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Ride comfort is the major concern to the roadway vehicle passengers, travelling in as it affects their health and efficiency to work. In the present study, a 9 DoF model of a three-wheel vehicle is developed with Lagrangian approach to investigate its ride behavior when subjected to random surface irregularities. The irregularities of the track are measured with a three-wheeled setup equipped with profilometer known as opto-coupler. The present model is validated in two ways, first by comparing the vertical-lateral PSD acceleration received from simulation and actual testing and second by comparing vertical seat to head transmissibility obtained from analysis (VSTH) with past reported studies. A 7 DoF bio-dynamic model of the seated human subject is formulated and integrated with the vehicle model, ride comfort of the vehicle and human body segments are assessed based on ISO specifications. Passenger Ride Comfort is optimized through non-linear optimization using Random Search Technique. The modified values of vehicle suspension parameters are presented to obtain optimum passenger comfort based on ISO-2631-1 criteria.

Formulation of a rail vehicle model using Lagrange’s method requires the system’s kinetic energy, potential energy, spring potential energy, Rayleigh’s dissipation energy and generalized forces to be determined. This article presents a detailed analysis of generalized forces developed at wheel–rail contact point for 27 degrees of freedom–coupled vertical–lateral model of a rail vehicle formulated using Lagrange’s method and subjected to random track irregularities. The vertical–lateral ride comfort of the vehicle and the ride index of the vehicle are evaluated based on ISO 2631-1 comfort specifications and stability is determined using eigenvalue analysis. The parameters that constitute the generalized forces and critically influence ride and stability have been identified and their influences on the same have been analysed in this work.

The nonextensive behavior of entropy is exploited to explain the regularity in multiplicity distributions in [Formula: see text] collisions at high energies. The experimental data are analyzed by using Tsallis [Formula: see text]-statistics. We propose a new approach of applying Tsallis [Formula: see text]-statistics, wherein the multiplicity distribution is divided into two components; two-jet and multijet components. A convoluted Tsallis distribution is fitted to the data. It is shown that this method gives the best fits which are several orders better than the conventional fit of Tsallis distribution.

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