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White dwarf-neutron star merger simulations show a Ca IR line feature in spectra, pointing to Ca-rich transients as a possible observation (Kaltenborn et al 2024, Gillanders et al 2024). Supernova and new/old kilonova (KN) models have been compared to recent peculiar long gamma-ray burst (GRB) with KN-like signal (Troja et al 2022).
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The Los Alamos National Laboratory (LANL), located in the state of New Mexico (United States), is one of the most iconic research centers in the world. Founded in 1943 as part of the Manhattan Project, it emerged from a global conflict and an unprecedented scientific emergency. At that time, the United States feared that Nazi Germany might develop an atomic weapon first. Under the direction of physicist J. Robert Oppenheimer, the U.S. government established a secret laboratory in an isolated region of the Los Alamos plateau, bringing together some of the greatest scientific minds of the era. This site, then known as Project Y, became the birthplace of the first atomic bomb.
A paradigm shift in chemical risk assessment is emphasizing mixture testing over single compound analysis, eliminating animal testing, and adopting advanced modeling approaches to understand mixture activity profiles. However, existing computational models largely focus on single chemicals, with few effective solutions for modeling complex mixtures that account for synergistic or antagonistic effects and multiple Modes of Action (MoA). Conventional methods like concentration addition (CA) and independent action (IA) are insufficient for this task as they are designed for simplistic interactions and struggle to account for the dynamic and multifaceted nature of chemical mixtures, such as overlapping MoA and non-linear interactions. Finch offers a novel approach utilizing deep learning (DL) embeddings and multi-task quantitative structure-activity relationship (QSAR) models to improve chemical exposure prediction. By leveraging molecular descriptors, physiochemical properties, and large language model (LLM) embeddings from SMILES inputs, Finch preserves critical information in a latent space thereby enhancing predictive accuracy. The multi-task learning aspect of Finch is highly advantageous, as it simultaneously optimizes multiple loss functions, leveraging all available data across tasks to develop generalized representations that effectively capture complex ingredient interactions within mixtures.
Physics Global Summit 2025 Conference
Item purpose – This poster will be presented at the SSA meeting Internal peer review status – This item has completed internal peer review. HQ review status – This item is being sent to HQ for review
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Vertical-axis wind turbines’ simpler design and low center of gravity make them ideal for floating wind applications. However, efficient design optimization of floating systems requires fast and accurate models. Low-fidelity vertical-axis turbine aerodynamic models, including double multiple streamtube and actuator cylinder theory, were created during the 1980s. Commercial development of vertical-axis turbines all but ceased in the 1990s until around 2010 when interest resurged for floating applications. Despite the age of these models, the original assumptions (2-D, rigid, steady, straight bladed) have not been revisited in full. When the current low-fidelity formulations are applied to modern turbines in the unsteady domain, aerodynamic load errors nearing 50% are found, consistent with prior literature. However, a set of steady and unsteady modifications that remove the majority of error is identified, limiting it near 5%. This paper shows how to reformulate the steady models to allow for unsteady inputs including turbulence, deforming blades, and variable rotational speed. A new unsteady approximation that increases numerical speed by 5–10× is also presented. Combined, these modifications enable full-turbine unsteady simulations with accuracy comparable to higher-fidelity vortex methods, but over 5000× faster.