Academic Journal
A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data
| Title: | A generative adversarial network (GAN) approach to creating synthetic flame images from experimental data |
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| Authors: | Anthony Carreon, Shivam Barwey, Venkat Raman |
| Source: | Energy and AI, Vol 13, Iss , Pp 100238- (2023) |
| Publisher Information: | Elsevier, 2023. |
| Publication Year: | 2023 |
| Collection: | LCC:Electrical engineering. Electronics. Nuclear engineering LCC:Computer software |
| Subject Terms: | Generative adversarial network, Combustion modeling, Data-driven modeling, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer software, QA76.75-76.765 |
| More Details: | Modern diagnostic tools in turbulent combustion allow for highly-resolved measurements of reacting flows; however, they tend to generate massive data-sets, rendering conventional analysis intractable and inefficient. To alleviate this problem, machine learning tools may be used to, for example, discover features from the data for downstream modeling and prediction tasks. To this end, this work applies generative adversarial networks (GANs) to generate realistic flame images based on a time-resolved data set of hydroxide concentration snapshots obtained from planar laser induced fluorescence measurements of a model combustor. The generative model is able to generate flames in attached, lifted, and intermediate configurations dictated by the user. Using k-means clustering and proper orthogonal decomposition, the synthetic image set produced by the GAN is shown to be visually similar to the real image set, with recirculation zones and burned/unburned regions clearly present, indicating good GAN performance in capturing the experimental data statistical structure. Combined with techniques for controlling the configuration of generated flames, this work opens new avenues towards tractable statistical analysis and modeling of flame behavior, as well as rapid and inexpensive flame data generation. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 2666-5468 |
| Relation: | http://www.sciencedirect.com/science/article/pii/S2666546823000101; https://doaj.org/toc/2666-5468 |
| DOI: | 10.1016/j.egyai.2023.100238 |
| Access URL: | https://doaj.org/article/78b07d1be068460ca6359c37c9128c0b |
| Accession Number: | edsdoj.78b07d1be068460ca6359c37c9128c0b |
| Database: | Directory of Open Access Journals |
| ISSN: | 26665468 |
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| DOI: | 10.1016/j.egyai.2023.100238 |
| Published in: | Energy and AI |
| Language: | English |