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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Self-Assembly and Critical Aggregation Concentration Measurements of ABA Triblock Copolymers with Varying B Block Types: Model Development, Prediction, and Validation

The dissipative particle dynamics (DPD) simulation technique is a coarse-grained (CG) molecular dynamics-based approach that can effectively capture the hydrodynamics of complex systems while retaining essential information about the structural properties of the molecular species. An advantageous feature of DPD is that it utilizes soft repulsive interactions between the beads, which are CG representation of groups of atoms or molecules. In this study, we used the DPD simulation technique to study the aggregation characteristics of ABA triblock copolymers in aqueous medium. Pluronic polymers (PEG-PPO-PEG) were modeled as two segments of hydrophilic beads and one segment of hydrophobic beads. Tyrosine-derived PEG 5K -b-oligo(desaminotyrosyl tyrosine octyl ester-suberate)-b-PEG 5K (PEG5K-oligo(DTO-SA)-PEG 5K ) block copolymers possess alternate rigid and flexible components along the hydrophobic oligo(DTO-SA) chain, and were modeled as two segments of hydrophilic beads and one segment of hydrophobic, alternate soft and hard beads. The formation, structure, and morphology of the initial aggregation of the polymer molecules in aqueous medium were investigated by following the aggregation dynamics. The dimensions of the aggregates predicted by the computational approach were in good agreement with corresponding results from experiments, for the Pluronic and PEG 5K -oligo(DTO-SA)-PEG 5K block copolymers. In addition, DPD simulations were utilized to determine the critical aggregation concentration (CAC), which was compared with corresponding results from an experimental approach. For Pluronic polymers F68, F88, F108, and F127, the computational results agreed well with experimental measurements of the CAC measurements. For PEG 5K -b-oligo(DTO-SA)-b-PEG 5K block polymers, the complexity in polymer structure made it difficult to directly determine their CAC values via the CG scheme. Therefore, we determined CAC values of a series of triblock copolymers with 3–8 DTO-SA units using DPD simulations, and used these results to predict the CAC values of triblock copolymers with higher molecular weights by extrapolation. In parallel, a PEG 5K -b-oligo(DTO-SA)-b-PEG 5K block copolymer was synthesized, and the CAC value was determined experimentally using the pyrene method. The experimental CAC value agreed well with the CAC value predicted by simulation. Furthermore, these results validate our CG models, and demonstrate an avenue to simulate and predict aggregation characteristics of ABA amphiphilic triblock copolymers with complex structures.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Age at exposure and attained age variations of cancer risk in the Japanese A-bomb and radiotherapy cohorts

Purpose: Phenomenological risk models for radiation-induced cancer are frequently applied to estimate the risk of radiation-induced cancers at radiotherapy doses. Such models often include the effect modification, of the main risk to radiation dose response, by age at exposure and attained age. The aim of this paper is to compare the patterns in risk effect modification by age, between models obtained from the Japanese atomic-bomb (A-bomb) survivor data and models for cancer risks previously reported for radiotherapy patients. Patterns in risk effect modification by age from the epidemiological studies of radiotherapy patients were also used to refine and extend the risk effect modification by age obtained from the A-bomb survivor data, so that more universal models can be presented here. Methods: Simple log-linear and power functions of age for the risk effect modification applied in models of the A-bomb survivor data are compared to risks from epidemiological studies of second cancers after radiotherapy. These functions of age were also refined and fitted to radiotherapy risks. The resulting age models provide a refined and extended functional dependence of risk with age at exposure and attained age especially beyond 40 and 65 yr, respectively, and provide a better representation than the currently available simple age functions. Results: It was found that the A-bomb models predict risk similarly to the outcomes of testicular cancer survivors. The survivors of Hodgkin’s disease show steeper variations of risk with both age at exposure and attained age. The extended models predict solid cancer risk increase as a function of age at exposure beyond 40 yr and the risk decrease as a function of attained age beyond 65 yr better than the simple models. Conclusions: The standard functions for risk effect modification by age, based on the A-bomb survivor data, predict second cancer risk in radiotherapy patients for ages at exposure prior to 40 yr and attained ages before 55 yr reasonably well. However, for larger ages, the refined and extended models can be applied to predict the risk as a function of age.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

The Sensitivity of Variational Bayesian Neural Network Performance to Hyperparameters

In scientific applications, predictive modeling is often of limited use without accurate uncertainty quantification (UQ) to indicate when a model may be extrapolating or when more data needs to be collected. Bayesian Neural Networks (BNNs) produce predictive uncertainty by propagating uncertainty in neural network (NN) weights and offer the promise of obtaining not only an accurate predictive model but also accurate UQ. However, in practice, obtaining accurate UQ with BNNs is difficult due in part to the approximations used for model training (such as those made in variational inference) and in part to the need to choose a suitable set of hyperparameters; these hyperparameters outnumber those needed for traditional NNs and often have opaque effects on the results. We aim to shed light on the effects of hyperparameter choices for variational BNNs by performing a global sensitivity analysis of variational BNN performance under varying hyperparameter settings. Our results indicate that many of the hyperparameters interact with each other to affect both predictive accuracy and UQ. For improved usage of variational BNNs in real-world applications, we suggest that thorough hyperparameter tuning, including tuning of prior hyperparameters and loss function parameters, is essential for accurate UQ in variational BNNs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Evaluation of the Near-Surface Variables in the HRRR Weather Model Using Observations from the ARM SGP Site

Abstract The performance of version 4 of the NOAA High-Resolution Rapid Refresh (HRRR) numerical weather prediction model for near-surface variables, including wind, humidity, temperature, surface latent and sensible fluxes, and longwave and shortwave radiative fluxes, is examined over the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) region. The study evaluated the model’s bias and bias-corrected mean absolute error relative to the observations on different time scales. Forecasts of near-surface geophysical variables at five SGP sites (HRRR at 3-km scale) were found to agree well with observations, but some consistent observation–forecast differences also occurred. Sensible and latent heat fluxes are the most challenging variables to be reproduced. The diurnal cycle is the main temporal scale affecting observation–forecast differences of the near-surface variables, and almost all of the variables showed different biases throughout the diurnal cycle. Results show that the overestimation of downward shortwave and the underestimation of downward longwave radiative flux are the two major biases found in this study. The timing and magnitude of downward longwave flux, wind speed, and sensible and latent heat fluxes are also different with contributions from model representations, data assimilation limitations, and differences in scales between HRRR and SGP sites. The positive bias in downward shortwave and negative bias in longwave radiation suggests that the model is underestimating cloud fraction in the study domain. The study concludes by showing a brief comparison with version 3 of the HRRR and shows that version 4 has better performance in almost all near-surface variables. Significance Statement A correct representation of the near-surface variables is important for numerical weather prediction models. This study investigates the capability of the latest NOAA High-Resolution Rapid Refresh (HRRRv4) model in simulating the near-surface variables by comparing against the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) in situ observations. Among others, we find that the surface heat fluxes, such as sensible and latent heat fluxes, are the most difficult variables to be reproduced. This study also shows that the diurnal cycle has the dominant impact on the model’s performance, which means the majority of the outputted near-surface variables have the strong diurnal cycle in their bias errors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

NOAA Air Resources Laboratory Atmospheric Turbulence and Diffusion Division Contribution to LAFE Field Campaign Report

The Land-Atmosphere Feedback Experiment (LAFE) was conducted between 1 and 31 August, 2017 at the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) observatory. The main scientific goal of LAFE was to investigate feedbacks occurring between different land surface types and the overlying atmosphere to improve turbulence parameterizations in numerical weather prediction models. LAFE collaborators included scientists from the following institutions: Cleveland State University Cooperative Institute for Meteorological Satellite Studies (CIMSS) Cooperative Institute for Research in Environmental Sciences (CIRES) Leibniz University Institute of Meteorology and Climatology Marquette University NASA Goddard Space Flight Center National Center for Atmospheric Research (NCAR) National Severe Storms Laboratory (NSSL) NOAA Earth System Research Laboratory (ESRL) Pacific Northwest National Laboratories (PNNL) University of Hohenheim Institute of Physics and Meteorology University of Tennessee Space Institute (UTSI). In addition to helping plan the scientific objectives of LAFE, collaborators deployed several lidars (i.e., temperature, water, vapor, and wind) and remote-sensing instruments (e.g., microwave radiometers, atmospheric emitted radiance interferometers [AERIs], etc.) to complement the suite of instrument platforms already present at the SGP site. The measurement platforms, as well as scientific objectives of LAFE, are summarized in Wulfmeyer et al. (2018). To complement these platforms, the NOAA Atmospheric Turbulence and Diffusion Division (ATDD) installed micrometeorological towers to continuously measure fluxes of momentum, sensible heat, latent heat, and CO 2 at 2 m and 10 m above ground level (AGL) at three sites during LAFE. Tower measurements also included temperature, humidity, wind speed and direction, pressure, rainfall, soil temperature, and soil moisture. The three micrometeorological towers were installed between 9 and 13 July, 2017 and were removed between 12 and 14 September, 2017. During the experiment, the footprint of Tower 1 included a mixture of soybean and grassland that transitioned to mature soybean crop during the second half of the experiment. Tower 2’s footprint encompassed a field of grazed pasture, and Tower 3’s footprint was a mature soybean crop. The meteorological and flux measurements from these towers were/are being used to: Increase the spatial density of measurement platforms that were already available at the ARM facility and that were deployed during LAFE Improve the characterization of surface fluxes around the SGP site Explore the validity of applying Monin-Obukhov Similarity Theory (MOST) over different land surface types and improve knowledge of flux-gradient relationships Provide initial conditions and be used for verification for large-eddy simulations (LES) Evaluate numerical weather prediction models, e.g., the High-Resolution Rapid Refresh (HRRR), to identify and correct for biases within these models Evaluate the role of low-level advection at each site, which would be present in the flux-divergence data sets obtained from the three towers.

Southern Great Plains, unmanned aerial systems, su↗
DOE OSTI2021

Occupant Preference-Aware Load Scheduling for Resilient Communities

The load scheduling of resilient communities in the islanded mode is subject to many uncertainties such as weather forecast errors and occupant behavior stochasticity. To date, it remains unclear how occupant preferences affect the effectiveness of the load scheduling of resilient communities. This paper proposes an occupant thermal preference-aware load scheduler for resilient communities operating in the islanded mode. First, key resilience indicators are selected to quantify its impacts on the load scheduling of a resilient community. A deterministic model predictive control-based load scheduling framework is adopted as the baseline. Then, a chance-constrained controller is proposed to address the occupant-induced uncertainty in room temperature setpoints. Finally, the chance-constrained controller is compared with the deterministic controller on a virtual community testbed based on a real-world net-zero energy community in Florida, U.S. Results have shown that the proposed chance-constrained controller performs better in terms of serving occupants’ thermal preference and the required battery sizes compared to the deterministic controller with the presence of the assumed stochastic occupant behavior. This work indicates that it is necessary to consider the stochasticity of the occupant behavior when designing optimal load schedulers for resilient communities.

Wang, Jing↗
DOE OSTI2017

Constraints on the Optical Depth of Galaxy Groups and Clusters

Future data from galaxy redshift surveys, combined with high-resolutions maps of the cosmic microwave background, will enable measurements of the pairwise kinematic Sunyaev–Zel’dovich (kSZ) signal with unprecedented statistical significance. This signal probes the matter-velocity correlation function, scaled by the average optical depth (τ) of the galaxy groups and clusters in the sample, and is thus of fundamental importance for cosmology. However, in order to translate pairwise kSZ measurements into cosmological constraints, external constraints on τ are necessary. In this work, we present a new model for the intracluster medium, which takes into account star formation, feedback, non-thermal pressure, and gas cooling. Our semi-analytic model is computationally efficient and can reproduce results of recent hydrodynamical simulations of galaxy cluster formation. We calibrate the free parameters in the model using recent X-ray measurements of gas density profiles of clusters, and gas masses of groups and clusters. Our observationally calibrated model predicts the average τ{sub 500} (i.e., the integrated τ within a disk of size R {sub 500}) to better than 6% modeling uncertainty (at 95% confidence level). If the remaining uncertainties associated with other astrophysical uncertainties and X-ray selection effects can be better understood, our model for the optical depth should break the degeneracy between optical depth and cluster velocity in the analysis of future pairwise kSZ measurements and improve cosmological constraints with the combination of upcoming galaxy and CMB surveys, including the nature of dark energy, modified gravity, and neutrino mass.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2015

Developing an Effective Model for Predicting Spatially and Temporally Continuous Stream Temperatures from Remotely Sensed Land Surface Temperatures

Although water temperature is important to stream biota, it is difficult to collect in a spatially and temporally continuous fashion. We used remotely-sensed Land Surface Temperature (LST) data to estimate mean daily stream temperature for every confluence-to-confluence reach in the John Day River, OR, USA for a ten year period. Models were built at three spatial scales: site-specific, subwatershed, and basin-wide. Model quality was assessed using jackknife and cross-validation. Model metrics for linear regressions of the predicted vs. observed data across all sites and years: site-specific r2 = 0.95, Root Mean Squared Error (RMSE) = 1.25 °C; subwatershed r2 = 0.88, RMSE = 2.02 °C; and basin-wide r2 = 0.87, RMSE = 2.12 °C. Similar analyses were conducted using 2012 eight-day composite LST and eight-day mean stream temperature in five watersheds in the interior Columbia River basin. Mean model metrics across all basins: r2 = 0.91, RMSE = 1.29 °C. Sensitivity analyses indicated accurate basin-wide models can be parameterized using data from as few as four temperature logger sites. This approach generates robust estimates of stream temperature through time for broad spatial regions for which there is only spatially and temporally patchy observational data, and may be useful for managers and researchers interested in stream biota.

McNyset, Kristina↗
DOE OSTI2025

Challenges in predicting protein-protein interactions of understudied viruses: Arenavirus-human interactions

Understanding protein-protein interactions (PPIs) between viruses and host organisms is crucial for uncovering infection mechanisms and identifying potential therapeutic targets. The ability to generalize PPI predictive models across understudied viruses presents a significant challenge. In this work, we use arenavirus-human PPIs to illustrate the difficulties associated with model generalization, which are compounded by a lack of both positive and negative data. We employ a Transfer Learning approach to investigate arenavirus-human PPIs by utilizing models trained on better-studied virus-human and human-human PPIs. Additionally, we curate and assess four types of negative sampling datasets to evaluate their impact on model performance. Despite the overall high accuracies (93–99 %) and AUPRC scores (0.8–0.9) appearing promising, further analysis indicates that these performance metrics can be misleading due to data leakage, data bias, and overfitting, especially concerning under-represented viral proteins. We reveal these gaps and assess the impact of data imbalance using standard k-fold cross-validation and Independent Blind Testing with a Balanced Dataset, resulting in a drop in accuracy below 50 %. We propose a viral protein-specific evaluation framework that categorizes viral proteins into majority and minority classes based on their representation in the dataset, enabling comparison of model performance across these groups using balanced accuracies. This framework offers a more robust evaluation of model generalizability, addressing biases inherent in standard evaluation techniques and paving the way for more reliable PPI prediction models for understudied viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

The mass–radius relation of young stars. I. USCO 5, an M4.5 eclipsing binary in upper Scorpius observed by K2

We present the discovery that UScoCTIO 5, a known spectroscopic binary in the Upper Scorpius star-forming region (P = 34 days, M{sub tot}sin(i)=0.64 M{sub ⊙}), is an eclipsing system with both primary and secondary eclipses apparent in K2 light curves obtained during Campaign 2. We have simultaneously fit the eclipse profiles from the K2 light curves and the existing RV data to demonstrate that UScoCTIO 5 consists of a pair of nearly identical M4.5 stars with M{sub A}=0.329±0.002 M{sub ⊙}, R{sub A}=0.834±0.006 R{sub ⊙}, M{sub B}=0.317±0.002 M{sub ⊙}, and R{sub B}=0.810±0.006 R{sub ⊙}. The radii are broadly consistent with pre-main-sequence ages predicted by stellar evolutionary models, but none agree to within the uncertainties. All models predict systematically incorrect masses at the 25%–50% level for the HR diagram position of these mid-M dwarfs, suggesting significant modifications to mass-dependent outcomes of star and planet formation. The form of the discrepancy for most model sets is not that they predict luminosities that are too low, but rather that they predict temperatures that are too high, suggesting that the models do not fully encompass the physics of energy transport (via convection and/or missing opacities) and/or a miscalibration of the SpT–T{sub eff} scale. The simplest modification to the models (changing T{sub eff} to match observations) would yield an older age for this system, in line with the recently proposed older age of Upper Scorpius (τ ∼ 11 Myr).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Studies of new Higgs boson interactions through nonresonant $HH$ production in the $ b\overline{b}\gamma \gamma $ final state in $pp$ collisions at $ \sqrt{s} $ = 13 TeV with the ATLAS detector

A search for nonresonant Higgs boson pair production in the $ b\overline{b}\gamma \gamma $ final state is performed using 140 fb –1 of proton-proton collisions at a centre-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. This analysis supersedes and expands upon the previous nonresonant ATLAS results in this final state based on the same data sample. The analysis strategy is optimised to probe anomalous values not only of the Higgs (H) boson self-coupling modifier κλ but also of the quartic HHVV (V = W, Z) coupling modifier κ 2V . No significant excess above the expected background from Standard Model processes is observed. An observed upper limit μHH < 4.0 is set at 95% confidence level on the Higgs boson pair production cross-section normalised to its Standard Model prediction. The 95% confidence intervals for the coupling modifiers are –1.4 < κ λ < 6.9 and –0.5 < κ 2V < 2.7, assuming all other Higgs boson couplings except the one under study are fixed to the Standard Model predictions. The results are interpreted in the Standard Model effective field theory and Higgs effective field theory frameworks in terms of constraints on the couplings of anomalous Higgs boson (self-)interactions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2017

Polarizable charge equilibration model for predicting accurate electrostatic interactions in molecules and solids

Electrostatic interactions play a critical role in determining the properties, structures, and dynamics of chemical, biochemical, and material systems. These interactions are described well at the level of quantum mechanics (QM) but not so well for the various models used in force field simulations of these systems. In this paper, we propose and validate a new general methodology, denoted PQEq, to predict rapidly and dynamically the atomic charges and polarization underlying the electrostatic interactions. Here the polarization is described using an atomic sized Gaussian shaped electron density that can polarize away from the core in response to internal and external electric fields, while at the same time adjusting the charge on each core (described as a Gaussian function) so as to achieve a constant chemical potential across all atoms of the system. The parameters for PQEq are derived from experimental atomic properties of all elements up to Nobelium (atomic no. = 102). Additionally, we validate PQEq by comparing to QM interaction energy as probe dipoles are brought along various directions up to 30 molecules containing H, C, N, O, F, Si, P, S, and Cl atoms. We find that PQEq predicts interaction energies in excellent agreement with QM, much better than other common charge models such as obtained from QM using Mulliken or ESP charges and those from standard force fields (OPLS and AMBER). Since PQEq increases the accuracy of electrostatic interactions and the response to external electric fields, we expect that PQEq will be useful for a large range of applications including ligand docking to proteins, catalytic reactions, electrocatalysis, ferroelectrics, and growth of ceramics and films, where it could be incorporated into standard force fields as OPLS, AMBER, CHARMM, Dreiding, ReaxFF, and UFF.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

A Modified Isotropic–Kinematic Hardening Model to Predict the Defects in Tube Hydroforming Process

Numerical simulations of tube hydroforming process of hollow crankshafts were conducted by using finite element analysis method. Moreover, the modified model involving the integration of isotropic–kinematic hardening model with ductile criteria model was used to more accurately optimize the process parameters such as internal pressure, feed distance and friction coefficient. Subsequently, hydroforming experiments were performed based on the simulation results. The comparison between experimental and simulation results indicated that the prediction of tube deformation, crack and wrinkle was quite accurate for the tube hydroforming process. Finally, hollow crankshafts with high thickness uniformity were obtained and the thickness distribution between numerical and experimental results was well consistent.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A mechanistic, multiscale model for predicting Pd penetration in TRISO fuels using BISON

TRistructural ISOtropic (TRISO) particles use silicon carbide (SiC) as the primary structural member and barrier against metallic fission product (FP) release. palladiums (PDs), produced by fission in the fuel kernel, can diffuse to and chemically interact with the SiC layer, degrading its structural integrity and ability to contain radioactive FPs. Existing temperature-dependent correlations for predicting Pd penetration rely on experimental data with significant scatter due to varying conditions, potentially complicating ongoing fuel qualification and licensing efforts for advanced reactors that would subject TRISO fuels to operating conditions outside of those examined in the experiments. A mechanistic model of Pd production, transport, and reaction is developed in this work to better understand and predict PD attack of SiC in TRISO particles. molecular dynamicss (MDs) simulations are utilized to calculate the Pd diffusivity in SiC grain bulk and grain boundaries. A mesoscale phase-field diffusion model, informed by the MD diffusivities, is used to develop a reduced order model (ROM) for the effect of SiC microstructure on the Pd penetration rate. The engineering scale BISON model calculates Pd production and transport, utilizing the ROM to predict penetration rates consistent with experimental data. This novel mechanistic ROM captures the effect of temperature, microstructure, and irradiation history on Pd penetration. In conclusion, these new capabilities are expected to support ongoing qualification and licensing efforts associated with near-term TRISO-fueled reactor applications.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2022

Development of advanced machine learning models for analysis of plutonium surrogate optical emission spectra

This work investigates and applies machine learning paradigms seldom seen in analytical spectroscopy for quantification of gallium in cerium matrices via processing of laser-plasma spectra. Ensemble regressions, support vector machine regressions, Gaussian kernel regressions, and artificial neural network techniques are trained and tested on cerium-gallium pellet spectra. A thorough hyperparameter optimization experiment is conducted initially to determine the best design features for each model. The optimized models are evaluated for sensitivity and precision using the limit of detection (LoD) and root mean-squared error of prediction (RMSEP) metrics, respectively. Gaussian kernel regression yields the superlative predictive model with an RMSEP of 0.33% and an LoD of 0.015% for quantification of Ga in a Ce matrix. This study concludes that these machine learning methods could yield robust prediction models for rapid quality control analysis of plutonium alloys.

Rao, Ashwin P. (ORCID:0000000319312568)↗
DOE OSTI2018

Modeling sequential context effects in diagnostic interpretation of screening mammograms

Prior research has shown that physicians’ medical decisions can be influenced by sequential context, particularly in cases where successive stimuli exhibit similar characteristics when analyzing medical images. This type of systematic error is known to psychophysicists as sequential context effect as it indicates that judgments are influenced by features of and decisions about the preceding case in the sequence of examined cases, rather than being based solely on the peculiarities unique to the present case. We determine if radiologists experience some form of context bias, using screening mammography as the use case. To this end, we explore correlations between previous perceptual behavior and diagnostic decisions and current decisions. We hypothesize that a radiologist’s visual search pattern and diagnostic decisions in previous cases are predictive of the radiologist’s current diagnostic decisions. To test our hypothesis, we tasked 10 radiologists of varied experience to conduct blind reviews of 100 four-view screening mammograms. Eye-tracking data and diagnostic decisions were collected from each radiologist under conditions mimicking clinical practice. Perceptual behavior was quantified using the fractal dimension of gaze scanpath, which was computed using the Minkowski–Bouligand box-counting method. To test the effect of previous behavior and decisions, we conducted a multifactor fixed-effects ANOVA. Further, to examine the predictive value of previous perceptual behavior and decisions, we trained and evaluated a predictive model for radiologists’ current diagnostic decisions. ANOVA tests showed that previous visual behavior, characterized by fractal analysis, previous diagnostic decisions, and image characteristics of previous cases are significant predictors of current diagnostic decisions. Additionally, predictive modeling of diagnostic decisions showed an overall improvement in prediction error when the model is trained on additional information about previous perceptual behavior and diagnostic decisions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Structural coherence model for predicting molten salt thermal conductivity informed by the pair distribution function

To enable thermal behavior prediction and design optimization of molten salt reactors, thermal conductivity of molten salts must be characterized in terms of salt composition and temperature. Current theoretical models fail to provide consistent approximations for all halide mixtures, particularly actinide-bearing melts. This study aims to link the short-range order structure of molten salts to the mean free path of energy carriers through a simple structural coherence model informed by the partial pair distribution function. The proposed method is used to predict the thermal conductivity of 33 alkali and alkaline earth halide salts. Predictions approximate experimental measurements with a mean absolute error of 15.7% for dissociating, complexing, and actinide salts, including unary LiCl, NaCl, and MgCl 2 as well as mixtures LiF–NaF–KF (FLiNaK), LiF–BeF 2 (FLiBe), and NaCl–UCl 3 . The work provides evidence for the validity of energy carrier descriptions of molecular-level heat transfer in molten salts, with implications for improved theories of liquid energy transport in general.

Actinide mixtures↗