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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Neutron Capture and Transmission Measurements of 54 Fe at the RPI LINAC [Slides]

Transmission and capture measurements provide a wholistic set of experimental data to start resonance evaluation on 54 Fe. Analysis thus far supports that changes are needed in 54 Fe evaluation. Major accomplishments include the completion of radiative capture measurements of 54 Fe and the completion of total cross section measurements of 54 Fe. Future work includes the completion of data analysis relevant to capture and transmission measurements (May 2022), complete fitting of new resonance parameters to 54 Fe data (early 2023), and completion of all required deliverables for graduation (May 2023).

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2023

Fundamental R&D for high-Q0 650 MHz SRF cavities for hadron linacs

Upcoming projects requiring high-Q0 ~650 MHz elliptical cavities, such as MSU’s Facility for Rare Isotope Beams’ energy upgrade and Fermilab’s PIP-II, drive a need to better understand RF losses due to magnetic flux. With sample sets from two different Niobium vendors, we explore the efficacy of the fast-cooldown method, and high- temperature annealing in promoting flux expulsion. Additionally, we report the results of vertical tests comparing the effect of electropolishing and furnace baking treatments on the cavity flux sensitivity. We also directly measure the flux pinning force in niobium samples, and correlate it with different heat treatments, and flux expulsion performance.

K.McGee, K.McGee↗
DOE OSTI2023

Controls at the Fermilab PIP-II Superconducting Linac

PIP-II is an 800 MeV superconducting RF linear accelerator under development at Fermilab. As the new first stage in our accelerator chain, it will deliver high-power beam to multiple experiments simultaneously and thus drive Fermilab's particle physics program for years to come. In a pivot for Fermilab, controls for PIP-II are based on EPICS instead of ACNET, the legacy control system for accelerators at the lab. This paper discusses the status of the EPICS controls work for PIP-II. We describe the EPICS tools selected for our system and the experience of operators new to EPICS. We introduce our continuous integration / continuous development environment. We also describe some efforts at cooperation between EPICS and ACNET, or efforts to move towards a unified interface that can apply to both control systems.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

LLRF System Analysis for the Fermilab PIP-II LINAC

Developing long-lived quantum processing units (QPUs) capable of supporting high-fidelity quantum operations is a crucial challenge on the path toward fault-tolerant quantum computing. TESLA-shaped superconducting RF (SRF) cavities, known for photon relaxation times on the order of seconds, provide an excellent foundation for 3D QPUs and quantum memory. This talk presents a novel design that leverages TESLA cavity modes coupled to ancillary transmon qubits, optimized to preserve coherence and control. By carefully engineering the package geometry, optimizing Hamiltonian parameters, and minimizing lossy participation ratios, we achieve photon relaxation times of over 16 ms and 20 ms for the two cavity modes, representing a significant improvement over previous multimode quantum memories. Despite the reduced coupling between the qubit and cavity modes, which is necessary to preserve long lifetimes, the platform supports robust and universal control schemes that are not limited by low coupling strength. We will also discuss how this architecture can lead to scalable, modular quantum computing systems.

Varghese, P. [Fermilab]↗
DOE OSTI2021

Autoencoder Based Analysis of RF Parameters in the Fermilab Low Energy Linac

Machine learning (ML) has the potential for significant impact on the modeling, operation, and control of particle accelerators due to its ability to model nonlinear behavior, interpolate on complicated surfaces, and adapt to system changes over time. Anomaly detection in particular has been highlighted as an area where ML can significantly impact the operation of accelerators. These algorithms work by identifying subtle behaviors of key variables prior to negative events. Efforts to apply ML to anomaly detection have largely focused on subsystems such as RF cavities, superconducting magnets, and losses in rings. However, dedicated efforts to understand how to apply ML for anomaly detection in linear accelerators have been limited. In this paper the use of autoencoders is explored to identify anomalous behavior in measured data from the Fermilab low-energy linear accelerator.

Edelen, Jonathan P. (ORCID:0000000215180652)↗