What are the challenges and opportunities of using machine learning for turbulence modeling?

1 ML techniques for turbulence modeling

ML is a branch of artificial intelligence that uses algorithms and data to learn from patterns and make predictions. ML techniques can be applied to different aspects of turbulence modeling, such as closure models, subgrid-scale models, wall models, and uncertainty quantification. Some of the common ML techniques for turbulence modeling are artificial neural networks (ANNs), genetic algorithms (GAs), support vector machines (SVMs), and random forests (RFs). These techniques can help improve the accuracy, efficiency, and robustness of turbulence models by learning from experimental or numerical data.

2 Challenges of using ML for turbulence modeling

Despite the potential benefits of ML for turbulence modeling, there are also several challenges that need to be addressed. One of the main challenges is the availability and quality of data. Turbulent flows are highly nonlinear, chaotic, and multiscale, which makes them difficult to measure and simulate. Data scarcity, noise, bias, and uncertainty can affect the performance and reliability of ML models. Another challenge is the interpretability and generalization of ML models. ML models often act as black boxes, meaning that they do not provide physical insight or explanation for their predictions. Moreover, ML models may not be able to extrapolate well to new or different flow conditions, geometries, or regimes.

3 Opportunities of using ML for turbulence modeling

Despite the challenges, ML also offers many opportunities for advancing turbulence modeling in naval architecture. One of the opportunities is the integration of ML with physics-based models, such as RANS or large-eddy simulation (LES). This can help overcome some of the limitations of both approaches and leverage their strengths. For example, ML can be used to correct or augment the closure terms in RANS models, or to model the unresolved scales in LES models. Another opportunity is the use of ML for design optimization and control of turbulent flows. ML can help identify the optimal parameters or strategies to reduce drag, noise, vibration, or cavitation in marine vehicles and structures. ML can also help develop active or passive control methods to manipulate or suppress turbulence.

4 Examples of using ML for turbulence modeling in naval architecture

There are already some examples of using ML for turbulence modeling in naval architecture, both in research and practice. For instance, researchers have used ANN to improve the prediction of skin friction and pressure coefficients for ship hulls, or to model the effect of free surface waves on turbulent boundary layers. Practitioners have used GA to optimize the shape and position of appendages, such as rudders, propellers, or fins, to enhance the hydrodynamic performance of ships. These examples demonstrate the feasibility and applicability of ML for turbulence modeling in naval architecture.

5 Tips and resources for using ML for turbulence modeling

If you are interested in using ML for turbulence modeling in naval architecture, here are some tips and resources to help you get started. First, you need to have a solid background in fluid mechanics, turbulence theory, and numerical methods. You also need to have some basic knowledge of ML concepts, such as supervised and unsupervised learning, classification and regression, and validation and testing. Second, you need to have access to relevant and reliable data sources, such as experimental facilities, computational fluid dynamics (CFD) software, or open databases. You also need to have the appropriate tools and platforms to process, analyze, and visualize the data, such as Python, MATLAB, or TensorFlow. Third, you need to keep up with the latest developments and trends in the field, such as new algorithms, models, or applications. You can do this by reading journals, books, blogs, or podcasts, or by attending workshops, webinars, or conferences.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?