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Session: High order methods
Session starts: Wednesday 19 June, 16:00
Presentation starts: 17:30
Room: Room 1.3

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Johan Jansson (KTH)
Kristoffer Wingstedt (SAAB)
Rebecca Durst (University of Pittsburgh)

Abstract:
We describe the Adaptive Euler methodology, and results from the High Lift Prediction Workshops with focus on the current HLPW5, showing good validation and high efficiency [1]. Adaptive Euler is first principles FEM simulation with adjoint-based adaptive error control, realized with automated discretization from mathematical notation in our FEniCS [8] framework. We describe a prototype extension of the methodology to aeroelasticity also with adjoint-based adaptive error control, such methods are highlighted as having “great potential” in the field of aeroelasticity in [7] We show that by the Adaptive Euler by the scientific method in our reproducible Digital Math framework predicts drag, lift, pitch moment and pressure distribution in close correspondence with experiments in the 4th and 5th High Lift Prediction Workshops, with very high efficiency, estimated to 100x faster and cheaper than RANS, the industry standard for efficient aerodynamics, corresponding to appx. 100 core hours on a commodity computational resource. The guiding incentive for this work is to develop an efficient and versatile tool for aeroelasticity modeling with the Adaptive Euler methodology. Such a product is highly sought after and is motivated in part by the CFD Vision 2030 set by NASA and the Certification by Analysis 2040 Vision set by Boeing. The consequences of this would include–but are not limited to–the eventual development of a full fluid-structure interaction (FSI) framework that may be used for applications in aerospace engineering. As such, we present numerical simulations designed to test benchmark problems in the field of aeroelasticity. These problems are chosen based on their relevance to current challenges and potential for extension, and the results are compared to experimental data when available. We view these simulations as critical building blocks towards the development of a full Adaptive Euler framework for aeroelasticity. References [1] Jansson, J., Johnson, C., & Scott, R. (2022). Predictive Euler CFD-Resolution of NASA Vision 2030. In AIAA AVIATION 2022 Forum (p. 3589). [2] Johan Jansson (jjan@kth.se), Claes Johnson, L. Ridgway Scott, Rebecca Durst, Predictive Aerodynamics: Adaptive Euler Real Flight Simulation http://digitalmath.tech/hiliftpw4-aiaa [3] http://digimat.tech/paper-euler-short/ [4] Certification by Analysis Vision 2040 https://ntrs.nasa.gov/citations/20210015404 [5] Daumas, L., Chalot, F., Forestier, N., & Johan, Z. (2009). Industrial use of linearized CFD tools for aeroelastic problems. IFASD, 54, 21-25. “The Galerkin/least-squares (GLS) formulation introduced by Hughes and Johnson, is a full space-time finite element technique …” [6] Cirrent (2024) Aeroelasticity Prediction Workshop: https://aiaa-dpw.larc.nasa.gov/ [7] Hulshoff, S. J. (2013). Aeroelasticity. Lecture notes Aerodynamics Master Track, TU Delft. [8] Logg, A., Mardal, K. A., & Wells, G. (Eds.). (2012). Automated solution of differential equations by the finite element method: The FEniCS book (Vol. 84). Springer Science & Business Media.