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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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152 records · Page 9

Understanding Transients: Needs from the Laboratory Astrophysics Community

A growing number of astrophysical transients are pushing astronomers to develop increasingly complex computational tools to model both radiation hydrodynamics and electron transport. Here we review the physics needs and simulation uncertainties associated with modeling these transients. Although this review will focus on theory and simulation aspects of this problem, we also review some experiments designed to study this physics.

79 ASTRONOMY AND ASTROPHYSICS↗

Modeling Hydrodynamic Instabilities, Shocks, and Radiation Waves in High Energy Density Experiments [Dissertation]

This thesis presents the computational design, modeling, and analysis of three experiments in high-energy density physics (HEDP), all of them concerning fundamental radiation flows. The first experiment is a laboratory astrophysics experiment to investigate the role of the Kelvin-Helmholtz instability (KHI) in the process of galactic filaments supplying gas to galactic halos. The achieved goal was to provide a first study in which the role of the instability is maximal and predict behavior in future iterations of an experiment accessing a more radiative regime where the role of the instability is stifled. This experiment would help answer how certain galaxies are able to grow so rapidly and produce many stars as the KHI limits this process. The second experiment, COAX, is a radiation flow experiment with a novel spectroscopy diagnostic configuration, designed to spatially measure the temperature of a radiation wave as it travels down a doped foam. A key result of this work was the development of a synthetic spectroscopy application and application of modern spectroscopy comparison techniques to provide our first temperature reconstructions from the experimental data. This experimental platform serves as the launching ground for a number of new experiments that vary the basic premise and thus is foundational to our ongoing research. The final experiment is a full integration of modeling, design, and theoretical development for the Radishock experiment. This experimental platform studies the head-on collision of a radiation wave with a counter-propagating shock, and like COAX, uses spectroscopy to diagnose and detect the interaction. My research analyzes the successful shots, indicating aspects of successful detections and suggests improvements to future iterations of the design.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

XFOL FY25 Omega 3 pager [Slides]

The XFOL campaign is continuing to study radiation flow in stochastic media. Purpose: Simultaneously measure the ionization state of the background and inclusions of a binary stochastic media using the COAX/OUTI diagnostic. Motivation: Radflow is stochastic media is of wide, growing interest and COAX is a new powerful diagnostic that can characterize the radflow physics and validate codes, models, and their underlying assumptions.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

AI/ML-Enhanced Wind Forecasts for Reducing Uncertainty in Prescribed Fire Planning

Prescribed fire is a vital tool for ecosystem management and wildfire risk reduction but its escalation is constrained by overly conservative burn windows because of uncertainties, for instance, in wind forecasts. This review describes the state of the art in weather product use by fire/smoke models and identifies three priority research gaps that artificial intelligence/machine learning (AI/ML) is well positioned to address: (1) spatial and temporal downscaling to meter-scale, sub-hourly wind fields; (2) bias correction for systematic model errors in complex terrain; and (3) robust uncertainty quantification to inform ensemble-based simulations. Emerging AI/ML techniques offer promising frameworks to address all three challenges. By providing high-resolution, bias-corrected, and probabilistic wind fields, AI/ML-enhanced forecasts will allow for expanded burn windows, improved ignition strategy design and a reduced reliance on expert intuition, especially when a prescribed fire is introduced into new areas.

54 ENVIRONMENTAL SCIENCES↗