, the steepest climb test was designed for six grain sizes to optimize the range of values. To find the optimal contact parameters for the different particle sizes based on the final results of Box–Behnken experiments, the discrete element parameters of black soil were optimized for the different particle sizes of black soil by using the multi-indicator total evaluation normalization method and response surface method. The results showed that the black soil–black soil static friction coefficient was 1.045, the black soil–black soil rolling friction coefficient was 0.464, and the black soil–stainless steel rolling friction coefficient was 0.215. The errors for each particle size were reduced by 0.89%, 0.7%, 0.84%, 0.57%, 0.71%, and 0.76% for the best combination of parameters before and after normalization, with an average error reduction of 0.745%. This data provides some reference value for the design and optimization of soil remediation equipment.

Joint inverse estimation of groundwater pollution source characteristics and model parameters based on an intelligent particle filter

Accurate groundwater pollution source estimation (GPSE) is the prerequisite for efficiently designing groundwater pollution remediation strategies and pollution risk assessment. However, deterministic point estimation of pollution source characteristics may not completely solve the problem for practical situations. Therefore, an interval estimation, capable of providing more comprehensive and abundant information for decision-makers, should also be considered. In GPSE, the particle filter (PF) method is a good means for the interval estimation. However, some inherent problems in the general PF method may compromise the estimation accuracy. Thus, this study made two improvements to the general PF method as a solution. First, to solve the problem of its weak local search ability when tackling complex problems, this study embedded the optimization method with strong local search ability into the importance sampling process, to strengthen the local search ability, better optimize the particle position, and guide the overall distribution of particles to move towards the true distribution. Secondly, in the resampling process, all particles need to be resampled in each iteration, which may lead to plunging into the local optima. Thus, this study introduced an intelligent resampling process that intelligently adjusted the number of resampling particles in each iteration to avoid plunging into the local optima. Through two improvements on the PF method, an intelligent PF method was innovatively constructed for GPSE, improving the estimation accuracy. Additionally, to alleviate the computational cost caused by the frequent simulation model invocation, we adopted a deep learning method (deep belief neural network method) to build the surrogate model of the simulation model, ensuring high approximation accuracy to the simulation model. Also, we applied the theories and methods mentioned above to two respective scenarios (inorganic and organic groundwater pollution). We simultaneously estimated the pollution source characteristics and simulation model parameters to verify the effectiveness. Finally, we obtained the point estimation, posterior probability distribution, and interval estimation results, capable of providing a more reliable and comprehensive reference for decision-makers.