A Neural Network Model to Estimate Aircraft Fuel Consumption

Trani, Antonio; Wing-Ho, F.; Schilling, Glen; Baik, Hojong; Seshadri, Anand

doi:10.2514/6.2004-6401

Cited by 50 publications

(30 citation statements)

References 2 publications

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“…In the last period of significant fuel cost increases, the early 1980s, researchers sought to improve measurements of airline fuel efficiency through more accurate modeling of fuel consumption, as exemplified by the work of Collins [4]. More recently, Trani et al [5] have investigated the use of neural networks to estimate fuel consumption. These models gained limited acceptance, perhaps because of their requirements for either detailed airplane aerodynamic information or a large database of airplane operations and associated airplane state data.…”

Section: Existing Fuel Consumption Modelsmentioning

confidence: 99%

Modeling of Terminal-Area Airplane Fuel Consumption

Senzig

Fleming

Iovinelli

2009

Journal of Aircraft

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Accurate modeling of airplane fuel consumption is necessary for air transportation policy-makers to properly adjudicate trades between competing environmental and economic demands. Existing public models used for computing terminal-area airplane fuel consumption have been shown to have substantial errors, potentially leading to erroneous policy and investment decisions. The method of modeling fuel consumption proposed in this paper was developed using data from a major airplane manufacturer. When compared with airline performance/operational data, this proposed method has been shown to accurately predict fuel consumption in the terminal area. The proposed method uses airplane performance data from publicly available environmental models supported by the Federal Aviation Administration and others. The proposed method has sufficient generality to protect the proprietary interests of the manufacturer, while still having adequate fidelity to analyze low-speed airplane operations in the terminal area. This improved methodology will enable more informed decisions by policy-makers seeking to account for the effects of fuel consumption and airplane emissions on plans for future airspace and airport designs.Nomenclature Fo = static thrust at sea level standard conditions F= = net corrected thrust h MSL = height above mean sea level K 1 = departure thrust specific fuel consumption constant coefficient K 2 = departure thrust specific fuel consumption Mach number coefficient K 3 = departure thrust specific fuel consumption altitude coefficient K 4 = departure thrust specific fuel consumption thrust coefficient M = Mach number = arrival thrust specific fuel consumption constant coefficient 1 = arrival thrust specific fuel consumption Mach number coefficient 2 = arrival thrust specific fuel consumption thrust term coefficient 3 = arrival thrust specific fuel consumption thrust coefficient = pressure ratio = temperature ratio

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Section: Existing Fuel Consumption Modelsmentioning

confidence: 99%

Modeling of Terminal-Area Airplane Fuel Consumption

Senzig

Fleming

Iovinelli

2009

Journal of Aircraft

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“…SIMMOD provided the capability of estimating the fuel consumption of aircraft, which was executed by a post-processor via a multivariate regression technique to compute fuel consumption under a given flight profile (Stolzer, 2002). Based on SIMMON, Trani and Wing-Ho (1997) developed a neural network-based processor to estimate fuel consumption. The Airborne Tactical Advantage Company developed a PC version of SIMMOD; it consisted of simulation engines, graphic user interfaces for data inputs, traffic animators and data post-processing (ATAC, 2006).…”

Section: Introductionmentioning

confidence: 99%

Support vector machine and ROC curves for modeling of aircraft fuel consumption

Wang

Chen

2015

Journal of Management Analytics

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To estimate the fuel consumption of a civil aircraft, we propose to use the receiver operating characteristic (ROC) curve to optimize a support vector machine (SVM) model. The new method and procedure has been developed to build, train, validate, and apply an SVM model. A conceptual support vector network is proposed to model fuel consumption, and the flight data collected from routes are used as the inputs to train an SVM model. During the training phase, an ROC curve is defined to evaluate the performance of the model. To validate the applicability of the trained model, a case study is developed to compare the data from an aircraft performance manual and from the implemented simulation model. The investigated aircraft in the case study is a Boeing 737-800 powered by CFM-56 engines. The comparison has shown that the trained SVM model from the proposed procedure is capable of representing a complex fuel consumption function accurately for all phases during the flight. The proposed methodology is generic, and can be extended to reliably model the fuel consumption of other types of aircraft, such as piston engine aircraft or turboprop engine aircraft.

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“…Daha sonra Trani ve ark. [9] yakıt tüketim tahmininde Yapay Sinir Ağı (NN-Neural Network) yöntemini kullanarak araştırmalar yapmıştır. Bu modeller, detaylı uçak aerodinamik bilgisi ve uçak operasyonları ile ilgili geniş bir veri tabanı gerektirmesi nedeniyle kısıtlı kabul görmüştür.…”

Section: Introductionunclassified

Orta Menzi̇lli̇ Bi̇r Uçak İçi̇n Yakit Modeli̇ni̇n Oluşturulmasi

Şahin

Aghayeva²

2017

Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi

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ÖZBu çalışmada; orta menzilli bir uçağın (Airbus 320) uçuş rotası tırmanma, düz uçuş ve alçalma olarak üç safhaya ayrılarak uçağın dinamik yörünge profili oluşturulmuştur. Her bir uçuş safhası için BADA (Base of Aircraft Database) verileri kullanılarak matematiksel yakıt modeli geliştirilmiştir. Aynı orta menzilli uçağın, iki farklı tarifeli uçuş rotasına ait uçuş bilgileri analiz edilmiştir. Bu kapsamda planlanan uçuş mesafesi ve gerçek uçuş mesafesi arasındaki fark hesaplanmış ve bu farkın maksimum (f max ), minimum (f min ) ve ortalama (f ort ) değerlerine karşılık gelen rotalar için yakıt tüketim hesabı tüm uçuş safhaları için hesaplanarak elde edilen yakıt tüketimi, farklı rotalardaki aynı tip uçak için karşılaştırılmıştır.Anahtar Kelimeler: Uçak, yakıt tüketimi, itki, dinamik yörünge profili MODELING OF FUEL CONSUMPTION FOR A MEDIUM RANGE AIRCRAFT ABSTRACTIn this study, the flight path for a medium-range aircraft (A320) is analyzed in three phases as climb, cruise, descent and a dynamic trajectory flight is performed. A mathematical fuel consumption model based on Base of Aircraft Database (BADA) for each flight phase is developed. Flight path data belong to two different scheduled flight operations are examined. The deviation of actual flight distance from scheduled ones are calculated, then the fuel consumption models are improved for routes corresponding to maximum (f max ), minimum (f min ) and average (f ort ) value of deviations. The fuel consumption is calculated for these specific routes. Finally, the fuel consumption is compared for medium range aircraft flying different routes.Keywords: Aircraft, fuel consumption, propulsion, dynamic trajectory profile GİRİŞYakıt fiyatlarının giderek artması, hava taşımacılığında yakıt tüketimini azaltarak ekonomik bir uçuş planlamasını gerektirmektedir. Yakıt tüketiminin ve dolayısıyla emisyon salınımının azalması ile, çevre üzerindeki olumsuz etkilerin de azalması beklenmektedir. Havacılık endüstrisi yakıt tüketiminden uzun yıllardır etkilenmektedir. Havayolu şirketlerinin operasyonel maliyetlerinin büyük oranını yakıtın oluşturması nedeniyle, havayolu şirketleri, uçak üretici firmaları yakıt tüketimini azaltıcı yöntemler aramaktadır.

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A Neural Network Model to Estimate Aircraft Fuel Consumption

Cited by 50 publications

References 2 publications

Modeling of Terminal-Area Airplane Fuel Consumption

Modeling of Terminal-Area Airplane Fuel Consumption

Support vector machine and ROC curves for modeling of aircraft fuel consumption

Orta Menzi̇lli̇ Bi̇r Uçak İçi̇n Yakit Modeli̇ni̇n Oluşturulmasi

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