SIMPACK An Analysis and Design Tool for Mechanical Systems

Background: Motion sickness is common within most forms of transport; it affects most of the population who experience varied symptoms at some stage in their lives. Thus far, there has been no specific method to quantify the predicted levels of motion sickness for a given vehicle design, task and route. Objective: To develop a motion sickness virtual prediction tool that includes the following inputs: human motion, vision, vehicle motion, occupant task and vehicle design. Method: A time domain analysis using a multi-body systems approach has been developed to provide the raw data for post-processing of vehicle motion, occupant motion and vision, based on a virtual route designed to provoke motion sickness, while the digital occupant undertakes a specific non-driving related task. Results: Predicted motion sickness levels are shared for a simple positional sweep of a vehicle cabin due to a prescribed motion and task. Two additional examples are shared within this study; first, it was found that the model can predict the difference found between sitting forwards and backwards in an autonomous vehicle. Second, analysis of a respected and independent study into auxiliary display height shows that the model can predict both relative and absolute levels between the two display heights congruent to the original physical experiment. Conclusion: It has been shown that the tool has been successful in predicting motion sickness in autonomous vehicles and is therefore of great use in guiding new future mobility solutions in the ability to tune vehicle dynamics and control alongside vision and design attributes.

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“…Steering and longitudinal control of the vehicle is achieved using the standard driver model using default values within SIMPACK 10.2.1 ® . 26,27…”

Section: Methodsmentioning

confidence: 99%

“…Steering and longitudinal control of the vehicle is achieved using the standard driver model using default values within SIMPACK 10.2.1 Ò . 26,27 The model has an initial speed of 40 km/h and varies according with the braking and throttle applied. The application percentages for brake and throttle refer to power train torque.…”

Section: Virtual Environmentmentioning

confidence: 99%

Model to predict motion sickness within autonomous vehicles

Salter

Diels

Herriotts

et al. 2019

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…The computational formula of the unsteady aerodynamic loads acting on a high-speed train subjected to fluctuating winds is discussed in the section 'Calculation of unsteady aerodynamic loads'. A vehicle system dynamics model is set-up using the commercial software SIMPACK, [33][34][35] and the aerodynamic loads are dealt with as external loads acting on the model, which is discussed in the section 'Vehicle system dynamics'. The evaluation of fuzzy random reliability based on importance sampling is discussed in the section 'Computation of fuzzy random reliability'.…”

Section: Evaluation Processmentioning

confidence: 99%

“…In addition, replacing " C F with " C M , " C 0 F with " C 0 M in equation (33), and adding the multiplication factor, i.e. the reference height H, we then obtain the computational formula of the unsteady aerodynamic moments.…”

Section: Calculation Of Unsteady Aerodynamic Loadsmentioning

confidence: 99%

Crosswind stability analysis of a high-speed train based on fuzzy random reliability

Maruyama

Zhang

et al. 2014

Proceedings of the Institution of Mechanical Engineers, Part F:

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The conventional crosswind stability analysis of high-speed trains is based on a deterministic approach with the final output being a characteristic wind curve (CWC). The CWC only provides the dividing line between the safe state and failure state of vehicles, thus it cannot be used to evaluate the overturning probability of vehicles subjected to strong winds. To overcome this shortcoming of the conventional CWC, a fuzzy stochastic approach is proposed that can make an effective assessment of the operational safety of high-speed trains exposed to stochastic winds. According to this methodology, the uncertain parameters existing in the system such as stochastic winds and aerodynamic coefficients are modelled as basic random variables, and the failure of the structure is considered as a fuzzy random event. An algorithm for computing the unsteady aerodynamic loads of high-speed trains exposed to stochastic winds is created and then the aerodynamic loads are applied to a dynamic model of the vehicle system in order to investigate the dynamic response. Importance sampling is used to conduct an analysis of the crosswind stability of high-speed trains based on fuzzy random reliability theory. This finally leads to the substitution of the conventional CWC by probabilistic characteristic wind curves (PCWCs). The conventional CWC is shown to be over-conservative, while the PCWCs can provide more significant reference for the safe operation of high-speed trains.

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“…For the multibody modelling and simulations the tool SIMPACK has been used; it is was originally developed at the DLR and is now commercially available, [RE93,SNMG99].…”

Section: Modelling and Simulationmentioning

confidence: 99%

Reliability based analysis of the crosswind stability of railway vehicles

Carrarini

2007

Journal of Wind Engineering and Industrial Aerodynamics

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SIMPACK An Analysis and Design Tool for Mechanical Systems

Cited by 14 publications

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Model to predict motion sickness within autonomous vehicles

Model to predict motion sickness within autonomous vehicles

Crosswind stability analysis of a high-speed train based on fuzzy random reliability

Reliability based analysis of the crosswind stability of railway vehicles

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