Hull Design Method Combining an Innovative Flow Solver Coupled With Efficient Multivariate Analysis and Optimization Strategies

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An important application of optimization of a ship is the minimization of calm-water resistance for a given displacement. In this work, an innovative flow solver that combines free-surface effects with a viscous solution allows for an accurate drag prediction with fast turnaround times ideally suited for an optimization study. A large number of geometrical-design variables are considered in early-stage design; thus, in this article, different techniques are examined to reduce the curse of dimensionality. Different methods such as multivariate analysis are used to optimize the hull with respect to resistance over a range of different speeds for a given displacement.

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... A nonlinear transformation method was developed in [ 5 ] to operate on a table of offsets in order to alter the shape of a vessel. Detailed hydrodynamic optimization using an advanced flow solver has been done in [ 6 ] and a genetic algorithm was used in [ 7 ] to optimize the design of a trimaran by minimizing the total resistance. A genetic algorithm has also been used to optimize a B-series propeller in [ 8 ] for hydrodynamic performance using a strength constraint based on [ 9 ]. ...
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The DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit provides a flexible and extensible interface between simulation codes and iterative analysis methods. DAKOTA contains algorithms for optimization with gradient and nongradient-based methods; uncertainty quantification with sampling, reliability, and stochastic finite element methods; parameter estimation with nonlinear least squares methods; and sensitivity/variance analysis with design of experiments and parameter study methods. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment for design and performance analysis of computational models on high performance computers. This report serves as a user's manual for the DAKOTA software and provides capability overviews and procedures for software execution, as well as a variety of example studies.
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The simulation of viscous free-surface water flow is a subject that has reached a certain maturity and is nowadays used in industrial applications, like the simulation of the flow around ships. While almost all methods used are based on the Navier-Stokes equations, the discretisation methods for the water surface differ widely. Many of these highly different methods are being used with success. We review three of these methods, by describing in detail their implementation in one particular code that is being used in industrial practice. The descriptions concern the principle of the method, numerical details, and the method’s strengths and limitations. For each code, examples are given of its use. Finally, the methods are compared to determine the best field of application for each. The following surface descretisation methods are reviewed. First, surface fitting/mesh deformation in PARNASSOS, developed by MARIN; the description focuses on the efficient steady-state solution method of this code. Then surface capturing with Volume-of-Fluid in ISIS-CFD, developed by CNRS/Ecole Centrale de Nantes; the main topic of this review are the compressive flux discretisation schemes for the volume fraction that are used in this code. And finally, the Level Set method in SURF, developed by NMRI; this description contains a modified formulation of the Level Set method that is optimised for ship flow computation.
Two types of sampling plans are examined as alternatives to simple random sampling in Monte Carlo studies. These plans are shown to be improvements over simple random sampling with respect to variance for a class of estimators which includes the sample mean and the empirical distribution function.
Use of data sampling, surrogate models, and numerical optimization in engineering design
  • A A Giunta
GIUNTA, A. A. 2002 Use of data sampling, surrogate models, and numerical optimization in engineering design, Proceedings, 40 th AIAA Aerospace Sciences Meeting and Exhibit, Reno, NV.
A scalable multiphase Rans capability based on object-oriented programming and its applications to ship hydrodynamics
  • S.-E Kim
  • B J Rhee
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  • K Maki
KIM, S.-E., RHEE, B. J., SHAN, H., GORSKI, J., PATERSON, E. G., AND MAKI, K. 2010 A scalable multiphase Rans capability based on object-oriented programming and its applications to ship hydrodynamics, Proceedings, Gothenburg 2010 Workshop on CFD in Ship Hydrodynamics.
Evolutionary Algorithms in Engineering and Computer Science
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MARCO, N., LANTERI, S., DESIDERI, J. A., AND PÉRIAUX, J. 1999 Evolutionary Algorithms in Engineering and Computer Science. Wiley, Chichester, United Kingdom, pp. 445-456.