AIRNOISE: a Tool for Preliminary Noise-Abatement Terminal Approach Route Design

Li, Jinhua; Sridhar, Banavar; Xue, Min; Ng, Hok K.

doi:10.2514/6.2016-3905

Cited by 3 publications

(1 citation statement)

References 10 publications

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“…Currently, most researches take the coverage of noise level as the evaluation indicator, but it cannot directly and accurately reflect the impact of aviation noise on the surrounding residents. This paper refers to the noise exposure population evaluation method proposed by Jinhua Li [13,14], divides the covered area of the terminal area into several grids with an area of S, and takes the center of the grid as the noise measuring point. According to the density distribution of residents, the total population of different noise level was calculated.…”

Section: Noisementioning

confidence: 99%

Optimization Method for Reducing the Air Pollutant Emission and Aviation Noise of Arrival in Terminal Area

Tian

Wan

et al. 2019

Sustainability

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In order to reduce the environmental impact of aircraft operation in the terminal area, this paper researched the route optimization method. Firstly, this paper constructed the air pollutant emission and noise assessment model, and the flight performance model. Secondly, aiming at reducing air emissions and noise level, the multi-objective terminal area route optimization model is established based on the principles of flight safety and flight procedure construction. Then this paper puts forward the path optimization method of emission and noise reduction of terminal area route network, through the research on the priority setting method of terminal area approach and departure route planning. The route segmentation method and NSGA-II algorithm are employed to solve the problem. Finally, a numerical case study is carried out for the Shanghai terminal area, and yields the following results: (1) Compared with the original route network, the optimized route network in the terminal area can significantly reduce emission and noise by reducing pollutant emission by 51.4% and noise influence by 21.5%; (2) The method can also reduce fuel consumption by 60.5% and the total route length by 21.1%. Sustainability 2019, 11, 4715 2 of 16 route by taking noise and fuel consumption into consideration [5]. Wang et al. designed a de-noising optimization method for departure route based on fuzzy theory [6]. Muller et al. took the arrival route of Seattle Airport as an example for analysis, and the results showed that the optimized single arrival route could save about 40% of fuel consumption at most [7]. Hartjes et al. proposed the design method of emission reduction and noise reduction of departure route in terminal area under the influence of single event, combining with the degree of residents' annoyance caused by noise [8]. Braakenburg et al. proposed a noise-reducing trajectory design method for arrival routes with the influence of multiple events, and designed a piecewise optimization method for approach routes based on Area Navigation (RNAV) [9]. Dougui et al. considered aircraft conflicts in the terminal area and proposed the arrival route design method based on the light propagation model by referring to the mode of light propagation in media [10,11]. Richter et al. used two-layer hybrid algorithm to calculate the minimum noise trajectory [12]. Li et al. designed an improvement method of the arrival route for preliminary noise reduction, by taking the population affected by noise as the evaluation reference, and changed the noise impact range by changing the horizontal flight path of aircraft [13,14]. Taghizadeh et al. used the atmospheric dispersion model to construct the dispersion model of air emissions in the terminal area, and the emission databank of International Civil Aviation Organization (ICAO) was utilized to estimate the pollutant emission rate of each aircraft [15]. On the route network path optimization of the terminal area, Xue et al. optimized the twodimensional route network path of the terminal area. The r...

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Section: Noisementioning

confidence: 99%