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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 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Final Report: A Multi-Channel Fusion Product

The goal of this project was to measure charged fusion products from the d(d,p)t reaction in MAST-U plasmas as a function of time and position with good energy resolution using a system of up to six charged particle detectors. The data from this new diagnostic will make it possible to determine the neutral beam ion density profile as a function of R, z, and t with reduced model dependency and contribute new information to a global analysis of fast ion diagnostic data needed for the determination of the fast ion distribution function (velocity space tomography).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Central Density and Low-Mode Perturbation Control of Inertial Confinement Fusion Dynamic-Shell Targets

The dynamic-shell target is a new class of design for inertial confinement fusion (ICF). These targets address some of the target fabrication challenges prevalent in current ICF targets and take advantage of advances in manufacturing technologies. This study first examines how the dynamic-shell design can be used to control the density of the central region and therefore convergence ratio, thus expanding the design space for ICF. Additionally, the concern of low-mode perturbation growth is considered. A new class of high-performing beam configurations, based on icosahedral polyhedra and charged-particle simulations is proposed. These configurations achieve low levels of irradiation nonuniformity through selection of beam shapes that suppress the dominant symmetrical mode.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Physical Background, Computations and Practical Issues of the Magnetohydrodynamic Pressure Drop in a Fusion Liquid Metal Blanket

In blankets of a fusion power reactor, liquid metal (LM) breeders, such as pure lithium or lead-lithium alloy, circulate in complex shape blanket conduits for power conversion and tritium breeding in the presence of a strong plasma-confining magnetic field. The interaction of the magnetic field with induced electric currents in the breeder results in various magnetohydrodynamic (MHD) effects on the flow. Of them, high MHD pressure losses in the LM breeder flows is one of the most important feasibility issues. To design new feasible LM breeding blankets or to improve the existing blanket concepts and designs, one needs to identify and characterize sources of high MHD pressure drop, to understand the underlying physics of MHD flows and to eventually define ways of mitigating high MHD pressure drop in the entire blanket and its sub-components. This article is a comprehensive review of earlier and recent studies of MHD pressure drop in LM blankets with a special focus on: (1) physics of LM MHD flows in typical blanket configurations, (2) development and testing of computational tools for LM MHD flows, (3) practical aspects associated with pumping of a conducting liquid breeder through a strong magnetic field, and (4) approaches to mitigation of the MHD pressure drop in a LM blanket.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Atmosphere Effects in Laser Powder Bed Fusion: A Review

The use of components fabricated by laser powder bed fusion (LPBF) requires the development of processing parameters that can produce high-quality material. Manipulating the most commonly identified critical build parameters (e.g., laser power, laser scan speed, and layer thickness) on LPBF equipment can generate acceptable parts for established materials and moderately intricate part geometries. The need to fabricate increasingly complex parts from unique materials drives the limited research into LPBF process control using underutilized parameters, such as atmosphere composition and pressure. As presented in this review, manipulating atmosphere composition and pressure in laser beam welding has been shown to expand processing windows and produce higher-quality welds. The similarities between laser beam welding and laser-based AM processes suggest that this atmosphere control research could be effectively adapted for LPBF, an area that has not been widely explored. Tailoring this research for LPBF has significant potential to reveal novel processing regimes. This review presents the current state of the art in atmosphere research for laser beam welding and LPBF, with a focus on studies exploring cover gas composition and pressure, and concludes with an outlook on future LPBF atmosphere control systems.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

What Machine Learning Can and Cannot Do for Inertial Confinement Fusion

Machine learning methodologies have played remarkable roles in solving complex systems with large data, well-defined input–output pairs, and clearly definable goals and metrics. The methodologies are effective in image analysis, classification, and systems without long chains of logic. Recently, machine-learning methodologies have been widely applied to inertial confinement fusion (ICF) capsules and the design optimization of OMEGA (Omega Laser Facility) capsule implosion and NIF (National Ignition Facility) ignition capsules, leading to significant progress. As machine learning is being increasingly applied, concerns arise regarding its capabilities and limitations in the context of ICF. ICF is a complicated physical system that relies on physics knowledge and human judgment to guide machine learning. Additionally, the experimental database for ICF ignition is not large enough to provide credible training data. Most researchers in the field of ICF use simulations, or a mix of simulations and experimental results, instead of real data to train machine learning models and related tools. They then use the trained learning model to predict future events. This methodology can be successful, subject to a careful choice of data and simulations. However, because of the extreme sensitivity of the neutron yield to the input implosion parameters, physics-guided machine learning for ICF is extremely important and necessary, especially when the database is small, the uncertain-domain knowledge is large, and the physical capabilities of the learning models are still being developed. In this work, we identify problems in ICF that are suitable for machine learning and circumstances where machine learning is less likely to be successful. This study investigates the applications of machine learning and highlights fundamental research challenges and directions associated with machine learning in ICF.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Fusion of Experiments and Simulations for Real-Time Identification of Pipeline Defects

In this study, we explored fusion of experiments and simulations for real time identification of pipeline defects across physical and non-physical domains. The challenges associated to data processing were addressed and a combined classification models was presented via CNN models. In addition, regression model based on XGBOOST is built to determine the defect location and defect dimension from data-driven features of guided wave signals captured by SMS fiber optic sensor.

deep learning↗
DOE OSTI2025

Foundational Research for Fusion Systems Safety Assessment Overview

Presentation detailing work package to develop comprehensive fusion device safety models over the course of the next three years, including references to a blanket, divertor and magnet design, and statistical reliability models

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

A Holistic Stabilization of the Anode in Lithium‐Sulfur Batteries Through a Ternary Alloy Fusion

The anode in lithium–sulfur batteries (LSBs) is plagued by not only inhomogeneous lithium (Li) deposition, but also by parasitic side-reactions. Often, a thick anode (∼400 µm) is used to compensate for these drawbacks, lowering gravimetric and volumetric energy densities. In this work, we demonstrate a ternary alloy anode fabricated via scalable thermal fusion of Li with aluminum (Al) and tellurium (Te) to cohesively address these issues. The Al–Li skeleton serves to homogenize Li deposition as well as reinforce the anode, allowing it to be rolled down to low thicknesses. Te incorporation suppresses the reaction of Li with polysulfides (Li 2 Sn) to form lithium sulfide (Li 2 S) and electrolyte degradation. It can also facilitate the formation of polytellurosulfides (Li 2 Te x Sy), which are far more conducive to Li + -ion diffusion. Even under a lean electrolyte/sulfur (E/S) ratio of 8 µL mg −1 and a low negative/positive (N/P) ratio of 3, capacities over 900 mA h g −1 at a C/5 rate are obtained in cells with Li–Te–Al anodes with a capacity retention of 80% after 70 cycles. In comparison, the baseline lithium-metal anode retains only 35% after 50 cycles. Furthermore, the Li–Te–Al anodes confer a 24% boost to gravimetric energy density at the pouch cell level.

25 ENERGY STORAGE↗
DOE OSTI2023

Uncovering acoustic signatures of pore formation in laser powder bed fusion

Abstract We present a machine learning workflow to discover signatures in acoustic measurements that can be utilized to create a low-dimensional model to accurately predict the location of keyhole pores formed during additive manufacturing processes. Acoustic measurements were sampled at 100 kHz during single-layer laser powder bed fusion (LPBF) experiments, and spatio-temporal registration of pore locations was obtained from post-build radiography. Power spectral density (PSD) estimates of the acoustic data were then decomposed using non-negative matrix factorization with custom $$\varvec{k}$$ k -means clustering (NMF $$\varvec{k}$$ k ) to learn the underlying spectral patterns associated with pore formation. NMF $$\varvec{k}$$ k returned a library of basis signals and matching coefficients to blindly construct a feature space based on the PSD estimates in an optimized fashion. Moreover, the NMF $$\varvec{k}$$ k decomposition led to the development of computationally inexpensive machine learning models which are capable of quickly and accurately identifying pore formation with classification accuracy of supervised and unsupervised label learning greater than 95% and 90%, respectively. The intrinsic data compression of NMF k , the relatively light computational cost of the machine learning workflow, and the high classification accuracy makes the proposed workflow an attractive candidate for edge computing toward in-situ keyhole pore prediction in LPBF.

36 MATERIALS SCIENCE↗
DOE OSTI2026

In-situ sensor monitoring of multi-class gas porosity formation in laser powder bed fusion using convolutional neural network

In-situ monitoring of defect formation remains a significant challenge in the laser powder bed fusion (LPBF) process. Recent advances have enabled real-time defect detection with machine learning and in-situ sensing technologies; however, most studies focus on binary classification of keyhole pores, limiting nuanced multi-class pore differentiation and formation mechanisms. This work introduces a multi-class pore detection framework (no pore, small pores < 15 µm, and large pores > 15 µm) by leveraging photodiode sensor data alongside high-fidelity synchrotron X-ray imaging. The 15 µm threshold is selected to distinguish between two fundamentally different defect mechanisms, following the physical size-mechanism boundary established by prior high-resolution synchrotron X-ray characterization of Al6061 LPBF. Distinguishing these classes is critical because large keyhole pores are structurally detrimental, whereas small gas pores are often benign, requiring different process control strategies. Thermal emission monitoring data collected simultaneously with high-speed X-ray imaging at the Stanford Synchrotron Radiation Lightsource (SSRL), are correlated with subsurface melt pool dynamics to establish ground truth. Continuous Wavelet Transform (CWT) with optimized parameters converts the photodiode time-series signals into time–frequency images, facilitating feature extraction. Convolutional Neural Networks (CNN) are then applied for real-time multi-class pore classification in an average inference time of 1 ms per signal window. It achieves 79% accuracy and an Area Under the Receiver Operating Characteristic curve (AUC ROC) score of 0.89 with five-fold cross-validation. The results demonstrate that coupling CWT-based feature engineering with CNN architecture enables reliable multi-class pore detection in Al6061 builds using affordable in-situ sensors. This approach advances scalable and affordable quality assurance in additive manufacturing by moving beyond binary defect detection toward more nuanced classification of porosity mechanisms with in-situ sensors and machine learning.

Laser powder bed fusion, Multi-class pores, In-sit↗
DOE OSTI2023

Active Lithium Injection for a Real Time Control of the Divertor Heat Flux for Fusion Devices

When the local heat flux exceeds specified flux limit, tungsten PFC surfaces can be damaged, which is not acceptable for a reliable reactor operations. The divertor PFCs are typically designed for a specific heat flux limit usually assuming an average steady-state heat flux which is typically 5–10 MW/m 2 . However, in addition to steady-state heat flux, fusion reactor divertor PFCs could experience transient heat fluxes such as ELMs and/or other magnetic reconnection events which can deposit large transient heat fluxes onto the divertor PFCs. The transient divertor heat flux could be significantly larger than the steady-state heat flux which could damage the solid PFC surfaces. The divertor heat flux can be subjected to additional complications such as the uncertainties in the the divertor strike point heat flux projection. Moreover, there are additional experimental observations of non-axisymmetric power flux which can occur under non-axisymmetric magnetic perturbations. The liquid lithium (LL) PFCs is more resilient against such transient heat fluxes as they could evaporate LL as needed and the lost LL can be then replenished afterward. In this paper, we analyze a case for a transient divertor heat pulse of 1 MJ in 10 ms for a ITER-size reactor. This is a small perturbation (~ 0.1%) to the expected plasma stored energy compared to the previously analyzed case of 20 MJ heat pulse. Even with this relatively modest heat pulse, the LL surface undergoes ~ 100 °C temperature rise. However, the resulting LL surface heating without rapid cooldown mechanism could lead to excessive LL evaporation continuing well after the transient heat flux resulting in a significant Li injection of ~ 0.6 mol in about a 200 ms period. This amount of Li injection could cause plasma dilution and performance degradation. On the other hand, an active Li injection capability if optimized could prevent the LL surface temperature rise and thus reducing subsequent Li evaporation into the plasma by a factor of 7 compared to the passive LL PFC case. A crucial tool of active Li injection is a rapid response pellet injector which could inject light impurity pellets before the excessive heat flux could reach the divertor plate causing serious damage. A simple pellet ablation model suggests a favorable pellet deposition profile for smaller ~ 0.1 mm radius pellet with ~ 10–20 m/s velocity. Moreover, if it is possible to inject from the private flux region, the pellet injection efficiency into the high heat flux strike point region can be as high as 80% compared to ~ 50% for the injection from outer radius region. The pellet deposition efficiency can be further improved by designing a shell-pellet which can burst when a certain ablation fraction is reached. Finally, a possible implementation technique using an inductive pellet injector with a rapid time response of a few msec is proposed here which can be tested in NSTX-U.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Effect of Volumetric Energy Density on Microstructure and Properties of Grade 300 Maraging Steel Fabricated by Laser Powder Bed Fusion

Here this study investigated the effect of laser powder bed fusion process parameters on the microstructure and properties of maraging steel. The results show that the process parameters can be finetuned to achieve both a high build rate/low hardness and low build rate/high hardness during fabrication, enabling the co-design of components where property gradients are desirable such as an injection molding tool where surface hardness is desired to be higher than the bulk, which needs to have higher fracture toughness.

36 MATERIALS SCIENCE↗
DOE OSTI2022

High temperature high strength austenitic steel fabricated by laser powder-bed fusion

Extremely fast cooling rates during laser powder-bed fusion (LPBF) can result in materials with unique microstructures. For LPBF 316L stainless steel, the formation of sub-grain cellular structures with high dislocation density has been linked to superior tensile properties at room temperature. This cellular structure offers also a new route for the development of high temperature LPBF steels with the nucleation of nano-size strengthening carbides in the cell walls. HK30Nb steel (Fe-25Cr-20Ni-Nb-C) was, therefore, fabricated by LPBF to evaluate its potential for high temperature applications. Optimization of the fabrication parameters yielded material with density greater than 99.7%, with nano Nb-rich precipitates in the cell walls. Annealing at 800 °C for 5h resulted in the nucleation and growth of additional precipitates mainly in the cell walls and at grain boundaries. Furthermore, the high dislocation density led to yield strength at 20–900 °C two to three times higher than yield strength for cast HK30Nb and the nano carbides in the cell walls significantly improved the cellular structure stability at 800 °C.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Long-term thermal aging behavior and strength reduction in a laser powder bed fusion 316H stainless steel

The long-term thermal stability of structural alloys is essential for ensuring the safe and reliable operation of nuclear reactors and other power plants. While extensive research has explored the effects of thermal aging on conventional stainless steels, the behavior of additively manufactured (AM) alloys remains less understood. This study examines the thermal aging response of laser powder bed fusion (LPBF) 316H stainless steel (SS) at temperatures ranging from 550 °C to 750 °C over durations of up to 10,000 h (approximately 1.14 years). Advanced characterization techniques, including electron microscopy and synchrotron X-ray diffraction, were used to investigate dislocation recovery and phase evolution. Based on these findings, a time-temperature-precipitation (TTP) diagram was developed for LPBF 316H SS, revealing a 10- to100-fold acceleration in precipitation kinetics compared to wrought 316H SS. A physics-informed model was calibrated using the short-term experimental data, enabling predictions of average precipitate sizes, volume fractions of M 23 C 6 and Laves phases, and changes in molybdenum solute concentration for aging up to 1 × 10⁶ h (114 years). These microstructural insights were further utilized to estimate yield strength and extrapolate strength reduction factors over the extended aging period. Despite the accelerated aging kinetics, LPBF 316H SS demonstrated superior yield strength retention compared to its wrought counterpart. In conclusion, this study establishes a framework for evaluating long-term performance using short-term experimental data and supports the accelerated qualification of AM materials for high-temperature structural applications.

Laser powder bed fusion↗