More accurate scientific simulations with high-fidelity nuclear data at low cost [Poster]
Multigroup neutron transport requires weighted averages of continuous-energy cross sections.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
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2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
2019 and 2022 Twin Cities Travel Behavior Inventory Surveys
# 2019 and 2022 Twin Cities Travel Behavior Inventory Surveys To help local and regional planning agencies understand shifting demographics and travel patterns, surveys were conducted in Minnesota’s greater Twin Cities region in 2019 and 2022. Survey results aided the Metropolitan Council in proposing practical transportation investments, preparing competitive grant applications, and prioritizing improvements to best fit regional needs. ## Data Collection Agency RSG conducted the surveys for the Metropolitan Council. ## Survey Methodology These mixed-mode surveys focused on bus, rail, car, micromobility, ride-hailing, and walking. Designed as household travel surveys, they were carried out in English, Spanish, Karen, Oromo, Somali, and Hmong during two timeframes: Oct. 1, 2018—Sept. 30, 2019 and June 22, 2021—Feb. 5, 2022. Participants accessed the surveys using a smartphone-based app, website, or call center. A questionnaire captured data about demographics, daily travel activities, and typical transportation patterns to inform model updates and gain information about emerging behavioral changes such as electric vehicle adoption and teleworking frequency. It also addressed the impacts of COVID-19 on participants’ typical travel behavior. ## Survey Records, Data, and Documentation Survey records include a total of 31,251 participants—16,152 participants from 7,837 households during the 2019 survey and 15,099 participants from 7,952 households during the 2022 survey.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Geant4Reweight: a framework for evaluating and propagating hadronic interaction uncertainties in Geant4
Geant4Reweight is an open-source C++ framework that allows users to weight tracks produced by the Geant4 particle transport Monte Carlo simulation according to hadron interaction cross section variations and estimate uncertainties in Geant4 interaction models by comparing the simulation’s hadron interaction cross section predictions to data. The ability to weight hadron transport as simulated by Geant4 is crucial to the propagation of systematic uncertainties related to secondary hadronic interactions in current and upcoming neutrino oscillation experiments, including MicroBooNE, NOvA, and DUNE, aswell as hadron test beam experiments such as ProtoDUNE. Here, we provide motivation for weighting hadron tracks in Geant4 in the context of systematic uncertainty propagation, a description of Geant4’s transport simulation technique, and a description of our weighting technique and fitting framework in the momentum range 0–10 GeV/c, which is typical for the hadrons produced by neutrino interactions in these experiments.
97 MATHEMATICS AND COMPUTING↗
Sensitivity/Uncertainty Comparison Study Involving IRSN, LANL, and ORNL Tools to Support Validation
Under a DOE Nuclear Criticality Safety Program (NCSP) task involving Analytical Methods, three Laboratories collaborated in a comparison of results obtained from Sensitivity/Uncertainty (S/U) packages relevant to validation of transport codes. The task involves Institut de Radioprotection et de Sûreté Nucléaire (IRSN), Los Alamos National Laboratory (LANL), and Oak Ridge National Laboratory (ORNL) comparing results of MORET 5/MACSENS V3.0, MCNP6.2/Whisper-1.1, and SCALE 6.2.3/TSUNAMI/USLSTATS respectively. All Monte Carlo transport code results utilize nuclear data from ENDF/B-VII.1 evaluation. This study examines five cases from the International Handbook of Evaluated Criticality Safety Benchmark Experiments (ICSBEP Handbook) selected as application models: IEU-MET- FAST-002-001, LEU-COMP-THERM-001-001, LEU-SOL-THERM-004-001, MIX-COMP- THERM-001-001, and U233-SOL-THERM-001-001. This is a continuation of a previous study to examine Pu and HEU cases: HEU-MET-FAST-013-001, HEU-SOL-THERM-001-008, PU-MET- FAST-022-001, and PU-SOL-THERM-001-001. Ultimately, comparison is made between Upper Subcritical Limits (USLs) obtained using each code package for each application case. Since differences exist in whether packages take into account margin of subcriticality (MOS), the USL is computed using only bias and bias uncertainty, also known as the calculational margin (CM) in ANSI/ANS-8.24. Results comparison appears to show that benchmark selection has a greater influence on the USL than the method used for calculation of bias and bias uncertainty.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Demand-Side Grid (dsgrid) TEMPO Light-Duty Vehicle Charging Profiles v2022
Simulated hourly electric vehicle charging profiles for light-duty household passenger vehicles in the contiguous United States, 2018-2050. Profiles are differentiated by scenario, county, household and vehicle types, and charging type. Data was produced in 2022 using the Transportation Energy & Mobility Pathway Options (TEMPO) model and published in demand-side grid (dsgrid) toolkit format. Data are available for three adoption scenarios: "AEO Reference Case", which is aligned with the U.S. EIA Annual Energy Outlook 2018 (linked below), "EFS High Electrification", which is aligned with the High Electrification scenario of the Electrification Futures Study (linked below), and "All EV Sales by 2035", which assumes that average passenger light-duty EV sales reach 50% in 2030 and 100% in 2035. The charging shapes are derived from two key assumptions of which data users should be aware: "ubiquitous charger access", meaning that drivers of vehicles are assumed to have access to a charger whenever a trip is not in progress, and "immediate charging", meaning that immediately after trip completion, vehicles are plugged in and charge until they are either fully recharged or taken on another trip. These assumptions result in a bounding case in which vehicles' state of charge is maximized at all times. This bounding case would minimize range anxiety, but is unrealistic from the point of view of both electric vehicle service equipment (EVSE) (i.e., charger) access, and plug-in behavior as it can result in dozens of charging sessions per week for battery electric vehicles (BEVs) that in reality are often only plugged in a few times per week.
Array↗
Review of quantitative methods to assess impacts of changing climate and socioeconomic conditions on Arctic transportation systems
Rapid climate and socioeconomic changes are transforming Arctic human- Earth systems. An integral part of these systems is mobility, which encompasses the transport of humans and goods into, out of, and between Arctic regions. Impacts of climate and socioeconomic drivers on Arctic mobility are heterogenous. Methodologies are needed to quantify these impacts in measures that can be linked with broader socioeconomic systems. This article reviews existing such methods and organizes them into a conceptual framework to understand trends and gaps in the literature. We found methods quantifying impacts of a range of climate drivers on most transportation modes present in the Arctic, but few methods focused on socioeconomic drivers. Also underrepresented were methods explicitly considering adaptive capacity of transportation systems. In conclusion, we provide insight into the data and relationships relevant to understanding impacts of Arctic change on transportation systems and how these impacts fit into broader human- Earth systems.
54 ENVIRONMENTAL SCIENCES↗
Barometric Pumping Through Fractured Rock: A Mechanism for Venting Deep Methane to Mars' Atmosphere
Abstract Both the source of methane on Mars and the mechanism for transmission from the subsurface to the atmosphere are not fully understood. Previous seepage simulations have invoked relatively shallow subsurface sources to explain observed methane signatures on Mars. We propose that barometric‐pressure pumping through fracture networks could be an effective mechanism for methane transport from the deep subsurface on Mars. Using atmospheric pressure data gathered by Curiosity as input, we simulate methane gas transport from depths of 200 m to the surface. Even with such a deep source, our model reproduces the observed seasonality of methane, and the simulated surface methane fluxes fall within the range of previous estimates derived from atmospheric observations. Because 200 m is the likely minimum hospitable depth for living methanogenic microbes, our fracture network model indirectly reinvigorates the possibility of a microbial source of methane on Mars.
58 GEOSCIENCES↗
Evidence for methanobactin “Theft” and novel chalkophore production in methanotrophs: impact on methanotrophic-mediated methylmercury degradation
Abstract Aerobic methanotrophy is strongly controlled by copper, and methanotrophs are known to use different mechanisms for copper uptake. Some methanotrophs secrete a modified polypeptide—methanobactin—while others utilize a surface-bound protein (MopE) and a secreted form of it (MopE*) for copper collection. As different methanotrophs have different means of sequestering copper, competition for copper significantly impacts methanotrophic activity. Herein, we show that Methylomicrobium album BG8, Methylocystis sp. strain Rockwell, and Methylococcus capsulatus Bath, all lacking genes for methanobactin biosynthesis, are not limited for copper by multiple forms of methanobactin. Interestingly, Mm. album BG8 and Methylocystis sp. strain Rockwell were found to have genes similar to mbnT that encodes for a TonB-dependent transporter required for methanobactin uptake. Data indicate that these methanotrophs “steal” methanobactin and such “theft” enhances the ability of these strains to degrade methylmercury, a potent neurotoxin. Further, when mbnT was deleted in Mm. album BG8, methylmercury degradation in the presence of methanobactin was indistinguishable from when MB was not added. Mc. capsulatus Bath lacks anything similar to mbnT and was unable to degrade methylmercury either in the presence or absence of methanobactin. Rather, Mc. capsulatus Bath appears to rely on MopE/MopE* for copper collection. Finally, not only does Mm. album BG8 steal methanobactin, it synthesizes a novel chalkophore, suggesting that some methanotrophs utilize both competition and cheating strategies for copper collection. Through a better understanding of these strategies, methanotrophic communities may be more effectively manipulated to reduce methane emissions and also enhance mercury detoxification in situ.
59 BASIC BIOLOGICAL SCIENCES↗
Slippery liquid infused porous surface (SLIPS) condensers for high efficiency air gap membrane distillation
Abstract To address growing water scarcity, we must improve the energy efficiency of thermal desalination technologies such as air gap membrane distillation. However, promising functional materials such as slippery liquid infused porous surfaces have not yet implemented for any desalination technology. Here, we fabricate and test slippery liquid infused porous surfaces (using Krytox 16,256 lubricant and CuO nanostructures) in an air gap membrane distillation apparatus. System-level transport models, validated by experimental data, establish a framework for improving performance through enhanced condensation surfaces. Results are obtained across a range of temperatures (50–80 °C), salinities (5–105 g/kg), and module lengths. We find that small air gap thickness and efficient droplet shedding significantly improves performance. The CuO Krytox process achieves these with a conductive-self-limiting coating, high nanostructure rugosity, strong covalent and metallic bonding, high hydrophobicity, minimal droplet pinning sites, and ultra-low contact angle hysteresis. The greatest efficiency enhancement from SLIPS is derived from the improved droplet shedding, which allows for reduced gap sizes without flooding, and is further augmented by the increased permeate flux.
Yogi, Yashwant S.↗