Assessing the Impact of Aerodynamic Modelling on Manoeuvring Aircraft

Ronch, Andrea Da; McCracken, A.J.; Tantaroudas, N.D.; Badcock, K. J.; Hesse, Henrik; Palacios, Rafael

doi:10.2514/6.2014-0732

Cited by 5 publications

(1 citation statement)

References 21 publications

(20 reference statements)

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“…The objective of this investigation is to validate the nonlinear aeroelastic toolbox developed, build on previous validations attempted by the authors for the rigid flight dynamics with the CFD and potential aerodynamics 22 and finally combine all previous work done based on H ∞ and adaptive control to design flight control systems based on nonlinear reduced models capable of capturing the nonlinear flight dynamic effect of the full order system under large wing deformations.…”

mentioning

confidence: 99%

Model Order Reduction for Control Design of Flexible Free-Flying Aircraft

Tantaroudas

Ronch

Badcock

et al. 2015

AIAA Atmospheric Flight Mechanics Conference

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This paper describes a systematic approach to nonlinear model order reduction of freeflying aircraft and the subsequent flight control design. System nonlinearities arise due to large wing deformation and the coupling between flexible and rigid body dynamics. The non-linear flight dynamics equations are linearised and the approach uses information on the eigenspectrum of the resulting coupled system Jacobian matrix and projects it through a series expansion onto a small basis of eigenvectors representative of the full-order dynamics. The testcase is the very flexible wing representative of the Helios wing which was developed by NASA and the aeroelastic solver is validated against other frameworks for gust responses. Nonlinear reduced order models are generated and used for faster parametric worst case gust searches of the full order nonlinear flight dynamic response and are exploited for complex control methodologies such as H∞, H2 and adaptive control designs.

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mentioning

confidence: 99%

Model Order Reduction for Control Design of Flexible Free-Flying Aircraft

Tantaroudas

Ronch

Badcock

et al. 2015

AIAA Atmospheric Flight Mechanics Conference

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Virtual Flight Simulation using Computational Fluid Dynamics

Akram¹,

Cristofaro

Ronch

2017

Advanced UAV Aerodynamics, Flight Stability and Control

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The aim of this Chapter is to present recent advances in the development of high-fidelity simulation software for virtual flight testing of unconventional and next-generation aircraft. The accurate and efficient simulation of the full set of physics associated with aircraft flight operations including flight control, aerodynamics, and structural dynamics is discussed. Pivotal to this framework is a novel adaptive design of experiment algorithm for the rapid generation of the aerodynamic database that is developed and demostrated on a complete aircraft with highly nonlinear aerodynamics. Throughout the Chapter, several worked-out examples and problems are introduced to aid the reader to gain competence and proficiency in the methods and computer codes accompanying this Chapter. The Chapter continues with Section 1.1 overviewing the state-of-the-art in virtual flight simulation. It continues in Section 1.2 with a discussion of the aerodynamic model traditionally used for flight simulation. Then, surrogate modelling and adaptive design of experiment are developed in Section 1.3. Finally, advanced models used for virtual flight simulation are presented in Section 1.4.

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Steady-State Transonic Flowfield Prediction via Deep-Learning Framework

Immordino,

Da Ronch,

Righi

2024

AIAA Journal

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This paper focuses on the development of a deep-learning framework for predicting distributed quantities around aircraft flying in the transonic regime. These quantities play a crucial role in determining aerodynamic loads and conducting aeroelastic analysis. Angle of attack and Mach number are chosen as the two independent parameters for the reduced-order models. A comparative assessment is conducted between the proposed deep-learning framework and the proper orthogonal decomposition approach to identify the strengths and weaknesses of each method. The accuracy of the data-driven machine-learning method in modeling steady-state transonic aerodynamics is assessed against three benchmark cases of three-dimensional test cases: Benchmark Super Critical Wing and ONERA M6 wings, and the wing–body Common Research Model configuration. Despite the challenges of the analyzed scenarios, promising results are obtained for each test case, showing the effectiveness of the model implemented. Furthermore, the paper demonstrates the application of the method for aeroelastic analysis and uncertainty quantification. This quantifies the robustness and versatility of the implemented model.

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Assessing the Impact of Aerodynamic Modelling on Manoeuvring Aircraft

Cited by 5 publications

References 21 publications

Model Order Reduction for Control Design of Flexible Free-Flying Aircraft

Model Order Reduction for Control Design of Flexible Free-Flying Aircraft

Virtual Flight Simulation using Computational Fluid Dynamics

Steady-State Transonic Flowfield Prediction via Deep-Learning Framework

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