Rotary drums are tools used extensively in various prominent industries for their utility in heating and transporting particulate products. These processes are often inefficient and studies on heat transfer in rotary drums will reduce energy consumption as operating parameters are optimized. Research on this subject has been ongoing at ASU; however, the design of the rotary drum used in these studies is restrictive and experiments using radiation heat transfer have not been possible.<br/><br/>This study focuses on recounting the steps taken to upgrade the rotary drum setup and detailing the recommended procedure for experimental tests using radiant heat transfer upon completed construction of the new setup. To develop an improved rotary drum setup, flaws in the original design were analyzed and resolved. This process resulted in a redesigned drum heating system, an altered thinner drum, and a larger drum box. The recommended procedure for radiant heat transfer tests is focused on determining how particle size, drum fill level, and drum rotation rate impact the radiant heat transfer rate.

Graph neural networks (GNN) offer a potential method of bypassing the Kohn-Sham equations in density functional theory (DFT) calculations by learning both the Hohenberg-Kohn (HK) mapping of electron density to energy, allowing for calculations of much larger atomic systems and time scales and enabling large-scale MD simulations with DFT-level accuracy. In this work, we investigate the feasibility of GNNs to learn the HK map from the external potential approximated as Gaussians to the electron density 𝑛(𝑟), and the mapping from 𝑛(𝑟) to the energy density 𝑒(𝑟) using Pytorch Geometric. We develop a graph representation for densities on radial grid points and determine that a k-nearest neighbor algorithm for determining node connections is an effective approach compared to a distance cutoff model, having an average graph size of 6.31 MB and 32.0 MB for datasets with 𝑘 = 10 and 𝑘 = 50 respectively. Furthermore, we develop two GNNs in Pytorch Geometric, and demonstrate a decrease in training losses for a 𝑛(𝑟) to 𝑒(𝑟) of 8.52 · 10^14 and 3.10 · 10^14 for 𝑘 = 10 and 𝑘 = 20 datasets respectively, suggesting the model could be further trained and optimized to learn the electron density to energy functional.