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This research compares the performance of the models to estimate monthly mean diffuse solar radiation (DSR) on a horizontal surface for composite climatic region of India. The goal is to identify the most accurate model using deterministic and probabilistic analysis through two levels of uncertainty in daily sunshine period to examine the accuracy of the model that will be used to estimate DSR in the region under consideration. The meteorological data were collected from Indian Meteorological Department for the city of New Delhi (28 34 0 N, 77 12 0 E) which comes under composite climatic region prescribed by the Energy Conservation Building Code for India.From the literature, 150 typical models were chosen and categorized into three categories correlating diffuse fraction to sky-clearness index and relative sunshine period.The models are statistically evaluated using well-known statistical indicators in a unique way. In addition, the global performance indicator (GPI) is calculated using scaled values of indicators. The GPI of the chosen models ranged from À2.2912 to 0.2584, with the greatest value indicating the best model. Following that, the models are then ranked in decreasing order of GPI. Finally, the performances of the models are also checked for different locations having similar climatic conditions. Thus, results of this work are useful for impoverished countries as well as remote areas having similar environment conditions.

In recent years, the suspension system in modern vehicles has played a key role both as far as driving safety and comfort is concerned. To satisfy these vehicle performance specifications,active suspension is currently studied and implemented in practice in recent decades.In contrast to passive suspensions, by introducing force into system, active suspension can alter the suspension dynamic in realtime. A design of a controller is needed for real-time tuning of the control force in an active suspension system (ASS) to fulfill the challenging control objectives of suspension system comprising road handling, ride convenience, and travel suspension. This research proposed a novel ant colony optimization (ACO) algorithm for solving multi-objective weight optimization problem of the linear quadratic regulator (LQR) for automobiles ASS. The optimization problem of ASS is to design a state-feedback controller (SFC) as a result ACO is used to find optimal LQR weights. Here bothQ and R weight matrix of LQR is tuned. On a quarter-car ASS (QCASS) system, the effectiveness of ACO-tuned LQR is analytically checked with hardware in loop (HIL) analysis for an irregular road surface. Here, forexperiment, ISO road D rough runway, bumpy path, and pulse-type road profile are taken into account. Experimental findings illustrate that the proposed procedure can substantially reduce the acceleration of the Car body due to irregular road profiles compared to classical tuned LQR and model predictive control (MPC). The proposed controller shows the profound impact on the efficiency of the control schemes for three different road profiles.

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