Enrique Casado

Sensitivity of Trajectory Prediction Accuracy to Aircraft Performance Uncertainty

The global Air Traffic Management (ATM) system is being transformed to enable increased capacity, efficiency and safety while contributing to reduce the environmental impact of aviation. A key element of this transformation is the introduction of TrajectoryBased Operations (TBO), which rely on advanced computer-based automation and digital data communications to enable airspace users and Air Navigation Service Providers (ASNPs) to collaboratively and strategically manage the aircraft’s intended trajectory. In the context of TBO, airborne and ground-based automation systems will predict trajectories and exchange trajectory information, both pre-departure and during the fight, to support a process aimed at assigning each aircraft a trajectory that achieves a compromise between the preferences of the airspace user and the constraints imposed by Air Traffic Control (ATC). It is anticipated that the automation infrastructure required to support TBO will require sophisticated trajectory prediction capabilities to accurately estimate the intended trajectory of the aircraft in different operational contexts and environmental conditions and for different look-ahead times. Such trajectory predictors will in turn need precise aircraft performance information to calculate the trajectory, in addition to other data such as wind and temperature forecasts, initial aircraft state or aircraft intent. This paper proposes a methodology to assess the impact on trajectory prediction accuracy of the aircraft performance uncertainty derived from the use of parametric models such as BADA. The proposed method, based on statistical modeling, enables the definition of performance uncertainty bounds for all aircraft of the same type and of the resulting bounds in trajectory prediction error.

Robustness of idle-throttle continuous descent approach trajectories against modified timing requirements

This paper reports on a study of the robustness of Continuous Descent Approach (CDA) trajectories in the face of late changes to the Required Time of Arrival (RTA). We demonstrate a method for determining limits on how much the RTA can be modied, as a function of notication lead-time, without signicantly impacting on optimality of the CDA. Our focus is on the period between Top Of Descent (TOD) from cruise level and arrival at a metering x. The aim is to help determine how exible airspace constraints would need to be in order to accommodate robust CDA. The Aircraft Intent Description Language (AIDL) is used as the modelling language.

Quantification of aircraft trajectory prediction uncertainty using polynomial chaos expansions

A novel approach to quantify the uncertainty associated with any aircraft trajectory prediction based on the application of the Polynomial Chaos (PC) theory is presented. The proposed method relies on univariate polynomial descriptions of the uncertainty sources affecting the trajectory prediction process. Those descriptions are used to build the multivariate polynomial expansions that represent the variability of the aircraft state variables along the predicted trajectory. A case study compares the results obtained by a classical Monte Carlo approach with those generated by applying the so-called arbitrary Polynomial Chaos Expansions (aPCE). The results provided herein lead to conclude that this new methodology can be used to accurately quantify trajectory prediction uncertainty with a very low computational effort, enabling the capability of computing the uncertainty of the individual trajectories of a traffic sample of thousands flights within very short time intervals.

Tumor pulmonar productor de alfafetoproteína

The secretion of alfafetoprotein (AFP) by lung carcinoma is rare and few cases have been described. Morphologically, these tumors present a characteristic hepatoid differentiation, thus the term hepatoid adenocarcinoma has been suggested for them. We report two cases of lung carcinoma with secretion of AFP and hepatoid differentiation.