Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network
Waste heat utilization has been prioritized especially in various industries and sectors. Many researchers have developed heat recovery processes by designing suitable waste heat recovery units (WRU), such as heat exchangers, using water as a coolants to receive heat from the waste heat fluid in the...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-03-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484722023472 |
| _version_ | 1797843883818745856 |
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| author | Chaiyanan Kamsuwan Xiaolin Wang Lee Poh Seng Cheng Kai Xian Ratchanon Piemjaiswang Pornpote Piumsomboon Yotsakorn Pratumwal Somboon Otarawanna Benjapon Chalermsinsuwan |
| author_facet | Chaiyanan Kamsuwan Xiaolin Wang Lee Poh Seng Cheng Kai Xian Ratchanon Piemjaiswang Pornpote Piumsomboon Yotsakorn Pratumwal Somboon Otarawanna Benjapon Chalermsinsuwan |
| author_sort | Chaiyanan Kamsuwan |
| collection | DOAJ |
| description | Waste heat utilization has been prioritized especially in various industries and sectors. Many researchers have developed heat recovery processes by designing suitable waste heat recovery units (WRU), such as heat exchangers, using water as a coolants to receive heat from the waste heat fluid in the production process. The conventional heat exchanger has limitations such as its equipment size, space for installation, and flexibility. The microchannel heat exchanger is one of many ideas for resolving these limitations. Moreover, the coolant on the cold side can be upgraded by adding nanometer-sized solid particles which is called “Nanofluid”. To reduce the high investigation cost and time, a new efficient and cost-effective simulation method was selected to use for investigating the performance of a microchannel heat exchanger with nanofluids in this study. To analyze the heat recovery at low temperature, i.e. around 100–200 °C, nanofluid property predictive models were developed using an artificial neural network (ANN). Then, the predictive models were embedded and integrated into computational fluid dynamics to design a microchannel heat exchanger. It is found that the use of nanofluids improved the heat transfer efficiency of this heat exchanger. The suitable nanofluid types and concentrations were selected based on the thermal–hydraulic performance. Here, the 3% weight TiO2/Water fluid with a 1.03 thermal–hydraulic performance ratio was found to be the most promising nanofluid for using in this condition. |
| first_indexed | 2024-04-09T17:13:18Z |
| format | Article |
| id | doaj.art-f64d71eef8334775b64bdb760e44ad5a |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-04-09T17:13:18Z |
| publishDate | 2023-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-f64d71eef8334775b64bdb760e44ad5a2023-04-20T04:36:51ZengElsevierEnergy Reports2352-48472023-03-019239247Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural networkChaiyanan Kamsuwan0Xiaolin Wang1Lee Poh Seng2Cheng Kai Xian3Ratchanon Piemjaiswang4Pornpote Piumsomboon5Yotsakorn Pratumwal6Somboon Otarawanna7Benjapon Chalermsinsuwan8Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, ThailandSchool of Engineering, The Australian National University, Canberra, ACT 2601, AustraliaDepartment of Mechanical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, 117576, SingaporeDepartment of Mechanical Engineering, Faculty of Engineering, National University of Singapore, 9 Engineering Drive 1, 117576, SingaporeEnvironmental Research Institute, Chulalongkorn University, Bangkok, 10330, ThailandFuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University, Bangkok 10330, ThailandNational Metal and Materials Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, ThailandNational Metal and Materials Technology Center, National Science and Technology Development Agency, Pathum Thani 12120, ThailandFuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand; Center of Excellence on Petrochemical and Materials Technology, Chulalongkorn University, Bangkok 10330, Thailand; Advanced Computational Fluid Dynamics Research Unit, Chulalongkorn University, Bangkok 10330, Thailand; Corresponding author at: Fuels Research Center, Department of Chemical Technology, Faculty of Science, Chulalongkorn University, Bangkok 10330, Thailand.Waste heat utilization has been prioritized especially in various industries and sectors. Many researchers have developed heat recovery processes by designing suitable waste heat recovery units (WRU), such as heat exchangers, using water as a coolants to receive heat from the waste heat fluid in the production process. The conventional heat exchanger has limitations such as its equipment size, space for installation, and flexibility. The microchannel heat exchanger is one of many ideas for resolving these limitations. Moreover, the coolant on the cold side can be upgraded by adding nanometer-sized solid particles which is called “Nanofluid”. To reduce the high investigation cost and time, a new efficient and cost-effective simulation method was selected to use for investigating the performance of a microchannel heat exchanger with nanofluids in this study. To analyze the heat recovery at low temperature, i.e. around 100–200 °C, nanofluid property predictive models were developed using an artificial neural network (ANN). Then, the predictive models were embedded and integrated into computational fluid dynamics to design a microchannel heat exchanger. It is found that the use of nanofluids improved the heat transfer efficiency of this heat exchanger. The suitable nanofluid types and concentrations were selected based on the thermal–hydraulic performance. Here, the 3% weight TiO2/Water fluid with a 1.03 thermal–hydraulic performance ratio was found to be the most promising nanofluid for using in this condition.http://www.sciencedirect.com/science/article/pii/S2352484722023472Artificial neural networkMicrochannelHeat exchangerNanofluidComputational fluid dynamics |
| spellingShingle | Chaiyanan Kamsuwan Xiaolin Wang Lee Poh Seng Cheng Kai Xian Ratchanon Piemjaiswang Pornpote Piumsomboon Yotsakorn Pratumwal Somboon Otarawanna Benjapon Chalermsinsuwan Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network Energy Reports Artificial neural network Microchannel Heat exchanger Nanofluid Computational fluid dynamics |
| title | Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| title_full | Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| title_fullStr | Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| title_full_unstemmed | Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| title_short | Simulation of nanofluid micro-channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| title_sort | simulation of nanofluid micro channel heat exchanger using computational fluid dynamics integrated with artificial neural network |
| topic | Artificial neural network Microchannel Heat exchanger Nanofluid Computational fluid dynamics |
| url | http://www.sciencedirect.com/science/article/pii/S2352484722023472 |
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