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Record W7018871301

Electrification of Airport Operations:
\nElectric Powered Tow-Truck Utilization
\nin Taxiing Operations

2019· dissertation· en· W7018871301 on OpenAlexaboutno aff

Bibliographic record

VenueSpectrum Research Repository (Concordia University) · 2019
Typedissertation
Languageen
FieldEnvironmental Science
TopicAdvanced Aircraft Design and Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsAviationFuel efficiencyElectrificationCivil aviationAirplaneGreenhouse gasRunwayConsumption (sociology)
DOInot available

Abstract

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ABSTRACT
\nCivil aviation has steadily increased over the past decades and plays an essential role in connecting
\npeople and countries across the world. According to the International Civil Aviation Organization
\n(ICAO, 2018), passenger traffic has grown with an average of 5.4% between 1995 and 2015. ICAO
\nestimates the demand for aviation to continue increasing by an annual rate of 4.3% until 2035 and
\n4.1% until 2045. Among several crucial objectives of air transportation system problems, the
\nminimization of fuel consumption has a profound impact on both the economic viability of airline
\ncompanies and the impact of air-transportation in the environment.
\nAlthough aviation is not currently the leading cause of global warming, industry development, and
\nthe increase in air transportation will make it a significant factor for global warming over the
\ncoming decades. Predicting the impact of aviation on economic and environmental systems
\nrequires investigations at different stages of air transport operations. One of the strategies to reduce
\nthe fuel consumption of aviation is to optimize the fuel burn during airplane ground movement
\n(taxiing) in airports. The main reason is that aircraft ground movement is a significant source of
\nfuel consumption and emissions at an airport (e.g., it is estimated that aircraft burn about 7% of
\ntheir fuel during this stage of the flight). Among different ways of taxiing operation in an airport,
\nelectrification of ground transportation has proven to be one of the most efficient ways which have
\nmany advantages such as reducing fuel consumption and emission of greenhouse gases with low
\nmaintenance cost. However, it should be noted that electric-powered vehicles can be a beneficial
\nand efficient way of taxiing in airports if the electricity is clean. Clean electricity is produced from 
\nIV
\nrenewable and non-emitting sources such as wind, sun, and water. Using electric-powered vehicles
\nin airports might not be the optimal option if the electricity is produced by burning fossil fuels like
\ncoal. Nowadays, in many provinces of Canada, the produced electricity is clean, and the
\ngovernment is determined to have 90% clean electricity across Canada by 2030.
\nThe presented study discusses the scheduling of aircraft towing tractors at the airport in order to
\nminimize the fuel consumption and environmental emission of airplane engines and towing
\ntractors. In this study, we developed a Mixed Integer-Linear Programming (MILP) model to
\nschedule electric-powered towing vehicles (pushback Tugs) to provide taxiing services to aircraft.
\nThe proposed MILP solution enables aircraft to request a towing vehicle when it is available or
\nperform traditional taxiing operations by using aircraft engines to minimize operating costs, which
\nincludes delay/earliness costs, fuel consumption cost, and towing cost. We concluded that the
\nhybrid system for taxiing operation which includes both traditional engine powered solutions and
\nthe proposed electric-powered towing vehicle approaches, is the optimal solution. Through
\nsensitivity analysis, the proposed taxiing operations planning model determines the optimum
\nnumber of towing vehicles in an airport.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.126
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.030
GPT teacher head0.281
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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