CFD modelling techniques are exploited to investigate the local velocity field around angle of attack and sideslip angle sensors fitted to the nose of a modified BAe Jetstream 3102 small airliner. Analysis of the flow angularity at the vane locations has allowed the vanes response to varying flight conditions to be predicted and errors in the readings to be quantified. Subsequently, a more accurate calibration of the system is applied to the current configuration on the Jetstream, and a better understanding of the position error with respect to the vane locations is obtained. The above aircraft was acquired by Cranfield University in 2003 with subsequent flow angle vane modifications taking place in 2005. The aircraft is currently in operation with the National Flying Laboratory Centre (NFLC) for research and demonstration purposes.
Fibre optic based sensors are becoming increasingly viable as replacements for traditional flight test sensors. Here we present laboratory, wind tunnel and flight test results of fibre Bragg gratings (FBG) used to measure surface strain and an extrinsic fibre Fabry-Perot interferometric (EFFPI) sensor used to measure unsteady pressure. The calibrated full scale resolution and bandwidth of the FBG and EFFPI sensors were shown to be 0.29% at 2.5 kHz up to 600 με and 0.15% at up to 10 kHz respectively up to 400 Pa. The wind tunnel tests, completed on a 30% scale model, allowed the EFFPI sensor to be developed before incorporation with the FBG system into a Bulldog aerobatic light aircraft. The aircraft was modified and certified based on Certification Standards 23 (CS-23) and flight tested with steady and dynamic manoeuvres. Aerobatic dynamic manoeuvres were performed in flight including a spin over a g-range −1g to +4g and demonstrated both the FBG and the EFFPI instruments to have sufficient resolution to analyse the wing strain and fuselage unsteady pressure characteristics. The steady manoeuvres from the EFFPI sensor matched the wind tunnel data to within experimental error while comparisons of the flight test and wind tunnel EFFPI results with a Kulite pressure sensor showed significant discrepancies between the two sets of data, greater than experimental error. This issue is discussed further in the paper.
The following paper presents detailed aerodynamic data of a Scottish Aviation Bulldog light aircraft. The data is taken from the pre-stall region of the aircraft flight envelope through two flight test methods and from a geometrically accurate computational fluid dynamics (CFD) model of the full scale aircraft, which was meshed in Ansys ICEM CFD and solved in Ansys Fluent. The fidelity of the CFD model was achieved by development of a CATIA solid model with surfaces matching a spatial point cloud of the aircraft taken using a 3D laser scanner. Following a CFD verification process, a 3·4m hybrid mesh with a Spalart-Allmaras (SA) turbulence model was found to give the best overall lift and drag characteristics. Further detailed comparisons with the glide flight test data showed the CFD drag polar to have 63% lower zero lift drag, although this discrepancy was related to the simplification of the original CATIA surface model, which excluded the undercarriage, aerials and other protuberance drags. Inclusion of estimates of these sources of drag resulted in a match in zero lift drag to within 15% and a maximum lift to drag of 10:1 which was within 11% of the glide flight test result. The remaining drag discrepancy is attributed to other effects including trim drag and the surface finish of the actual aircraft.
Lift and drag flight test data is presented from the National Flying Laboratory Centre, Jetstream 31 aircraft. The aircraft has been modified as a flying classroom for completing flight test training courses, for engineering degree accreditation. The straight and level flight test data is compared to data from 10% and 17% scale wind tunnel models, a Reynolds Averaged Navier Stokes steady-state computational fluid dynamics model and an empirical model. Estimated standard errors in the flight test data are ±2.4% in lift coefficient, ±2.7% in drag coefficient. The flight test data also shows the aircraft to have a maximum lift to drag ratio of 10.5 at Mach 0.32, a zero lift drag coefficient of 0.0376 and an induced drag correction factor of 0.0607. When comparing the characteristics from the other models, the best overall comparison with the flight test data, in terms of lift coefficient, was with the empirical model. For the drag comparisons, all the models under predicted levels of drag by up to 43% when compared to the flight test data, with the best overall match between the flight test data and the 10% scale wind tunnel model. These discrepancies were attributed to various factors including zero lift drag Reynolds number effects, omission of a propeller system and surface excrescences on the models, as well as surface finish differences.
With the increased use of unmanned aerial systems (UAS) for civil and commercial applications, there is a strong demand for new regulations and technology that will eventually permit for the integration of UAS in unsegregated airspace. This requires new technology to ensure sufficient safety and a smooth integration process. The absence of a pilot on board a vehicle introduces new problems that do not arise in manned flight. One challenging and safety-critical issue is flight in known icing conditions. Whereas in manned flight, dealing with icing is left to the pilot and his appraisal of the situation at hand; in unmanned flight, this is no longer an option and new solutions are required. To address this, an icing-related decision-making system (IRDMS) is proposed. The system quantifies in-flight icing based on changes in aircraft performance and measurements of environmental properties, and evaluates what the effects on the aircraft are. Based on this, it determines whether the aircraft can proceed, and whether and which available icing protection systems should be activated. In this way, advice on an appropriate response is given to the operator on the ground, to ensure safe continuation of the flight and avoid possible accidents.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2025 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.