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Aircraft Mass Estimation using Quick Access Recorder Data

机译:使用快速访问记录器数据进行飞机质量估计

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Aircraft mass is the most important parameter airliners use to determine how much cargo and fuel they should take for the flight to maximize their profit while keep the total weight under the safety limit. For many practical reasons, airliners can not get the accurate aircraft mass data by weighting every cargo and passenger for each flight. Several studies have proposed methods to estimate aircraft mass based on radar data or ADS-B data. But due to the measurement errors in the data, and also due to uncertainties in aircraft configuration, it is hard to calculate accurate aircraft mass data for every flight. In the paper, the quick access recorder data are used for analysis, parameters that are not available in radar data or ADS-B data can now be used to eliminate parameters with large errors in the level flight point-mass dynamics model. Equations are reformulated as a set of overdetermined linear equations with nonlinearly structured errors in system matrix. The set of equations does not depend on inaccurate parameters like thrust and it does not require accurate knowledge of the aircraft like geometry, aerodynamic coefficients, etc. The set of equations is solved by an improved structured nonlinear total least squares method using Monte Carlo method. The method is applied to 120 real flights of Boeing 777-300ER aircraft, the result shows a good accuracy for some type of flights.
机译:飞机质量是客机用来确定为使利润最大化同时将总重量保持在安全极限以内的飞行所应携带的货物和燃料的最重要参数。由于许多实际原因,客机无法通过加权每个航班的每个货物和乘客的重量来获得准确的飞机质量数据。一些研究提出了基于雷达数据或ADS-B数据估算飞机质量的方法。但是由于数据中的测量误差以及飞机配置的不确定性,很难为每次飞行计算准确的飞机质量数据。在本文中,快速访问记录器数据用于分析,雷达数据或ADS-B数据中不可用的参数现在可以用于消除在水平飞行点质量动力学模型中存在较大误差的参数。方程被重新构造为一组在系统矩阵中具有非线性结构误差的超线性方程组。方程组不依赖于不准确的参数(例如推力),也不需要飞机的精确知识,例如几何形状,空气动力学系数等。方程组通过使用蒙特卡洛方法的改进的结构化非线性总最小二乘法求解。该方法应用于波音777-300ER飞机的120次真实飞行,结果表明对于某些类型的飞行具有良好的准确性。

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