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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

SIMS and HR-XPS characterization of lithiated graphite from the magnetic fusion device RFX-mod

Lithium wall conditioning has improved the performance of many magnetic fusion devices. Li conditioning in the RFX-mod device was performed by: (1) a single Li pellet injector and (2) a multi-Li pellet injector during He plasma discharges; (3) a Li evaporator after He glow discharge cleaning and He plasma discharge exposure. This report compares the spatial and depth distributions of Li deposited on polycrystalline graphite witness samples at different locations in RFX-mod and the elemental and chemical compositions of the resulting surfaces. The sample surfaces were analyzed ex situ using secondary ion mass spectrometry (SIMS) and high-resolution X-ray photoelectron spectroscopy (HR-XPS). The results showed that Li pellet injection provided a relatively uniform toroidal coverage while Li evaporation produced highly localized Li deposition. A Li 1s HR-XPS peak at 56.5 eV binding energy (BE) characteristic of lithium-intercalated graphite was only observed with the sample exposed to Li evaporation. All of the samples exhibited a HR-XPS C 1s peak at 285.1–285.2 eV BE that is largely attributed to hydrogenated graphite. This finding suggests that hydrogenation of fresh graphite occurs during He plasma discharge exposures. Finally, our results have implications for density control and the selection of Li conditioning techniques in magnetic fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Predictive process mapping for laser powder bed fusion: A review of existing analytical solutions

One of the main challenges in the laser powder bed fusion (LPBF) process is making dense and defect-free components. These porosity defects are dependent upon the melt pool geometry and the processing conditions. Power-velocity (PV) processing maps can aid in visualizing the effects of LPBF processing variables and mapping different defect regimes such as lack-of-fusion, under-melting, balling, and keyholing. This work presents an assessment of existing analytical equations and models that provide an estimate of the melt pool geometry as a function of material properties. The melt pool equations are then combined with defect criteria to provide a quick approximation of the PV processing maps for a variety of materials. Finally, the predictions of these processing maps are compared with experimental data from the literature. Here, the predictive processing maps can be computed quickly and can be coupled with dimensionless numbers and high-throughput (HT) experiments for validation. The present work provides a boundary framework for designing the optimal processing parameters for new metals and alloys based on existing analytical solutions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

OpenEdge: A collaborative, open-source, multi-purpose direct simulation Monte Carlo for plasma simulation in magnetic fusion environments

OpenEdge is a collaborative, open-source, object-oriented Direct Simulation Monte Carlo (DSMC) code, designed specifically for plasma simulations in magnetic fusion environments. Here, the code features include advanced structures, robust capabilities, and an effective parallelization strategy, all of which significantly enhance performance. It includes specialized modules for managing complex particle interactions, including collisions, ionization/recombination, and reflection/sputtering. Benchmarks and performance analyses have confirmed its efficiency and scalability. Versatile and adaptable, OpenEdge is applied across a broad spectrum of plasma-material interaction studies and charged particle transport in various fusion research settings.

Boundary plasma↗
DOE OSTI2020

An Evaluation of the Global Effects of Tritium Emissions from Nuclear Fusion Power

We report that tritium, like all hydrogen isotopes, is difficult to confine and easily diffuses through most materials. As currently planned, fusion power plants will process and handle large quantities of deuterium and tritium as fuel, and therefore, will become sources of tritium input into the environment. Tritium releases from a worldwide distribution of tritium sources (fusion or fission) will lead to higher tritium levels overall and have global impact. This report investigates the hydrologic partitioning of yearly tritium releases assuming 1 g/yr loss per 500 MW of nuclear power generation for varying scales of power generation. On large scales, it is found that the worldwide average levels of tritium could exceed some public health goals and regulatory guidelines. Tritium concentrations at such levels may also have other impacts on the environment through increased ion-pair generation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Radiation effects on stress evolution and dimensional stability of large fusion energy structures

Here we assess the effects of neutron irradiation on the deformation and stress evolution of large-scale fusion energy structures. This is accomplished through non-linear finite element structural analysis of the coupled thermal and mechanical fields at the Beginning-Of-Life (BOL), at 45 dpa, and at 90 dpa. Radiation effects include volumetric swelling and the influence of radiation on the mechanical properties. The system studied here is a large section of a full inboard module of an integrated structure comprising the First Wall and Blanket (FW/B) of a Dual Cooled Lithium-Lead (DCLL) energy conversion unit in the Fusion Nuclear Science Facility (FNSF). The structural material is the ferritic/martensitic steel F82H. We analyze several radiation effects phenomena that can lead to significant impact on the mechanical design and lifetime of the structure. These include volumetric swelling effects on the assembly and disassembly of modules and the impact of spatial gradients in neutron damage and helium generation on deformation, stress evolution, and plastic strain accumulation. We show that the stress state is strongly influenced by the accumulation of swelling strains, radiation hardening and softening, and by spatial gradients in displacement damage and helium gas generation. Several key regions in the FW/B structure are identified where severe plastic strains accumulate and may be potential sites for failure, especially during cyclic reactor operations. We finally show that minimum gaps between Inboard (IB) blanket sectors must be present during assembly so as to avoid excessive stress and strain in FW/B modules critical regions and raising chances of failure. A range of structural displacements are shown, from as low as 4 mm at BOL for free side walls to as high as 46 mm at 90 dpa for fully constrained side walls.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Steady state thermo-mechanics and material property definition framework for analyzing DCLL blanket in the fusion nuclear science facility

In this work, a thermo-mechanics model that relies on creating the material property definition framework (MPDF) and multiphysics coupling of the heat transfer and the solid mechanics modules is developed to determine the structural integrity of the recently designed dual cooled lead lithium (DCLL) inboard blanket (IB) for the Fusion Nuclear Science Facility under steady state loads. The MPDF is called to supply fusion relevant neutron irradiation and temperature induced changes in material properties during multiphysics finite element runs, and PbLi temperature profiles are used to approximate Magnetohydrodynamics effect and the nuclear volumetric heating on the PbLi. Neutron irradiation and temperature induced reduction of the yield and ultimate strengths of F82H steel at the first wall (FW) are quantified for one year. A blanket in an assembly with gaps between blanket sectors and another blanket in an assembly with no gaps between blanket sectors, both exposed to radiation damage that lasted for one year are analyzed. Analysis using the elastic ITER structural design criteria for in-vessel components (ITER SDC-IC) design rules and a linear isotropic-hardening-type elastoplastic material model are used where most appropriate. The IB blanket with gaps between blanket sectors will withstand the steady state combined thermal and coolant loads for one year operational period but will fail if no gaps are allowed between blanket sectors. It is recommended that a gap of about 7.62 mm should be provided between IB blanket sectors during assembly which would close up during service, stop neutron streaming, reduce stresses and reduce bending of the FW into the scrape-off layer.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Monitoring and prediction of porosity in laser powder bed fusion using physics-informed meltpool signatures and machine learning

In this work we accomplished the monitoring and prediction of porosity in laser powder bed fusion (LPBF) additive manufacturing process. This objective was realized by extracting physics-informed meltpool signatures from an in-situ dual-wavelength imaging pyrometer, and subsequently, analyzing these signatures via computationally tractable machine learning approaches. Porosity in LPBF occurs despite extensive optimization of processing conditions due to stochastic causes. Hence, it is essential to continually monitor the process with in-situ sensors for detecting and mitigating incipient pore formation. In this work a tall cuboid-shaped part (10 mm × 10 mm × 137 mm, material ATI 718Plus) was built with controlled porosity by varying laser power and scanning speed. This test caused various types of porosity, such as lack-of-fusion and keyhole formation, with varying degrees of severity in the part. The meltpool was continuously monitored using a dual-wavelength imaging pyrometer installed in the machine. Physically intuitive process signatures, such as meltpool length, temperature distribution, and ejecta (spatter) characteristics, were extracted from the meltpool images. Subsequently, relatively simple machine learning models, e.g., K-Nearest Neighbors, were trained to predict both the severity and type of porosity as a function of these physics-informed meltpool signatures. These models resulted in a prediction accuracy exceeding 95% (statistical F1-score). The same analysis was carried out with a complex, black-box deep learning convolutional neural network which directly used the meltpool images instead of physics-informed features. The convolutional neural network produced a comparable F1-score in the range of 89–97%. Finally, these results demonstrate that using pragmatic, physics-informed meltpool signatures within a simple machine learning model is as effective for flaw prediction in LPBF as using a complex and computationally demanding black-box deep learning model.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Unsupervised multimodal fusion of in-process sensor data for advanced manufacturing process monitoring

Effective monitoring of manufacturing processes is crucial for maintaining product quality and operational efficiency. Modern manufacturing environments often generate vast amounts of complementary multimodal data, including visual imagery from various perspectives and resolutions, hyperspectral data, and machine health monitoring information such as actuator positions, accelerometer readings, and temperature measurements. However, fusing and interpreting this complex, high-dimensional data presents significant challenges, particularly when labeled datasets are unavailable or impractical to obtain. This paper presents a novel approach to multimodal sensor data fusion in manufacturing processes, inspired by the Contrastive Language-Image Pre-training (CLIP) model. We leverage contrastive learning techniques to correlate different data modalities without the need for labeled data, overcoming limitations of traditional supervised machine learning methods in manufacturing contexts. Our proposed method demonstrates the ability to handle and learn encoders for five distinct modalities: visual imagery, audio signals, laser position (x and y coordinates), and laser power measurements. By compressing these high-dimensional datasets into low-dimensional representational spaces, our approach facilitates downstream tasks such as process control, anomaly detection, and quality assurance. The unsupervised nature of our method makes it broadly applicable across various manufacturing domains, where large volumes of unlabeled sensor data are common. We evaluate the effectiveness of our approach through a series of experiments, demonstrating its potential to enhance process monitoring capabilities in advanced manufacturing systems. This research contributes to the field of smart manufacturing by providing a flexible, scalable framework for multimodal data fusion that can adapt to diverse manufacturing environments and sensor configurations. The proposed method paves the way for more robust, data-driven decision-making in complex manufacturing processes.

Contrastive Learning↗
DOE OSTI2025

Suppression of irradiation hardening in tungsten-coated ferritic steel for fusion reactor blanket applications

W-coated reduced activation ferritic steels have been developed for use as plasma facing components in fusion reactor blankets, offering excellent sputtering resistance and structural strength. Previous high-temperature coating methods, such as diffusion bonding and brazing, caused interfacial deterioration due to thermal stress from mismatched thermal expansion between W and reduced activation ferritic steel. To address this, underwater explosive welding was introduced as a high-velocity cold process that joins dissimilar materials while maintaining a strong, thin interface without the thermal issues associated with traditional methods. In this study, the effects of neutron irradiation on the hardness and microstructure in W-coated F82H reduced activation ferritic steel (W/F82H) joined by underwater explosive welding are investigated. Following neutron irradiation at 290 °C, irradiation hardening is suppressed in W, F82H, and their interface within the W/F82H material. Furthermore, microstructural observations indicate that the recovery of work hardening and relaxation of elastic strain introduced during coating significantly contribute to the suppression of irradiation hardening in W/F82H. In conclusion, W/F82H exhibits significantly suppressed irradiation hardening compared with those in stand-alone materials. This suppression is explained by residual stress from thermal expansion mismatch and the unique microstructure at the interface. These results provide valuable insights for the development of more durable materials in nuclear fusion applications.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Directed energy deposition of functionally graded V-4Cr-4Ti to Fe-9Cr transition for fusion power systems

This study proposes a graded structure via additive manufacturing for divertor and first wall blanket applications in fusion reactors. Materials were selected based on thermodynamic calculations to operate from 1100 °C at the plasma-facing level to 550 °C at the structural steel level. Conventional joining methods often lead to failures due to discrete reaction layers with significant mechanical property differences. Using laser beam-directed energy deposition (LB-DED), this study demonstrates the fabrication of a VCrTi-Gr91 steel functionally graded component through a novel process parameter optimization framework. A systematic approach included powder characterization, single-track depositions, and construction of printability maps. Near full-density specimens of each interlayer were additively manufactured, and a transition from V-based alloys to reduced activation ferritic martensitic steels was achieved. Computational material selection of interlayer alloys and thermodynamic/diffusion kinetics simulations prevented most interface incompatibilities. A brittle intermetallic formed at one interface, causing cracking, which was not predicted by current thermodynamic models. Transition alloy design approach was updated with a more recent database and a mitigation strategy has been proposed to eliminate the formation of deleterious intermetallic phases. Ultimately, LB-DED has proven effective for producing multi-material graded systems for fusion applications, with the demonstrated process parameter optimization framework applicable to various materials.

Additive manufacturing↗
DOE OSTI2025

A high strength Al-2Ni-0.5Zr conductor alloy fabricated via laser powder bed fusion

There is a current need for new aluminum alloy design strategies to target applications requiring high strength and conductivity with reductions in mass. A new lightweight Al-2Ni-0.5Zr (wt. %) conductor alloy was fabricated using laser powder bed fusion. A design of experiments probed the alloy's solidification cracking susceptibility. It was observed that solidification cracking was generally reduced with fast scan speeds, above 1500 mm/s, and smaller hatch spacings. The different cooling rates throughout the melt pool produced a heterogeneous distribution of cellular and equiaxed Al 3 Ni precipitates in the as-printed alloy. Additionally, the rapid solidification characteristic of laser powder bed fusion created a super-saturated Zr solid solution. An aging heat treatment at 375 °C for 24 h imparted strengthening through the precipitation of L1 2 -Al 3 Zr nanoprecipitates, which counteracted the softening caused by the fragmentation and coarsening of Al 3 Ni precipitates. The yield strength increased from 138 MPa in the as-printed condition to 168 MPa after aging, while the ductility remained constant at ∼21%. The aging treatment simultaneously increased the electrical conductivity from 40.8% IACS (International Annealed Copper Standard) to 53.5% IACS. Modeling of the strengthening mechanisms and electrical conductivity contributions rationalized the simultaneous increase in strength and conductivity upon aging. Furthermore, the strengthening efficacy of the Al 3 Ni and L1 2 -Al 3 Zr precipitates, combined with the low Ni and Zr solubility in the FCC Al matrix, facilitated both high strength and electrical conductivity. Overall, the combination of strength and electrical conductivity positions this alloy as a suitable choice for additively manufactured lightweight conductors.

Additive manufacturing↗
DOE OSTI2025

The FABLE silicon detector array for comprehensive studies of fusion and breakup dynamics

The Fusion and Breakup of Light Elements (FABLE) array, comprised of ΔE-E telescopes of neutron-transmutation doped (NTD) Double-Sided Silicon Strip Detectors (DSSDs), has been developed and commissioned for future experiments at the Facility for Rare Isotope Beams (FRIB). FABLE was designed to perform measurements of complete and incomplete fusion cross sections, to help address the interplay between breakup and clustering in outcomes of reactions with exotic nuclides. Here, the FABLE array has been commissioned in a comprehensive study at the ReAccelerator facility ReA6 of the Facility for Rare Isotope Beams (FRIB), investigating the dynamics in the weakly-bound proton rich 7 Be reaction on 208 Pb targets.

Breakup↗
DOE OSTI2025

Enhancing the Quantification of Critical Elements in WTE and Coal Ash via Alkaline Fusion: Superiority of Lithium Metaborate (LiBO 2 )

Ashes generated from coal combustion, as well as waste incineration, can be a potential source of critical elements necessary for the ongoing transition towards electrification and greener energy technologies. For the quantification of critical elements, traditional methods such as acid digestion are time-intensive and can fail to dissolve critical elements in refractory minerals. One potential solution is to adopt alkaline fusion for faster, total digestion. However, the role of flux choice and the subsequent digestion efficiency (DE) is unknown. Here, we report a systematic investigation on the feasibility of alkaline fusion with Lithium Metaborate (LiBO 2 ) and Lithium Tetraborate (Li 2 B 4 O 7 ) as fluxes for the digestion of two standard reference materials (BCR 176R and SRM 1633c). Our findings suggest that LiBO 2 yields higher DE values than Li 2 B 4 O 7 for several critical REEs and volatiles, such as Pb and Cd. Specifically, for REEs in SRM 1633c, the DE values with LiBO 2 are, on average, ~16 percent higher than those with Li 2 B 4 O 7 . Similarly, for Pb and Cd in BCR 176R, the DE values with LiBO 2 are ~20 percent higher than with Li 2 B 4 O 7 . These results suggest that LiBO 2 is a superior flux for rapid ash digestion.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2020

Observation of persistent species temperature separation in inertial confinement fusion mixtures

The injection and mixing of contaminant mass into the fuel in inertial confinement fusion (ICF) implosions is a primary factor preventing ignition. ICF experiments have recently achieved an alpha-heating regime, in which fusion self-heating is the dominant source of yield, by reducing the susceptibility of implosions to instabilities that inject this mass. We report the results of unique separated reactants implosion experiments studying pre-mixed contaminant as well as detailed high-resolution three-dimensional simulations that are in good agreement with experiments. At conditions relevant to mixing regions in high-yield implosions, we observe persistent chunks of contaminant that do not achieve thermal equilibrium with the fuel throughout the burn phase. The assumption of thermal equilibrium is made in nearly all computational ICF modeling and methods used to infer levels of contaminant from experiments. We estimate that these methods may underestimate the amount of contaminant by a factor of two or more.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The impact of low-mode symmetry on inertial fusion energy output in the burning plasma state

Indirect Drive Inertial Confinement Fusion Experiments on the National Ignition Facility (NIF) have achieved a burning plasma state with neutron yields exceeding 170 kJ, roughly 3 times the prior record and a necessary stage for igniting plasmas. The results are achieved despite multiple sources of degradations that lead to high variability in performance. Results shown here, for the first time, include an empirical correction factor for mode-2 asymmetry in the burning plasma regime in addition to previously determined corrections for radiative mix and mode-1. Analysis shows that including these three corrections alone accounts for the measured fusion performance variability in the two highest performing experimental campaigns on the NIF to within error. Here we quantify the performance sensitivity to mode-2 symmetry in the burning plasma regime and apply the results, in the form of an empirical correction to a 1D performance model. Furthermore, we find the sensitivity to mode-2 determined through a series of integrated 2D radiation hydrodynamic simulations to be consistent with the experimentally determined sensitivity only when including alpha-heating.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Tracking blobs in the turbulent edge plasma of a tokamak fusion device

Abstract The analysis of turbulence in plasmas is fundamental in fusion research. Despite extensive progress in theoretical modeling in the past 15 years, we still lack a complete and consistent understanding of turbulence in magnetic confinement devices, such as tokamaks. Experimental studies are challenging due to the diverse processes that drive the high-speed dynamics of turbulent phenomena. This work presents a novel application of motion tracking to identify and track turbulent filaments in fusion plasmas, called blobs, in a high-frequency video obtained from Gas Puff Imaging diagnostics. We compare four baseline methods (RAFT, Mask R-CNN, GMA, and Flow Walk) trained on synthetic data and then test on synthetic and real-world data obtained from plasmas in the Tokamak à Configuration Variable (TCV). The blob regime identified from an analysis of blob trajectories agrees with state-of-the-art conditional averaging methods for each of the baseline methods employed, giving confidence in the accuracy of these techniques. By making a dataset and benchmark publicly available, we aim to lower the entry barrier to tokamak plasma research, thereby greatly broadening the community of scientists and engineers who might apply their talents to this endeavor.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

How turbulence spreading improves power handling in quiescent high confinement fusion plasmas

Viable magnetic fusion devices necessitate combining good confinement with effective power flux handling. A major concern for ITER, and devices beyond, is the divertor heat load width, which sets peak boundary heat loads on the plasma-facing materials. Current estimates of the heat flux width are narrow for future reactors. Here, we demonstrate how pedestal turbulence can expand into, or entrain, the stable scrape-off-layer and so broaden the heat flux width beyond these neoclassical predictions. Employing combined theoretical, computational, and experimental approaches, we focus on quiescent high confinement discharges on the DIII-D tokamak, but the results are of broader significance. Our findings uncover common trends in the edge turbulence intensity flux, the pressure perturbation skewness, and the turbulence mixing length, which together determine the heat flux width. This research demonstrates the physics of scrape-off-layer broadening by turbulence and highlights the promise of a turbulent pedestal for successful core-edge integration in ITER and future fusion devices.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗