Calibration Of Model Parameters Research Articles

Digital twinning of cardiac electrophysiology for congenital heart disease.

In recent years, blending mechanistic knowledge with machine learning has had a major impact in digital healthcare. In this work, we introduce a computational pipeline to build certified digital replicas of cardiac electrophysiology in paediatric patients with congenital heart disease. We construct the patient-specific geometry by means of semi-automatic segmentation and meshing tools. We generate a dataset of electrophysiology simulations covering cell-to-organ level model parameters and using rigorous mathematical models based on differential equations. We previously proposed Branched Latent Neural Maps (BLNMs) as an accurate and efficient means to recapitulate complex physical processes in a neural network. Here, we employ BLNMs to encode the parametrized temporal dynamics of in silico 12-lead electrocardiograms (ECGs). BLNMs act as a geometry-specific surrogate model of cardiac function for fast and robust parameter estimation to match clinical ECGs in paediatric patients. Identifiability and trustworthiness of calibrated model parameters are assessed by sensitivity analysis and uncertainty quantification.

Cyclic Void Growth Model Parameter Calibration of Q460D Steel and ER55-G Welds after Exposure to High Temperatures

When high-strength steel is heated to high temperatures and then cooled naturally, its ductility decreases. In earthquake-prone areas, it is necessary to evaluate the ultra-low cycle fatigue fracture (ULCF) behavior of high-strength steel structures after a fire if these structures are used continuously. However, the ULCF fracture model of high-strength steel subjected to high temperatures followed by natural cooling has not been deeply studied. In view of this, twelve notched, round bar specimens fabricated from Q460D steel and ER55-G welds were heated to 900 °C followed by natural cooling and then cyclic loading experiments and finite element analyses (FEA) were performed on these specimens. The fracture deformation obtained from the experiments was used in the FEA to calibrate the damage degradation parameter of a Cyclic Void Growth Model (CVGM) of Q460D steel and ER55-G welds under this condition. The calibrated values were 0.30 and 0.20, respectively. The calibrated CVGM was employed to predict the number of cycles and the force and displacement at the fracture moment of the notched round bar specimens. The predicted results aligned closely with the experimental results, indicating that CVGM is effective in predicting the fracture of Q460D steel and ER55-G welds following exposure to 900 °C and subsequent natural cooling.

Predictive abilities of constitutive models for clay under monotonic and cyclic loading: element tests and centrifuge experiments

A critical investigation of three constitutive models for clay by means of analyses of a sophisticated laboratory testing program and of centrifuge tests on monopiles in clay subjected to (cyclic) lateral loading is presented. Constitutive models of varying complexity, namely the basic Modified Cam Clay model, the hypoplastic model with Intergranular Strain (known as Clay hypoplasticity model) and the more recently proposed anisotropic visco-ISA model, are considered. From the simulations of the centrifuge tests with monotonic loading it is concluded that all three constitutive models give satisfactory results if a proper calibration of constitutive model parameters and proper initialisation of state variables is ensured. In the case of cyclic loading, the AVISA model is found to perform superior to the hypoplastic model with Intergranular Strain.

Optimal charging for lithium-ion batteries to avoid lithium plating based on ultrasound-assisted diagnosis and model predictive control

Lithium plating in lithium-ion batteries for electric vehicles, occurring due to low-temperature or high-rate charging, is a significant factor impacting safety and service life. To address this issue, a novel adaptive charging approach is proposed, combining ultrasound-assisted diagnosis and model predictive control (MPC). In the method, a discrete state-space electrochemical model is used to describe the dynamic characteristics of the battery, and a model predictive controller (MPC) is utilized to optimize the charging current to avoid lithium plating. Considering that factors such as battery performance degradation and variable working temperature affect the battery model's judgment of lithium plating, an ultrasound-assisted diagnosis method is used to determine the critical point of lithium plating. The effectiveness of the method is validated through low-temperature charging and cycle aging experiments. The results indicate that without complex model parameter calibration in different temperatures, the new charging method not only has a higher charging speed than constant current charging, but also can effectively suppress the occurrence of lithium plating on the negative electrode of the battery. The method is expected to be applied in electrochemical energy storage systems to enhance safety and service life.

Addressing equifinality in agent-based modeling: a sequential parameter space search method based on sensitivity analysis

This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.

Calibration of visual measurement system for excavator manipulator pose

The automatic control of excavator operation trajectories is a pivotal technology for autonomous excavators, with the essential prerequisite being the real-time measurement of manipulator poses. Given the complexity of the operating environment, traditional sensor-based measurement methods face limitations, whereas visual measurement emerges as a promising technique. Accurately measuring excavator manipulator poses involves a crucial aspect: mapping the relationship between image information and poses. First, to address the significant errors in pose prediction encountered with machine learning techniques like artificial neural networks, this work introduces a mathematical model for mapping this relationship, referred to as the pose mapping mathematical model, which includes calibrating model parameters. Second, to address the sensitivity of initial values in the calibration process, we propose a residual-guided initialization algorithm. This algorithm aims to ensure that initial values closely approximate the ground truth values, thus preventing matrix singularity at the source and avoiding parameter estimation divergence. Third, to tackle challenges such as unstable lighting conditions and discrepancies between the dataset and the mathematical model, we introduce the random sample consensus-driven Levenberg–Marquardt parameter optimization algorithm to enhance parameter estimation accuracy. Experiments with static and dynamic online measurement demonstrate that our method reduces pose measurement errors compared to existing methods. This research lays a solid foundation for developing visual measurement techniques for excavators and automated manipulator control based on visual measurements, also serving as a valuable reference for research on mechanical arms.

Modeling dynamic crush behavior of carbon fiber reinforced polymer composite structures using MAT213

Modeling crushing of carbon fiber reinforced polymer (CFRP) composites is challenging, and current simulation methodologies involve tuning of non-physical parameters. MAT213, a next-generation material model, has advanced functionality to better simulate dynamic impact loading. The objective of the present investigation is to evaluate the potential for using MAT213 to simulate dynamic crushing of CFRPs. Two sets of simulations were performed: one for calibration based on a set of coupon-level experiments and another for prediction of the response of structural elements. Simulations involving dynamic crushing of flat specimens were iteratively run to calibrate model parameters. The calibration demonstrated that MAT213 could produce a simulated force–displacement response within experimental scatter. The simulated failure morphology was also comparable to the experiments. After successful calibration, predictive simulations of dynamic crushing of C-channel shaped specimens were completed using a simulated crash sled test rig and two pairs of impactor mass/velocity conditions. The simulated force–displacement curve in the crash sled simulations for the lower-velocity condition fell within the experimental scatter, but the stable crush force was underpredicted by 27 % in the higher-velocity simulations. Better correlation in the lower-velocity test condition likely results from the calibration condition being a similar velocity to the lower-velocity crash sled condition.

Abstract 7374: Mathematical modeling of targeted radionuclide therapy and CAR-T cell immunotherapy for maximizing therapeutic efficacy in multiple myeloma

Abstract Background: Resistance of cancer cells to monotherapies has led to the development of sequential or combination therapy regimens. However, dosing and scheduling of these therapies is challenging due to the numerous dosing and scheduling combinations that can be given. Mathematical models are thus critical tools for addressing this challenge as multiple therapy combinations can be tested in silico to finalize a patient-specific therapeutic regimen in vivo. Here we develop a mathematical framework for combining targeted radiation therapy (TRT) with Chimeric Antigen Receptor (CAR)-T cell immunotherapy and demonstrate the use of in silico techniques to schedule these therapies for maximizing survival. Methods: In the mathematical framework tumor growth is assumed to be exponential and the effect of radiation on both tumor cells and CAR-T cells is modeled using the linear-quadratic model of cell survival to radiation. A predator-prey model is used to characterize the dynamics of tumor and CAR-T cells. Using a preclinical disseminated mouse model of multiple myeloma (MM1S), we evaluate tumor response to 200 nCi of 225Ac-DOTA-Daratumumab (TRT) and 1 million cells of CS1 CAR-T cell therapy both as monotherapies as well as in combination. Tumor burden tracked using bioluminescence imaging from six groups of mice is used to calibrate model parameters: (a) No treatment. (b) Day 7 TRT. (c) Day 7 CAR-T cell therapy. (d) Day 7 TRT + Day 18 CAR-T cell therapy. (e) Day 7 TRT + Day 25 CAR-T cell therapy. (f) Day 7 TRT + Day 32 CAR-T cell therapy. Response to therapy is evaluated using progression-free survival (PFS), overall survival (OS) and time to minimum tumor burden (tmin), all of which are calculated using the predicted in silico tumor burden dynamics and the tumor burden at the start of first therapy. We evaluate these response metrics for various dosing and scheduling regimens. Results: Therapy intervals that were too short or too long are shown to be detrimental for therapeutic efficacy. TRT too close to CAR-T cell therapy results in radiation related CAR-T cell killing, while the interval being too long results in tumor regrowth, negatively impacting tumor control and survival. If a single dose is split into multiple doses, the splitting is advantageous only if the first therapy delivered can produce a significant benefit as a monotherapy. Based on the model parameters we estimate the minimum required TRT activity and CAR-T cell dose to demonstrate an improvement in PFS. Conclusions: The proposed model demonstrates the impact of different dosing and scheduling regimens of TRT and CAR-T therapy on survival metrics. It is a potent tool for translating preclinical results to the clinic and eventually tailor therapy regimens for patients. Citation Format: Vikram Adhikarla, Dennis Awuah, Enrico Caserta, Megan Minnix, Maxim Kuznetsov, Amrita Krishnan, Jeffrey Y. Wong, John E. Shively, Xiuli Wang, Flavia Pichiorri, Russell C. Rockne. Mathematical modeling of targeted radionuclide therapy and CAR-T cell immunotherapy for maximizing therapeutic efficacy in multiple myeloma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7374.

Modeling and multi-objective optimization of forward osmosis process

In order to ensure efficient wastewater treatment and seawater desalination, adequate modeling and optimization of the forward osmosis (FO) process has the potential to be very helpful. This paper deals with the FO model parameters calibration and FO process optimization by a gradient-based optimization method. For this purpose, an upgraded FO model, which involves temperature- and agent-dependent parameters, was developed. The FO model calibration was done using NaCl as agents in draw solution, while MgCl2 was used for model validation. The agreements between simulated and measured FO performance were satisfactory; relative index of agreement are higher than 0.99. By using the proposed FO model, the optimization of FO process conditions was performed with various definitions of the objective and constraint functions. In case of maximizing the water flux, minimizing reverse solute flux, and fulfilling the required constraints, the ratio of water flux and reverse solute flux increased up to 40 % for NaCl and up to 20 % for MgCl2; meanwhile the effective osmotic pressure difference was improved 2-times for NaCl and up to 3.8-times for MgCl2. The optimization process proved to be stable and efficient and can easily be adapted or upgraded for more complex dynamic FO modeling.

Application of real-time water temperature prediction system in winter for long-distance water diversion projects

ABSTRACT Water diversion projects in high-latitude areas often reduce the risk of ice jams in winter by reducing the water transfer flow, which might cause the waste of water transfer benefits. This paper establishes a real-time prediction system of water temperature in winter, which can predict the change in water temperature by inputting the air temperature forecast data and the current hydraulic data. Taking the middle route of the south-to-north water diversion project as the background, the model parameters calibration and system application testing at different time periods are carried out. The results show that the prediction errors of water temperature for the 1 and 7 days are relatively small, and the prediction errors of water temperature at four observation stations can be controlled within ±0.3 and ±0.6 °C, with the root mean square error (RMSE) ranging from 0.07 to 0.25 and 0.12 to 0.36, respectively. The 15-day water temperature prediction results are greatly affected by air temperature input conditions. The prediction errors for the first 7 days are relatively small, ranging from −0.59 to 0.36 °C, and the errors for the last 8 days increase as the accuracy of the air temperature forecast decreases, ranging from −2.42 to 0.22 °C.

A model of erosion rate prediction for component with complex geometry based on numerical simulation

The structural components prone to erosion damage often exhibit irregular and complex shapes. Predicting erosion rates for such irregular geometries is a field that needs more attention, since it can be used to evaluate the performance of the component. This study aims to develop an evaluation method for predicting erosion rates of components with complex geometry. Standard erosion tests were performed on small plates to investigate the effects of different impact angles and particle velocities on the erosion rate of the target material. Based on these test results, the Newton iteration method was employed to determine the parameters of the Tabakoff and Grant erosion model. Moreover, these parameters were used in computational fluid dynamics simulation of components with complex geometries. Besides, a model was developed to convert the erosion rate density into erosion rate in numerical simulation, allowing quantitative comparison of simulation results with experimental data. The calibration of the erosion model parameters was conducted by using 1060 aluminum alloy specimens. The predicted erosion rate changes with impact angle showed consistency good agreement with the testing data. Subsequently, the erosion rate of a simplified similar structure for aircraft engine blade made of 1060 aluminum alloy was predicted and verified.

A Coupled Parameter Automation Calibration Module for Urban Stormwater Modelling

In the context of accelerating urbanisation, the issue of urban stormwater flooding security has garnered increasing attention. Further development of urban stormwater management techniques is imperative to achieve more stable, precise, and expeditious simulation outcomes. The calibration of model parameters, which is a pivotal phase in stormwater simulation endeavours, is hampered by challenges such as substantial subjectivity, time intensiveness, and low efficiency. Therefore, this study introduces a parameter calibration model coupled with the Non-dominated Sorting Genetic Algorithm III (NSGA-III). This model utilises the Nash–Sutcliffe efficiency (NSE) and peak relative error (PE) values for various rainfall events as objective functions to calibrate and assess the study target. The two rainfalls used for rate determination had NSE values greater than 0.9 and absolute PE values less than 0.17; the rainfall used for validation had NSE values greater than 0.9 and absolute PE values less than 0.27. Thus, the results of the model for the rate determination of the parameters are reliable. In addition, the inverted generation distance and hypervolume values indicate that the iterative process of the algorithm during population evolution demonstrated stable iterative outcomes and ensured sound population quality. Both reach relative stability after 40 iterations. In conclusion, the proposed multi-objective parameter calibration model integrated with NSGA-III offers dependable calibration results and robust computational efficacy, presenting novel avenues and perspectives for urban stormwater model parameter calibration and simulation.

Assessing the impact of climate change on surface runoff of the Ubolratana Reservoir, Thailand

Assessing surface runoff in river basins is paramount for effectively managing water resources. Climate change significantly impacts the availability of water within these basins. The Ubolratana reservoir is vital for sustainable water supply, aquaculture, agricultural practices, and domestic needs. This research aims to evaluate the runoff availability of the reservoir by using the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) under distinct climate change scenarios. The HEC-HMS model was calibrated based on observed rainfall and runoff data. To project future runoff dynamics, calibrated model parameters were coupled with the bias-corrected rainfall data of Can-ESM5, CESM2, and GFDL-ESM4 models for two Shared Socioeconomic Pathways (SSPs) climate scenarios (SSP 245 and SSP 585). The results show upward trends in both projected rainfall and runoff within the drainage area of the Ubolratana reservoir between 2022 and 2061. In summary, the findings of this research may be useful for regional water resources management and strategic planning endeavors.

CALIBRATION OF MICROSCOPIC TRAFFIC SIMULATION OF URBAN ROAD NETWORK INCLUDING MINI-ROUNDABOUTS AND UNSIGNALIZED INTERSECTION USING OPEN-SOURCE SIMULATION TOOL

Microscopic traffic simulation models offer an effective way to analyze and assess different transportation systems thanks to their efficiency and reliability. As traffic management issues become more prevalent, notably in urban areas, simulation tools enable a significant opportunity to replicate real-world conditions before implementation. Therefore, the calibration of traffic simulation models plays a substantial role in obtaining accurate and confidential results. Nowadays, urban regions are facing the challenge of restricted space for developing traffic solutions. As a consequence of environmental restrictions, the use of mini-roundabouts rather than larger roundabouts is increasing. Based on the given literature review, it is seen that not much attention was given to the complex modeling and calibration of microsimulation models of mini-roundabouts and unsignalized intersections. The objective of this study is to offer the calibration of microscopic traffic simulation of urban road network, including closely located mini-roundabouts and unsignalized intersection. To this end, an open-source tool called SUMO (Simulation of Urban Mobility) was utilized as a simulation environment in this study. The necessary data for developing a microsimulation model in SUMO was gathered using a videography technique. The traffic count data and speed were considered performance measures between field observations and simulation outputs. The routeSampler tool of SUMO, which has recently emerged in the literature, was used to match traffic count data and the corresponding time interval for traffic volume data calibration. The calibration of car-following model parameters using a trial-and-error approach was employed based on mean absolute percent error (MAPE) between simulated speeds and field-measured speeds. According to the findings of the study, the simulation model fulfilled the calibration aims of the FHWA guideline and is suitable for further research.

Daily Simulation of the Rainfall–Runoff Relationship in the Sirba River Basin in West Africa: Insights from the HEC-HMS Model

This study focuses on the Sirba River Basin (SRB), a transboundary West African catchment of 38,950 km2 shared by Burkina Faso and Niger, which contributes to flooding downstream in Niamey (Niger). The study uses the HEC-HMS hydrological model to explore the dynamics of the daily rainfall–runoff relationship over the period 2006–2020. The model is calibrated using observed rainfall at 13 meteorological stations within the river basin and observed discharges at the Garbey Kourou hydrometric station outlet. Two types of simulation are compared: (i) a continuous simulation (CS) over the period 2006–2020 and (ii) an event-based simulation (ES) using selected major flood events in 2010, 2012, 2013, 2015 and 2020. The results showed satisfactory model performance under both modeling schemes (R2 = 0.84–0.87 for CS and R2 = 0.94–0.98 for ES), with a superior performance of ES over CS. Also, significant differences in the distribution of calibrated model parameters for the percent impervious and the attenuation flood wave factor were observed. A sensitivity analysis revealed that the curve number, initial abstraction, lag time and routing time factors were influential on the model outputs. The study therefore underscores the model’s robustness and contributes crucial insights for flood control management and infrastructure planning in the SRB.

Automatic calibration of SWMM parameters based on multi-objective optimisation model

Abstract To address the issue of low accuracy and inefficiency in the traditional parameter calibration methods for the SWMM model, this paper constructs an automatic parameter calibration model based on multi-objective optimisation algorithms. Firstly, the Sobol method and GLUE method are utilised to determine sensitive parameters and their ranges, aiming to narrow down the solution space and expedite the model-solving speed. Secondly, the NSGA-3 multi-objective optimisation algorithm based on the Pareto theory is applied for the optimisation and calibration of sensitive parameter sets. The model is validated in the rainwater drainage system with independent runoff in a residential area in a northwestern city in China. The results show that parameters such as N-Imperv and KSlope are highly sensitive to the model output under the land-use conditions of the study area. The simulation accuracy of the multi-objective continuous optimisation algorithm is significantly better than that of the single-objective genetic algorithm. The simulation results of the SWMM model under multi-objective optimisation demonstrate a certain level of reliability and stability. The research findings can provide technical support for the automatic calibration of SWMM model parameters, accurate model simulation, and application.

SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation

Abstract. Maintaining soil organic matter (SOM) is crucial for healthy and productive agricultural soils and requires understanding at the process level, including the role of SOM protection by soil aggregates and the connection between microbial growth and aggregate formation. We developed the Soil Aggregation through Microbial Mediation (SAMM) model, to represent this important connection. The pools of SAMM are fully measurable, and we calibrated and evaluated it against data from a long-term bare fallow experiment in a tropical sandy soil. This experiment received additions of plant litter of different compositions, which resulted in twice the soil carbon stocks in the best treatment compared to the control (about 8 vs. 4 t C ha−1 in 0–15 cm soil depth) after 25 years. As hypothesized, the SAMM model effectively represented the microbial growth response after the addition of litter and the subsequent formation and later destabilization of aggregates. The low correlations between different calibrated model parameters (r<0.5 for all parameters; r>0.4 for only 4 of 22) showed that SAMM is parsimonious. SAMM was able to capture differences between treatments in soil organic carbon (Nash–Sutcliffe modeling efficiency (EF) of 0.68), microbial nitrogen (EF of 0.24), and litter carbon (EF of 0.80). The amount of carbon within the aggregates (EF of 0.60) and in the free silt and clay fraction (EF of 0.24) was also simulated very well to satisfactorily. Our model results suggested that in spite of the sandy soil, up to 50 % of carbon stocks were stabilized through aggregate protection mechanisms; and that microbial and physical aggregate formation coexists. A version of the SAMM model without aggregate protection (SAMMnoAgg) initially failed to stabilize soil organic carbon (EF decreased to −3.68) and the simulation of microbial nitrogen worsened (EF of 0.13). By recalibrating SAMMnoAgg, it was possible to partially correct for the lack of aggregate protection by reducing the rate of mineral-attached carbon decomposition by about 85 % (EF of 0.68, 0.75, and 0.18 for SOC, litter carbon, and microbial nitrogen, respectively). However, the slightly better evaluation statistics of SAMM (e.g., Akaike information criterion of 5351 vs. 5554) suggest that representing aggregate dynamics in SOM models can be beneficial and necessary to understand the mechanism behind SOM dynamics. Our results indicate that current models without aggregate formation partly compensate for the absence of aggregate protection by lowering the turnover rates of other pools. Thus, they remain suitable options where data on aggregate associated carbon are not available.

Simulation‐based inference for reliability model selection and parameter calibration: An application to fatigue

AbstractSelecting a “useful” model among a set of competing candidate models is a common problem in many disciplines and particularly in engineering applications. For a modeller, the best model fulfills a certain purpose best (e.g., the ability to accurately predict new data, good interpolation, precise estimates of lower/upper percentiles, etc.), which is typically assessed by comparing model simulations to the observed data. Approximate Bayesian computation (ABC) which is a statistical learning technique is used in this work for reliability model selection and parameter calibration using small/moderate fatigue life data sets. This is always the case in material fatigue due to the high cost and low efficiency of fatigue tests which are bottleneck problem when designing components/structures with regard to fatigue. The ABC is a likelihood‐free based method which means that it needs only a generative model to simulate the data. The proposed method provides a formal rank of the competing reliability models by eliminating gradually the least likely models in a parsimonious manner. It is flexible since it offers the possibility to use different metrics to measure the discrepancy between simulated and observed data. The choice of the appropriate distance function depends essentially on the purpose of model selection. Through various examples, it has been demonstrated that the ABC method has a number of very attractive properties, which makes it especially useful in probabilistic risk assessment and reliability analysis.

A novel fast and efficient adaptive shuffled complex evolution algorithm for model parameter calibration

The research and optimization of hydrological forecasting models are among the most crucial components in the scope of water management and flood protection. Optimizing the calibration of hydrological forecasting models is crucial for forecasting performance. A rapid adaptive Shuffled Complex Evolution (SCE) method called Fast Adaptive SCE (FASCE) is proposed for calibrating model parameters. It builds upon the previously established SCE-UA, known for its effectiveness and robustness in the same calibration context. The robustness of the original SCE-UA is expanded upon, introducing a revised adaptive simplex search to bolster efficiency. Additionally, a new strategy for setting up the initial population base enhances explorative capacities. FASCE’s performance has been assessed alongside numerous methods from prior studies, demonstrating its effectiveness. Initial tests were conducted on a set of functions to assess FASCE’s efficacy. Findings revealed that FASCE could curtail the failure rate by a minimum of 80%, whereas the requirement for function evaluations fell between 30% and 60%. Two hydrological models - Support Vector Machine (SVM) and Xinanjiang rainfall-runoff model were employed to estimate the new algorithm’s performance. No failures were reported, and there was a reduction of at least 30% in function evaluations using FASCE. The outcomes from these studies affirm that FASCE can considerably reduce both the number of failures and the count of function evaluations required to reach the global maximum. Hence, FASCE emerges as a viable substitute for model parameter calibration.

Discrete modelling of the mechanical response of Cuxhaven sand under shear and oedometric conditions using the rolling resistance contact model

The Distinct element method (DEM) is a promising approach to model the microscopic behaviour of granular materials, but the capability of the simulations to reproduce the mechanical response of real granular materials depends strongly on the contact model parameters utilized. The present study focuses on the calibration and validation of the rolling resistance linear model parameters for Cuxhaven sand based on the experimental results from triaxial and oedometer tests. A sensitivity analysis was conducted to explore the influence of contact model parameters on the sample preparation and the shear stage of triaxial tests. The influence of parameters like rolling resistance friction coefficient, inter-particle friction coefficient, effective modulus, and normal-to-shear stiffness ratio were investigated. For calibration of the DEM model, the input parameters were selected such that the simulations reproduce the macro mechanical characteristics like dilation angle, peak stress, and stiffness. The calibrated parameters were then validated by simulating a one-dimensional compression test. The results are in good agreement with the experiments, which proves the suitability of the calibrated parameters. In addition, the validated parameters were applied to investigate the mechanical behaviour including the evolution of contact force chains, non-coaxiality of principal stress and strain rate, and sample inhomogeneity during a simple shear test.