Identifying the Origin of Turbulence Using Convolutional Neural Networks
Though turbulence is often thought to have universal behavior regardless of origin, it may be possible to distinguish between the types of turbulence generated by different sources. Prior work in turbulence modeling has shown that the fundamental “constants” of turbulence models are often problem-de...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-07-01
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| Series: | Fluids |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2311-5521/7/7/239 |
| _version_ | 1797433598161190912 |
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| author | Justin Brown Jacqueline Zimny Timour Radko |
| author_facet | Justin Brown Jacqueline Zimny Timour Radko |
| author_sort | Justin Brown |
| collection | DOAJ |
| description | Though turbulence is often thought to have universal behavior regardless of origin, it may be possible to distinguish between the types of turbulence generated by different sources. Prior work in turbulence modeling has shown that the fundamental “constants” of turbulence models are often problem-dependent and need to be calibrated to the desired application. This has resulted in the introduction of machine learning techniques to attempt to apply the general body of turbulence simulations to the modeling of turbulence at the subgrid-scale. This suggests that the inverse is likely also possible: that machine learning can use the properties of turbulence at small scales to identify the nature of the original source and potentially distinguish between different classes of turbulence-generating systems, which is a novel pursuit. We perform numerical simulations of three forms of turbulence—convection, wake, and jet—and then train a convolutional neural network to distinguish between these cases using only a narrow field of view of the velocity field. We find that the network is capable of identifying the correct case with 86% accuracy. This work has implications for distinguishing artificial sources of turbulence from natural ones and aiding in identifying the mechanism of turbulence in nature, permitting more accurate mixing models. |
| first_indexed | 2024-03-09T10:19:16Z |
| format | Article |
| id | doaj.art-6a9796fa960245c68d1202a13e8634fe |
| institution | Directory Open Access Journal |
| issn | 2311-5521 |
| language | English |
| last_indexed | 2024-03-09T10:19:16Z |
| publishDate | 2022-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Fluids |
| spelling | doaj.art-6a9796fa960245c68d1202a13e8634fe2023-12-01T22:08:33ZengMDPI AGFluids2311-55212022-07-017723910.3390/fluids7070239Identifying the Origin of Turbulence Using Convolutional Neural NetworksJustin Brown0Jacqueline Zimny1Timour Radko2Naval Postgraduate School, One University Circle, Monterey, CA 93943, USANaval Postgraduate School, One University Circle, Monterey, CA 93943, USANaval Postgraduate School, One University Circle, Monterey, CA 93943, USAThough turbulence is often thought to have universal behavior regardless of origin, it may be possible to distinguish between the types of turbulence generated by different sources. Prior work in turbulence modeling has shown that the fundamental “constants” of turbulence models are often problem-dependent and need to be calibrated to the desired application. This has resulted in the introduction of machine learning techniques to attempt to apply the general body of turbulence simulations to the modeling of turbulence at the subgrid-scale. This suggests that the inverse is likely also possible: that machine learning can use the properties of turbulence at small scales to identify the nature of the original source and potentially distinguish between different classes of turbulence-generating systems, which is a novel pursuit. We perform numerical simulations of three forms of turbulence—convection, wake, and jet—and then train a convolutional neural network to distinguish between these cases using only a narrow field of view of the velocity field. We find that the network is capable of identifying the correct case with 86% accuracy. This work has implications for distinguishing artificial sources of turbulence from natural ones and aiding in identifying the mechanism of turbulence in nature, permitting more accurate mixing models.https://www.mdpi.com/2311-5521/7/7/239wakesmachine learningjets |
| spellingShingle | Justin Brown Jacqueline Zimny Timour Radko Identifying the Origin of Turbulence Using Convolutional Neural Networks Fluids wakes machine learning jets |
| title | Identifying the Origin of Turbulence Using Convolutional Neural Networks |
| title_full | Identifying the Origin of Turbulence Using Convolutional Neural Networks |
| title_fullStr | Identifying the Origin of Turbulence Using Convolutional Neural Networks |
| title_full_unstemmed | Identifying the Origin of Turbulence Using Convolutional Neural Networks |
| title_short | Identifying the Origin of Turbulence Using Convolutional Neural Networks |
| title_sort | identifying the origin of turbulence using convolutional neural networks |
| topic | wakes machine learning jets |
| url | https://www.mdpi.com/2311-5521/7/7/239 |
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