The continuous adjoint approach to the k–ε turbulence model for shape optimization and optimal active control of turbulent flows

The continuous adjoint to the incompressible Reynolds-averaged Navier–Stokes equations coupled with the low Reynolds number Launder–Sharma k–ε turbulence model is presented. Both shape and active flow control optimization problems in fluid mechanics are considered, aiming at minimum viscous losses. In contrast to the frequently used assumption of frozen turbulence, the adjoint to the turbulence model equations together with appropriate boundary conditions are derived, discretized and solved. This is the first time that the adjoint equations to the Launder–Sharma k–ε model have been derived. Compared to the formulation that neglects turbulence variations, the impact of additional terms and equations is evaluated. Sensitivities computed using direct differentiation and/or finite differences are used for comparative purposes. To demonstrate the need for formulating and solving the adjoint to the turbulence model equations, instead of merely relying upon the ‘frozen turbulence assumption’, the gain in the optimization turnaround time offered by the proposed method is quantified.

[1]  C. M. Reeves,et al.  Function minimization by conjugate gradients , 1964, Comput. J..

[2]  Asitav Mishra,et al.  Time Dependent Adjoint-based Optimization for Coupled Aeroelastic Problems , 2013 .

[3]  W. Tollmien,et al.  Über Flüssigkeitsbewegung bei sehr kleiner Reibung , 1961 .

[4]  Antony Jameson,et al.  Aerodynamic design via control theory , 1988, J. Sci. Comput..

[5]  M. Giles,et al.  Adjoint equations in CFD: duality, boundary conditions and solution behaviour , 1997 .

[6]  Jaime Peraire,et al.  Aerodynamic design using unstructured meshes , 1996 .

[7]  Frank Thiele,et al.  Optimal Control of Unsteady Flows Using a Discrete and a Continuous Adjoint Approach , 2011, System Modelling and Optimization.

[8]  Paolo Luchini,et al.  Algebraic growth in boundary layers: optimal control by blowing and suction at the wall , 2000 .

[9]  F. Thiele,et al.  Discrete Adjoint based Sensitivity Analysis for Optimal Flow Control of a 3D High-Lift Configuration , 2013 .

[10]  A. Jameson,et al.  Optimum Aerodynamic Design Using the Navier–Stokes Equations , 1997 .

[11]  C. Othmer A continuous adjoint formulation for the computation of topological and surface sensitivities of ducted flows , 2008 .

[12]  Juan J. Alonso,et al.  A hybrid adjoint approach applied to turbulent flow simulations , 2013 .

[13]  Kyriakos C. Giannakoglou,et al.  Adjoint wall functions: A new concept for use in aerodynamic shape optimization , 2010, J. Comput. Phys..

[14]  Qiqi Wang,et al.  Unsteady Aerostructure Coupled Adjoint Method for Flutter Suppression , 2015 .

[15]  Jacques Periaux,et al.  Drag reduction by active control for flow past cylinders , 2000 .

[16]  Pavel Grinfeld,et al.  Hadamard’s Formula Inside and Out , 2010 .

[17]  Kyriakos C. Giannakoglou,et al.  A continuous adjoint method with objective function derivatives based on boundary integrals, for inviscid and viscous flows , 2007 .

[18]  Dan S. Henningson,et al.  Adjoint-based optimization of steady suction for disturbance control in incompressible flows , 2002, Journal of Fluid Mechanics.

[19]  B. Launder,et al.  Application of the energy-dissipation model of turbulence to the calculation of flow near a spinning disc , 1974 .

[20]  Johanna Weiss,et al.  Optimal Shape Design For Elliptic Systems , 2016 .

[21]  Richard P. Dwight,et al.  Efficient Algorithms for Solution of the Adjoint Compressible Navier-Stokes Equations with Applications , 2006 .

[22]  Oktay Baysal,et al.  Three-dimensional aerodynamic shape optimization using discrete sensitivity analysis , 1996 .

[23]  Kyriakos C. Giannakoglou,et al.  Continuous adjoint approach to the Spalart–Allmaras turbulence model for incompressible flows , 2009 .

[24]  K. Giannakoglou,et al.  Aerodynamic design using the truncated Newton algorithm and the continuous adjoint approach , 2012 .

[25]  D. Darmofal,et al.  An implicit, exact dual adjoint solution method for turbulent flows on unstructured grids , 2004 .

[26]  Juan J. Alonso,et al.  Automatic aerodynamic optimization on distributed memory architectures , 1996 .

[27]  W. K. Anderson,et al.  Airfoil Design on Unstructured Grids for Turbulent Flows , 1999 .

[28]  D. Spalding,et al.  A calculation procedure for heat, mass and momentum transfer in three-dimensional parabolic flows , 1972 .

[29]  W. K. Anderson,et al.  Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation , 1997 .

[30]  K. Giannakoglou,et al.  The continuous adjoint method as a guide for the design of flow control systems based on jets , 2013 .

[31]  Niles A. Pierce,et al.  An Introduction to the Adjoint Approach to Design , 2000 .

[32]  A. Jameson,et al.  STUDIES OF THE CONTINUOUS AND DISCRETE ADJOINT APPROACHES TO VISCOUS AUTOMATIC AERODYNAMIC SHAPE OPTIMIZATION , 2001 .

[33]  W. K. Anderson,et al.  Aerodynamic design optimization on unstructured grids with a continuous adjoint formulation , 1997 .

[34]  Uwe Naumann,et al.  The Art of Differentiating Computer Programs - An Introduction to Algorithmic Differentiation , 2012, Software, environments, tools.

[35]  Enrique Zuazua,et al.  Continuous adjoint approach for the Spalart−Allmaras model in aerodynamic optimization , 2012 .

[36]  Michael B. Giles,et al.  The harmonic adjoint approach to unsteady turbomachinery design , 2002 .

[37]  Ronald D. Joslin,et al.  Issues in active flow control: theory, control, simulation, and experiment , 2004 .

[38]  Byung Joon Lee,et al.  Automated design methodology of turbulent internal flow using discrete adjoint formulation , 2007 .

[39]  Hussaini M. Yousuff,et al.  A Self-Contained, Automated Methodology for Optimal Flow Control , 1997 .

[40]  Juan J. Alonso,et al.  Aerodynamic shape optimization of supersonic aircraft configurations via an adjoint formulation on distributed memory parallel computers , 1996 .

[41]  Ionel M. Navon,et al.  Optimal control of cylinder wakes via suction and blowing , 2003, Computers & Fluids.