
You have already added 0 works in your ORCID record related to the merged Research product.
You have already added 0 works in your ORCID record related to the merged Research product.
<script type="text/javascript">
<!--
document.write('<div id="oa_widget"></div>');
document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=undefined&type=result"></script>');
-->
</script>Review of Deterministic and AI-Based Methods for Fluid Motion Modelling and Sloshing Analysis
doi: 10.3390/en18051263
Contemporary fluid motion modelling techniques, including the phenomenon of liquid sloshing in tanks, are increasingly associated with the use of artificial intelligence methods. In addition to the still frequently used traditional analysis methods and techniques, such as FEM, CFD, VOF and FSI, there is an increasing number of publications that use elements of artificial intelligence. Among others, artificial neural networks and deep learning techniques are used here in the field of prediction and approximation, as well as genetic and other multi-agent algorithms for optimization. This article analyses of the current state of research using the above techniques and the possibilities and main directions of their further development.
liquid sloshing, modelling, Technology, T, deep learning, artificial intelligence, neural networks
liquid sloshing, modelling, Technology, T, deep learning, artificial intelligence, neural networks
