Continuous Adjoint Methods for Turbulent Flows, Applied to Shape and Topology Optimization: Industrial Applications

Papoutsis‐Kiachagias, Evangelos M.; Giannakoglou, Kyriakos C.

doi:10.1007/s11831-014-9141-9

Cited by 160 publications

(174 citation statements)

References 137 publications

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“…The interested reader could find more about the adjoint method used in [18,19,13]. Since both geometries to be studied operate at low air speeds, the development is based on the incompressible RANS equations.…”

Section: The Continuous Adjoint Methodsmentioning

confidence: 99%

“…In the applications presented in section 5, the Spalart-Allmaras, [16], and k-ω SST, [10], models are used. Though the continuous adjoint to both turbulence models has been developed by some of the authors, [18,13,8], the remainder of the continuous adjoint formulation will neglect their differentiation in the interest of space.…”

Section: Flow Equationsmentioning

confidence: 99%

“…For the development of the adjoint to the turbulence model PDEs, the reader is referred to [13]. The differentiation of eq.…”

Section: Continuous Adjoint Formulationmentioning

confidence: 99%

“…1 and applying the Green-Gauss theorem, where necessary. This development can be found in [18,8,13].…”

Section: Continuous Adjoint Formulationmentioning

confidence: 99%

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

Papoutsis‐Kiachagias¹,

Andrejašič²,

Porziani³

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. In this paper, the continuous adjoint method, developed by NTUA in the Open-FOAM R environment, is coupled with an RBF-based morpher developed by UTV to tackle optimization problems in low-speed aeronautics. The adjoint method provides a fast and accurate way for computing the sensitivity derivatives of the objective functions (here, drag, lift and losses) with respect to the design variables. The latter are defined as a set of variables controlling a group of RBF control points used to deform both the surface and volume mesh of the computational domain. The use of the RBF-based morpher provides a fast and robust way of handling mesh and geometry deformations, facing two challenging tasks related to shape optimization with the same tool. The coupling of the above-mentioned tools is used to tackle (a) the minimization of the cooling losses for an electric motor installed on a lightweight aircraft, by controlling the cooling air intake shape and (b) the shape optimization of a glider geometry targeting maximum lift-to-drag ratio by mainly optimizing the wing-fuselage junction. Regarding problem (a), a porous media is utilized to simulate the pressure drop caused by the radiator; the adjoint to this porosity model is developed and presented. This work was carried out in the framework of the EU-funded RBF4AERO project and the presented methods are available through the RBF4AERO platform (www.rbf4aero.eu).

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Section: The Continuous Adjoint Methodsmentioning

confidence: 99%

Section: Flow Equationsmentioning

confidence: 99%

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

Papoutsis‐Kiachagias¹,

Andrejašič²,

Porziani³

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 aerodynamics application, after the work on inviscid Euler equation system (Jameson, 1988), much work has been done, and even the wing design for full-scale transonic jet has been reported (Reuther et al, 1996;Mohammadi & Pironneau, 2004). Very recently, further applications to high Reynolds number flow have been tackled (Zymaris et al, 2010;Walther & Nadarajah, 2012;Papoutsis-Kiachagias & Giannakoglou, 2016). The applications of optimal control theory to turbulence control problems (Bewley et al, 2001;Kasagi et al, Morimoto, Kinoshita and Suzuki, Journal of Thermal Science and Technology, Vol.11, No.3 (2016) 2012) have also brought about great success.…”

Section: Introductionmentioning

confidence: 99%

Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

Morimoto

Kinoshita

Suzuki

2016

JTST

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In the present study, an adjoint-based shape-optimization method is formulated for heat transfer enhancement in liquid-solid phase change problems, in which heat conduction is dominant. In the present shape-optimization scheme, extended heat transfer surfaces with constant wall temperature are allowed to deform based on variational information of a cost functional, which is obtained from the physical temperature and adjoint enthalpy fields. In the computation of the developed scheme with an enthalpy-based formulation, a meshless local Petrov-Galerkin (MLPG) method is implemented for dealing with the complex boundary shape. For high-resolution analyses, a bubble-mesh method for boundary-fitted node arrangement in a well-controlled manner is employed, combined with a high-efficiency searching algorithm for choosing the neighboring bubbles interacted with each other. In the shape evolution process for different initial fin shapes, it is demonstrated that, within a certain range of the initial state, the present shape-optimization scheme leads to tree-like fin shapes that achieve the temperature field with global similarity.

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Manufactured chemistry: Rethinking unit operation design in the age of additive manufacturing

Stark

2018

AIChE Journal

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Continuous Adjoint Methods for Turbulent Flows, Applied to Shape and Topology Optimization: Industrial Applications

Cited by 160 publications

References 137 publications

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

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

Adjoint-based shape optimization of fin geometry for heat transfer enhancement in solidification problem

Manufactured chemistry: Rethinking unit operation design in the age of additive manufacturing

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