Prediction of aircraft engine emissions using ADS-B flight data

2021 ◽  
pp. 1-25
Author(s):  
A. Filippone ◽  
B. Parkes ◽  
N. Bojdo ◽  
T. Kelly
Keyword(s):  
Real Time ◽  
Water Vapour ◽  
Long Range ◽  
Aircraft Engine ◽  
Software Systems ◽  
Engine Emissions ◽  
Flight Data ◽  
Seasonal Basis ◽  
Emissions Inventories ◽  
Aviation Emissions

ABSTRACT Real-time flight data from the Automatic Dependent Surveillance–Broadcast (ADS-B) has been integrated, through a data interface, with a flight performance computer program to predict aviation emissions at altitude. The ADS-B, along with data from Mode-S, are then used to ‘fly’ selected long-range aircraft models (Airbus A380-841, A330-343 and A350-900) and one turboprop (ATR72). Over 2,500 flight trajectories have been processed to demonstrate the integration between databases and software systems. Emissions are calculated for altitudes greater than 3,000 feet (609m) and exclude landing and take-off cycles. This proof of concept fills a gap in the aviation emissions inventories, since it uses real-time flights and produces estimates at a very granular level. It can be used to analyse emissions of gases such as carbon dioxide ( $\mathrm{CO}_2$ ), carbon monoxide (CO), nitrogen oxides ( $\mathrm{NO}_x$ ) and water vapour on a specific route (city pair), for a specific aircraft, for an entire fleet, or on a seasonal basis. It is shown how $\mathrm{NO}_x$ and water vapour emissions concentrate around tropospheric altitudes only for long-range flights, and that the cruise range is the biggest discriminator in the absolute value of these and other exhaust emissions.

Validation of Approach and Climb-Out Times-in-Mode for Aircraft Emissions Computation

2003 ◽  
Vol 1850 (1) ◽  
pp. 79-82 ◽  
Author(s):  
Colin C. Rice
Keyword(s):  
Environmental Protection Agency ◽  
Aircraft Engine ◽  
Radar Data ◽  
Aircraft Emissions ◽  
Engine Emissions ◽  
Emissions Inventory ◽  
The Past ◽  
Emissions Inventories ◽  
Discrete Values ◽  
The Impact

The first aircraft emissions standards were proposed by the U.S. Environmental Protection Agency (EPA) in “Proposed Standards for Control of Air Pollution,” in 1972. Landing–takeoff cycles (LTOs), aircraft engine emissions factors, and times-in-mode (TIMs) were the basis of computing aircraft emissions. Discrete values were assigned to each TIM. No formal record of the derivation of these TIMs is available. Thirty years later, the same TIMs presented by EPA in 1972 are still used in developing aircraft emissions inventories. Today’s aircraft perform much differently than did aircraft from the past. A study was attempted to validate the TIM values currently used with radar data. A sample emissions inventory will be developed for Chicago O’Hare International Airport to show the impact of the proposed changes of TIMs.

Determination of fuel consumption and pollutant emissions with the real-time engine running data of aircrafts in the taxi-out period

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mehmet Kadri Akyüz
Keyword(s):  
Real Time ◽  
Fuel Consumption ◽  
Aircraft Engine ◽  
Pollutant Emissions ◽  
Civil Aviation ◽  
Exhaust Emission ◽  
Content Type ◽  
Flight Data ◽  
Total Fuel Consumption ◽  
Fuel Consumption And Emissions

Purpose The purpose of this paper is to calculate the fuel consumption and emissions of carbon monoxide (CO), nitrogen oxide (NOx) and hydrocarbons (HC) in the taxi-out period of aircraft at the International Diyarbakir Airport in 2018 and 2019. Design/methodology/approach Calculations were performed by determining the engine operating times in the taxi-out period with the flight data obtained from the airport authority. In the analyses, aircraft series and aircraft engine types were determined, and the Engine Exhaust Emission Databank of the International Civil Aviation Authority (ICAO) were used for the calculation. Findings Total fuel consumption in the taxi-out period in 2018 and 2019 was calculated as 525.64 and 463.69 tons, respectively. In 2018, HC, CO and NOx emissions caused by fuel consumption were found to be 1,109, 10,668 and 2,339 kg, respectively. In 2019, the total HC, CO and NOx emissions released to the atmosphere during the taxi-out phase are 966, 9,391 and 2,126 kg, respectively. B737 Series aircraft have the largest share in total fuel consumption and pollutant emissions. Practical implications This study explains the importance of determining fuel consumption and pollutant emissions by considering engine operating times in the taxi-out period. The study provides aviation authorities with scientific methods to follow in calculating fuel consumption and emissions from aircraft operations. Originality/value The originality of this study is the calculation of fuel consumption and pollutant emissions by determining real-time engine running times in the taxi-out period. In addition, calculations were made with real engine operating times determined in the taxi-out period using real flight data.

scholarly journals Improving the Stochastic Model of Ionospheric Delays for BDS Long-Range Real-Time Kinematic Positioning

2021 ◽  
Vol 13 (14) ◽  
pp. 2739
Author(s):  
Huizhong Zhu ◽  
Jun Li ◽  
Longjiang Tang ◽  
Maorong Ge ◽  
Aigong Xu
Keyword(s):  
Real Time ◽  
Long Range ◽  
Ionospheric Delay ◽  
Time Interval ◽  
Short Time Interval ◽  
Dual Frequency ◽  
Triple Frequency ◽  
Ambiguity Fixing ◽  
Power Spectral ◽  
Observation Noise

Although ionosphere-free (IF) combination is usually employed in long-range precise positioning, in order to employ the knowledge of the spatiotemporal ionospheric delays variations and avoid the difficulty in choosing the IF combinations in case of triple-frequency data processing, using uncombined observations with proper ionospheric constraints is more beneficial. Yet, determining the appropriate power spectral density (PSD) of ionospheric delays is one of the most important issues in the uncombined processing, as the empirical methods cannot consider the actual ionosphere activities. The ionospheric delays derived from actual dual-frequency phase observations contain not only the real-time ionospheric delays variations, but also the observation noise which could be much larger than ionospheric delays changes over a very short time interval, so that the statistics of the ionospheric delays cannot be retrieved properly. Fortunately, the ionospheric delays variations and the observation noise behave in different ways, i.e., can be represented by random-walk and white noise process, respectively, so that they can be separated statistically. In this paper, we proposed an approach to determine the PSD of ionospheric delays for each satellite in real-time by denoising the ionospheric delay observations. Based on the relationship between the PSD, observation noise and the ionospheric observations, several aspects impacting the PSD calculation are investigated numerically and the optimal values are suggested. The proposed approach with the suggested optimal parameters is applied to the processing of three long-range baselines of 103 km, 175 km and 200 km with triple-frequency BDS data in both static and kinematic mode. The improvement in the first ambiguity fixing time (FAFT), the positioning accuracy and the estimated ionospheric delays are analysed and compared with that using empirical PSD. The results show that the FAFT can be shortened by at least 8% compared with using a unique empirical PSD for all satellites although it is even fine-tuned according to the actual observations and improved by 34% compared with that using PSD derived from ionospheric delay observations without denoising. Finally, the positioning performance of BDS three-frequency observations shows that the averaged FAFT is 226 s and 270 s, and the positioning accuracies after ambiguity fixing are 1 cm, 1 cm and 3 cm in the East, North and Up directions for static and 3 cm, 3 cm and 6 cm for kinematic mode, respectively.

scholarly journals Real-Time Onboard Global Nonlinear Aerodynamic Modeling from Flight Data

2016 ◽  
Vol 53 (5) ◽  
pp. 1261-1297 ◽  
Author(s):  
Jay M. Brandon ◽  
Eugene A. Morelli
Keyword(s):  
Real Time ◽  
Flight Data ◽  
Aerodynamic Modeling ◽  
Nonlinear Aerodynamic
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