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Results for “anomalous transport”

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 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Advancing understanding and predictive capability for fast-ion driven instabilities and associated anomalous transport in NSTX-U

This project advanced predictive capability for the spherical tokamak (ST) by (a) experimental validation of physics models in GTC for simulating fast-ion driven instabilities and their fast-ion and energy transport in the ST, and (b) verification of GTC with linear theory and validation using experimental measurements in the high frequency regime in the ST.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1986

Effects of anomalous transport on the potentials of discrete auroral arcs

The evolution of U-shaped double layers in discrete auroral arcs occurs much faster than the global current in the arc flux tube can be redistributed. For asymptotic stability of the arc on MHD time scales, this implies that local transport mechanisms are required to charge the nearly field-aligned flanks of the equipotential structure over an extended region along the magnetic field. The diversion of the uniform upstream current provides a constraint which limits the double-layer potential.

Smith, R. A.↗
DOE OSTI2022

Machine-Learning of Nonlocal Kernels for Anomalous Subsurface Transport from Breakthrough Curves

Anomalous behavior is ubiquitous in subsurface solute transport due to the presence of high degrees of heterogeneity at different scales in the media. Although fractional models have been extensively used to describe the anomalous transport in various subsurface applications, their application is hindered by computational challenges. Simpler nonlocal models characterized by integrable kernels and finite interaction length represent a computationally feasible alternative to fractional models; yet, the informed choice of their kernel functions still remains an open problem. We propose a general data-driven framework for the discovery of optimal kernels on the basis of very small and sparse data sets in the context of anomalous subsurface transport. Using spatially sparse breakthrough curves recovered from fine-scale particle-density simulations, we learn the best coarse-scale nonlocal model using a nonlocal operator regression technique. Predictions of the breakthrough curves obtained using the optimal nonlocal model show good agreement with fine-scale simulation results even at locations and time intervals different from the ones used to train the kernel, confirming the excellent generalization properties of the proposed algorithm. A comparison with trained classical models and with black-box deep neural networks confirms the superiority of the predictive capability of the proposed model.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Fractional Modeling in Action: A Survey of Nonlocal Models for Subsurface Transport, Turbulent Flows, and Anomalous Materials

Modeling of phenomena such as anomalous transport via fractional-order differential equations has been established as an effective alternative to partial differential equations, due to the inherent ability to describe large-scale behavior with greater efficiency than fully-resolved classical models. In this review article, we first provide a broad overview of fractional-order derivatives with a clear emphasis on the stochastic processes that underlie their use. We then survey three exemplary application areas – subsurface transport, turbulence, and anomalous materials – in which fractional-order differential equations provide accurate and predictive models. For each area, we report on the evidence of anomalous behavior that justifies the use of fractional-order models, and survey both foundational models as well as more expressive state-of-the-art models. We also propose avenues for future research, including more advanced and physically sound models, as well as tools for calibration and discovery of fractional-order models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Anomalous exciton transport in response to a uniform in-plane electric field

Excitons are neutral objects that, naively, should have no response to a uniform electric field. Could the Berry curvature of the underlying electronic bands alter this conclusion? In this work, we show that Berry curvature can indeed lead to anomalous transport for excitons in two-dimensional materials subject to a uniform in-plane electric field. Further, by considering the constituent electron and hole dynamics, we demonstrate that there exists a regime for which the corresponding anomalous velocities are in the same direction. We establish the resulting center-of-mass motion of the exciton through both a semiclassical and fully quantum mechanical analysis, and elucidate the critical role of Bloch oscillations in achieving this effect. We identify transition metal dichalcogenide heterobilayers as candidate materials to observe the effect.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Anomalous electrical transport behavior in the vicinity of the first-order magnetostructural transition in the giant magnetocaloric Gd 4 ScGe 4

Here, magnetic, specific heat, and electrical transport measurements of Gd 4 ScGe 4 single crystals reveal sharp, discontinuous, nearly anhysteretic first-order magnetostructural transformation at $T_C$ = 63 K. The electrical resistivity exhibits two distinct regions where it increases with decreasing temperature: between $T_C$ and 120 K, as well as below 3 K; electronic transport remains conventionally metallic at all other measured temperatures, up to 325 K. The dispersion of charge carriers due to electron-paramagnon scattering is the likely reason for the observed anomalous transport above $T_C$. Additionally, the existence of intermediate lattice states near the transition recognized by the spike in the interslab Ge-Ge distances is expected to reduce the mean free path of the electrons contributing to the unusual behavior of the electrical resistivity between $T_C$ and 120 K. Beyond conventional electronic and lattice terms, the third component of likely magnetic origin contributes to the low-temperature heat capacity; the presence of spin waves may be responsible for the increased electron-magnon scattering below 3 K. Minor magnetocrystalline anisotropy is observed with the b axis as the easy magnetization axis in Gd 4 ScGe 4 . A negative deviation from linearity in the temperature dependence of the inverse magnetic susceptibility is detected below 150 K.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Statistical inference of anomalous thermal transport with uncertainty quantification for interpretive 2D SOL models

The critical task of inferring anomalous cross-field transport coefficients is addressed in simulations of boundary plasmas with fluid models. A workflow for parameter inference in the UEDGE fluid code is developed using Bayesian optimization with parallelized sampling and integrated uncertainty quantification. In this workflow, transport coefficients are inferred by maximizing their posterior probability distribution, which is generally multidimensional and non-Gaussian. Uncertainty quantification is integrated throughout the optimization within the Bayesian framework that combines diagnostic uncertainties and model limitations. As a concrete example, we infer the anomalous electron thermal diffusivity $\chi_\perp$ from an interpretive 2D model describing electron heat transport in the conduction-limited region with radiative power loss. The workflow is first benchmarked against synthetic data and then tested on H-, L-, and I-mode discharges to match their midplane temperature and divertor heat flux profiles. We demonstrate that the workflow efficiently infers diffusivity and its associated uncertainty, generating 2D profiles that match 1D measurements. Future efforts will focus on incorporating more complicated fluid models and analyzing transport coefficients inferred from a large database of experimental results.

Bayesian optimization↗
DOE OSTI2022

Normalizing flows for likelihood-free inference with fusion simulations

Fluid-based scrape-off layer transport codes, such as UEDGE, are heavily utilized in tokamak analysis and design, but typically require user-specified anomalous transport coefficients to match experiments. Determining the uniqueness of these parameters and the uncertainties in them to match experiments can provide valuable insights to fusion scientists. Here, we leverage recent work in the area of likelihood-free inference (‘simulation-based inference’) to train a neural network, which enables accurate statistical inference of the anomalous transport coefficients given experimental plasma profile input. UEDGE is treated as a black-box simulator and runs multiple times with anomalous transport coefficients sampled from priors, and the neural network is trained on these simulations to emulate the posterior. The neural network is trained as a normalizing flow model for density estimation, allowing it to accurately represent complicated, high-dimensional distribution functions. With a fixed simulation budget, we compare a single-round procedure to a multi-round approach that guides the training simulations toward a specific target observation. Finally, we discuss the future possibilities for use of amortized models, which train on a wide range of simulations and enable fast statistical inference for results during experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗