Combining an RBF-Based Morpher With Continuous Adjoint for Low-Speed Aeronautical Optimization Applications

Papoutsis‐Kiachagias, Evangelos M.; Andrejašič, Matej; Porziani, Stefano; Groth, Corrado; Erzen, David; Biancolini, Marco Evangelos; Costa, Emiliano; Giannakoglou, Kyriakos C.

doi:10.7712/100016.2270.15521

Cited by 9 publications

(6 citation statements)

References 7 publications

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“…This case comes from the RBF4AERO Project which aimed at developing the RBF4AERO Benchmark Technology and was presented in Ref. 36. The flow Reynolds number is Re = 1.55 × 10 6 based on the wing chord, the Spalart-Allmaras turbulence model is used, the mesh consists of about 4.7 million cells and the far-field flow angle is 10 o .…”

Section: B Shape Optimization: Lift-to-drag Ratio Maximization For Amentioning

confidence: 99%

“…The flow Reynolds number is Re = 1.55 × 10 6 based on the wing chord, the Spalart-Allmaras turbulence model is used, the mesh consists of about 4.7 million cells and the far-field flow angle is 10 o . The geometry is parameterized using four RBF-based design variables 36,37 depicted in figure 13, controlling the wing-fuselage junction close to the leading and trailing edges as well as parts of the upper fuselage surface. The convergence of the steepest descent-driven algorithm can be found in figure 14(a).…”

Section: B Shape Optimization: Lift-to-drag Ratio Maximization For Amentioning

confidence: 99%

See 1 more Smart Citation

A Continuous Adjoint Framework for Shape and Topology Optimization and their Synergistic Use

Papoutsis‐Kiachagias

Koch

Gkaragounis

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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In fluid mechanics, two of the most widely used optimization approaches are shape and topology optimization, which are generally treated as mutually-exclusive. This paper presents a general continuous adjoint formulation for both shape and topology optimization, focusing on (a) crucial aspects of computing accurate shape sensitivity derivatives such as the differentiation of the turbulence model PDEs and the proper treatment of grid sensitivities and (b) a synergistic, sequential application of topology and shape optimization, in which topology is used to define a preliminary solution from which shape optimization can be initiated. To achieve this, a transition process is used to accurately represent and parameterize the topological solution with NURBS surfaces. The flow cases presented in this work and the in-house code pertaining to the optimization and transition process are implemented within OpenFOAM. Results from purely aerodynamic as well as multidisciplinary applications including Conjugate Heat Transfer (CHT) are presented.

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Section: B Shape Optimization: Lift-to-drag Ratio Maximization For Amentioning

confidence: 99%

Section: B Shape Optimization: Lift-to-drag Ratio Maximization For Amentioning

confidence: 99%

A Continuous Adjoint Framework for Shape and Topology Optimization and their Synergistic Use

Papoutsis‐Kiachagias

Koch

Gkaragounis

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…*1+ 3 5 = A , 1 ≤ 6 ≤ 7; , -C1+ D 5 = 0 . /0 (11) for all polynomials C with a degree less or equal to that of polynomial '. Coefficientsand : can be obtained by solving the system…”

Section: Rbf-based Morphingmentioning

confidence: 99%

“…Since radial basis functions (RBFs) have been proven in the past as a robust choice for mesh morphing [6][7][8], they are used as an interpolation basis also in the present work. Morphing is done by exploitation of RBF Morph™ software that has been shown as a powerful and effective tool for solving challenging aerospace [9][10][11][12] and non-aerospace [13,14] engineering applications.…”

Section: Introductionmentioning

confidence: 99%

A Mesh Morphing Based Fsi Method Used in Aeronautical Optimization Applications

Andrejašič¹,

Erzen²,

Costa³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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In this paper a fast and efficient mesh morphing based technique to perform FSI analyses for aeroelastic design and optimization applications is presented. The procedure is based on the finite volume CFD solver (OpenFOAM® and SU2) coupled with the RBF Morph™ tool capable of deforming the surface and volume mesh of the computational domain according to the mode superposition method. Structural vibration modes of the geometry of interest are calculated in a pre-processing stage by means of a FEM solver and later imported into the RBF Morph™ tool to create a set of individual basic deformations. Aerodynamic loads calculated with a CFD solver are then projected onto the accounted structural modes to get modal loads and modal coordinates which are applied to the computational model in order to obtain the deformed configuration. An FSI cycle incorporating a CFD simulation and morphing of its mesh can be iteratively repeated upon convergence to the final deformed shape. Since the modal parameterization and the mesh calculation have to be prepared only once per FSI analysis, its computation time is drastically reduced compared to a standard two-way coupling method in which a structural analysis has to be done at each cycle. Present procedure was applied to two geometries, HIRENASD fuselage-wing geometry for the purpose of testing the procedure and a Pipistrel's electric aircraft propeller for the purpose of optimization of its shape. By utilizing a DoE and a response surface method an increase of four percent of propeller efficiency was obtained by converging to a most favourable propeller pitch and twist configuration incorporating also FSI deformation. The abovementioned procedure was developed in the framework of the EU-funded RBF4AERO project (Grant Agreement No: 605396) and is available through the RBF4AERO platform.

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“…In the case the CFD solver is OpenFOAM and the objective function is the drag, lift or the pressure loss, the user can also exploit the capabilities of the continuous adjoint solver and of the innovative adjoint-morphing coupling. In particular, two algorithms foreseeing the coupling between the adjoint solver and the MT, called gradient-based [2] and Adjoint Self Sculpting algorithms, can be used to perform shape optimization. Moreover the Adjoint Preview feature, in the case multiple shape variations are available, can be adopted to identify the most influent ones.…”

Section: Benchmark Technology Infrastructure Capabilitesmentioning

confidence: 99%

The Rbf4aero Benchmark Technology Platform

Bernaschi¹,

Sabellico²,

Urso³

et al. 2016

Proceedings of the VII European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS Congress 2016)

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Abstract. This paper presents the RBF4AERO benchmark technology platform, developed in the framework of the EU-funded RBF4AERO project. The platform enables the so-called Benchmark Management System (BMS) used for benchmark submission and results reporting. The BMS is deployed using three modules, namely the Graphical User Interface (GUI), the Workflow Manager (WM) and the Benchmarking Database System (BDS) which cooperate during the whole optimization benchmark life-cycle. The GUI is the only component which interacts with the end-user. It enables the optimization benchmark submission, along with the progress, results and computational platform resources monitoring. The configuration of the Optimization (OT) and the Morpher Tool (MT) is a pre-requisite for the optimization benchmark submission. In an optimization scenario the WM, which is practically the controller of the system, queries the OT in order to get a table of samples and gives back the results of the simulator (for instance a CFD tool). The evaluated individuals serve as training patterns of a Response Surface Model (RSM) which is, then, used for an Evolutionary Algorithms based optimization. The resulting 'optimal' solution(s) are delivered back to the WM for re-evaluation on the CFD tool. For each evaluation on the CFD tool, when a new geometrical shape is required, the computational grid is morphed using the MT based on radial basis functions. 4156Massimo Bernaschi et al.

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Combining an RBF-Based Morpher With Continuous Adjoint for Low-Speed Aeronautical Optimization Applications

Cited by 9 publications

References 7 publications

A Continuous Adjoint Framework for Shape and Topology Optimization and their Synergistic Use

A Continuous Adjoint Framework for Shape and Topology Optimization and their Synergistic Use

A Mesh Morphing Based Fsi Method Used in Aeronautical Optimization Applications

The Rbf4aero Benchmark Technology Platform

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