In a groundbreaking study led by Ahmed Hadi from the Department of Maritime and Transport Technology at Delft University of Technology, researchers have tackled the complex challenge of segregation in granular materials—a concern that resonates across various industries, including maritime operations. This innovative research, published in ‘Scientific Reports’, explores how adaptive machine learning techniques can streamline the calibration of discrete element method (DEM) simulations, ultimately paving the way for more efficient handling of bulk materials like grains, coal, and even ship ballast.
Segregation of granular materials is a common headache, especially when dealing with mixtures that can separate during transport or storage. The traditional approach to understanding this phenomenon involves extensive DEM simulations, which can be both time-consuming and resource-intensive. Hadi and his team have introduced machine learning-based surrogate models (SMs) to simplify this process. These SMs can predict how different parameters affect segregation, significantly cutting down the amount of data needed for accurate modeling.
One of the standout features of this research is the incorporation of transfer learning (TL). This technique allows the model to adapt knowledge from previously studied scenarios to new, unseen situations. Hadi explains, “By leveraging just a handful of samples from new scenarios, we can enhance the model’s performance without the need for a full dataset.” This is particularly valuable in maritime contexts where conditions can vary widely—think different cargo types or varying sea states.
The implications for the maritime sector are substantial. Efficient calibration of DEM models means that shipping companies can optimize how they load and transport materials, reducing the risk of segregation and ensuring cargo integrity. Moreover, the ability to adapt models quickly to new conditions can lead to improved safety and operational efficiency.
Hadi’s findings indicate that Gaussian process regression (GPR) stands out as an effective method for building these SMs, even with minimal data. The study shows that incorporating just a few samples from new scenarios can improve model accuracy by a remarkable 17% to 47%. This not only streamlines the modeling process but also reduces the workload for engineers and researchers.
The research opens up exciting opportunities for maritime professionals looking to enhance their operations. By adopting these advanced modeling techniques, companies can gain a competitive edge through better material handling and reduced operational risks. As the maritime industry continues to evolve, leveraging such innovative technologies will be crucial for staying ahead of the curve.
In summary, Ahmed Hadi’s work at Delft University of Technology sheds light on a promising avenue for managing granular materials more effectively, with far-reaching implications for the maritime sector. The study, published in ‘Scientific Reports’, could very well be a game-changer for how the industry approaches the complex dynamics of material segregation.
