Engineering topics

Gaffney, Jim

Publications and source records attributed to Gaffney, Jim.

2 records

DOE OSTI2022

Hydroscaling indirect-drive implosions on the National Ignition Facility

A goal of the laser-based National Ignition Facility (NIF) is to increase the liberated fusion energy “yield” in inertial confinement fusion experiments well past the ignition threshold and the input laser energy. One method of increasing the yield, hydrodynamic scaling of current experiments, does not rely on improving compression or implosion velocity, but rather increases the scale of the implosion to increase hotspot areal density and confinement time. Indirect-drive (Hohlraum driven) implosions carried out at two target sizes, 12.5% apart, have validated hydroscaling expectations. Moreover, extending comparisons to the best-performing implosions at five different capsule sizes shows that their performance also agrees well with hydroscaling expectations even though not direct hydroscales of one another. In the future, by switching to a reduced loss Hohlraum geometry, simulations indicate that we can drive 20% larger-scale implosions within the current power and energy limitations on the NIF. Finally, at the demonstrated compression and velocity of these smaller-scale implosions, these 1.2× hydroscaled implosions should put us well past the ignition threshold.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Designing accurate emulators for scientific processes using calibration-driven deep models

Abstract Predictive models that accurately emulate complex scientific processes can achieve speed-ups over numerical simulators or experiments and at the same time provide surrogates for improving the subsequent analysis. Consequently, there is a recent surge in utilizing modern machine learning methods to build data-driven emulators. In this work, we study an often overlooked, yet important, problem of choosing loss functions while designing such emulators. Popular choices such as the mean squared error or the mean absolute error are based on a symmetric noise assumption and can be unsuitable for heterogeneous data or asymmetric noise distributions. We propose Learn-by-Calibrating, a novel deep learning approach based on interval calibration for designing emulators that can effectively recover the inherent noise structure without any explicit priors. Using a large suite of use-cases, we demonstrate the efficacy of our approach in providing high-quality emulators, when compared to widely-adopted loss function choices, even in small-data regimes.

97 MATHEMATICS AND COMPUTING↗