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Estimating the model-specific uncertainty of aircraft noise calculations

机译：估计飞机噪声计算的模型特定不确定性

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摘要

Aircraft noise contours are estimated with model calculations. Due to their impact, e.g., on land use planning, calculations need to be highly accurate, but their uncertainty usually remains unaccounted for. The objective of this study was therefore to quantify the uncertainty of calculated average equivalent continuous sound levels (L_(Aeq)) of complex scenarios such as yearly air operations, and to establish uncertainty maps. The methodology was developed for the simulation program FLULA2. In a first step, the partial uncertainties of modelling the aircraft as a sound source and of modelling sound propagation were quantified as a function of aircraft type and distance between aircraft and receiver. Then, these uncertainties were combined for individual flights to obtain the uncertainty of the single event level (L_(AE)) at a specified receiver grid. The average L_(Aeq) of a scenario results from the combination of the L_(AE) of many single flights, each of which has its individual uncertainties. In a last step, the uncertainties of all L_(AE) were therefore combined to the uncertainty of the L_(Aeq), accounting also for uncertainties of the number of movements and of prognoses. Uncertainty estimations of FLULA2 calculations for Zurich and Geneva airports revealed that the standard uncertainty of the L_(Aeq) ranges from 0.5 dB (day) to 1.0 dB (night) for past-time scenarios when using radar data as input, and from 1.0 dB (day) to 1.3 dB (night) for future scenarios, in areas where L_(Aeq) ≥ 53 dB (day) and L_(Aeq) ≥ 43 dB (night), respectively. Different uncertainty values may result for other models and/or airports, depending on the model sophistication, traffic input data, available sound source data, and airport peculiarities such as the specific aircraft fleet or prevailing departure and arrival procedures. The methodology, while established for FLULA2 on Zurich and Geneva airports, may be applied to other models and/or airports, but the partial uncertainties have to be specifically reestablished to account for individual models and underlying sound source data.
机译：飞机噪声等高线通过模型计算来估算。由于它们的影响，例如对土地使用规划的影响，计算需要高度准确，但通常仍无法确定其不确定性。因此，本研究的目的是量化复杂情景（例如每年的空中运行）的平均等效等效连续声级（L_（Aeq））的不确定性，并建立不确定性图。该方法是为仿真程序FLULA2开发的。第一步，根据飞机类型和飞机与接收器之间的距离，量化将飞机建模为声源和建模声音传播的部分不确定性。然后，将这些不确定性针对单个飞行进行组合，以获得指定接收方网格处单个事件级别（L_（AE））的不确定性。情景的平均L_（Aeq）由许多单个航班的L_（AE）组合得出，每个航班都有其各自的不确定性。因此，在最后一步中，将所有L_（AE）的不确定性与L_（Aeq）的不确定性相结合，同时还要考虑运动次数和预后的不确定性。苏黎世和日内瓦机场的FLULA2计算的不确定性估计显示，过去使用雷达数据作为输入时，L_（Aeq）的标准不确定性范围从0.5 dB（白天）到1.0 dB（夜晚），从1.0 dB开始L_（Aeq）≥53 dB（白天）和L_（Aeq）≥43 dB（夜晚）的区域中，将来的情况从（白天）到1.3 dB（夜晚）。对于其他模型和/或机场，可能会得出不同的不确定性值，具体取决于模型的复杂程度，交通输入数据，可用的声源数据和机场特性，例如特定的飞机机队或现行的起飞和降落程序。该方法虽然在苏黎世和日内瓦机场上针对FLULA2建立，但可以应用于其他模型和/或机场，但是必须专门重新建立部分不确定性，以说明各个模型和基础声源数据。

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  • 来源
    《Applied Acoustics》 |2014年第10期|58-72|共15页
  • 作者

    Beat Schaeffer; Stefan Pluess; Georg Thomann;

  • 作者单位

    Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Acoustics/Noise Control, 8600 Duebendorf, Switzerland;

    Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Acoustics/Noise Control, 8600 Duebendorf, Switzerland;

    Empa, Swiss Federal Laboratories for Materials Science and Technology, Laboratory for Acoustics/Noise Control, 8600 Duebendorf, Switzerland;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Aircraft noise; Noise maps; Model uncertainty; Uncertainty maps;

    机译：飞机噪音;噪声图;模型不确定性;不确定度图;

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