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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Development of a compact magnetic spectrometer for use at the OMEGA Laser Facility and the National Ignition Facility

Measurement of proton spectra is an important diagnostic for a variety of high energy density physics experiments. Current diagnostics are either not designed to capture the spectrum of low-energy protons or are unsuitable for high debris experiments. To bridge the gap, a new CR-39 based compact magnetic spectrometer (MagSpec) has been developed to measure proton spectra in the 1–20 MeV energy range, with a particular focus on the low-energy (1–6 MeV) spectrum, for use in experiments at the OMEGA Laser Facility and the National Ignition Facility (NIF). In the MagSpec diagnostic, protons of different energies are dispersed as they pass through a magnetic field before impinging on a differentially filtered CR-39 surface, resulting in a spatial distribution of CR-39 tracks that corresponds to the energy spectrum. In this paper, we discuss details of the design and implementation of MagSpec on the NIF and OMEGA.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Platform development for dE/dx measurements on short-pulse laser facilities

We illustrate generating benchmark data on charged-particle stopping power in dense plasma is a challenge, motivating new experimental platforms for facilities such as short-pulse lasers. We report on several experiments that aimed to develop a platform for high-precision stopping power measurements at OMEGA EP, specifically developing target-normal sheath acceleration (TNSA) sources, proton isochoric heating, and radiatively heated foams. Integrated experiments were performed to measure stopping power, but several phenomena have been identified that would need to be mitigated for a stopping-power measurement, including irreproducibility of the TNSA sources, spurious electromagnetic fields around proton-heated targets, and cross-talk between the source and subject targets. This work provides a basis for potential future work towards mitigating these problems and performing stopping-power measurements, and is also valuable data for general understanding of TNSA and proton isochoric heating experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The Effect of Intensity Fluctuations on Sequential X-ray Photon Correlation Spectroscopy at the X-ray Free Electron Laser Facilities

How materials evolve at thermal equilibrium and under external excitations at small length and time scales is crucial to the understanding and control of material properties. X-ray photon correlation spectroscopy (XPCS) at X-ray free electron laser (XFEL) facilities can in principle capture dynamics of materials that are substantially faster than a millisecond. However, the analysis and interpretation of XPCS data is hindered by the strongly fluctuating X-ray intensity from XFELs. Here we examine the impact of pulse-to-pulse intensity fluctuations on sequential XPCS analysis. We show that the conventional XPCS analysis can still faithfully capture the characteristic time scales, but with substantial decrease in the signal-to-noise ratio of the g2 function and increase in the uncertainties of the extracted time constants. We also demonstrate protocols for improving the signal-to-noise ratio and reducing the uncertainties.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Prediction of laser beam spatial profiles in a high-energy laser facility by use of deep learning

We adapt the significant advances achieved recently in the field of generative artificial intelligence/machine-learning to laser performance modeling in multipass, high-energy laser systems with application to high-shot-rate facilities relevant to inertial fusion energy. Advantages of neural-network architectures include rapid prediction capability, data-driven processing, and the possibility to implement such architectures within future low-latency, low-power consumption photonic networks. Four models were investigated that differed in their generator loss functions and utilized the U-Net encoder/decoder architecture with either a reconstruction loss alone or combined with an adversarial network loss. We achieved inference times of 1.3 ms for a 256 × 256 pixel near-field beam with errors in predicted energy of the order of 1% over most of the energy range. It is shown that prediction errors are significantly reduced by ensemble averaging the models with different weight initializations. These results suggest that including the temporal dimension in such models may provide accurate, real-time spatiotemporal predictions of laser performance in high-shot-rate laser systems.

47 OTHER INSTRUMENTATION↗
DOE OSTI2025

A platform to measure isentropes from proton-heated warm dense matter on short pulse laser facilities

We describe the development of an experimental platform that measures the release isentrope of materials heated isochorically to temperatures of a few electron volts, using short-pulse laser-produced protons to heat the sample and long-pulse laser-produced x rays to perform streaked x-ray radiography. The density profiles derived from the radiography data are integrated to generate pressure–density isentropes, independent of prior knowledge of the equation of state of the sample material. In order to understand the sensitivities of isentrope extraction from radiography data, we analyze synthetic radiographs generated by a radiation hydrodynamics code. Noise reduction and high spatial resolution are critical for isentrope reconstruction, as demonstrated by the analysis of a proof-of-principle shot day on the OMEGA-EP facility. In conclusion, the data demonstrate the feasibility of the platform for characterizing isentropes, and we discuss the necessary improvements to enhance precision in differentiating between equation-of-state models.

Equations of state↗
DOE OSTI2024

Three-dimensional reconstruction of laser-direct-drive inertial confinement fusion hot-spot plasma from x-ray diagnostics on the OMEGA laser facility (invited)

A deep-learning convolutional neural network (CNN) is used to infer, from x-ray images along multiple lines of sight, the low-mode shape of the hot-spot emission of deuterium–tritium (DT) laser-direct-drive cryogenic implosions on OMEGA. The motivation of this approach is to develop a physics-informed 3-D reconstruction technique that can be performed within minutes to facilitate the use of the results to inform changes to the initial target and laser conditions for the subsequent implosion. The CNN is trained on a 3D radiation-hydrodynamic simulation database to relate 2D x-ray images to 3D emissivity at stagnation. The CNN accounts for the lack of an absolute spatial reference and the different bands of photon energies in the x-ray images. While previous works studied the effect of mode-1 asymmetries on implosion performance using nuclear diagnostics, this work focuses on the effect of mode 2 inferred from x-ray diagnostics on implosion performance. A current analysis of 19 DT cryogenic implosions indicates there is an upper limit of ~20% reduction in the neutron yield caused by an ℓ = 2 amplitude for ℓ 2 /ℓ 0 ≤ 0.32. Here, these conclusions are supported by 2D simulations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware

The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Processing Units and Reconfigurable Dataflow Units. We show the landscape of training times with a goal of full model retraining in under 15 min. Although a full from scratch retraining of a model may not be required in all cases, we nevertheless present an example of the application of emerging computational hardware for adapting machine learning models to changing environments in real-time, during streaming data acquisition, at the rates expected for the data fire hoses of accelerator-based user facilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

LIGHT Laser Facility at LLNL

Explore the source record for details and available documents.

Jin, Frank [Lawrence Livermore National Laboratory↗
NASA NTRS1987

Studies of CO oxidation on Pt/SnO2 catalyst in a surrogate CO2 laser facility

Samples of 1% Pt/SnO2 catalyst were exposed to a stoichiometric gas mixture of 1% CO and 1.2% O2 in helium over a range of flowrates from 5 to 15 sccm and temperatures from 338 to 394 Kelvin. Reaction rate constants for the catalytic oxidation of carbon monoxide and their temperature dependence were determined and compared with previous literature values.

Batten, Carmen E.↗