... Emerald Publishing Limited Licensed re-use rights only Ellis nanofluid Internal heating Thermal radiation Machine learning Mixed convection Artificial neural network algorithm α = Material parameter (–); γ = Curvature parameter (–); N = Local...

... storage system efficiency by integrating numerical simulations with machine learning for enhanced predictive accuracy. Design/methodology/approach The incompressible smoothed particle hydrodynamics (ISPH) method is used for solving the governing equations of heat, mass and momentum transfer...

... are used. Results, performance, accuracy and correlation are examined with neural networking, Levenberg-Marquardt, machine learning, artificial intelligence (AI) algorithms and linear regression. Findings Numerical and graphical results are presented to observe the impact of physical parameters...

... in flows that have high non-equilibrium effects like separation or transition. This study aims to present a novel methodology of using high-fidelity data and machine learning (ML) techniques to capture these non-equilibrium effects. Design/methodology/approach A precursor to this methodology has...

... the control theory. This paper aims to propose a hybrid method (i.e. machine learning and control theory) for feedback control of fluid flows, by which the flow is mapped to the latent space in such a way that the linear control theory can be applied therein. Design/methodology/approach The authors...

... correlations from the literature. Originality/value Techniques of machine learning, such as filling methods, were used to improve the performance of the neural models. Besides the typical comparisons with conventional correlations, comparisons with three main equations derived from multiple regression...

... Deep learning Machine learning Data exchange AI = Artificial intelligence ML = Machine learning DL = Deep learning DT = Digital twin FEM = Finite element method (D)NN = (Deep) neural network SL = Supervised learning DRL = Deep reinforcement...

... I ) 2 + ( δ A A ) 2 ] 1 2 Table 3 shows the results of the uncertainty analysis associated with the measurements done in this present study. Reliability Two-phase Dynamic loading Machine learning Data centre Predictive model...

...Cornelia Grabe; Florian Jäckel; Parv Khurana; Richard P. Dwight Purpose This paper aims to improve Reynolds-averaged Navier Stokes (RANS) turbulence models using a data-driven approach based on machine learning (ML). A special focus is put on determining the optimal input features used for the ML...

... made. Rainald Löhner can be contacted at: rlohner@gmu.edu 01 11 2020 05 12 2020 06 12 2020 © Emerald Publishing Limited 2020 Emerald Publishing Limited Licensed re-use rights only Machine learning Deep neural networks Forced and natural convection Interpolation...