** NEW PUBLICATION ** The operation of unmanned aerial vehicles (UAVs) in icing conditions can be significantly limited because of aerodynamic performance penalties. For example, ice accretions on wings reduce lift and increase drag. The increased drag requires the aircraft to increase thrust, thereby increasing the required energy and limiting flight endurance and range.
Good mission planning and energy-efficient ice protection systems are required to maintain acceptable flight endurance and range. One piece of information that can help is the expected increase in drag under given icing conditions. Using this information, the expected reduction in flight endurance can be calculated, helping the operator to update his mission. Additionally, if the aircraft is equipped with a de-icing system, the drag prediction can help to control when to de-ice.
Existing drag predictions under icing conditions were based on manned aircraft Reynolds numbers and have not been extensively tested under UAV conditions. This new publication investigates the accuracy of existing correlations and proposes a new prediction of the increase in drag under icing conditions.
Method – To obtain as many data points as possible for comparison with existing correlations or for developing new predictions, numerical simulations were used. FENSAP-ICE simulations were run in a batch-automated way to reduce the manual setup time to setting up the baseline simulation and the condition matrix. The simulations predict ice growth under the selected conditions and the corresponding increases in drag. In total, 1798 data points were simulated.
Comparison with existing correlations – Each data point was compared to existing correlations. In general, all existing correlations showed the same phenomenon of a significantly wider range of predicted drag coefficients than the range of simulated drag coefficients, exemplarily shown here:
New prediction – Because of the bad fit of existing correlations with the simulation data, new correlations were developed that fit the simulated UAV case much better. Using separate equations for 3 different freezing fraction ranges, a correlation can be found that has a maximum deviation of only 23% from the simulated drag increases:
Summary – Predicting the increase in drag under given icing conditions can help operators optimize their in-flight mission planning. Existing correlations have been found to show bad agreement with a simulated UAV case. A new prediction has been developed that shows excellent agreement with the simulated data.
The numerical data require experimental validation, and the prediction method must be tested over a wider parameter range to verify its performance beyond the very specific case tested in this publication. Nevertheless, the good agreement between the new prediction and the simulation data indicates that this approach can be used to develop drag increase predictions for UAVs under icing conditions.
Reference: Wallisch, J.; Lindner, M.; Borup, K.T.; Hann, R. UAV Icing at Low Reynolds Numbers: RG-15 Airfoil Drag Increase Prediction Based on Batch-Automated CFD Simulations. J. Aerosp. Eng. 2026, 39, https://doi.org/10.1061/jaeeez.aseng-6806.
Text & pictures by Joachim Wallisch.
