Sustainable aviation fuels approval streamlining: auxiliary power unit lean blowout testing
Abstract
An underpinning hindrance in the market penetration of sustainable aviation fuel is the approval process for alternative jet fuels. One solution to this is to develop low-cost screening tools that can be implemented earlier in the approval process. Auxiliary power unit combustors historically show the most sensitivity to physical and volatile fuel properties, making it a useful tool in assessing potential alternative jet fuel effects at test conditions representative of operability stability limits. It is hypothesized that these observations can be explained via timescale analysis considering fuel droplet breakup and evaporation, combustor mixing, and chemical reactivity timescales on the progression to lean blowout. This paper combines timescale theory with reduced-order fuel properties and random forest regressions to represent each of the identified timescales. Random forest regressions with only these timescale representative properties account for better than 95% of experimental variance across seven very different test conditions. An additional sensitivity analysis corroborates previous observations in which auxiliary power units are most sensitive to mixing, atomization, and evaporative timescales. Testing of these key timescale representative fuel properties requires 280 mL of fuel and could be used as a new screening tool for alternative jet fuels, reducing the time and cost for their approval.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Combined properties at various temperatures were able to predict fuel effects on the Figure of Merit combustor behavior across multiple architectures for LBO and ignition for selected fuels.
“Combined, these properties at various temperatures were able to predict fuel effects on the Figure of Merit combustor behavior across multiple architectures for LBO [11,12] and ignition [13] for selected fuels.”
From Sustainable aviation fuel prescreening tools and procedures (2021) · cites this paper as [11]
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538 · see citing works
Data Mining: Practical Machine Learning Tools and Techniques
Witten, I. H., Frank, E., & Hall, M. A. (2011) · Elsevier eBooks · doi:10.1016/c2009-0-19715-5 · Cited by 25,493 · see citing works
The Elements of Statistical Learning
Hastie, T., Friedman, J., & Tibshirani, R. (2001) · Springer Series in Statistics · doi:10.1007/978-0-387-21606-5 · Cited by 24,162 · see citing works
Climate Science Special Report: Fourth National Climate Assessment, Volume I
USGCRP (2017) · doi:10.7930/j0j964j6 · Cited by 1,190 · see citing works
Electrical Power Generation in Aircraft: Review, Challenges, and Opportunities
Madonna, V., Giangrande, P., & Galea, M. (2018) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2018.2834142 · Cited by 640 · see citing works
Predicting stock market index using fusion of machine learning techniques
Patel, J., Shah, S. S., Thakkar, P., & Kotecha, K. V. (2014) · Expert Systems with Applications · doi:10.1016/j.eswa.2014.10.031 · Cited by 611 · see citing works
Business data mining — a machine learning perspective
Bose, I., & Mahapatra, R. (2001) · Information & Management · doi:10.1016/s0378-7206(01)00091-x · Cited by 437 · see citing works
Overview of the National Jet Fuels Combustion Program
Colket, M., Heyne, J., Rumizen, M., Gupta, M., Edwards, T., Roquemore, W. M., Andac, G., Boehm, R., Lovett, J., Williams, R., et al. (2017) · AIAA Journal · doi:10.2514/1.j055361 · Cited by 279 · see citing works
Self-citationReference Jet Fuels for Combustion Testing
Edwards, J. T. (2017) · 55th AIAA Aerospace Sciences Meeting · doi:10.2514/6.2017-0146 · Cited by 230 · see citing works
Hybrid-Electric Propulsion for Aircraft
Friedrich, C., & Robertson, P. (2014) · Journal of Aircraft · doi:10.2514/1.c032660 · Cited by 204 · see citing works
Fuel effects on lean blow-out in a realistic gas turbine combustor
Esclapez, L., Ma, P., Mayhew, E. K., et al. (2017) · Combustion and Flame · doi:10.1016/j.combustflame.2017.02.035 · Cited by 189 · see citing works
Analysis and design of hybrid electric regional turboprop aircraft
Voskuijl, M., van Bogaert, J., & Rao, A. G. (2017) · CEAS Aeronautical Journal · doi:10.1007/s13272-017-0272-1 · Cited by 177 · see citing works
Characteristic time correlation for lean blowoff of bluff-body-stabilized flames☆
Plee, S. L., & Mellor, A. M. (1979) · Combustion and Flame · doi:10.1016/0010-2180(79)90007-5 · Cited by 173 · see citing works
Won, S. H., Dooley, S., Dryer, F. L., & Ju, Y. (2011) · Combustion and Flame · doi:10.1016/j.combustflame.2011.08.020 · Cited by 117 · see citing works
Semi-empirical correlations for gas turbine emissions, ignition, and flame stabilization
Mellor, A. M. (1980) · Progress in Energy and Combustion Science · doi:10.1016/0360-1285(80)90010-6 · Cited by 73 · see citing works
Stouffer, S. D., Hendershott, T. H., Monfort, J. R., Diemer, J., Corporan, E., Wrzesinski, P. J., & Caswell, A. W. (2017) · AIAA Aerospace Sciences Meeting · doi:10.2514/6.2017-1954 · Cited by 62 · see citing works
Heyne, J. S., Peiffer, E. E., Colket, M. B., Jardines, A., Shaw, C., Moder, J. P., Roquemore, W. M., Edwards, J. T., Li, C., Rumizen, M., et al. (2018) · AIAA Aerospace Sciences Meeting · doi:10.2514/6.2018-1667 · Cited by 40 · see citing works
Self-citationHeyne, J. S., Colket, M. B., Gupta, M. L., Jardines, A., Moder, J. P., Edwards, J. T., Roquemore, M., Li, C., & Rumizen, M. (2017) · AIAA Aerospace Sciences Meeting · doi:10.2514/6.2017-0145 · Cited by 29 · see citing works
Self-citationOpacich, K. C., Heyne, J. S., Peiffer, E., & Stouffer, S. D. (2019) · AIAA Scitech 2019 Forum · doi:10.2514/6.2019-1434 · Cited by 21 · see citing works
Self-citationThe influence of fuel properties on threshold combustion in aviation gas turbine engines
Burger, V. (2017) · Open University of Cape Town (University of Cape Town) · Cited by 12 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Sustainable aviation fuel prescreening tools and procedures
Heyne, J., Rauch, B., Le Clercq, P., & Colket, M. (2021) · Fuel · doi:10.1016/j.fuel.2020.120004 · Cited by 170 · see citing works
Self-citation
- Authors
- Peiffer, E. E., Heyne, J. S., & Colket, M.
- Affiliations
- University of Dayton (US)
- United Technologies (United States) (US)
- Venue
- AIAA Journal
- Published
- 2019-11
- Volume
- 57
- Issue
- 11
- Pages
- 4854-4862
- ISSN
- 0001-1452 (ISSN-L)
- 1533-385X
- OpenAlex
- W2954777328
- Cited by
- 33
- References
- 20
- Citations by year
- 2026: 3 · 2025: 2 · 2024: 6 · 2023: 2 · 2022: 3 · 2021: 8 · 2020: 8 · 2019: 1
- FWCI
- 3.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- AIAA Journal (publishedVersion)
- Funders
- Federal Aviation Administration (13-C-AJFE-UD-18)
- Air Force Research Laboratory
- Topics
- Physical Sciences › Engineering › Computational Mechanics › Combustion and flame dynamics
- Physical Sciences › Engineering › Computational Mechanics › Heat transfer and supercritical fluids
- Physical Sciences › Chemical Engineering › Fluid Flow and Transfer Processes › Advanced Combustion Engine Technologies
- Keywords
- sustainable aviation fuel, alternative jet fuels, auxiliary power unit, lean blowout, screening tool, time-scale analysis, atomization, fuel properties, random forest regression, sensitivity analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
