Abstract
A computational approach is presented for optimizing new riblet surface designs in turbulent channel flow for drag reduction, utilizing design-by-morphing (DbM), large Eddy simulation (LES), and Bayesian optimization (BO). The design space is generated using DbM to include a variety of novel riblet surface designs, which are then evaluated using LES to determine their drag-reducing capabilities. The riblet surface geometry and configuration are optimized for maximum drag reduction using the mixed-variable Bayesian optimization (MixMOBO) algorithm. A total of 125 optimization epochs are carried out, resulting in the identification of three optimal riblet surface designs that are comparable to or better than the reference drag reduction rate of 8%. The Bayesian-optimized designs commonly suggest riblet sizes of around 15 wall units, relatively large spacing compared to conventional designs, and spiky tips with notches for the riblets. Our overall optimization process is conducted within a reasonable physical time frame with up to 12-core parallel computing and can be practical for fluid engineering optimization problems that require high-fidelity computational design before materialization.
References
1.
Yu
, C.
, Liu
, M.
, Zhang
, C.
, Yan
, H.
, Zhang
, M.
, Wu
, Q.
, Liu
, M.
, and Jiang
, L.
, 2020
, “Bio-inspired Drag Reduction: From Nature Organisms to Artificial Functional Surfaces
,” Giant
, 2
, p. 100017
. 2.
Liu
, G.
, Yuan
, Z.
, Qiu
, Z.
, Feng
, S.
, Xie
, Y.
, Leng
, D.
, and Tian
, X.
, 2020
, “A Brief Review of Bio-Inspired Surface Technology and Application Toward Underwater Drag Reduction
,” Ocean. Eng.
, 199
, p. 106962
. 3.
Ball
, P.
, 1999
, “Engineering Shark Skin and Other Solutions
,” Nature
, 400
(6744
), pp. 507
–509
. 4.
Bhasin
, D.
, McAdams
, D. A.
, and Layton
, A.
, 2021
, “A Product Architecture-Based Tool for Bioinspired Function-Sharing
,” J. Mech. Des.
, 143
(8
), p. 081401
. 5.
Ott
, J.
, Lazalde
, M.
, and Gu
, G. X.
, 2020
, “Algorithmic-Driven Design of Shark Denticle Bioinspired Structures for Superior Aerodynamic Properties
,” Bioinspiration Biomimetics
, 15
(2
), p. 026001
. 6.
Parfitt
, A. R.
, and Vincent
, J. F. V.
, 2005
, “Drag Reduction in a Swimming Humboldt Penguin, Spheniscus Humboldti, When the Boundary Layer is Turbulent
,” J. Bionic Eng.
, 2
(2
), pp. 57
–62
. 7.
Bannasch
, R.
, 1995
, Hydrodynamics of Penguins—An Experimental Approach
, Surrey Beatty & Sons
, Chipping Norton, NSW
.8.
Choi
, K.-S.
, 1989
, “Near-Wall Structure of a Turbulent Boundary Layer With Riblets
,” J. Fluid. Mech.
, 208
, pp. 417
–458
. 9.
Bixler
, G. D.
, and Bhushan
, B.
, 2013
, “Fluid Drag Reduction With Shark-Skin Riblet Inspired Microstructured Surfaces
,” Adv. Funct. Mater.
, 23
(36
), pp. 4507
–4528
. 10.
Han
, M.
, Lim
, H.
, Jang
, Y.-G.
, Lee
, S.
, and Lee
, S.-J.
, 2003
, “Fabrication of a Micro-Riblet Film and Drag Reduction Effects on Curved Objects
,” 12th International Conference on Solid-State Sensors, Actuators and Microsystems. Digest of Technical Papers (Cat. No.03TH8664), Vol. 1
, IEEE
, Boston, MA
, pp. 396
–399
.11.
Sundaram
, S.
, Viswanath
, P. R.
, and Rudrakumar
, S.
, 1996
, “Viscous Drag Reduction Using Riblets on NACA 0012 Airfoil to Moderate Incidence
,” AIAA. J.
, 34
(4
), pp. 676
–682
. 12.
Dai
, W.
, Alkahtani
, M.
, Hemmer
, P. R.
, and Liang
, H.
, 2019
, “Drag-reduction of 3D Printed Shark-Skin-Like Surfaces
,” Friction
, 7
(6
), pp. 603
–612
. 13.
Wen
, L.
, Weaver
, J. C.
, and Lauder
, G. V.
, 2014
, “Biomimetic Shark Skin: Design, Fabrication and Hydrodynamic Function
,” J. Experimental Biol.
, 217
(10
), pp. 1656
–1666
. 14.
Li
, Y.
, Mao
, H.
, Hu
, P.
, Hermes
, M.
, Lim
, H.
, Yoon
, J.
, Luhar
, M.
, Chen
, Y.
, and Wu
, W.
, 2019
, “Bioinspired Functional Surfaces Enabled by Multiscale Stereolithography
,” Adv. Mater. Technol.
, 4
(5
), p. 1800638
. 15.
Sheikh
, H. M.
, and Marcus
, P. S.
, 2022
, “Bayesian Optimization for Mixed-Variable, Multi-objective Problems
,” Structural Multidisciplinary Optim.
, 65
(11
), p. 331
. 16.
Oh
, S.
, Jiang
, C.-H.
, Jiang
, C.
, and Marcus
, P. S.
, 2018
, “Finding the Optimal Shape of the Leading-and-Trailing Car of a High-Speed Train Using Design-by-Morphing
,” Comput. Mech.
, 62
(1
), pp. 23
–45
. 17.
Sheikh
, H. M.
, Lee
, S.
, Wang
, J.
, and Marcus
, P. S.
, 2023
, “Airfoil Optimization Using Design-by-Morphing
,” J. Comput. Design Eng.
, 10
(4
), pp. 1443
–1459
. 18.
Sheikh
, H. M.
, Callan
, T. A.
, Hennessy
, K. J.
, and Marcus
, P. S.
, 2022
, “Optimization of the Shape of a Hydrokinetic Turbine’s Draft Tube and Hub Assembly Using Design-by-Morphing With Bayesian Optimization
,” Comput. Methods. Appl. Mech. Eng.
, 401
, p. 115654
. 19.
Vangelatos
, Z.
, Sheikh
, H. M.
, Marcus
, P. S.
, Grigoropoulos
, C. P.
, Lopez
, V. Z.
, Flamourakis
, G.
, and Farsari
, M.
, 2021
, “Strength Through Defects: A Novel Bayesian Approach for the Optimization of Architected Materials
,” Sci. Adv.
, 7
(41
), p. eabk2218
. 20.
Sheikh
, H. M.
, Meier
, T.
, Blankenship
, B.
, Vangelatos
, Z.
, Zhao
, N.
, Marcus
, P. S.
, and Grigoropoulos
, C. P.
, 2022
, “Systematic Design of Cauchy Symmetric Structures Through Bayesian Optimization
,” Int. J. Mech. Sci.
, 236
, p. 107741
. 21.
Walsh
, M.
, 1982
, “Turbulent Boundary Layer Drag Reduction Using Riblets
,” 20th Aerospace Sciences Meeting
, Orlando, FL
, Jan. 11–14
.22.
Walsh
, M.
, 1985
, “Riblets for Aircraft Skin-Friction Reduction,” Langley Symposium on Aerodynamics
, S.H.
Stack
, ed., NASA Langley Research Center
, Hampton, VA
, pp. 557
–572
. https://ntrs.nasa.gov/citations/1988000554423.
Bechert
, D. W.
, Bruse
, M.
, Hage
, W.
, Van der Hoeven
, J. G. T.
, and Hoppe
, G.
, 1997
, “Experiments on Drag-Reducing Surfaces and Their Optimization With an Adjustable Geometry
,” J. Fluid. Mech.
, 338
, pp. 59
–87
. 24.
Bechert
, D. W.
, Bruse
, M.
, Hage
, W.
, and Meyer
, R.
, 2000
, “Fluid Mechanics of Biological Surfaces and Their Technological Application
,” Naturwissenschaften
, 87
(4
), pp. 157
–171
. 25.
Bai
, X.
, Zhang
, X.
, and Yuan
, C.
, 2016
, “Numerical Analysis of Drag Reduction Performance of Different Shaped Riblet Surfaces
,” Marine Technol. Soc. J.
, 50
(1
), pp. 62
–72
. 26.
Heidarian
, A.
, Ghassemi
, H.
, and Liu
, P.
, 2018
, “Numerical Analysis of the Effects of Riblets on Drag Reduction of a Flat Plate
,” J. Appl. Fluid Mech.
, 11
(3
), pp. 679
–688
.27.
Ao
, M.
, Wang
, M.
, and Zhu
, F.
, 2021
, “Investigation of the Turbulent Drag Reduction Mechanism of a Kind of Microstructure on Riblet Surface
,” Micromachines
, 12
(1
), p. 59
. 28.
Klocke
, F.
, Feldhaus
, B.
, and Mader
, S.
, 2007
, “Development of an Incremental Rolling Process for the Production of Defined Riblet Surface Structures
,” Production Eng.
, 1
(3
), pp. 233
–237
. 29.
Gao
, X.
, Zhu
, G.
, Wang
, H.
, Guo
, N.
, and Wang
, Z.
, 2021
, “Research on Micro Riblets Rolling Process Based on Uncertainty Analysis
,” Mater. Today Commun.
, 27
, p. 102302
. 30.
Blumenson
, L. E.
, 1960
, “A Derivation of N-Dimensional Spherical Coordinates
,” Amer. Math. Monthly
, 67
(1
), pp. 63
–66
.31.
GEOS contributors
, 2021
, “GEOS Coordinate Transformation Software Library
, Open Source Geospatial Foundation
, https://libgeos.org/, Accessed December 11, 2023.32.
Walsh
, M.
, and Lindemann
, A.
, 1984
, “Optimization and Application of Riblets for Turbulent Drag Reduction
,” 22nd Aerospace Sciences Meeting
, Reno, NV
, Jan. 9–12
.33.
Choi
, H.
, Moin
, P.
, and Kim
, J.
, 1993
, “Direct Numerical Simulation of Turbulent Flow Over Riblets
,” J. Fluid. Mech.
, 255
, pp. 503
–539
. 34.
García-Mayoral
, R.
, and Jiménez
, J.
, 2011
, “Drag Reduction by Riblets
,” Philos. Trans. Royal Soc. Math. Phys. Eng. Sci.
, 369
(1940
), pp. 1412
–1427
.35.
Martin
, S.
, and Bhushan
, B.
, 2016
, “Fluid Flow Analysis of Continuous and Segmented Riblet Structures
,” RSC. Adv.
, 6
(13
), pp. 10962
–10978
. 36.
Lee
, S.
, and Hwang
, W.
, 2019
, “Development of an Efficient Immersed-Boundary Method With Subgrid-Scale Models for Conjugate Heat Transfer Analysis Using Large Eddy Simulation
,” Int. J. Heat. Mass. Transfer.
, 134
, pp. 198
–208
. 37.
Baek
, S.
, Lee
, S.
, Hwang
, W.
, and Park
, J. S.
, 2019
, “Experimental and Numerical Investigation of the Flow in a Trailing Edge Ribbed Internal Cooling Passage
,” ASME J. Turbomach.
, 141
(1
), p. 011012
. 38.
Abdulbari
, H. A.
, Mahammed
, H. D.
, and Hassan
, Z. B. Y.
, 2015
, “Bio-Inspired Passive Drag Reduction Techniques: A Review
,” ChemBioEng Rev.
, 2
(3
), pp. 185
–203
. 39.
Choi
, H.
, and Moin
, P.
, 2012
, “Grid-Point Requirements for Large Eddy Simulation: Chapman’s Estimates Revisited
,” Phys. Fluids.
, 24
(1
), p. 011702
. 40.
Kim
, J.
, Kim
, D.
, and Choi
, H.
, 2001
, “An Immersed-Boundary Finite-Volume Method for Simulations of Flow in Complex Geometries
,” J. Comput. Phys.
, 171
(1
), pp. 132
–150
. 41.
Kim
, J.
, Moin
, P.
, and Moser
, R.
, 1987
, “Turbulence Statistics in Fully Developed Channel Flow at Low Reynolds Number
,” J. Fluid. Mech.
, 177
(13800
), pp. 133
–166
. 42.
Tsukahara
, T.
, Seki
, Y.
, Kawamura
, H.
, and Tochio
, D.
, 2005
, “DNS of Turbulent Channel Flow at Very Low Reynolds Numbers
,” Fourth International Symposium on Turbulence and Shear Flow Phenomena
, Williamsburg, VA
, June 27–29
, pp. 935
–940
.43.
Vreman
, A. W.
, and Kuerten
, J. G.
, 2014
, “Comparison of Direct Numerical Simulation Databases of Turbulent Channel Flow at
,” Phys. Fluids.
, 26
(1
), p. 015102
. 44.
Park
, N.
, Lee
, S.
, Lee
, J.
, and Choi
, H.
, 2006
, “A Dynamic Subgrid-Scale Eddy Viscosity Model With a Global Model Coefficient
,” Phys. Fluids.
, 18
(12
), p. 125109
. 45.
Vreman
, A. W.
, 2004
, “An Eddy-Viscosity Subgrid-Scale Model for Turbulent Shear Flow: Algebraic Theory and Applications
,” Phys. Fluids.
, 16
(10
), pp. 3670
–3681
. 46.
Smagorinsky
, J.
, 1963
, “General Circulation Experiments With the Primitive Equations
,” Monthly Weather Rev.
, 91
(3
), pp. 99
–164
. 47.
Iliescu
, T.
, and Fischer
, P. F.
, 2003
, “Large Eddy Simulation of Turbulent Channel Flows by the Rational Large Eddy Simulation Model
,” Phys. Fluids.
, 15
(10
), pp. 3036
–3047
. 48.
Lee
, J.
, Kim
, J.
, Choi
, H.
, and Yang
, K. S.
, 2011
, “Sources of Spurious Force Oscillations From an Immersed Boundary Method for Moving-Body Problems
,” J. Comput. Phys.
, 230
(7
), pp. 2677
–2695
. 49.
De Villiers
, E.
, 2006
, “The Potential of Large Eddy Simulation for the Modeling of Wall Bounded Flows
,” Ph d. thesis, Imperial College London
, UK
.50.
Brochu
, E.
, Cora
, V. M.
, and de Freitas
, N.
, 2010
, “A Tutorial on Bayesian Optimization of Expensive Cost Functions, With Application to Active User Modeling and Hierarchical Reinforcement Learning
,” arXiv:1012.2599.51.
Frazier
, P. I.
, and Wang
, J.
, 2016
, “Bayesian Optimization for Materials Design,” Information Science for Materials Discovery and Design
, Vol. 225
, Lookman
, T.
, Alexander
, F. J.
, and Rajan
, K.
, eds., Springer
, Cham
, pp. 45
–75
.52.
Chen
, D.
, Skouras
, M.
, Zhu
, B.
, and Matusik
, W.
, 2018
, “Computational Discovery of Extremal Microstructure Families
,” Sci. Adv.
, 4
(1
), p. eaao7005
. 53.
Chen
, W.
, Watts
, S.
, Jackson
, J. A.
, Smith
, W. L.
, Tortorelli
, D. A.
, and Spadaccini
, C. M.
, 2019
, “Stiff Isotropic Lattices Beyond the Maxwell Criterion
,” Sci. Adv.
, 5
(9
), p. eaaw1937
. 54.
Shaw
, L. A.
, Sun
, F.
, Portela
, C. M.
, Barranco
, R. I.
, Greer
, J. R.
, and Hopkins
, J. B.
, 2019
, “Computationally Efficient Design of Directionally Compliant Metamaterials
,” Nat. Commun.
, 10
(1
), p. 291
. 55.
Song
, J.
, Wang
, Y.
, Zhou
, W.
, Fan
, R.
, Yu
, B.
, Lu
, Y.
, and Li
, L.
, 2019
, “Topology Optimization-Guided Lattice Composites and Their Mechanical Characterizations
,” Composites Part B: Eng.
, 160
, pp. 402
–411
. 56.
Snoek
, J.
, Larochelle
, H.
, and Adams
, R. P.
, 2012
, “Practical Bayesian Optimization of Machine Learning Algorithms
,” 26th Annual Conference on Neural Information Processing Systems
, Lake Tahoe, NV
, Dec 3–8
.57.
Chen
, Y.
, Huang
, A.
, Wang
, Z.
, Antonoglou
, I.
, Schrittwieser
, J.
, Silver
, D.
, and de Freitas
, N.
, 2018
, “Bayesian Optimization in AlphaGo
,” arXiv:1812.06855 .58.
Oh
, C.
, Gavves
, E.
, and Welling
, M.
, 2018
, “BOCK: Bayesian Optimization With Cylindrical Kernels
,” 35th International Conference on Machine Learning
, Stockholm, Sweden
, July 10–15
.59.
Pyzer-Knapp
, E. O.
, 2018
, “Bayesian Optimization for Accelerated Drug Discovery
,” IBM. J. Res. Dev.
, 62
(6
), pp. 1
–7
. 60.
Korovina
, K.
, Xu
, S.
, Kandasamy
, K.
, Neiswanger
, W.
, Poczos
, B.
, Schneider
, J.
, and Xing
, E. P.
, 2020
, “ChemBO: Bayesian Optimization of Small Organic Molecules With Synthesizable Recommendations
,” 23rd International Conference on Artificial Intelligence and Statistics
, Online
, Aug. 26-28
.61.
Sheikh
, H. M.
, Shabbir
, Z.
, Ahmed
, H.
, Waseem
, M. H.
, and Sheikh
, M. Z.
, 2017
, “Computational Fluid Dynamics Analysis of a Modified Savonius Rotor and Optimization Using Response Surface Methodology
,” Wind Eng.
, 41
(5
), pp. 285
–296
.62.
Tušar
, T.
, Brockhoff
, D.
, and Hansen
, N.
, 2019
, “Mixed-Integer Benchmark Problems for Single- and Bi-objective Optimization
,” GECCO '19: Genetic and Evolutionary Computation Conference
, Prague, Czech Republic
, July 13–17
.63.
Bechert
, D. W.
, and Bartenwerfer
, M.
, 1989
, “The Viscous Flow on Surfaces With Longitudinal Ribs
,” J. Fluid. Mech.
, 206
, pp. 105
–129
. 64.
Jeong
, J.
, and Hussain
, F.
, 1995
, “On the Identification of a Vortex
,” J. Fluid. Mech.
, 285
, pp. 69
–94
. 65.
Lee
, S.-J.
, and Lee
, S.-H.
, 2001
, “Flow Field Analysis of a Turbulent Boundary Layer Over a Riblet Surface
,” Experiments in Fluids
, 30
(2
), pp. 153
–166
. 66.
Bacher
, E.
, and Smith
, C.
, 1985
, “A Combined Visualization-Anemometry Study of the Turbulent Drag Reducing Mechanisms of Triangular Micro-groove Surface Modifications
,” Shear Flow Control Conference
, Boulder, CO
, Mar. 12–14
.67.
Savill
, A. M.
, 1990
, “Drag Reduction by Passive Devices—a Review of some Recent Developments,” Structure of Turbulence and Drag Reduction
, A.
Gyr
, ed., Springer
, Berlin, Heidelberg
, pp. 429
–465
. 68.
Choi
, K.-S.
, 1990
, “Drag Reduction Mechanisms and Near-wall Turbulence Structure with Riblets,” Structure of Turbulence and Drag Reduction
, A.
Gyr
, ed., Springer
, Berlin/Heidelberg
, pp. 553
–560
. 69.
San Diego Supercomputer Center (SDSC)
, 2023
, “Expanse User Guide
,” https://www.sdsc.edu/support/user_guides/expanse, Accessed December 11, 2023.Copyright © 2024 by ASME
You do not currently have access to this content.
