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This paper focuses on the estimation of the aircraft mass in ground-based applications. Mass is a key parameter for climb prediction. It is currently not available to groundbased trajectory predictors because it is considered a competitive parameter by many airlines. There is hope that the aircraft mass might become widely available someday, but in the meantime it is possible to estimate an equivalent mass from the data already available, assuming the thrust to be known (maximum or reduced climb thrust for example). In this paper, we compare the performances of two mass estimation methods proposed in recent publications. Both methods estimate the aircraft mass by fitting the modeled energy rate (i.e. the power of the forces acting on the aircraft) with the energy rate observed at several points of the past trajectory. The first method, proposed by Schultz et al. ([1]), dynamically adjusts the weight parameter so as to fit the energy rate, using an adaptive sensitivity parameter to weight each observation. The second method, introduced in one of our previous publications ([2]), estimates the mass by minimizing the quadratic error on the observed energy rate, taking advantage of the polynomial expression of the modeled power when using the BADA model. The robustness of both methods to the observation errors is assessed, using simulated data with various distributions of the noise added to the observed state variables. The results show that both methods are able to find mass estimates that are very close to the "actual" mass, with slightly better performances for the least squares method.

This paper focuses on the estimation of the aircraft mass in ground-based applications. Mass is a key parameter for climb prediction. It is currently not available to groundbased trajectory predictors because it is considered a competitive parameter by many airlines. There is hope that the aircraft mass might become widely available someday, but in the meantime it is possible to estimate an equivalent mass from the data already available, assuming the thrust to be known (maximum or reduced climb thrust for example). In a previous paper, two mass estimation methods were compared using simulated data. In this paper, we compare these two mass estimation methods using Mode-C radar data. Both methods estimate the aircraft mass by fitting the modeled energy rate (i.e. the power of the forces acting on the aircraft) with the energy rate observed at several points of the past trajectory. The first method dynamically adjusts the weight parameter so as to fit the energy rate, using an adaptive sensitivity parameter to weight each observation. The second method, introduced in one of our previous publications, estimates the mass by minimizing the quadratic error on the observed energy rate, taking advantage of the polynomial expression of the modeled power when using the BADA model.The actual mass is unavailable in our radar data. However, we can use the estimated mass to compute a trajectory prediction. This prediction is then compared to the actual trajectory giving us some insight on the accuracy of the estimated mass. We have compared the obtained predictions with the ones obtained using the BADA reference mass. The root mean square error on the predicted altitude is reduced by 45 % using the least squares method. With the adaptive method this error is divided by two.

Owing to their professional activity, flight crews may receive a dose of some millisieverts within a year; airline passengers may also be concerned. The effective dose is to be estimated using various experimental and calculation tools. The European project DOSMAX (Dosimetry of Aircrew Exposure during Solar Maximum) was initiated in 2000 extending to 2004 to complete studies over the current solar cycle during the solar maximum phase. To compare various dosemeters in real conditions simultaneously in the same radiation field, an intercomparison was organised aboard a Paris-Tokyo round-trip flight. Both passive and active detectors were used. Good agreement was observed for instruments determining the different components of the radiation field; the mean ambient dose equivalent for the round trip was 129 +/- 10 microSv. The agreement of values obtained for the total dose obtained by measurements and by calculations is very satisfying.