Cognitive Digital Twin for Emergency Survival Decision-Support in Deep-Ocean Submersible Matsya6000

Abstract

In deep-ocean human submersibles, effective management of on-board oxygen, energy and human cabin microclimate are essential for survivability of crew during emergency situations. This article describes the development and validation of Prana and Garuda modules of Situation-aware Cognitive Digital Twin Chaitanya, based on machine-learnt ocean environment, battery performance under varied temperature conditions, Matsya’s hydrodynamics, crew physiology and thermal model of human cabin. As a digital co-pilot, Prana and Garuda modules of Chaitanya supports the mission with advisories for maintaining conducive human cabin microclimate, ensuring availability of propulsion power during delayed retrievals and optimizing oxygen usage for crew survivability beyond emergency periods. The novelty of this work lies in the development of a situation-aware Cognitive Digital Twin that integrates machine-learnt ocean environment prediction, thermal–physiological cabin modelling, hydrodynamic behavior, and battery performance under temperature variations. Unlike existing literature that treats life-support, hydrodynamics, and energy systems independently, the proposed Prana and Garuda modules operate as AI-enabled digital co-pilots that jointly optimize oxygen usage, cabin microclimate, and propulsion survivability during emergency retrieval delays. The oxygen consumption model of Prana is found to match the crew oxygen consumption during surface floating condition logged during the field tests with an accuracy of 96% and is used for training the Cognitive Digital Twin. Simulation results of Garuda indicate that residual propulsion energy of 20 kWh could support 64 h of subsurface hovering at 220 m water depth in Central Indian Ocean.

Keywords

Cognitive digital twin / Human scientific submersible / Matsya6000 / Life support cabin / Flow rate estimation

References

[1] ANSI/ASHRAE. ANSI/ASHRAE Standard 55-2020-Thermal environmental conditions for human occupancy, 2025[Accessed on July 23, 2025]

[2] Chen K, Luo J, Huang Y. Impact of low temperature exposure on lithium-ion batteries: A multi scale study of performance degradation, predictive signals, and underlying mechanisms. Chemical Engineering Journal, 2025, 503: 158260

[3] ISO. ISO 22252: 2020, Manned submersibles-Breathing air supply and CO₂ absorption systems-Performance requirements and recommendations, 20201st EdnGeneva, International Organization for Standardization

[4] Immas A, Do N, Alam MR. Real time in situ prediction of ocean currents. Ocean Engineering, 2021, 228: 108922

[5] Jiang L, Ye C, Li H, Wang Y, Jiang N. Analysis of different standards for life support technology in manned submersible. Front. Physiol., 2024, 15: 1304250

[6] Jyothi VBN, Ramesh R, Vedachalam N, Ramadass GA. Assessment of the technological maturity of manned submersible navigation positioning system. Marine Technology Society Journal, 2021, 55(5): 1948-1209

[7] Kohnen W. MTS Manned underwater vehicles 2017–2018 Global industry overview. Marine Technology Society Journal, 2018, 52(5): 125-151

[8] Majumdar D, Chatterjee S. Modeling accumulation of respiratory CO₂ in closed rooms leading to decision making on room occupancy. MAPAM Journal of Metrology Society of India, 2020, 35(3): 323-332

[9] Palaniappan M, Bala Naga Jyothi V, Chowdhary T, Sathiyanarayanan D, Vedachalam N. A method for evaluating safety reliability descend/ascend drop weight system and mission abort decision for deep ocean scientific human submersibles. Scientific Reports, 2024, 14: 19266

[10] Ramesh S, Ramadass GA, Ravichandran M, Atmanand MA. Dissolved oxygen as a tracer for intermediate water mixing characteristics in the Indian Ocean. Research Communications, Current Science, 2013, 105(12): 1724-1729

[11] Ravichandran M. India’s deep ocean mission. Current Science, 2025, 128: 227-228

[12] Roger C. No time on our side, 1975, New York, USA, W. W. Norton & Company Inc.

[13] Sagalevich AM. 30 years experience of Mir submersibles for the ocean operations. Deep Sea Research Part II: Tropical Studies in Oceanography, 2018, 155: 83-95

[14] Trstenjak M, Benesova A, Opetuk T, Cajner H. Human factors and ergonomics in Industry 5.0- A systematic literature review. Applied Sciences, 2025, 15(4): 2123

[15] Uc Castillo JL, Marin Celestino AE, Martinez Cruz DA, Tuxpan Vargas J, Ramos Leal JA, Morán Ramirez J. A systematic review of machine learning and deep learning approaches in Mexico, challenges and opportunities. Frontier Artificial Intelligence, 2024, 7: 1479855

[16] Vedachalam N. Ampacity derating analysis of winch-wound power cables: A study based on deep-water ROV umbilical. IEEE Journal of Oceanic Engineering, 2016, 412462-470

[17] Zhang T, Tang J, Qin S, Wang X. Review of navigation and positioning of deep sea manned submersibles. Journal of Navigation, 2019, 72(4): 1021-1034

RIGHTS & PERMISSIONS

Harbin Engineering University and Springer-Verlag GmbH Germany, part of Springer Nature