Author
A. Mitra
Other affiliations: Budge Budge Institute of Technology
Bio: A. Mitra is an academic researcher from Indian Institute of Technology Kharagpur. The author has contributed to research in topics: Turbulence & Reynolds stress. The author has an hindex of 5, co-authored 9 publications receiving 96 citations. Previous affiliations of A. Mitra include Budge Budge Institute of Technology.
Topics: Turbulence, Reynolds stress, Drag, Underwater, Turbulence kinetic energy
Papers
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01 Feb 2021
TL;DR: Autonomous underwater vehicles play an essential role in geophysical data collection, deep water mining, seafloor mapping, ocean exploration, and in many other related activities starting from mili... as mentioned in this paper.
Abstract: Autonomous underwater vehicles play an essential role in geophysical data collection, deep water mining, seafloor mapping, ocean exploration, and in many other related activities starting from mili...
37 citations
29 citations
TL;DR: In this article, experimental and numerical studies carried out in conjunction, to investigate the hydrodynamic characteristics of AUV hulls at different Reynolds numbers over sloped channel-beds are presented.
27 citations
TL;DR: In this paper, experimental and numerical studies on the effect of free stream turbulence (FST) on evolution of flow over an AUV hull form at three Reynolds numbers with different submergence depths and angles of attack.
25 citations
TL;DR: In this article, the authors present experimental and numerical analysis of grid generated turbulence with and without the effects of applied mean strain. But the experimental data of turbulence statistics including Reynolds stress anisotropies is collected, analyzed and then compared to the predictions of Reynolds stress models to assess their accuracy.
21 citations
Cited by
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TL;DR: Current research trends in the field of AUVs and future research directions are presented and localization and navigation techniques such as inertial navigation to simultaneous localization and mapping being used in current AUVs are discussed in detail.
250 citations
TL;DR: Gaussian process regression is combined with sliding mode control for the dynamic positioning of underwater robotic vehicles to enhance the tracking performance, by predicting unknown hydrodynamic effects and compensating for them.
Abstract: Sliding mode control is a very effective strategy in dealing not only with parametric uncertainties, but also with unmodeled dynamics, and therefore has been widely applied to robotic agents. However, the adoption of a thin boundary layer neighboring the switching surface to smooth out the control law and to eliminate the undesired chattering effect usually impairs the controller’s performance and leads to a residual tracking error. As a matter of fact, underwater robots are very sensitive to this issue due to their highly uncertain plants and unstructured operating environments. In this work, Gaussian process regression is combined with sliding mode control for the dynamic positioning of underwater robotic vehicles. The Gaussian process regressor is embedded within the boundary layer in order to enhance the tracking performance, by predicting unknown hydrodynamic effects and compensating for them. The boundedness and convergence properties of the tracking error are analytically proven. Numerical results confirm the improved performance of the proposed control scheme when compared with the conventional sliding mode approach.
37 citations
01 Feb 2021
TL;DR: Autonomous underwater vehicles play an essential role in geophysical data collection, deep water mining, seafloor mapping, ocean exploration, and in many other related activities starting from mili... as mentioned in this paper.
Abstract: Autonomous underwater vehicles play an essential role in geophysical data collection, deep water mining, seafloor mapping, ocean exploration, and in many other related activities starting from mili...
37 citations
TL;DR: In this paper , the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity is explored.
Abstract: This paper aims to provide an overview of the capabilities of unmanned systems to monitor and manage aquaculture farms that support precision aquaculture using the Internet of Things. The locations of aquaculture farms are diverse, which is a big challenge on accessibility. For offshore fish cages, there is a difficulty and risk in the continuous monitoring considering the presence of waves, water currents, and other underwater environmental factors. Aquaculture farm management and surveillance operations require collecting data on water quality, water pollutants, water temperature, fish behavior, and current/wave velocity, which requires tremendous labor cost, and effort. Unmanned vehicle technologies provide greater efficiency and accuracy to execute these functions. They are even capable of cage detection and illegal fishing surveillance when equipped with sensors and other technologies. Additionally, to provide a more large-scale scope, this document explores the capacity of unmanned vehicles as a communication gateway to facilitate offshore cages equipped with robust, low-cost sensors capable of underwater and in-air wireless connectivity. The capabilities of existing commercial systems, the Internet of Things, and artificial intelligence combined with drones are also presented to provide a precise aquaculture framework.
27 citations