Purpose

The purpose of this paper is to create a new fuel flow rate model for the descent phase of the flight using particle swarm optimization (PSO).

Design/methodology/approach

A new fuel flow rate model was developed for the descent phase of the B737-800 aircraft, which is frequently used in commercial air transport using PSO method. For the analysis, the actual flight data records (FDRs) data containing the fuel flow rate, speed, altitude, engine speed, time and many more data were used. In this regard, an empirical formula has been created that gives real fuel flow rate values as a function of altitude and true airspeed. In addition, in the fuel flow rate predictions made for the descent phase of the specified aircraft, a different model has been created that can be used without any optimization process when FDR data are not available for a specific aircraft take-off weight condition.

Findings

The error analysis applied to the models showed that both models predict real fuel flow rate values with high precision.

Practical implications

Because of the high accuracy of the PSO model, it is thought to be useful in air traffic management, decision support systems, models used for trajectory prediction, aircraft performance models, strategies used to reduce fuel consumption and emissions because of fuel consumption.

Originality/value

This study is the first fuel flow rate model for descent flight using PSO algorithm. The use of real FDR data in the analysis shows the originality of this study.

中文翻译：

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使用粒子群算法对商用飞机下降飞行的燃油流量建模

目的

本文的目的是使用粒子群算法（PSO）为飞行下降阶段创建一个新的燃油流量模型。

设计/方法/方法

针对B737-800飞机下降阶段开发了一种新的燃油流速模型，该模型经常用于使用PSO方法进行商业航空运输。为了进行分析，使用了实际的飞行数据记录（FDR）数据，其中包含燃油流量，速度，高度，发动机速度，时间以及更多数据。在这方面，已经创建了一个经验公式，该公式给出了实际燃料流量值与海拔高度和实际空速之间的函数关系。此外，在为指定飞机的下降阶段做出的燃油流量预测中，创建了一个不同的模型，当FDR数据不适用于特定的飞机起飞重量条件时，可以在没有任何优化过程的情况下使用该模型。

发现

应用于模型的误差分析表明，两个模型都可以高精度预测实际燃油流量值。

实际影响

由于PSO模型具有很高的准确性，因此被认为可用于空中交通管理，决策支持系统，用于轨迹预测的模型，飞机性能模型，用于减少因油耗而减少油耗和排放的策略。

创意/价值

这项研究是第一个使用PSO算法进行下降飞行的燃油流速模型。分析中使用实际FDR数据显示了这项研究的独创性。