A Three-Dimensional Parametric Biomechanical Rider Model for Multibody Applications

Predicting preferred motorcycle riding postures to support human factors/ergonomic trade-off analyses within a multi-objective optimisation-based digital human model

Digital human models (DHM) can predict how users might interact with new vehicle geometry during early-stage design, an important precursor to conducting trade-off analyses. However, predicting human postures requires assumptions about which performance criteria best predict realistic postures. Focusing on the design of motorcycles, we do not know what performance criteria drive preferred riding postures. Addressing this gap, we aimed to identify which performance criteria and corresponding weightings best predicted preferred motorcycle riding postures when using a DHM. To address our aim, we surveyed the literature to find experimental data specifying joint angles that correspond to preferred riding postures. We then deployed a response surface methodology to determine which performance criteria and weightings optimally predicted the preferred riding postures when using a DHM. Weighting the minimisation of the discomfort performance criteria (an aggregate of joint range of motion, displacement from neutral and joint torque) best predicted preferred motorcycle riding postures.

A Screw-Axis Approach to the Stability of Two-Wheeled Vehicles

The stability of two-wheeled vehicles is predominantly characterized by the well-known weave and wobble vibration modes, which have been extensively investigated in the literature, mainly in terms of their frequencies and damping ratios. In this work the focus is towards their mode shapes, which are investigated using the screw-axis (also called Mozzi-axis), instead of the classic compass diagrams, for a better understanding of their three-dimensional patterns. The analysis is then carried out using the velocity centres for a characterization from the top, rear and side view of the vehicle. The multibody vehicle model employed for the numerical analysis is built in Adams. The dataset resembles that of a 250cc sport motorcycle, and has been derived from laboratory tests. The stability analysis is carried out in the frequency domain. It is found that, depending on the selected plane for the projection of the three-dimensional vibration motion, the trajectories of the velocity centres of the weave and wobble can cross either aft or fore the centre of mass, which has been associated to the under- and over-steering behaviour in the literature.

A Neural-Network-Based Methodology for the Evaluation of the Center of Gravity of a Motorcycle Rider

A correct reproduction of a motorcycle rider’s movements during driving is a crucial and the most influential aspect of the entire motorcycle–rider system. The rider performs significant variations in terms of body configuration on the vehicle in order to optimize the management of the motorcycle in all the possible dynamic conditions, comprising cornering and braking phases. The aim of the work is to focus on the development of a technique to estimate the body configurations of a high-performance driver in completely different situations, starting from the publicly available videos, collecting them by means of image acquisition methods, and employing machine learning and deep learning techniques. The technique allows us to determine the calculation of the center of gravity (CoG) of the driver’s body in the video acquired and therefore the CoG of the entire driver–vehicle system, correlating it to commonly available vehicle dynamics data, so that the force distribution can be properly determined. As an additional feature, a specific function correlating the relative displacement of the driver’s CoG towards the vehicle body and the vehicle roll angle has been determined starting from the data acquired and processed with the machine and the deep learning techniques.