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

Identification of marginal and joint CDFs using bivariate type I interval multiply censored data for RBDO of a pick-up device of a pilot mining robot

“…Most of these requirements are significantly affected by environmentally uncontrollable factors such as the shear strength of seafloor sediment, speed of the bottom current, size and mass of manganese nodules, and their chemical and mineralogical composition (Lee et al 2007;Choi et al 2011;Park et al 2014;Kim et al 2019b). The identification of the probability distributions or statistical modeling of these uncontrollable environmental uncertainties is an essential prerequisite for improving the prediction of various performances of the deepsea pilot mining robot and applying various uncertainty-based design optimization techniques (Park et al 2014;Cho et al 2016;Kim et al 2019a).…”

“…The Coandă effect-based pick-up device, which is illustrated in Fig. 13, has received much attention in recent years and has been adopted to the mining robot because of its simple structure, high reliability, and robustness against altitude variations in the seafloor (Kim et al 2019a). The Coandă effect is the tendency of a fluid to cling or to stay attached to a curved surface that is near an orifice from which the fluid emerges (Reba 1966).…”

“…14. The numerical simulation of the Coandă effectbased pick-up device that has been developed as an inhouse code in the previous study performed by our research team was used (Kim et al 2019a). To accurately predict the collection efficiency of the Coandă effectbased pick-up device mounted at the pilot mining robot, the size and mass of the manganese nodules are fundamentally important.…”

“…12 Illustration of a deep-sea mining system (Kim et al 2019b) Fig. 13 Pick-up device using the Coandă effect (Kim et al 2019a) manganese nodules are presented in Figs. 15 and 16, respectively.…”

“…The RBDO of the pick-up device of the pilot mining robot is performed by using the probability distribution of size and mass of manganese nodules. As the joint probability distribution was directly identified using the bivariate type I interval multiply censored manganese nodule data, the collection efficiency of the mining robot was substantially improved compared with RBDO using the processed data (Kim et al 2019a).…”