S.P. Nigam

A Study on a Vibratory Model of a Human Body

This paper is concerned with the modeling of the human body as a spring mass system. Based on certain assumptions, an analysis for evaluating the mass and stiffness values of the model is developed. As an illustration of the modeling procedure, a 15-degree-of-freedom model of a male body is considered. The computed natural frequencies of the model are found to be within the range of available experimental values.

A study on mode of failures in spur gears under optimized conditions

The concept of design space formed with module and pinion number of teeth has been utilized in establishing an optimized design of a spur gear mesh and studying the dominating mode of failure under such condition. An attempt has been made in the present work for critically studying the behaviour of mode of failure for a large range of parameter variations for 20 commonly used gear materials for both 20° and 25° pressure angle, full depth spur gears. Minimum centre distance has been chosen as the optimization objective function and an in-depth study of bending, pitting and scoring mode of failures has been attempted.

Vehicular traffic noise prediction using soft computing approach.

A new approach for the development of vehicular traffic noise prediction models is presented. Four different soft computing methods, namely, Generalized Linear Model, Decision Trees, Random Forests and Neural Networks, have been used to develop models to predict the hourly equivalent continuous sound pressure level, Leq, at different locations in the Patiala city in India. The input variables include the traffic volume per hour, percentage of heavy vehicles and average speed of vehicles. The performance of the four models is compared on the basis of performance criteria of coefficient of determination, mean square error and accuracy. 10-fold cross validation is done to check the stability of the Random Forest model, which gave the best results. A t-test is performed to check the fit of the model with the field data.

Modal analysis of human body vibration model for Indian subjects under sitting posture

Need and importance of modelling in human body vibration research studies are well established. The study of biodynamic responses of human beings can be classified into experimental and analytical methods. In the past few decades, plenty of mathematical models have been developed based on the diverse field measurements to describe the biodynamic responses of human beings. In this paper, a complete study on lumped parameter model derived from 50th percentile anthropometric data for a seated 54- kg Indian male subject without backrest support under free un-damped conditions has been carried out considering human body segments to be of ellipsoidal shape. Conventional lumped parameter modelling considers the human body as several rigid masses interconnected by springs and dampers. In this study, concept of mass of interconnecting springs has been incorporated and eigenvalues thus obtained are found to be closer to the values reported in the literature. Results obtained clearly establish decoupling of vertical and fore-and-aft oscillations. Practitioner Summary: The mathematical modelling of human body vibration studies help in validating the experimental investigations for ride comfort of a sitting subject. This study clearly establishes the decoupling of vertical and fore-and-aft vibrations and helps in better understanding of possible human response to single and multi-axial excitations.

Honking noise contribution to road traffic noise prediction

The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.The implementation of Road Traffic Noise predictive Models (RTNMs) is crucial in order to be able to predict noise in urban areas strongly affected by vehicular traffic. These RTNMs can have in input a small or large number of inputs, according to the implemented function. Among these inputs, honking cannot be neglected in some specific areas in which drivers are used to horn in traffic jam or in proximity of intersections or other vehicles. In this paper, starting from a field measurement campaign in India, the authors highlight the shortcomings of standard RTNMs, that are not able to include random noisy events such as low or high pressure honking. Once the differences will be evaluated, the contribution of honking will be estimated and added to the predictions, to achieve a new model that is able to provide results in good agreement with field measurements.