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Results for “Iterative limiters”

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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Asymptotic Expansion of the Impedance Per Unit Length for Rectangular Conductors

An iteration method is introduced to obtain the asymptotic form of the impedance per unit length of a rectangular conductor when the half side lengths are large compared to the skin depth. The first terms of the asymptotic expansion are extracted in closed form. The manner in which the corner corrections fit into the expansion are illustrated. The asymptotic results are compared to a numerical solution in the square limit. The odd corner correction for a right angle edge is also discussed.

42 ENGINEERING↗
DOE OSTI2023

Cost-optimized Automated Variance Reduction for Highly Angle-dependent Radiation Transport Analyses [Dissertation]

Monte Carlo variance-reduction techniques that directly bias particle direction have historically received limited attention despite being useful for many radiation transport applications such as those in which radiation travels through large regions with a low probability of colliding. One such technique is known as DXTRAN (short for deterministic transport) in the MCNP Monte Carlo radiation transport code. Until now, effectively applying DXTRAN in calculations is largely based on empirical observations of computational performance. Optimal DXTRAN parameters are identified through manual iteration. This work develops new mathematical descriptions of the DXTRAN variance-reduction process and demonstrates a new automated variance-reduction method that applies these mathematical formulations to determine the optimal application of DXTRAN in a given problem.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Multi-objective sizing and dispatch for building thermal and battery storage towards economic and environmental synergy

The role of building thermal and battery storage is pivotal in advancing smart cities and achieving sustainability goals through effective energy management. Despite their significance, there are several limitations in the sizing approach and value stream analysis with various objectives for their widespread adoption in buildings. This work proposes a flexible and scalable multi-objective optimization framework for optimal sizing and dispatch of building thermal and battery storage, addressing conflicting objectives simultaneously using mixed-integer linear programming. The weighted-sum method is adapted, combining multiple objectives into a single function. The two-stage procedure iterates over different weights, generating optimal solutions and forming the Pareto front. Case studies are performed to assess the energy, economic, and environmental benefits of building energy storage systems for a large office building in three climate locations. The results demonstrate that the proposed framework efficiently determines optimal sizing and dispatch strategies, addressing the balance between economic viability and emission reduction. The dynamic relationship between time-of-use energy charges and emission factors leads to significantly different strategies based on whether economic or environmental concerns are prioritized. This research enhances our understanding of the benefits of TES and BES systems in buildings, providing valuable guidance to stakeholders.

25 ENERGY STORAGE↗
DOE OSTI2024

Exploring toroidal anvil profiles for larger sample volumes above 4 Mbar

Abstract With the advent of toroidal and double-stage diamond anvil cells (DACs), pressures between 4 and 10 Mbar can be achieved under static compression, however, the ability to explore diverse sample assemblies is limited on these micron-scale anvils. Adapting the toroidal DAC to support larger sample volumes offers expanded capabilities in physics, chemistry, and planetary science: including, characterizing materials in soft pressure media to multi-megabar pressures, synthesizing novel phases, and probing planetary assemblages at the interior pressures and temperatures of super-Earths and sub-Neptunes. Here we have continued the exploration of larger toroidal DAC profiles by iteratively testing various torus and shoulder depths with central culet diameters in the 30–50 µm range. We present a 30 µm culet profile that reached a maximum pressure of 414(1) GPa based on a Pt scale. The 300 K equations of state fit to our P–V data collected on gold and rhenium are compatible with extrapolated hydrostatic equations of state within 1% up to 4 Mbar. This work validates the performance of these large-culet toroidal anvils to > 4 Mbar and provides a promising foundation to develop toroidal DACs for diverse sample loading and laser heating.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

The role of ion-scale micro-turbulence in pedestal width of the DIII-D wide-pedestal QH mode

The low-edge rotation, intrinsically ELM-free, and improved confinement wide-pedestal quiescent H-mode (QH-mode), discovered in DIII-D tokamak, has pedestal widths exceeding the EPED-kinetic-ballooning mode (KBM) model scaling typically by at least 25%. Ion-scale ($k_yp_s$ < 1) microturbulence and its role in setting the pedestal structure is investigated using the radially local δ$f$ gyrokinetic code CGYRO. The electromagnetic trapped electron mode (TEM) is unstable at the pedestal top, while plasma beta (β$_e$) is ∼60% below the KBM onset threshold and the electron temperature gradient mode is found to be unstable in the peak gradient region. Nonlinear simulation reveals that the ion-scale turbulence could produce electron energy flux consistent with the flux inferred from power balance at the pedestal top, with a reasonable variation of the local shearing rate; and the local neoclassical transport from NEO is dominant over the simulated turbulent transport in the ion energy flux channel. The simulated ion-scale turbulence produces much lower electron energy flux than inferred from experiment in the pedestal peak gradient region. A correction to the EPED-KBM pedestal width scaling is obtained based on the two-dimensional scan of pedestal top plasma beta (β$_e$) and normalized electron density and temperature scale lengths,${a}$/${L_n}_e$, ${a}$/${L_T}_n$using CGYRO linear simulations. Mode transitions among TEM, micro-tearing mode, ion-temperature gradient mode and KBM, are observed in the 2D scan at the pedestal top. A fixed normalized growth rate for these drift-type modes is taken to determine the pedestal width scaling, which shows good consistency with the QH experimental database on pedestal heights and widths. The onset of KBM instabilities and the local E × B shear suppression criterion set the lower and upper limit for the pedestal width of standard QH-mode, wide-pedestal QH-mode and type-I ELMy H mode. A potentially higher and wider pedestal is expected from the new scaling of pedestal width. This work presents an improved understanding of the ion-scale micro-turbulence of wide-pedestal QH-mode and sheds light on a promising scenario for future reactors, including ITER and beyond.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Effects of nitrogen on tungsten surfaces investigated with low energy ion scattering

The effect of nitrogen on the surfaces of polycrystalline ITER-grade tungsten and a tungsten single crystal were studied with low energy ion scattering (LEIS) and direct recoil spectroscopy (DRS). LEIS and DRS measurements on both tungsten surfaces were performed in an ultra-high vacuum system as various quantities of N 2 were introduced into the chamber through a variable leak valve. The obtained ion energy spectra reveal that nitrogen was readily adsorbed onto the surface, in turn limiting the amount of hydrogen that could be adsorbed onto the surface. These results not only provide insight into how the presence of nitrogen on tungsten surfaces may play a role in hydrogen adsorption and retention, but also serve to benchmark models being developed to describe the H-N-W system.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Data Collection and Analysis Challenges and Mitigation Strategies for Quantitative Human Factors Research Studies in Nuclear Power Plant Modernization

The United States (U.S.) Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) program Plant Modernization Pathway is conducting targeted research and development (R&D) to address aging and reliability concerns with the legacy instrumentation and control and related information systems of the U.S. LWR fleet. In this effort, the application of human factors engineering (HFE) provides an important role in ensuring new digital plant technologies enable broad innovation and business improvement with continued operational safety. Evaluation is a key activity in HFE, which often occurs iteratively through the system design lifecycle. While qualitative methods are important in collecting information of how users perform tasks through observations, quantitative methods are equally important in assessing system design based on performance. In collecting and analyzing this quantitative data, there are notable challenges in the nuclear HFE domain that may threaten the validity and reliability of the inferences made in these studies. Notable challenges include small sample size and limited resources, large error variance and small effect size, an “adding test features to losing degrees of freedom” dilemma, non-normal distribution, and heterogeneity of variance. The results in control room usability studies are often statistically non-significant, which makes it hard to interpret. This work discusses these challenges across different scientific viewpoints and provides real-world examples of these challenges in practice. Collectively, the objective of this work is to position these challenges to the larger data science community as a means of identifying future opportunities to address these issues.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Distributed Stochastic Optimization of a Neural Representation Network for Time-Space Tomography Reconstruction

4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an important inverse problem in non-destructive evaluation. Conventional back-projection based reconstruction methods assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. Here, we use a forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued 4D object coordinates. Unlike previous neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

SCIMON: Scientific Inspiration Machines Optimized for Novelty

We explore and enhance the ability of neu- ral language models to generate novel scien- tific directions grounded in literature. Work on literature-based hypothesis generation has traditionally focused on binary link prediction— severely limiting the expressivity of hypothe- ses. This line of work also does not focus on optimizing novelty. We take a dramatic depar- ture with a novel setting in which models use as input background contexts (e.g., problems, experimental settings, goals), and output natu- ral language ideas grounded in literature. We present SCIMON, a modeling framework that uses retrieval of “inspirations” from past scien- tific papers, and explicitly optimizes for novelty by iteratively comparing to prior papers and up- dating idea suggestions until sufficient novelty is achieved. Comprehensive evaluations reveal that GPT-4 tends to generate ideas with over- all low technical depth and novelty, while our methods partially mitigate this issue. Our work represents a first step toward evaluating and developing language models that generate new ideas derived from the scientific literature.

Ji, Heng↗
DOE OSTI2020

Divertor heat flux challenge and mitigation in SPARC

Owing to its high magnetic field, high power, and compact size, the SPARC experiment will operate with divertor conditions at or above those expected in reactor-class tokamaks. Power exhaust at this scale remains one of the key challenges for practical fusion energy. Based on empirical scalings, the peak unmitigated divertor parallel heat flux is projected to be greater than 10 GW m -2 . This is nearly an order of magnitude higher than has been demonstrated to date. Furthermore, the divertor parallel Edge-Localized Mode (ELM) energy fluence projections (~11–34 MJ m -2 ) are comparable with those for ITER. However, the relatively short pulse length (~25 s pulse, with a ~10 s flat top) provides the opportunity to consider mitigation schemes unsuited to long-pulse devices including ITER and reactors. The baseline scenario for SPARC employs a ~1 Hz strike point sweep to spread the heat flux over a large divertor target surface area to keep tile surface temperatures within tolerable levels without the use of active divertor cooling systems. In addition, SPARC operation presents a unique opportunity to study divertor heat exhaust mitigation at reactor-level plasma densities and power fluxes. Not only will SPARC test the limits of current experimental scalings and serve for benchmarking theoretical models in reactor regimes, it is also being designed to enable the assessment of long-legged and X-point target advanced divertor magnetic configurations. Experimental results from SPARC will be crucial to reducing risk for a fusion pilot plant divertor design.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

MHD stability and disruptions in the SPARC tokamak

SPARC is being designed to operate with a normalized beta of $β_N$ = 1.0, a normalized density of $n_G$ = 0.37 and a safety factor of $q_{95}$ ≈ 3.4, providing a comfortable margin to their respective disruption limits. Further, a low beta poloidal $β_p$ = 0.19 at the safety factor $\textit{q}$ = 2 surface reduces the drive for neoclassical tearing modes, which together with a frozen-in classically stable current profile might allow access to a robustly tearing-free operating space. Although the inherent stability is expected to reduce the frequency of disruptions, the disruption loading is comparable to and in some cases higher than that of ITER. The machine is being designed to withstand the predicted unmitigated axisymmetric halo current forces up to 50 MN and similarly large loads from eddy currents forced to flow poloidally in the vacuum vessel. Runaway electron (RE) simulations using GO+CODE show high flattop-to-RE current conversions in the absence of seed losses, although NIMROD modelling predicts losses of ~80 %; self-consistent modelling is ongoing. A passive RE mitigation coil designed to drive stochastic RE losses is being considered and COMSOL modelling predicts peak normalized fields at the plasma of order 10 –2 that rises linearly with a change in the plasma current. Massive material injection is planned to reduce the disruption loading. A data-driven approach to predict an oncoming disruption and trigger mitigation is discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

The Impact of Disruptions on the Economics of a Tokamak Power Plant

Tokamaks are often considered to be a leading candidate for near-term, cost-effective fusion energy, but these devices are susceptible to sudden loss of confinement events called disruptions. The threat of disruptions has garnered serious attention in research for the next generation of burning plasma experiments, such as ITER, but has received little treatment in economic studies of magnetic fusion energy. In this paper, we present a model for quantifying the effect of disruptions on the cost of electricity produced by a tokamak power plant (TPP). We outline the various ways disruptions increase costs and decrease revenues, introduce metrics to quantify these effects, and add them to a levelized cost of electricity (LCOE) model. Additionally, we identify several rate-limiting repair steps and introduce a classification system of disruption types based on the time to return to operations. We demonstrate how the LCOE model can be used to find the cost of electricity and the requirements for disruption handling of a TPP, and we further highlight where future research can have a strong impact in neutralizing the “showstopping” potential of disruptions.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Control of neutral fueling and helium exhaust to NSTX-U plasmas by means of three-dimensional magnetic control fields

Resonant magnetic perturbations are used to stabilize plasma edge instabilities, so-called edge localized modes (ELMs), in high-performance (H-mode) plasmas explored for fusion energy. These ELMs cause repetitive outburst of confined energy and particles and cause cyclic loading of plasma facing components (PFCs). This endangers the integrity of the PFCs and hence limits the lifetime and hence commercial viability of fusion. It was shown that these ELMs can be stabilized by application of small amplitude resonant magnetic perturbation (RMP) fields. This removes the ELMs as edge instabilities but also induces three-dimensional (3D) effects to the plasma boundary, which was formerly toroidally axisymmetric. The impact of this 3D perturbation on the plasma boundary solution needs to be understood to extrapolate the effects to ITER and towards a fusion reactor, if ELM control by RMP fields would be utilized.

3D Boundary Plasmas↗
DOE OSTI2021

GANISP (GAN-assisted Importance SPlitting)

Genealogical importance splitting marches towards a rare event by iteratively selecting and replicating realizations that are headed towards a rare event. The replication step is made difficult when applied to deterministic systems as the initial conditions of the offspring realizations need to be adjusted. Typically, a random perturbation is applied to the offspring. For some cases, this cloning technique may not be adequate and prevent variance reduction in the probability estimate. A GAN-based replication process is proposed to address this limitation. The perturbations applied to the clones are physically consistent instead of being randomly chosen. The proposed method allows reducing the variance in the probability estimation.

Hassanaly, Malik↗
DOE OSTI2025

Characterization of W production during ICRF operations: experiments and modeling

For successfully heating plasma with waves in the ion cyclotron range of frequencies (ICRFs), mitigating impurity production is just as crucial as maximizing power coupling, especially in high-Z environments. ICRF can effectively deposit energy on ions, modify turbulence-driven transport, and enhance fusion reaction efficiency, but only when its power coupling has minimal impact on impurity production. To do so, one must rely on a toroidal array of at least three active elements excited with appropriate phasing and power ratio to reduce the currents induced on the antenna frame below levels critical for physical sputtering. In contrast to classic two-strap antennas, which are optimized for dipole phasing with equal power on both straps, three-strap antennas in ASDEX Upgrade (AUG)—but also four-strap antennas in JET, Alcator C-Mod, SPARC and ITER—offer the possibility to act also on the power ratio between the central and outer straps. With optimal settings, impurity production can be reduced substantially, making the ICRF compatible with the high-Z wall. This paper explores the characteristics of the AUG three-strap antennas in terms of impurity production, as well as the key role of plasma composition in this process. Numerical simulations were performed using SSWICH and Petra-M (finite element codes) to quantify impurity production and compare with experimental results. Energies of ions falling on antenna limiters (measured with probes) are well predicted by both codes. These tools are then used to further describe the source of the impurity, namely the gross erosion of tungsten from an ICRF antenna, for different plasma mixtures. Results are also compared to spectroscopy data. Ultimately, we show that deleterious effects of the ICRF on plasma surface interactions will be weaker in plasmas containing larger fractions of highly ionized heavier low-Z impurity, which is typically relevant for experiments relying on impurity seeding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Impact of nitrogen on lithium-induced corrosion and cracking of RAFM steel

The emergence of private fusion enterprises, combined with advancements in U.S. and EU ITER-TBM as well as EU DEMO liquid breeder blanket concepts, has renewed focus on the materials challenges. This study examines the compatibility of reduced activation ferritic/martensitic (RAFM) steel in liquid Lithium (Li) containing various Nitrogen (N) concentrations, tested at 600 and 700 °C for 500 and 1000 h under static conditions. Microstructural characterization revealed the corrosion-induced dissolution and precipitation in Li containing 0.082 and 0.17 wt.% N at 600 °C, while limited dissolution and precipitation were observed in N-gettered Li using zirconium (Zr) and titanium (Ti) foils at 600 °C. Tensile testing at room temperature after Li exposure revealed a decrease in strength and ductility of the RAFM steel as a function of N content in Li. Severe embrittlement was observed after 500 h in Li containing 0.17 wt.% N at 600 °C and after 500 h in hot-gettered Li at 700 °C. Chromium (Cr) depletion from the steel matrix was observed, leading to the formation of M6C-type carbides on the surface identified by electron microscopy (SEM), X-ray diffraction (XRD), in agreement with thermodynamic calculations. These findings highlight the critical materials challenges posed by liquid Li breeder blanket concepts, emphasizing the need for improved strategies to address Li-induced dissolution, precipitation, and steel embrittlement due to N contamination of the coolant.

Romedenne, Marie [ORNL] (ORCID:0000000317936561)↗