Simulation is one possibility to gain insight into the behaviour of tracked vehicles on deformable soils. A lot of publications are known on this topic, but most of the simulations described there cannot be run in real-time. The ability to run a simulation in real-time is necessary for driving simulators. This article describes an approach for real-time simulation of a tracked vehicle on deformable soils. The components of the real-time model are as follows: a conventional wheeled vehicle simulated in the Multi Body System software TRUCKSim, a geometric description of landscape, a track model and an interaction model between track and deformable soils based on Bekker theory and Janosi–Hanamoto, on one hand, and between track and vehicle wheels, on the other hand. Landscape, track model, soil model and the interaction are implemented in MATLAB/Simulink. The details of the real-time model are described in this article, and a detailed description of the Multi Body System part is omitted. Simulations with the real-time model are compared to measurements and to a detailed Multi Body System–finite element method model of a tracked vehicle. An application of the real-time model in a driving simulator is presented, in which 13 drivers assess the comfort of a passive and an active suspension of a tracked vehicle.

Sustainable modes of transport are being promoted to achieve global climate goals. The perceived user experience is decisive for the choice of transport mode. To increase the share of sustainable transport in total traffic, the user experience is placed into the spotlight, raising the need for appropriate exploration methods. Machine learning (ML) techniques have become increasingly popular in the transport domain, but the black-box nature of ML models poses significant challenges in interpreting the relationship between model input and output. Explainable AI methods (XAI) can fill this gap by providing post hoc interpretation methods for black-box models. The aim of the present work was therefore to assess the potential of XAI to explore user experience in transport. The introduced method was based on a popular XAI method named SHAP (SHapley Additive exPlanations). Applied to the use case of e-bikes, we aimed to explore factors influencing the riding experience on e-bikes. We applied Gaussian process regression to data collected in a cycling study from 55 e-bike riders including rider behaviour, motor power and riding dynamics. Applying SHAP, we compared the riding experience of four rider types identified by hierarchical cluster analysis. The results provide insights into the riding experience on e-bikes: motor power, rider behaviour and riding dynamics were found to be meaningful predictors differing in their impact between rider types. Our results can be regarded as a proof of concept and demonstrate the potential of XAI to enhance the understanding of user experience in transport.

The discrete element method (DEM) is suitable to investigate problems where large deformations occur especially in granular material. The fitting of reliable DEM parameters is crucial and a challenge which is caused by the high number of DEM parameters and the computational effort. Despite its drawbacks, a trial and error approach is often used for the DEM parameter calibration. The knowledge of the DEM parameter influence on the model response is necessary to improve the calibration and to check whether the experiment is suitable to calibrate specific parameters or not. It is possible to reduce the dimensionality of the optimisation problem by omitting parameters whose influence on the model response is negligibly small. One approach is to perform a global sensitivity analysis based on Sobol’ indices. A frequently used calibration experiment in literature is the pile experiment. The deviation between the experiment and the simulation is evaluated with the angle of repose. In the present paper, an algorithm to determine the angle of repose considering the three-dimensional shape of the heap is discussed. The global sensitivity analysis is performed for two different experimental heap set-ups. To decrease the computational effort of the sensitivity analysis, the model response is approximated with metamodels whose predictability is evaluated using the root mean squared error (RMSE) based on a separate sampling point set.

The discrete element method (DEM) is frequently used to investigate the behaviour of granular media (Bravo in Simulation of soil and tillage-tool interaction by the discrete element method, 2013; Tijskens et al. in J Sound Vib 266:493–514, 2003; Langston et al. in Chem Eng Sci 50:967–987, 1995; Kohring et al. in Comput Methods Appl Mech Eng 124:273–281, 1995; Stahl et al. in Granul Matter 13:417–428, 2011). The parameter calibration is a challenging task due to the large number of input parameters and the computational effort. Sometimes, this is performed with a trial-and-error approach as mentioned in Roessler et al. (Powder Technol 343:803–812, 2019), Rackl and Hanley (Powder Technol 307:73–83, 2017) based on laboratory tests, e.g. the pile experiment, the oedometer experiment and the shear test. To achieve a more suitable calibration, a better model understanding is necessary in which the influence of the DEM parameters is analysed. Consequently, the calibration can be focused on specific parameters, which have a significant influence on thef model response. If parameters with a negligibly small influence exist, the number of calibration parameters can be reduced. On this basis, it is possible to decide whether the laboratory test is suitable for the calibration of specific parameters or not. This is demonstrated with a sensitivity analysis based on Sobol’ indices for the oedometer laboratory test. In order to reduce the computational effort, the sensitivity analysis is performed with different metamodels of the oedometer simulation. The metamodels are fitted and validated with two separate sampling point sets. It is shown that the Young’s modulus for the investigated input space is the most significant parameter. This knowledge can be used to only focus the calibration on this significant parameter which enables an easier calibration and makes clear that for calibrating of other parameters this laboratory test is inappropriate. An algorithm of a force-driven plate is developed and shown which prevents non-physical states in which the interaction force between the particles and the loadplate exceeds the applied force.

This paper presents a method for replicating transient signals induced in a passenger car by single-obstacle crossings. These replicated signals should be used in a ride comfort simulator. By using feedforward-inverse-model-control with direct identification of the inverse transfer function, time-consuming iterations of conventional time waveform replication methods are eliminated. The quality of the system identification for approximating the inverse transfer function is significantly increased by decomposing the frequency band and the complexity is considerably reduced. The use of nonlinear block-oriented models significantly decreases the replication errors in frequency bands with high nonlinearity influence.

The high prediction accuracy of FEM calculations is an important prerequisite for their growing use in the field of crash load cases for vehicle body design. Although the simulation accuracy of crash calculations for steel structures already is at a high standard, continuous improvements and a further development of the simulation models are crucial in the field of vehicle design. Existing studies on the simulation accuracy for steel structures under crash load focus on the deformation behaviour of idealized component-like structures. These studies do not allow for conclusions on the simulation accuracy of crash calculations based on vehicle-related components with increased geometric complexity as well as with different material combinations, joining techniques and bead structures. This was the reason for conducting the experimental and numerical investigations on crash boxes outlined in this paper. The goal was to investigate whether the FE code is capable of increasing the accuracy of the simulation of the experimental results by considering the information obtained from technical measurements.