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NASA NTRS1993

Nonlinear and progressive failure aspects of transport composite fuselage damage tolerance

The purpose is to provide an end-user's perspective on the state of the art in life prediction and failure analysis by focusing on subsonic transport fuselage issues being addressed in the NASA/Boeing Advanced Technology Composite Aircraft Structure (ATCAS) contract and a related task-order contract. First, some discrepancies between the ATCAS tension-fracture test database and classical prediction methods is discussed, followed by an overview of material modeling work aimed at explaining some of these discrepancies. Finally, analysis efforts associated with a pressure-box test fixture are addressed, as an illustration of modeling complexities required to model and interpret tests.

Walker, Tom↗

DOE OSTI2022

Measurement of the anomalous precession frequency in the Muon $g-2$ experiment at Fermilab

The muon anomalous magnetic moment, $a_\mu=\frac{g-2}{2}$, is a low energy observable that can be both measured and computed with high precision, therefore it can provide an important test of the Standard Model and it is a sensitive probe for new physics. In particular, any discrepancy between the experimental value and the theoretical prediction can be due to Beyond Standard Model physics contribution. The E989 Muon $g-2$ Experiment at Fermilab aims to measure $a_\mu$ with a precision of 140 parts per billion, four time more precisely than the previous experiment at Brookhaven National Laboratory (BNL). E989 seeks to either resolve or confirm the observed discrepancy of $3.7\sigma$ between the Standard Model value and the experimental one. The $a_\mu$ measurement requires a precise determination of both the muon spin anomalous precession frequency and the average magnetic field seen by the muons as they circulate in a storage ring. The anomalous precession frequency measurement is b ased on the time distribution of high-energy decay positrons observed by 24 electromagnetic calorimeters placed around the inner circumference of the ring, while the magnetic field is constantly monitored by NMR probes. Last spring, E989 has published a new measurement of $a_\mu$ from the 2018 data-taking campaign, confirming the previous BNL value with comparable precision, bringing the discrepancy with the theoretical value to $4.2\sigma$. The candidate worked on the measurement of the anomalous precession frequency and on the estimate of the related systematic uncertainties of the 2018 data, giving an important contribution to the data analysis. The result presented in chapters 4 and 5 of this dissertation was included, among three other analyses, to determine the estimate of the anomalous magnetic moment by the Muon g-2 experiment. This work will present the anomalous precession frequency analysis technique and its result, the related systematics and finally the latest $a_\mu$ result.

Sorbara, Matteo↗

DOE OSTI2021

LANL Critical Benchmark Comparison Study and Subsequent Revision for Cases Involving LEU and MIX

As part of an international collaboration within the DOE Nuclear Criticality Safety Program (NCSP), LANL is involved in a comparison study to quantify differences in k-effective results from neutron transport simulations of critical benchmark experiments. The DOE NCSP Mission and Vision details the activity in which the French Institut de Radioprotection et de Sûreté Nucléaire (IRSN) leads the study with LANL, ORNL, and LLNL to compare results of various neutron transport codes and nuclear data libraries for ICSBEP benchmarks held in common by the entities. The task statement from the DOE NCSP Five-Year Execution Plan is given as: "The proposal is for IRSN to lead a new intercomparison based on the MORET code with the latest JEFF-3.3 data and ENDF/B-VIII.0 data, when available, using their existing comprehensive selection of 2,714 benchmarks and collate their results together with those from LLNL (COG), LANL (MCNP) and ORNL (SCALE). Due to the large number of benchmarks involved, this effort is envisioned to take three years with an additional year for IRSN to complete a summary report. The benchmark development will be performed independently to minimize modeling errors through discovery and resolution of discrepant results. A summary report will be generated (led by IRSN) to document the results of this study." This report documents results obtained for revisions made to cases involving Intermediate Enriched Uranium (IEU) and a mixture of Pu and Uranium (MIX), with a focus on the changes made to LANL benchmarks modeled with MCNP6 using ENDF/B-VII.1 nuclear data that appeared to have discrepant results when compared with results of other codes. Feedback was used to pinpoint review of particular benchmark input files and to revise them when necessary. This report documents the results of review and revision of specific benchmarks highlighted as possibly discrepant in the comparison study. In addition, there is an effort tied to this work involving collaboration between LANL XCP and NCS Divisions in the development of a shared review/revision procedure and use of a new benchmark repository.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

NASA NTRS2000

Fully Three-Dimensional Helical RF Field Effects on TWT Interaction

The phenomenal growth of the satellite communications industry has created a large demand for traveling wave-tubes (TWTs) operating with unprecedented specifications requiring design and production of many novel devices in record time. To achieve this, the TWT industry heavily relies upon computational modeling. However, there is a need for improvement in the TWT industry's computational modeling capabilities as there are often discrepancies between measured TWT data and that predicted by conventional two-dimensional helical TWT interaction codes. This severely limits the analysis and design of novel devices or TWTs with parameters differing from what is conventionally manufactured. In addition, the inaccuracy of current computational tools limits achievable TWT performance because optimized designs require highly accurate models. In particular, collector efficiency, which significantly affects overall TWT efficiency, is difficult to optimize unless extremely accurate spent beam data is predicted by the interaction code.

Kory, Carol L.↗

DOE OSTI2024

Causes of and Solutions to Wind Speed Bias in NREL's 2020 Offshore Wind Resource Assessment for the California Pacific Outer Continental Shelf

This report provides the results of a detailed analysis into the causes of high wind speed bias in the 20-year wind resource data set for offshore California the National Renewable Energy Laboratory (NREL) released in 2020, herein called CA20. The data set was developed using the state-of-the-art Weather Research and Forecasting (WRF) model. Notably, no floating lidars were available at the time in offshore California to validate offshore hub-height wind speeds. In late 2020, the Pacific Northwest National Laboratory (PNNL) deployed two floating lidars in the California outer continental shelf (OCS), near the Bureau of Ocean Energy Management (BOEM) call areas of Humboldt and Morro Bay. Using these observations through 2021, NREL found considerable bias in modeled hub-height winds at both locations: up to +2 m/s at Humboldt over a 6-month period, and up to +1 m/s at Morro Bay over a one-year period. Upon the discovery of this bias, the Department of Energy (DOE) and BOEM funded NREL and PNNL to investigate the causes of, impacts of, and solutions to the bias in the CA20 data set. This report summarizes the findings of this research. We first investigated whether different WRF model setups could lead to reduced bias. We found that the choice of planetary boundary layer (PBL) scheme - which controls the vertical turbulent mixing of momentum, heat, and moisture in the lowermost part of the atmosphere - greatly affected hub-height wind speeds in the region. Specifically, switching from the Mellor-Yamada-Nakanishi-Niino (MYNN) scheme used in CA20 (and widely used across a range of operational and research weather models) to the less common Yonsei University (YSU) scheme nearly eliminated the bias at both the Humboldt and Morro Bay lidar locations. The large discrepancy between the MYNN- and YSU-modeled hub-height winds pointed towards the role of atmospheric stability. In general, PBL schemes agree well in conditions of high turbulence and mixing, normally referred to as "unstable" conditions. By contrast, PBL schemes start to diverge in "stable" conditions, where turbulence is low and thermal stratification (i.e., higher temperature air sitting on top of colder air) greatly suppresses vertical mixing. Under such conditions, winds aloft can decouple from surface effects and greatly accelerate, causing high wind speeds at hub-height and frequent low-level jets (LLJs). We determined that these stable conditions are in fact dominant in offshore California. The region is characterized by moderate-to-extreme stable stratification with a LLJ on average around 200 meters above sea-level. To our knowledge, no wind energy area globally has as strongly stable stratification as offshore California. Under these extreme conditions, we determined that the MYNN scheme models higher stability than YSU, resulting in less vertical turbulent mixing than YSU, allowing for the acceleration of hub-height winds, more intense LLJs, and higher-amplitude inertial oscillations. Using surface observations, we found that MYNN overestimates near-surface stability, whereas YSU tends to model stability better. We then considered several short-term case studies to assess additional meteorological drivers of the bias at Humboldt. We found that during synoptic scale northerly flows driven by the North Pacific High and inland thermal low, a coastal warm bias in the MYNN case studies contributes to the modeled wind speed bias by altering the boundary layer thermodynamics via a thermal wind mechanism. Given the strong performance of the YSU-based runs in offshore California, NREL has produced and published an updated version of the CA20 data set with YSU as the PBL scheme. This updated data set is now part of NREL's 2023 National Offshore Wind (NOW-23) data set, which covers all the U.S. offshore waters. The development and final validation of the NOW-23 data set in offshore California is documented in this report.

17 WIND ENERGY↗

NASA NTRS2008

5th Annual AGILE Science Workshop

The EGRET model of the galactic diffuse gamma-ray emission (GALDIF) has been extended to provide full-sky coverage and improved to address the discrepancies with the EGRET data. This improved model is compared with the AGILE results from the Galactic center. The comparison is discussed.

Hunter, Stanley↗

NASA NTRS2012

Assessing Confidence in Pliocene Sea Surface Temperatures to Evaluate Predictive Models

In light of mounting empirical evidence that planetary warming is well underway, the climate research community looks to palaeoclimate research for a ground-truthing measure with which to test the accuracy of future climate simulations. Model experiments that attempt to simulate climates of the past serve to identify both similarities and differences between two climate states and, when compared with simulations run by other models and with geological data, to identify model-specific biases. Uncertainties associated with both the data and the models must be considered in such an exercise. The most recent period of sustained global warmth similar to what is projected for the near future occurred about 3.33.0 million years ago, during the Pliocene epoch. Here, we present Pliocene sea surface temperature data, newly characterized in terms of level of confidence, along with initial experimental results from four climate models. We conclude that, in terms of sea surface temperature, models are in good agreement with estimates of Pliocene sea surface temperature in most regions except the North Atlantic. Our analysis indicates that the discrepancy between the Pliocene proxy data and model simulations in the mid-latitudes of the North Atlantic, where models underestimate warming shown by our highest-confidence data, may provide a new perspective and insight into the predictive abilities of these models in simulating a past warm interval in Earth history.This is important because the Pliocene has a number of parallels to present predictions of late twenty-first century climate.

simulation↗

DOE OSTI2021

Spinning no-scale $${\mathcal {F}}$$-SU(5) in the right direction

Abstract The Fermi National Accelerator Laboratory (FNAL) recently announced confirmation of the Brookhaven National Lab (BNL) measurements of the $$g-2$$ g - 2 of the muon that uncovered a discrepancy with the theoretically calculated Standard Model value. We suggest an explanation for the combined BNL+FNAL 4.2 $$\sigma $$ σ deviation within the supersymmetric grand unification theory (GUT) model No-Scale $${\mathcal {F}}$$ F - $$SU(5)$$ S U ( 5 ) supplemented with a string derived TeV-scale extra $$10+\overline{10}$$ 10 + 10 ¯ vector-like multiplet and charged vector-like singlet $$(XE,XE^c)$$ ( X E , X E c ) , dubbed flippons. We introduced these vector-like particles into No-Scale Flipped SU (5) many years ago, and as a result, the renormalization group equation (RGE) running was immediately shaped to produce a distinctive and rather beneficial two-stage gauge coupling unification process to avoid the Landau pole and lift unification to the string scale, in addition to contributing through 1-loop to the light Higgs boson mass. The flippons have long stood ready to tackle another challenge, and now do so yet again, where the charged vector-like “lepton”/singlet couples with the muon, the supersymmetric down-type Higgs $$H_d$$ H d , and a singlet S , using a chirality flip to easily accommodate the muonic $$g-2$$ g - 2 discrepancy in No-Scale $${\mathcal {F}}$$ F - $$SU(5)$$ S U ( 5 ) . Considering the phenomenological success of this string derived model over the prior 11 years that remains accommodative of all presently available LHC limits plus all other experimental constraints, including no fine-tuning, and the fact that for the first time a Starobinsky-like inflationary model consistent with all cosmological data was derived from superstring theory in No-Scale Flipped SU (5), we believe it is imperative to reconcile the BNL+FNAL developments within the model space.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

Towards a NEAMS-based high-fidelity model of the MARVEL reactor

This report outlines the progress of Idaho National Laboratory in developing a high-fidelity and high-resolution model of the Microreactor Applications Research Validation and Evaluation reactor. The model was developed under the Nuclear Energy Advanced Modeling and Simulation microreactor application driver at Idaho National Laboratory. The overarching objective of this activity is the development of a high-fidelity multiphysics MARVEL model using NEAMS tools, and to verify and validate NEAMS tools against MARVEL reference simulation and experimental data, respectively. This is a unique opportunity to conduct multiphysics analysis on a soon-to-be-deployed microreactor. This multiphysics model developed under the NEAMS-funded INL microreactor application driver leverages three single-physics models coupled via the MOOSE’s MultiApp and Transfer systems. The latter systems enable in-memory data transfer between MOOSE-based and MOOSE-wrapped applications. The first single-physics model, that functions as main application, leverages Griffin to model the neutron transport in the core through the discontinuous finite element (DFEM) discrete ordinates solver (SN). Several optimization flags that were developed by the Griffin developer team were beta-tested to enhance the solver’s performance. These include the combined use of using_average_xs and update_averaged_xs_on that enable to avoid expensive on-the-fly cross sections evaluations at each linear iterations in favor of evaluations of the macroscopic cross sections at each Picard iteration. The second single-physics model uses BISON to handle solid heat transfer and asymptotic hydrogen redistribution analysis in the fuel. While the model returns consistent results for the temperature and hydrogen distribution in the fuel, a mismatch was noticed in the calculated temperature in the reflector due to the value of the gap conductance used in our model. Ongoing investigations are being performed to assess the origin of this discrepancy. Finally, the System Analysis Module (SAM) was used to model the flow of the sodium-potassium eutectic in the primary loop. A first verification was also performed showing good agreement in terms of mass flow rate and inlet temperature. All mesh files were generated using the MOOSE Reactor module, removing the need for external meshing tools. Notably, this workscope represents one of the initial applications of the MOOSE Reactor module for modeling highly irregular geometries. The use of the reactor module significantly streamlined the mesh generation process. The full multiphysics mode, that combines all the single physics models, was leveraged to conduct initial steady-state multiphysics simulations to compute power, and temperature distribution in the reactor. Initial testing was performed for transient simulations as well. In this case, the new checkpoint restart capability for eigenvalue calculations was tested showing the capability for streamlined restart of transient calculations. Future work will focus on improving the fidelity of the model by performing comprehensive code-to-code comparisons. For instance, the full-core Griffin neutronics model will be benchmarked against MCNP reference results, that were provided by the MARVEL design team. Additionally, the SAM T/H model will be verified against reference RELAP-5 results for selected accident scenarios. Besides code-to-code verification exercises, the model fidelity will be improved by replacing the single-channel SAM model with a more complex SAM-Pronghorn coupled model, in which the sub-channel capability is deployed to obtain radial temperature resolution in the coolant. This model will be developed in synergy with the NEAMS thermal hydraulics team.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2006

Experiments on the Motion of Drops on a Horizontal Solid Surface due to a Wettability Gradient

Results from experiments performed on the motion of drops of tetraethylene glycol in a wettability gradient present on a silicon surface are reported and compared with predictions from a recently developed theoretical model. The gradient in wettability was formed by exposing strips cut from a silicon wafer to decyltrichlorosiland vapors. Video images of the drops captured during the experiments were subsequently analyzed for drop size and velocity as functions of position along the gradient. In separate experiments on the same strips, the static contact angle formed by small drops was measured and used to obtain the local wettability gradient to which a drop is subjected. The velocity of the drops was found to be a strong function of position along the gradient. A quasi-steady theoretical model that balances the local hydrodynamic resistance with the local driving force generally describes the observations; possible reasons for the remaining discrepancies are discussed. It is shown that a model in which the driving force is reduced to accomodate the hysteresis effect inferred from the data is able to remove most of the discrepancy between the observed and predicted velocities.

Moumen, Nadjoua↗

NASA NTRS2013

Bayesian Revision of Residual Detection Power

This paper addresses some issues with quality assessment and quality assurance in response surface modeling experiments executed in wind tunnels. The role of data volume on quality assurance for response surface models is reviewed. Specific wind tunnel response surface modeling experiments are considered for which apparent discrepancies exist between fit quality expectations based on implemented quality assurance tactics, and the actual fit quality achieved in those experiments. These discrepancies are resolved by using Bayesian inference to account for certain imperfections in the assessment methodology. Estimates of the fraction of out-of-tolerance model predictions based on traditional frequentist methods are revised to account for uncertainty in the residual assessment process. The number of sites in the design space for which residuals are out of tolerance is seen to exceed the number of sites where the model actually fails to fit the data. A method is presented to estimate how much of the design space in inadequately modeled by low-order polynomial approximations to the true but unknown underlying response function.

DeLoach, Richard↗

DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗

DOE OSTI2021

Interpreting internal consistency of DES measurements

ABSTRACT Bayesian evidence ratios are widely used to quantify the statistical consistency between different experiments. However, since the evidence ratio is prior dependent, the precise translation between its value and the degree of concordance/discordance requires additional information. The most commonly adopted metric, the Jeffreys scale, can falsely suggest agreement between data sets when priors are chosen to be sufficiently wide. This work examines evidence ratios in a DES-Y1 simulated analysis, focusing on the internal consistency between weak lensing and galaxy clustering. We study two scenarios using simulated data in controlled experiments. First, we calibrate the expected evidence ratio distribution given noise realizations around the best-fitting DES-Y1 ΛCDM cosmology. Secondly, we show the behaviour of evidence ratios for noiseless fiducial data vectors simulated using a modified gravity model, which generates internal tension in the LCDM analysis. We find that the evidence ratio of noise realizations generated at all confidence levels was biased towards agreement and show, with a modified gravity model, that the choice of prior could conceal the discrepancies between weak lensing and galaxy clustering induced by prior effects in unlike cosmological models, concluding that the evidence ratio in a DES-Y1 study is, indeed, biased towards agreement. Boundary effects can also influence conclusions about the inconsistency induced by modified gravity, even in a noiseless data vector simulation.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Probing O 2 dependence of hydroperoxy-butyl reactions via isomer-resolved speciation

Degenerate chain-branching mechanisms of n-alkanes are centered on the formation of hydroperoxy-alkyl radicals ($\dot{Q}$OOH), formed via $\dot{R}$ + O 2 reactions, and the ensuing competition between unimolecular decomposition and second-O 2 -addition. Quantitative measurements of partially oxidized intermediates formed via reactions of $\dot{Q}$OOH provide critical constraints that are required for accurate modeling of combustion chemistry. To examine the influence of temperature and oxygen concentration on intermediates from unimolecular decomposition of $\dot{Q}$OOH, isomer-resolved speciation measurements were conducted on n-butane oxidation at 835 Torr in a jet-stirred reactor (JSR) from 500 – 900 K. Resulting from negative-temperature coefficient behavior, cyclic ether formation peaked at two temperatures, 650 K and 800 K, which were selected for separate experiments to quantify the O 2 -dependence of species profiles using O 2 concentrations of 4.2 · 10 17 – 1.1 · 10 19 molecules cm –3 . Utilizing vacuum-ultraviolet absorption spectroscopy and electron-impact mass spectrometry, cyclic ether isomers were quantified separately, including explicit resolution of cis– and trans– isomers of 2,3-dimethyloxirane. Stereoisomers of 2-butene were also quantified explicitly. For all cyclic ethers, a common trend in O 2 -dependence emerged: species concentrations reach a maximum near 3.0 · 10 18 molecules cm –3 (equivalence ratio of 0.5). Although quantitative disparities are evident, chemical kinetics modeling qualitatively reproduces the O 2 dependence of species at 650 K. However, at 800 K, weak dependence on O 2 is predicted, which is in contrast with the measurements. Two carbonyls, diacetyl and methyl vinyl ketone, were also quantified and follow similar dependence on [O 2 ] and temperature as the cyclic ethers, which indicates some fraction forms via $\dot{Q}$OOH-mediated reactions. The discrepancies between the measured and model-predicted species profiles indicate that sub-mechanisms for important intermediates may require additional elementary reactions, including stereochemical-specific reactions, to improve the fidelity of n-alkane combustion modeling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Physical and Thermodynamic Properties of Secondary Organic Materials for Modeling (Final Technical Report)

Secondary organic material constitutes more than 50% of the submicron component of atmospheric aerosol particles. This organic material is produced by oxidation of biogenic and anthropogenic volatile organic compounds. The physicochemical properties of these aerosol particles and their variability with atmospheric conditions influence many climate-relevant endpoints, such as rates of chemical reactions, production of secondary organic material, number-diameter distributions, direct radiative forcing, and cloud condensation nuclei. The results of the project research highlight a nonlabile-to-labile transition for material representative of anthropogenic secondary organic particulate matter at a threshold humidity range. This behavior differs markedly from materials representing biogenic aerosol sources, which lack such a transition. More specifically, the mass lability of atmospheric organic particulate matter, meaning its tendency to evaporate, is an important property in the mechanisms governing the climate effects. This property is affected by both the volatility and the molecular diffusivity of the constituent organic species. Via a series of laboratory experiments, the project directly determined the mass labilities of films of secondary organic material representative of anthropogenic and biogenic organic particulate matter (PM). The evaporation rates for anthropogenic organic PM increased above a threshold relative humidity (RH) between 20% and 30%, indicating rapid partitioning above a transition RH. Below the threshold, the characteristic time for equilibration was estimated at up to 1 week for a typically sized particle. In contrast, for films representing biogenic PM, no RH threshold was observed, suggesting equilibrium partitioning is rapidly obtained for all RHs. The effective molecular diffusivity for the biogenic case was at least 1000 times greater than that of the anthropogenic case. The results allow for an improved representation of anthropogenic and biogenic secondary organic aerosol formation in climate models. The results of the project also provide new measurements on how organic aerosol particles take up water and serve as cloud condensation nuclei, thus explaining the observed complex thermodynamic behaviors using a simple thermodynamic model. The project results reconciled the discrepancy between sub-saturation hygroscopicity and super-saturation cloud condensation nuclei activity for organic aerosol particles. The key to the explanation is the phase separation between hydrophobic and hydrophilic organics and the induced surface tension lowering. The results are essential for the assessment of the aerosol indirect effect on climate. In more detail, hygroscopic growth and cloud condensation nuclei activation are key processes for accurately modeling the climate impacts of organic particulate matter. Nevertheless, the microphysical mechanisms of these processes remain unresolved. The project results focus on complex thermodynamic behaviors, including humidity-dependent hygroscopicity, diameter-dependent cloud condensation nuclei activity, and liquid-liquid phase separation in the laboratory for biogenically derived secondary organic material representative of similar atmospheric organic particulate matter. These behaviors can be explained by the non-ideal mixing of water with hydrophobic and hydrophilic organic components. The non-ideality-driven liquid-liquid phase separation further enhances water uptake and induces lowered surface tension at high relative humidity, which results in a lower barrier to cloud condensation nuclei activation. By comparison, secondary organic material representing anthropogenic sources does not exhibit complex thermodynamic behavior. The combined results highlight the importance of detailed thermodynamic representations of the hygroscopicity and cloud condensation nuclei activity in models of the Earth’s climate system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Characterization of Partially Observed Epidemics - Application to COVID-19

This report documents a statistical method for the "real-time" characterization of partially observed epidemics. Observations consist of daily counts of symptomatic patients, diagnosed with the disease. Characterization, in this context, refers to estimation of epidemiological parameters that can be used to provide short-term forecasts of the ongoing epidemic, as well as to provide gross information for the time-dependent infection rate. The characterization problem is formulated as a Bayesian inverse problem, and is predicated on a model for the distribution of the incubation period. The model parameters are estimated as distributions using a Markov Chain Monte Carlo (MCMC) method, thus quantifying the uncertainty in the estimates. The method is applied to the COVID-19 pandemic of 2020, using data at the country, provincial (e.g., states) and regional (e.g. county) levels. The epidemiological model includes a stochastic component due to uncertainties in the incubation period. This model-form uncertainty is accommodated by a pseudo-marginal Metropolis-Hastings MCMC sampler, which produces posterior distributions that reflect this uncertainty. We approximate the discrepancy between the data and the epidemiological model using Gaussian and negative binomial error models; the latter was motivated by the over-dispersed count data. For small daily counts we find the performance of the calibrated models to be similar for the two error models. For large daily counts the negative-binomial approximation is numerically unstable unlike the Gaussian error model. Application of the model at the country level (for the United States, Germany, Italy, etc.) generally provided accurate forecasts, as the data consisted of large counts which suppressed the day-to-day variations in the observations. Further, the bulk of the data is sourced over the duration before the relaxation of the curbs on population mixing, and is not confounded by any discernible country-wide second wave of infections. At the state-level, where reporting was poor or which evinced few infections (e.g., New Mexico), the variance in the data posed some, though not insurmountable, difficulties, and forecasts were able to capture the data with large uncertainty bounds. The method was found to be sufficiently sensitive to discern the flattening of the infection and epidemic curve due to shelter-in-place orders after around 90% quantile for the incubation distribution (about 10 days for COVID-19). The proposed model was also used at a regional level to compare the forecasts for the central and north-west regions of New Mexico. Modeling the data for these regions illustrated different disease spread dynamics captured by the model. While in the central region the daily counts peaked in the late April, in the north-west region the ramp-up continued for approximately three more weeks.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Influence of Numerical Modeling Approaches on Damped Behavior of Flexible Beams: Preprint

Composites structures are widely used in aerospace and wind energy applications for their excellent stiffness and strength-to-weight properties. In these structures, structural damping is critical to predict vibration amplitudes, performance, and reliability. Structural damping is of particular interest for slender wings, rotorcraft blades, and wind turbine blades that can exhibit complex vibration phenomena and are frequently modeled with geometrically exact beam theory (GEBT). Standard approaches of stiffness proportional or modal damping merely assign user defined values and cannot predict damping behavior. This work compares stiffness proportional damping to two more advanced damping approaches: modal strain energy and Prony series. The modal strain energy approach uses a sectional analysis tool to calculate the beam stiffness and postprocess internal stresses from GEBT simulations. The internal stresses are then used to calculate modal damping factors. The Prony series is implemented within GEBT to directly model viscoelastic behavior of the composites. These approaches are compared by modeling the evolution of the damping factors of a realistic flexible wind turbine blade with varying rotational speed. Discrepancies between the approaches suggest areas for future modeling development, but differences in nonlinear damping values are less than current uncertainties about the magnitude of structural damping.

17 WIND ENERGY↗

NASA NTRS2023

An Evaluation of A Physics-Based Firn Model and A Semi-Empirical Firn Model Across the Greenland Ice Sheet (1980–2020)

The Greenland Ice Sheet's (GrIS) firn layer buffers the ice sheet's contribution to sea level rise by storing meltwater in its pore space. However, available pore space and meltwater retention capability is lost due to ablation of the firn layer and refreezing of meltwater as near-surface ice slabs in the firn. Understanding how firn properties respond to climate is important for constraining the GrIS's future contribution to sea level rise in a warming climate. Observations of firn density provide detailed information about firn properties, but they are spatially and temporally limited. Here we use two firn models, the physics-based SNOWPACK model and the Community Firn Model configured with a semi-empirical densification equation (CFM-GSFC), to quantify firn properties across the GrIS from 1980 through 2020. We use an identical forcing (Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) atmospheric reanalysis) for SNOWPACK and the CFM-GSFC in order to isolate firn model differences. To evaluate the models, we compare simulated firn properties, including firn air content (FAC), to measurements from the Surface Mass Balance and Snow on Sea Ice Working Group (SUMup) dataset of snow and firn density. Both models perform well (mean absolute percentage errors of 14 % in SNOWPACK and 16 % in the CFM-GSFC), though their performance is hindered by the spatial resolution of the atmospheric forcing. In the ice-sheet-wide simulations, the 1980–1995 average spatially integrated FAC (i.e., air volume in the firn) for the upper 100 m is 34 645 km 3 from SNOWPACK and 28 581 km 3 from the CFM-GSFC. The discrepancy in the magnitude of the modeled FAC stems from differences in densification with depth and variations in the sensitivity of the models to atmospheric forcing. In more recent years (2005–2020), both models simulate substantial depletion of pore space. During this period, the spatially integrated FAC across the entire GrIS decreases by 3.2 % (−66.6 km 3 yr −1 ) in SNOWPACK and 1.5 % (−17.4 km 3 yr −1 ) in the CFM-GSFC. These differing magnitudes demonstrate how model differences propagate throughout the FAC record. Over the full modeled record (1980–2020), SNOWPACK simulates a loss of pore space equivalent to 3 mm of sea level rise buffering, while the CFM-GSFC simulates a loss of 1 mm. The greatest depletion in FAC is along the margins and especially along the western margin where observations and models show the formation of near-surface, low-permeability ice slabs that may inhibit meltwater storage.

Megan Thompson-Munson↗