Adjoint-Based Uncertainty Quantification and Calibration of RANS-Based Transition Modeling

[1]  Ardeshir Hanifi,et al.  A Gradient-based Optimization Method for Natural Laminar Flow Design : OPTLAM Project Final Report , 2010 .

[2]  Shivaji Medida,et al.  Correlation-based Transition Modeling for External Aerodynamic Flows , 2014 .

[3]  R. E. Mayle,et al.  The 1991 IGTI Scholar Lecture: The Role of Laminar-Turbulent Transition in Gas Turbine Engines , 1991 .

[4]  S. Hosder,et al.  Effect of Turbulence Model Uncertainty on Scramjet Isolator Flowfield Analysis , 2020 .

[5]  G. Janiga,et al.  Optimization and Computational Fluid Dynamics , 2008 .

[6]  B. J. Abu-Ghannam,et al.  Natural Transition of Boundary Layers—The Effects of Turbulence, Pressure Gradient, and Flow History , 1980 .

[7]  G. Barakos,et al.  Assessment and Calibration of the γ-Equation Transition Model at Low Mach , 2017 .

[8]  Paola Cinnella,et al.  Quantification of model uncertainty in RANS simulations: A review , 2018, Progress in Aerospace Sciences.

[9]  Chao Yan,et al.  Uncertainty and sensitivity analysis of SST turbulence model on hypersonic flow heat transfer , 2019, International Journal of Heat and Mass Transfer.

[10]  James George Coder Development of a CFD-compatible transition model based on linear stability theory , 2014 .

[11]  Hideyuki Azegami,et al.  Numerical Solution for Min-Max Shape Optimization Problems (Minimum Design of Maximum Stress and Displacement) , 1998 .

[12]  D. Zingg,et al.  Aerodynamic Shape Optimization for Natural Laminar Flow Using a Discrete-Adjoint Approach , 2015 .

[13]  Joaquim R. R. A. Martins,et al.  Natural Laminar-Flow Airfoil Optimization Design Using a Discrete Adjoint Approach , 2020, AIAA Journal.

[14]  M. Giles,et al.  Viscous-inviscid analysis of transonic and low Reynolds number airfoils , 1986 .

[15]  F. Menter Two-equation eddy-viscosity turbulence models for engineering applications , 1994 .

[16]  Paola Cinnella,et al.  Bayesian Predictions of Reynolds-Averaged Navier-Stokes Uncertainties Using Maximum a Posteriori Estimates , 2018 .

[17]  James G. Coder,et al.  Computational Fluid Dynamics Compatible Transition Modeling Using an Amplification Factor Transport Equation , 2014 .

[18]  Karthik Duraisamy,et al.  Turbulence Modeling in the Age of Data , 2018, Annual Review of Fluid Mechanics.

[19]  F. Menter,et al.  A One-Equation Local Correlation-Based Transition Model , 2015 .

[20]  Todd A. Oliver,et al.  Bayesian uncertainty quantification applied to RANS turbulence models , 2011 .

[21]  Florian R. Menter,et al.  Correlation-Based Transition Modeling for Unstructured Parallelized Computational Fluid Dynamics Codes , 2009 .

[22]  Yulun Zhang,et al.  Calibration of a γ-Reθ transition model and its validation in low-speed flows with high-order numerical method , 2015 .

[23]  Joaquim R. R. A. Martins,et al.  An adaptive approach to constraint aggregation using adjoint sensitivity analysis , 2007 .

[24]  Kivanc Ekici,et al.  FDOT: A Fast, memory-efficient and automated approach for Discrete adjoint sensitivity analysis using the Operator overloading Technique , 2019, Aerospace Science and Technology.

[25]  J. Martins,et al.  Adjoint-based aerodynamic shape optimization including transition to turbulence effects , 2020 .

[26]  Jason Howison,et al.  Dynamic stall analysis using harmonic balance and correlation-based γ– Reθt¯ transition models for wind turbine applications† , 2015 .