AUV hull lines optimization with uncertainty parameters based on six sigma reliability design

Abstract Autonomous Underwater Vehicle (AUV), which are becoming more and more important in ocean exploitation tasks, needs energy conservation urgently when sailing the complex mission path in long time cruise. As hull lines optimization design becomes the key factor, which closely related with resistance, in AUV preliminary design stage, uncertainty parameters need to be considered seriously. In this research, Myring axial symmetry revolution body with parameterized expression is assumed as AUV hull lines, and its travelling resistance is obtained via modified DATCOM formula. The problems of AUV hull lines design for the minimum travelling resistance with uncertain parameters are studied. Based on reliability-based optimization design technology, Design For Six Sigma (DFSS) for high quality level is conducted, and is proved more reliability for the actual environment disturbance.

[1]  Guoqing Xia,et al.  Multi-objective optimization for AUV conceptual design based on NSGA-II , 2016, OCEANS 2016 - Shanghai.

[2]  Daniele Peri,et al.  Robust optimization for ship conceptual design , 2010 .

[3]  Apostolos Papanikolaou,et al.  Stochastic uncertainty modelling for ship design loads and operational guidance , 2014 .

[4]  Damian Derlukiewicz,et al.  Method of assessing the quality of the design process of construction equipment with the use of DFSS (design for Six Sigma) , 2012 .

[5]  Nickolas Vlahopoulos,et al.  Introducing Uncertainty in Multidiscipline Ship Design , 2010 .

[6]  M. Diez,et al.  Uncertainty quantification of Delft catamaran resistance, sinkage and trim for variable Froude number and geometry using metamodels, quadrature and Karhunen–Loève expansion , 2014 .

[7]  Lester Ingber,et al.  Adaptive Simulated Annealing , 2012 .

[8]  Rakesh K. Kapania,et al.  New Approach for System Reliability-Based Design Optimization , 2005 .

[9]  Matteo Diez,et al.  URANS study of Delft catamaran total/added resistance, motions and slamming loads in head sea including irregular wave and uncertainty quantification for variable regular wave and geometry , 2013 .

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

[11]  Miguel A. Vega-Rodríguez,et al.  A FPGA Optimization Tool Based on a Multi-island Genetic Algorithm Distributed over Grid Environments , 2008, 2008 Eighth IEEE International Symposium on Cluster Computing and the Grid (CCGRID).

[12]  Hu Lin,et al.  Reliability-based optimization design of structure subjected to vehicle frontal impact based on probability-convex hybrid model , 2016 .

[13]  E. A. de Barros,et al.  Investigation of a method for predicting AUV derivatives , 2008 .

[14]  Zenghui Wang,et al.  Chaotic particle swarm optimization , 2009, GEC '09.

[15]  Karl Sammut,et al.  A Study On the Design Optimization of an AUV By Using Computational Fluid Dynamic Analysis , 2009 .