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首页> 外文会议>AIAA/3AF aircraft noise and emissions reduction symposium 2014 >Development of Generic Vehicles for Fleet-Level Analysis of Noise and Emissions Tradeoffs
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Development of Generic Vehicles for Fleet-Level Analysis of Noise and Emissions Tradeoffs

机译：用于噪声和排放权衡的舰队水平分析的通用车辆的开发

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摘要

A fleet-level analysis of technology impacts on environmental metrics via generic vehicle design is proposed. Vehicles are grouped into "vehicle classes" distinguished by the vehicle-level environmental metrics, which include fuel burn (as a surrogate of CO_2 emissions), NO_x emissions, and SEL noise contours. These groupings are compared against traditional seat class groupings. Target metrics are established for a subset of 94 airports by designing a series of tests of sequentially increasing complexity, with ideal generic vehicle designs minimizing the error distributions across these airports 'when vehicle-level performance is aggregated to fleet-levels. Latin hypercube design of experiments are employed to explore the Environmental Design Space (EDS), and Stochastic Multicriteria Acceptability Analysis (SMAA) is used to evaluate potential generic vehicle designs against each other and identify the best designs for simultaneously matching aggregate fuel burn, NO_x emissions, and DNL contours. In general, the average generic vehicles provide greater accuracy for each of these metrics across across the 94 airports for a representative six weeks of operations at these airports derived from various sources such as the Bureau of Transportation Statistics (BTS), as compared to the traditional approach of choosing a representative vehicle per class. The average generic vehicle approach works well for both vehicle-class and seat-class groupings, but the former leads to slightly tighter error distributions for all of the metrics.
机译：提出了通过通用车辆设计对技术对环境指标的影响进行车队级分析的方法。根据车辆级别的环境指标将车辆分为“车辆类别”，其中包括燃油消耗（作为CO_2排放的替代物），NO_x排放和SEL噪声等值线。将这些分组与传统的座位类别分组进行比较。通过设计一系列依次提高复杂性的测试，为94个机场的子集建立了目标度量标准，当将车辆级别的性能汇总到机队级别时，理想的通用车辆设计可将这些机场之间的误差分布最小化。采用拉丁超立方体实验设计来探索环境设计空间（EDS），并使用随机多标准可接受性分析（SMAA）相互评估潜在的通用车辆设计，并确定同时匹配总燃料消耗，NO_x排放量的最佳设计和DNL等高线。一般而言，与传统的传统交通工具相比，一般的通用交通工具在94个机场中为这些指标中的每一个指标提供了更高的准确性，这些指标来自各种来源（例如运输统计局（BTS）），在这些机场进行了具有代表性的六周运营每个班级选择代表性车辆的方法。普通的通用车辆方法对于车辆类别和座位类别分组均适用，但是前者会导致所有指标的误差分布稍紧。

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