TY - GEN
T1 - Aerodynamic Model Identification of the Flying V from Sub-Scale Flight Test Data
AU - Ruiz Garcia, A.
AU - Brown, M.T.H.
AU - Atherstone, D.M.
AU - van Arnhem, N.
AU - Vos, Roelof
PY - 2022
Y1 - 2022
N2 - This paper presents the identification of the aerodynamic model of the "Flying-V", a novel aircraft configuration. The aerodynamic model is estimated using flight test data from a 4.6\% sub-scale model. The dataset includes longitudinal and lateral-directional maneuvers performed by both the pilot and the autopilot to excite the aircraft dynamic modes. The so-called Two-Step Method is used to decouple and simplify the aerodynamic identification problem; the state estimation step is performed by an Iterated Extended Kalman Filter, and the parameter-estimation step using ordinary least squares. A stepwise regression technique and previous knowledge from wind-tunnel tests are combined to select the model structure, and the identified model is validated using a third of the gathered data. The estimated models are parsimonious and considered adequate in terms of model fit, with a maximum relative Root Mean Square Error of 10% for the worst validation case. For the considered location of the center of gravity and flight conditions, the estimated aerodynamic derivatives confirm that the aircraft is longitudinally stable, both statically and dynamically; and that it is also laterally and directionally statically stable. The analysis of the dynamic modes of the sub-scale model showed stable short period and roll subsidence modes, a lightly damped Dutch roll mode, and lightly damped/unstable phugoid and spiral modes.
AB - This paper presents the identification of the aerodynamic model of the "Flying-V", a novel aircraft configuration. The aerodynamic model is estimated using flight test data from a 4.6\% sub-scale model. The dataset includes longitudinal and lateral-directional maneuvers performed by both the pilot and the autopilot to excite the aircraft dynamic modes. The so-called Two-Step Method is used to decouple and simplify the aerodynamic identification problem; the state estimation step is performed by an Iterated Extended Kalman Filter, and the parameter-estimation step using ordinary least squares. A stepwise regression technique and previous knowledge from wind-tunnel tests are combined to select the model structure, and the identified model is validated using a third of the gathered data. The estimated models are parsimonious and considered adequate in terms of model fit, with a maximum relative Root Mean Square Error of 10% for the worst validation case. For the considered location of the center of gravity and flight conditions, the estimated aerodynamic derivatives confirm that the aircraft is longitudinally stable, both statically and dynamically; and that it is also laterally and directionally statically stable. The analysis of the dynamic modes of the sub-scale model showed stable short period and roll subsidence modes, a lightly damped Dutch roll mode, and lightly damped/unstable phugoid and spiral modes.
UR - http://www.scopus.com/inward/record.url?scp=85123299621&partnerID=8YFLogxK
U2 - 10.2514/6.2022-0713
DO - 10.2514/6.2022-0713
M3 - Conference contribution
T3 - AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2022
BT - AIAA SCITECH 2022 Forum
T2 - AIAA SCITECH 2022 Forum
Y2 - 3 January 2022 through 7 January 2022
ER -