Data-Driven Model-Form Uncertainty with Bayesian Statistics and Neural Differential Equations.
Abstract not provided.
Acquesta, Erin↗
SEARCH · Engineering Papers
Results for “STATISTICAL MODELS”
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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Design Methods, Tools, and Data for Ceramic Solar Receivers
This report presents the development of tools and methods for evaluating the reliability and performance of ceramic materials in high temperature solar receivers. As Concentrating Solar Power (CSP) technologies aim for higher operating temperatures to enhance efficiency and meet industrial process heat requirements, current high temperature metallic materials face challenges in maintaining structural integrity. This report explores advanced ceramics as a promising alternative, given their superior high temperature strength and lower thermal expansion, compared to metals. To address the need for effective ceramic receiver design tools, this report integrates statistical failure models of ceramics into the existing srlife tool: an open-source software package designed to estimate the life of high temperature CSP components. These failure models account for the inherent variability and flaw distribution in ceramics, as well as the impact of subcritical crack growth under high temperature cyclic loads. The report also presents experimental data collected for a commercially available ceramic material, SiC, and details the process of estimating reliability model parameters from these data. A comparative design analysis is then performed between ceramic (SiC) and metallic (current nickel-based superalloys A740H and A282) receiver. This comparison demonstrates that SiC receivers can achieve service life exceeding 30 years under high incident heat flux conditions, compared to just a few years for metallic receivers.
14 SOLAR ENERGY↗
Robust Dark Energy Constraints with the Dark Energy Spectroscopic Survey (Final Technical Report)
This project developed and applied advanced theoretical, computational, and data-analysis methodologies to extract robust and precise cosmological constraints from the Dark Energy Spectroscopic Instrument (DESI). The work focused on maximizing the scientific return of DESI through optimized survey strategy, novel higher-order clustering statistics, improved modeling of small-scale structure, and rigorous mitigation of observational systematics. Over the award period, the project made substantial contributions to DESI science planning, produced new methods for bispectrum and three-point correlation function analyses, advanced constraints on primordial non-Gaussianity, and delivered widely used software tools. The project also played a major role in training graduate students and a postdoctoral researcher who contributed directly to DESI key projects. The results have significantly enhanced the cosmological reach of DESI and provide a strong foundation for future surveys such as DESI-II and Stage-V experiments.
79 ASTRONOMY AND ASTROPHYSICS↗
Long-Term Stochasticity Combines With Short-Term Variability in Assembly Processes to Underlie Rice Paddy Sustainability
Revealing temporal patterns of community assembly processes is important for understanding how microorganisms underlie the sustainability of agroecosystem. The ancient terraced rice paddies at Longji provide an ideal platform to study temporal dynamics of agroecosystem sustainability due to their chronosequential records of soil physicochemistry and well-archived microbial information along 630-year rice cultivation. We used statistical null models to evaluate microbial assembly processes along the soil chronosequences of Longji rice paddies through time. Stochastic and deterministic assembly processes jointly governed microbial community composition within successional eras (less than 250 years), and within-era determinism was mainly driven by soil fertility and redox conditions alone or in combination. Conversely, across successional eras (i.e., over 300 years), stochasticity linearly increased with increasing duration between eras and was eventually predominant for the whole 630 years. We suggest that the impact of stochasticity vs. determinism on assembly is timescale-dependent, and we propose that the importance of stochastic assembly of microbial community at longer timescales is due to the gradual changes in soil properties under long-term rice cultivation, which in turn contribute to the sustainability of paddy ecosystem by maintaining a diverse community of microorganisms with multi-functional traits. In total, our results indicate that knowledge on the timescales at which assembly processes govern microbial community composition is key to understanding the ecological mechanisms generating agroecosystem sustainability.
59 BASIC BIOLOGICAL SCIENCES↗
Kepler Data Analysis: Non-Gaussian Noise and Fourier Gaussian Process Analysis of Stellar Variability
We develop a statistical analysis model of Kepler stellar flux data in the presence of planet transits, non-Gaussian noise, and stellar variability. We first develop a model for the Kepler noise probability distribution in the presence of outliers, which make the noise probability distribution non-Gaussian. We develop a signal likelihood analysis based on this probability distribution, in which we model the signal as a sum of the star variability and planetary transits. We argue that these components need to be modeled together if optimal signal is to be extracted from the data. For the stellar variability model we develop an optimal Gaussian process analysis using a Fourier-based Wiener filter approach, where the power spectrum is non-parametric and learned from the data. We develop high dimensional optimization of the objective function, where we jointly optimize all the model parameters, including thousands of star variability modes, and planet transit parameters. We apply the method to Kepler-90 data and show that it gives a better match to the stellar variability than the existing methods, and robustly handles noise outliers. As a consequence, the planet radii have a higher value than what the existing methods give, including splines and celerite.
79 ASTRONOMY AND ASTROPHYSICS↗
XRISM Spectroscopy of the Fe Kα Emission Line in the Seyfert Active Galactic Nucleus NGC 4151 Reveals the Disk, Broad-line Region, and Torus
Abstract We present an analysis of the first two XRISM/Resolve spectra of the well-known Seyfert-1.5 active galactic nucleus (AGN) in NGC 4151, obtained in 2023 December. Our work focuses on the nature of the narrow Fe K α emission line at 6.4 keV, the strongest and most common X-ray line observed in AGN. The total line is found to consist of three components. Even the narrowest component of the line is resolved with evident Fe K α ,1 (6.404 keV) and K α ,2 (6.391 keV) contributions in a 2:1 flux ratio, fully consistent with neutral gas with negligible bulk velocity. Subject to the limitations of our models, the narrowest and intermediate-width components are consistent with emission from optically thin gas, suggesting that they arise in a disk atmosphere and/or wind. Modeling the three line components in terms of Keplerian broadening, they are readily associated with (1) the inner wall of the “torus,” (2) the innermost optical “broad-line region” (or “X-ray BLR”), and (3) a region with a radius of r ≃ 100 GM / c 2 that may signal a warp in the accretion disk. Viable alternative explanations of the broadest component include a fast-wind component and/or scattering; however, we find evidence of variability in the narrow Fe K α line complex on timescales consistent with small radii. The best-fit models are statistically superior to simple Voigt functions, but when fit with Voigt profiles the time-averaged lines are consistent with a projected velocity broadening of FWHM = 1600 − 200 + 400 km s − 1 . Overall, the resolution and sensitivity of XRISM show that the narrow Fe K line in AGN is an effective probe of all key parts of the accretion flow, as it is currently understood. We discuss the implications of these findings for our understanding of AGN accretion, future studies with XRISM, and X-ray-based black hole mass measurements.
Audard, Marc (ORCID:000000034721034X)↗
Warming of the Willamette River, 1850–present: the effects of climate change and river system alterations
Abstract. Using archival research methods, we recovered and combined data from multiple sources to produce a unique, 140-year record of daily water temperature (Tw) in the lower Willamette River, Oregon (1881–1890, 1941–present). Additional daily weather and river flow records from the 1850s onwards are used to develop and validate a statistical regression model of Tw for 1850–2020. The model simulates the time-lagged response of Tw to air temperature and river flow and is calibrated for three distinct time periods: the late 19th, mid-20th, and early 21st centuries. Results show that Tw has trended upwards at 1.1 ∘C per century since the mid-19th century, with the largest shift in January and February (1.3 ∘C per century) and the smallest in May and June (∼ 0.8 ∘C per century). The duration that the river exceeds the ecologically important threshold of 20 ∘C has increased by about 20 d since the 1800s, to about 60 d yr−1. Moreover, cold-water days below 2 ∘C have virtually disappeared, and the river no longer freezes. Since 1900, changes are primarily correlated with increases in air temperature (Tw increase of 0.81 ± 0.25 ∘C) but also occur due to alterations in the river system such as depth increases from reservoirs (0.34 ± 0.12 ∘C). Managed release of water affects Tw seasonally, with an average reduction of up to 0.56 ∘C estimated for September. River system changes have decreased variability (σ) in daily minimum Tw by 0.44 ∘C, increased thermal memory, reduced interannual variability, and reduced the response to short-term meteorological forcing (e.g., heat waves). These changes fundamentally alter the response of Tw to climate change, posing additional stressors on fauna.
54 ENVIRONMENTAL SCIENCES↗
statMechCrack
SAND2022-12599 O statMechCrack is a Python package providing an API to run calculations based on certain statistical mechanical models for cracks and crack growth. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Grutzik, Scott [Sandia National Lab. (SNL-CA), Liv↗
Race-Specific Risk Factors for Homeownership Disparity in the Continental United States
The United States has a racial homeownership gap due to a legacy of historic inequality and discriminatory policies, but factors that contribute to the racial disparity in homeownership rates between White Americans and people of color have not been fully characterized. In order to alleviate this issue, policymakers need a better understanding of how risk factors affect the homeownership rates of racial and ethnic groups differently. In this study, data from several publicly available surveys, including the American Community Survey and United States Census, were leveraged in combination with statistical learning models to investigate potential factors related to homeownership rates across racial and ethnic categories, with a focus on how risk factors vary by race or ethnicity. Our models indicated that job availability for specific demographics, and specific regions of the United States were factors that affect homeownership rates in Black, Hispanic, and Asian populations in different ways. Based on the results of this study, it is recommended policymakers promote strategies to increase access to jobs for people of color (POC), such as vocational training and programs to reduce implicit bias in hiring practices. These interventions could ultimately increase homeownership rates for POC and be a step toward reducing the racial wealth gap.
99 GENERAL AND MISCELLANEOUS↗
Retrofit-ability: A supplementary metric to inform energy efficiency policies and programs
Building Energy Use Intensity (EUI) has been commonly used to facilitate policy makers and utilities to design energy efficacy programs and help building managers prioritize investment in building upgrades. Buildings with higher EUIs are usually chosen as better candidates for retrofit. Our study of nearly one hundred buildings in Seattle’s Building Tune Up Accelerator program and thousands of buildings in the Asset Score database reveals that EUI (after weather/location/use type normalization) alone is not the best indicator of a building’s potential to save energy, especially for those buildings in the mid-range (30-70 percentile EUI). In this paper, we introduce the concept of “retrofit-ability” — a building’s real potential to reduce its energy use cost-effectively — as a supplementary metric to inform energy efficiency policy and investment. Currently, such potential analyses are predicted for a building stock using prototypical building models or statistical data. However, typical buildings or general historical data may not sufficiently represent building configurations in a portfolio and predict their saving potentials. We utilized machine learning to investigate how key building characteristics (such as envelope attributes, HVAC type, location, use type, etc.) affect a building’s improvement potentials and developed a low-cost method to quantify such potentials for a portfolio of buildings. The supplementary perspective provided by “retrofit-ability” highlights which building assets correlate most with a building’s energy savings potential across region, building use type and more.
Wang, Na↗
DOE-BU-26096-4
The infiltration of nanoparticle electrocatalysts into solid oxide fuel cell (SOFC) electrodes has been proven to produce a high density of electrochemically active sites, and reduce charge transfer polarization losses in SOFC electrodes. This is crucial for intermediate temperature operation, as these losses increase greatly at lower temperatures. Nickel-yttria stabilized zirconia (Ni-YSZ) cermets are low-cost, and exhibit excellent stability, but their main disadvantage stems from nickel coarsening and performance loss over their operational lifetimes. Infiltration of electrocatalyst nanoparticles has been shown to mitigate nickel coarsening and the consequent anode degradation. In this work, the effects of these infiltrants have been observed in a standard Ni-YSZ electrode. In addition to nickel, mixed ionic and electronic conducting (MIEC) phases have been infiltrated into Ni-YSZ scaffolds and their performance characterized using electrochemical impedance spectroscopy (EIS). Cross-sectional microscopy of fractured cells has been used to compare electrode microstructure and particle statistics. A model has been proposed to explain the origin of anode performance enhancement from nanoscale electrocatalysts.
Mo, Boshan↗
Fracture statistics and integration into computational models.
Abstract not provided.
Portone, Teresa↗
First results from the search of $ν_μ$ disappearance with ICARUS
his work presents a search for muon neutrino disappearance signal using the ICARUS detector. ICARUS is currently working as the far detector of the Short-Baseline Neutrino (SBN) program. Its main goal is to investigate the possible oscillation of a sterile neutrino state ($\Delta m^2 \sim \text{eV}^2$), that would drive short baseline oscillations at the scale of L/E $\sim$ km/GeV. Neutrino interactions from the Booster Neutrino Beam (BNB) are investigated, looking for simple final state topology with a single muon and at least one proton (1$\mu$Np). This analysis tests the 3+1 model using the two flavor neutrino approximation, where careful consideration has been given to the systematic uncertainties arising from flux, neutrino interaction, and detector models. No statistically significant $\nu_\mu$ disappearance is observed, hence 90\% C.L. exclusion contours are reported in the $\Delta m^2_{41} - \sin^22\theta_{\mu\mu}$ parameter space.
Artero Pons, Maria [INFN, Padua; U. Padua (main)]↗
Probing the limits of statistical neutron capture for the r process: Experimental constraints on 141 Cs nuclear level densities
The r-process abundance peaks, particularly near mass number A ∼ 130, reflect underlying nuclear structure effects such as closed neutron shells, yet modeling the nucleosynthesis in this region remains hindered by uncertain neutron-capture rates. These rates are especially sensitive to nuclear level densities (NLDs) and γ-ray strength functions of neutron-rich nuclei, where experimental data are scarce. We present the first experimental constraint on the NLD of 141 Cs using the β-Oslo method, extending sensitivity to the neutron-rich regime near the N = 82 closed shell. Our data allow for critical calibration of microscopic NLD models and reveal that 141 Cs lies near the limit of statistical model applicability. Using this experimental input, we evaluate radiative neutron-capture rates across neighboring isotones using both Hauser–Feshbach (HF) and High Fidelity Resonance (HFR) models. Our results show order-of-magnitude rate increases for nuclei along the N = 86 line, signaling a transition to resonance-dominated capture in this region. These findings underscore the importance of constraining NLDs to improve r-process reaction network predictions, particularly in environments where the validity of statistical models breaks down.
Nuclear level density↗
A large-scale comparison of Artificial Intelligence and Data Mining (AI&DM) techniques in simulating reservoir releases over the Upper Colorado Region
In recent years, the Artificial Intelligence and Data Mining (AI&DM) models have become popular tools in assisting various aspects of reservoir operation. However, the practical uses are still rarely reported. Comparison experiment of many AI&DM models over a large number of reservoir cases is particularly valuable to help reservoir operators first examine the usefulness and transferability of different AI&DM models, and then identify the most stable and reliable AI&DM model in assist of various decision-making processes. In this study, a total of 12 AI&DM models with different parameterizations and simulation scenarios are comprehensively tested out and compared in simulating the controlled reservoir outflows of 33 reservoir cases over the Upper Colorado Region, United States. Results show that the Random Forecast and the Long-Short-Term-Memory model could consistently derive the best statistical performance than other models under the baseline simulation scenario. The employed AI&DM models could obtain satisfactory statistical interquartile ranges (25–75%) between [0.6–0.9], [0.3–0.8], and [0.2–0.8], for CORR, NSE, and KGE measurements, respectively, and [1.5–6.5], [–15 to 20], and [0.5–8.5] for the normalized RMSE, PBIAS and RSR measurements, respectively. Results also show Multi-Layer Perceptron model and Extreme Gradient Boosting Tree Algorithm produced more stable and superior performance than other models under more complex input scenarios. We also found that the performance of different AI&DM models are closely relevant to the reservoir elevations, sizes, and functionalities. Discussions were made about the sensitivity of AI&DM models’ parameterizations and the key advantages of AI&DM models over the rule-based reservoir models. We further identify that the main advantage of AI&DM models is the flexibility in designing input structures, whereas the rule-based simulation model is rather limited. Future studies were suggested regarding the best way reservoir operators and researchers could use, select, and apply different AI&DM models in simulating reservoir releases under different natural and modeling environments. Finally, this comparison study also serves as a reference and a piece of groundwork for further promoting the practical uses of AI&DM models in assisting reservoir operation.
54 ENVIRONMENTAL SCIENCES↗
Stokes-dependent droplet collection efficiency on a NACA 0012 airfoil from droplet-informed simulations with statistical overloading
Accurate modelling of ice accretion on aircraft wings requires analysing droplet impingement on the surface to optimize the design of ice-protection systems. We perform Euler–Lagrange simulations of a droplet-laden flow impinging on a NACA 0012 airfoil. Our study includes water droplets with eight discrete sizes ranging from 1 to 160 microns. We vary the free-stream velocity of the incoming airflow in the range 60 ≤ U ≤ 240 m s −1 and the chord length of the airfoil in the range 0.5 ≤ c ≤ 2 m. Due to the dilute nature of supercooled clouds, one-way coupling is used in the simulations. The effects of droplet breakup and collision are also neglected. To reduce the computational cost, we employ statistical overloading of droplets, allowing us to simulate millions of impinging droplets in a time span on the order of milliseconds. Our results show that the droplet collection efficiency, which measures the likelihood of droplet impingement on the airfoil surface, increases with droplet size and free-stream velocity but decreases with airfoil size. We demonstrate that collection efficiency, impingement velocity and impingement angle are primarily dictated by a single non-dimensional parameter, the droplet Stokes number. We also identify a critical stagnation-streamline Stokes number below which impingements do not occur and use it to estimate the minimum droplet size for impingement. In addition, we observe droplet behaviour to become Stokes number independent at large values of the Stokes number. This article is part of the theme issue ‘Heat and mass transfer in frost and ice’.
Science & Technology - Other Topics↗
Application of the immobilized low-activity waste glass corrosion model to the static dissolution of 24 statistically-designed alkali-borosilicate waste glasses
Glass corrosion models that capture the complex mechanisms of the glass-water reaction enable the prediction of nuclear waste glass durability in disposal scenarios. Parameterization of such models is challenging because of the need to capture changes in corrosion behavior with time, reaction conditions, and glass composition. In this report we describe and employ the immobilized low activity waste (ILAW) glass corrosion model (IGCM) in geochemical simulations of static dissolution tests, at two temperatures (40 °C and 90 °C), for a matrix of 24 enhanced low activity waste (eLAW) glasses statistically designed to cover a processable composition space defined by 8 major glass components (Al 2 O 3 , B 2 O 3 , CaO, Na 2 O, SiO 2 , SnO 2 , ZrO 2 , and Others as defined in the text). The IGCM includes a first-order chemical affinity term and an ion-exchange term that represents the net exchange of Na+ ions in the pristine glass with protons in aqueous solution. Constant, time-dependent, and time- and pH-dependent functional forms of the ion-exchange term are evaluated to reproduce the change in corrosion behavior with time in saturated, static dissolution tests. The agreement with measured aqueous concentrations of the main glass components (B, Na, and Si) improved significantly upon addition of a time-dependent term, which therefore constitutes a simple representation of the glass-water reaction progress. Due to the limited changes in pH in the static dissolution tests, past a short initial period of rapid increase, addition of a pH dependent term did not appreciably improve the fits, indicating that comprehensive model parameterization requires more than one type of glass corrosion test to capture a wide range of solution chemistries. IGCM parameters were found to be dependent on glass composition, and the parameter sets generated in this work will enable the development of composition–parameter correlation models that offer the promise of predicting IGCM parameters, and thus glass corrosion behavior, solely based on glass composition.
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
Impact of solar geoengineering on temperatures over the Indonesian Maritime Continent
Climate change has been projected to increase the intensity and magnitude of extreme temperature in Indonesia. Solar radiation management (SRM) has been proposed as a strategy to temporarily combat global warming, buying time for negative emissions. Though the global impacts of SRM have been extensively studied in recent years, regional impacts, especially in the tropics, have received much less attention. This paper investigates the potential stratospheric sulfate aerosol geoengineering to modify mean and extreme temperature, as well as the relative humidity and wet bulb temperature (WBT) change over Indonesian Maritime Continent (IMC) based on simulations from three different earth system models. We applied statistical downscaling and corrected the bias of model output to reproduce historical temperatures and relative humidity over IMC. We compared SRM as specified by the Geoengineering Model Intercomparison Project (GeoMIP) G4 experiment – 5 Tg of SO 2 into the lower stratosphere between 2020 and 2069 – concurrent with the RCP4.5 emissions scenario. SRM is able to significantly reduce the temperature means and extremes, and although differences in magnitude of response and spatial pattern occur, there is a generally consistent response. Additionally, the spatial response of changes forced by RCP4.5 scenario and SRM impact is notably heterogeneous in the archipelago, highlighting uncertainties that would be critical in assessing socio-economic consequences of both doing, and not doing SRM. In general, SRM has bigger impacts in reducing temperatures over land than oceans, and the southern monsoon region shows more variability. SRM is also effective at reducing the likelihood of WBT > 27OC events compared to RCP4.5 after some years of SRM deployment as well as during the post-termination period of SRM. Regional downscaling may be an effective tool in obtaining policy-relevant information about local effects of different future scenarios involving SRM.
54 ENVIRONMENTAL SCIENCES↗