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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Integrated modeling of advanced tokamaks for tearing mode avoidance on DIII-D

Understanding the physics of the onset and evolution of tearing modes (TMs) in tokamak plasmas is crucial for high-performance steady-state operations. The onset condition of (m,n) = (2,1) tearing stability, $Δ′$ > $Δ_c^′$> 0, has been studied in DIII-D steady-state hybrid discharges with accurate equilibrium reconstruction and well-measured plasma profiles. Here, m is the poloidal mode number and n is the toroidal mode number. The onset of n = 1 TMs is carefully identified using the Mirnov array to determine in-phase signals for the onset time and using electron cyclotron emission measurements to trace back from the saturated island width in the experiments for both initial island width and its growth rate. The tearing stability index $Δ′$ at TM onset is calculated in multiple ways, using the modified Rutherford equation and using the PEST-III or resistive DCON magnetohydrodynamic codes, and the results are in reasonable agreement with each other. The calculated $Δ′$ is compared with an analytical formula of the tearing stability threshold $Δ_c^′$ to determine the condition for the mode onset, $Δ′$ > $Δ_c^′$. Finally, the local gradient effect of the plasma current and the pressure profiles on the tearing stability is investigated in terms of the difference between $Δ′$ and $Δ_c^′$ through a series of modeled equilibria with the parametric variations. A database of experimental mode instability onsets is compatible with the simulated regions of instability obtained from the parametric variations.

Linear stability analysis↗
DOE OSTI2023

Integrated modeling of cracking during deep penetration laser welding of nickel alloys

During deep penetration laser welding of nickel alloys, interactions between composition and processing conditions can lead to the formation of defects. Inconel 740H, for example, has demonstrated a susceptibility to horizontal fusion zone cracking at locations between 70% and 80% of the weld depth during laser welding at powers above 5 kW. Coupling three-dimensional heat transfer, fluid flow, and stress modeling tools allowed that both the strain rate and stress normal to the solidification direction to be calculated. In the Inconel 740H welds, cracks formed at locations where the strain rate and stress simultaneously reached critical levels. No cracking was observed in the Inconel 690 welds, since the strain rate and stress did not simultaneously reach these critical levels.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Integrable model of topological SO(5) superfluidity

Assisted by general symmetry arguments and a many-body invariant, we introduce a phase of matter that constitutes a topological SO(5) superfluid. Key to this finding is the realization of an exactly solvable model that displays some similarities with a minimal model of superfluid 3 He. We study its quantum phase diagram and correlations, and find exotic superfluid as well as metallic phases in the repulsive sector. At the critical point separating trivial and nontrivial superfluid phases, our Hamiltonian reduces to the globally SO(5)-symmetric Gaudin model with a degenerate ground manifold that includes quartet states. Most importantly, the exact solution permits uncovering of an interesting non-pair-breaking mechanism for superfluids subject to external magnetic fields. Furthermore, nonintegrable modifications of our model lead to a strong-coupling limit of our metallic phase with a ground-state manifold that shows an extensive entropy.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Integrated Modeling and Optimal Operation of Multi-Energy System for Coastal Community

Focusing on remote, isolated, and underserved communities, a multi-energy system is designed in this research which is capable of utilizing different energy sources in a more coordinated and energy-efficient way to support various demands, such as fresh water, electricity, hydrogen, thermal demand, etc. The energy sources considered are renewables (wind, solar, marine) and natural gas. The energy conversion process includes water desalination, gas combustion, water electrolyzation, and different types of storage (hydrogen tank, electricity, thermal, etc.) are designed to serve as buffers in supply-demand balancing. Sets of experiments are designed to demonstrate the effectiveness of the proposed operating model and investigate the impact of uncertainties from renewable generations and demands.

Chen, Yang↗
DOE OSTI2020

Predictive Integrated Modeling of Heated Tokamak Plasmas (Final Technical Report)

The research carried out by the Lehigh University fusion group during this reporting period: (1) Self-consistent predictive core-pedestal ITER scenario modeling; (2) Microtearing instabilities and electron thermal transport in standard and spherical tokamak discharges; (3) Neural network model of the Multi-Mode anomalous transport module; (4) Anomalous transport in KSTAR, JET, EAST, Alcator C-Mod and DIII-D discharges; (5) Theory for transport in magnetized plasmas; (6) Control-oriented modeling of the q-profile dynamic response in preparation for advanced scenario control in KSTAR and in EAST; (7) Modeling of edge harmonic oscillation in DIII-D QH mode discharges; (8) Elucidating plasma dynamics in Hasegawa-Wakatani turbulence by information geometry; (9) Non-linear Langevin and Fractional Fokker–Planck equations for anomalous diffusion by Levy stable processes; and (10) Investigation of the plasma shaping effects on the H-mode pedestal structure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

AI-Based Integrated Modeling and Observational Framework for Improving Seasonal to Decadal Prediction of Terrestrial Ecohydrological Extremes

Focal Areas: (1) Insight gleaned from complex data (both observed and simulated) using artificial intelligence(AI), big data analytics, and other advanced methods, including explainable AI and physics- or knowledge-guided AI (2) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing).

54 ENVIRONMENTAL SCIENCES↗