Robust Design of Transonic Natural Laminar Flow Wings under Environmental and Operational Uncertainties
暂无分享,去创建一个
Stefan Görtz | Philipp Bekemeyer | Christian Sabater Campomanes | S. Görtz | P. Bekemeyer | Christian Sabater
[1] Jens Neumann,et al. The Parallel Mesh Deformation of the DLR TAU-Code , 2007 .
[2] Stefan Goertz,et al. An Efficient Bi-Level Surrogate Approach for Optimizing Shock Control Bumps under Uncertainty , 2019, AIAA Scitech 2019 Forum.
[3] Siegfried Wagner,et al. Recent Results in Laminar-Turbulent Transition , 2004 .
[4] Richard L. Campbell,et al. Natural Laminar Flow Design for Wings with Moderate Sweep , 2016 .
[5] I. Sobol. On the distribution of points in a cube and the approximate evaluation of integrals , 1967 .
[6] J. V. Ingen. A suggested semi-empirical method for the calculation of the boundary layer transition region , 1956 .
[7] Emiliano Iuliano,et al. Robust Design of a Supersonic Natural Laminar Flow Wing-Body , 2017, IEEE Computational Intelligence Magazine.
[8] Thomas Streit,et al. DLR natural and hybrid transonic laminar wing design incorporating new methodologies , 2015, The Aeronautical Journal.
[9] Philip L. Roe,et al. Understanding Aerodynamics: Arguing from the Real Physics , 2014 .
[10] K. H. Horstmann,et al. Design for a natural laminar flow glove for a transport aircraft , 1990 .
[11] Zhonghua Han,et al. Aerodynamic Shape Optimization of Natural-Laminar-Flow Wing Using Surrogate-Based Approach , 2018, AIAA Journal.
[12] Johnathan Green. Laminar Flow Control - Back to the Future? , 2008 .
[13] Nicolas Gayton,et al. AK-MCS: An active learning reliability method combining Kriging and Monte Carlo Simulation , 2011 .
[14] Stefan Görtz,et al. Improving variable-fidelity surrogate modeling via gradient-enhanced kriging and a generalized hybrid bridge function , 2013 .
[15] Stefan Görtz,et al. Efficient Quantification of Aerodynamic Uncertainty due to Random Geometry Perturbations , 2014 .
[16] Zhonghua Han,et al. Efficient Uncertainty Quantification using Gradient-Enhanced Kriging , 2009 .
[17] Melissa B. Rivers,et al. Assessment of the National Transonic Facility for Natural Laminar Flow Testing , 2010 .
[18] Doug McLean,et al. Understanding Aerodynamics: Arguing from the Real Physics , 2012 .
[19] Sally A. Viken,et al. Preliminary Results from an Experimental Assessment of a Natural Laminar Flow Design Method , 2019, AIAA Scitech 2019 Forum.
[20] Nils Beck,et al. Drag reduction by laminar flow control , 2018 .
[21] H. Schrauf. EVALUATION OF THE A320 HYBRID LAMINAR FIN EXPERIMENT , 2000 .
[22] Jens K. Fassbender,et al. MEGAFLOW - Numerical Flow Simulation for Aircraft Design , 2005 .
[23] Michael Meinel,et al. The FlowSimulator framework for massively parallel CFD applications , 2010 .
[24] Thomas Gerhold,et al. Overview of the Hybrid RANS Code TAU , 2005 .
[25] Ning Qin,et al. Robustness of Natural Laminar Flow Airfoil Drag Optimization to Transition Amplification Factor , 2017 .
[26] G. Schrauf. Industrial View on Transition Prediction , 2004 .
[27] G. Schrauf,et al. Status and perspectives of laminar flow , 2005, The Aeronautical Journal (1968).
[28] Z. Gao,et al. Robust Design of High Speed Natural-Laminar-Flow Airfoil for High Lift , 2017 .
[29] Andy J. Keane,et al. Recent advances in surrogate-based optimization , 2009 .
[30] Gerhart I. Schuëller,et al. Computational methods in optimization considering uncertainties – An overview , 2008 .
[31] Geza Schrauf,et al. LARGE-SCALE LAMINAR FLOW TESTS EVALUATED WITH LINEAR STABILITY THEORY , 2001 .
[32] J. E. Green,et al. Civil aviation and the environment – the next frontier for the aerodynamicist , 2006, The Aeronautical Journal (1968).
[33] Régis Duvigneau. Aerodynamic Shape Optimization with Uncertain Operating Conditions using Metamodels , 2007 .
[34] B. Kulfan. Universal Parametric Geometry Representation Method , 2008 .
[35] Hossein Zare-Behtash,et al. State-of-the-art in aerodynamic shape optimisation methods , 2018, Appl. Soft Comput..
[36] Ling Li,et al. Sequential design of computer experiments for the estimation of a probability of failure , 2010, Statistics and Computing.