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The advent of artificial intelligence has sparked transformative changes across various sectors, including marine environments.
Researchers at Osaka Metropolitan University have introduced a novel machine learning-driven fluid simulation model that dramatically cuts down computation time while maintaining high accuracy. This innovative method holds promise for diverse applications ranging from offshore energy production to the design of vessels and real-time monitoring of ocean conditions.
Precise predictions of fluid dynamics are essential for industries that depend on harnessing wave and tidal energy, as well as those involved in building maritime infrastructure and vessels. Traditional particle methods, which use particles to mimic fluid flow, are prevalent but are known for their hefty demands in terms of computing resources and time. By streamlining and expediting fluid simulations, AI-enhanced surrogate models are revolutionizing research in fluid dynamics.
Nonetheless, the application of AI is not without challenges.
“While AI excels at solving specific problems, it can struggle when placed in different scenarios,” remarked Takefumi Higaki, assistant professor at Osaka Metropolitan University’s Graduate School of Engineering and the principal author of the study.
To forge a consistently rapid and precise tool, the research team created a new surrogate model leveraging graph neural networks—an advanced deep learning technology. They initiated their process by assessing various training conditions to identify essential factors that contribute to highly accurate fluid calculations. Following this, they systematically tested their model’s adaptability to varying simulation speeds, and different fluid dynamics.
The outcomes revealed impressive generalization abilities across a spectrum of fluid behaviors.
“Our model provides an accuracy level comparable to that of traditional particle-based simulations across multiple fluid scenarios, while slashing computation time from approximately 45 minutes down to merely three minutes,” stated Higaki.
This research signifies a substantial advancement in high-performance fluid simulation, presenting a flexible and broadly applicable solution that effectively balances precision with speed. The implications of these enhancements extend beyond academic inquiry.
“Enhanced speed and precision in fluid simulations can significantly expedite the design processes for maritime vessels and offshore energy installations,” Higaki noted. “Moreover, they facilitate real-time analysis of fluid behavior, which has the potential to optimize the efficiency of oceanic energy systems.”
The findings were published in Applied Ocean Research.
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