Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Forecasting of Engine Performance for Gasoline-Ethanol Blends using Machine Learning

The incorporation of alternative fuels in the automotive domain has brought a new paradigm to tackle the environmental and energy crises. Therefore, it is of interest to test and forecast engine performance with blended fuels. This paper presents an experimental study on gasoline-ethanol blends to test and forecast engine behavior due to changes in the fuel. This study employed a machine learning (ML) technique called TOPSIS to forecast the performance of a slightly higher blend fuelled engine based on experimental data obtained from the same engine running on 0% ethanol blend (E0) and E10 fuels under full load conditions. The engine performance predictions of this ML model were validated for 15% ethanol blend (E15) and further used to predict the engine performance of 20% ethanol blend fuel. The prediction R2 score for the ML model was found to be greater than 0.95 and the MAPE range was 1% to 5% for all observed engine performance attributes. Thus, this paper presents the potential of TOPSIS methodology-based ML predictions on blended fuel engine performance to shorten the testing efforts of blended fuel engines. This methodology may help to faster incorporate higher blended fuels in the automotive sector

Estimation of Heat Diffusion in Human Tissue at Adverse Temperatures Using the Cylindrical Form of Bioheat Equation

Background: The assessment of the evolution or fall of the temperature distribution of all biological tissues, and particularly human in vivo tissues at adverse temperatures, is crucial because excess cold or heat can impair the human body and its physiological processes. However, this estimation through experimental investigations is challenging due to the ability of the human body to bear a wide range of unfavourable temperatures. Thus, it becomes imperative to frame a mathematical model and its solution for the measurement of the temperature distribution in the selected tissue. Method: The three-dimensional cylindrical bioheat equation, with initial and boundary conditions, was used to formulate a mathematical model. The model was solved using the variables-separable method. Results: The model was solved analytically, and MATLAB software was used for numerical calculations and a graphical representation. The model was applied to display the temperature distributions in human skin and in the head. Conclusions: The paper helps predict the distribution of heat and corresponding burn or cold injuries in human tissue well in advance of applying any thermal treatment such as targeted tumour hyperthermia or cryosurgery