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P. Sandeep Varma

Bio: P. Sandeep Varma is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Diesel fuel & Common rail. The author has co-authored 2 publications. Previous affiliations of P. Sandeep Varma include National Institute of Technology Agartala.

Papers
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Journal ArticleDOI
TL;DR: In this paper, the impact of Undi biodiesel blended diesel on combustion, performance, and exhaust fume profiles of a single-cylinder, four-stroke diesel engine was investigated.
Abstract: The present investigation accentuates the impact of Undi biodiesel blended diesel on combustion, performance, and exhaust fume profiles of a single-cylinder, four-stroke diesel engine. Five Undi biodiesel-diesel blends were prepared and tested at four variable loads over a constant speed of 1500 (±10) rpm. The Undi biodiesel incorporation to diesel notably improved the in-cylinder pressure and heat release rate (HRR) of the engine. The higher amount of Undi biodiesel addition enhanced the brake thermal efficiency (BTE) and brake specific energy consumption (BSEC) of the engine. In addition, the Undi biodiesel facilitated the reduction of the major pollutants, such as unburned hydrocarbon (UHC), carbon monoxide, and particulate matter (PM) emissions with slightly higher oxides of nitrogen emissions of the engine. To this end, a trade-off study was introduced to locate the favorable diesel engine operating conditions under Undi biodiesel-diesel strategies. The optimal results of the engine operation were found to be 32.65% of brake thermal efficiency, 1.21 g/kWh of brake specific cumulated oxides of nitrogen and unburned hydrocarbon, 0.94 g/kWh of brake specific carbon monoxide (BSCO), and 0.32 g/kWh of brake specific particulate matter (BSPM) for 50% (by volume) Undi biodiesel blend at 5.6 bar brake mean effective pressure (BMEP) with a relative closeness value of 0.978, which brings up the pertinence of the trade-off study in diesel engine platforms.

1 citations

Book ChapterDOI
01 Jan 2021
TL;DR: In this article, an Artificial Neural Network (ANN) was used to predict brake thermal efficiency, oxides of nitrogen, unburned hydrocarbon and carbon monoxide emissions of a single-cylinder, common rail direct injection (CRDI) engine.
Abstract: The current investigation highlights the impact of Diesel–biodiesel blends on performance and exhaust emission profiles of a single-cylinder, common rail direct injection (CRDI) engine. Experiments were performed at constant engine speed (1500 rpm) and three engine loads (50, 75 and 100%) under high fuel injection pressure (900 bar) with volume proportions (10, 20 and 30%) of Karanja with Diesel. Utilizing CRDI engine experimental data, an artificial intelligence (AI)-affiliated artificial neural network (ANN) model has been created with the intention of forecasting brake thermal efficiency, oxides of nitrogen, unburned hydrocarbon and carbon monoxide emissions. From various tested ANN models, one hidden layer with three neurons along with logsig transfer function has been noticed to be optimum network for Diesel-Karanja paradigms under high fuel injection pressure. While developing the optimum model, standard Levenberg–Marquardt training algorithm has been employed. The optimum ANN model is capable to estimate the CRDI engine performance–emission profiles with an overall correlation coefficient value of 0.99742, wherein 0.99783, 0.99951 and 0.99969 for training, validation and testing datasets, respectively. Results made clear that the formulated AI-based ANN model is viable for predicting the existing CRDI engine performance and emission profiles of Diesel-Karanja blends operating under high fuel injection pressure.

1 citations


Cited by
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Journal ArticleDOI
14 Jun 2022-Energies
TL;DR: In this paper , a regression algorithm was used to construct the engine torque and NOx emission prediction model, and the regression algorithm reached 4.9208 and 99.2%, indicating that the model was relatively accurate.
Abstract: Low accuracy is the main challenge that plagues the application of engine modeling technology at present. In this paper, correlation analysis technology is used to analyze the main influencing factors of engine torque and NOx (nitrogen oxides) raw emission performance from a statistical point of view, and on this basis, the regression algorithm is used to construct the engine torque and NOx emission prediction model. The prediction RMSE between engine torque prediction value and true value reaches 4.6186, and the torque prediction R2 reaches 1.00. Prediction RMSE between NOx emission prediction value and true value reaches 67.599, and NOx emission prediction R2 reaches 0.99. When using the new WHTC data for model prediction verification, the RMSE between the engine torque predicted value and true value reaches 4.9208, and the prediction accuracy reaches 99.60%, the RMSE between NOx emission prediction value and true value reaches 72.38, and the prediction accuracy reaches 99.2%, indicating that the model is relatively accurate. The evaluation result of the ambient temperature impact on torque shows that ambient temperature is positively correlated with engine torque.

2 citations