Faculty Publications
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Item Optimum selection of phase change material for solar box cooker integrated with thermal energy storage unit using multi-criteria decision-making technique(Elsevier Ltd, 2021) Anilkumar, B.C.; Maniyeri, R.; Anish, S.Various thermal energy storage (TES) materials are used to increase the efficacy of solar cooker in off-sun hours. For the past few decades, phase change materials (PCMs) used as heat storage medium have become research interest. Selection of optimum PCM is important for the effective and efficient heat storage. Therefore, the main objective of the current study is to select the optimum PCM among the alternatives to be used for TES unit incorporated in solar box cooker (SBC). The PCMs are pre-screened among the alternatives used in earlier works based on the melting temperature. The optimum PCM is then selected with the aid of different multi-criteria decision -making (MCDM) techniques like TOPSIS, EDAS and MOORA. The criteria weights required for the optimization algorithm is found by using AHP, ENTROPY and CRITIC methods. Also, compromised values between the weights obtained through these methods are computed. The optimization algorithms are solved using MATLAB. The results of all MCDM techniques show that erythritol is the best alternative for the TES medium incorporated in the SBC. Further, the optimum mass of PCM and dimensions of the TES unit required for the SBC to operate during sun down hours for some specific duration is calculated by using a simple iterative solver developed with MATLAB. There is good agreement between the computational procedure and experimental study using paraffin wax as the TES medium. The iterative solution procedure also selects erythritol to be the best alternative as it required lesser quantity compared with other PCMs. Therefore, we recommend erythritol as the best PCM for the SBC incorporated with TES unit. © 2021Item Thermal performance assessment of a cylindrical box solar cooker fitted with decahedron outer reflector(SAGE Publications Inc., 2023) Anilkumar, B.C.; Maniyeri, R.; Anish, S.One of the important issues humankind globally faces in recent years is the scarcity of non-renewable energy resources. Solar energy is considered safe and renewable, which can fulfil the demand and supply chain requirements. Solar box cookers (SBCs) are popular in domestic cooking due to their ease of use and handling. The prime objective of the present work is to develop and test the performance of a cylindrical SBC fitted with decahedron-shaped reflector (CSBC-FDR). The CSBC is designed using minimum entropy generation (MEG) method. Through experiments, we observed that absorber plate attains peak temperature of about 138°C–150°C with the aid of decahedron reflector. The first figure of merit (F1) is found to be 0.13, indicating better optical efficiency and low heat loss coefficient for the SBC. The second figure of merit (F2) is obtained as 0.39, which indicates good heat exchange efficiency (F') and less heat capacity for cooker's interior. The average energy efficiency, exergy efficiency, and standardized cooking power values are 21.93%, 3.04%, and 25.28W, respectively. These results show that the present CSBC-FDR is able to cook food in a shorter period with better efficiency. The experimental and numerical values of overall heat loss coefficient of the developed SBC are in close agreement. The experimentally assessed performance parameters reveal superior performance of the present cylindrical SBC in comparison with many conventional rectangular and trapezoidal box solar cookers. © The Author(s) 2021.Item Performance Prediction Model Development for Solar Box Cooker Using Computational and Machine Learning Techniques(American Society of Mechanical Engineers (ASME), 2023) Anilkumar, B.C.; Maniyeri, R.; Anish, S.The development of prediction models for solar thermal systems has been a research interest for many years. The present study focuses on developing a prediction model for solar box cookers (SBCs) through computational and machine learning (ML) approaches. The prime objective is to forecast cooking load temperatures of SBC through ML techniques such as random forest (RF), k-nearest neighbor (k-NN), linear regression (LR), and decision tree (DT). ML is a commonly used form of artificial intelligence, and it continues to be popular and attractive as it finds new applications every day. A numerical model based on thermal balance is used to generate the dataset for the ML algorithm considering different locations across the world. Experiments on the SBC in Indian weather conditions are conducted from January through March 2022 to validate the numerical model. The temperatures for different components obtained through numerical modeling agree with experimental values with less than 7% maximum error. Although all the developed models can predict the temperature of cooking load, the RF model outperformed the other models. The root-mean-square error (RMSE), determination coefficient (R2), mean absolute error (MAE), and mean square error (MSE) for the RF model are 2.14 (°C), 0.992, 1.45 (°C), and 4.58 (°C), respectively. The regression coefficients indicate that the RF model can accurately predict the thermal parameters of SBCs with great precision. This study will inspire researchers to explore the possibilities of ML prediction models for solar thermal conversion applications. © © 2023 by ASME.