Multidisciplinary optimization design of a new underwater vehicle with highly efficient gradient calculation

In order to reduce the cost of oceanographic exploration, a new underwater vehicle is designed to sail the required distance with the lowest energy consumed. Since the new underwater vehicle is a complicated multidisciplinary system, it is firstly decomposed into four smaller disciplines and then a multidisciplinary design optimization (MDO) problem is built based on these disciplines. The Multidisciplinary Feasible (MDF) architecture is adopted as the solution strategy to this optimization problem considering that it is easily implemented and a multidisciplinary feasible solution is always guaranteed throughout the optimization process. To solve this optimization problem efficiently, the coupled adjoint method is firstly introduced to improve the efficiency of gradient calculation and then a discipline-merging method is proposed to further enhance the computational efficiency. After this, the discipline-merging method is verified against the finite difference method in two aspects of solution accuracy and computational costs and the results show it is an effective and high efficient gradient calculation method. Finally, the multidisciplinary design optimization of the new underwater vehicle is performed efficiently under the MDF architecture with the discipline-merging method to calculate gradients.

[1]  Robert D. Braun,et al.  Collaborative optimization: an architecture for large-scale distributed design , 1996 .

[2]  Li Jia-wang Simulation of Long-distance AUV in Low Speed Maneuvers , 2007 .

[3]  John T. Hwang,et al.  Review and Unification of Methods for Computing Derivatives of Multidisciplinary Computational Models , 2013 .

[4]  Christina Bloebaum,et al.  A simulation-based comparison of multidisciplinary design optimization solution strategies using CASCADE , 2000 .

[5]  Yuan Charles,et al.  Evaluation of Methods for Multidisciplinary Design Optimization (MDO), Part II , 2000 .

[6]  Jeremy S. Agte,et al.  Bi-Level Integrated System Synthesis , 1998 .

[7]  Jaroslaw Sobieszczanski-Sobieski,et al.  Integrated System-of-Systems Synthesis , 2008 .

[8]  Paul S Granville GEOMETRICAL CHARACTERISTICS OF STREAMLINED SHAPES , 1969 .

[9]  John R. Olds,et al.  Evaluation of Multidisciplinary Optimization Techniques Applied to a Reusable Launch Vehicle , 2006 .

[10]  Christina Bloebaum,et al.  NON-HIERARCHIC SYSTEM DECOMPOSITION IN STRUCTURAL OPTIMIZATION , 1992 .

[11]  Natalia Alexandrov,et al.  Analytical and Computational Aspects of Collaborative Optimization for Multidisciplinary Design , 2002 .

[12]  Tapabrata Ray,et al.  A brief taxonomy of autonomous underwater vehicle design literature , 2014 .

[13]  R. Haftka Simultaneous analysis and design , 1985 .

[14]  Jaroslaw Sobieszczanski-Sobieski,et al.  OPTIMIZATION OF COUPLED SYSTEMS: A CRITICAL OVERVIEW OF APPROACHES , 1994 .

[15]  D. Cecchi,et al.  Autonomous underwater vehicles for scientific and naval operations , 2004 .

[16]  Ilan Kroo,et al.  Use of the Collaborative Optimization Architecture for Launch Vehicle Design , 1996 .

[17]  R. Goodson,et al.  THE OPTIMUM SHAPING OF AXISYMMETRIC BODIES FOR MINIMUM DRAG IN INCOMPRESSIBLE FLOW , 1972 .

[18]  Gyung-Jin Park,et al.  Comparison of MDO methods with mathematical examples , 2008 .

[19]  J. Sobieszczanski-Sobieski,et al.  Bilevel Integrated System Synthesis for Concurrent and Distributed Processing , 2002 .

[20]  Baowei Song,et al.  Research on Multi-objective Optimization Design of the UUV Shape Based on Numerical Simulation , 2010, ICSI.

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

[22]  Ilan Kroo,et al.  Implementation and Performance Issues in Collaborative Optimization , 1996 .

[23]  Joaquim R. R. A. Martins,et al.  Benchmarking multidisciplinary design optimization algorithms , 2010 .

[24]  Joaquim R. R. A. Martins,et al.  Multidisciplinary design optimization: A survey of architectures , 2013 .

[25]  J. Renaud,et al.  Approximation in nonhierarchic system optimization , 1994 .

[26]  B. Bett,et al.  Autonomous Underwater Vehicles (AUVs): Their past, present and future contributions to the advancement of marine geoscience , 2014 .

[27]  Eric Jones,et al.  SciPy: Open Source Scientific Tools for Python , 2001 .

[28]  Layne T. Watson,et al.  Multidisciplinary Design Optimization with Quasiseparable Subsystems , 2005 .

[29]  Roy M. Turner,et al.  The Development of Autonomous Underwater Vehicles (AUV); A Brief Summary , 2001 .

[30]  John E. Dennis,et al.  Problem Formulation for Multidisciplinary Optimization , 1994, SIAM J. Optim..