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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

FNAL PIP-II Accumulator Ring

The FNAL accelerator complex is poised to reach MW neutrino beams on target for the exploration of the dark sector physics and rare physics program spaces. Future operations of the complex will include CW linac operations at beam intensities that have not been seen before \cite{PIP2,RCS_LOI}. The ambitious beam program relies on multi-turn H$^{-}$ injection into the FNAL Booster and then extracted into delivery rings or the Booster Neutrino Beam (BNB) 8 GeV HEP program. A new rapid-cycling synchrotron (RCS) will be required to reach the LBNF goal of 2.4 MW because of intense space-charge limitations. There are many accelerator engineering challenges that are already known and many that will be discovered. This proposal calls for an intermediate step that will both facilitate the operation of Booster in the PIP-II era and gain operational experience associated with high power injection rings. This step includes the design, construction and installation of a 0.8 GeV accumulator ring (upgradeable to 1+ GeV) to be located in the PIP-II Booster Transfer Line (BTL). The PIP-II accumulator ring (PAR) may be primarily designed around permanent magnets or use standard iron core magnet technology with an aperture selected to accommodate the desired high intensity protons at 0.8 GeV.

43 PARTICLE ACCELERATORS↗

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗

Corrosion Testing of Refractory inContact with Molten Glasses Designed for Waste Vitrification - VSL Touchpoint Matrix Glasses

It is known that the predictive life of the refractory ceramic liner of nuclear waste glass melters is conservative, as demonstrated by performance of these materials such as in the Defense Waste Processing Facility (DWPF). The motivation for this task is to maximize the useful life of the melters that will be operated at the Waste Treatment and Immobilization Plant (WTP), which will in turn minimize procurement and disposal costs and melter outage times, as well as to identify maximum loadings in the waste glass of those species that corrode melter components. This task was initiated jointly with Pacific Northwest National Laboratory (PNNL) with the objective to develop a methodology and model to enable more accurate prediction of refractory service life under prototypic conditions from laboratory-scale material corrosion tests. Refractory corrosion is generally reported as physical material loss, measured in units of distance (e.g., inch) or as physical material loss rate, measured in units of distance per time (e.g., inch/day). In post-operational melters, the refractory corrosion is measured directly, sometimes reported as corrosion depth. Crucible tests are used in the laboratory to accelerate the refractory corrosion to facilitate a meaningful measurement in a commensurate amount of time. Crucible tests are particularly useful in understanding refractory corrosion across a large glass composition space, where operational testing would be prohibitive. Some of the critical parameters that are known to influence refractory corrosion by molten glass in a crucible test are temperature, system redox, molten salt phases, glass chemistry, and test duration. The majority of data collected for Monofrax® K-3 (hereafter referred to as K-3) corrosion is from crucible tests, but a small amount comes directly from scaled and production melters. Crucible test data has been collected under varying conditions, whereas data collected from operational melters is relatively fewer and represents conditions specific to the melter campaign. The result is that the published data can be grouped and analyzed in multiple ways, not all of which are readily comparable. The Standard Test Method for Isothermal Corrosion Resistance of Refractories to Molten Glass (ASTM C621) outlines the general guidelines used across industry. That method describes a sealed, static test in which the surface area of the refractory coupon and the volume of glass are fixed. A significant portion of the crucible data pertaining to nuclear waste glasses has been collected in a modified configuration; the most notable differences being the surface area of the refractory coupon to volume of the glass and use of a method for bubbling the melt. To our knowledge, the influence of those parameters on the refractory corrosion has not been quantified. In this work, it was determined that static tests and bubbled tests would be performed. Savannah River National Laboratory (SRNL) was tasked with setting up and performing static testing while PNNL was tasked with setting up and performing bubbled testing. Initial activities were performed to establish laboratory methods that reproduce data comparable to existing data sets of K-3 refractory corrosion by low activity waste (LAW) and high-level waste (HLW) glass compositions. Later activities were focused on refining the test parameters to establish a standard test practice to be used between Laboratories and collecting additional data to be used in the enhanced waste glass model development. This document serves primarily to convey the refractory loss measurement results from corrosion testing of K-3 refractory with waste glass compositions developed for use in the WTP melters. The data will be used in the enhanced property/composition models being developed for waste glass vitrification and melter operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

An FFT-based approach for Bloch wave analysis: application to polycrystals

A method based on the Fast Fourier Transform is proposed to obtain the dispersion relation of acoustic waves in heterogeneous periodic media with arbitrary microstructures. The microstructure is explicitly considered using a voxelized Representative Volume Element (RVE). The dispersion diagram is obtained solving an eigenvalue problem for Bloch waves in Fourier space. To this aim, two linear operators representing stiffness and mass are defined through the use of differential operators in Fourier space. The smallest eigenvalues are obtained using the implicitly restarted Lanczos and the subspace iteration methods, and the required inverse of the stiffness operator is done using the conjugate gradient with a preconditioner. The method is used to study the propagation of acoustic waves in elastic polycrystals, showing the strong effect of crystal anistropy and polycrystaline texture on the propagation. It is shown that the method combines the simplicity of classical Fourier series analysis with the versatility of Finite Elements to account for complex geometries proving an efficient and general approach which allows the use of large RVEs in 3D.

97 MATHEMATICS AND COMPUTING↗

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗

Kernelized approaches to streaming compression of scientific data

In this paper three algorithms are developed for the streaming compression of scientific data. The algorithms presented are reliant on the theory of vector-valued reproducing kernel Hilbert spaces and operator valued kernel. Further, the scientific data is modeled as a snapshot of time dependent vector field F(x, t) over a manifold M and the recovery of the data is framed as a learning problem. These processes are then appropriately modified and ana lyzed for the streaming scenario in which data is generated without the ability to revisit past entries.

97 MATHEMATICS AND COMPUTING↗

High-Temperature Ceramic-Carbonate Dual-Phase Membrane Reactor for Pre-combustion Carbon Dioxide Capture (Final Scientific/Technical Report)

Arizona State University, in collaboration with University of South Carolina, worked on a project aimed at development of a new high temperature, high pressure CO 2 perm-selective membrane reactor for water-gas-shift reaction (WGS) with simulated gasifier syngas to produce a high concentration H 2 stream with CO 2 capture. The membrane reactor is made of a CO 2 semi-permeable ceramic-carbonate dual-phase (CCDP) membrane with high CO 2 perm-selectivity/permeance and thermal/mechanical stability for application in WGS reaction. The objectives of this project were to (1) synthesize the chemically/thermally stable tubular CCDP membranes with CO 2 permeance and selectivity (with respect to H 2 , CO or H 2 O) larger than 6.5×10-7 mol/m2·s·Pa and 500, respectively; (2) establish CCDP membrane reactor setup and study high pressure CO 2 permeation and WGS reaction with CO 2 capture using the setup; and (3) identify conditions for WGS in the CCDP membrane reactor that produce CO 2 and H 2 streams with purity of >99% and >90% respectively at CO conversion >95% and overall carbon capture >90%. The work in this project included both membrane development and membrane reactor process study. The membrane development efforts were focused on investigating a H 2 S resistant and highly oxygen-ionic conducting metal oxide material and membrane for CO 2 separation, fabrication of tubular samaria-doped-ceria/molten-carbonate CCDP membrane with high mechanical strength, and experimental and modeling study of high-pressure CO 2 permeation of the CCDP membranes. Mathematical models were developed to describe WGS in the CCDP membrane reactor without a catalyst or packed with a commercial high temperature WGS catalyst. Experiments on WGS in the CCDP membrane reactor with the commercial WGS catalyst, guided by the model analysis, were performed to identify optimum conditions for achieving the CO conversion, carbon capture, and the purity of the H 2 and CO 2 streams mentioned above. At 30 atm feed pressure, 750°C operation temperature, space velocity of 250 h-1, and with steam sweep, a single-stage CCDP membrane reactor with average CO 2 permeation flux of 0.5 cm3(STP)/min.cm2 can achieve CO 2 conversion of 95% and overall carbon capture of 94%, and produce CO 2 and H 2 streams with dry-based purity of >99% and 92% respectively. The project also included process design and techno-economic analysis (TEA) for a CCDP membrane reactor process for WGS reaction with CO 2 capture for a 550 MW coal-fired IGCC power plant, and its comparison with the conventional fixed-bed reactor system for WGS with follow-up CO 2 capture by an amine absorption process. The target performance for the reactor for WGS with CO 2 capture includes CO conversion >95%, hydrogen stream purity >90%, CO 2 stream purity >95%, and total carbon capture >90%. The CCDP membrane developed in this project can achieve the performance target, without subsequent CO 2 capture process at the optimum conditions identified in this project. The outcome of the process design and TEA analysis shows that the membrane reactor for WGS with in-situ CO 2 capture has an operating cost about 40% lower than that for the conventional fixed-bed reactor with a separate amine absorption process for CO 2 capture. However, the capital cost of the membrane reactor process is about twice that of the conventional process because of the higher cost of the CCDP membrane. Modeling analysis shows that a membrane reactor using a CCDP membrane with higher CO 2 permeance (about three times the current value) can deliver the targeted performance for WGS reaction with CO 2 capture at a much higher space velocity and lower membrane surface area to catalyst volume ratio, leading to a smaller catalyst amount and/or membrane area and hence significantly reduced membrane reactor capital costs.

20 FOSSIL-FUELED POWER PLANTS↗

Anatomy of Continuous Mars SEIS and Pressure Data from Unsupervised Learning

The seismic noise recorded by the Interior Exploration using Seismic Investigations, Geodesy, and Heat Transport (InSight) seismometer (Seismic Experiment for Interior Structure [SEIS]) has a strong daily quasi-periodicity and numerous transient microevents, associated mostly with an active Martian environment with wind bursts, pressure drops, in addition to thermally induced lander and instrument cracks. That noise is far from the Earth’s microseismic noise. Quantifying the importance of nonstochasticity and identifying these microevents is mandatory for improving continuous data quality and noise analysis techniques, including autocorrelation. Cataloging these events has so far been made with specific algorithms and operator’s visual inspection. We investigate here the continuous data with an unsupervised deep-learning approach built on a deep scattering network. This leads to the successful detection and clustering of these microevents as well as better determination of daily cycles associated with changes in the intensity and color of the background noise. We first provide a description of our approach, and then present the learned clusters followed by a study of their origin and associated physical phenomena. We show that the clustering is robust over several Martian days, showing distinct types of glitches that repeat at a rate of several tens per sol with stable time differences. We show that the clustering and detection efficiency for pressure drops and glitches is comparable to or better than manual or targeted detection techniques proposed to date, noticeably with an unsupervised approach. Finally, here we discuss the origin of other clusters found, especially glitch sequences with stable time offsets that might generate artifacts in autocorrelation analyses. We conclude with presenting the potential of unsupervised learning for long-term space mission operations, in particular, for geophysical and environmental observatories.

58 GEOSCIENCES↗

Removing numerical pathologies in a turbulence parameterization through convergence testing

Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e. clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, this noise can be amplified by an advective term in CLUBB's background diffusion. Smoothing the limiters and removing the advective component of the background diffusion reduces the noise and restores the expected first-order convergence in CLUBB's solutions. These model reformulations improve the results at coarser, near-operational grid spacing and time step in cumulus cloud and dry turbulence tests. In addition, convergence testing is proved to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.

Mathematics and Computing, Geosciences↗

Development of data‐driven exponential integrators with application to modeling of delay photocurrents

Abstract Radiation‐induced photocurrent effects represent a threat to microelectronic components operating in space, manmade and terrestrial radiation environments. Analysis of these threats by circuit simulations requires accurate and computationally efficient compact models. Most existing compact models are based on closed form analytic solutions of the governing equations and require empirical assumptions and idealizations that can limit their validity. In this paper we formulate an alternative numerical, data‐driven approach that learns a compact model from data representative of the type of measurements one can obtain in an experimental facility. To develop the model we start from a generic discrete‐time dynamical system and then use physics knowledge to refine its structure. Numerical studies demonstrate the potential of the model and establish some empirical guidelines for its training.

Bochev, Pavel↗

High-performance windows improve thermal survivability of occupants during cold snaps

Exposure to low indoor air temperature is a major contributor to temperature-related mortality during extreme cold events, especially when power outages disrupt operation of space heating systems. This study explores the impact of high-performance windows on the thermal resilience of residential buildings during extreme cold weather and grid power outages, as well as their long-term benefits through energy efficiency and reduced risk of property damage. Building performance simulations were conducted for reference residential buildings in three construction vintages and two major U.S. cities located in cold climate zones, considering two types of extreme cold events: short and severe, and long and milder. Our research found that even houses compliant with current energy codes struggle to maintain safe indoor temperatures for more than a few hours during power outages, necessitating rapid evacuations. High-performance windows can extend the thermal survivability time by up to 3.8 days within a 7-day cold snap and significantly reduce risk of bursting frozen water pipes, depending on the building’s insulation and infiltration level, cold event severity, and occupant vulnerability. This extended thermal safety time is crucial in scenarios where reduced mobility complicates emergency responses in senior housing. In addition to boosting thermal resilience, upgrading older homes with high-performance windows can reduce heating energy consumption by over 18% and cooling energy by 15%. Our findings highlight the need to incorporate thermal resilience assessments into new designs or major retrofits, including the use of typical and extreme weather scenarios and advanced technologies like high-performance windows.

Krelling, Amanda F↗

Removing Numerical Pathologies in a Turbulence Parameterization Through Convergence Testing

Abstract Discretized numerical models of the atmosphere are usually intended to faithfully represent an underlying set of continuous equations, but this necessary condition is violated sometimes by subtle pathologies that have crept into the discretized equations. Such pathologies can introduce undesirable artifacts, such as sawtooth noise, into the model solutions. The presence of these pathologies can be detected by numerical convergence testing. This study employs convergence testing to verify the discretization of the Cloud Layers Unified By Binormals (CLUBB) model of clouds and turbulence. That convergence testing identifies two aspects of CLUBB's equation set that contribute to undesirable noise in the solutions. First, numerical limiters (i.e., clipping) used by CLUBB introduce discontinuities or slope discontinuities in model fields. Second, nonlinear artificial diffusion employed for improving numerical stability can introduce unintended small‐scale features into the solution of the model equations. Smoothing the limiters and using linear artificial diffusion reduces the noise and restores the expected first‐order convergence in CLUBB's solutions. These model reformulations enhance our confidence in the trustworthiness of solutions from CLUBB by eliminating the unphysical oscillations in high‐resolution simulations. The improvements in the results at coarser, near‐operational grid spacing and timestep are also seen in cumulus cloud and dry turbulence tests. In addition, convergence testing is proven to be a valuable tool for detecting pathologies, including unintended discontinuities and grid dependence, in the model equation set.

58 GEOSCIENCES↗

Achieving a robust grassy-ELM operation regime in CFETR

We have identified a robust grassy-edge localized mode (ELM) operation regime for future tokamak reactors. The regime exists within a pedestal top electron collisionality (ν*) window at high global poloidal beta (β p ). The existence of an upper ν* limit for grassy-ELMs is consistent with results previously reported in experiments (Oyama et al 2010 Nucl. Fusion 50 064014), while the existence of a lower ν* limit has not been reported previously. Using EPED and BOUT++, a theoretical model that quantitatively explains the physics of the grassy-ELMs within the window, which distinguishes them from the small mixed-ELMs at lower ν*, is presented for the first time. A peeling-ballooning stability boundary is obtained by scanning the operating density space. The change in density corresponds to a change in ν* that affects the pedestal bootstrap current. High β p leads to a strong Shafranov shift, which affects the flux surface averaged pressure drive. The two effects combine to create a peeling-dominated window in intermediate ν* buffered by ballooning-dominated regimes. Only the peeling-dominated regime shows a cyclic behavior in the perturbed pressure during the nonlinear simulation of an ELM crash, reminiscent of grassy-ELM dynamics. Similarly, the energy released across the separatrix is demonstrated to be significantly smaller. The quick recovery of the ELM crash is explainable by the rapid rise of a low n kink-peeling instability when the pedestal current I ped exceeds a threshold at high β p . It minimizes the excursion beyond marginal stability and is absent in the ballooning-dominated regime. Comparison with recent experiments over a range of β p and ν* strongly supports the physical picture proposed by the modeling.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Alfvén eigenmode classification based on ECE diagnostics at DIII-D using deep recurrent neural networks

Modern tokamaks have achieved significant fusion production, but further progress towards steady-state operation has been stymied by a host of kinetic and MHD instabilities. Control and identification of these instabilities is often complicated, warranting the application of data-driven methods to complement and improve physical understanding. In particular, Alfvén eigenmodes are a class of ubiquitous mixed kinetic and MHD instabilities that are important to identify and control because they can lead to loss of confinement and potential damage to the walls of a plasma device. In the present work, we use reservoir computing networks to classify Alfvén eigenmodes in a large labeled database of DIII-D discharges, covering a broad range of operational parameter space. However, despite the large parameter space, we show excellent classification and prediction performance, with an average hit rate of 91% and false alarm ratio of 7%, indicating promise for future implementation with additional diagnostic data and consolidation into a real-time control strategy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Absolute timing calibration of the Resolve microcalorimeter spectrometer on the X-ray Imaging and Spectroscopy Mission

The Resolve microcalorimeter spectrometer on the X-ray Imaging and Spectroscopy Mission (XRISM) is designed to have a good timing capability with the mission-level requirement of 1 ms as the absolute time tagging accuracy to suffice the needs for observatory science. In the ground calibration campaign, the absolute and relative timing offsets were measured using pulsed X-rays from the modulated X-ray sources. These offsets were used to determine calibration parameters, the Resolve timing coefficients, which are used in the offline correction of event times. In the orbit, reevaluation of the timing coefficients was carried out using the Crab pulsar, which was observed in two periods, one in the performance verification phase and the other in the guest observation phase. We report the absolute timing calibration of XRISM/Resolve using the ground and in-orbit data. Although the requirement is likely satisfied with the ground parameters, the timing coefficients have been refined using the in-orbit calibration to improve the timing accuracy. In addition, for the first time, we present the interpretation of the absolute timing offset originating from the analog and digital processing of X-ray events unique to a microcalorimeter spectrometer and present a complete view of the timing error of the Resolve instrument.

Astronomy and AstroPhysics↗