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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 1,297 records · Page 72

Non-dimensional confinement scaling in similar negative triangularity plasmas on the DIII-D and TCV tokamaks

Similarity experiments were performed on the DIII-D and TCV tokamaks to explore the scaling of energy confinement in negative triangularity (NT) plasmas using non-dimensional variables. Near up-down symmetric plasmas with large top-bottom averaged NT were created in a lower single null configuration, with the shape of the separatrix being closely matched between the two devices. The normalized energy confinement is found to weakly improve at increasing collisionality and, between the two devices, shows a machine size scaling behavior between Bohm and gyro-Bohm. Engineering scaling on a large DIII-D dataset is in agreement with the non-dimensional experiment.

confinement scaling↗

Time Correlations from Steady-State Expectation Values

Recovering properties of correlation functions is typically challenging. On the one hand, experimentally, it requires measurements with a temporal resolution finer than the system’s dynamics. On the other hand, analytical or numerical analysis requires solving the system evolution. Here, we use recent results of quantum metrology with continuous measurements to derive general lower bounds on the relaxation and second-order correlation times that are both easy to calculate and measure. These bounds are based solely on steady-state expectation values and their derivatives with respect to a system parameter, and can be readily extended to the autocorrelation of arbitrary observables. We validate our method on two examples of critical quantum systems: a critical driven-dissipative resonator, where the bound matches analytical results for the dynamics, and the infinite-range Ising model, where only the steady state is solvable, and thus the bound provides information beyond the reach of existing analytical approaches. Our results can be applied to the experimental characterization of ultrafast systems and to the theoretical analysis of many-body models whose dynamics are hard to compute.

Górecki, Wojciech [INFN, Pavia] (ORCID:00000001991↗

redis-pvxs-ioc

redis-pvxs-ioc is a standalone PVAccess service for hot-reloadable, Redis-backed EPICS process variables (PV) defined by YAML. The core runtime is PVA-only. It does not load EPICS databases, call iocInit(), or serve Channel Access.

Steinkamp, Derek [Fermi National Accelerator Labor↗

fermi-ad/flutter-template

This code is a collection of repositories for code that will help modernize the AD controls system. This is a collection of backend services for applications, libraries with common code, and templates to help initialize applications. The individual repositories are: 1. https://github.com/fermi-ad/flutter-template 2. https://github.com/fermi-ad/grpc-alarms-db 3. https://github.com/fermi-ad/alarms-actions-synchronizer 4. https://github.com/fermi-ad/aeolisten-async 5. https://github.com/fermi-ad/aeolisten 6. https://github.com/fermi-ad/acorn-alarms-service 7. https://github.com/fermi-ad/ap-python-template 8. https://github.com/fermi-ad/rust-template 9. https://github.com/fermi-ad/acsys-python-container-example 10. https://github.com/fermi-ad/dev-containers 11. https://github.com/fermi-ad/grpc-reflection-catalog 12. https://github.com/fermi-ad/rust-db-lib 13. https://github.com/fermi-ad/ap-python-launcher

Neswold, Rich [Fermi National Accelerator Laborato↗

fermi-ad/rust-pubsub-lib

A Rust library for publisher-subscriber interactions. Used to hide the implementation of whatever pub-sub service is in use (e.g., Kafka, Redis, etc.).

Curley, Jacob [Fermi National Accelerator Laborato↗

CosmoLensNRE

Cosmology Inference from Strong Gravitational Lensing using Neural Ratio Estimation

Jarugula, Sreevani [Fermi National Accelerator Lab↗

redis-tui

A terminal UI client for Redis inspired by Redis Insight, built with Rust and ratatui.

Santucci, Bobby [Fermi National Accelerator Labora↗

lanl/bacterial-classification

bacterial-classification is a sub repository of the Intelligent Immunity project specific to training and testing machine learning models for bacterial classification (Gram-positive, Gram-negative, and non-bacterial samples). Specifically, this repository aims to compare ML classification performance across different dataset curation strategies.

Mancuso, Marina [Los Alamos National Laboratory]↗