Yazar "Sundara, Vikram" seçeneğine göre listele

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    Production of HHO gas in the water-electrolysis unit and the influences of its introduction to CI engine along with diesel-biodiesel blends at varying injection pressures
    (Pergamon-Elsevier Science Ltd, 2024) Babu, J. M.; Kumar, K. Sunil; Kumar, R. Ramesh; Agbulut, Umit; Razak, Abdul; Thakur, Deepak; Sundara, Vikram
    Hydrogen has been identified as a clean and renewable energy source that has a significant potential to replace fossil fuels. The effective production of hydrogen on a commercial scale, however, presents a vital obstacle in today's world. Water splitting electrolysis, which offers high energy conversion and storage capabilities, has emerged as a promising approach for achieving the efficient hydrogen production to address this issue. This experimental research focuses on the production of hydrogen-oxygen gas through the electrolysis process. HHO gas provides promising benefits in terms of better combustion and lower emissions. Therefore, it also focuses on how best HHO gas can be utilized in diesel engines to improve the performance and to reduce the emissions. HHO gas produced at 60 L per hour through electrolysis process is mixed with air in a mixing chamber. Also, Palm munja methyl ester of 10% and diesel fuel of 90% was volumetrically blended to get B10 biodiesel. Therefore, totally four test fuels (Diesel, Diesel + HHO, B10, and B10 + HHO) were tested on a single-cylinder Kirloskar water-cooled direct injection diesel engine under different engine speeds ranging from 1447 to 1550 rpm. The engine injector pressure was varied from 200, 220, and 240 bar during the experiments with an interval of 20 bar. The engine has been modified for hydrogen and oxygen inlet at the entrance manifold 6 cm away at an angle of 30 degrees. The results shows that at 200 bar injection pressure with B10 + HHO blend exhibits better performance and released lower emissions. The perfor-mance results also show that the brake thermal efficiency was improved up to 14.16%, and the brake power was increased by 3.22%, while the brake-specific fuel consumption is reduced by 11.53%. The emission results show that CO, HC, NOx, and CO2 emissions were reduced by 20.87%, 11.47%, 1.96%, and 5.22%, respectively. Therefore, it can be concluded that B10 + HHO provides better performance and the lowest emissions compared with other blends. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.
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    Use of modern algorithms for multi-parameter optimization and intelligent modelling of sustainable battery performance
    (Elsevier, 2023) Afzal, Asif; Buradi, Abdulrajak; Jilte, Ravindra; Sundara, Vikram; Shaik, Saboor; Agbulut, Umit; Alwetaishi, Mamdooh
    The focus of this computational work is to predict and optimize the battery thermal performance indicators for its sustainable operation using different meta-heuristic optimization algorithms and machine learning models. The contribution of this work is two-fold, first, the heat removal ability from battery indicated by average Nusselt number (Nuavg) and hotspots (MaxT) to avoid battery thermal runaway are optimized as single objective optimization (SOO) and as multi-level objective optimization (MOO) problem. Second, intelligent algorithms: Gradient boosting (GB) algorithm and Gaussian process regressor (GPR) algorithm are used for modelling of Nuavg and MaxT. For SOO, Multi-verse optimization (MVO) and Grey wolf optimization (GWO) algorithms are used for individual battery performance indicators. Similarly, the enhanced version of MVO and GWO for MOO (MMVO and MGWO) algorithms is customized. Each algorithm is operated for five cycles and 100 iterations in each cycle of execution. In GB algorithm the effect of different loss functions and in GPR algorithm the effect of parameter alpha (alpha) is analyzed. SOO gives highest fitness of Nuavg and lowest hotspots occurrence from both the algorithms with same converged positions of operating parameters. MMVO and MGWO relatively provide lower Nuavg with MaxT in the same range of SOO. The MOO provides different set of particle positions compared to SOO. MGWO algorithm has outperformed in providing the best non-dominated solution. The GB and GPR algorithm are good enough for the forecasting of battery thermal parameters. GPR is even accurate, however the range of alpha is important during training and testing. The best Nuavg obtained from SOO using MVO algorithm is around 82.06 while MaxT is 0.34. The same from GWO algorithm is 82.05 and 0.33 respectively. MGWO algorithm in MOO provides Nuavg and MaxT around 75.57 and 0.34 while MMWO provides 66.76 and 0.33 respectively. GPR algorithm gives accuracy as close as 98 % for MaxT while it gives 94 % accuracy for Nuavg. On the other hand GB algorithm gives 99 % and 97.5 % accuracy for MaxT and Nuavg respectively.