A neural network based approach for thrust prediction in cold gas propulsion systems
Abstract In this paper, we present a machine learning method to accurately predict thrust in a cold gas thruster using a feedforward neural network (FFNN). The model leverages critical operational parameters, such as storage pressure, mass flow rate, nozzle length, exit pressure, and propellant mass...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-12705-0 |
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| author | Morteza Farhid Mohammad Reza Ghavidel Aghdam Moharram Shameli |
| author_facet | Morteza Farhid Mohammad Reza Ghavidel Aghdam Moharram Shameli |
| author_sort | Morteza Farhid |
| collection | DOAJ |
| description | Abstract In this paper, we present a machine learning method to accurately predict thrust in a cold gas thruster using a feedforward neural network (FFNN). The model leverages critical operational parameters, such as storage pressure, mass flow rate, nozzle length, exit pressure, and propellant mass density, to achieve high precision in thrust predictions. To make this technology accessible and practical, we introduce an intuitive graphical user interface (GUI) that allows users to estimate thrust in real-time systems. This tool simplifies design and analysis processes, offering engineers a powerful resource for optimizing the performance of the cold gas thrusters. Based on the simulation results, our proposed method achieves an accuracy of 0.98 and an F1 score of 0.981, showcasing its robustness and generalizability across various test cases. Our work highlights how machine learning methods can be effectively integrated into propulsion system development, paving the way for more innovative, more efficient designs. |
| format | Article |
| id | doaj-art-d25c832ef30c4b0b97d10c721d461f40 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-d25c832ef30c4b0b97d10c721d461f402025-08-20T03:05:26ZengNature PortfolioScientific Reports2045-23222025-07-0115111010.1038/s41598-025-12705-0A neural network based approach for thrust prediction in cold gas propulsion systemsMorteza Farhid0Mohammad Reza Ghavidel Aghdam1Moharram Shameli2Iranian Space Research CenterIranian Space Research CenterIranian Space Research CenterAbstract In this paper, we present a machine learning method to accurately predict thrust in a cold gas thruster using a feedforward neural network (FFNN). The model leverages critical operational parameters, such as storage pressure, mass flow rate, nozzle length, exit pressure, and propellant mass density, to achieve high precision in thrust predictions. To make this technology accessible and practical, we introduce an intuitive graphical user interface (GUI) that allows users to estimate thrust in real-time systems. This tool simplifies design and analysis processes, offering engineers a powerful resource for optimizing the performance of the cold gas thrusters. Based on the simulation results, our proposed method achieves an accuracy of 0.98 and an F1 score of 0.981, showcasing its robustness and generalizability across various test cases. Our work highlights how machine learning methods can be effectively integrated into propulsion system development, paving the way for more innovative, more efficient designs.https://doi.org/10.1038/s41598-025-12705-0Thrust predictionCold gas thrusterNeural networkGUI |
| spellingShingle | Morteza Farhid Mohammad Reza Ghavidel Aghdam Moharram Shameli A neural network based approach for thrust prediction in cold gas propulsion systems Scientific Reports Thrust prediction Cold gas thruster Neural network GUI |
| title | A neural network based approach for thrust prediction in cold gas propulsion systems |
| title_full | A neural network based approach for thrust prediction in cold gas propulsion systems |
| title_fullStr | A neural network based approach for thrust prediction in cold gas propulsion systems |
| title_full_unstemmed | A neural network based approach for thrust prediction in cold gas propulsion systems |
| title_short | A neural network based approach for thrust prediction in cold gas propulsion systems |
| title_sort | neural network based approach for thrust prediction in cold gas propulsion systems |
| topic | Thrust prediction Cold gas thruster Neural network GUI |
| url | https://doi.org/10.1038/s41598-025-12705-0 |
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