Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network
Abstract Embracing an interaction between the phase change material (PCM) and the droplets of a heat transfer fluid, the direct contact (DC) method suggests a cutting-edge solution for expediting the phase change rates of PCMs in thermal energy storage (TES) units. In the direct contact TES configur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2023-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-023-37712-x |
| _version_ | 1797789771090624512 |
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| author | Shahin Faghiri Parham Poureslami Hadi Partovi Aria Mohammad Behshad Shafii |
| author_facet | Shahin Faghiri Parham Poureslami Hadi Partovi Aria Mohammad Behshad Shafii |
| author_sort | Shahin Faghiri |
| collection | DOAJ |
| description | Abstract Embracing an interaction between the phase change material (PCM) and the droplets of a heat transfer fluid, the direct contact (DC) method suggests a cutting-edge solution for expediting the phase change rates of PCMs in thermal energy storage (TES) units. In the direct contact TES configuration, when impacting the molten PCM pool, droplets evaporate, provoking the formation of a solidified PCM area (A). Then, they reduce the created solid temperature, leading to a minimum temperature value (T min). As a novelty, this research intends to maximize A and minimize T min since augmenting A expedites the discharge rate, and by lowering T min, the generated solid is preserved longer, resulting in a higher storage efficacy. To take the influences of interaction between droplets into account, the simultaneous impingement of two ethanol droplets on a molten paraffin wax is surveyed. Impact parameters (Weber number, impact spacing, and the pool temperature) govern the objective functions (A and T min). Initially, through high-speed and IR thermal imaging, the experimental values of objective functions are achieved for a wide range of impact parameters. Afterward, exploiting an artificial neural network (ANN), two models are fitted to A and T min, respectively. Subsequently, the models are provided for the NSGA-II algorithm to implement multi-objective optimization (MOO). Eventually, utilizing two different final decision-making (FDM) approaches (LINMAP and TOPSIS), optimized impact parameters are attained from the Pareto front. Regarding the results, the optimum amount of Weber number, impact spacing, and pool temperature accomplished by LINMAP and TOPSIS procedures are 309.44, 2.84 mm, 66.89 °C, and 294.98, 2.78 mm, 66.89 °C, respectively. This is the first investigation delving into the optimization of multiple droplet impacts for TES applications. |
| first_indexed | 2024-03-13T01:55:24Z |
| format | Article |
| id | doaj.art-572e63fadd594eb4aa57e1678bb4cd85 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-03-13T01:55:24Z |
| publishDate | 2023-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-572e63fadd594eb4aa57e1678bb4cd852023-07-02T11:14:57ZengNature PortfolioScientific Reports2045-23222023-06-0113112110.1038/s41598-023-37712-xMulti-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural networkShahin Faghiri0Parham Poureslami1Hadi Partovi Aria2Mohammad Behshad Shafii3Department of Mechanical Engineering, Sharif University of TechnologyDepartment of Mechanical Engineering, Sharif University of TechnologySchool of Mechanical Engineering, College of Engineering, University of TehranDepartment of Mechanical Engineering, Sharif University of TechnologyAbstract Embracing an interaction between the phase change material (PCM) and the droplets of a heat transfer fluid, the direct contact (DC) method suggests a cutting-edge solution for expediting the phase change rates of PCMs in thermal energy storage (TES) units. In the direct contact TES configuration, when impacting the molten PCM pool, droplets evaporate, provoking the formation of a solidified PCM area (A). Then, they reduce the created solid temperature, leading to a minimum temperature value (T min). As a novelty, this research intends to maximize A and minimize T min since augmenting A expedites the discharge rate, and by lowering T min, the generated solid is preserved longer, resulting in a higher storage efficacy. To take the influences of interaction between droplets into account, the simultaneous impingement of two ethanol droplets on a molten paraffin wax is surveyed. Impact parameters (Weber number, impact spacing, and the pool temperature) govern the objective functions (A and T min). Initially, through high-speed and IR thermal imaging, the experimental values of objective functions are achieved for a wide range of impact parameters. Afterward, exploiting an artificial neural network (ANN), two models are fitted to A and T min, respectively. Subsequently, the models are provided for the NSGA-II algorithm to implement multi-objective optimization (MOO). Eventually, utilizing two different final decision-making (FDM) approaches (LINMAP and TOPSIS), optimized impact parameters are attained from the Pareto front. Regarding the results, the optimum amount of Weber number, impact spacing, and pool temperature accomplished by LINMAP and TOPSIS procedures are 309.44, 2.84 mm, 66.89 °C, and 294.98, 2.78 mm, 66.89 °C, respectively. This is the first investigation delving into the optimization of multiple droplet impacts for TES applications.https://doi.org/10.1038/s41598-023-37712-x |
| spellingShingle | Shahin Faghiri Parham Poureslami Hadi Partovi Aria Mohammad Behshad Shafii Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network Scientific Reports |
| title | Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network |
| title_full | Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network |
| title_fullStr | Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network |
| title_full_unstemmed | Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network |
| title_short | Multi-objective optimization of multiple droplet impacts on a molten PCM using NSGA-II optimizer and artificial neural network |
| title_sort | multi objective optimization of multiple droplet impacts on a molten pcm using nsga ii optimizer and artificial neural network |
| url | https://doi.org/10.1038/s41598-023-37712-x |
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