Search for: All records

Abstract Immersed boundary methods (IBMs) have evolved over the past 50 years from a specialized technique in biofluid dynamics and applied mathematics to a cornerstone of computational fluid dynamics. Many recent advancements in immersed boundary methods have centered on sharp-interface immersed boundary methods, which offer enhanced accuracy and fidelity for flow simulations. This paper outlines the key principles that have driven our own efforts in the development of sharp-interface immersed boundary methods over the past 25 years. We also highlight the power and versatility of these methods by showcasing a range of applications, spanning biolocomotion (i.e., swimming and flying), physiological flows, compressible aerodynamics, fluid–structure interaction (FSI), and flow-induced noise. 

Per- and polyfluoroalkyl substances (PFASs) have recently garnered considerable concerns regarding their impacts on human and ecological health. Despite the important roles of polyamide membranes in remediating PFASs contaminated water, the governing factors influencing PFAS transport across these membranes remain elusive. In this study, we investigate PFAS rejection by polyamide membranes using two machine learning (ML) models, namely XGBoost and multimodal transformer models. Utilizing the Shapley additive explanation method for XGBoost model interpretation unveils the impacts of both PFAS characteristics and membrane properties on model predictions. The examination of the impacts of chemical structure involves interpreting the multimodal transformer model incorporated with simplified molecular input line entry system strings through heatmaps, providing a visual representation of the attention score assigned to each atom of PFAS molecules. Both ML interpretation methods highlight the dominance of electrostatic interaction in governing PFAS transport across polyamide membranes. The roles of functional groups in altering PFAS transport across membranes are further revealed by molecular simulations. The combination of ML with computer simulations not only advances our knowledge of PFAS removal by polyamide membranes, but also provides an innovative approach to facilitate data-driven feature selection for the development of high-performance membranes with improved PFAS removal efficiency. 

ABSTRACT AST3-2 is the second of the three Antarctic Survey Telescopes, aimed at wide-field time-domain optical astronomy. It is located at Dome A, Antarctica, which is by many measures the best optical astronomy site on the Earth’s surface. Here we present the data from the AST3-2 automatic survey in 2016 and the photometry results. The median 5σ limiting magnitude in i-band is 17.8 mag and the light-curve precision is 4 mmag for bright stars. The data release includes photometry for over 7 million stars, from which over 3500 variable stars were detected, with 70 of them newly discovered. We classify these new variables into different types by combining their light-curve features with stellar properties from surveys such as StarHorse.