Hybrid Electric Aircraft Thermal Management: Now, New Visions and Future Concepts and Formulation
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,002 |
| Communication savante | 0,004 | 0,006 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».