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Byline: K.V. Gangadharan, C. Sujatha, V. Ramamurti A very elaborate Finite Element (FE) model and a rigid body model of a typical electrical multiple unit trailer coach are described. These models were used to find the dynamic response to track irregularities in the frequency domain. The Power Spectral Density (PSD) of track irregularities was used as input to the system. The influence of different track irregularities on dynamic response and coupling between vertical and lateral dynamics was investigated. Extensive experiments were carried out, and analytical results were compared with the measured response.

Byline: K.V. Gangadharan, C. Sujatha, V. Ramamurti One rigid body and three finite element models have been used to study the dynamic behaviour of a typical Electrical Multiple Unit Trailer coach running on Indian suburban tracks. Following eigenvalue analysis, dynamic responses to input excitation from rail joints and Average Vertical Profile (AVP) have been determined in the time domain, using mode superposition technique. Acceleration and displacement response at Centres of Gravity (cg) of car and bogie have been predicted. The analytical results compare well with experimental results and highlight the need to include superstructure elasticity to predict the dynamic response accurately.

In the present study, performance, emission, and vibro-acoustic studies were conducted on a spark ignition (SI) engine fueled with gasoline and an n-propanol blend at variable compression ratio (CR), speed, and propanol blend fraction (PBF). Experimental data were used to model an artificial neural network (ANN) trained with a genetic algorithm (GA). ANN predictive responses were employed to establish regression relationships between brake power (BP), brake specific fuel consumption (BSFC), brake thermal efficiency (BTE), oxides of nitrogen (NO x), carbon monoxide (CO), hydrocarbon (HC), resultant vibration acceleration (RVA), and sound pressure level (SPL) with operating parameters using response surface methodology (RSM). These models served as objective functions in the non-dominated sorting genetic algorithm-3 (NSGA3), a multi-objective optimization (MOO) technique, to optimize responses and obtain non-dominated solutions. These solutions were filtered using a modified technique for order preference by similarity to the ideal solution (TOPSIS) to obtain a compromised optimal solution. ANN-GA model outcomes showed high accuracy, with coefficient of determination (R2) and root mean square error (RMSE) values ranging from 0.979 to 0.993 and 0.0381 to 0.0643, respectively. NSGA3 coupled with modified TOPSIS identified optimal operating conditions at 1271.77 RPM, a CR of 11.96, and a PBF of 33.26 %. • N-propanol and gasoline blend used in a PFI-SI engine under half-throttle conditions. • The addition of n-propanol reduces emissions and vibro-acoustic behavior. • ANN trained with GA for accurate prediction of performance, emission, and vibro-acoustic features of the engine. • Exploration of Pareto solutions through the application of the NSGA3 algorithm. • Identification of the compromised optimal solution achieved by employing a modified version of the TOPSIS method. [ABSTRACT FROM AUTHOR]