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CC BY V4.0, Open Access
Journal article
A machine learning framework for drop-in volume swell characteristics of sustainable aviation fuel
Fuel (Guildford), Vol.274, p.117832
08/15/2020
Handle:
https://hdl.handle.net/2376/117206
Appears in Aviation Sustainability Center (ASCENT)
Abstract
A neural network is used to predict volume swell for 10 non-metallic materials.•Holdout predictions for 3 of 10 materials had errors of 12.4% or better.•Optimization indicates that cycloalkanes can replace aromatics in jet fuel.•High performance fuel optimization is reported with benefits to aircraft operation.
A machine learning framework has been developed to predict volume swell for 10 non-metallic materials submerged in neat compounds. The non-metallic materials included nitrile rubber, extracted nitrile rubber, fluorosilicone, low temp fluorocarbon, lightweight polysulfide, polythioether, epoxy (0.2 mm), epoxy (0.04 mm), nylon, and Kapton. Volume swell, a material compatibility concern, serves as a significant impediment for the minimization of the greenhouse gas emissions of aviation. Sustainable aviation fuels, the only near and mid-term solution to mitigating greenhouse gas emissions, are limited to low blend limits with conventional fuel due to material compatibility issues (i.e. O-ring swell). A neural network was trained to predict volume swell for non-metallic materials submerged in neat compounds. Subsequent blend optimization incorporated nitrile rubber volume swell predictions for iso- and cycloalkanes to create a high-performance jet fuel within ‘drop-in’ limits.
The results of this study are volume swell predictions for 3 of the 10 materials -nitrile rubber, extracted nitrile rubber, and polythioether- with holdout errors of 12.4% or better relative to mean volume swell values. Optimization considering nitrile rubber volume swell achieved median specific energy [MJ/kg] and energy density [MJ/L] increases of 1.9% and 5.1% relative to conventional jet fuel and an average volume swell of 6.2% v/v which is within the range of conventional fuels. Optimized solutions were heavily biased toward monocycloalkanes, indicating that they are a suitable replacement for aromatics. This study concludes that cycloalkanes can replace aromatics in jet fuel considering volume swell and other operability requirements while significantly reducing soot and particulate matter emissions.
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Details
- Title
- A machine learning framework for drop-in volume swell characteristics of sustainable aviation fuel
- Creators
- Shane Kosir - University of Dayton, Department of Mechanical & Aerospace Engineering, 300 College Park, Dayton, OH 45469, United StatesJoshua Heyne - University of Dayton, Department of Mechanical & Aerospace Engineering, 300 College Park, Dayton, OH 45469, United StatesJohn Graham - University of Dayton Research Institute, Nonstructural Materials Division, 300 College Park, Dayton, OH 45469, United States
- Publication Details
- Fuel (Guildford), Vol.274, p.117832
- Academic Unit
- Aviation Sustainability Center (ASCENT); Alternative Jet Fuel
- Publisher
- Elsevier Ltd
- Grants
- 13-C-AJFE-UD-22, Federal Aviation Administration (United States, Washington) - FAA
- Identifiers
- 99900581259701842
- Language
- English
- Resource Type
- Journal article