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The numerical modelling of influence of engine design and operating variables such as types of fuels, calorific value, piston bowl diameter and emission variables on engine emission have been developed through empirical equation, the equation have been constituted with variables and were obtained from the engine test results. The MATLAB coding has been developed with respect to available testing data. The developed model have shown the results agreed with closest to the real time values. Therefore the influence of piston bowl diameter, calorific value and various types fuels such as isobutanol, methanol, ethanol and gasoline on NOx emission have been simulated with the help of developed model. The results were seen closest to the real time value.

The mathematical modelling programme MATLAB was used to examine the effective thermal conductivity of heterogeneous materials, and the results are given in this research. The prediction of metal matrix composite materials' thermal conductivity in relation to different relevant variables such as inclusion size and volume %. Ceramic compounds and non-thermal conductive inclusions are common.It appears that determining the effect of inclusion on the thermal conductivity of heterogeneous materials using standardised tests is challenging. As a result, the created mathematical tool may be used to demonstrate the effect of inclusions on thermal conductivity. For the evaluation of heterogeneous metals, the volume proportion of inclusion has been estimated to be up to 50%. For the thermal conductivity test, metals such as aluminium, iron, magnesium, and zinc were chosen.

An expanding requirement for clean air motivates the vehicle makers to contribute a lot of time and cash to reduce toxic emissions. The utilization of catalytic converters is one of the well-known strategies to clean the exhaust. The catalytic converters oxidize the destructive carbon monoxide, hydrocarbon emissions into harmless CO2 and water vapor. In this experiment, the essential objective is to develop a catalytic converter with combined methods like selective catalytic reduction, NOx storage and reduction (SCR-NSR) procedure and test its effectiveness towards the NOx reduction from the exhaust of diesel engine. The combination of the two systems is proposed in this experimentation for building up an effective catalytic converter and distinguishing the optimal emission processes. The fabricated SCR-NSR catalytic converter fitted to the exhaust system and the emission rates are estimated with the help of a gas analyzer. The testing is done by utilizing diesel as fuel in the Kirloskar TV1 engine with NSR, Non-filter and combined NSR-SCR system. The investigation is experiencing for 20 trials with different emission parameters is analyzed. For improving the discharge level in the engine, the test data are predicted with the proposed numerical model and tested in Mat Lab software. The invasive weed optimization and particle swarm optimization together with a recurrent neural network are utilized for the prediction and optimization process and parameters like compression ratio, input power, and load are utilized as input to the experimentation. In the 20 trial, a low level of discharge is attained in the 17th trial. Subsequently, the prediction goes for the 20 sets individually and compared with 17th set and the experimental results demonstrates that the discharge rates acquired for consolidated NSR-SCR observed to be 0.01% of CO, 3% of HC, 0.1% of CO2, 20.74% of O2, and 68 ppm of NOx. At the point when compared to predicted outcomes demonstrates the ideal level of 0.001% of CO, 0.5% of HC, 0% of CO2, 21.76% of O2, and 60.6ppm of NOx respectively. The proposed invasive weed optimization and particle swarm optimization obviously reduce the emission rates of diesel engine than the other comparative methods.