Hybrid Electric Aircraft Thermal Management: Now, New Visions and Future Concepts and Formulation
Bibliographic record
Abstract
The global fuel consumption by commercial airlines has increased each year since 2009 and is predicted to reach an all-time high of 97 billion gallons in 2019. There is also an environmental impact from this: CO2 emissions from commercial passenger and freight operations totaled 918 Mt in 2018 (ICCT, 2019), or around 2.5% of global energy-related CO2 emissions. Passenger transport accounted for 81% of the total. Emissions from aviation have grown 32% over the past five years. Coupled with this aspect, there is a continuous and growing need to satisfy ever-growing electrical power needs on commercial and military aircraft. All the armed services (Army, Navy, and Air Force) are continuously trying to enhance UAV (unmanned aerial vehicle) endurance and range across a broad fleet of different aircraft. The commercial Boeing 787 requires about 1.2MWe and that is expected to grow. Current technologies used to supply increased on-board electrical power are generally: 1) "burn more fuel and convert through on-board generators" and 2) use additional heavy (i.e., weight-inefficient) and sometimes unsafe battery systems on-board the aircraft. The aircraft industry is seeking new, innovative ways to satisfy this increasing power demand. One as-yet-untapped power source is the enormous amount of "waste" thermal energy flowing out the jet engine exhaust; some estimates in smaller "by-pass" flow jet engines is several hundreds of kilowatts (e.g., Pratt & Whitney Canada PW545B turbofan). This quantity is much higher in large jet engines associated with commercial aircraft. This large waste thermal energy manifests itself in large temperature differences within the by-pass-flow engine exhaust system relative to outside ambient conditions, because of the actual by-pass engine design configuration. There is strong need to develop thermal technologies and systems that could harness and convert at least a portion of this thermal energy into useful electrical energy to satisfy growing on-board electrical needs. In addition, there is a strong desire within the aircraft and engine manufacturing community to reduce the "carbon footprint" of the industry though reduced fuel usage worldwide. NASA has a robust aircraft electrification program to meet these desires and support industry in its aircraft electrification objectives. This program is integrating thermoacoustic systems, advanced lightweight heat exchanger technology, and advanced heat pipe technology to capture and transport large amounts of engine waste thermal energy for on-board power conversion, advanced heat-pump cooling, and exergy enhancement (i.e., temperature lift). Advanced lightweight heat exchangers are envisioned to capture engine exhaust thermal energy at approximately 673 K and deliver it to efficient thermoacoustic power conversion systems operating at temperature ratios (Thot/Tcold) > 1.6. Advanced heat pipe systems are envisioned to transport thermal energy from low temperature sources, through thermoacoustic heat pumps, to high temperature needs such as wing anti-icing, fuel pre-heating, and combustion air pre-heating. The paper will discuss the current state-of-the-art, objectives, system design architecture, and remaining technical challenges in system formulation in the NASA aircraft electrification program.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".