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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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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Tent-induced perturbations on areal density of implosions at the National Ignition Facility

Areal density non-uniformities seeded by time-dependent drive variations and target imperfections in Inertial Confinement Fusion (ICF) targets can grow in time as the capsule implodes, with growth rates that are amplified by instabilities. Here, we report on the first measurements of the perturbations on the density and areal density profiles induced by the membranes used to hold the capsule within the hohlraum in indirect drive ICF targets. The measurements are based on the reconstruction of the ablator density profiles from 2D radiographs obtained using pinhole imaging coupled to area backlighting, as close as 150 ps to peak compression. Our study shows a clear correlation between the modulations imposed on the areal density and measured neutron yield, and a 3× reduction in the areal density perturbations comparing a high-adiabat vs. low-adiabat pulse shape.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Observation of hohlraum-wall motion with spectrally selective x-ray imaging at the National Ignition Facility

The high fuel capsule compression required for indirect drive inertial confinement fusion requires careful control of the X-ray drive symmetry throughout the laser pulse. When the outer cone beams strike the hohlraum wall, the plasma ablated off the hohlraum wall expands into the hohlraum and can alter both the outer and inner cone beam propagations and hence the X-ray drive symmetry especially at the final stage of the drive pulse. To quantitatively understand the wall motion, we developed a new experimental technique which visualizes the expansion and stagnation of the hohlraum wall plasma. Details of the experiment and the technique of spectrally selective x-ray imaging are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Time-Sequenced Flow Field Prediction in an Optima Spark-Ignition Direct-Injection Engine Using Bidirectional Recurrent Neural Network (bi-RNN) with Long Short-Term Memory

To further improve the energy conversion efficiency of internal combustion engine, the transient and complex air flow movement inside the cylinder needs to be better understood and controlled. Although the in-cylinder flow fields are highly stochastic with strong cycle-to-cycle fluctuations, machine learning can still provide an efficient way to learn and regress the complex flow movement process inside the cylinder. In this work, a bidirectional recurrent neural network (bi-RNN) model with long short-term memory was applied to predict the in-cylinder flow fields at different time steps using training data from mull-cycle particle image velocimetry (PIV) measurements. To evaluate the agreement between the true and predicted flow fields, structure and magnitude comparison indices are calculated both globally and locally. The comparison results show that the bi-RNN model can accurately predict the bulk flow and vortex motions from early intake stroke to compression stroke. This work demonstrates that the machine learning model has the potential to predict the underlying dynamics of the interaction between in-cylinder flows and provides a reliable way to improve temporal resolution in PIV flow data to better reveal transient in-cylinder flow features.

Bi-RNN model↗