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The purpose of this study is to examine the influence of biodiesel (BD) fuel properties on different characteristics of the engine and to compare with the baseline petroleum diesel (PD) fuel. This study consists of two parts, first one is biodiesel characterization and the second one is testing in the engine. Two BD fuels namely, the medium chain (C6–C24) coconut oil methyl ester (COME) and the long chain (C16–C18) sesame seed oil methyl ester (SSME) were selected. It is observed that, the physical and chemical properties such as viscosity, density, bulk modulus, calorific value, C/H ratio, and iodine value of SSME are higher than that of COME, while the cetane number, saturation% and oxygen% of the COME is higher than that of SSME. Experiments were conducted in a naturally aspirated, single cylinder, four-stroke, stationary, water cooled, constant rpm (1500), in-line (pump-high pressure tube-fuel injector) direct injection compression ignition (DI–CI) engine with COME, SSME (with and without preheating), and PD as fuels. The performance was evaluated in terms of fuel consumption (FC), brake specific energy consumption (BSEC), and thermal efficiency (BTE). Except for COME at full load, the BTE of the esters over all load ranges were less than that of PD fuel. Also, a significant improvement in BTE was observed, when the SSME is tested at PD’s viscosity by using preheating technique. At full load, the BSFC of COME and SSME are increased by 16.61% and 18.24% respectively. The minimum BSEC (at full load) of COME is decreased by 1.3% and while that of SSME is increased by 4.5%, as compared to that of PD fuel. The full load peak pressures for COME, SSME and PD fuel are 63.8 bar, 65.8 bar, and 62.9 bar respectively. The high peak pressures of the methyl esters are probably due to dynamic injection advance, caused by their higher bulk modulus. The net heat release rate (HRR) and cumulative heat release (CHR) were calculated from the acquired data. The results show that, at all loads there is a slight increase in peak HRR for COME and large increase in peak HRR for SSME against PD fuel. The higher values of peak HRR indicate better premixed combustion with the methyl esters. However, the peak HRR for preheated SSME (SSME_H) decreases due to late injection and faster evaporation of the fuel. It was observed that, at full load the nitric oxide (NO) emission of SSME is increased by 12.9%, while that of COME is decreased by 13.8% as compared to that of PD fuel. The smoke is increasing linearly with the fuels ‘C/H’ ratio regardless of their molecular structure. The HC emissions of both the esters are very low and are reduced by up to 73%, as compared PD. Also, there is a significant reduction in all exhaust emissions, and in particular the NO emission is observed with preheated SSME, due to change in premixed combustion phase.

A prediction model based on the perfect prognosis method was developed to predict the probability of lightning and probable time of its occurrence over the southeast Indian region. In the perfect prognosis method, statistical relationships are established using past observed data. For real time applications, the predictors are derived from a numerical weather prediction model. In the present study, we have developed the statistical model based on Binary Logistic Regression technique. For developing the statistical model, 115 cases of lightning that occurred over the southeast Indian region during the period 2006-2009 were considered. The probability of lightning (yes or no) occurring during the 12-hour period 0900-2100 UTC over the region was considered as the predictand. The thermodynamic and dynamic variables derived from the NCEP Final Analysis were used as the predictors. A three-stage strategy based on Spearman Rank Correlation, Cumulative Probability Distribution and Principal Component Analysis was used to objectively select the model predictors from a pool of 61 potential predictors considered for the analysis. The final list of six predictors used in the model consists of the parameters representing atmospheric instability, total moisture content in the atmosphere, low level moisture convergence and lower tropospheric temperature advection. For the independent verifications, the probabilistic model was tested for 92 days during the months of May, June and August 2010. The six predictors were derived from the 24-h predictions using a high resolution Weather Research and Forecasting model initialized with 00 UTC conditions. During the independent period, the probabilistic model showed a probability of detection of 77% with a false alarm rate of 35%. The Brier Skill Score during the independent period was 0.233, suggesting that the prediction scheme is skillful in predicting the lightning probability over the southeast region with a reasonable accuracy.

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