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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Comparison of Machine Learning Methods of Association Tested on Dense Nodal Arrays

The association of phase picks to form events is one of the fundamental components of seismology. Large and dense sensor networks, such as >1000 geophone arrays (and distributed acoustic sensing), offer unique challenges in association due to the vast numbers of observations and high likelihood of errant picks. In addition, the large number of stations can greatly increase the time it takes to perform the association. For this reason, machine learning (ML) methods might provide a more optimal method of association for such networks. In this work, we examine how well ML methods (e.g., Gaussian mixture model association, PhaseLink, and Graph Earthquake Neural Interpretation Engine) can incorporate dense seismic arrays into regional networks and how well they handle the increasing numbers of stations. Here, we test their capabilities on two dense seismic deployments, one within Rock Valley Nevada (52 nodes and a 9-station sparse local network), and the LArge-n Seismic Survey in Oklahoma dense nodal array (>1800 vertical-component geophones). Processing data from these two different styles of dense seismic deployments allows testing of how the ML algorithms can merge array data with a broader regional network, how they deal with poorly picked phases, and how they handle anthropogenic noise. We compare the ML-associated bulletins to those obtained using the Rapid Earthquake Association and Location algorithm, a more traditional method of association. We find that there are very small differences in results between the methods for small networks (<100 stations) with low pick rates. For large networks (>1000), there are enough errant picks that some of the ML methods start to create false events out of noise. We also find that the ML methods vary in computation time significantly but are all faster than the traditional method tested here.

58 GEOSCIENCES↗
DOE OSTI2025

FNCL Enhancements Implementation (FY25 Annual Report)

The FNCL investigations team at Lawrence Livermore National Laboratory (LLNL) has completed research and development of hardware, signal processing, and analysis tools to enhance the measurement capabilities of both the current CAEN SyS VeryFuel Fast Neutron Collar (FNCL) instrument and a next-generation FNCL prototype. The team successfully built and commissioned the LLNL Demonstrator System: a fully integrated, three-panel detector system featuring higher segmentation, plastic scintillators (EJ-276D), Silicon Photomultipliers (SiPMs), no high-voltage requirement, a reduced electronic footprint, and the LLNL-developed Gaussian Mixture Model Pulse Shape Discrimination (GMM-PSD) signal processing. An extensive experimental campaign was conducted at LLNL’s Inherently Safe Subcritical Assembly (ISSA) facility using both the baseline FNCL and the LLNL Demonstrator. The campaign results validated system performance, calibration stability, and the effectiveness of advanced signal processing and analysis algorithms in a relevant environment.

and physical protection↗
DOE OSTI2022

Infrared-Fused Vision-Based Thermoregulation Performance Estimation for Personal Thermal Comfort-Driven HVAC System Controls

Thermal comfort is one of the primary factors influencing occupant health, well-being, and productivity in buildings. Existing thermal comfort systems require occupants to frequently communicate their comfort vote via a survey which is impractical as a long-term solution. Here, we present a novel thermal infrared-fused computer vision sensing method to capture thermoregulation performance in a non-intrusive and non-invasive manner. In this method, we align thermal and visible images, detect facial segments (i.e., nose, eyes, face boundary), and accordingly read the temperatures from the appropriate coordinates in the thermal image. We focus on the human face since it is often clearly visible to cameras and is not merged into a hot background (unlike hands). We use a regularized Gaussian Mixture model to track the thermoregulation changes over time and apply a heuristic algorithm to extract hot and cold indices. We present a personalized and a generalized comfort modeling method, selected based on the availability of the occupant historical indices measurements in a neutral environment, and use the time-series of the hot and cold indices to define corrections to HVAC system operations in the form of setpoint constraints. To evaluate the efficacy of our proposed approach in responding to thermal stimuli, we designed a series of controlled experiments to simulate exposure to cold and hot environments. While applying personalized modeling showed an acceptable average accuracy of 91.3%, the generalized model’s average accuracy was only 65.2%. This shows the importance of having access to physiological records in modeling and assessing comfort. We also found that individual differences should be considered in selecting the cooling and heating rates when some knowledge of the occupant’s overall thermal preference is available.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Entropy-Assisted Quality Pattern Identification in Finance

Short-term patterns in financial time series form the cornerstone of many algorithmic trading strategies, yet extracting these patterns reliably from noisy market data remains a formidable challenge. In this paper, we propose an entropy-assisted framework for identifying high-quality, non-overlapping patterns that exhibit consistent behavior over time. We ground our approach in the premise that historical patterns, when accurately clustered and pruned, can yield substantial predictive power for short-term price movements. To achieve this, we incorporate an entropy-based measure as a proxy for information gain: patterns that lead to high one-sided movements in historical data yet retain low local entropy are more “informative” in signaling future market direction. Compared to conventional clustering techniques such as K-means and Gaussian Mixture Models (GMMs), which often yield biased or unbalanced groupings, our approach emphasizes balance over a forced visual boundary, ensuring that quality patterns are not lost due to over-segmentation. By emphasizing both predictive purity (low local entropy) and historical profitability, our method achieves a balanced representation of Buy and Sell patterns, making it better suited for short-term algorithmic trading strategies. This paper offers an in-depth illustration of our entropy-assisted framework through two case studies on Gold vs. USD and GBPUSD. While these examples demonstrate the method’s potential for extracting high-quality patterns, they do not constitute an exhaustive survey of all possible asset classes.

Physics↗
DOE OSTI2025

SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data

We present the SpecDis value-added stellar distance catalog accompanying DESI Data Release 1. SpecDis trains a feed-forward neural network (NN) with Gaia parallaxes and gets the distance estimates. To build up an unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead, we incorporate parallax error into the loss function. Moreover, we employ principal component analysis to reduce the noise and dimensionality of stellar spectra. Validated by independent external samples of member stars with precise distances from globular clusters, dwarf galaxies, stellar streams, combined with blue horizontal branch stars, we demonstrate that our distance measurements show no significant bias up to 100 kpc, and are much more precise than Gaia parallax beyond 7 kpc. The median distance uncertainties are 23%, 19%, 11%, and 7% for S/N < 20, 20 ≤ S/N < 60, 60 ≤ S/N < 100, and S/N ≥ 100. Selecting stars with ${\mathrm{log}}\,g\lt 3.8$ and distance uncertainties smaller than 25%, we have more than 74,000 giant candidates within 50 kpc of the Galactic center and 1500 candidates beyond this distance. Additionally, we develop a Gaussian mixture model to identify unresolvable equal-mass binaries by modeling the discrepancy between the NN-predicted and the geometric absolute magnitudes from Gaia parallaxes and identify 120,000 equal-mass binary candidates. Our final catalog provides distances and distance uncertainties for >4 million stars, offering a valuable resource for Galactic astronomy.

astronomy data analysis↗
DOE OSTI2021

Distant Relatives: The Chemical Homogeneity of Comoving Pairs Identified in Gaia

Comoving pairs, even at the separations of $\mathcal{O}$(10 6 ) are a predicted reservoir of conatal stars. Here we present detailed chemical abundances of 62 stars in 31 comoving pairs with separations of 10 2 –10 7 au and 3D velocity differences <2 km s -1 . This sample includes both bound comoving pairs/wide binaries and unbound comoving pairs. Observations were taken using the Magellan Inamori Kyocera Echelle (MIKE) spectrograph on board the Magellan/Clay Telescope at high resolution (R~ 45,000) with a typical signal-to-noise ratio of 150 pixel -1 . With these spectra, we measure surface abundances for 24 elements, including Li, C, Na, Mg, Al, Si, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, Sr, Y, Zr, Ba, La, Nd, and Eu. Taking iron as the representative element, our sample of wide binaries is chemically homogeneous at the level of 0.05 dex, which agrees with prior studies on wide binaries. Importantly, even systems at separations 2 × 10 5 –10 7 au are homogeneous to 0.09 dex, as opposed to the random pairs, which have a dispersion of 0.23 dex. Assuming a mixture model of the wide binaries and random pairs, we find that 73 ± 22% of the comoving pairs at separations 2 × 10 5 –10 7 au are conatal. Our results imply that a much larger parameter space of phase space may be used to find conatal stars, to study M-dwarfs, star cluster evolution, exoplanets, chemical tagging, and beyond.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Contribution of Gaia Sausage to the Galactic Stellar Halo Revealed by K Giants and Blue Horizontal Branch Stars from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope, Sloan Digital Sky Survey, and Gaia

We explore the contribution of the Gaia Sausage to the stellar halo of the Milky Way by making use of a Gaussian mixture model (GMM) and applying it to halo star samples of Large Sky Area Multi-Object Fiber Spectroscopic Telescope K giants, Sloan Extension for Galactic Understanding and Exploration K giants, and Sloan Digital Sky Survey blue horizontal branch stars. The GMM divides the stellar halo into two parts, of which one represents a more metal-rich and highly radially biased component associated with an ancient, head-on collision referred to as the Gaia Sausage, and the other one is a more metal-poor and isotropic halo. A symmetric bimodal Gaussian is used to describe the distribution of spherical velocity of the Gaia Sausage, and we find that the mean absolute radial velocity of the two lobes decreases with the Galactocentric radius. We find that the Gaia Sausage contributes about 41%–74% of the inner (Galactocentric radius r gc < 30 kpc) stellar halo. The fraction of stars of the Gaia Sausage starts to decline beyond r gc ~ 25–30 kpc, and the outer halo is found to be significantly less influenced by the Gaia Sausage than the inner halo. After the removal of halo substructures found by integrals of motion, the contribution of the Gaia Sausage falls slightly within r gc ~ 25 kpc but is still as high as 30%–63%. Finally, we select several possible Sausage-related substructures consisting of stars on highly eccentric orbits. The GMM/Sausage component agrees well with the selected substructure stars in their chemodynamical properties, which increases our confidence in the reliability of the GMM fits.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

The SDSS-Gaia View of the Color–Magnitude Relation for Blue Horizontal-branch Stars

We present an updated sample of blue horizontal-branch (BHB) stars selected from the photometric and spectroscopic data from Sloan Digital Sky Survey and its associated project Sloan Extension for Galactic Understanding and Exploration (SEGUE). With these data, we selected candidates for A-type stars in the color–color space and then a mixture modeling technique was implemented in order to distinguish between BHB and main-sequence/blue-straggler stars based on their surface gravity values (log g) estimated by the SEGUE Stellar Parameter Pipeline. Our robust approach allows us to attribute individual probabilities of each star truly being in the BHB stage. Hence, our method is advantageous in comparison to previous SEGUE BHB selections that adopted simple log g cuts. We also revisit the color–magnitude relation for these stars and propose two calibrations, based on updated distances for Galactic globular clusters, to estimate absolute magnitudes with (g – r) 0 and (u – r) 0 colors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Proper Motions, Orbits, and Tidal Influences of Milky Way Dwarf Spheroidal Galaxies

We combine Gaia early data release 3 astrometry with accurate photometry and utilize a probabilistic mixture model to measure the systemic proper motion of 52 dwarf spheroidal (dSph) satellite galaxies of the Milky Way (MW). For the 46 dSphs with literature line-of-sight velocities we compute orbits in both a MW and a combined MW + Large Magellanic Cloud (LMC) potential and identify Car II, Car III, Hor I, Hyi I, Phx II, and Ret II as likely LMC satellites. 40% of our dSph sample has a >25% change in pericenter and/or apocenter with the MW + LMC potential. For these orbits, we use a Monte Carlo sample for the observational uncertainties for each dSph and the uncertainties in the MW and LMC potentials. We predict that Ant II, Boo III, Cra II, Gru II, and Tuc III should be tidally disrupting by comparing each dSph's average density relative to the MW density at its pericenter. dSphs with large ellipticity (CVn I, Her, Tuc V, UMa I, UMa II, UMi, Wil 1) show a preference for their orbital direction to align with their major axis even for dSphs with large pericenters. We compare the dSph radial orbital phase to subhalos in MW-like N-body simulations and infer that there is not an excess of satellites near their pericenter. With projections of future Gaia data releases, we find that dSph's orbital precision will be limited by uncertainties in the distance and/or MW potential rather than in proper motion precision. Finally, we provide our membership catalogs to enable community follow-up.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

New Measurements of the Lyα Forest Continuum and Effective Optical Depth with LyCAN and DESI Y1 Data

Abstract We present the Ly α Continuum Analysis Network (LyCAN), a convolutional neural network that predicts the unabsorbed quasar continuum within the rest-frame wavelength range of 1040–1600 Å based on the red side of the Ly α emission line (1216–1600 Å). We developed synthetic spectra based on a Gaussian mixture model representation of nonnegative matrix factorization (NMF) coefficients. These coefficients were derived from high-resolution, low-redshift ( z < 0.2) Hubble Space Telescope/Cosmic Origins Spectrograph (COS) quasar spectra. We supplemented this COS-based synthetic sample with an equal number of DESI Year 5 mock spectra. LyCAN performs extremely well on testing sets, achieving a median error in the forest region of 1.5% on the DESI mock sample, 2.0% on the COS-based synthetic sample, and 4.1% on the original COS spectra. LyCAN outperforms principal component analysis (PCA) and NMF-based prediction methods using the same training set by 40% or more. We predict the intrinsic continua of 83,635 DESI Year 1 spectra in the redshift range of 2.1 ≤ z ≤ 4.2 and perform an absolute measurement of the evolution of the effective optical depth. This is the largest sample employed to measure the optical depth evolution to date. We fit a power law of the form τ ( z ) = τ 0 ( 1 + z ) γ to our measurements and find τ 0 = (2.46 ± 0.14) × 10 −3 and γ = 3.62 ± 0.04. Our results show particular agreement with high-resolution, ground-based observations around z = 2, indicating that LyCAN is able to predict the quasar continuum in the forest region with only spectral information outside the forest.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The Velocity Field of Our Milky Way Outer Stellar Halo Based on DESI DR2

Using 64,000 halo K giants from the Dark Energy Spectroscopic Instrument second Data Release (DR2), we decompose the Milky Way (MW) stellar halo between 3 and 160 kpc into metal-rich (MR) and metal-poor (MP) components via a Gaussian mixture model. The two populations are nearly equal in number but chemically and kinematically distinct: MR stars occupy highly radial orbits with velocity anisotropy of β ≈ 0.94 and metallicity dispersion σ [Fe/H] ≈ 0.17 dex, without obvious dependence on distance, and are mainly contributed by Gaia-Sausage/Enceladus (GSE) debris. The MR component dominates the inner 30 kpc and reemerges beyond 50 kpc, implying GSE debris can extend to ∼70–80 kpc. MP stars exhibit a weaker radial bias of β ≈ 0.46, decreasing to −0.5 beyond 80 kpc, and with a larger metallicity dispersion of σ [Fe/H] ≈ 0.46 dex, showing signatures of multiple minor mergers. Both components exhibit net prograde rotation at ∼10–30 kpc with a stronger azimuthal signal in the MP population. The nonequilibrium motions of the outer halo (>50 kpc) are quantified with a dipole-plus-contraction velocity field. We find that the outer halo is simultaneously contracting (μ compr = −19 km s −1 , distance-independent) and subject to reflex motions (μ dipole increases from −19 to −44 km s −1 with radius), reflecting the perturbation from the Large Magellanic Cloud (LMC). We also confirm a linear dependence of mean polar velocity for the outer stellar halo on μ dipole , a direct consequence of the LMC and MW interaction. Our results provide a quantitative distance-resolved description of the MW’s last major accretion event and its ongoing response to the first infall of the LMC.

Li, Songting [Shanghai Jiao Tong University; Shang↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy’s gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a GC. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper-motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicities for over 10 million stars and reaches significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$km s −1 and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel “spur” feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a “hot,” metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [University of Toronto; Dunlap In↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗
DOE OSTI2026

The Kinematically Hot, Extremely Metal-Poor C-19 Stellar Stream in DESI DR2

Stellar streams are the result of a host galaxy's gravitational potential tidally disrupting satellite dwarf galaxies and globular clusters (GCs), causing them to grow leading and trailing tidal tails. The C-19 stellar stream is an extremely metal-poor stellar population, showing chemical abundance patterns characteristic of a globular cluster. However, its large velocity dispersion is difficult to reconcile with a conventional, purely baryonic, disrupting-GC progenitor. Current techniques for stream characterization are primarily applied to Gaia DR3, relying heavily on proper motion measurements. Using the Dark Energy Spectroscopic Instrument (DESI), which provides radial velocities and metallicites for over 10 million stars reaching significantly fainter magnitudes than comparable surveys, we employ a mixture model approach to jointly characterize stream populations in proper motions, radial velocities, and metallicities against a Milky Way halo background. By applying this framework to the C-19 stellar stream, we identify a total of 47 spectroscopically confirmed member stars, of which 41 are newly identified and only 6 were previously reported in the literature. In this work, we measure a velocity dispersion of $7.8^{+1.5}_{-1.3}$ km s$^{-1}$ and a mean metallicity of [Fe/H] = $-3.36^{+0.12}_{-0.10}$. We further identify a novel 'spur' feature within the stream. We conclude that our measurements are in line with previous works identifying C-19 as a 'hot', metal-poor stream. In forthcoming work, we will apply this approach to many more streams in the DESI footprint, enabling population-level comparisons with predictions from simulations.

Mohammed, Nasser [Toronto U.] (ORCID:0009000812240↗
DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($σ_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($σ_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
DOE OSTI2025

Sockeye Code Validation Against UNIST Heat Pipe - Poster - Internship 2025

Sockeye is an engineering level code being developed under the MOOSE framework for modeling heat pipes. A heat pipe is a sealed tube with a wick structure filled with a working fluid that transfers heat efficiently and passively via phase change of the working fluid. Heat applied to the evaporator end creates a pressure gradient which causes vapor to migrate to the condenser end where the vapor deposits its energy and condenses. Capillary force generated by the wick structure then draws the liquid back to the evaporator. A validation study was performed to compare the Sockeye code against data produced at the Ulsan National Institute of Standards and Technology for a slightly overfilled sodium heat pipe. The heat pipe was operated under natural convection cooling and a decreasing inactive length was observed. The experiment was modeled in Sockeye using the condenser pool model, the front non-condensable gas (NCG) model, and the mixture NCG model separately to reproduce the inactive length phenomenon. Also, a new capability was implemented in MOOSE to allow for conjugate heat transfer from the condenser based on the Churchill-Chu correlation for natural convection. The condenser pool model showed an increasing inactive length, demonstrating that the behavior was likely not caused by a pool of excess liquid. The front NCG model was able to show good agreement with the experiment, but it suffered convergence issues at the front. Finally, the mixture NCG model gave good results when the axial mesh was sufficiently refined. This work culminated in additions to the Sockeye documentation and a contribution to a journal article that will be published later. This poster is a summary of my work which I can take back to my university for presentation.

42 - ENGINEERING↗