Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …

Modal-decomposition techniques are computational frameworks based on data aimed at
identifying a low-dimensional space for capturing dominant flow features: the so-called …

The aim of this work is to analyse the formation mechanisms of large-scale coherent
structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant …

This article presents an innovative open-source software named ModelFLOWs-app, 1
written in Python, which has been created and tested to generate precise and robust hybrid …

This study uses higher-order dynamic mode decomposition to analyze a high-fidelity
database of the turbulent flow in an urban environment consisting of two buildings separated …

The objective of this study is to assess the capability of convolution-based neural networks
to predict the wall quantities in a turbulent open channel flow, starting from measurements …

Data-driven modal analysis methods provide a powerful way to decompose data into a sum
of modes. The spatiotemporal Koopman decomposition (STKD) enables the computation of …

Accurately predicting three-dimensional power distribution within a reactor core during
reactivity transients is crucial for optimizing reactor operational control. This paper proposes …

Modal decomposition techniques are important tools for the analysis of unsteady flows and,
in order to provide meaningful insights with respect to coherent structures and their …

Bubble columns play a crucial role in a wide range of industries, including chemical and
biochemical processes, petrochemicals, and environmental engineering. Understanding the …