A machine learning framework for drop-in volume swell characteristics of sustainable aviation fuel. (15th August 2020)
- Record Type:
- Journal Article
- Title:
- A machine learning framework for drop-in volume swell characteristics of sustainable aviation fuel. (15th August 2020)
- Main Title:
- A machine learning framework for drop-in volume swell characteristics of sustainable aviation fuel
- Authors:
- Kosir, Shane
Heyne, Joshua
Graham, John - Abstract:
- Graphical abstract: Highlights: 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. Abstract: 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 holdoutGraphical abstract: Highlights: 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. Abstract: 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. … (more)
- Is Part Of:
- Fuel. Volume 274(2020)
- Journal:
- Fuel
- Issue:
- Volume 274(2020)
- Issue Display:
- Volume 274, Issue 2020 (2020)
- Year:
- 2020
- Volume:
- 274
- Issue:
- 2020
- Issue Sort Value:
- 2020-0274-2020-0000
- Page Start:
- Page End:
- Publication Date:
- 2020-08-15
- Subjects:
- Sustainable aviation fuel -- Material compatibility -- High-performance jet fuel -- Volume swell -- Neural network -- Principal component analysis
Fuel -- Periodicals
Coal -- Periodicals
Coal
Fuel
Periodicals
662.6 - Journal URLs:
- http://www.sciencedirect.com/science/journal/latest/00162361 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.fuel.2020.117832 ↗
- Languages:
- English
- ISSNs:
- 0016-2361
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 4048.000000
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British Library HMNTS - ELD Digital store - Ingest File:
- 13429.xml