Electrification of Airport Operations:Electric Powered Tow-Truck Utilizationin Taxiing Operations
Notice bibliographique
Résumé
ABSTRACT Civil aviation has steadily increased over the past decades and plays an essential role in connecting people and countries across the world. According to the International Civil Aviation Organization (ICAO, 2018), passenger traffic has grown with an average of 5.4% between 1995 and 2015. ICAO estimates the demand for aviation to continue increasing by an annual rate of 4.3% until 2035 and 4.1% until 2045. Among several crucial objectives of air transportation system problems, the minimization of fuel consumption has a profound impact on both the economic viability of airline companies and the impact of air-transportation in the environment. Although aviation is not currently the leading cause of global warming, industry development, and the increase in air transportation will make it a significant factor for global warming over the coming decades. Predicting the impact of aviation on economic and environmental systems requires investigations at different stages of air transport operations. One of the strategies to reduce the fuel consumption of aviation is to optimize the fuel burn during airplane ground movement (taxiing) in airports. The main reason is that aircraft ground movement is a significant source of fuel consumption and emissions at an airport (e.g., it is estimated that aircraft burn about 7% of their fuel during this stage of the flight). Among different ways of taxiing operation in an airport, electrification of ground transportation has proven to be one of the most efficient ways which have many advantages such as reducing fuel consumption and emission of greenhouse gases with low maintenance cost. However, it should be noted that electric-powered vehicles can be a beneficial and efficient way of taxiing in airports if the electricity is clean. Clean electricity is produced from IV renewable and non-emitting sources such as wind, sun, and water. Using electric-powered vehicles in airports might not be the optimal option if the electricity is produced by burning fossil fuels like coal. Nowadays, in many provinces of Canada, the produced electricity is clean, and the government is determined to have 90% clean electricity across Canada by 2030. The presented study discusses the scheduling of aircraft towing tractors at the airport in order to minimize the fuel consumption and environmental emission of airplane engines and towing tractors. In this study, we developed a Mixed Integer-Linear Programming (MILP) model to schedule electric-powered towing vehicles (pushback Tugs) to provide taxiing services to aircraft. The proposed MILP solution enables aircraft to request a towing vehicle when it is available or perform traditional taxiing operations by using aircraft engines to minimize operating costs, which includes delay/earliness costs, fuel consumption cost, and towing cost. We concluded that the hybrid system for taxiing operation which includes both traditional engine powered solutions and the proposed electric-powered towing vehicle approaches, is the optimal solution. Through sensitivity analysis, the proposed taxiing operations planning model determines the optimum number of towing vehicles in an airport.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,000 |
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 tête enseignante, 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 ».