Purpose This paper aims to present a numerical study that investigates the flow of MgO-
Al2O3/water hybrid nanofluid inside a porous elliptical-shaped cavity, in which we aim to …

This paper introduces a novel surrogate model for two-dimensional adaptive steady-state
thermal convection fields based on deep learning technology. The proposed model aims to …

To alleviate the computational burden associated with the computational fluid dynamics
(CFD) simulation stage and improve aerodynamic optimization efficiency, this work develops …

This study proposes a deep Hybrid Transformer-Convolutional Neural Network (HTCNN) to
reconstruct the temperature field in the nanofluid-filled Parabolic Trough Collector receivers …

We develop and computationally analyze a mathematical model for natural convection to a
non-Newtonian fluid in a long and thin channel. The channel is bounded by antisymmetric …

This paper proposes a transfer learning approach to reduce the dependence of the neural
network model on dataset size for multi-chip modules (MCMs) thermal estimation. The …

Computational efficiency and precision pose a classic contradiction in aerodynamic shape
optimization. To address this challenge, this study introduces an effective optimization …

Comprehending and managing the transport characteristics of nanofluids is critical for
improving their efficacy in heat transfer applications, thereby improving thermal …

This paper investigates the optimization of the inner thermal layout in nanofluid-filled
horizontal annular pipes under natural convection conditions. Two-dimensional models of …

This work proposes an innovative approach for supersonic flow field modeling around
airfoils based on sparse convolutional neural networks (SCNNs) and Bézier generative …