Engineering topics

Sprouse, Trevor Michael

Publications and source records attributed to Sprouse, Trevor Michael.

3 records

DOE OSTI2024

Emergent Nucleosynthesis from a 1.2 s Long Simulation of a Black Hole Accretion Disk

We simulate a black hole accretion disk system with full-transport general relativistic neutrino radiation magnetohydrodynamics for 1.2 s. This system is likely to form after the merger of two compact objects and is thought to be a robust site of r-process nucleosynthesis. We consider the case of a black hole accretion disk arising from the merger of two neutron stars. Our simulation time coincides with the nucleosynthesis timescale of the r-process (~1 s). Because these simulations are time-consuming, it is common practice to run for a “short” duration of approximately 0.1–0.3 s. We analyze the nucleosynthetic outflow from this system and compare the results of stopping at 0.12 and 1.2 s. We find that the addition of mass ejected in the longer simulation as well as more favorable thermodynamic conditions from emergent viscous ejecta greatly impacts the nucleosynthetic outcome. We quantify the error in nucleosynthetic outcomes between short and long cuts.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2022

β — -delayed fission in the coupled quasiparticle random-phase approximation plus Hauser-Feshbach approach

Beta-delayed neutron emission and β-delayed fission (βdf) probabilities were calculated for heavy, neutronrich nuclei using the Los Alamos coupled quasiparticle random-phase approximation plus Hauser-Feshbach (QRPA + HF) approach. In this model, the compound nucleus is initially populated by β decay and is followed through subsequent statistical decays taking into account competition among neutrons, γ rays, and fission. The primary output of these calculations includes branching ratios along with neutron and γ -ray spectra. Here, we find a relatively large region of heavy nuclides where the probability of βdf is near 100%. For a subset of nuclei near the neutron drip line, delayed neutron emission and the probability of fission are both large, which leads to the possibility of multichance βdf (mc-βdf). We comment on prospective neutron-rich nuclei that could be probed by future experimental campaigns and provide a full table of branching ratios in ASCII format in the Supplemental Material for use in various applications.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Nuclear masses learned from a probabilistic neural network

Machine learning methods and uncertainty quantification have been gaining interest throughout the last several years in low-energy nuclear physics. In particular, Gaussian processes and Bayesian neural networks have increasingly been applied to improve mass model predictions while providing well-quantified uncertainties. In this work, we use the probabilistic Mixture Density Network (MDN) to directly predict the mass excess of the 2016 Atomic Mass Evaluation within the range of measured data, and we extrapolate the inferred models beyond available experimental data. The MDN provides not only mean values but also full posterior distributions both within the training set and extrapolated testing set. We show that the addition of physical information to the feature space increases the accuracy of the match to the training data as well as provides for more physically meaningful extrapolations beyond the the limits of experimental data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗