Yazar "Sharma, Prabhakar" seçeneğine göre listele

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    Enhancing the performance of renewable biogas powered engine employing oxyhydrogen: Optimization with desirability and D-optimal design
    (Elsevier Sci Ltd, 2023) Sharma, Prabhakar; Balasubramanian, Dhinesh; Khai, Chu Thanh; Venugopal, Inbanaathan Papla; Alruqi, Mansoor; Josephin, J. S. Femilda; Sonthalia, Ankit
    The performance and exhaust characteristics of a dual-fuel compression ignition engine were explored, with biogas as the primary fuel, diesel as the pilot-injected fuel, and oxyhydrogen as the fortifying agent. The trials were carried out with the use of an RSM-based D-optimal design. ANOVA was used to create the relationship functions between input and output. Except for nitrogen oxide emissions, oxyhydrogen fortification increased biogas-diesel engine combustion and decreased carbon-based pollutants. For each result, RSM-ANOVA was utilized to generate mathematical formulations (models). The output of the models was predicted and compared to the observed findings. The prediction models showed robust prediction efficiency (R2 greater than 99.21%). The optimal engine operating parameters were discovered by desirability approach-based optimization to be 24 degrees crank angles before the top dead center, 10.88 kg engine loading, and 1.1 lpm oxyhydrogen flow rate. All outcomes were within 3.75% of the model's predicted output when the optimized parameters were tested experimentally. The current research has the potential to be widely used in compression ignition engine-based transportation systems.
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    Exploration of low heat rejection engine characteristics powered with carbon nanotubes-added waste plastic pyrolysis oil
    (Elsevier, 2023) Murugesan, Parthasarathy; Elumalai, P. V.; Balasubramanian, Dhinesh; Padmanabhan, S.; Murugunachippan, N.; Afzal, Asif; Sharma, Prabhakar
    Compression ignition (CI)-powered alternative energy sources are currently the main focus due to the constantly rising worldwide demand for energy and the growing industrialization of the automotive sector. Due to their difficulty of disposal, non-degradable plastics contribute significantly to solid waste and pollution. The waste plastics were simply dropped into the sea, wasting no energy in the process. Attempts have been made to convert plastic waste into usable energy through recycling. Waste plastic oil (WPO) is produced by pyrolyzing waste plastic to produce a fuel that is comparable to diesel. Initially, a standard CI engine was utilized for testing with diesel and WPO20 (20% WPO+80% diesel). When compared to conventional fuel, the brake thermal efficiency (BTE) of WPO20 dropped by 3.2%, although smoke, carbon monoxide (CO), and hydrocarbon (HC) emissions were reasonably reduced. As a result, nitrogen oxide (NOx) emissions decreased while HC and CO emissions marginally increased in subsequent studies utilizing WPO20 with the addition of 5% water. When combined with WPO20 emulsion, nanoadditives have the potential to significantly cut HC and CO emissions without impacting performance. The possibility of incorporating nanoparticles into fuel to improve performance and lower NOx emissions should also be explored. In order to reduce heat loss through the coolant, prevent heat transfer into the cylinder liner, and increase combustion efficiency, the thermal barrier coating (TBC) material is also coated inside the combustion chamber surface. In this work, low heat rejection (LHR) engines powered by emulsion WPO20 containing varying percentages of carbon nanotubes (CNT) are explored. The LHR engine was operated with a combination of 10 ppm, 20 ppm, and 30 ppm CNT mixed with WPO20. It was shown that while using 20 ppm of CNT with WPO20, smoke, hydrocarbons, and carbon monoxide emissions were reduced by 11.9%, 21.8%, and 22.7%, respectively, when compared to diesel operating in normal mode. The LHR engine achieved the greatest BTE of 31.7% as a result of the improved emulsification and vaporization induced by CNT-doped WPO20. According to the study's findings, WPO20 with 20 ppm CNT is the most promising low-polluting fuel for CI engines.
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    Exploration of the dual fuel combustion mode on a direct injection diesel engine powered with hydrogen as gaseous fuel in port injection and diesel-diethyl ether blend as liquid fuel
    (Pergamon-Elsevier Science Ltd, 2024) Barik, Debabrata; Bora, Bhaskor Jyoti; Sharma, Prabhakar; Medhi, Bhaskar Jyoti; Balasubramanian, Dhinesh; Krupakaran, R. L.; Ramegowda, Ravikumar
    The present study explores the possibilities of the use of diesel-diethyl ether (DDEE) blends as pilot fuel, and hydrogen (H2) as inducted gaseous fuel in a diesel engine operated on dual fuel mode (DFM). DEE was added to diesel in ratios of 5-25% in increasing steps of 5%, to prepare the DDEE5, DDEE10, DDEE15, DDEE20, and DDEE25 blends that were used as pilot fuel. In this current study, for hydrogen gas generation, a hydrogen production kit was fabricated which was powered by solar energy. The hydrogen gas was produced from the electrolysis of water-KOH solution. During the experiment, hydrogen was inducted through the engine intake port employing an electronic gas injector. The quantity of hydrogen injection was set constant of 0.2 lpm for all the test cases. DDEE-hydrogen (DDEE+H2) blends accomplished overall good results compared to diesel. DDEE20+H2 furnished optimal results compared to diesel and other DDEE+H2 blends. Peak cylinder pressure for DDEE20+H2 was 66.91 bar at 5.2oCA aTDC, and the maximum HRR was 32.75 J/ deg.CA. Compared to diesel, the BTE of engine for DDEE20+H2 was augmented by about 0.6% and the BSFC was diminished by about 3.7%, at maximum load conditions. A decline in CO and HC emissions of 29.6%, and 35% were observed for DDEE20+H2 at maximum load condition, but the NO and CO2 emanation was observed to be higher by around 29.4%, and 17.4% in comparison to diesel respectively.(c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Maximizing efficiency and environmental benefits of an algae biodiesel-hydrogen dual fuel engine through operational parameter optimization using response surface methodology
    (Pergamon-Elsevier Science Ltd, 2024) Mohite, Avadhoot; Bora, Bhaskor Jyoti; Sharma, Prabhakar; Medhi, Bhaskar Jyoti; Barik, Debabrata; Balasubramanian, Dhinesh; Nguyen, Van Giao
    The utilization of clean and renewable fuels has become increasingly significant in the power generation and transportation sectors. Dual-fuel engines that employ hydrogen and algal biodiesel are potential alternatives. This study investigated the impact of pilot fuel injection pressures and engine loads on the performance and emissions of an algal biodiesel-hydrogen dual-fuel engine. The engine was optimized using response surface methodology under various operating conditions. The highest brake thermal efficiency (28.71 %) was obtained at 240 bar pilot fuel injection pressure and 100 % engine load, significantly reducing carbon monoxide and hydrocarbon emissions. The optimum parameters were identified using response surface methodology at 67.63 % engine load and 245.48 bar pilot fuel injection pressure, with a high model fit (R2) range of 88.89 %-99.59 % and composite desirability of 96.1 %. The potential for optimizing algal biodiesel-hydrogen dual-fuel engines to achieve greater efficiency and environmental benefits is highlighted in this work, as is the relevance of applying response surface methods to optimize engine performance.
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    A novel intelligent transport system charging scheduling for electric vehicles using Grey Wolf Optimizer and Sail Fish Optimization algorithms
    (TAYLOR & FRANCIS, 2022) Rajamoorthy, Rajasekaran; Arunachalam, Gokulalakshmi; Kasinathan, Padmanathan; Devendiran, Ramkumar; Ahmadi, Pouria; Pandiyan, Santhiya; Muthusamy, Suresh; Panchal, Hitesh; Kazem, Hussein A.; Sharma, Prabhakar
    Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered.