Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Ouro-Koura, Habilou; Jung, Hyunjun; Li, Jinglun; Borca-Tasciuc, Diana-Andra; Copping, Andrea E.; Deng, Zhiqun Daniel

doi:10.1016/j.energy.2024.131660

Predictive model using artificial neural network to design phase change material-based ocean thermal energy harvesting systems for powering uncrewed underwater vehicles

Journal Article · Wed May 15 00:00:00 EDT 2024 · Energy

DOI:https://doi.org/10.1016/j.energy.2024.131660· OSTI ID:2496206

^[1]; Jung, Hyunjun ^[2]; ^[3]; ^[4]; Copping, Andrea E. ^[2]; ^[5]

Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Rensselaer Polytechnic Inst., Troy, NY (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)
ASM America, Phoenix, AZ (United States)
Rensselaer Polytechnic Inst., Troy, NY (United States)
Pacific Northwest National Laboratory (PNNL), Richland, WA (United States); Univ. of Michigan, Ann Arbor, MI (United States)

Uncrewed Underwater Vehicles (UUVs) are a major beneficiary of the phase change material (PCM)-based ocean thermal energy harvesting technology for their mission needs. However, this technology relies on different parameters and energy conversion steps that could be critical to the general energy generation efficiency. Sea trials showed that the design performed lower than their laboratory design specifications. This underperformance results from different factors, mainly the UUV’s trajectory, travel time, underwater ocean currents, temperature fluctuations, and biofouling on the heat exchanger due to long term underwater operations. Therefore, there exists a need to continuously monitor the ambient energy harvesting system and predict system performance, for mission planning purposes. Two major parameters influencing the energy harvesting system include the final pressure inside the hydraulic energy storage vessel or accumulator, and the electrical load value. Here, this work focuses on the hydraulic to electric energy conversion system. Therefore, a combination of numerical model and experimental testing is used to develop a predictive model using artificial neural network using MATLAB. After validation with experimental testing, 1000 data samples obtained from the numerical model are used to train the ANN. Compared to the experimental results, the developed ANN model can predict in less than a second the designed benchtop system’s total efficiency with less than 15 percent maximum error range. This predictive model development represents a cost-effective way for optimization and a computational energy efficient mode aboard UUVs for mission planning for deployed UUVs using PCM-based ocean thermal energy harvesting technology.

View Accepted Manuscript (DOE)

Research Organization:: Pacific Northwest National Laboratory (PNNL), Richland, WA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Water Power Technologies Office

Grant/Contract Number:: AC05-76RL01830; SC0014664

OSTI ID:: 2496206

Report Number(s):: PNNL-SA--189773

Journal Information:: Energy, Journal Name: Energy Vol. 301; ISSN 0360-5442

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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