Accuracy Evaluation of an Aircraft Performance Model with Airliner Flight Data

Harada, Akinori; Miyamoto, Yuto; Miyazawa, Yoshikazu; Funabiki, Kozo

doi:10.2322/tastj.11.79

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…the estimated ḟ of individual waypoints at cruise agree to ± 10%, and the estimated TFC is within ± 3%. However, we note two limitations of BADA3: (i) a decrease in the accuracy of the modelled ḟ at suboptimal flight conditions (suboptimal altitudes and high speeds) 43 ; and (ii) a comparison with the actual ḟ from flight data recorders showed that the uncertainty in ḟ (from BADA3) is larger at climb and descent 44 . For this study, limitation (ii) is of minor importance because contrails predominantly form at cruise.…”

Section: Aircraft Fuel Consumption and Thrust Settingsmentioning

confidence: 93%

“…However, we note two limitations of BADA3: (i) a decrease in the accuracy of the modeled ṁf at suboptimal flight conditions (suboptimal altitudes and high speeds); 43 and (ii) a comparison with the actual ṁf from flight data recorders showed that the uncertainty in ṁf (from BADA3) is larger at climb and descent. 44 For this study, limitation (ii) is of minor importance because contrails predominantly form at cruise. However, limitation (i) remains an open issue and BADA4, which was developed as an improvement to BADA3, was not accessible.…”

Section: Methodsmentioning

confidence: 98%

See 1 more Smart Citation

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption

Teoh

Schumann

Majumdar

et al. 2020

Environ. Sci. Technol.

130

126

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The climate forcing of contrails and induced-cirrus cloudiness is thought to be comparable to the cumulative impacts of aviation CO2 emissions. This paper estimates the impact of aviation contrails on climate forcing for flight track data in Japanese airspace and propagates uncertainties arising from meteorology and aircraft black carbon (BC) particle number emissions. Uncertainties in contrail age, coverage, optical properties, radiative forcing and energy forcing (EF) from 2 individual flights can be two orders of magnitude larger than the fleet-average values. Only 2.2% [2.0%, 2.5%] of flights contribute to 80% of the contrail EF in this region. A small-scale strategy of selectively diverting 1.7% of the fleet could reduce the contrail EF by up to 59.3% [52.4%, 65.6%], with only a 0.014% [0.010%, 0.017%] increase in total fuel consumption and CO2 emissions. A low-risk strategy of diverting flights only if there is no fuel penalty, thereby avoiding additional long-lived CO2 emissions, would reduce contrail EF by 20.0% [17.4%, 23.0%]. In the longer term, widespread use of new engine combustor technology, which reduces BC particle emissions, could achieve a 68.8% [45.2%, 82.1%] reduction in the contrail EF. A combination of both interventions could reduce the contrail EF by 91.8% [88.6%, 95.8%].

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Section: Aircraft Fuel Consumption and Thrust Settingsmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 98%

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption

Teoh

Schumann

Majumdar

et al. 2020

Environ. Sci. Technol.

130

126

View full text Add to dashboard Cite

show abstract

“…The operational limitations of each aircraft, such as its maximum operating Mach number (MMO) and maximum operating altitude (HMO), are also provided. The fuel flow calculated by the BADA model has been evaluated with reference to an airliner's onboard flight data, indicating that the model is sufficiently accurate for trajectory optimization calculations 18 . Fig.…”

Section: B Aircraft Performance Modelmentioning

confidence: 99%

Dynamic Programming Trajectory Optimization by Piecewise Linear Approximation

Harada

Matsuda

Miyazawa

2015

AIAA Guidance, Navigation, and Control Conference

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The rapid increase of computational capability due to high-performance processors and parallel processing, as well as continual decreases of hardware costs, have revived interest in previously overlooked numerical methods. The practical application of Dynamic Programming, one such method for trajectory optimization, has been prevented by the "menace of the expanding grid," otherwise known as the dimensional difference problem. This problem occurs in cases where the number of control variables is fewer than that of state variables. The present paper proposes a promising method that overcomes the problem by piecewise linear approximation to obtain the optimum return function. The accuracy achieved by the method is illustrated with a simple example in which the exact solution is provided analytically. Furthermore, the method may be applied to aircraft longitudinal flight optimization, where it generates the most efficient and practically applicable reference flight trajectory in real time. NomenclatureB A, = system matrix of the continuous time system equation C = constraints D = drag f = function vector g = gravity acceleration H = geopotential altitude J = objective function L = Lagrangian, lift m = dimension of the control variable, mass of aircraft M = Mach number n = dimension of the state variable, number of divisions P = solution of the Riccati equation Q = weight matrix for state variables in an objective function r = weight for control variables in an objective function, dimension of constraints R = weight matrix for control variables in an objective function 0 R = radius of the Earth R = set of real numbers S = wing area t = time T = thrust 1 Graduate student, School of Engineering,AIAA SciTech u = control variable V = velocity x = state vector, down range γ = flight path angle η = cross range θ = longitude µ = fuel flow ξ = down range ρ = air density φ = latitude, objective function at terminal ψ = constraint function at terminal Subscripts f , 0 = initial, final c = continuous-time system d = discrete-time system j i, = element numbers for vector and matrix k = time stage number in the discrete time system max = maximum min = minimum MO = maximum operating opt = optimal Superscripts  = zeroth-order hold

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“…According to another analysis for the flight data, the numerical error of fuel consumption calculated from the flight variables is ±5% compared to the onboard measurement. 10) Since the same BADA model is used to calculate the fuel consumption for both real flight data and proposed optimal trajectory, the model error does not directly affect the difference of the fuel consumptions.…”

Section: Aircraft Performance Modelmentioning

confidence: 99%

Analysis of Fuel-Efficient Airliner Flight via Dynamic Programming Trajectory Optimization

Miyamoto

Wickramasinghe

Harada

et al. 2013

AEROSPACE TECHNOLOGY JAPAN

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Fuel-efficient flight is in strong demand due to environmental concerns and skyrocketing fuel prices. This paper analyzes airliner flight trajectory from the perspective of fuel efficiency through comparison with the optimal trajectory. Since the optimal trajectory gives the maximum achievable performance without any constraint of air traffic control, it introduces reference data for quantitative merit of the ideal flight. Dynamic programming is adopted to calculate the optimal trajectory. Examples show that considerable reduction in the fuel consumption from the estimated values for actual flights could be achieved by the trajectory optimization, where wind conditions are taken into account as well.

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Accuracy Evaluation of an Aircraft Performance Model with Airliner Flight Data

Cited by 26 publications

References 5 publications

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption

Mitigating the Climate Forcing of Aircraft Contrails by Small-Scale Diversions and Technology Adoption

Dynamic Programming Trajectory Optimization by Piecewise Linear Approximation

Analysis of Fuel-Efficient Airliner Flight via Dynamic Programming Trajectory Optimization

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