The transportation demand in India is increasing tremendously, which arouses the energy consumption by 4.1 to 6.1% increases each year from 2010 to 2050. In addition, the private vehicle ownership keeps on increasing almost 10% per year during the last decade and reaches 213 million tons of oil consumption in 2016. Thus, this makes India the third largest importer of crude oil in the world. Because of this problem, there is a need of promoting the alternative fuels (biodiesel) which are from different feedstocks for the transportation. This alternative fuel has better emission characteristics compared to neat diesel, hence the biodiesel can be used as direct alternative for diesel and it can also be blended with diesel to get better performance. However, the effect of compression ratio, injection timing, injection pressure, composition-blend ratio and air-fuel ratio, and the shape of the cylinder may affect the performance and emission characteristics of the diesel engine. This article deals with the effect of compression ratio in the performance of the engine while using Honne oil diesel blend and also to find out the optimum compression ratio. So the experimentations are conducted using Honne oil diesel blend-fueled CI engine at variable load conditions and at constant speed operations. In order to find out the optimum compression ratio, experiments are carried out on a single-cylinder, four-stroke variable compression ratio diesel engine, and it is found that 18:1 compression ratio gives better performance than the lower compression ratios. Engine performance tests were carried out at different compression ratio values. Using experimental data, regression model was developed and the values were predicted using response surface methodology. Then the predicted values were validated with the experimental results and a maximum error percentage of 6.057 with an average percentage of error as 3.57 were obtained. The optimum numeric factors for different responses were also selected using RSM.

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This chapter describes the basic concepts of aerodynamics, evolution of lift and drag, types of drag, reduction of wing tip vortices, non-planar wing concepts for increased aerodynamic efficiency, various methods for determination of aerodynamic forces of an airplane, classification of wind tunnels, blower balance tunnels, and a case study report on aerodynamic force measurement of the non-planar wing systems. To increase the aerodynamic efficiency of the monoplane configuration, the ‘C-wing' configuration is presented in this chapter. The aim is to prove, at all angles of attack, C-wing produces a higher (L/D) ratio than straight wing for the same wetted surface area. The aerodynamic characteristics of three different wing models with NACA-64215 aerofoil such as straight wing, C-wing, and inverted C-wing at different angles of attack and low Reynolds number are shown. The inverted C-wing created more lift but produced more vibration, which may lead to lesser structural integrity.

In machinery, it is evident that the computing system for self‐automated machinery derives nonlinear and complex equations by comparing the machinery's different input parameters with their corresponding performance output parameters. In order to operate the machinery with good performance and better efficiency, the computing system needs a machine‐learning algorithm. Most recent researchers have concentrated more on self‐driving vehicle, which seems to be lack of developing a strong algorithm for compression ignition (CI) engines to predict the performance and emission output parameter. Thus, this article deals with the prediction of performance and emission characteristics of CI engine fueled with 25% Calophyllum inophyllum and 75% diesel blend (CIB25) at various compression ratios using artificial neural network (ANN). Performance and emission tests were conducted in a single‐cylinder four‐stroke variable‐compression‐ratio CI engine fueled with CIB25 with varying loads and at a constant speed of operation. Experimental investigation indicates that 18:1 compression ratio gives better performance results when CIB25 is used as the fuel. Emission test results show better emission characteristics at 17:1 compression ratio. These results show that some input factors affect the output factors under some set of operating conditions, while some input factors improve them. ANN developed for the CI engine learns how the input factors affect and improve the output factors. Also, developed neural network is found to be satisfactory, and it predicts the output at a regression value of 0.998 with an average error of 1.77% in the case of CIB25.

This chapter is dedicated to the shock wave reflections and intersections. Each topic offers a pictorial representation of the physical and shock polar plane for a better understanding of the shock wave reflections and intersections. This chapter contains an introduction to the shock-shock interference under the various real-life examples of the intersection of different and same family shock waves in a solid boundary, wave reflections from the free boundary, Mach reflections (lambda shock wave), intersection of shock from different or opposite families (Type I Interference), intersection of intense shocks of opposite families forms normal shock (Type II Interference), intersection of strong and weak oblique shocks of different families (Type III Interference), intersection of normal shock with oblique shock (Type IV Interference), intersection of weak oblique shock with intense shock (Type V Interference), intersection of the weak shock of same families (Type VI Interference).