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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Photoluminescence Induced in Mineral Oil by Ionizing Radiation

We have studied photoluminescence (PL) properties of vacuum pump mineral oil as a function of the type and intensity of ionizing radiation exposure. The mineral oil has a simple aliphatic structure, which possesses no chromophore in the traditional sense. Unexpected PL in the mineral oil has characteristic features such as variation of the emission peak wavelength depending on the excitation wavelength and intensity dramatically increasing with radiation dose. The observed behavior can be understood in the framework of a previously suggested model introducing aggregation-induced PL and the formation of conjugated clusters comprising nontraditional chromophores. Our findings can be used for the development of optical sensors for real-time monitoring of oil utilized in vacuum pumps in nuclear fusion reactors.

Bliznyuk, Valery N.↗
DOE OSTI2020

Measurements of enhanced performance in an indirect drive inertial confinement fusion experiment when reducing the contact area of the capsule support

Experimental results from indirectly driven inertial confinement fusion experiments testing the performance gained from using an alternate capsule tent support are reported. The polar tent describes an alternate geometry for the thin membrane used to support the Deuterium–Tritium (DT) filled capsule. In this work, the contact area is reduced by 23 times by locating the tent support close to the poles of the capsule. The polar tent experiments are repeats of previous 3 shock 1.63 MJ, 400 TW high foot experiments and use a 165 μm thick silicon doped carbon hydrogen plastic (CH) shell. Using the polar tent support, we report a DT neutron yield of 1.07 ×1016, 76% higher than the expected Y DT ∝V 7.7 scaling. This is, at the time of writing, the highest neutron yield to date from a CH shell implosion. Furthermore, we find that the inferred pressure when using the polar tent is significantly above the model based on analytic scaling even when accounting for tent effects. Analysis of x-ray and neutron images shows the reduction of lobes produced by nominal tent features. The reduction of these features in the polar tent experiments leads to decreased low mode (P2 and P4) asymmetry compared to the nominal tent results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

High temperature tensile properties of H13 tool steel fabricated via binder jet additive manufacturing

H13 is one of the most used steels for hot work tooling applications like die casting. Binder jet additive manufacturing (BJAM) offers a cost-effective solution to fabricate tools with performance enhancing features like internal cooling channels. While full densification of BJAM H13 has been demonstrated in the literature, the room and high temperature tensile properties of the material, and the effect of post-process treatments like hot isostatic pressing (HIP), and heat treatment (HT) are unknown. Here we use HIP+HT to obtain a combination of ultimate tensile strength (UTS) and elongation to failure that is superior to H13 fabricated via laser powder bed fusion and wire arc additive manufacturing. HIP+HT BJAM H13 has a superior UTS (1836 MPa) compared with conventionally processed (1671 MPa) H13 but has lower plastic elongation. We report that as-sintered samples have an extensive network of continuous grain boundary (GB) carbides that contribute to brittle fracture. HIP+HT results in partial dissolution of GB carbides and formation of a fine martensitic microstructure resulting in superior tensile strength and elongation compared with as-sintered H13. We use thermo-kinetic calculations to rationalize the effects of HIP+HT on microstructure and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Nonlinear models for coupling the effects of stimulated Raman scattering to inertial confinement fusion codes

Laser plasma instabilities (LPI) reduce driver-target coupling, alter implosion symmetry, and therefore can fundamentally limit fusion performance in inertial confinement fusion (ICF). Developing a predictive modeling capability for LPI effects can critically advance the success of the field. We perform vector particle-in-cell simulations of multi-speckled laser beams undergoing stimulated Raman scattering (SRS) at various densities and intensities relevant to mainly indirectly driven and a subset of parameter space for directly driven ICF systems, focusing on the regimes with intensities above the onset of electron trapping. Based on the wavenumber of the SRS daughter electron plasma wave, we identify several regions with underpinning SRS saturation physics: the electron-trapping dominated region with intermediate kλD values, the strong Landau damping region at larger kλD values, and the region where the Langmuir decay instability arises at lower kλD values. We develop a nonlinear SRS reflectivity model that features the base trapping-dominated scaling of (kλD)−4 and its modifications. Electron trapping deforms the initialized electron distribution functions, and we have developed a new δf-Gaussian-mixture algorithm for an accurate characterization of the trapped hot electron population. With this SRS hot electron description, we construct a nonlinear energy deposition model and a hot electron source model—based on a modified Manley–Rowe relation—suitable for including SRS effects as a sub-grid module in a high-fidelity ICF design code.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

How does ion temperature gradient turbulence depend on magnetic geometry? Insights from data and machine learning

Magnetic geometry has a significant effect on the level of turbulent transport in fusion plasmas. Here, we model and analyse this dependence using multiple machine learning methods and a dataset of >200 000 nonlinear gyrokinetic simulations of ion-temperature-gradient turbulence in diverse non-axisymmetric geometries. The dataset is generated using a large collection of both optimised and randomly generated stellarator equilibria. At fixed gradients and other input parameters, the turbulent heat flux varies between geometries by several orders of magnitude. Trends are apparent among the configurations with particularly high or particularly low heat flux. Regression and classification techniques from machine learning are then applied to extract patterns in the dataset. Due to a symmetry of the gyrokinetic equation, the heat flux and regressions thereof should be invariant to translations of the raw features in the parallel coordinate, similar to translation invariance in computer vision applications. Multiple regression models including convolutional neural networks (CNNs) and decision trees can achieve reasonable predictive power for the heat flux in held-out test configurations, with highest accuracy for the CNNs. Using Spearman correlation, sequential feature selection and Shapley values to measure feature importance, it is consistently found that the most important geometric lever on the heat flux is the flux surface compression in regions of bad curvature. The second most important geometric feature relates to the magnitude of geodesic curvature. These two features align remarkably with surrogates that have been proposed based on theory, while the methods here allow a natural extension to more features for increased accuracy. The dataset, released with this publication, may also be used to test other proposed surrogates, and we find that many previously published proxies do correlate well with both the heat flux and stability boundary.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Classification and regression models of audio and vibration signals for machine state monitoring in precision machining systems

Here we present a data-driven method for monitoring machine status in manufacturing processes. Audio and vibration data from precision machining are used for inference in two operating scenarios: (a) variable machine health states (anomaly detection); and (b) settings of machine operation (state estimation). Audio and vibration signals are first processed through Fast Fourier Transform and Principal Component Analysis to extract transformed and informative features. These features are then used in the training of classification and regression models for machine state monitoring. Specifically, three classifiers (K-nearest neighbors, convolutional neural networks and support vector machines) and two regressors (support vector regression and neural network regression) were explored, in terms of their accuracy in machine state prediction. It is shown that the audio and vibration signals are sufficiently rich in information about the machine that 100% state classification accuracy could be accomplished. Data fusion was also explored, showing overall superior accuracy of data-driven regression models.

42 ENGINEERING↗
DOE OSTI2025

Direct Evidence of Multispecies Hydrodynamics in Ignition-Scale Hohlraums

A targeted experiment at the National Ignition Facility (NIF) confirms the presence of multispecies hydrodynamics in inertial confinement fusion hohlraums relevant to ignition. Here, the effects are identified by filling the gold hohlraum with a deuterium-tritium (DT) gas mixture instead of helium. As the hohlraum is heated by the NIF lasers, it implodes inward, compressing and heating the DT, which leads to fusion. The resulting DT-fusion neutrons are measured in space, time, yield, angle, and energy. A distinct, peaked, triangular shape in the radial neutron emission profile provides evidence of a “leaky piston” effect caused by the interpenetration of DT into the expanding gold. This process reduces the reversibility of DT compression and decreases neutron generation on the temporal trailing edge compared to the leading edge. These results are well described by multispecies hydrodynamics simulations, which reproduce the observed spatial and temporal features, as well as the total neutron yield and angularly resolved energy spectra. In contrast, conventional simulations that use only single-species physics fail to match the experimental data. They overpredict the neutron yield, produce a flatter-than-observed spatial profile, and show excessive emission at late times compared to the experimental measurements.

Higginson, Drew P. [Lawrence Livermore National La↗
DOE OSTI2023

Machine learning-based real-time kinetic profile reconstruction in DIII-D

Abstract Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q , toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

An asymptotic-preserving semi-Lagrangian algorithm for the anisotropic heat transport equation with arbitrary magnetic fields

Here, we extend the recently proposed semi-Lagrangian algorithm for the extremely anisotropic heat transport equation [Chacón et al., J. Comput. Phys ., 272 (2014)] to deal with arbitrary magnetic field topologies. The original scheme (which showed remarkable numerical properties) was valid for the so-called tokamak-ordering regime, in which the magnetic field magnitude was not allowed to vary much along field lines. The proposed extension maintains the attractive features of the original scheme (including the analytical Green's function, which is critical for tractability) with minor modifications, while allowing for completely general magnetic fields. The accuracy and generality of the approach are demonstrated by numerical experiment with an analytical manufactured solution.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Local conservation of energy in fully implicit PIC algorithms

We consider the issue of strict, fully discrete local energy conservation for a whole class of fully implicit local-charge- and global-energy-conserving particle-in-cell (PIC) algorithms. Earlier studies demonstrated these algorithms feature strict global energy conservation. However, whether a local energy conservation theorem exists (in which the local energy update is governed by a flux balance equation at every mesh cell) for these schemes is unclear. In this study, we show that a local energy conservation theorem indeed exists. We begin our analysis with the 1D electrostatic PIC model without orbit-averaging, and then generalize our conclusions to account for orbit averaging, multiple dimensions, and electromagnetic models (Darwin). In all cases, a temporally, spatially, and particle-discrete local energy conservation theorem is shown to exist, proving that these formulations (as originally proposed in the literature), in addition to being locally charge conserving and globally energy conserving, are strictly locally energy conserving as well. In contrast to earlier proofs of local conservation in the literature, which only considered continuum time, our result is valid for the fully implicit time-discrete version of all models considered, including important features such as orbit averaging. We demonstrate the local-energy-conservation property numerically with a paradigmatic numerical example.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

On the strength and fracture toughness of an additive manufactured CrCoNi medium-entropy alloy

An additively manufactured (nominally equiatomic) CrCoNi alloy was processed by laser powder bed fusion (LPBF). At ambient temperatures (298 K), this medium-entropy alloy displayed a yield strength, σ y of ~691 ± 9 MPa, and an ultimate tensile strength, σ u of ~926 ± 15.2 MPa; at cryogenic temperatures (77 K), yield and tensile strengths increased respectively to σ y ~ 944 ± 6 MPa and σ u ~ 1382 ± 11 MPa. These strength levels are 57 and 44% higher than that of the wrought alloy, due to strengthening from the solidification cellular structures intertwined with dislocations in the LPBF CrCoNi. The crack-initiation fracture toughness, K JIc was measured to be ~183.7 ± 28 MPa√m at 298 K; this value marginally decreased by ~4% to ~176 ± 11 MPa√m at 77 K. These K JIc values of the LPBF CrCoNi were 11% and 35% lower than the wrought CrCoNi alloy at 298 K and 77 K, respectively. Here, the resistance to crack growth of the LPBF CrCoNi from its hierarchical micro- and meso-structures was evaluated using nonlinear-elastic fracture mechanics by measuring R-curve behavior in the form of the J-integral as a function of crack extension. The specific features of the hierarchical microstructures at different length-scales provide a basis for the strengthening and toughening properties of this additively manufactured medium-entropy alloy. This correlation between the deformation and the hierarchical microstructures at different length-scales may provide future guidance for improving the fracture toughness properties of medium-entropy alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Fill tube dynamics in inertial confinement fusion implosions with high density carbon ablators

Plasma jets, such as γ-ray burst jets, Herbig–Haro jets, μ-quasar jets, and active galactic nuclei jets, are found throughout the universe [S. Mendoza et al., Rev. Mex. Astron. Astrofis. 41, 453 (2005)]. Plasma jets are also present in indirect drive inertial confinement fusion experiments originating from the capsule's fill tube and occasionally from divots and voids in the capsules, particles on the exterior of the capsule, or from the tent holding the capsule in the target. This paper looks at two different gas-filled capsule implosions containing a plasma jet resulting from a capsule fill tube and fill channel, both of which utilized high density carbon ablators. Two models were developed, a drag and a snowplow model, which use the time-dependent motion of the injected mass through the hotspot to estimate the mass injected into the hotspot from the fill tube and channel, arriving at an average injected mass of ~84.5 ± 25.5 ng for the first experiment and 91 ± 20 ng for the second experiment. Furthermore, unlike previous methods to estimate fill tube injected mass, these techniques do not assume that the mixed mass is in thermal equilibrium with the hotspot or that the x-ray emission is only coming from within the hotspot itself. This paper also discusses the features seen in these experiments which include limb brightening in the shell for undoped ablators and flattening in the ablator from shadowing by the fill tube.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

$\mathrm{T}$ransient $\mathrm{CHI}$ System Design Studies for $\mathrm{P}$ EGASUS -$\mathrm{III}$

We report transient coaxial helicity injection (transient CHI), first developed on the Helicity Injected Torus-II (HIT-II) and later on the National Spherical Torus Experiment (NSTX) for implementing solenoid-free plasma current startup capability in a spherical tokamak (ST), is now planned to be tested on the Pegasus-III ST using a novel double-biased configuration. Such a configuration is likely needed for transient CHI deployment in a reactor. The transient CHI system optimization will be studied on Pegasus-III to enable startup toroidal persisting currents at the limits permitted by the external poloidal field coils. A transient CHI discharge is generated by driving injector current along magnetic field lines that connect the inner and outer divertor plates on one end of the ST. Simulations using the Tokamak Simulation Code are used to assess the transient CHI toroidal current generation potential and electrode gap location on the Pegasus-III. While past transient CHI systems have used high-voltage, oil-filled capacitors for driving the injector current, for improved safety, Pegasus-III will use a high-current capacitor bank based on low-voltage electrolytic capacitors. The designed and fabricated system is capable of over 32 kA. The modular design features permit the system to be upgraded to higher currents, as needed, to meet the future needs of the Pegasus-III facility.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Prediction of DIII-D Pedestal Structure From Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Physics-guided machine learning approaches to predict the ideal stability properties of fusion plasmas

One of the biggest challenges to achieve the goal of producing fusion energy in tokamak devices is the necessity of avoiding disruptions of the plasma current due to instabilities. The disruption event characterization and forecasting (DECAF) framework has been developed in this purpose, integrating physics models of many causal events that can lead to a disruption. Two different machine learning approaches are proposed to improve the ideal magnetohydrodynamic (MHD) no-wall limit component of the kinetic stability model included in DECAF. First, a random forest regressor (RFR), was adopted to reproduce the DCON computed change in plasma potential energy without wall effects for a large database of equilibria from the national spherical torus experiment (NSTX). This tree-based method provides an analysis of the importance of each input feature, giving an insight into the underlying physics phenomena. Secondly, a fully-connected neural network has been trained on sets of calculations with the DCON code, to get an improved closed form equation of the no-wall β limit as a function of the relevant plasma parameters indicated by the RFR. The neural network has been guided by physics theory of ideal MHD in its extension outside the domain of the NSTX experimental data. The estimated value has been incorporated into the DECAF kinetic stability model and tested against a set of experimentally stable and unstable discharges. Moreover, the neural network results were used to simulate a real-time stability assessment using only quantities available in real-time. Finally, the portability of the model was investigated, showing encouraging results by testing the NSTX-trained algorithm on the mega ampere spherical tokamak (MAST).

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Active interlocking metasurfaces enabled by shape memory alloys

Interlocking metasurfaces (ILMs) are a newly developed joining technology that relies on arrays of interlocking features that transmit force and constrain motion between adjoining bodies in one or more directions. This study explores harnessing the shape memory effect (SME) in Nickel-Titanium shape memory alloys (NiTi SMAs) in structures fabricated using additive manufacturing (AM) to advance the development of active ILMs by creating unit cells that open or close at specific temperatures. The study encompasses designing and fabricating two distinct interlocking array configurations using near-equiatomic NiTi powder and the laser powder bed fusion (L-PBF) AM technique, following a previously developed AM process optimization framework to manufacture defect-free parts. To guide the design process, finite element analysis (FEA) was employed to predict strain values during engage-disengage cycles. The martensitic transformation characteristics of the ILMs were characterized. Thermomechanical testing revealed that the ILMs demonstrate high locking force once engaged, coupled with complete shape recovery and good cyclic stability. Digital image correlation (DIC) was also employed to validate the FEA predictions during the engage-disengage cycles. The results indicate that NiTi SMA-based ILMs can be designed and fabricated into complex shapes using L-PBF. By leveraging the SME, the functionality of an ILM can be improved upon. The combination of computational modeling, additive manufacturing, and thermomechanical and physical property characterization provides a framework for designing future ILMs out of active materials.

Additive manufacturing↗
DOE OSTI2024

Transport and stability in sustained high q min , high β N discharges on DIII-D

Abstract To address the needs for a fusion pilot plan design, DIII-D/EAST joint experiments on DIII-D have demonstrated high normalized beta β N ∼ 4.2, toroidal beta β T ∼ 3.3% with q min > 2, q 95 ⩽ 8 sustained for more than six energy confinement times in high poloidal beta regime. The excellent energy confinement quality ( H 98y2 ∼ 1.8) is achieved with an internal transport barrier at high line-averaged Greenwald density fraction f Gr > 0.9. The trapped gyro-Landau fluid (TGLF) modeling of the transport characteristics shows that the beam-driven rotation does not play an important role in the high confinement quality. The modeling also captures very well several transport features, giving us confidence in using integrated modeling to project these experimental results to future machines. The high-performance phase is terminated by fast-growing modes triggered near the n = 1 ideal-wall kink stability limit. New radio frequency (RF) capabilities for off-axis current drive could remove the residual ohmic current to achieve a fully non-inductive state, and improve the mode–wall coupling to increase the ideal-wall β N limit, enabling sustainment of the fully non-inductive high performance plasma in stationary conditions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗