Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data

Patterson, Judith G.; Noel, George; Senzig, David A.; Roof, Christopher J.; Fleming, Gregg G.

doi:10.2514/1.42432

Cited by 31 publications

(16 citation statements)

References 1 publication

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“…A review of existing literature on fuel burn estimation indicate that, using the standard International Civil Aviation Organization (ICAO) fuel burn rate and time in mode can result in overestimation of total fuel burn 7 . Instead, the fuel burn can be estimated with 5.4% of the actual value using a model based on suitable track position report 8 .…”

Section: B Fuel Burn Modelmentioning

confidence: 99%

Estimated Fuel Burn Performance for MDW Arrivals

Belle

Sherry

2013

2013 Aviation Technology, Integration, and Operations Conference

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The results of the analysis indicate that, during Instrument Meteorological Conditions (IMC), the RNP approach to 13C uses an average of 14% less fuel than the ILS approach on to 13C. For narrow body aircrafts, this is equivalent to on an average a savings of $47 per flight per approach (at $3 a gallon). During Visual Meteorological Conditions (VMC) the least average fuel burn arrival flow for VFR approach procedures is to runways 4R or 31C (174.9 kg) followed by 13C (+11%), and 22L (+19%). During Instrument Meteorological Conditions (IMC) the least average fuel burn arrival flow for ILS approach procedures is to runway 31C (207.06 kg) followed by, 4R (+6.5%) and 13C (+33%). To ensure equity in fuel burn between flights arriving from the east and the west during VMC, 13C is the most equitable runway with the 14% difference in fuel burn between east and west flows, followed by 4R (18% difference), 22L (96% difference) and 31C (148% difference). During IMC, 13C (56%) is the most equitable runway followed by 4R (72%) and 31C (211%).For narrow body aircrafts at MDW, the fuel burn rate in level flight (35 kg/min) is 108% greater than the fuel burn for near-idle descent segments (15 kg/min) and 25% greater than fuel burn on the final approach segment (25 kg/min). These results quantify the benefits of the RNP approach onto 13C and identify an opportunity for a decision-support tool to select runway configuration based on wind magnitude/direction, as well as fuel burn.

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Section: B Fuel Burn Modelmentioning

confidence: 99%

Estimated Fuel Burn Performance for MDW Arrivals

Belle

Sherry

2013

2013 Aviation Technology, Integration, and Operations Conference

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“…However, this data is not able to completely cover and describe the operation of a specific fleet. Models for a global and averaged view on fuel consumption exist 2,3 as well as fuel flow estimations for taxi, departure and landing 4,5 . QAR data is also used for trend monitoring of engines 6 , for engine performance studies 7 and for anomaly detection during cruise 8 .…”

Section: Introductionmentioning

confidence: 99%

Cluster Analysis of Fuel Flow in Operational Flight Data

Koppitz

Sauterleute

Holzapfel

2016

16th AIAA Aviation Technology, Integration, and Operations Conference

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With fuel being one of the main cost drivers of airlines, a possibility to measure the reduction of fuel consumption during regular operational based on QAR (Quick Access Recorder) data is assessed. The fuel flow data recorded during regular operation is analyzed by clustering it based on influencing factors such as pressure altitude or Mach number. One fuel flow cluster represents a narrow point of operation for a certain combination of narrow intervals of influencing factors. This paper shows how well the fuel flow in a point of operation can be determined by clustering. With a fixed combination of influencing factors, the effect of the minimum number of data points in one cluster to be considered a full cluster, of the interval width, and the separation between clusters through a single influencing factor are analyzed. Pairs of full clusters are used to calculate the difference in fuel flow between two sets of QAR data based on the mean fuel flow and the H-test developed by Kruskal and Wallis. Real flight data of two Boeing 737-500 aircraft in a certain period of time show significant difference in fuel flow. Nomenclature h = pressure altitude Hnorm = normal deviate H-test = confidence interval of mean value ̅ = confidence interval of mean value normed by mean value = critical level for Hnorm m = total mass of aircraft ̇ = fuel flow ̅ ̅̅ = mean fuel flow of one cluster Ma = Mach number n = number of data points per cluster = number of flights N = number of all data points in a pair of full clusters from two data sets N1= fan speed R = sum of ranks from one compared cluster zx = x-quantile of the standard normal distribution = confidence level = standard deviation ̅ = standard deviation normed by mean value

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“…Being non-operational in nature, such data may not be re ective of the performance of a real engine in ight. Indeed, statistically signi cant di erences have been shown to exist between the fuel burn estimates from ground test-based methods (for example, the International Civil Aviation Organization Aircra Engine Emissions Databank [12]) and the actual values seen in ight operations [6,17]. erefore, the use of operational ight data to develop models of fuel ow rate is expected to give estimates which be er re ect the behavior of an aircra in ight.…”

Section: Introductionmentioning

confidence: 99%

A Gaussian process regression approach to model aircraft engine fuel flow rate

Chati

Balakrishnan

2017

Proceedings of the 8th International Conference on Cyber-Physical Systems

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e problem of building statistical models of cyber-physical systems using operational data is addressed in this paper, using the case study of aircra engines. ese models serve as a complement to physics-based models, which may not accurately re ect the operational performance of systems. e accurate modeling of fuel ow rate is an essential aspect of analyzing aircra engine performance. In this paper, operational data from Flight Data Recorders are used to model the fuel ow rate. e independent variables are restricted to those which are obtainable from trajectory data. Treating the engine as a statistical system, an algorithm based on Gaussian Process Regression (GPR) is developed to estimate the fuel ow rate during the airborne phases of ight. e algorithm propagates the uncertainty in the estimates in order to determine prediction intervals. e proposed GPR models are evaluated for their predictive performance on an independent set of ights. e resulting estimates are also compared with those given by the Base of Aircra Data (BADA) model, which is widely used in aircra performance studies. e GPR models are shown to perform statistically signi cantly be er than the BADA model. e GPR models also provide interval estimates for the fuel ow rate which re ect the variability seen in the data, presenting a promising approach for data-driven modeling of cyber-physical systems. CCS CONCEPTS •Computing methodologies →Modeling methodologies; Modeling and simulation; Model development and analysis; •Applied computing →Aerospace; Physical sciences and engineering;

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Analysis of Departure and Arrival Profiles Using Real-Time Aircraft Data

Cited by 31 publications

References 1 publication

Estimated Fuel Burn Performance for MDW Arrivals

Estimated Fuel Burn Performance for MDW Arrivals

Cluster Analysis of Fuel Flow in Operational Flight Data

A Gaussian process regression approach to model aircraft engine fuel flow rate

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