Design optimization for self-propulsion of a bulk carrier hull using a discrete adjoint method

Abstract Computational fluid dynamics (CFD) based optimization is becoming increasingly popular in hydrodynamic design of ship hulls because it provides a fully automatic framework with a shorter design cycle than a human-supervised design tool. Despite the above advantage, CFD-based optimization requires careful attention to relevant design considerations, such that the final design is useful in practice. These considerations include all relevant objectives (such as drag and wake distortion) and constraints (such as volume, thickness, and curvature). Although constraints have been included in previous hull shape optimization studies, these studies have typically considered only one objective. To address this shortcoming, we conduct design optimization for self-propulsion by simultaneously considering drag and propeller-wake distortion. We use a gradient-based optimization framework that includes a discrete adjoint method for efficient derivative computation, which allows us to use a large number of design variables to parameterize the complex hull shape and thus gain a large amount of freedom for geometric modification. We impose appropriate geometric constraints (volume, thickness, and curvature) on the hull surface to ensure a practical design. In addition, we use a weighted objective function that includes drag and wake distortion to construct a Pareto front with five optimizations. We also consider hull-propeller interaction by comparing optimization results with and without a propeller. We use the Japan bulk carrier (JBC) as the baseline model and focus on optimizing the stern region. We find that optimizing for only one objective results in a large penalty on the other objective, whereas a weighted objective balances the penalty and achieves simultaneous improvement in drag and wake distortion. Moreover, we observe that the suction effect of the propeller suppresses the flow separation near the bilge tube and smooths out the velocity distortion at the propeller plane; these are effects that would end up affecting the optimized shapes. Our results demonstrate that it is necessary to simultaneously consider drag and wake distortion in hull-shape-optimization studies, and that constrained shape optimization with a large number of design variables is possible with the discrete-adjoint method.

[1]  Joaquim R. R. A. Martins,et al.  Multipoint High-Fidelity Aerostructural Optimization of a Transport Aircraft Configuration , 2014 .

[2]  J. Eric,et al.  Aerodynamic Design Optimization on Unstructured Meshes Using the Navier-Stokes Equations , 1998 .

[3]  Joaquim R. R. A. Martins,et al.  A laminate parametrization technique for discrete ply-angle problems with manufacturing constraints , 2013 .

[4]  Jean-Louis Boulanger,et al.  Verification and Validation , 2017 .

[5]  Lars Larsson,et al.  Numerical Ship Hydrodynamics - An Assessment of the Gothenburg 2010 Workshop , 2014 .

[6]  R. F. Warming,et al.  Upwind Second-Order Difference Schemes and Applications in Aerodynamic Flows , 1976 .

[7]  Michael A. Saunders,et al.  SNOPT: An SQP Algorithm for Large-Scale Constrained Optimization , 2002, SIAM J. Optim..

[8]  Joaquim R. R. A. Martins,et al.  Multimodality in aerodynamic wing design optimization , 2017 .

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

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

[11]  Joaquim R. R. A. Martins,et al.  An aerodynamic design optimization framework using a discrete adjoint approach with OpenFOAM , 2018 .

[12]  M. Diez,et al.  Design-space dimensionality reduction in shape optimization by Karhunen–Loève expansion , 2015 .

[13]  Eric Blades,et al.  A fast mesh deformation method using explicit interpolation , 2012, J. Comput. Phys..

[14]  Rainald Löhner,et al.  An Adjoint-Based Design Methodology for CFD Optimization Problems , 2003 .

[15]  Stefano Lucidi,et al.  Ship hydrodynamic optimization by local hybridization of deterministic derivative-free global algorithms , 2016 .

[16]  Niklas Kühl,et al.  Adjoint volume-of-fluid approaches for the hydrodynamic optimisation of ships , 2018 .

[17]  Saad Ragab Shape optimization in free surface potential flow using an adjoint formulation - Surface ships , 2001 .

[18]  Joaquim R. R. A. Martins,et al.  High-fidelity multipoint hydrostructural optimization of a 3-D hydrofoil , 2017 .

[19]  Joaquim R. R. A. Martins,et al.  Effective adjoint approaches for computational fluid dynamics , 2019, Progress in Aerospace Sciences.

[20]  Nancy Wilkins-Diehr,et al.  XSEDE: Accelerating Scientific Discovery , 2014, Computing in Science & Engineering.

[21]  M. Visonneau,et al.  On the role played by turbulence closures in hull shape optimization at model and full scale , 2003 .

[22]  Philip A. Wilson,et al.  An Effective Approximation Modeling Method for Ship Resistance in Multidisciplinary Ship Design Optimization , 2014 .

[23]  Joaquim R. R. A. Martins,et al.  On the influence of optimization algorithm and initial design on wing aerodynamic shape optimization , 2018 .

[24]  Joaquim R. R. A. Martins,et al.  Simultaneous optimization of propeller–hull systems to minimize lifetime fuel consumption , 2013 .

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

[26]  Richard P. Dwight,et al.  Uncertainty quantification for a sailing yacht hull, using multi-fidelity kriging , 2015 .

[27]  P. Spalart A One-Equation Turbulence Model for Aerodynamic Flows , 1992 .

[28]  X. Chen,et al.  High-fidelity global optimization of shape design by dimensionality reduction, metamodels and deterministic particle swarm , 2015 .

[29]  Hyunyul Kim,et al.  A new surface modification approach for CFD-based hull form optimization , 2010 .

[30]  Alex Pothen,et al.  What Color Is Your Jacobian? Graph Coloring for Computing Derivatives , 2005, SIAM Rev..

[31]  Suak-Ho Van,et al.  Wind tunnel tests on flow characteristics of the KRISO 3,600 TEU containership and 300K VLCC double-deck ship models , 2003 .

[32]  Daniele Peri,et al.  High-Fidelity Models and Multiobjective Global Optimization Algorithms in Simulation-Based Design , 2005 .

[33]  Joaquim R. R. A. Martins,et al.  High-Fidelity Hydrodynamic Shape Optimization of a 3-D Hydrofoil , 2015 .

[34]  William Gropp,et al.  Efficient Management of Parallelism in Object-Oriented Numerical Software Libraries , 1997, SciTools.

[35]  Patrick J. Roache,et al.  Verification and Validation in Computational Science and Engineering , 1998 .

[36]  Roque Corral,et al.  Comparison between aerodynamic designs obtained by human driven and automatic procedures , 2018 .

[37]  Arthur Stück,et al.  Adjoint Navier-Stokes methods for hydrodynamic shape optimisation , 2012 .

[38]  C. Rhie,et al.  Numerical Study of the Turbulent Flow Past an Airfoil with Trailing Edge Separation , 1983 .

[39]  Timothy R. Brooks,et al.  Multidisciplinary Design Optimization of Aircraft Configurations Part 2 : High-fidelity aerostructural optimization , 2016 .

[40]  Dae Hyun Kim,et al.  Bulbous bow retrofit of a container ship using an open-source Computational Fluid Dynamics (CFD) toolbox , 2014 .

[41]  Daniele Peri,et al.  Computational fluid dynamics-based multiobjective optimization of a surface combatant using a global optimization method , 2008 .

[42]  D. X. Wang,et al.  Adjoint Aerodynamic Design Optimization for Blades in Multistage Turbomachines—Part I: Methodology and Verification , 2010 .

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

[44]  M. Hoekstra,et al.  A RANS-based analysis tool for ducted propeller systems in open water condition , 2006 .

[45]  J. Martins,et al.  Hull form hydrodynamic design using a discrete adjoint optimization method , 2018 .

[46]  김진 Tokyo 2015 A Workshop on CFD in Ship Hydrodynamics , 2016 .

[47]  Graeme J. Kennedy,et al.  An evaluation of constraint aggregation strategies for wing box mass minimization , 2017 .

[48]  Carsten Othmer,et al.  Adjoint methods for car aerodynamics , 2014, Journal of Mathematics in Industry.

[49]  Joaquim R. R. A. Martins,et al.  Aerothermal optimization of a ribbed U-bend cooling channel using the adjoint method , 2019, International Journal of Heat and Mass Transfer.

[50]  LUIGI MARTINELLI,et al.  An adjoint method for design optimization of ship hulls , 2007 .

[51]  M. Powell,et al.  On the Estimation of Sparse Jacobian Matrices , 1974 .

[52]  Joaquim R. R. A. Martins,et al.  pyOpt: a Python-based object-oriented framework for nonlinear constrained optimization , 2011, Structural and Multidisciplinary Optimization.

[53]  Arthur Stück,et al.  Adjoint-based Hull Design for Wake Optimisation , 2011 .

[54]  Kadir Burak Korkmaz,et al.  CFD Predictions of Resistance and Propulsion for the JAPAN Bulk Carrier (JBC) with and without an Energy Saving Device , 2015 .

[55]  Joaquim R. R. A. Martins,et al.  A CAD-Free Approach to High-Fidelity Aerostructural Optimization , 2010 .

[56]  Cheng-Hung Huang,et al.  An Inverse Hull Design Problem in Optimizing the Desired Wake of a Ship , 2002 .

[57]  Gregory A. Wrenn,et al.  An indirect method for numerical optimization using the Kreisselmeir-Steinhauser function , 1989 .

[58]  Weilin Luo,et al.  Design Optimization of the Lines of the Bulbous Bow of a Hull Based on Parametric Modeling and Computational Fluid Dynamics Calculation , 2017 .

[59]  Tahsin Tezdogan,et al.  Computational fluid dynamics-based hull form optimization using approximation method , 2018 .

[60]  Hugh W. Coleman,et al.  Comprehensive Approach to Verification and Validation of CFD Simulations—Part 1: Methodology and Procedures , 2001 .

[61]  Joaquim R. R. A. Martins,et al.  Aerodynamic Shape Optimization Investigations of the Common Research Model Wing Benchmark , 2015 .

[62]  E. Campana,et al.  Shape optimization in ship hydrodynamics using computational fluid dynamics , 2006 .