Ever increasing usage of fossil fuels and dwindling natural resources led researchers to concentrate on investigating other sources which can satisfy our demands and reduce pollution levels. Present research work aims to investigate the performance and emission characteristics of plastic, diesel and biogas as fuel blend operated in a dual-fuel engine with biogas as a primary fuel and plastic oil – diesel blends as secondary fuel and also predict the output variable using artificial neural network. A modified four-stroke single cylinder CI engine was used for experiments conducted at varying load, percentage of plastic oil percentage in diesel and biogas flow rate. Based on the levels and factors a Taguchi L9 orthogonal matrix was designed to find the optimal combination of input indices. The signal to noise ratios in taguchi method were applied based on the desired output characteristics and according to the respective SNR ratios an ANOVA table was created to determine the major contributor effecting output parameters such as brake thermal efficiency, CO, HC NOx and smoke emissions. ANN model helped to predict BTE with same input parameters but with an increased set of sample data. Based on Gradient descent and Levenberg-Marquardt algorithm the ANN architecture was trained, validated and tested to predict the response with least error. The ANOVA calculated indicates load to be the prime factor effecting BTE and NOx emission
Similar to an IC (Internal combustion) engine which requires cooling to operate at optimum temperature for better efficiency; electric vehicles do require a similar system. There are various methods used in the current market for thermal management of batteries, of these our paper focuses on phase change materials (PCM). This cooling strategy can store an enormous amount of heat produced inside a battery because of its high latent heat capability. A 3D model of the battery using the multi-scale multi-dimension model (MSMD) for battery simulation and Solidification/melting models were used to showcase the melting of PCM due to the heat generated from a cell. ANSYS fluent was used to carry out the simulations. These computations are carried out at different C-rate to find the time taken for a battery to discharge and to find the impact of C-rate on PCM performance. Besides, temperature data for the cell was recorded before and after PCM was involved to compare the temperature difference between various PCM's.
As we are probably aware of certain infectious diseases that transmit from body to body because of perspiration or respiration of air from a human being containing strains of the infection, the goal of this investigation is to see how the infection is getting spread from a human residing in a closed area provided with air conditioner and with an appropriate ventilation framework that need to be involved to diminish infection dissemination in this enclosed area. Considering the present COVID-19 situation, it is important to discover the effect of infection spread to an individual contagion source. An appropriate CFD-model giving analysis of infection transmission from individual to individual in an air-conditioned room would give results to understand such situations. Likewise, this examination would help in determining the velocity, temperature, and particle contours in a characterized walled area. Besides, we have displayed various nooks utilizing different ventilation frameworks to discover which framework would give better outcomes to decrease infection transmission. Our investigation would provide how varying flow rates in a room at an outlet could be effective in reducing virus dissemination, as this model could be applied to cafes, cinemas, inns, and above all emergency clinics where individuals remain in an enclosed air-conditioned room.
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