Search NASA⌕ Search

SEARCH · Search NASA

Results for “GPC”

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.

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

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Calculation of monomer yields, verification of PG concentration approximation, catalyst comparison for neat aromatic RCF, SEM micrographs for postreaction pulps, yields of aqueous-phase sugar derivatives after PG-H2O RCF, reactor pressure transients, GPC comparing pre- and postreaction organic solvent composition, PG-H2O miscibility testing, GPC probing dimer/oligomer fractionation in PG-H2O mixtures for

Reductive catalytic fractionation (RCF) is an effective lignin-first biorefining method to extract lignin as a stabilized oil from lignocellulosic biomass. To realize RCF at scale, process modeling has shown that minimizing the use of exogenous organic solvents is critical. To this end, here we investigate the ability of lignin-derived monomers to act as either solvents or cosolvents for RCF. We begin by examining the influence of lignin-derived aromatic compounds (4-propylguaiacol, 4-propylphenol, and propylbenzene) on RCF monomer yields and subsequently extend our analysis to mixtures of 4-propylguaiacol and either methanol or water. We demonstrate that 4-propylguaiacol is an effective solvent for lignin extraction and depolymerization during RCF, especially when used in combination with water as a cosolvent. Cosolvent mixtures of 4-propylguaiacol and water enable up to 81% lignin extraction, monomer yields up to 25 wt %, and postreaction phase separation. However, unlike methanol, water as a cosolvent fails to inhibit aromatic ring hydrogenation when conducted over Ru/C as a catalyst, potentially leading to excess hydrogen consumption in a process utilizing this approach. Nonetheless, these results suggest a promising strategy for eliminating external organic solvents from RCF by utilizing mixtures of lignin-derived compounds and water as alternative extraction solvents.

biorefining↗
DOE OSTI2022

Delineating the mechanism of anti-Lassa virus GPC-A neutralizing antibodies

Lassa virus (LASV) is the etiologic agent of Lassa Fever, a hemorrhagic disease that is endemic to West Africa. During LASV infection, LASV glycoprotein (GP) engages with multiple host receptors for cell entry. Neutralizing antibodies against GP are rare and principally target quaternary epitopes displayed only on the metastable, pre-fusion conformation of GP. Currently, the structural features of the neutralizing GPC-A antibody competition group are understudied. Structures of two GPC-A antibodies presented here demonstrate that they bind the side of the pre-fusion GP trimer, bridging the GP1 and GP2 subunits. Complementary biochemical analyses indicate that antibody 25.10C, which is broadly specific, neutralizes by inhibiting binding of the endosomal receptor LAMP1 and also by blocking membrane fusion. The other GPC-A antibody, 36.1F, which is lineage-specific, prevents LAMP1 association only. These data illuminate a site of vulnerability on LASV GP and will guide efforts to elicit broadly reactive therapeutics and vaccines.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Multiresolution GPC-Structured Control of a Single-Loop Cold-Flow Chemical Looping Testbed

Chemical looping is a near-zero emission process for generating power from coal. It is based on a multi-phase gas-solid flow and has extremely challenging nonlinear, multi-scale dynamics with jumps, producing large dynamic model uncertainty, which renders traditional robust control techniques, such as linear parameter varying H ∞ design, largely inapplicable. This process complexity is addressed in the present work through the temporal and the spatiotemporal multiresolution modeling along with the corresponding model-based control laws. Namely, the nonlinear autoregressive with exogenous input model structure, nonlinear in the wavelet basis, but linear in parameters, is used to identify the dominant temporal chemical looping process dynamics. The control inputs and the wavelet model parameters are calculated by optimizing a quadratic cost function using a gradient descent method. The respective identification and tracking error convergence of the proposed self-tuning identification and control schemes, the latter using the unconstrained generalized predictive control structure, is separately ascertained through the Lyapunov stability theorem. The rate constraint on the control signal in the temporal control law is then imposed and the control topology is augmented by an additional control loop with self-tuning deadbeat controller which uses the spatiotemporal wavelet riser dynamics representation. The novelty of this work is three-fold: (1) developing the self-tuning controller design methodology that consists in embedding the real-time tunable temporal highly nonlinear, but linearly parametrizable, multiresolution system representations into the classical rate-constrained generalized predictive quadratic optimal control structure, (2) augmenting the temporal multiresolution loop by a more complex spatiotemporal multiresolution self-tuning deadbeat control loop, and (3) demonstrating the effectiveness of the proposed methodology in producing fast recursive real-time algorithms for controlling highly uncertain nonlinear multiscale processes. The latter is shown through the data from the implemented temporal and augmented spatiotemporal solutions of a difficult chemical looping cold flow tracking control problem.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A New Proposal Generalized Predictive Control Algorithm With Polynomial Reference Tracking Applied for Sodium Fast Reactors

This paper proposes a generalized predictive control (GPC) with constraints and orthonormal Laguerre functions using the simplified model of the primary system (reactor core and intermediate heat exchanger (IHX)) of a prototypical sodium fast reactor (SFR). This paper develops a multiple-input multiple-output (MIMO) GPC with input constraints able to track polynomial references of any degree applied in coolant temperature difference across the core and fractional power. The manipulated variables of the GPC-SFR are the reactivity and the sodium flow rate of the primary and secondary pipes. Moreover, orthonormal Laguerre functions and step down condition number techniques were also applied to avoid the numerical ill-conditioning issue in quadratic programming of large systems. Thus, a GPC type-2 was designed to control fractional power, coolant temperature difference across the core and sodium tank temperature of the SFR primary system when temperature references change according to a linear ramp after reaching their steady-state operation, sustaining 100% power operation on the reactor. In order to analyze the load tracking capability of the GPC-SFR type-2, the load following from 100% fractional power (FP) to 60% FP at 0.8% FP/min rate is simulated. Constraints on the rate of coolant temperature difference across the core and reactivity were applied for the design safety. For comparison criteria, this paper compares the GPC-SFR type-2 with the GPC-SFR type-1, i.e., standard model predictive control (MPC), to verify the viability and superior performance of the proposal regarding: (a) ramp-tracking capability of temperature and load; (b) the rejections of a reactivity disturbance of -1 cent and a secondary sodium inlet temperature disturbance of +10°F; and (c) a simulation with uncertainty in reactor design. The simulations show that the GPC-SFR type-2 overcome the GPC-SFR type-1 robustness and performance.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2021

Phosphorus speciation analysis of fatty-acid-based feedstocks and fast pyrolysis biocrudes via gel permeation chromatography inductively coupled plasma high-resolution mass spectrometry

Renewable feedstocks, such as lignocelulosic fast pyrolysis oils and both vegetable oil and animal fats, are becoming a viable alternative to petroleum for producing high-quality renewable transportation fuels. However, the presence of phosphorus-containing compounds, mainly from phospholipids, in these renewable feedstocks is known to poison and deactivate hydrotreating catalysts during fuel production. In this work, gel permeation chromatography (GPC) combined with inductively coupled plasma high-resolution mass spectrometry (ICP-HRMS) was used to analyze feedstocks including unprocessed soybean oil, animal fat, and pyrolysis oils from red oak and milorganite to identify phosphorus species. The results have shown the presence of a wide range of different phosphorous compounds among all the samples analysed in this work. The GPC-ICP-HRMS analyses of a vegetable oil and two animal fats have shown different fingerprints based on the molecular weight of each of the samples, highlighting the structural differences among their corresponding phosphorus-containing compounds. While the presence of low-molecular-weight species, such as phospholipids, was expected, several high-molecular-weight species (MW > 10 000 Da) have been found, suggesting that high-molecular-weight micelles or liposomes might have been formed due to the high concentration of phospholipids in these samples. Results obtained through the hydroxylation of a mix of phospholipids (asolectin) and its posterior GPC-ICP-HRMS agree with this hypothesis. With respect to the lignocellulosic catalytic fast pyrolysis oil samples, the GPC-ICP-HRMS results obtained suggest that either aggregation or polymerization reactions might have occurred during the pyrolysis process, yielding phosphorus-containing compounds with an approximate molecular weight above 91 000 kDa. In addition, an aggregation phenomenom has been observed for those phosphorus species present within the fast pyrolysis oils after being stored for 3 months, especially for those pyrolysis oils contaning pre-processed feedstocks, such as milorganite.

09 BIOMASS FUELS↗
DOE OSTI2021

Efficient Active Learning for Gaussian Process Classification by Error Reduction

Active learning sequentially selects the best instance for labeling by optimizing an acquisition function to enhance data/label efficiency. The selection can be either from a discrete instance set (pool-based scenario) or a continuous instance space (query synthesis scenario). In this work, we study both active learning scenarios for Gaussian Process Classification (GPC). The existing active learning strategies that maximize the Estimated Error Reduction (EER) aim at reducing the classification error after training with the new acquired instance in a onestep-look-ahead manner. The computation of EER-based acquisition functions is typically prohibitive as it requires retraining the GPC with every new query. Moreover, as the EER is not smooth, it can not be combined with gradient-based optimization techniques to efficiently explore the continuous instance space for query synthesis. To overcome these critical limitations, we develop computationally efficient algorithms for EER-based active learning with GPC. Further, we derive the joint predictive distribution of label pairs as a one-dimensional integral, as a result of which the computation of the acquisition function avoids retraining the GPC for each query, remarkably reducing the computational overhead. We also derive the gradient chain rule to efficiently calculate the gradient of the acquisition function, which leads to the first query synthesis active learning algorithm implementing EER-based strategies. Our experiments clearly demonstrate the computational efficiency of the proposed algorithms. We also benchmark our algorithms on both synthetic and real-world datasets, which show superior performance in terms of sampling efficiency compared to the existing state-of-the-art algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Flow-driven spectral chaos (FSC) method for simulating long-time dynamics of arbitrary-order non-linear stochastic dynamical systems

Uncertainty quantification techniques such as the time-dependent generalized polynomial chaos (TD-gPC) use an adaptive orthogonal basis to better represent the stochastic part of the solution space (aka random function space) in time. However, because the random function space is constructed using tensor products, TD-gPC-based methods are known to suffer from the curse of dimensionality. Here, we introduce a new numerical method called the flow-driven spectral chaos (FSC) which overcomes this curse of dimensionality at the random-function-space level. The proposed method is not only computationally more efficient than existing TD-gPC-based methods but is also far more accurate. The FSC method uses the concept of enriched stochastic flow maps to track the evolution of a finite-dimensional random function space efficiently in time. To transfer the probability information from one random function space to another, two approaches are developed and studied herein. In the first approach, the probability information is transferred in the mean-square sense, whereas in the second approach the transfer is done exactly using a new theorem that was developed for this purpose. The FSC method can quantify uncertainties with high fidelity, especially for the long-time response of stochastic dynamical systems governed by ODEs of arbitrary order. Six representative numerical examples, including a nonlinear problem (the Van-der-Pol oscillator), are presented to demonstrate the performance of the FSC method and corroborate the claims of its superior numerical properties. Finally, a parametric, high-dimensional stochastic problem is used to demonstrate that when the FSC method is used in conjunction with Monte Carlo integration, the curse of dimensionality can be overcome altogether.

(nonlinear) stochastic dynamical systems↗
DOE OSTI2024

Connecting radiation-driven changes in structural, thermal, and mechanical properties in several medical device polymers

Stemming from security threats and shortages in supply, a recent push has emerged to develop alternatives to radioisotope Cobalt-60 gamma radiation for sterilization of polymeric medical products, including electron beam (e-beam) and X-ray machine-based sources. However, before large-scale implementation, the effects of these non-isotope-based methods must be thoroughly investigated from several perspectives, involving their potential detrimental effects on the structural, thermal, and mechanical properties of polymers in medical devices. This paper investigates such effects in commonly used medical device polymers, including polypropylene homopolymer (PPH), polyolefin elastomer (POE), low-density polyethylene (LDPE), acrylonitrile butadiene styrene (ABS), and chlorobutyl rubber (CIIR). To connect radiation-driven changes in polymer structural and thermal properties and corresponding changes in mechanical properties, several characterization techniques were utilized, including gel permeation chromatography (GPC), Fourier transform infrared spectroscopy (FTIR), dynamic mechanical analysis (DMA) and differential scanning calorimetry (DSC). We found that the tensile strength of X-ray irradiated PPH, as well as X-ray, e-beam, and gamma irradiated CIIR decreases due to chain scission, which is corroborated by GPC and DMA. The GPC results of LDPE suggest chain scission, but interestingly without a corresponding decline in tensile strength. By comparison, POE and ABS appear to undergo further crosslinking with increasing irradiation dose, which generally strengthens them. Importantly, in the vast majority of the cases, there were small to no changes in properties upon changing radiation technologies (at a given dose), i.e., no radiation source dependence. However, in a few specific cases, radiation method dependencies did arise, for instance, in terms of the molecular weight of PPH being significantly increased upon X-ray irradiation, while the melting temperature, glass transition temperature, and elongation at break reduced compared to gamma. Overall, this study provides valuable insight into radiation-driven chemical and structural changes in polymers that produce changes in physical and functional properties.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Intelligent Agents for the Optimization of Atomic Layer Deposition

Atomic layer deposition (ALD) is a highly controllable thin film synthesis approach with applications in computing, energy, and separations. The flexibility of ALD means that it can access a massive chemical catalogue; however, this chemical and process diversity results in significant challenges in determining processing parameters that result in stable and uniform film growth with minimal precursor consumption. In situ measurements of the ALD growth per cycle (GPC) can accelerate process development but it still requires expert intuition and time-consuming trial and error to identify acceptable processing parameters. This procedure is made more difficult by the presence of experimental noise in the GPC values and the complexity of ALD surface chemistries. Here, a need exists for efficient optimization approaches capable of autonomously determining processing conditions resulting in optimal ALD film growth. In this work, we present the development of three optimization strategies and compare their performance in optimizing four simulated ALD processes. Furthermore, the effect of noise in the GPC measurements on optimization convergence is studied.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Population of Merging Compact Binaries Inferred Using Gravitational Waves through GWTC-3

We report on the population properties of compact binary mergers inferred from gravitational-wave observations of these systems during the first three LIGO-Virgo observing runs. The Gravitational-Wave Transient Catalog 3 (GWTC-3) contains signals consistent with three classes of binary mergers: binary black hole, binary neutron star, and neutron star–black hole mergers. We infer the binary neutron star merger rate to be between 10 and 1700 Gpc − 3 yr − 1 and the neutron star–black hole merger rate to be between 7.8 and 140 Gpc − 3 yr − 1 , assuming a constant rate density in the comoving frame and taking the union of 90% credible intervals for methods used in this work. We infer the binary black hole merger rate, allowing for evolution with redshift, to be between 17.9 and 44 Gpc − 3 yr − 1 at a fiducial redshift ( z = 0.2 ). The rate of binary black hole mergers is observed to increase with redshift at a rate proportional to ( 1 + z ) κ with κ = 2. 9 − 1.8 + 1.7 for z ≲ 1 . Using both binary neutron star and neutron star–black hole binaries, we obtain a broad, relatively flat neutron star mass distribution extending from 1.2 − 0.2 + 0.1 to 2.0 − 0.3 + 0.3 M ⊙ . We confidently determine that the merger rate as a function of mass sharply declines after the expected maximum neutron star mass, but cannot yet confirm or rule out the existence of a lower mass gap between neutron stars and black holes. We also find the binary black hole mass distribution has localized over- and underdensities relative to a power-law distribution, with peaks emerging at chirp masses of 8.3 − 0.5 + 0.3 and 27.9 − 1.8 + 1.9 M ⊙ . While we continue to find that the mass distribution of a binary’s more massive component strongly decreases as a function of primary mass, we observe no evidence of a strongly suppressed merger rate above approximately 60 M ⊙ , which would indicate the presence of a upper mass gap. Observed black hole spins are small, with half of spin magnitudes below χ i ≈ 0.25 . While the majority of spins are preferentially aligned with the orbital angular momentum, we infer evidence of antialigned spins among the binary population. We observe an increase in spin magnitude for systems with more unequal-mass ratio. We also observe evidence of misalignment of spins relative to the orbital angular momentum. Published by the American Physical Society 2023

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Inverse Modeling of Hydrologic Parameters in CLM4 via Generalized Polynomial Chaos in the Bayesian Framework

In this work, generalized polynomial chaos (gPC) expansion for land surface model parameter estimation is evaluated. We perform inverse modeling and compute the posterior distribution of the critical hydrological parameters that are subject to great uncertainty in the Community Land Model (CLM) for a given value of the output LH. The unknown parameters include those that have been identified as the most influential factors on the simulations of surface and subsurface runoff, latent and sensible heat fluxes, and soil moisture in CLM4.0. We set up the inversion problem in the Bayesian framework in two steps: (i) building a surrogate model expressing the input–output mapping, and (ii) performing inverse modeling and computing the posterior distributions of the input parameters using observation data for a given value of the output LH. The development of the surrogate model is carried out with a Bayesian procedure based on the variable selection methods that use gPC expansions. Our approach accounts for bases selection uncertainty and quantifies the importance of the gPC terms, and, hence, all of the input parameters, via the associated posterior probabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Physics-Based Limiter Redesign and Bit Performance Analysis at The Geysers

As part of a U.S. DOE Geothermal Technologies Office funding opportunity, Geysers Power Company, LLC (GPC), an indirect subsidiary of Calpine Corporation, partnered with Sandia National Labs, EGI at the University of Utah, and Texas A&M University to demonstrate increased drilling performance at The Geysers Geothermal Field. The performance target in the drilling demonstrations is at least a 25% improvement in rates of penetration, with increased footage on bottom for each bit coupled with increased bit life and time drilling. The project leverages advances in oil and gas drilling technologies including PDC bits, along with the physics-based limiter redesign techniques championed in drilling demonstrations conducted at the Utah FORGE geothermal site. The planned drilling demonstrations are being conducted as part of an existing drilling campaign intended to enhance reservoir utilization. The wells are typically drilled to the top of the reservoir with mud and then air-drilled to total depth (TD) through fractured zones at temperatures ≥ 450°F. A major goal of the project is to assess the effectiveness of implementing mechanical specific energy (MSE) and drilling dysfunction diagnosis and remediation in these challenging environments, as well as alternate rock reduction technologies. The first demonstration well has been completed, with 15 PDC bit runs in the 17.5”, 12.25” and 8.5” sections. Initial analysis shows ROP gains in all three sections, especially in the 17.5” and 12.25” sections, compared with conventional roller cone bit runs in the demonstration well and offset wells. However, in the 8.5” hole, wear and damage to the PDC bits resulted in relatively short bit runs. Analysis is underway to take advantage of the positive results and remediate the challenges. This paper provides updates on drilling activities conducted since the Phase 1 demonstration well at GDC-36 which was drilled from November 2023-January 2024. Additional analysis of the bit performance has been conducted. Furthermore, in subsequent wells drilled by GPC, PDC bits have been used extensively, building on the gains realized at GDC-36. GPC has continued to work with bit vendors to identify designs that last longer in the harsh, air-drilled 8.5” portions of the wells. Planning for the Phase 2 demonstration at Prati-44 is ongoing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Flow-driven spectral chaos (FSC) method for long-time integration of second-order stochastic dynamical systems

For decades, uncertainty quantification techniques based on the spectral approach have been demonstrated to be computationally more efficient than the Monte Carlo method for a wide variety of problems, particularly when the dimensionality of the probability space is relatively low. The time-dependent generalized polynomial chaos (TD-gPC) is one such technique that uses an evolving orthogonal basis to better represent the stochastic part of the solution space in time. Here in this paper, we present a new numerical method that uses the concept of enriched stochastic flow maps to track the evolution of the stochastic part of the solution space in time. The computational cost of this proposed flow-driven stochastic chaos (FSC) method is an order of magnitude lower than TD-gPC for comparable solution accuracy. This gain in computational cost is realized because, unlike most existing methods, the number of basis vectors required to track the stochastic part of the solution space does not depend upon the dimensionality of the probability space. Four representative numerical examples are presented to demonstrate the performance of the FSC method for long-time integration of second-order stochastic dynamical systems in the context of stochastic dynamics of structures.

FSC↗
DOE OSTI2024

Crimean Congo hemorrhagic fever virus nucleoprotein and GP38 subunit vaccine combination prevents morbidity in mice

Immunizing mice with Crimean-Congo hemorrhagic fever virus (CCHFV) nucleoprotein (NP), glycoprotein precursor (GPC), or with the GP38 domain of GPC, can be protective when the proteins are delivered with viral vectors or as a DNA or RNA vaccine. Subunit vaccines are a safe and cost-effective alternative to some vaccine platforms, but Gc and Gn glycoprotein subunit vaccines for CCHFV fail to protect despite eliciting high levels of neutralizing antibodies. Here, we investigated humoral and cellular immune responses and the protective efficacy of recombinant NP, GP38, and GP38 forms (GP85 and GP160) associated with the highly glycosylated mucin-like (MLD) domain, as well as the NP + GP38 combination. Vaccination with GP160, GP85, or GP38 did not confer protection, and vaccination with the MLD-associated GP38 forms blunted the humoral immune responses to GP38, worsened clinical chemistry, and increased viral RNA in the blood compared to the GP38 vaccination. In contrast, NP vaccination conferred 100% protection from lethal outcome and was associated with mild clinical disease, while the NP + GP38 combination conferred even more robust protection by reducing morbidity compared to mice receiving NP alone. Thus, recombinant CCHFV NP alone is a promising vaccine candidate conferring 100% survival against heterologous challenge. Moreover, incorporation of GP38 should be considered as it further enhances subunit vaccine efficacy by reducing morbidity in surviving animals.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

The DESI N -body Simulation Project – II. Suppressing sample variance with fast simulations

ABSTRACT Dark Energy Spectroscopic Instrument (DESI) will construct a large and precise three-dimensional map of our Universe. The survey effective volume reaches $\sim 20\, h^{-3}\, \mathrm{Gpc}^{3}$. It is a great challenge to prepare high-resolution simulations with a much larger volume for validating the DESI analysis pipelines. AbacusSummit is a suite of high-resolution dark-matter-only simulations designed for this purpose, with $200\, h^{-3}\, \mathrm{Gpc}^{3}$ (10 times DESI volume) for the base cosmology. However, further efforts need to be done to provide a more precise analysis of the data and to cover also other cosmologies. Recently, the CARPool method was proposed to use paired accurate and approximate simulations to achieve high statistical precision with a limited number of high-resolution simulations. Relying on this technique, we propose to use fast quasi-N-body solvers combined with accurate simulations to produce accurate summary statistics. This enables us to obtain 100 times smaller variance than the expected DESI statistical variance at the scales we are interested in, e.g. $k \lt 0.3\, h\, \mathrm{Mpc}^{-1}$ for the halo power spectrum. In addition, it can significantly suppress the sample variance of the halo bispectrum. We further generalize the method for other cosmologies with only one realization in AbacusSummit suite to extend the effective volume ∼20 times. In summary, our proposed strategy of combining high-fidelity simulations with fast approximate gravity solvers and a series of variance suppression techniques sets the path for a robust cosmological analysis of galaxy survey data.

cosmology: theory↗
DOE OSTI2020

GW190521: A Binary Black Hole Merger with a Total Mass of 150 M ⊙

On May 21, 2019 at 03:02:29 UTC Advanced LIGO and Advanced Virgo observed a short duration gravitational-wave signal, GW190521, with a three-detector network signal-to-noise ratio of 14.7, and an estimated false-alarm rate of 1 in 4900 yr using a search sensitive to generic transients. If GW190521 is from a quasicircular binary inspiral, then the detected signal is consistent with the merger of two black holes with masses of 8 5 - 14 + 21 M ⊙ and 6 6 - 18 + 17 M ⊙ (90% credible intervals). We infer that the primary black hole mass lies within the gap produced by (pulsational) pair-instability supernova processes, with only a 0.32% probability of being below 65 M ⊙ . We calculate the mass of the remnant to be 14 2 - 16 + 28 M ⊙ , which can be considered an intermediate mass black hole (IMBH). The luminosity distance of the source is 5.3 - 2.6 + 2.4 Gpc , corresponding to a redshift of 0.82 - 0.34 + 0.28 . The inferred rate of mergers similar to GW190521 is 0.13 - 0.11 + 0.30 Gpc - 3 yr - 1 .

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

The influence of secondary electron yield uncertainty on the single-sided multipacting in dielectrics

Multipacting is an electrical discharge caused by the emission of secondary electrons which can occur in vacuum radio frequency systems. Generally multipacting is highly undesirable obstacle, which increases the noise level as well as the return loss of radio frequency systems. Therefore, a prediction of multipacting is necessary to avoid it during radio frequency system design. In this regard, the accurate evaluation of the factors that affect multipacting is required. One of the critical factors determining the multipacting development is the secondary electron yield of the material. In practice the emission properties of materials are not known very accurately. There are uncertainties in the measured values of this yield. In this paper, the generalized Polynomial Chaos (gPC) method is used to quantify uncertainty of the secondary electron yield. This method is verified by Monte Carlo simulation. The effect of uncertainty for two secondary electron yield parameters is then investigated on the multipacting using gPC method.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗