Probabilistic assessment of fleet-level noise impacts of projected technology improvements |
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| Institution: | 1. School of Aerospace Engineering, Aerospace Systems Design Laboratory, Georgia Institute of Technology, 275 Ferst Dr., Atlanta, GA 30332-0150, USA;2. Civil Aviation Research Division, School of Aerospace Engineering, Aerospace Systems Design Laboratory, Georgia Institute of Technology, 275 Ferst Dr., Atlanta, GA 30332-0150, USA;3. Boeing Regents Professor of Advanced Aerospace Systems Analysis, School of Aerospace Engineering, Aerospace Systems Design Laboratory, Georgia Institute of Technology, 275 Ferst Dr., Atlanta, GA 30332-0150, USA;1. Department of Transport and Planning, University of Westminster, 35 Marylebone Road, London NW1 5LS, United Kingdom;2. Dipartimento di Fisica e Chimica, Università di Palermo, Viale delle Scienze, Ed. 18, I-90128, Palermo, Italy;3. Deep Blue Srl, P.zza Buenos Aires, 20, I-00100, Rome, Italy;1. Associate Professor of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba''i University, Dehkadeh-ye-Olympic, 1997967556, Tehran, Iran;2. Professor of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Dehkadeh-ye-Olympic, 1997967556, Tehran, Iran;3. Assistant Professor of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Dehkadeh-ye-Olympic, 1997967556, Tehran, Iran;4. Ph.D. Student of Industrial Management, Faculty of Management and Accounting, Allameh Tabataba’i University, Dehkadeh-ye-Olympic, 1997967556, Tehran, Iran;1. Department of Economics, National and Kapodistrian University of Athens, 1, Sofokleous St., Athens 10559, Greece;2. Department of Business Administration, University of the Aegean, Chios, Greece;1. Institute of Psychology, University of Gdansk, Gdańsk, Poland;2. Gdansk University of Physical Education and Sport, Gdańsk, Poland;3. Polish Airlines LOT, Poland;4. University of Finances and Management in Warsaw, Warsaw, Poland |
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| Abstract: | Demand projections for civil aviation have forecast increases in operations in future decades. Increases in demand are beneficial to the growth and advancement of the aviation industry, but also come with the threat of significant increase in environmental impacts. In response, the industry is focusing on programs to develop technologies for reductions in fuel burn, NOx emissions, and noise. While aircraft-level impacts are an obvious metric of success, it is difficult to make informed robust technology investment decisions with respect to noise without understanding the fleet-level impacts. Fleet-level predictions of noise for technology explorations are especially complicated because it is computationally expensive, highly combinatorial, and airport-specific. Recently, rapid automated airport noise models have been developed, which can be simulated using Design of Experiments (DOE). The results of these simulations are used to generate surrogate models for airport noise contour area, which can be summed to yield a fleet-level impact. These models make use of simplifying assumptions to provide estimates of airport-level noise that are substantially cheaper to compute. They can be used to perform parametric trade-off analyses in conjunction with the equivalency assumption. Equivalency asserts that environmental impacts of a technology infused aircraft can be represented by scaled operations of the baseline aircraft in the same class. This simple assumption allows for the modeling of technology and market penetration factors under the same units: operations. This research uses surrogate models in conjunction with the equivalency assumption to examine two potential technology scenarios in a target forecast year, simulating technology and market performance factors to identify vehicle classes that could have the greatest impact in reducing contour area. Results show that technology and market performance of future notional Small Single Aisle and Large Single Aisle vehicle aircraft have the highest positive correlations with potential reductions in contour area. |
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| Keywords: | Airport noise Fleet noise Fleet analysis Technology impacts DNL Day-Night Average Level Large Single-Aisle Large Twin-Aisle Regional Jet Correlation between variables x y Correlation between variable x and response z Correlation between variable y and response z Multiple correlation between variables x y and response z Small Single-Aisle Small Twin-Aisle Very Large Aircraft |
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