AI Slashes Fluid Simulation Times Fifteenfold

Osaka researchers have developed an AI model that performs complex fluid simulations in minutes instead of hours, potentially transforming offshore engineering while maintaining high accuracy. This advancement could accelerate development cycles for maritime technologies and enable real-time monitoring systems previously considered computationally impossible.

Traditional particle-based fluid simulations, essential for predicting wave behavior in oceanic applications, typically require extensive computational resources. The new graph neural network (GNN) approach developed at Osaka Metropolitan University reduces computation time from approximately 45 minutes to just 3 minutes while preserving simulation quality.

“AI can deliver exceptional results for specific problems but often struggles when applied to different conditions,” said Takefumi Higaki, assistant professor at Osaka Metropolitan University’s Graduate School of Engineering and lead researcher on the study.

The breakthrough addresses AI’s notorious challenge with generalization. Previous machine learning models for fluid dynamics have struggled when confronted with scenarios different from their training data. The Osaka team systematically evaluated multiple approaches to create a model capable of handling varied fluid phenomena with consistent accuracy.

For investors monitoring computational technology markets, advancements in computational fluid dynamics (CFD) are converging with several high-growth sectors. The global CFD market was valued at approximately $2.6 billion in 2023 and is projected to reach $5.3 billion by 2033, growing at a compound annual growth rate (CAGR) of 7.2%, according to Allied Market Research. Other major forecasts place the 2030 market near $4.2 billion, as highlighted in reports by GlobeNewswire, with annual growth estimates ranging from 6.8% to 9.5% depending on the source, such as Technavio, Digital Engineering 24/7, and DataHorizzon Research.

Marine engineering is a particularly significant segment within this expanding market. CFD is transforming the design and optimization of vessels, offshore structures, and propulsion systems by providing precise simulations of hydrodynamic performance, fuel efficiency, and operational stability. Applications in this area are showcased by MR-CFD, Regalia Marine, and FLOW-3D. Adoption is accelerating as the industry seeks to address challenges like fuel efficiency, emission standards, and operational safety.

Meanwhile, AI acceleration hardware is rapidly evolving to support physics simulations. Technologies such as AI-accelerated solvers and specialized processors (notably NVIDIA GPUs) are enabling dramatic improvements in simulation speed and efficiency—sometimes reducing analysis times from hours to seconds, as detailed in this Nafems article. NVIDIA’s announcement of Newton, an open-source physics engine for robotics simulation, illustrates how these advances are opening new opportunities for specialized hardware in engineering and scientific applications, further enhancing the performance and scalability of CFD and related technologies.

The research also carries implications for renewable energy development. Offshore wind turbine design and tidal energy systems require precise fluid modeling to optimize placement and energy capture. Faster simulation capabilities could compress development timelines for these technologies while improving their economic viability through more accurate performance forecasting.

What separates this research from previous efforts is its methodical approach to improving generalization. Rather than focusing solely on speed, the team systematically analyzed what features were essential for accuracy across different simulation conditions. Their graph neural network-based simulator with wall boundary nodes and pressure estimation (GNS-WP) maintained high precision even when applied to fluid scenarios not included in its training data.

The model’s ability to handle larger time steps than conventional methods is particularly notable. While traditional computational fluid dynamics (CFD) approaches face stability issues when using larger time increments, the AI model remained stable and accurate with time steps up to ten times larger than its training data.

“Faster and more precise fluid simulations can mean a significant acceleration in the design process for ships and offshore energy systems,” Higaki noted. “They also enable real-time fluid behavior analysis, which could maximize the efficiency of ocean energy systems.”

For maritime technology developers, the implications are substantial. Ship hull designs typically undergo extensive fluid dynamics testing before physical prototypes are built. Accelerating this process from hours to minutes could translate to weeks or months saved during design iterations. Similarly, offshore platform designers could evaluate more design variations in less time, potentially leading to more optimized structures.

While the current research focused on two-dimensional fluid simulations, the team plans to extend their approach to more complex three-dimensional scenarios. They’re also exploring the potential to train models directly from experimental data rather than traditional simulation outputs, which could further enhance real-world applicability.

As computational resources become increasingly constrained relative to simulation demands, approaches that maintain accuracy while dramatically reducing processing requirements will likely find rapid adoption across industries. This research represents a significant step toward making sophisticated fluid dynamics accessible to a broader range of applications and users.

The study was published in the January 2025 issue of Applied Ocean Research.