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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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DOE OSTI2022

Physics-informed Machine Learning for Modeling Turbulence in Supernovae

Abstract Turbulence plays an important role in astrophysical phenomena, including core-collapse supernovae (CCSNe), but current simulations must rely on subgrid models, since direct numerical simulation is too expensive. Unfortunately, existing subgrid models are not sufficiently accurate. Recently, machine learning (ML) has shown an impressive predictive capability for calculating turbulence closure. We have developed a physics-informed convolutional neural network to preserve the realizability condition of the Reynolds stress that is necessary for accurate turbulent pressure prediction. The applicability of the ML subgrid model is tested here for magnetohydrodynamic turbulence in both the stationary and dynamic regimes. Our future goal is to utilize this ML methodology (available on GitHub) in the CCSN framework to investigate the effects of accurately modeled turbulence on the explosion of these stars.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1994

Research in astrophysical processes

Work completed under this grant is summarized in the following areas:(1) radio pulsar turn on and evaporation of companions in very low mass x-ray binaries and in binary radio pulsar systems; (2) effects of magnetospheric pair production on the radiation from gamma-ray pulsars; (3) radiation transfer in the atmosphere of an illuminated companion star; (4) evaporation of millisecond pulsar companions;(5) formation of planets around pulsars; (6) gamma-ray bursts; (7) quasi-periodic oscillations in low mass x-ray binaries; (8) origin of high mass x-ray binaries, runaway OB stars, and the lower mass cutoff for core collapse supernovae; (9) dynamics of planetary atmospheres; (10) two point closure modeling of stationary, forced turbulence; (11) models for the general circulation of Saturn; and (12) compressible convection in stellar interiors.

Ruderman, Malvin A.↗

DOE OSTI2023

Neutrino-driven Winds in Three-dimensional Core-collapse Supernova Simulations

Abstract In this paper, we analyze the neutrino-driven winds that emerge in 12 unprecedentedly long-duration 3D core-collapse supernova simulations done using the code F ornax . The 12 models cover progenitors with zero-age main-sequence mass between 9 and 60 solar masses. In all our models, we see transonic outflows that are at least 2 times as fast as the surrounding ejecta and that originate generically from a proto−neutron star surface atmosphere that is turbulent and rotating. We find that winds are common features of 3D simulations, even if there is anisotropic early infall. We find that the basic dynamical properties of 3D winds behave qualitatively similarly to those inferred in the past using simpler 1D models, but that the shape of the emergent wind can be deformed, very aspherical, and channeled by its environment. The thermal properties of winds for less massive progenitors very approximately recapitulate the 1D stationary solutions, while for more massive progenitors they deviate significantly owing to aspherical accretion. The Y e temporal evolution in winds is stochastic, and there can be some neutron-rich phases. Though no strong r -process is seen in any model, a weak r -process can be produced, and isotopes up to 90 Zr are synthesized in some models. Finally, we find that there is at most a few percent of a solar mass in the integrated wind component, while the energy carried by the wind itself can be as much as 10%–20% of the total explosion energy.

79 ASTRONOMY AND ASTROPHYSICS↗