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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Stellar feedback-regulated black hole growth: driving factors from nuclear to halo scales

ABSTRACT Several recent simulations of galaxy formation predict two main phases of supermassive black hole (BH) accretion: an early, highly intermittent phase (during which BHs are undermassive relative to local scaling relations), followed by a phase of accelerated growth. We investigate physical factors that drive the transition in BH accretion in cosmological zoom-in simulations from the FIRE project, ranging from dwarf galaxies to galaxies sufficiently massive to host luminous quasars. The simulations model multichannel stellar feedback, but neglect AGN feedback. We show that multiple physical properties, including halo mass, galaxy stellar mass, and depth of the central gravitational potential correlate with accelerated BH fuelling: constant thresholds in these properties are typically crossed within ∼0.1 Hubble time of accelerated BH fuelling. Black hole masses increase sharply when the stellar surface density in the inner 1 kpc crosses a threshold $\Sigma^\star _{1\,\rm kpc}\approx 10^{9.5} \, {\rm M_{\odot }}\,{\rm kpc}^{-2}$, a characteristic value above which gravity prevents stellar feedback from ejecting gas, and similar to the value above which galaxies are observed to quench. We further show that accelerated BH growth correlates with the emergence of long-lived thin gas discs, as well as with virialization of the inner circumgalactic medium. The halo mass Mhalo ∼ 1012 M⊙ and stellar mass M* ∼ 1010.5 M⊙ at which BH growth accelerates correspond to ∼L⋆ galaxies. The fact that stellar feedback becomes inefficient at ejecting gas from the nucleus above this mass scale may play an important role in explaining why AGN feedback appears to be most important in galaxies above L⋆.

Astronomy & Astrophysics↗
DOE OSTI2022

Evaluating the Effects of Nitrogen Doping and Oxygen Doping on SRF Cavity Performance

Superconducting radiofrequency (SRF) cavities are resonators with extremely low surface resistance that enable accelerating cavities to have extremely high quality factors (Q₀). High Q₀ decreases the capital required to keep the accelerators cold by reducing power loss. The performance of SRF cavities is largely governed by the surface composition of the first §I{100}{nm} of the cavity surface. Impurities such as oxygen and nitrogen have been observed to yield high Q₀, but their precise roles are still being studied. Here, we compare the performance of cavities doped with nitrogen and oxygen in terms of surface composition and heating behavior with field. A simulation of the diffusion of oxygen into the bulk of the cavity was built using COMSOL Multiphysics software. Simulated results were compared to the actual surface composition of the cavities as determined from secondary ion mass spectrometry analysis. Understanding how these impurities affects performance allows us to have further insight into the underlying mechanisms that enable these surface treatments to yield high Q₀.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Development of the first 1 GHz niobium-tin quarter-wave cavity

Niobium-Tin (Nb3Sn) is a promising alternative to pure niobium for low-beta ion accelerators due to low RF losses even at high frequencies. At 4.5 K, the expected Bardeen-Cooper-Schrieffer resistance of Nb3Sn is a few nano-Ohms at 1 GHz, with two orders of magnitude improvement compared to pure niobium. The low Nb3Sn RF losses allow the use of higher frequency, much smaller cavities while maintaining 4.5 K operation that is compatible with cryocoolers. This work aims to demonstrate the feasibility of coating a high-frequency 1 GHz, compact quarter-wave cavity. The development of this cavity has the potential to transform low-beta ion accelerators through size reductions and by enabling the replacement of large helium cryoplants with small plug-in cryocoolers. We have designed, built, and tested a 1GHz Nb3Sn coated quarter-wave cavity. Nb3Sn coating has been performed once by vapor diffusion at Fermilab. Cryogenic cold tests show a quality factor of ~1E9 at low accelerating electric fields or ~7 times higher than the theoretical limit for pure niobium at 1GHz. This first coating does not yet meet our quality factor or gradient goals; however, we plan to continue to develop this cavity.

Zhou, Yang [Argonne]↗
DOE OSTI2022

Impact of specimen preparation method on photovoltaic backsheet degradation during accelerated aging test

In this study, we highlight some important factors in the specimen preparation methods for evaluating photovoltaic backsheet properties after accelerated aging. Two different sequences are considered: Method (I) cut-then-age: cut into 1-cm wide strips and then expose to stress, and Method (II) age-then-cut: expose a larger sheet to stress and then cut into 1-cm widths for mechanical property measurements. We also compare the effect of three cutting methods, (a) tensile specimen punch, (b) paper cutter, and (c) fresh razor blade. Several commercial backsheets were evaluated, with stress exposures including (a) pressure cooker test (PCT), (b) dry ultraviolet (UV) radiation exposure, and (c) UV combined damp heat tests. Fourier transform infrared spectrometer (FTIR) and intrinsic viscosity (IV) were used to analyze the materials on unstressed materials and samples exposed to PCT. The results show that both the cutting method and the time of cutting have an impact on the backsheet mechanical properties. Additionally, under UV exposure, Method II, age-then-cut, generally resulted in a higher average value, with more variation than Method I; however, if the side strips from Method II were excluded, the variation dropped to the same level. This is because the specimens at the sides of the sheet get additional damage from UV light from the exposed sides of the sample. In contrast to UV exposure, PCT specimens prepared by Method II result in lower average values and higher variability. This is attributed to embrittlement through the bulk of the sample where the cutting of embrittled specimens appears to result in more edge defects which can then initiate a break at smaller strains. The data suggest that for UV exposures, the specimens should be cut after aging and the exposed side specimens discarded, and that for PCT exposures, the specimens should be cut before the exposure.

14 SOLAR ENERGY↗
DOE OSTI2020

Combined-Accelerated Stress Testing for Advanced Reliability Assessment of Photovoltaic Modules

New and previously undiscovered degradation mechanisms continue to be identified after field exposure in new photovoltaic module designs or materials oftentimes results in significant losses in investment. This is despite passing certification tests. Testing is insufficient largely due to their single-or double-stress nature. A Combined-Accelerated Stress Test (C-AST) has recently been developed. The test combines multiple stress factors of the natural environment to better replicate conditions experienced by modules in the field and detect mechanisms not a-priori known in new materials and designs.

accelerator factor modeling↗
DOE OSTI2020

Simple descriptor derived from symbolic regression accelerating the discovery of new perovskite catalysts

Symbolic regression (SR) is an approach of interpretable machine learning for building mathematical formulas that best fit certain datasets. In this work, SR is used to guide the design of new oxide perovskite catalysts with improved oxygen evolution reaction (OER) activities. A simple descriptor, μ/t, where μ and t are the octahedral and tolerance factors, respectively, is identified, which accelerates the discovery of a series of new oxide perovskite catalysts with improved OER activity. We successfully synthesise five new oxide perovskites and characterise their OER activities. Remarkably, four of them, Cs 0.4 La 0.6 Mn 0.25 Co 0.75 O 3 , Cs 0.3 La 0.7 NiO 3 , SrNi 0.75 Co 0.25 O 3 , and Sr 0.25 Ba 0.75 NiO 3 , are among the oxide perovskite catalysts with the highest intrinsic activities. Our results demonstrate the potential of SR for accelerating the data-driven design and discovery of new materials with improved properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Physics Design of Proton/Ion SRF Accelerators

Superconducting Radio Frequency (SRF) technology has become foundation of the modern high energy particle accelerators. The technology enables a higher accelerating field with minimal power dissipation and a stronger compact magnetic field. These advancements have broadened the operational horizon of the particle accelerators making a higher duty factor and Continuous Wave (CW) beam operations both feasible and economically viable. The intensity frontier research founded on generation of intense beam of exotic particles including but not limited to neutrinos, muons, kaons and neutrons as well as practical applications involving transmutation of nuclear reactor waste, rare isotopes generation, sub-critical nuclear power generation etc. could be accomplished efficiently only by using superconducting proton/ions particle accelerators. As a result, many newly constructed or under construction high energy accelerator facilities around the world such as Proton Improvement Plan-II (PIP-II) at Fermilab, FRIB at MSU, ESS at Sweden are utilizing SRF accelerators. This talk presents the design philosophy of SRF proton/ion particle accelerator, with a focus on optimizing its performance while addressing physics and operational challenges including the fault scenarios, machine availability and preservation of crucial beam parameters.

Saini, Arun↗
DOE OSTI2020

Stereoscopic detection of hot spots in superfluid 4 He (He II) for accelerator-cavity diagnosis

Superconducting radio-frequency (SRF) cavities cooled by superfluid 4He (He II) are building blocks of many modern particle accelerators due to their high quality factor. However, Joule heating from sub-millimeter surface defects on cavities can lead to cavity quenching, which limits the maximum acceleration gradient of the accelerators. Developing a non-contacting detection technology to accurately locate these surface hot spots is the key to improve the performance of SRF cavities and hence the accelerators. In a recent proof-of-concept experiment, we demonstrated that a molecular tagging velocimetry (MTV) technique based on the tracking of a He molecular tracer line created nearby a surface hot spot in He II can be utilized to locate the hot spot. In order to make this technique practically useful, here we describe our further development of a stereoscopic MTV setup for tracking the tracer line’s motion in three-dimensional (3D) space. We simulate a quench spot by applying a transient voltage pulse to a small heater mounted on a substrate plate. Images of the drifted tracer line, taken with two cameras from orthogonal directions, are used to reconstruct the line profile in 3D space. Here, a new algorithm for analyzing the 3D line profile is developed, which incorporates the finite size effect of the heater. We show that the center location of the heater can be reproduced on the substrate surface with an uncertainty of only a few hundred microns, thereby proving the practicability of this method.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Dependence of emittance on the length of an electron bunch during laser-plasma acceleration in guiding structures

We report a theoretical analysis and numerical simulation of the dynamics of transverse emittance of an electron bunch during its acceleration in wake fields generated by a laser pulse in a weakly nonlinear mode. Analytical expressions are obtained for the main factors affecting the emittance growth during acceleration and the case is considered when the characteristic transverse size of the injected bunch exceeds the matched radius determined by the focusing force at the injection point, the initial emittance and the electron bunch energy, and the resulting value of emittance is much larger than the initial one. The dynamics of the emittance growth during acceleration as a function of the length of the electron bunch is described, and the length of the bunch is found at which there occurs a complete phase mixing of betatron oscillations of electrons and the emittance increases to its maximum value determined by the bunch parameters and the focusing force at the injection point. Analytical expressions are in good agreement with the results of numerical simulation. (paper)

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Momentum Compaction, Phase-Slip Factor and Gamma Transition in a Synchrotron

A synchrotron is the workhorse in charged particle acceleration and is applied for charged particles acceleration and ion acceleration to the highest energies [1, p. 77]. During the acceleration of a charged particle, each particle experiences a longitudinal force opposite in sign to it’s “displacement from the central particle” like in a harmonic oscillator. This motion is similar also to the transverse motion of the particle in the accelerator (betatron oscillations) and this is the reason that the particle stays inside the bucket during the acceleration. In this technical note, we discuss the concepts like, transition energy, momentum compaction and phase slip factor associated with a synchrotron accelerator, and we use a simple example to present the physical meaning of the “transition energy”. We present a simple model of a circular accelerator having straight sections and bends only to provide an explanation and the physical meaning of transition energy.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Commissioning of a two-target DC cylindrical magnetron sputter coater for depositing Nb 3 Sn film on Nb superconducting radiofrequency cavities

A DC cylindrical magnetron sputter coater was commissioned and used to coat Nb 2.6 GHz superconducting radiofrequency (SRF) cavity with Nb 3 Sn. The sputter coater has two identical cylindrical magnetrons that can move, with a controlled speed, along the axis of the SRF cavity to coat the inside surface of the cavity. The design of the sputter coater is discussed, along with its performance. Initially, a sample holder that allows coating on flat substrates at positions similar to the equator and beam tubes of a 2.6 GHz SRF cavity was used to test conditions for fabricating Nb 3 Sn layers. Multilayers of Nb and Sn were sequentially sputtered on flat Nb and sapphire substrates mounted on the equivalent positions of the cavity's beam tubes and the equator using the two identical cylindrical magnetrons. Then, the Nb/Sn multilayers were annealed at 950 °C for 3 h. Here, the ~1.2 μm thick Nb 3 Sn film did not show any other Nb–Sn compounds and had a superconducting transition temperature of 17.61–17.76 K. The 2.6 GHz SRF cavity was coated using similar conditions as flat samples. Cryogenic RF testing of the Nb 3 Sn-coated cavity demonstrated a quality factor of 3.2 × 10 8 at an accelerating gradient of 5 MV/m at 4.4 K.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Alkali Metal Alkoxides in Noyori-Type Hydrogenations

Chemical modification of the catalyst via the formation of the amidate HM-N-M a + complexes and/or physical activation of the substrate through C=O…M a + non-covalent interactions are two factors responsible for the rate acceleration in Noyori-type hydrogenations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Efficiency analysis of helium-cooled MAS DNP: case studies of surface-modified nanoparticles and homogeneous small-molecule solutions

Despite the growing number of successful applications of dynamic nuclear polarization (DNP)-enhanced magic-angle spinning (MAS) NMR in structural biology and materials science, the nuclear polarizations achieved by current MAS DNP instrumentation are still considerably lower than the theoretical maximum. Here, the method could be significantly strengthened if experiments were performed at temperatures much lower than those currently widely used (~100 K). Recently, the prospects of helium (He)-cooled MAS DNP have been increased with the instrumental developments in MAS technology that uses cold helium gas for sample cooling. Despite the additional gains in sensitivity that have been observed with He-cooled MAS DNP, the performance of the technique has not been evaluated in the case of surfaces and interfaces that benefit the most from DNP. Herein, we studied the efficiency of DNP at temperatures between ~30 K and ~100 K for organically functionalized silica material and a homogeneous solution of small organic molecules at a magnetic field B 0 = 16.4 T. We recorded the changes in signal enhancement, paramagnet-induced quenching and depolarization effects, DNP build-up rate, and Boltzmann polarization. For these samples, the increases in MAS-induced depolarization and DNP build-up times at around 30 K were not as severe as anticipated. In the case of the surface species, we determined that MAS DNP at 30 K provided ~10 times higher sensitivity than MAS DNP at 90 K, which corresponds to the acceleration of experiments by multiplicative factors of up to 100.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Accelerating multilevel Markov Chain Monte Carlo using machine learning models

Here, this work presents an efficient approach for accelerating multilevel Markov Chain Monte Carlo (MCMC) sampling for large-scale problems using low-fidelity machine learning models. While conventional techniques for large-scale Bayesian inference often substitute computationally expensive high-fidelity models with machine learning models, thereby introducing approximation errors, our approach offers a computationally efficient alternative by augmenting high-fidelity models with low-fidelity ones within a hierarchical framework. The multilevel approach utilizes the low-fidelity machine learning model (MLM) for inexpensive evaluation of proposed samples thereby improving the acceptance of samples by the high-fidelity model. The hierarchy in our multilevel algorithm is derived from geometric multigrid hierarchy. We utilize an MLM to accelerate the coarse level sampling. Training machine learning model for the coarsest level significantly reduces the computational cost associated with generating training data and training the model. We present an MCMC algorithm to accelerate the coarsest level sampling using MLM and account for the approximation error introduced. We provide theoretical proofs of detailed balance and demonstrate that our multilevel approach constitutes a consistent MCMC algorithm. Additionally, we derive the expression for cost reduction due to machine learning model to facilitate cost analysis of the hierarchical sampling algorithm. Our technique is demonstrated on a standard benchmark inference problem in groundwater flow, where we estimate the probability density of a quantity of interest using a four-level MCMC algorithm. Our proposed algorithm accelerates multilevel sampling by a factor of two while achieving similar accuracy compared to sampling using the standard multilevel algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Surrogate modeling of Cellular-Potts agent-based models as a segmentation task using the U-Net neural network architecture

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigate in vitro vasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Design, Fabrication and Concept for the Surface Treatment of the SRF Cavity Prototype for the CLIC Damping Rings

The Compact Linear Collider (CLIC) Damping Rings (DRs) need to generate ultra-low emittance bunches to achieve high luminosity in CLIC. This requires many wiggler magnets with big energy loss which is compensated by the Radio Frequency (RF) system. The resulting strong beam loading transients lead to a challenging design for the RF system. A novel SRF cavity at 2 GHz with an ultra-low R/Q parameter of below 1 Ω is proposed to minimize the transient beam loading effects below acceptable level. The design and fabrication of the bulk Nb prototype based on turning from a single piece of Nb and EB welding is presented. Moreover, conceptual study of the system for cavity surface treatment to achieve the highest surface magnetic field which is the main goal of the prototype cold test is described as well. To enable excellent performance in this cavity, we plan to apply the 75/120C modified low temperature bake in combination with the cold electropolishing process. This surface treatment approach has been shown to consistently deliver high accelerating gradients and improved quality factors in TESLA-shaped 1.3 GHz SRF cavities. By adapting and implementing this process for the 2 GHz ultra-low R/Q design, we aim to maximize the achievable surface magnetic field while minimizing residual resistance and field emission. This treatment strategy will be critical for demonstrating that the cavity can meet the demanding performance requirements of the CLIC damping ring RF system under high beam loading conditions.

Bafia, Daniel P. [Fermilab]↗
DOE OSTI2021

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118- and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

alternating direction method of multipliers↗