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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 OSTI2024

Boron carbide films with reduced nodular defect density deposited by full-face erosion radio-frequency magnetron sputtering

Boron carbide coatings deposited by sputtering with conventional planar magnetrons exhibit nodular growth defects. Here, we demonstrate that the density of nodular defects in B 4 C films can be greatly reduced by using a full-face-erosion magnetron source that achieves a more uniform target erosion by sweeping the confining magnetic field over the target surface. Here, we systematically study properties of B 4 C films deposited on substrates with different tilt angles and demonstrate deposition of ultrathick films with compressive residual stress below 200MPa.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Radio Frequency Field Programable Gate Array Implementation of Reflectometry Cable Monitoring

PNNL has developed an innovative cable testing system that leverages the Xilinx RF System-on-Chip (RFSoC) technology to create a more flexible and capable measurement tool than traditional approaches. The architecture is designed on the ZCU111 development board and takes advantage of the high-speed digital to analog converters (DAC) and analog to digital converters (ADC) to generate and capture digitally synthesized waveforms. The platform allows engineers to easily adjust power, frequency, duration and modulation on the fly rather than being locked into fixed hardware configurations

Tedeschi, Jonathan [Pacific Northwest National Lab↗
DOE OSTI2021

Superconducting Radio-frequency Cavity Fault Classification Using Machine Learning at Jefferson Laboratory

We report on the development of machine learning models for classifying C100 superconducting radiofrequency (SRF) cavity faults in the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab. Of the 418 SRF cavities in CEBAF, 96 are designed with a digital low-level RF system configured such that a cavity fault triggers recordings of RF signals for each of eight cavities in the cryomodule. Subject matter experts analyze the collected time-series data and identify which of the eight cavities faulted first and classify the type of fault. This information is used to find trends and strategically deploy mitigations to problematic cryomodules. However, manually labeling the data is laborious and time-consuming. By leveraging machine learning, near real-time - rather than postmortem - identification of the offending cavity and classification of the fault type has been implemented. We discuss the performance of the machine learning models during a recent physics run. We also discuss efforts for further insights into fault types through unsupervised learning techniques and present preliminary work on cavity and fault prediction using data collected prior to a failure event.

Tennant, C. D.↗
DOE OSTI2021

Adapting Secure MultiParty Computation to Support Machine Learning in Radio Frequency Sensor Networks

In this project we developed and validated algorithms for privacy-preserving linear regression using a new variant of Secure Multiparty Computation (MPC) we call "Hybrid MPC" (hMPC). Our variant is intended to support low-power, unreliable networks of sensors with low-communication, fault-tolerant algorithms. In hMPC we do not share training data, even via secret sharing. Thus, agents are responsible for protecting their own local data. Only the machine learning (ML) model is protected with information-theoretic security guarantees against honest-but-curious agents. There are three primary advantages to this approach: (1) after setup, hMPC supports a communication-efficient matrix multiplication primitive, (2) organizations prevented by policy or technology from sharing any of their data can participate as agents in hMPC, and (3) large numbers of low-power agents can participate in hMPC. We have also created an open-source software library named "Cicada" to support hMPC applications with fault-tolerance. The fault-tolerance is important in our applications because the agents are vulnerable to failure or capture. We have demonstrated this capability at Sandia's Autonomy New Mexico laboratory through a simple machine-learning exercise with Raspberry Pi devices capturing and classifying images while flying on four drones.

42 ENGINEERING↗
DOE OSTI2021

Effect of Low Temperature Nitrogen Baking on Superconducting Radio Frequency Cavities With Different Frequency

We report the rf performance of several single-cell superconducting radiofrequency cavities frequency ranging from 1.3-3.0 GHz after low temperature baking in nitrogen environment The cavities are treated at different temperature in the range of (120-160oC) for extended period of time (~24-48 hours) with nitrogen gas injection in the furnace. The improvement in Q0 with Q-rise in some case was observed when nitrogen gas was injected at elevated temperature (~250-290 oC) and held at the temperature range 120-200 oC without any degradation in accelerating gradient over the baseline performance. The temperature and time of baking play significant role in dependence of quality factor on accelerating gradient.

Khanal, B.↗
DOE OSTI2023

Development of a Prototype Superconducting Radio-Frequency Cavity for Conduction-Cooled Accelerators

Recent progress in the development of high-quality Nb?Sn film coatings along with the availability of cryocoolers with high cooling capacity at 4 K makes it feasible to operate SRF cavities cooled by thermal conduction at relevant accelerating gradients for use in accelerators. We have developed a prototype single-cell cavity to prove the feasibility of operation up to the accelerating gradient required for 1 MeV energy gain, cooled by conduction with cryocoolers. The cavity has a ~3 ¿m thick Nb?Sn film on the inner surface, deposited on a ~4 mm thick bulk Nb substrate and a bulk ~7 mm thick Cu outer shell with three Cu attachment tabs. The cavity was tested up to a peak surface magnetic field of 53 mT in liquid He at 4.3 K. A horizontal test cryostat was designed and built to test the cavity cooled with three cryocoolers. The rf tests of the conduction-cooled cavity achieved a peak surface magnetic field of 50 mT and stable operation was possible with up to 18.5 W of rf heat load. The peak frequency shift due to microphonics was 23 Hz. These results represent the highest peak surface magnetic field achieved in a conduction-cooled SRF cavity to date

Ciovati, Gianluigi↗
DOE OSTI2024

Case Study: Leveraging GenAI to Build AI-based Surrogates and Regressors for Modeling Radio Frequency Heating in Fusion Energy Science

This work presents a detailed case study on using Generative AI (GenAI) to develop AI surrogates for simulation models in fusion energy research. The scope includes the methodology, implementation, and results of using GenAI to assist in model development and optimization, comparing these results with previous manually developed models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗