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Öğe Experimental and numerical analysis of the thermal performance of pebble solar thermal collector(Cell Press, 2024) Naik, N. Channa Keshava; Priya, R. Krishna; Agulut, Umit; Gurel, Ali Etem; Shaik, Saboor; Alzaed, Ali Nasser; Alwetaishi, MamdoohIn this work, pebbles of higher specific heat than the conventional absorber materials like aluminium or copper are proposed as a absorber in the solar flat plate collector. The proposed collector are integrated into the building design and constructed with masonry. Tests were conducted by varying the operating parameters which influence its performance, like the flow rate of the heat-absorbing medium, and the tilt of the collector using both coated and uncoated pebbles. The maximum temperature difference that could be measured for a conventional absorber was approximately 8 degrees C for a flow rate of 0.6 L/min. While for a coated and uncoated absorber, it was 7 degrees C and 5.5 degrees C respectively. This difference decreased with an increase in flow rates from 0.6 L/min to 1.2 L/min. For all the flow rates, it was observed that the average difference in efficiency between the coated and the conventional absorber collector is 5.82 %, while the difference between the coated and uncoated absorber collector is 15.68 %. Thus, it is very much evident that by replacing the conventional absorber with the proposed coated pebble absorber, the overall loss in efficiency is just 5.82 %, but the advantages are enormous. Along with the experimental study, numerical analysis was also carried out with CFD modeling. The numerical results agreed well with experimental results with the least error. Therefore, CFD simulation can be further used to optimize the design of the collector.Öğe Hybrid optimization and modelling of CI engine performance and emission characteristics of novel hybrid biodiesel blends(Pergamon-Elsevier Science Ltd, 2022) Viswanathan, Vinoth Kannan; Kaladgi, Abdul Razak; Thomai, Pushparaj; Ağbulut, Ümit; Alwetaishi, Mamdooh; Said, Zafar; Shaik, SaboorDifferent meta-heuristic optimization algorithms have been used in a variety of fields due to their intelligent behavior and fast convergence. However, use of these algorithms in the engine behavior optimization is very-limited. The development of so-called hybrid optimization technique when these algorithms are combined with experimental design technique is an upcoming method in the field of renewable energy. Hence in this research, meta-heuristic optimization algorithms and experimental design methods were combined to optimize the engine behavior. Additionally, artificial neural networks (ANN) were employed to forecast the performance and emission behaviors of a CI engine running on a novel hybrid biodiesel blend of Cucurbita pepo. L (pumpkin) and Prosopis juliflora, mixed with a novel Elaeocarpus ganitrus (Rudraksha) additive. To assess the success of the ANN, four statistical benchmarks (R-2, and MSE) were used. Experiments were designed according to Design of Experiments (DOE) rules with performance and emission parameters as outputs. Response surface methodology (RSM) was employed to find the effect of interaction factors. Single objective and multi-objective optimization using highly efficient hybrid RSM-particle swarm optimization (RPSO) and dragon fly algorithm (RMODA) were employed to optimize the response of the obtained RSM equations. The outcomes demonstrated that RSM and ANN were excellent modelling techniques for these kinds of situations, with good accuracy. In addition, ANN's prediction performance (R-2 = 0.978 for BTE) was somewhat better than RSM's (R-2 = 0.960 for BTE). On the other hand, the PJB20 blend with 5 mL additive increased BTE by 52.8% and reduced BSFC by 34.9% at maximum load. The smoke opacity was lowered by 7.1% when compared to pure diesel, without any engine modifications. CO2 emission was seen to be shortened by 19.14%. Finally, it can be concluded that this novel biodiesel can be possibly utilized in CI engines with no modification and the engine characteristics can be controlled by optimization and prediction models.Öğe Hydrogen and dual fuel mode performing in engine with different combustion chamber shapes: Modelling and analysis using RSM-ANN technique(Elsevier Ltd, 2022) Khandal, Sanjeevakumar Veerasangappa; Razak, A.; Veza, I.; Afzal, Asif; Alwetaishi, Mamdooh; Shaik, S.; Ağbulut, ÜmitThis study investigates the impacts of hydrogen (H2) induction along with injected liquid honne biodiesel (BHO)/uppage biodiesel (BUO) as secondary pilot fuel in diesel engine. The effects of compression ratio (CR), hydrogen fuel flow rate (HFR) and different combustion chamber shapes in dual fuel (DF) mode were investigated. In the first phase of experiments, the effects of three different CR (15.5, 16.5, and 17.5) on engine efficacy and emission were presented. In the second phase, the effects of three HFR (0.1, 0.17, and 0.24 kg/h) on engine efficacy and emission, as well as the maximum possible HFR were reported. In the last phase, performance with different combustion chambers i.e., Hemispherical Combustion Chamber (HCC), Toroidal Reentrant Combustion Chamber (TRCC), and Toroidal Combustion Chamber (TCC) at maximum possible CR and HFR was highlighted. The study revealed that for knock free operation of the DF engine, the highest probable HFR was 0.24 kg/h at a CR of 17.5, fuel IT of 27obefore top dead center (bTDC) and injector opening pressure (IOP) of 250 bar. The toroidal re-entrant combustion chamber (TRCC) shape yielded 8%–12% better brake thermal efficiency (BTE) with lower emissions but 20–29% higher oxides of nitrogen (NOx) at 80% load in DF mode as contrasted to the single CI mode. Both peak pressure (PP) and heat release rate (HRR) were 12–15% higher. Response surface methodology (RSM) was used to design the experiments and to carry the optimization process. Artificial Neural Network (ANN) was used to forecast the performance and emission behaviors of the test engine. The findings demonstrated that RSM and ANN were excellent modelling techniques with good accuracy. In addition, ANN's prediction performance (R2 = 0.975 for BTE) was somewhat better than RSM's (R2 = 0.974 for BTE). Both the techniques were found to be successful in terms of agreement with experimental findings with ratios varying from 95% to 98% respectively. The prediction of BTE and NOx was also carried using different machine learning algorithms. It can be seen that R2 value for these models were slightly lower than ANN and RSM models indicating good predicting capability of ANN modelling. © 2022 Hydrogen Energy Publications LLCÖğe Melting numerical simulation of hydrated salt phase change material in thermal management of cylindrical battery cells using enthalpy-porosity model(Elsevier, 2023) Afzal, Asif; Jilte, Ravindra; Samee, Mohammed; Agbulut, Umit; Shaik, Saboor; Park, Sung Goon; Alwetaishi, MamdoohBattery thermal management using different cooling techniques is rapidly growing. Understanding the proper cooling and melting process when phase change materials (PCM) are used is of prime importance in this area. Hence, a transient thermal-fluid and melting process of hydrated salt PCM enclosed in a battery module with six cylindrical cells is numerically investigated to understand the melting process of the PCM. Four structural models S1, S2, S3, and S4 are constructed for the present numerical simulation. The battery cell wall is kept at a constant temperature of 35celcius, while the rectangular enclosure walls are assumed to be insulated. A finite volume scheme -based CFD (computational fluid dynamics) software is used to simulate the melting process of hydrated salt PCM. In order to capture the phase change phenomenon from solid to liquid, an enthalpy-porosity equation is solved. The temporal temperature distribution, liquid fraction, velocity and enthalpy are analyzed. The results obtained by the numerical computation suggest that the battery cell arrangement used in S1 and S2 model at the initial time step gives better space for temperature distribution and liquid fraction up to the time step of 420 s, while S3 and S4 model after a time interval of 420 s provide better scope for temperature distribution and complete melting of hydrated PCM.Öğe Parametric optimization of an impingement jet solar air heater for active green heating in buildings using hybrid CRITIC-COPRAS approach(Elsevier France-Editions Scientifiques Medicales Elsevier, 2024) Kumar, Raj; Kumar, Sushil; Agbulut, Uemit; Guerel, Ali Etem; Alwetaishi, Mamdooh; Shaik, Saboor; Saleel, C. AhamedThis work aimed to optimize the parameters of discrete multi-arc shaped ribs (DMASRs) in a solar air heating system (SAHS) through multi-criteria decision-making techniques. In the experiment, the roughness parameters of DMASRs were varied to find the best parameter combination for optimal SAHS performance. The relative rib height (Hr /H) was varied from 0.025 to 0.047 , and the relative rib pitch (Pr /H) was varied from 0.58 to 3.1. The results obtained for the Nusselt number and friction factor, which determine the performance of the SAHS system, depend on the geometrical parameters of the roughness. The parameters of DMASRs did not show any discernible trend. Hence, a multi-decision criteria approach that uses criteria importance through inter-criteria correlation (CRITIC) and complex proportional assessment (COPRAS) hybrid techniques was employed to determine the best parameter combination for optimal performance. The novel aspect of this study includes the use of a hybrid method (experimental and analytical) to optimize the performance of SAHS roughened with DMASRs hindrance promoters and predictions of outcomes using a hybrid CRITIC-COPRAS approach. The experimental and analytical examination through the use of the hybrid CRITIC-COPRAS approach is an essential component of this research that contributes to the optimization of the design parameters of such SAHS. The finding demonstrated that when Re = 19000, Pr/H = 1.7, and Hr/H = 0.047 were reached, the SAHS obtained an optimal thermohydraulic performance of 4.1.Öğe Pore size variation of nano-porous material fixer on the engine bowl and its combined effects on hybrid nano-fuelled CI engine characteristics(Elsevier Sci Ltd, 2023) Sathish, T.; Agbulut, Umit; Muthukumar, K.; Saravanan, R.; Alwetaishi, Mamdooh; Shaik, Saboor; Saleel, C. AhamedEnvironmental research is currently one of the most significant and pertinent fields of studies. Due to CI engi-nes' unique nature, they are widely used in densely populated cities for a variety of purposes. However, due to their improper combustion for various acceptable reasons at the engine cylinder, they considerably pollute the environment. This study prepared hybrid nano fuel from waste cooking oil by adding Al2O3 (aluminum oxide) and MWCNT (multi-wall carbon nanotubes) particles and adding ZnO (zinc oxide) nanoporous material fixture in the combustion chamber as an attachment for enhancing combustion efficiency to meet the aim of mitigating the CI engine emissions significantly. The research was evaluated in a 5.2 kW CI engine, and the ZnO nano-porous material is fixed to the combustor. Four distinct pores per inch (PPI) nanoporous materials of pore counts such as 60, 45, 30, and 15 PPI were considered to test the fuels such as diesel and hybrid nanofuel. The hybrid nano-fuel was created from the WCO biodiesel by mixing nanoparticles of MWCNT and Al2O3 nanoparticles in the ultra-sonicator. The experiments were carried out at different engine loads from no load to full load with a 25% step -up. The performance of the results was compared with conventional diesel fuel with and without ZnO nano-porous material fixtures and ZnO nano-porous material fixtures with different PPI. The result exhibited that the nanoparticles-added biodiesel fuel in 15 PPI has produced less NOx emission, CO emission, and heat release rate by 55.2%, 7%, 26.5%, and 22.67%, respectively, and this combination also exhibited an improvement of 2.62% in the brake thermal efficiency. Finally, the present work proves that better engine characteristics are generally obtained as the pore size of nanoporous materials in the engine bowl gets smaller, and the hybrid nanoparticles usage in the biodiesel fuel ensures more efficient engine combustion, performance, and emission characteristics.Öğe Poultry fat biodiesel as a fuel substitute in diesel-ethanol blends for DI-CI engine: Experimental, modeling and optimization(Elsevier Ltd, 2023) N., Santhosh; Afzal, Asif; V., Srikanth H.; Ağbulut, Ümit; Alahmadi, Ahmad Aziz; Gowda, Ashwin C.; Alwetaishi, MamdoohThe purpose of the present study is to evolve an alternate non-edible source for the synthesis of biodiesel and use it as a fuel substitute in diesel-ethanol blends for DI-CI engines. The use of discarded poultry fat feedstocks for the sustainable production of biofuels in the current day scenario is a novel approach that is still in its embryonic stage. For the effective utilization of these processed biofuels, it is very much required to ascertain the characteristics and their performance attributes for different blends. In this regard, a set of experiments are planned to study the emission and performance attributes of a direct injection (DI) diesel engine operating on poultry fat biodiesel, and the three proportions of diesel-biodiesel-ethanol blends with varying vol. % over the wide load range on a diesel engine. The ethanol percentage in the blend is varied from 5 vol % to 15 vol % in increments of 5 vol % with the amount of poultry fat-based biodiesel kept constant at 10 vol %. The performance and emission characteristics, particularly, the CO, CO2, NOx, unused Oxygen, and hydrocarbon emissions are experimentally determined for different fuel blends. From the results, it is evident that the performance characteristics of the fuel blends improve with the addition of ethanol in the diesel-biodiesel blend. Further, regression modeling of the performance characteristics is carried out to optimize the blend and operating load conditions, and the regression model is evolved for developing a mathematical relation for predictions of the results for different operating conditions. Also, Artificial Neural Network (ANN) modeling of the performance characteristics is carried out at each stage to predict the outcomes for different blends and load conditions and provide a set of empirical relations for analyzing the performance characteristics of the engines operating on poultry fat-based biodiesel-diesel-ethanol blends. Excellent predictions are obtained using regression modeling and ANN with R-squared values above 0.9. Thus, the present work provides a newer model of effectively using the ANN for the systematic study of the performance characteristics of the biodiesel blends obtained from a set of experiments through various optimization methods for better performance and a significant reduction in emissions. © 2023 Elsevier LtdÖğe Single- and combined-source typical metrological year solar energy data modelling(Springer, 2023) Afzal, Asif; Buradi, Abdulrajak; Alwetaishi, Mamdooh; Agbulut, Umit; Kim, Boyoung; Kim, Hyun-Goo; Park, Sung GoonPrediction of solar energy data is very crucial for the effective utilization of freely available renewable energy abundantly in nature. Solar energy data are widely available which must be carefully prepared and arranged for modelling. In this work, typical meteorological year (TMY) data made available by the Korea institute of energy research (KIER) and the National renewable energy laboratory (NREL) are used for modelling in different phases. TMY data at single-point location and multiple locations from KIER are initially used for training of machine learning (ML) algorithms. Later, the TMY data from NREL and KIER are combined and then modelled using radius nearest neighbour (RNN), decision tree regressor (DTR), random forest regressor (RFR), and X-gradient boosting (XGB) algorithms. The solar energy parameters modelled in this work are dew point temperature (DPT), dry bulb temperature (DBT), relative humidity (RH), surface pressure (SP), windspeed (WS), and solar insolation of horizontal plane (IHP). Quantitative analysis of the algorithms is also performed in each stage of the work. The modelling indicates that the DBT, DPT, RH, and SP are able to be predicted with a minimum accuracy of over 90% in each stage. The WS and IHP data when modelled from a single-source TMY data provide superior accuracy than when they are combined. RFR and XGB have outperformed overall as they provide good accuracy for WS and IHP data as well. RNN and DTR achieved 100% accuracy in training, while RFR and XGB showed slightly lower training accuracy due to their avoidance of overfitting. There are errors in testing for RNN/DTR. Using RNN/DTR, the training errors are 0% in all cases, while in some cases like DTP the error by RFR/XGB up to 3%, whereas RNN/DTR testing errors go up to 5% and in case of RFR/XGB they are up to 7.5%. For RH modelling RFR/XGB, training errors are max 6%. RNN/DTR testing errors go up to 11%, while for RFR/XGB up to 7.5% which indicates their robustness. It is observed that many solar parameters, when combined with different source data, can be predicted easily with good accuracy, while WS and IHP become a little bit challenging to model.Öğe A study on a milk chiller latent storage system with phase change material encapsulated spherical balls(Elsevier Ltd, 2023) Jilte, Ravindra; Afzal, Asif; Ağbulut, Ümit; Alahmadi, Ahmad Aziz; Alwetaishi, Mamdooh; Alzaed, Ali NasserFor dispersed or remotely located families, the collection of raw milk takes place less frequently or transportation to the nearest center is not feasible. It requires chilling of collected milk from udder temperature (?35 °C) to storage temperature (?4 °C) and maintaining it throughout thus it demands running chilling at the discrete locations. In this study, a novel design of a milk chiller for coolness storage of 12/24 h based on phase change material is presented. System performance has been demonstrated following the prevailing practice of milk collection and loading/unloading of milk. By switching off the refrigeration after a certain interval, the coolness storage was demonstrated to meet the chilling conditions even during the non-availability of power. The study proposes an integrated portable mobile milk chilling system that can move between solar PV plants and the nearest electric grid during non-sunny days. The proposed milk chiller latent storage system (MC-LSS) contains three major components: a helical coil for refrigerant circulation during charging of the system, spherical capsules for encapsulating phase change materials and interspaced occupied brine solution for storing coolness and circulating throughout PCM-filled capsules. MC-LSS is tested under two cases: FLS-12(first loading of milk chiller and storage for 12 h) and SLS-12 (second loading of milk and storage for 12 h). The temperature history of the Milk chiller latent storage system for the FLS-12 h case is qualitatively analyzed which shows an appreciable reduction in milk temperature around 10–15 °C within the first 20 min and in another ?40 min of further cooling, milk temperature attains the desired storage temperature (4–5 °C). © 2023 Elsevier LtdÖğe 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, MamdoohThe 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.
