SEARCH · Search NASA

Results for “rf stabilization”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5

All sources NASA NTRS DOE OSTI

DOE OSTI2022

Development of Integrated Education, Training and Research Program with Interdisciplinary Applications

Plasma models have been formulated including realistic spatial profiles of both flow and radio frequency induced ponderomotive force. With these inclusions the picture of stability of various plasma and fluid instabilities, as expected, changed drastically with ground-breaking consequences. The inhomogeneous parallel flow and the radio frequency waves can actually shown to stabilize turbulence. This is different from the prevalent notion that both parallel flow shear and radio frequency waves are responsible for the excitation (destabilization) of plasma turbulence. This has several ground-breaking consequences:- (1) the stabilization by parallel flow clearly goes against the conventional notion of the origin of ionospheric oscillation which invokes parallel flow destabilization as the origin, (2) the stabilization by parallel flow opens us a new avenue for improved mode formation in fusion devices - which mostly rely on the perpendicular flow shear stabilization for improved mode formation but the perpendicular flow is damped in a tokamak - so the improved mode formed by the parallel flow can sustain longer and has more prospect for ignition, (3) the complete stabilization of the ITG mode (and consequent suppression of transport) not only explain many unknown phenomena in the space physics but it also raises a prospect for transport barrier formation by the RF waves but not by the RF induced flow (as most works suggest) which is never observed in a tokamak of that magnitude to create a barrier. These are indeed ground-breaking consequences.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Development of Integrated Education, Training and Research Program with Interdisciplinary Applications II

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

Multiple interaction points in ghost collisions

The Ghost Collider makes use of unique “ghost” bunches, which are electrically neutral combinations of electrons and positrons within the same RF bucket, eliminates the beam-beam effects typically present at the interaction point (IP) in conventional colliders. This allows for the novel possibility of placing multiple interaction regions in series, achieving additive luminosity without introducing significant disruption. However, to get higher luminosity, the beta functions at the IP reaches millimeter scale, which in turn adds significant chromatic contribution to the collider. Correcting these chromatic effects is essential to maintain beam stability and ensure high luminosity during collider operation. By carefully adjusting the phase advance between two IRs that are placed in series, it becomes possible to cancel chromaticity globally, enabling stable collider operation while preserving high luminosity. In this paper we discuss the design of such IR/IRs to be used in a ghost collider.

Accelerator Physics↗

DOE OSTI2025

Virtual Critical Coupling in High-Power Resonant Systems

Power reflections are a fundamental challenge in high-power resonant systems, causing energy loss, limiting per- formance, and potentially damaging critical components. This problem is particularly acute in applications such as lower hybrid current drive (LHCD) for tokamak-based nuclear fusion, where efficient, stable power transfer to the plasma is paramount. In this work, we introduce and experimentally demonstrate the virtual critical coupling (VCC) mechanism, a nonmechanical method to achieve reflection-free excitation in high-power S-band resonators. VCC utilizes a temporally shaped excitation signal with a precise complex frequency, tailored to the resonator’s characteristics to enable complete energy absorption. Using a custom low-level RF system and a 5-MW S-band klystron, we conducted experiments from low power (32 mW) to high power (600 kW). Furthermore, the results demonstrate a greater than ninefold reduction in the reflection coefficient compared to conventional monochromatic excitation. This signal processing-based approach offers a robust path toward enhancing the efficiency and stability of high-power resonant systems, with significant implications for particle accelerators and the advancement of nuclear fusion energy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Machine Learning for Improving Surface-Layer-Flux Estimates

Abstract Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946), $$z/L$$ z / L . However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisture based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

Meteorology & Atmospheric Sciences↗

DOE OSTI2021

Advances in physics understanding of high poloidal beta regime toward steady-state operation of CFETR

Experimental and modeling investigations of the high βp scenarios on the DIII-D and EAST tokamaks show advantages in high energy confinement, avoidance of n = 1 MHD, and core-edge integration with reduced heat flux, making this scenario an attractive option for CFETR steady-state operation. Experiments show that plasmas with high confinement and high density can be achieved with neutral beam injection on DIII-D (β p ~ 2.2, β N ~ 3.5, f BS ~ 50%, f Gw ~ 1.0, H 98y2 ~ 1.5) and pure RF power on EAST (β P ~ 2.0, β N ~ 1.6, f BS ~ 50%, f Gw ~ 0.8, H 98y2 > 1.3). By tailoring the current density profile, a q-profile with local (off-axis) negative shear is achieved that yields improved confinement and MHD stability. Transport analysis and simulation suggest that the combination of high density gradient and high Shafranov shift allows turbulence stabilization and higher confinement. Using on-axis ECH injection, tungsten accumulation is avoided on EAST, and this is reproduced in modeling. Reduced heat flux (by >40%) and maintenance of high core confinement is achieved with active feedback control of the radiated divertor, an important result for long pulse operation in tokamaks. In conclusion, the improved physics understanding and validated modeling tools are used to design a 1GW steady-state scenario for CFETR.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

A data-driven framework for predicting machining stability: employing simulated data, operational modal analysis, and enhanced transfer learning

Chatter, a self-excited vibration phenomenon, presents a significant challenge in machining operations, particularly in high-speed milling, where it can degrade tool life, reduce material removal efficiency, and compromise workpiece quality. Addressing this challenge requires a reliable predictive model that can accommodate the complex dynamics of various machining scenarios. This study introduces a novel, data-driven approach to predicting machining stability, leveraging over 140,000 simulated datasets and employing advanced techniques such as operational modal analysis (OMA), enhanced transfer learning (TL), and receptance coupling substructure analysis (RCSA). By integrating these methodologies, the framework effectively classifies and predicts chatter across diverse operational modes, achieving robust and accurate outcomes. Our model utilizes a Random Forest (RF) classifier trained with the comprehensive dataset, which demonstrates substantial improvements in both predictive accuracy and robustness. Specifically, the RF model achieved an accuracy rate of 85%, an area under the curve (AUC) of 0.90, and an F1 score of 0.88, underscoring its capability to adapt to varying machining configurations. These results highlight the framework’s potential to enhance operational efficiency and machining quality by providing reliable chatter predictions across a broad range of machining parameters. In conclusion, this research thus offers a significant advancement in predictive maintenance for machining processes, enabling more stable and efficient manufacturing operations.

42 ENGINEERING↗

DOE OSTI2025

The fixed probe storage ring magnetometer for the Muon g-2 experiment at Fermi National Accelerator Laboratory

The goal of the FNAL E989 experiment is to measure the muon magnetic anomaly to unprecedented accuracy and precision at the Fermi National Accelerator Laboratory. To meet this goal, the time and space averaged magnetic environment in the muon storage volume must be known to better than 70 ppb. A new pulsed proton nuclear magnetic resonance (NMR) magnetometer was designed and built at the University of Washington, Seattle to track the temporal stability of the 1.45 T magnetic field in the muon storage ring at this precision. It consists of an array of 378 petroleum jelly based NMR probes that are embedded in the walls of muon storage ring vacuum chambers and custom electronics built with readily available modular radio frequency (RF) components. We give NMR probe construction details and describe the functions of the custom electronic subsystems. The excellent performance metrics of the magnetometer are discussed, where after 8 years of operation the median single shot resolution of the array of probes remains at 650 ppb.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

Roles of temperature, materials, and domain inversion in high-performance, low-bias-drift thin film lithium niobate blue light modulators

We demonstrate a thin film lithium niobate electro-optic modulator operating at 456 nm with an RF voltage-length product of 0.38 V-cm and a bandwidth of 6.9 GHz. We test the dielectric relaxation of the modulator with sweeps of temperature and optical input power, and compare equivalent modulators with electrode materials of Cr-Au, Ti-Au and Al in terms of bias stability and current-voltage characteristics. We demonstrate bias stability over at least 8 hours with Al devices, and show relationships between drift, I-V characteristics and ferroelectric domain switching.

Celik, Oguz Tolga (ORCID:0000000264542306)↗

DOE OSTI2023

Study of Microphonic Effects on the C100 Cryomodule for High Energy Electron Beam Accelerators

The Continuous Electron Beam Accelerator Facility (CEBAF) at Thomas Jefferson National Laboratory (JLab) is a particle accelerator which can accelerate an electron beam to relativistic speeds and apply the beam onto target samples. The C100 superconducting radio frequency (SRF) cavity is the primary accelerating structure of the C100 cryomodule, one of the many cryomodules which compose the CEBAF linear accelerator. SRF cavities are particularly sensitive to internal and external vibrations that can result in a phenomenon called microphonics which degrade the operational stability of a cryomodule. The purpose of this thesis is to investigate the significance of mechanical disturbances on the electromagnetic resonant frequency of a C100 SRF cavity. Knowledge of the mechanical resonance of the cavities and cryomodule sheds light into how these disturbances are most easily realized as deformation which causes radio frequency (RF) detuning. Three studies were conducted: the development and hammer test calibration of a Finite Element Analysis (FEA) model of a C100 cavity, the development and hammer test calibration of an FEA model of a C100 cavity string, and the hammer test of the C100-10R cryomodule at the Cryomodule Test Facility (CMTF). The cavity FEA model was found to accurately predict two modes found in two real cavities in a simply supported configuration. The cavity string FEA model leveraged the calibrated cavity FEA model but was not found to accurately predict the modal behavior of a real cavity string. Even so, the modal behavior of the cavity string inside the C100-10R cryomodule was captured during a hammer test while it was partially assembled. Finally, the C100-10R cryomodule was placed in the CMTF to study RF detuning. The RF detuning spectra during hammer hits and background noise was captured. The results of the hammer testing indicate two strong peaks at low frequencies (9-10 Hz and 22-23 Hz). These two frequencies were found to be nearly coincident to four instances of mechanical resonance found during the hammer testing done on the partially-assembled C100-10R. Because of this, these two modes are believed to contribute to RF detuning of the cryomodule. This test event also included the testing of the effectiveness of a configuration of BNNT canisters designed to act as dampers. While these tests show promising results, the lurking variables render these tests somewhat inconclusive.

Hull, Caleb James↗

DOE OSTI2023

Long-pulse high-performance H-mode plasmas achieved on EAST

A record duration of a 310 s H-mode plasma (H 98y2 ~ 1.3, n e /n GW ~ 0.7, f BS > 50%) has been recently achieved on experimental advanced superconducting tokamak (EAST) with metal walls, exploiting the device's improved long-pulse capabilities. The experiment demonstrates good control of tungsten concentration, core/edge MHD stability, and particle and heat exhaust with an ITER-like tungsten divertor and zero injected torque, establishing a milestone on the path to steady-state long-pulse high-performance scenarios in support of ITER and CFETR. Important synergistic effects are leveraged toward this result, which relies purely on radio frequency (RF) powers for heating and current drive (H&CD). On-axis electron cyclotron heating enhances the H&CD efficiency from lower hybrid wave injection, increasing confinement quality and enabling fully non-inductive operation at high density (n e /n GW ~ 70%) and high poloidal beta (β P ~ 2.5). A small-amplitude grassy edge localized mode regime facilitates the RF power coupling to the H-mode edge and reduces divertor sputtering/erosion. The high energy confinement quality (H 98y2 ~ 1.3) is achieved with the experimental and simulated results pointing to the strong effect of Shafranov shift on turbulence. Transport analysis suggests that trapped electron modes dominate in the core region during the record discharge. The detailed physics processes (RF synergy, core-edge integration, confinement properties, etc.) of the steady-state operation will be illustrated in the content. In the future, EAST will aim at accessing more relevant dimensionless parameters to develop long-pulse high-performance plasma toward ITER and CFETR steady-state advanced operation.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Correction for Neutral Pressure-Driven Signal in Radiated Power Measurements on Proto-MPEX

A pinhole camera using four-channel resistive bolometers was designed and implemented on the Prototype Material Plasma Exposure eXperiment (Proto-MPEX) to measure the plasma radiated power. Quantification of the radiated power is required for comprehensive power balance assessments but faces unique challenges on linear plasma devices due to weak absolute signal, compared to typical use in toroidal fusion plasmas, and high neutral pressure changes. Around 1 mTorr, the pressure-driven signal contribution was shown to overwhelm the radiated power signal in Proto-MPEX, found to be ~3 μW . Since the pressure-driven signals could not be predicted from first principle arguments, the signals were corrected using empirical models that take two experimental pressure measurements and the raw bolometer signal to reveal the radiated power signals. Four “gas-only” Proto-MPEX shots were taken, without the radio frequency (rf) plasma discharge, to train, optimize, and validate empirical models. The data included two pressure measurements at different locations along with the bolometer signal. Multiple models were tested; partial least-squares regression with a cross correlation method to account for the time offset in the pressure measurements provided the best combination of accuracy and stability. The model was validated to have a modeling error of 0.0036 V, which was lower than 10% of the expected radiated power signal. The model was tested on a different “gas-only” shot with a complete time-series data. For plasma operation shots, the corrected bolometer measurements were compared with an absolute extreme ultraviolet photodiode measurement and similarities and differences are discussed.

Lee, Seungsup↗

DOE OSTI2023

Phase Locking of a THz QC-VECSEL to a Microwave Reference

We report high-resolution frequency study and phase locking have been performed on a terahertz (THz) quantum-cascade vertical-external-cavity surface-emitting laser (QC-VECSEL) operating around 2.5 THz. A subharmonic diode mixer is used to down convert the THz signal to a 100 MHz intermediate frequency that is phase locked to a stable 100 MHz microwave reference. Between 90% and 95% of the QC-VECSEL signal is locked within 2 Hz of the multiplied RF reference, and amplitude fluctuations on the order of 1%–10% are observed, depending on the bias point of the QC-VECSEL. The bandwidth of the locking loop is ~1 MHz. Many noise peaks in the IF signal are observed, likely corresponding to mechanical resonances in the 10 Hz–10 kHz. These peaks are generally -30 to -60 dB below the main tone and are below the phase noise level of the multiplied RF reference that ultimately limits the phase noise of the locked QC-VECSEL.

42 ENGINEERING↗

DOE OSTI2023

Recent Beam Stability Analysis for the EIC

This document summarizes work done in WBS 6.02.02 during FY23. Additional details can be found in the EIC overview paper presented at IPAC’23 [1], and the references therein. Progress has been made in the ESR and HSR lattice design, dynamic aperture optimization, and the RCS lattice design. The impedance and Collective effects are progressing and include the impedance optimization process of the vacuum systems for RCS, HSR, and ESR; collective effects studies; collective effects and beam-beam interaction; coupled bunch instabilities and the crab cavities; low-level RF feedback system design and beam-ion instability. The reversed phasing RF system has been numerically studied for the ESR to mitigate Robinson instability and demonstrate reliable stable beam operation. Various codes, including C++, SPACE [2], ELEGANT [3], and Mbtrack2 [4] have been employed to benchmark the results. The simulation of HSR bunch splitting with beam loading has been performed at 275 GeV energy. The evaluation of Beam Position Monitors aimed to validate their expected performance, with the primary objective being to verify their accuracy. Calculations of Electron Polarization in the ESR, RCS, and HSR are showing good progress. In the RCS, preliminary studies indicate excellent polarization transmission over intrinsic spin resonances, achieving over 90% transmission with improved performance compared to the previous lattice. Preliminary simulations in the ESR indicate encouraging results in minimizing depolarization and improving equilibrium polarization.

43 PARTICLE ACCELERATORS↗

DOE OSTI2023

Multi-Line Superconducting Waveguides Based on YBCO-on-Kapton Technology

Coplanar (with the ground) and stripline 25 cm long waveguides are manufactured from the YBCO-Kapton material. The waveguides are intended as the key components of a scalable signal delivery for large quantum computing systems and detectors. The waveguide is coupled with an integrated power divider that replaces the standard attenuator. The divider is designed to return most of the power to the previous thermalization stage. The method is projected to reduce the active dissipation by ≈10. We investigate the effect of internal strain in YBCO layer on the device's mechanical stability. Here, we show that adhesives with high stiffness are essential for the reliability of YBCO-Kapton devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2026

Enhanced electrochemical performance and extended cycling of resorcinol-formaldehyde derived N-doped carbon xerogel for alkali metal-ion (Li/Na/K) batteries

Resorcinol formaldehyde-derived carbon xerogel (RFC) is a versatile material with tuneable properties, synthesized through a simple sol-gel method. This study presents nitrogen-doped RF carbon xerogel (N-RFC) with 11.8 at% nitrogen doping, offering a microporous architecture ideal for alkali metal-ion (Li, Na, K) batteries. The porous N-doped framework enhances electrochemical performance by improving ion transport, increasing active storage sites, and significantly boosting metal-ion adsorption, particularly through pyrrolic nitrogen, as revealed by first-principles calculations supported by XPS analysis. N-RFC anodes showed excellent cycling stability, high-capacity retention, and fast charge/discharge capabilities, rendering them suitable for commercial applications. Notably, the N-RFC anode demonstrates high-rate long-term cycling stability, retaining its capacity of 83.5 % (188 mAh/g at 2 C-rate) and 50 % (133 mAh/g at 1250 mA/g) over 1000 cycles for Li and Na-ion batteries, respectively, favorable for commercial battery applications. Additionally, N-RFC demonstrates a reversible capacity of 120 mAh/g after 394 cycles with a retention of 82 % for K-ion batteries. The ability of the material to accommodate larger ions like Na + and K + further emphasizes its versatility and potential application in diverse alkali metal-ion battery systems.

25 ENERGY STORAGE↗

DOE OSTI2025

Machine learning and process-based modeling of spatiotemporal changes in active layer thickness across Alaska

Permafrost degradation poses a growing threat to infrastructure stability and ecosystem resilience in the rapidly warming Arctic. We investigated the spatiotemporal dynamics of active layer thickness (ALT) across Alaska by integrating field observations, environmental datasets, a physically based Stefan model, and machine learning (ML) techniques. Using weather projections from the Coupled Model Intercomparison Project Phase 6 under two Shared Socioeconomic Pathways (SSP 2-4.5 and SSP 5-8.5), we assessed ALT sensitivity to projected future weather conditions. The random forest (RF) model outperformed the Stefan approach in predicting ALT on the training dataset (R² = 0.84 vs. 0.53) but demonstrated lower generalizability on the test dataset (R² = 0.24 vs. 0.54). The root mean square error (RMSE) for the RF model for training and testing ranged from 14 to 22 cm, compared to 17 and 18 cm for the Stefan model. Variable importance analysis revealed that mean annual temperature and slope angle were the strongest predictors of ALT, accounting for 19% and 18% of the variance, respectively, followed by sediment transport index (14%) and stream power index (11%). Comparative analysis of baseline ALT predictions showed the Stefan model tended to project a thicker active layer (mean ± SD: 65 ± 16 cm), compared to the RF model (mean ± SD: 59 ± 8.8) cm). Both models indicated a latitudinal gradient in ALT, with shallower depths at higher latitudes. Projected ALT increases by 2100 were estimated at 3.3 ± 2.2 cm under SSP 2-4.5 and 5.9 ± 4.0 cm under SSP 5-8.5 for the ML model, whereas the Stefan model projected substantially larger increases of 13 ± 2.6 cm (SSP 2-4.5) and 28 ± 4.4 cm (SSP5-8.5). Spatial analysis showed the greatest ALT increases in northern Alaska, with relatively smaller changes in southern regions. These findings highlight the complex, multifactorial nature of ALT dynamics and the value of hybrid modeling approaches. As rising temperatures accelerate permafrost thaw, changes in ALT can disrupt ecosystems, damage infrastructures, and enhance the release of stored soil carbon, highlighting the urgent need for improved predictive capabilities to inform adaptation strategies in the Arctic.

Climate sciences↗

DOE OSTI2025

From pixels to patterns: Coupling Optical Coherence Tomography and machine learning for monitoring coastal wetland root systems

Coastal wetlands are crucial in shoreline stabilization, carbon sequestration, and storm protection. Yet, due to limitations in traditional destructive sampling techniques, the belowground biomass (live root mass) and necromass (dead and decaying roots) remain difficult to assess in coastal wetlands, limiting our understanding on coastal resilience, nutrient cycling, and soil structure. This study employs Optical Coherence Tomography (OCT) as a high-resolution imaging technique to analyze root biomass and necromass in the Terrebonne Basin, Louisiana. A Random Forest (RF) model was developed to classify root health states based on OCT-derived features, achieving an accuracy of 70% in distinguishing live from dead root segments. The results demonstrate that OCT, combined with ML, offers a promising novel approach to root analysis, providing fine-scale insights into root morphology and decay patterns that are not easily captured by conventional methods. This research lays the foundation for future integration of OCT with complementary imaging modalities such as X-ray Computed Tomography (XCT) and advanced ML algorithms to enhance classification accuracy and scalability. Future work aims to expand the dataset diversity across different wetland types and apply the methodology for large-scale, repeatable assessments of root biomass turnover and accumulation, with important implications for wetland monitoring, conservation, and restoration under changing environmental conditions.

AI/ML↗