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S. de Boer
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In line with recent advancements in aviation, which lead to more fuel-efficient aircraft, this paper presents a novel continuous movable parameterisation methodology. The methodology takes advantage of the ability of the doublet lattice method (DLM) to describe aerodynamic forces using downwash. The movables are described in the continuous space using a downwash distribution generated using a B-spline surface. To demonstrate and assess the movable modelling methodology, the U-HARWARD aircraft model has been used, with the performance of the continuous parameterisation compared to a reference movable parameterisation for roll control, manoeuvre load alleviation and cruise performance. The results show that the continuous parameterisation can determine a downwash distribution that is at least equal in performance – during roll – or has better performance – for manoeuvre load alleviation and cruise performance – than the reference parameterisation. The continuous parameterisation showed a 3 percentage points improvement with respect to the reference parameterisation for manoeuvre load alleviation and a 2.4 percentage points improvement for the induced drag coefficient. The results in the paper demonstrated the successful application of a movable parameterisation methodology, which can be applied to an aircraft for which only the planform and initial structural parameters are known.
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In line with recent advancements in aviation, which lead to more fuel-efficient aircraft, this paper presents a novel continuous movable parameterisation methodology. The methodology takes advantage of the ability of the doublet lattice method (DLM) to describe aerodynamic forces using downwash. The movables are described in the continuous space using a downwash distribution generated using a B-spline surface. To demonstrate and assess the movable modelling methodology, the U-HARWARD aircraft model has been used, with the performance of the continuous parameterisation compared to a reference movable parameterisation for roll control, manoeuvre load alleviation and cruise performance. The results show that the continuous parameterisation can determine a downwash distribution that is at least equal in performance – during roll – or has better performance – for manoeuvre load alleviation and cruise performance – than the reference parameterisation. The continuous parameterisation showed a 3 percentage points improvement with respect to the reference parameterisation for manoeuvre load alleviation and a 2.4 percentage points improvement for the induced drag coefficient. The results in the paper demonstrated the successful application of a movable parameterisation methodology, which can be applied to an aircraft for which only the planform and initial structural parameters are known.
A new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections is presented. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. By exciting the stabilising mass in one of the primary (i.e., x, y and z) directions while simultaneously measuring the response in these directions and repeating the excitation in other directions, the flutter margins that are associated with the original model can be determined. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection, onto which a flutter pod consisting of a shaker and stabilising mass was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into an LCO. SISO and MIMO PFM tests were then performed to obtain the nominal flutter boundaries without actually hitting flutter, showing a maximum difference of 4.4 % between each other at an angle of attack of 6°. The difference between the MIMO and SISO PFM was found to be increasing with increasing angle of attack, which was as expected. Compared to the directly measured flutter tests, the MIMO PFM results identified a flutter velocity of 4.8 % lower than the directly measured flutter offset velocity at an angle of attack of 4°, and the SISO PFM results identified the flutter velocity to be 8.2 % lower than the offset velocity at 4° angle of attack. The PFM-identified flutter frequencies showed a difference of less than 2 % compared to the direct flutter test, with the difference in identified flutter frequency between the SISO and MIMO PFM reaching a maximum of 2 %. The acquired data shows the potential of the PFM method for performing safer, shorter and consequently cheaper flight tests for the certification procedure of new aircraft configurations.
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A new flutter test version of the Parametric Flutter Margin (PFM) method, specifically applied to wings undergoing large deflections is presented. The PFM method adds a stabilising parameter, such as a stabilising mass, to the model such that the flutter velocity is increased. By exciting the stabilising mass in one of the primary (i.e., x, y and z) directions while simultaneously measuring the response in these directions and repeating the excitation in other directions, the flutter margins that are associated with the original model can be determined. To demonstrate the method a wind tunnel test campaign was performed at TU Delft using the Delft Pazy Wing which can exhibit large nonlinear deflection, onto which a flutter pod consisting of a shaker and stabilising mass was placed at the mid-span position at the leading edge of the wing. During the test campaign, three test series were performed. The first identified the flutter boundary through direct flutter tests, with the flutter onset and offset velocities being determined by actually hitting flutter that turned into an LCO. SISO and MIMO PFM tests were then performed to obtain the nominal flutter boundaries without actually hitting flutter, showing a maximum difference of 4.4 % between each other at an angle of attack of 6°. The difference between the MIMO and SISO PFM was found to be increasing with increasing angle of attack, which was as expected. Compared to the directly measured flutter tests, the MIMO PFM results identified a flutter velocity of 4.8 % lower than the directly measured flutter offset velocity at an angle of attack of 4°, and the SISO PFM results identified the flutter velocity to be 8.2 % lower than the offset velocity at 4° angle of attack. The PFM-identified flutter frequencies showed a difference of less than 2 % compared to the direct flutter test, with the difference in identified flutter frequency between the SISO and MIMO PFM reaching a maximum of 2 %. The acquired data shows the potential of the PFM method for performing safer, shorter and consequently cheaper flight tests for the certification procedure of new aircraft configurations.