Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm
The aim of this study is to explore the characteristics of an active Free-Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time-intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to ex...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-04-01
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| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844024044189 |
| _version_ | 1797234069005664256 |
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| author | A.P. Masoumi A.R. Tavakolpour-Saleh V. Bagherian |
| author_facet | A.P. Masoumi A.R. Tavakolpour-Saleh V. Bagherian |
| author_sort | A.P. Masoumi |
| collection | DOAJ |
| description | The aim of this study is to explore the characteristics of an active Free-Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time-intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to expedite the process. To construct a nonlinear model, 5000 samples are extracted from simulation results. Input parameters included in the model are the hot and cold source temperatures, the voltage given to the DC motor, spring stiffness, and the mass of the power piston, while output parameters are the amplitude and frequency of power piston displacement. The proposed ANN model structure comprises two hidden layer with 10 and 20 neurons, respectively, indicating the applicability of the ANN model in estimating significant parameters of AFPSE in a shorter amount of time. The firefly optimization algorithm is utilized to determine the unknown input parameters of ANN and maximize the output power. Results indicate that a maximum output power of 23.07 W can be attained by applying 8.5 V voltage on the DC motor. This study highlights the potential of machine learning techniques to explore the primary features of AFPSE. |
| first_indexed | 2024-04-24T16:26:12Z |
| format | Article |
| id | doaj.art-cb74c9e4b642420e827326514611917a |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-24T16:26:12Z |
| publishDate | 2024-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-cb74c9e4b642420e827326514611917a2024-03-31T04:37:35ZengElsevierHeliyon2405-84402024-04-01107e28387Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithmA.P. Masoumi0A.R. Tavakolpour-Saleh1V. Bagherian2Department of Mechanical and Aerospace Engineering, Shiraz University of Technology, Shiraz, IranDepartment of Mechanical and Aerospace Engineering, Shiraz University of Technology, Shiraz, Iran; Corresponding author.Department of Mechanical Engineering, Shiraz University, Shiraz, IranThe aim of this study is to explore the characteristics of an active Free-Piston Stirling Engine (AFPSE) through the use of machine learning methods. Due to the time-intensive nature of extracting simulation results from complex thermal equations, an Artificial Neural Network (ANN) is utilized to expedite the process. To construct a nonlinear model, 5000 samples are extracted from simulation results. Input parameters included in the model are the hot and cold source temperatures, the voltage given to the DC motor, spring stiffness, and the mass of the power piston, while output parameters are the amplitude and frequency of power piston displacement. The proposed ANN model structure comprises two hidden layer with 10 and 20 neurons, respectively, indicating the applicability of the ANN model in estimating significant parameters of AFPSE in a shorter amount of time. The firefly optimization algorithm is utilized to determine the unknown input parameters of ANN and maximize the output power. Results indicate that a maximum output power of 23.07 W can be attained by applying 8.5 V voltage on the DC motor. This study highlights the potential of machine learning techniques to explore the primary features of AFPSE.http://www.sciencedirect.com/science/article/pii/S2405844024044189Active free piston stirling engineArtificial neural networkFirefly optimization algorithmNonlinear model |
| spellingShingle | A.P. Masoumi A.R. Tavakolpour-Saleh V. Bagherian Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm Heliyon Active free piston stirling engine Artificial neural network Firefly optimization algorithm Nonlinear model |
| title | Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm |
| title_full | Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm |
| title_fullStr | Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm |
| title_full_unstemmed | Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm |
| title_short | Performance investigation of an active free-piston Stirling engine using artificial neural network and firefly optimization algorithm |
| title_sort | performance investigation of an active free piston stirling engine using artificial neural network and firefly optimization algorithm |
| topic | Active free piston stirling engine Artificial neural network Firefly optimization algorithm Nonlinear model |
| url | http://www.sciencedirect.com/science/article/pii/S2405844024044189 |
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