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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Experimental Study on the Effect of Localized Blockages on the Friction Factor of a 61-Pin Wire-Wrapped Bundle

Abstract The thermal-hydraulic behavior of the flow in rod bundles has motivated numerous experimental and computational investigations. Previous studies have identified potential for accumulation of debris within the small subchannels of typical wire-wrapped assemblies with subsequent total or partial blockage of subchannel coolant flow. A test campaign was conducted to study the effects of localized blockages on the bundle averaged friction factor of a tightly packed wire-wrapped rod bundle. Blockages were installed within the bundle, and fluid pressure drop was measured across one wire pitch for a Reynolds number range of 500–17,200. The Darcy–Weisbach friction factor of the perturbed rod bundle geometry was compared with that of the unblocked bundle, as well as with the predictions of a well-established friction factor correlation. Differing effects based on blockage size and location for various flow regimes were studied. A number of conclusions can be made about the effects of the blockages on the friction factor, such as an increasing effect of the blockage on friction factor with an increase in Reynolds number, a change in flow behavior in the turbulent transition flow regime near Reynolds number 3000, differences in effect on friction factor for different types of subchannel blockage, and a nonlinear trend in friction factor variation with flow area impeded for edge subchannels. To this end, all data and quantified uncertainty produced in this study are made available for comparison and validation of advanced computational tools.

Engineering↗
DOE OSTI2014

SU-E-T-246: MU Model Implementation for a Passively Scattered Proton Beam: Inclusion of the Bolus Gap and Nozzle Extension Factors

Purpose: To develop and implement an MU model for a passively scattered proton beam, and eliminate the need for patient specific calibrations for field sizes from 3cm to 15cm. This would enable consistent and timely calibrations for a wide variety of patient portals, streamlining the treatment process. Methods: Measurements were initially made using a Standard Imaging A-16 ion chamber and a modified water tank to determine the bolus gap factors (BGF) for multiple combinations of aperture size, bolus thickness, and air gap. The BGF was then separated into two component factors: the bolus thickness factor (BTF) and the nozzle extension factor (NEF). Polynomial curves were generated using the measured data to produce BTF tables for air gaps from 0cm to 30cm and for bolus thicknesses from 0cm to 10cm, and NEF tables for the full range of clinically used nozzle extensions. Additionally, data tables were created for every factor that affects beam output in the MU model. The MUs were then modeled for 487 patient portals and retrospectively compared to the MUs generated from the physical calibrations previously performed. Results: Of the 487 patient portals tested, 100% of the portals used for the comparison were within 2.5% from the MUs generated using a physical calibration, and 95.9% of the MU model portals tested were within 2%. The patient portals tested had field sizes ranging from 2.1cm to 10.1cm, with air gaps from 2cm to 25cm. Output factors for field sizes below 3cm with irregularly shaped fields demonstrated inconsistent results and will be further studied. Conclusion: The most problematic output factor, the BGF, was modeled accurately and consistently using the lowest order polynomial curve fits and interpolation between measured data. The study results demonstrate the robustness of the MU model and the potential for saving valuable personnel and beam time.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2014

SU-E-T-491: Importance of Energy Dependent Protons Per MU Calibration Factors in IMPT Dose Calculations Using Monte Carlo Technique

Purpose: In intensity modulated proton therapy (IMPT), energy dependent, protons per monitor unit (MU) calibration factors are important parameters that determine absolute dose values from energy deposition data obtained from Monte Carlo (MC) simulations. Purpose of this study was to assess the sensitivity of MC-computed absolute dose distributions to the protons/MU calibration factors in IMPT. Methods: A “verification plan” (i.e., treatment beams applied individually to water phantom) of a head and neck patient plan was calculated using MC technique. The patient plan had three beams; one posterior-anterior (PA); two anterior oblique. Dose prescription was 66 Gy in 30 fractions. Of the total MUs, 58% was delivered in PA beam, 25% and 17% in other two. Energy deposition data obtained from the MC simulation were converted to Gy using energy dependent protons/MU calibrations factors obtained from two methods. First method is based on experimental measurements and MC simulations. Second is based on hand calculations, based on how many ion pairs were produced per proton in the dose monitor and how many ion pairs is equal to 1 MU (vendor recommended method). Dose distributions obtained from method one was compared with those from method two. Results: Average difference of 8% in protons/MU calibration factors between method one and two converted into 27 % difference in absolute dose values for PA beam; although dose distributions preserved the shape of 3D dose distribution qualitatively, they were different quantitatively. For two oblique beams, significant difference in absolute dose was not observed. Conclusion: Results demonstrate that protons/MU calibration factors can have a significant impact on absolute dose values in IMPT depending on the fraction of MUs delivered. When number of MUs increases the effect due to the calibration factors amplify. In determining protons/MU calibration factors, experimental method should be preferred in MC dose calculations. Research supported by National Cancer Institute grant P01CA021239.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

TU-H-CAMPUS-IeP1-03: Comparison of Monte Carlo Simulation and Conversion Factor Based Method On Estimation of Effective Dose in Pediatric Patients Undergoing Interventional Cardiac Procedures

Purpose: Interventional cardiac procedures utilize frequent fluoroscopy and cineangiography, which impose considerable radiation risk to patients, especially pediatric patients. Accurate calculation of effective dose is important in order to estimate cancer risk over the rest of their lifetime. This study evaluates the difference in effective dose calculated by Monte Carlo simulation with those estimated by locally-derived conversion factors (CF-local) and by commonly quoted conversion factors from Karambatsakidou et al (CF-K). Methods: Effective dose (E),of 12 pediatric patients, age between 2.5–19 years old, who had undergone interventional cardiac procedures, were calculated using PCXMC-2.0 software. Tube spectrum, irradiation geometry, exposure parameters and dose-area product (DAP) of each projection were included in the software calculation. Effective doses for each patient were also estimated by two Methods: 1) CF-local: conversion factor derived locally by generalizing results of 12 patients, multiplied by DAP of each patient gives E-local. 2) CF-K: selected factor from above-mentioned literature, multiplied by DAP of each patient gives E-K. Results: Mean of E, E-local and E-K were 16.01 mSv, 16.80 mSv and 22.25 mSv respectively. A deviation of −29.35% to +34.85% between E and E-local, while a greater deviation of −28.96% to +60.86% between E and EK were observed. E-K overestimated the effective dose for patients at age 7.5–19. Conclusion: Effective dose obtained by conversion factors is simple and quick to estimate radiation risk of pediatric patients. This study showed that estimation by CF-local may bear an error of 35% when compared with Monte Carlo calculation. If using conversion factors derived by other studies may result in an even greater error, of up to 60%, due to factors that are not catered for in the estimation, including patient size, projection angles, exposure parameters, tube filtration, etc. Users must be aware of these potential inaccuracies when simple conversion method is employed.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Virulence factors and antimicrobial resistance profiles of Campylobacter isolates recovered from consecutively reused broiler litter

ABSTRACT Campylobacterinfections are a leading cause of bacterial diarrhea in humans globally. Infections are due to consumption of contaminated food products and are highly associated with chicken meat, with chickens being an important reservoir forCampylobacter. Here, we characterized the genetic diversity ofCampylobacter jejuni(C. jejuni) andCampylobacter coli(C. coli) detected in broiler chicken litter over three consecutive flocks and determined their antimicrobial resistance (ARM) and virulence factor (VF) profiles.Campylobacterwas detected in 9.38% (27/288) of litter samples collected. Antimicrobial susceptibility testing and whole genome sequencing were performed onC. jejuni(n= 39) andC. coli(n= 5) isolates.Campylobactervirulence factors differed within and across broiler houses but were explained by the broiler flock cohort raised on litter,Campylobacterspecies andCampylobactermultilocus sequence type (MLST). Virulence factors involved in the ability to invade and colonize host tissues and evade host defenses were present inC. jejuniisolates (ST-464) from flock cohorts 1 and 2 but absent inC. jejuniisolates (ST-48) from flock cohort 3.C. jejuniisolates from house three harbored a significantly higher proportion of virulence genes with functions related to glycosylation and immune evasion thanC. jejuniisolates from houses 1 and 2 (P< 0.01). AllC. jejuniisolates were susceptible to all antibiotics tested whileC. coli(n= 4) were resistant to tetracycline and harbored the tetracycline resistant ribosomal protection protein (TetO). Our results suggest that house environment and broiler management practices imposed selective pressures on virulence factors and antimicrobial resistance genes ofCampylobacter. IMPORTANCE Campylobacteris a leading cause of foodborne illness in the United States due to consumption of contaminated or mishandled food products, often associated with chicken meat.Campylobacteris common in the microbiota of avian and mammalian gut; however, acquisition of antimicrobial resistance genes (ARGs) and virulence factors (VFs) may result in strains that pose significant threat to public health. Although there are studies investigating the genetic diversity ofCampylobacterstrains isolated from post-harvest chicken samples, there are limited data on the genome characteristics of isolates recovered from preharvest broiler production. Here, we show thatCampylobacter jejuniandCampylobacter colidiffer in their carriage of antimicrobial resistance and virulence factors may also differ in their ability to persist in litter during consecutive grow-out of broiler flocks. We found that presence/absence of virulence factors needed for evasion of host defense mechanisms and gut colonization played an integral role in differentiatingCampylobacterstrains.

Microbiology↗
DOE OSTI2023

NREM sleep as a novel protective cognitive reserve factor in the face of Alzheimer's disease pathology

Alzheimer’s disease (AD) pathology impairs cognitive function. Yet some individuals with high amounts of AD pathology suffer marked memory impairment, while others with the same degree of pathology burden show little impairment. Why is this? One proposed explanation is cognitive reserve i.e., factors that confer resilience against, or compensation for the effects of AD pathology. Deep NREM slow wave sleep (SWS) is recognized to enhance functions of learning and memory in healthy older adults. However, that the quality of NREM SWS (NREM slow wave activity, SWA) represents a novel cognitive reserve factor in older adults with AD pathology, thereby providing compensation against memory dysfunction otherwise caused by high AD pathology burden, remains unknown. Here, we tested this hypothesis in cognitively normal older adults (N = 62) by combining 11 C-PiB (Pittsburgh compound B) positron emission tomography (PET) scanning for the quantification of β-amyloid (Aβ) with sleep electroencephalography (EEG) recordings to quantify NREM SWA and a hippocampal-dependent face-name learning task. We demonstrated that NREM SWA significantly moderates the effect of Aβ status on memory function. Specifically, NREM SWA selectively supported superior memory function in individuals suffering high Aβ burden, i.e., those most in need of cognitive reserve (B = 2.694, p = 0.019). In contrast, those without significant Aβ pathological burden, and thus without the same need for cognitive reserve, did not similarly benefit from the presence of NREM SWA (B = -0.115, p = 0.876). This interaction between NREM SWA and Aβ status predicting memory function was significant after correcting for age, sex, Body Mass Index, gray matter atrophy, and previously identified cognitive reserve factors, such as education and physical activity (p = 0.042). These findings indicate that NREM SWA is a novel cognitive reserve factor providing resilience against the memory impairment otherwise caused by high AD pathology burden. Furthermore, this cognitive reserve function of NREM SWA remained significant when accounting both for covariates, and factors previously linked to resilience, suggesting that sleep might be an independent cognitive reserve resource. Beyond such mechanistic insights are potential therapeutic implications. Unlike many other cognitive reserve factors (e.g., years of education, prior job complexity), sleep is a modifiable factor. As such, it represents an intervention possibility that may aid the preservation of cognitive function in the face of AD pathology, both present moment and longitudinally.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Weak Dispersion of Exciton Landé Factor with Band Gap Energy in Lead Halide Perovskites: Approximate Compensation of the Electron and Hole Dependences

The optical properties of lead halide perovskite semiconductors in vicinity of the bandgap are controlled by excitons, so that investigation of their fundamental properties is of critical importance. The exciton Landé or g-factor g X is the key parameter, determining the exciton Zeeman spin splitting in magnetic fields. The exciton, electron, and hole carrier g-factors provide information on the band structure, including its anisotropy, and the parameters contributing to the electron and hole effective masses. Here, g X is measured by reflectivity in magnetic fields up to 60 T for lead halide perovskite crystals. The materials band gap energies at a liquid helium temperature vary widely across the visible spectral range from 1.520 up to 3.213 eV in hybrid organic–inorganic and fully inorganic perovskites with different cations and halogens: FA 0.9 Cs 0.1 PbI 2.8 Br 0.2 , MAPbI 3 , FAPbBr 3 , CsPbBr 3 , and MAPb(Br 0.05 Cl 0.95 ) 3 . The exciton g-factors are found to be nearly constant, ranging from +2.3 to +2.7. Thus, the strong dependences of the electron and hole g-factors on the bandgap roughly compensate each other when combining to the exciton g-factor. The same is true for the anisotropies of the carrier g-factors, resulting in a nearly isotropic exciton g-factor. The experimental data are compared favorably with model calculation results.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Human Factors and Technologies Design to Improve User Acceptance of Pooled Rideshare for Increasing Transportation System Energy Efficiency

This multi-year project delivered a comprehensive, human-factors-driven framework to understand, model, and improve pooled rideshare (PR) adoption in the United States. Through three large-scale national survey studies involving more than 16,000 participants across multiple cities and demographic groups, the research established one of the most extensive datasets to date on user perceptions, behavioral barriers, and service expectations related to pooled rideshare. These data revealed key human factors barriers of user acceptance of PR and suggested potential actionable experience optimizations that could lead to increased PR usage. This foundational knowledge guided the development of novel human-factors models and behavioral choice models that quantify how psychological, demographic, and trip-level factors influence willingness to pool. Building on these empirical insights, the project developed advanced behavioral modeling tools, including mixed logit and integrated choice and latent variable models, to capture both observable and latent influences on PR adoption. These models significantly improved the ability to predict riders’ acceptance of pooled trips, explaining choice heterogeneity through latent constructs such as safety, service experience, privacy concerns, time sensitivity, and environmental attitudes. Together, these models provide a robust analytical foundation for designing PR systems that more effectively meet user needs. The project translated human-factors insights and behavioral models into actionable technology innovations by extending POLARIS—an agent-based, activity-based travel simulation platform—into a fully functional pooled rideshare simulation environment. New PR modules, acceptance models, and regional scenarios were implemented for Greenville, SC and Austin, TX, enabling high-fidelity validation of algorithmic strategies under realistic demand and traffic conditions. The simulation platform supported the development and evaluation of adaptive discount-based assignment algorithms, enhanced willingness-to-pay formulations, demographic-aware incentive mechanisms, and a proactive joint assignment and repositioning strategy. Simulation results demonstrated substantial gains in pooling uptake, average vehicle occupancy, energy efficiency, and fleet profitability. In Greenville, pooling adoption more than doubled, while reductions in vehicle-miles traveled and energy consumption were significant. In Austin, pooling improvements were achieved with minimal service-quality trade-offs, and profitability increased across all fleet sizes. Through this research, we developed a comprehensive understanding of the human factors barriers that limit user acceptance of pooled rideshare services. These insights enabled the design of human-factors-aware pooled rideshare technologies that more effectively address user concerns and improve adoption rates. By integrating these models into an advanced agent-based simulation framework, we demonstrated that higher adoption of pooled rideshare can lead to measurable improvements in energy efficiency and system performance. Together, these contributions establish a validated pathway from human-centered analysis to technology development and energy-saving outcomes, supporting national goals for more sustainable and efficient mobility systems.

Jia, Yunyi↗
DOE OSTI2025

Factorized QED Contribution to Lepton-Hadron DIS

We present the first calculation of next-to-leading order (NLO) factorized QED contributions to the short-distance hard coefficients of inclusive lepton-hadron deep inelastic scattering (DIS) in a joint QCD and QED factorization approach. We demonstrate how the joint factorization consistently factorize all perturbative collinear sensitivities of partonic scattering in both QCD and QED into corresponding universal hadron and lepton distribution functions without the need of any parameters other than the standard factorization scale. We discuss the necessary modification to DGLAP-type evolution of the parton and lepton distribution functions in this joint factorization approach. We also discuss the potential impact of this joint factorization approach on the extraction of partonic information from lepton-hadron DIS.

Qiu, Jianwei↗
DOE OSTI2024

Factorized visual representations in the primate visual system and deep neural networks

Object classification has been proposed as a principal objective of the primate ventral visual stream and has been used as an optimization target for deep neural network models (DNNs) of the visual system. However, visual brain areas represent many different types of information, and optimizing for classification of object identity alone does not constrain how other information may be encoded in visual representations. Information about different scene parameters may be discarded altogether (‘invariance’), represented in non-interfering subspaces of population activity (‘factorization’) or encoded in an entangled fashion. In this work, we provide evidence that factorization is a normative principle of biological visual representations. In the monkey ventral visual hierarchy, we found that factorization of object pose and background information from object identity increased in higher-level regions and strongly contributed to improving object identity decoding performance. We then conducted a large-scale analysis of factorization of individual scene parameters – lighting, background, camera viewpoint, and object pose – in a diverse library of DNN models of the visual system. Models which best matched neural, fMRI, and behavioral data from both monkeys and humans across 12 datasets tended to be those which factorized scene parameters most strongly. Notably, invariance to these parameters was not as consistently associated with matches to neural and behavioral data, suggesting that maintaining non-class information in factorized activity subspaces is often preferred to dropping it altogether. Thus, we propose that factorization of visual scene information is a widely used strategy in brains and DNN models thereof.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Convolution Correction Factor Adjustments on Static PRA Models for Event Assessment

Current probabilistic risk assessment (PRA) models will contain cut sets that have time-constrained basic events such as switched components, recovery of failed components, and failures in time. For these cut sets that have multiple time-constrained events that interact (e.g., offsite power recover at the same time as multiple diesel generators fail to run), there is the possibility that the minimal cut set quantified result is too large. Since current PRA models typically assume that the accident scenario starts at “time zero,” the fails-to-run basic events use this starting time as the basis for their respective reliability models. Over time, the standard practice was to recognize this issue and accept it as part of the conservatism built into the event tree/fault tree PRA models. However, now that these same PRA models are being used in a regulatory forum, this conservatism needs to be addressed. Further, this issue exists not only for the baseline PRA results, but for deficiencies that have been observed or those that have the potential of occurrence, whether the deficiency is complete failure or just a degraded condition. We have found that this conservatism becomes very important when evaluating scenarios such as station blackout sequences and other loss of offsite power sequences. This paper will address the theory and implemented process used by the Systems Analysis Programs for Hands-on Integrated Reliability Evaluations (SAPHIRE) PRA software. Starting with SAPHIRE version 8, the process that was developed is to calculate a “convolution correction factor” based on three distinct calculations. The first calculation is the cut set based frequency calculated via the standard event tree/fault tree PRA model (assuming all run failures start at time equal to zero). The second calculation is to perform the exact time-constrained calculation by integrating the component failure models over the time period of interest (e.g., time to recover, a mission time). For example, this exact calculation for the case of the diesel generators is to convolve their failure models with the offsite power recovery model to obtain the exact frequency. The third calculation is to produce the convolution correction factor by dividing the exact frequency calculation by the standard cut set frequency. This convolution correction factor is then applied to the PRA cut sets in order to remove the time-constrained conservatism. The process used to obtain the convolution correction factor for the baseline PRA is important; however, in this paper we also describe what happens when there is degradation of a component or a potential deficiency. These issues become very important in order to ensure that the convolution correction factor is re-calculated correctly. If the correction factor does not get re-calculated and the same baseline convolution correction factor is used, the final analysis result could be over conservative or under conservative. This paper will discuss how these issues are addressed and provide examples on how SAPHIRE automatically makes the necessary adjustments. MathCad verification results can be computed using the provided equations and input data.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2022

Demystify the 1.1 Factor for Ultimate Tensile Strength Above Room Temperature in Development of the Code Stress Tables

As the Boiler and Pressure Vessel Code increasingly gains popularity worldwide, it is not uncommon that many users are mystified by the 1.1 factor required for ultimate tensile strength above room temperature in developing the allowable design stresses of Section II Part D Stress Tables. Questions often arise about the origin and purpose of the factor, the reason for it not applied to the yield strength, its due considerations when evaluating an alloy for acceptance to construction use from the perspective of the alloy’s tested tensile strength value, and more. Not knowing reliable sources for explicit explanations, some users tend to misinterpret the factor, abuse its application, incorrectly define their alloy acceptance or qualification criteria, and mistakenly manipulate the safety margin in structural component design.To help the Code users who struggle with these frequent and confusing issues, particularly those in nuclear industry where rigorous criteria are required for component design and alloy acceptance or qualification, this paper is intended to demystify the 1.1 factor and facilitate knowledgeable interpretation and use of the Section II Part D Stress Tables as well as relevant Mandatory Appendices.A brief review is first given on the background of the 1.1 factor, followed by a summary of its application in the Stress Tables. The provenance and purpose of the factor are then discussed in detail with graphic examples. Finally, current applicability and necessity of the factor are considered through a demonstration using example alloys.

Ren, Weiju↗
DOE OSTI2017

Application of peaking factor credit in spent fuel pool criticality analysis

Higher moderator temperature (lower density) and higher fuel temperatures used in the depletion analysis of spent fuel increases the reactivity. Modern criticality analyses for spent fuel pools typically determine the highest power assembly and use the temperatures from this high power assembly. The high power assembly is determined by finding the maximum assembly averaged relative power or peaking factor. For the depletion analysis it is appropriate to use the average peaking factor over the life of the assembly. This can be determined by dividing the assembly burnup by the sum of the core cycle burnups for the cycles the assembly was in the core. A bounding assembly averaged peaking factor is typically about 1.4. Clearly this is 40% higher than the average assembly power. The reactivity limiting fuel assembly generally has low burnup and thus a low peaking factor. Rather than assume a limiting peaking factor to determine the temperatures in the depletion analysis, the depletion analysis can be performed multiple times with different temperature sets so the depleted isotopic content is a function of the peaking factor. This would allow more fuel assemblies to satisfy the loading criteria while maintaining the same criticality margin. The peaking factor credit can be about 2% in k or about 3.5 GWd/MTU change in the burnup requirements. (authors)

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Measurement of the Neutron Magnetic Form Factor at Large Momentum Transfer Using the Super-Bigbite Apparatus in Jefferson Lab Hall-A

The nucleon elastic electromagnetic form factors help us study the electromagnetic structure of the nucleon, benchmark theoretical models, and improve our understanding of non-perturbative quantum chromodynamics and confinement. The Nobel Prize-winning electron-nucleon scattering experiments by Robert Hofstadter and collaborators in the 1950s at Stanford High Energy Physics Lab were the first nucleon form factor measurements performed using leptonic probes. The Super Bigbite Spectrometer (SBS) program at Hall-A of Jefferson Lab represents the latest efforts to measure nucleon form factors. This ambitious program aims to significantly extend the current data set in terms of square momentum transfer (Q2) with high precision. The advent of novel detector technologies, like Gas Electron Multipliers (GEM), which provide excellent position resolution (< 100 ?m) while withstanding high background particle rates (several hundred MHz/cm2) over a large active area, has paved the way for open-geometry, moderate solid angle spectrometers, which are central to all form factor experiments in the SBS program. The first experimental run group in SBS ran successfully between September 2021 and February 2022, collecting data for the measurement of the magnetic form factor of the neutron Gn M at five squared momentum-transfer values: 3.0, 4.5, 7.5, 9.8, and 13.5 (GeV /c)2. This extends the existing high-precision data for Gn M by about a factor of four. The ratio technique was used, which involved the simultaneous measurement of exclusive quasielastic scattering of D(e,e?n)p and D(e,e?p)n from a deuterium target. Pre-preliminary results for D(e,e?n)p and D(e,e?p)n quasi-elastic ratio, and the neutron magnetic form factor Gn M , for Q2 points 3.0, 9.8, and 13.5 (GeV /c)2 are presented.

Mudiyanselage, Anuruddha Rathnayake↗
DOE OSTI2020

Tucker-1 Boolean Tensor Factorization with Quantum Annealers

Quantum annealers are an emerging computational architecture that have the potential to address some challenging computational issues that will be left unresolved as we approach the end of the Moore's Law era of computing. D-Wave quantum annealers are designed to solve a challenging set of problems - quadratic unconstrained binary optimization problems. This makes them a natural fit for solving problems with binary or Boolean variables. Here, we explore the use of a quantum annealer to solve Boolean tensor factorization. The goal of Boolean tensor factorization is to represent a high-dimensional tensor filled with Boolean values as a product of Boolean matrices and a Boolean core tensor. We show that a particular Boolean tensor factorization problem (called Tucker-1 factorization) can be decomposed into a sequence of quadratic unconstrained binary optimization problems that can be solved with a D-Wave 2000Q quantum annealer. While quantum annealers specifically and quantum computers in general are at a fairly early stage in their development, they are currently capable of solving these Boolean tensor factorization problems. Importantly, our results show that for fairly small tensors, we are frequently able to obtain an accurate (sometimes exact) factorization using quantum annealing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Characterizing peak electricity demand for U.S. households: an assessment of end-use loads and demand factors

Understanding household peak electricity demand is critical to evaluate the technical need for electrical infrastructure upgrades. This study characterizes peak loads for existing and new equipment using metered data from a convenience sample of 11,940 U.S. dwellings from four sources, including 911 from two sources with end-use metering. After standardized data cleaning and labeling, we derived descriptive statistics for key metrics, such as maximum demand and demand factors, and developed predictive models relating 60- to 15-min demand for the National Electrical Code (NEC). Mean 15-min maximum demand was 9.7 kW (median 9.0 kW; IQR 7.0–11.5 kW, 95% CI 9.6–9.8 kW), indicating spare capacity in 98% of homes with hypothetical 100 A panels. Maximum demand increased with floor area and number of high-demand loads. Dwelling maximum demand was driven by higher-power, longer-duration heating appliances and vehicle charging, while most user-operated appliances contributed little. Demand factors are used to account for how most devices contribute less than their rated power to maximum demand. Existing load mean demand factors (28%; median 10%; IQR 0–58%; CI 28–29%) were higher than those for new loads (21%; median 7%; IQR 0–35%; CI 20–21%), because new loads changed the timing and magnitude of maximum demand. New high-demand loads had higher than average demand factors (40–60%). Whole dwelling demand factors support the NEC's 40% assumption, but they challenge its conservative 100% treatment of new HVAC. We propose a data-driven 50% demand factor for new equipment, which would align with metered data, improve affordability, and modernize electrical codes.

Appliances↗
DOE OSTI2016

Radiation-Induced Esophagitis In Vivo and In Vitro Reveals That Epidermal Growth Factor Is a Potential Candidate for Therapeutic Intervention Strategy

Purpose: To establish and characterize radiation-induced esophagitis (RIE) in vivo and in vitro. Methods and Materials: Fractionated thoracic irradiation at 0, 8, 12, or 15 Gy was given daily for 5 days to Balb/c or C57Bl/6 mice. Changes in body weight gain and daily food intake were assessed. At the end of the study, we removed the esophagus and examined histology by hematoxylin and eosin staining, immune cell infiltration and apoptosis by fluorescence-activated cell sorting, and gene expression changes by quantitative real-time polymerase chain reaction. Het-1A human esophageal epithelial cells were irradiated at 6 Gy, treated with recombinant human growth factors, and examined for gene expression changes, apoptosis, proliferation, and signal transduction pathways. Results: We observed that irradiation at 12 Gy or 15 Gy per fraction produced significant reduction in body weight and decreased food intake in Balb/c mice but not as much in C57Bl/6 mice. Further analyses of Balb/c mice irradiated at 12 Gy/fraction revealed attenuated epithelium, inflamed mucosa, and increased numbers of infiltrating CD4+ helper T cells and apoptotic cells. Moreover, we found that expression of tissue inhibitor for metalloproteinase-1, plasminogen activator inhibitor-1, granulocyte macrophage-colony stimulating factor, vascular endothelial growth factor, and stromal-derived factor-1 were increased, whereas epidermal growth factor (EGF) was decreased. Irradiated Het-1A cells similarly showed a significant decrease in expression of EGF and connective tissue growth factor (CTGF). Treatment of EGF but not CTGF partially protected Het-1A cells from radiation-induced apoptosis and revealed phosphorylation of EGFR, AKT, and ERK signaling pathways. Conclusions: We established a mouse model of RIE in Balb/c mice with 12 Gy × 5 fractions, which showed reduced body weight gain, food intake, and histopathologic features similar to those of human esophagitis. Decreased EGF expression in the irradiated esophagus suggests that EGF may be a potential therapeutic intervention strategy to treat RIE.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2016

Broken symmetry dielectric resonators for high quality factor Fano metasurfaces

We present a new approach to dielectric metasurface design that relies on a single resonator per unit cell and produces robust, high quality factor Fano resonances. Our approach utilizes symmetry breaking of highly symmetric resonator geometries, such as cubes, to induce couplings between the otherwise orthogonal resonator modes. In particular, we design perturbations that couple “bright” dipole modes to “dark” dipole modes whose radiative decay is suppressed by local field effects in the array. Our approach is widely scalable from the near-infrared to radio frequencies. We first unravel the Fano resonance behavior through numerical simulations of a germanium resonator-based metasurface that achieves a quality factor of ~1300 at ~10.8 μm. Then, we present two experimental demonstrations operating in the near-infrared (~1 μm): a silicon-based implementation that achieves a quality factor of ~350; and a gallium arsenide-based structure that achieves a quality factor of ~600, the highest near-infrared quality factor experimentally demonstrated to date with this kind of metasurface. Importantly, large electromagnetic field enhancements appear within the resonators at the Fano resonant frequencies. Here, we envision that combining high quality factor, high field enhancement resonances with nonlinear and active/gain materials such as gallium arsenide will lead to new classes of active optical devices.

36 MATERIALS SCIENCE↗