Computational methods in fluid research have been progressing during the past few years, driven by the incorporation of massive amounts of data, either in textual or graphical form, generated from multi-scale simulations, laboratory experiments, and real data from the field. Artificial Intelligence (AI) and its adjacent field, Machine Learning (ML), are about to reach standardization in most fields of computational science and engineering, as they provide multiple ways for extracting information from data that turn into knowledge, with the aid of portable software implementations that are easy to adopt. There is ample information on the historical and mathematical background of all aspects of AI/ML in the literature. Thus, this review article focuses mainly on their impact on fluid research at present, highlighting advances and opportunities, recognizing techniques and methods having been proposed, tabulating, and testing some of the most popular algorithms that have shown significant accuracy and performance on fluid applications. We also investigate algorithmic accuracy on several fluid datasets that correspond to simulation results for the transport properties of fluids and suggest that non-linear, decision tree-based methods have shown remarkable performance on reproducing fluid properties.

<abstract> <p>This paper incorporates a number of parameters, such as nanopore size, wall wettability, and electric field strength, to assess their effect on ion removal from nanochannels filled with water. Molecular dynamics simulations are incorporated to monitor the process and a numerical database is created with the results. We show that the movement of ions in water nanochannels under the effect of an electric field is multifactorial. Potential energy regions of various strength are formed inside the nanochannel, and ions are either drifted to the walls and rejected from the solution or form clusters that are trapped inside low potential energy regions. Further computational investigation is made with the incorporation of machine learning techniques that suggest an alternative path to predict the water/ion solution properties. Our test procedure here involves the calculation of diffusion coefficient values and the incorporation of four ML algorithms, for comparison reasons, which exploit MD calculated results and are trained to predict the diffusion coefficient values in cases where no simulation data exist. This two-fold computational approach constitutes a fast and accurate solution that could be adjusted to similar ion separation models for property extraction.</p> </abstract>