Abstract: A method for detecting the presence of ice on an aircraft, including: obtaining, in real time, measurements (provided by sensors) of a set of flight parameters comprising a slope, an incidence, a Mach number, a rotational speed of a low-pressure propulsion engine assembly of the aircraft, a fuel flow, a mass of the aircraft, a position of a trimmable horizontal stabilizer, a static temperature and a static pressure; injecting the obtained measurements into an artificial-intelligence model, trained and configured to calculate a presence of ice indicator based on measurements of the set of flight parameters; and providing the presence of ice indicator calculated by the model. This novel indirect method exhibits good performance because it is based on an artificial-intelligence model using a large number of flight parameters.

Abstract: A method for determining a risk of landing failure of an aircraft on a landing location is based on a reference table constructed by making a selection of a set of values of M variables representative of landing conditions independent of potential turbulences, uniformly between a minimum value and a maximum value of each variable, performing a number P of closed-loop simulations by applying random turbulence conditions, from the set of values of the M variables, calculating a mean and a standard deviation of touchdown parameters from the P closed-loop simulations, and by complementing the reference table on each iteration for a number N of sets of values of the M variables. Next, the reference table is used to determine the risk of landing failure, in light of envisaged landing conditions.