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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 973 records · Page 54

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DOE OSTI2025

Pointing stabilization of a 1 Hz high-power laser via machine learning

Abstract High-power lasers are vital for particle acceleration, imaging, fusion and materials processing, requiring precise control and high-energy delivery. Laser plasma accelerators (LPAs) demand laser positional stability at focus to ensure consistent electron beams in applications such as X-ray free-electron lasers and high-energy colliders. Achieving this stability is especially challenging for the low-repetition-rate lasers in current LPAs. We present a machine learning method that predicts and corrects laser pointing instabilities in real-time using a high-frequency pilot beam. By preemptively adjusting a correction mirror, this approach overcomes traditional feedback limits. Demonstrated on the BELLA petawatt laser operating at the terawatt level (30 mJ amplification), our method achieved root mean square pointing stabilization of 0.34 and 0.59 $\unicode{x3bc} \mathrm{rad}$ in the x and y directions, reducing jitter by 65% and 47%, respectively. This is the first successful application of predictive control for shot-to-shot stabilization in low-repetition-rate laser systems, paving the way for full-energy petawatt lasers and transformative advances across science, industry and security.

Amodio, Alessio↗

DOE OSTI2023

Siderocalin fusion proteins enable a new 86 Y/ 90 Y theranostic approach

The mammalian protein siderocalin binds bacterial siderophores and their iron complexes through cation-π and electrostatic interactions, but also displays high affinity for hydroxypyridinone complexes of trivalent lanthanides and actinides. In order to circumvent synthetic challenges, the use of siderocalin-antibody fusion proteins is explored herein as an alternative targeting approach for precision delivery of trivalent radiometals. We demonstrate the viability of this approach in vivo, using the theranostic pair 90 Y (β − , t 1/2 = 64 h)/ 86 Y (β + , t 1/2 = 14.7 h) in a SKOV-3 xenograft mouse model. Ligand radiolabeling with octadentate hydroxypyridinonate 3,4,3-LI(1,2-HOPO) and subsequent protein binding were achieved at room temperature. The results reported here suggest that the rapid non-covalent binding interaction between siderocalin fusion proteins and the negatively charged Y(III)-3,4,3-LI(1,2-HOPO) complexes could enable purification-free, cold-kit labeling strategies for the application of therapeutically relevant radiometals in the clinic.

Cosby, Alexia G.↗

DOE OSTI2024

Simulations and models for the Richtmyer–Meshkov instability with broadband perturbations

The Richtmyer–Meshkov instability (RMI) is shock driven and affects many phenomena from inertial fusion to supernova explosions. The behavior of single-modes in the RMI has been studied extensively but less is known with the broadband perturbations that occur in applications. Here, we describe extensive numerical simulations and modeling of the RMI with broadband perturbations with an initial power spectrum of the form P ∝ km, where k is the wavenumber. The hydrodynamic condition is the same as in the θ-Group Collaboration [Thornber et al., “Late-time growth rate, mixing, and anisotropy in the multimode narrowband Richtmyer–Meshkov instability: The θ-group collaboration,” Phys. Fluids 29, 105107 (2017)] with a Mach ∼1.86 shock and gamma-law = 5/3 fluids with Atwood number A = −0.5. The bubble amplitude hB is found to grow in two stages. Initially, hB undergoes a phase-inversion and grows linearly in time (t) at a rate consistent with a linear theory. Asymptotically, hB grows as a power law ∼tθ when k|hB| > O(1), where k is the average wavenumber for the initial spectrum. The RMI behavior in simulations and modeling agree over a wide range of exponent m, spectral width, initial amplitude, and time. The agreement is quantified objectively using statistical analysis.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Microscale thermophoresis as a powerful tool for screening glycosyltransferases involved in cell wall biosynthesis

Background: Identification and characterization of key enzymes associated with cell wall biosynthesis and modification is fundamental to gain insights into cell wall dynamics. However, it is a challenge that activity assays of glycosyltransferases are very low throughput and acceptor substrates are generally not available. Results: We optimized and validated microscale thermophoresis (MST) to achieve high throughput screening for glycosyltransferase substrates. MST is a powerful method for the quantitative analysis of protein-ligand interactions with low sample consumption. The technique is based on the motion of molecules along local temperature gradients, measured by fluorescence changes. We expressed glycosyltransferases as YFP-fusion proteins in tobacco and optimized the MST method to allow the determination of substrate binding affinity without purification of the target protein from the cell lysate. The application of this MST method to the β-1,4-galactosyltransferase AtGALS1 validated the capability to screen both nucleotide-sugar donor substrates and acceptor substrates. We also expanded the application to members of glycosyltransferase family GT61 in sorghum for substrate screening and function prediction. Conclusions: This method is rapid and sensitive to allow determination of both donor and acceptor substrates of glycosyltransferases. MST enables high throughput screening of glycosyltransferases for likely substrates, which will narrow down their in vivo function and help to select candidates for further studies. Additionally, this method gives insight into biochemical mechanism of glycosyltransferase function.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Application of deep learning to single-shot gas-phase laser-induced breakdown spectroscopy

Single-shot fs laser-induced breakdown spectroscopy (LIBS) has the potential to capture ns-scale electrode desorption phenomena in pulsed power fusion drivers. However, the successful implementation of the diagnostic for this purpose is challenging, as it requires interpreting single-shot measurements collected from low-density gas mixtures. In this work, we demonstrate the efficacy of a Bayesian-optimized convolutional neural network (CNN) to interpret these measurements. We generated 256 distinct measurement conditions at relevant gas pressures ranging from 80–530 mTorr by mixing 100–250 sccm H 2 and 50–200 sccm CH 4 in increments of 10 sccm. Despite the considerable overlap between signals separated by 20 sccm, the CNN is able to predict the H 2 flow rate with a root-mean-square error (RMSE) of 15.9 sccm and the CH 4 flow rate with an RMSE of 12.0 sccm. The average relative prediction error is <9% for each gas and largely remains below or near 10%.

Brown, Nathan Parnell [Sandia National Lab. (SNL-N↗

DOE OSTI2022

Report on PTT Imaging of Defects in AM Metallic Materials-Part 2

Metal Additive Manufacturing (AM) is a promising method for cost-efficient fabrication of complex shape structures for applications in harsh environment, such as in a nuclear reactor. However, internal defects (pores) occur in high-strength AM alloys, which are manufactured with Laser Powder Bed Fusion (LPBF) AM method. Pulsed Infrared Thermography (PIT) is an efficient nondestructive evaluation (NDE) method to examine actual structures, because this method offers one-sided non-contact measurements, and fast processing of large sample areas. However, imaging of material defects, particularly defects with sizes at microscopic level, is challenging. In this report, we benchmark the performance of several Unsupervised Learning (UL) algorithms designed to enhance imaging of microscopic defects in metals with PIT. UL aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Performance of Independent Component Analysis (ICA), Sparse Coding (SC), Principal Component Analysis (PCA) and Exploratory Factor Analysis (EFA) was compared using F-score, UL model training time and defects reconstruction time. We obtained the average F-score of 0.75, and a highest F-score of 0.89 for the EFA algorithm. Overall, EFA outperforms other UL algorithms considered in this study.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Scalable in situ non-destructive evaluation of additively manufactured components using process monitoring, sensor fusion, and machine learning

Laser Powder Bed Fusion (L-PBF) Additive Manufacturing (AM) is among the metal 3D printing technologies most broadly adopted by the manufacturing industry. However, the current industry qualification paradigm for critical-application L-PBF parts relies heavily on expensive non-destructive inspection techniques, which significantly limits the use-cases of L-PBF. In situ monitoring of the process promises a less expensive alternative to ex situ testing, but existing sensor technologies and data analysis techniques struggle to detect sub-surface flaws (e.g., porosity and cracking) on production-scale L-PBF printers. In this work, an in situ NDE (INDE) system was engineered to detect subsurface flaws detected in X-Ray Computed Tomography (XCT) directly from process monitoring data. A multilayer, multimodal data input allowed the INDE system to detect numerous subsurface flaws in the size range of 200–1000µm using a novel human-in-the-loop annotation procedure. Furthermore, a framework was established for generating probability-of-detection (POD) and probability-of-false-alarm (PFA) curves compliant with NDE standards by systematically comparing instances of detected subsurface flaws to post-build XCT data. Here, we also introduce for the first time in the AM in situ sensing literature the a 90/95 – the flaw size corresponding to a 90% detection rate on the lower 95% confidence interval of the POD curve. The INDE system successfully demonstrated POD capabilities commensurate with traditional NDE methods. Traditional ML performance metrics were also shown to be inadequate for assessing the ability of the INDE system’s flaw detection performance. It is the hope of the authors that future studies will adopt the POD and PFA approach outlined here to provide better insight into the utility of process monitoring for AM.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Numerical investigation of liquid wall ablation in inertial fusion energy chambers

This paper presents a novel approach for modeling liquid wall ablation in liquid wall-protected inertial fusion energy (IFE) chambers. These systems are promising candidates for the implementation of fusion technology, yet significant gaps remain in understanding the underlying physical processes and their implications for design. Following target ignition, a portion of the fusion energy is released as x-rays, which deposit their energy into an array of liquid jets, leading to partial vaporization. Accurately modeling this heat deposition and vaporization process remains challenging due to the complex geometries typical of (pre-conceptual) IFE chamber designs. Furthermore, the subsequent expansion of vaporized material into the chamber’s vacuum environment poses difficulties for conventional CFD methods based on continuum assumptions, which can lead to significant inaccuracies. To address some aspects of these challenges, this work introduces a ray-tracing-based methodology to map the spatial distribution of ablated material in liquid wall-protected systems. In addition, a vacuum-tracking scheme is developed to extend the applicability of an OpenFOAM-based solver to gas dynamics in rarefied environments. The proposed approach has been verified through numerical benchmarks and applied to a practical case involving the HYLIFE-II (High Yield Lithium Injection Fusion Energy) chamber. The methodology advances the modeling capabilities for liquid wall-protected IFE systems and provides valuable tools to support their design and optimization.