Reliability-based design optimization of a pick-up device of a manganese nodule pilot mining robot using the Coandă effect

Design of a pick-up device using the Coandă effect in a deep-sea mining robot is vital to develop a reliable and sustainable deep-sea mining system. One of the crucial performance metrics of this device is the collection efficiency since it affects the mining efficiency of the entire system. However, the collection efficiency is significantly affected by the uncertainties of shape, size and mass of manganese nodules on the seabed. In this study, reliability-based design optimization (RBDO) was performed to improve the reliability of the collection efficiency of the pick-up device under these environmental uncertainties. First, a computational model based on the Coandă effect that predicts the collection efficiency of the pick-up device was developed. Next, RBDO based on the Akaike information criterion method was employed to design the pick-up device by using this model. The results demonstrated that the proposed design methodology significantly improved the design of the pick-up device for the pilot mining robot.

[1]  Tae Hee Lee,et al.  Reliability-Based Design Optimization Using Akaike Information Criterion for Discrete Information , 2012 .

[2]  H. Joe.  Multivariate models and dependence concepts , 1998 .

[3]  Jong-Su Choi,et al.  Track velocity control of crawler type underwater mining robot through shallow-water test , 2012 .

[4]  Wolfgang Rodi,et al.  The turbulent wall jet , 1979 .

[5]  O-Kaung Lim,et al.  Development of a kernel density estimation with hybrid estimated bounded data , 2018, Journal of Mechanical Science and Technology.

[6]  L. Roberts,et al.  Experimental investigation of straight and curved annular wall jets , 1989 .

[7]  Sanghyun Park,et al.  Nonparametric approach for uncertainty-based multidisciplinary design optimization considering limited data , 2016 .

[8]  Jong-Su Choi,et al.  Probability distribution for the shear strength of seafloor sediment in the KR5 area for the development of manganese nodule miner , 2011 .

[9]  M. Hohenbichler,et al.  Improvement Of Second‐Order Reliability Estimates by Importance Sampling , 1988 .

[10]  여태경,et al.  다중 집광로봇의 수평대형유지를 위한 기초 알고리즘 연구 , 2014 .

[11]  Mustapha Benabed.  Computational Optimization of Coanda Effect on Film-Cooling Performance , 2015 .

[12]  Anirban Mazumdar,et al.  Pulse Width Modulation of Water Jet Propulsion Systems Using High-Speed Coanda-Effect Valves , 2013 .

[13]  Jae-Won Oh,et al.  On-board measurement methodology for the liquid-solid slurry production of deep-seabed mining , 2018 .

[14]  Anil B. Valsangkar,et al.  Deep-sea Polymetallic Nodule Mining: Challenges Ahead for Technologists and Environmentalists , 2003 .

[15]  Jong-Su Choi,et al.  Probability distribution for size and mass of a nodule in the KR5 area for the development of a manganese nodule miner , 2019, Ocean Engineering.

[16]  B. Youn,et al.  An Investigation of Nonlinearity of Reliability-Based Design Optimization Approaches , 2004 .

[17]  Hyouk Ryeol Choi,et al.  Design Optimization, Modeling, and Control of Unmanned Aerial Vehicle Lifted By Coandă Effect , 2017, IEEE/ASME Transactions on Mechatronics.

[18]  Hyun-Bok Lee,et al.  Status of Manganese Nodule Samples in the Library of Marine Samples , 2014 .

[19]  Jong-Su Choi,et al.  Metamodel-Based Multidisciplinary Design Optimization of a Deep-Sea Manganese Nodules Test Miner , 2012, J. Appl. Math..

[20]  Jin-Ha Kim,et al.  A Note On Design And Operation of Waterjet Nodule Lifter of Manganese Nodule Collector , 2001 .

[21]  Jungho Kim,et al.  Reliability-based design optimization of an automotive structure using a variable uncertainty , 2016 .

[22]  K. K. Choi,et al.  Reliability-based design optimization of problems with correlated input variables using a Gaussian Copula , 2009 .

[23]  Chang-Ho Lee,et al.  A study of the kinematic characteristic of a coupling device between the buffer system and the flexible pipe of a deep-seabed mining system , 2014 .

[24]  Imants Reba,et al.  Applications of the Coanda Effect , 1966 .

[25]  Young Bae Jun,et al.  Coderivations of Ranked Bigroupoids , 2012, J. Appl. Math..

[26]  S. Rahman,et al.  A univariate dimension-reduction method for multi-dimensional integration in stochastic mechanics , 2004 .

[27]  K. Breitung.  Asymptotic approximations for multinormal integrals , 1984 .

[28]  Yoojeong Noh,et al.  A new method to determine the number of experimental data using statistical modeling methods , 2017 .

[29]  Niels C. Lind,et al.  Methods of structural safety , 2006 .

[30]  D. Geropp,et al.  Drag reduction of motor vehicles by active flow control using the Coanda effect , 2000 .

[31]  Joo-Ho Choi,et al.  Improved dimension reduction method (DRM) in uncertainty analysis using kriging interpolation , 2009 .

[32]  Chang-Ho Lee,et al.  A Self-Propelled Deep-Seabed Miner and Lessons From Shallow Water Tests , 2010 .

[33]  Kyung K. Choi,et al.  Identification of marginal and joint CDFs using Bayesian method for RBDO , 2009 .

[34]  R. Nelsen.  An Introduction to Copulas , 1998 .

[35]  Abd. Rahim Abu Talib,et al.  Aerodynamics and flight mechanics of MAV based on Coandă effect , 2017 .

[36]  B. Youn,et al.  Eigenvector dimension reduction (EDR) method for sensitivity-free probability analysis , 2008 .

[37]  Khairul Salleh Mohamed Sahari,et al.  Numerical investigation on Coanda flow over a logarithmic surface , 2015 .

[38]  Rahul Sharma,et al.  Deep-sea mining: Economic, technical, technological, and environmental considerations for sustainable development , 2011 .