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At least 289 records · Page 16

Simulation-aided occupant-centric building design: A critical review of tools, methods, and applications

Occupants are active participants in their built environment, affecting its performance while simultaneously being affected by its design and indoor environmental conditions. With recent advances in computer modeling, simulation tools, and analysis techniques, topics such as human-building interactions and occupant behavior have gained significant interest in the literature given their premise of improving building design processes and operating strategies. In practice, the focus of occupant-centric literature has been mostly geared towards the latter (i.e., operation), leaving the implications on building design practices underexplored. This paper fills the gap by providing a critical review of existing studies applying computer-based modeling and simulation to guide occupant-centric building design. The reviewed papers are organized along four main themes, namely occupant-centric: (i) metrics of building performance, (ii) modeling and simulation approaches, (iii) design methods and applications, and (iv) supporting practices and mechanisms. Important barriers are identified for a more effective application of occupant-centric building design practices, including the limited consideration of metrics beyond energy efficiency (e.g., occupant well-being and space planning), the limited implementation and validation of the proposed methods, and the lack of integration of occupant behavior modeling in existing building performance simulation tools. Finally, future research directions are discussed, covering large-scale international data collection efforts to move from generic assumptions about occupant behavior to specific/localized knowledge, improved metrics of measuring building performance, and improved industry practices, such as building codes, to promote an occupant-in-the-loop approach to the building design process.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Thermo-Oxidation Analysis of Structural Adhesives: An Experimental Study

Abstract Cross-linked polymers play an important role in structural applications as adhesives. In this article, we performed an experimental study to understand the effects of high temperature i.e. thermo-oxidative aging on the mechanical behavior of polymers. Subsequently, the constitutive behavior, i.e. stress-strain relation of these adhesives before and after the accelerated aging tests were studied on samples aged at different temperature and different time durations at zero humidity. The aging conditions and periods selected for the tests have been specifically designed to simulate the effects of aging under the working conditions that the materials will be subjected to in their standard applications. The focus of this study is on the investigation of the effects of aging on the polymeric adhesives used in automotive applications. Consequently, four different polymeric adhesives (one silicone-based, two polyurethane-based, and one acrylic type), which are being used extensively in these applications, have been chosen. The quasi-static tensile loading was used on these four adhesives and the changes in the constitutive behavior were measured. The failure tensile tests as well as the cyclic tensile tests were performed in our lab using a Test-Resources tensile testing machine. The temperatures that were used in this work are 60, 80 and 95°C for varying aging periods of 24hrs, 240hrs, and 720hrs. Then, all the data was collected and consequently analyzed, and the resulting behavior was compared to that of the unaged material, showing varying results depending on the material, temperature, and aging period. Different behaviors have been observed after aging. The polymers’ behavior was expanding from no effect to significant damage caused by aging conditions. This work provides the core understanding required for modeling the material behavior during aging and lays down the groundwork for further data-infused model-free approaches.

Alazhary, Sharif↗

Early life stress induces irritable bowel syndrome from childhood to adulthood in mice

Background Irritable bowel syndrome (IBS) is one of the most common functional gastrointestinal disorder. Traditionally, early life stress (ELS) is predisposed to IBS in adult. However, whether ELS induces IBS in early life remains unclear. Methods Separated cohort studies were conducted in neonatal male pups of C57BL/6 mice by maternal separation (MS) model. MS and non-separation mice were scheduled to be evaluated for prime IBS-phenotypes, including visceral hypersensitivity, intestinal motility, intestinal permeability, and anxiety-like behavior. Ileal contents and fecal samples were collected and analyzed by 16S rRNA gene sequencing and bacterial community analyses. Subcellular structures of intestinal epithelial, such as epithelial tight junctions and mitochondria, were observed under transmission electron microscopy. Results MS induced visceral hypersensitivity and decreased total intestinal transit time from childhood to adulthood. In addition, MS induced intestinal hyperpermeability and anxiety-like behavior from adolescence to adulthood. Besides, MS affected intestinal microbial composition from childhood to adulthood. Moreover, MS disrupted intestinal mitochondrial structure from childhood to adulthood. Conclusion The study showed for the first time that MS induced IBS from early life to adulthood in mice. The disrupted intestinal mitochondrial structure and the significant dysbiosis of intestinal microbiota in early life may contribute to the initiation and progress of IBS from early life to adulthood.

Tao, Enfu↗

Dynamic Modeling and Characterization of Nuclear-grade Graphite

Idaho National Labs serves as the spearhead for many innovative energy solutions to the world's energy crisis. One such solution is the INL's Microreactor which is designed to deploy to extreme/remote environments where other sources of power are either unavailable or unreliable. In order to best design these energy solutions for their operational environments, it is crucial to understand how the design, components, and materials will respond to the environmental conditions. One key material in these innovative designs is a nuclear-grade graphite known as PCEA. This study examines the behavior of PCEA graphite under dynamic loading, similar to that which may occur in extreme environments. The objective is to characterize the dynamic behavior and produce an accurate, reliable constitutive model suitable for use in simulation tools such as INL's MOOSE. Graphite specimens were tested using a Split Hopkinson Pressure Bar (SHPB) to administer the dynamic compressive load. The SHPB was charged at various pressures to produce a range of strain rates on the material in compression. Data was acquired via strain gauges on the SHPB setup, from which stress, strain, and time data were collected. Analysis revealed the stress-strain behavior of the material as well as insights into the material behavior's relationship to strain rate. Further work must continue to characterize the various other dynamic behaviors of the material which will combine to create a substantially trustworthy constitutive model for this grade of nuclear-grade graphite. Ultimately, this will allow for realistic simulation of the material in reactor designs, allowing for prediction of design weaknesses and leading to improved designs for increased resilience, security, and reliability.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Investigating the collective nature of cavity-modified chemical kinetics under vibrational strong coupling

Abstract In this paper, we develop quantum dynamical methods capable of treating the dynamics of chemically reacting systems in an optical cavity in the vibrationally strong-coupling (VSC) limit at finite temperatures and in the presence of a dissipative solvent in both the few and many molecule limits. In the context of two simple models, we demonstrate how reactivity in the collective VSC regime does not exhibit altered rate behavior in equilibrium but may exhibit resonant cavity modification of reactivity when the system is explicitly out of equilibrium. Our results suggest experimental protocols that may be used to modify reactivity in the collective regime and point to features not included in the models studied, which demand further scrutiny.

Lindoy, Lachlan P.↗

Advances in Multimodal Characterization of Structural Materials

The myriad detectors and instruments now available for materials characterization provide researchers with an ever-growing suite of tools to probe material behavior. Progress in the development of instrumentation and workflows that enable the collection, and leverage the potential, of various data modalities have provided novel insights into material behavior. Using data across multiple length scales, or performing complementary analyses of in situ and ex situ data, can help reveal a more complete picture of dynamic processes or material structure. However, the accurate combination, or fusion, of these disparate data modalities presents new challenges. Differences in resolution, as well as the varying length scales at which physical phenomena are exploited to generate these data, necessitate novel approaches to accurately interpret and combine these data. Furthermore, the papers within this special topic focus on the collection and fusion of multimodal data to better understand structural materials. From new frameworks and workflows for data segmentation and analysis, process monitoring, enhancing simulations, or interrogating mechanical response, these papers reveal the potential benefits of utilizing multimodal data.

36 MATERIALS SCIENCE↗

Spatiotemporal Plasma-Particle Characterization of Dry Aerosols Using Nanosecond, Femtosecond, and Filament Laser-Produced Plasmas

The ability to rapidly characterize dry aerosols in air using laser-induced breakdown spectroscopy (LIBS) with femtosecond laser pulses promises advancement towards real-time atmospheric sampling and standoff capabilities. Of particular interest is the ability to apply LIBS in the context of low-particle loaded environments where discrete particle interactions must be observed within the sampling volume of the laser-produced plasma (LPP). In this study, dry nanoparticles in suspension are generated from a standard solution and sampled in air using Q-switched nanosecond (ns-) pulses, short-focus (SF) femtosecond (fs-) pulses, and filaments. Short time-gated plasma images are captured to observe spatially and temporally varying discrete plasma-particle interactions, which is shown to influence early air breakdown behavior and subsequent plasma evolution. Along with images, photo-multiplier tube (PMT) measurements are conducted where strong spatiotemporal dependencies are exhibited by the collected emission signal on particle proximity and plasma expansion behavior. Finally, conditional analysis is performed on LIBS measurements to determine associated sampling probabilities and filter out spectra with poor or absent emission peaks with an adaptive threshold algorithm.

42 ENGINEERING↗

Back to functional hydrides: Effects of neutron-irradiated microstructure on hydrogen retention in yttrium hydride

Functional hydrides are promising candidates for advanced nuclear reactors, particularly in portable or transportable applications, due to their high hydrogen-retention capabilities, enabling efficient neutron moderation, and compact reactor design. However, hydrogen mobility in hydrides at elevated irradiation temperatures poses significant technological challenges, necessitating a comprehensive understanding of their irradiation behavior. Furthermore, this study investigated the microstructural and chemical stability of neutron-irradiated yttrium hydrides to assess their hydrogen-retention capacity. A targeted literature review was also conducted to contextualize neutron-irradiation effects on functional hydrides with regards to structural stability and hydrogen retention. Experimental characterizations revealed that, at high temperatures, irradiated hydrides retained their phase stability, which was likely enhanced by irradiation-induced microstructure evolution. Notably, an amorphous yttrium and oxygen -rich surface layer was present at the free surface of the hydride. Its thickness decreased while a continuous crystalline Y-O-rich layer was formed with increasing neutron damage. Additionally, the number density of dislocation loops and cavities generally increased as a function of neutron dose. First-principles calculations of hydrogen behavior within yttrium vacancy clusters in yttrium hydrides demonstrated vacancy-size-dependent hydrogen stability and configuration, highlighting the role of vacancy geometry in regulating hydrogen retention. Thus, the presence of irradiation-induced dislocation loops and cavities were hypothesized to improve hydrogen retention. Collectively, these findings advance the understanding of hydride behavior under neutron irradiation as well as their technological readiness for portable or transportable nuclear reactors.

Defect clusters↗

Tutorial: Lessons Learned for Behavior Analysts from Data Scientists

Big data is a computing term used to refer to large and complex data sets, typically consisting of terabytes or more of diverse data that is produced rapidly. The analysis of such complex data sets requires advanced analysis techniques with the capacity to identify patterns and abstract meanings from the vast data. The field of data science combines computer science with mathematics/statistics and leverages artificial intelligence, in particular machine learning, to analyze big data. This field holds great promise for behavior analysis, where both clinical and research studies produce large volumes of diverse data at a rapid pace (i.e., big data). This article presents basic lessons for the behavior analytic researchers and clinicians regarding integration of data science into the field of behavior analysis. We provide guidance on how to collect, protect, and process the data, while highlighting the importance of collaborating with data scientists to select a proper machine learning model that aligns with the project goals and develop models with input from human experts. Here, we hope this serves as a guide to support the behavior analysts interested in the field of data science to advance their practice or research, and helps them avoid some common pitfalls.

42 ENGINEERING↗

Large Deviations Anomaly Detection (LAD) for collection of multivariate time series data: Applications to COVID-19 data

Time series anomaly detection is frequently used to identify extreme behaviors within a single time series. Identifying extreme trends in relation to a collection of other time series, on the other hand, is frequently of significant interest, such as in public health policy, social justice, and pandemic propagation. Using concepts from large deviations theory , we propose an algorithm that can scale to large collections of time series data. This paper expands on the LAD algorithm presented in Guggilam et al. (2022). The proposed algorithm is an online anomaly detection method for identifying anomalies in a collection of multivariate time series that takes advantage of the algorithm’s ability to scale to high-dimensional data. We show how the proposed Large Deviations Anomaly Detection (LAD) algorithm can be used to identify regions with anomalous trends in COVID-19 cases, deaths, biweekly growth rates, vaccinations, and fatality rates. Several of the observed anomalous trends are associated with regions that have demonstrated poor response to the COVID pandemic.

97 MATHEMATICS AND COMPUTING↗

Molecular structure models of amorphous bismuth and cerium carboxylate catalyst precursors

As our societal need for materials and energy has grown, so has our need for catalyst processes in hydrogen production. A major function in these applications, for both homogenous and heterogeneous catalysis processes, is the synthesis of an active metal catalyst. It must first be soluble to control the physical properties of the metal being used. Recent work in metal precursors has begun to turn toward these metal carboxylate types of material. Here, structural models are proposed for bismuth 2-ethylhexanoate and 2,2-dimethyloctanoate and cerium 2-ethylhexanoate. The bismuth compounds have been characterized at different ratios of bismuth to carboxylate as solutions of the free acids. Their structures are most consistent with a Bi 4 (RCO 2 ) 12 motif where the Bi ions are arranged in a flattened tetrahedron with Bi – Bi distances of about 4.3 Å. There is evidence for Bi – O – Bi linkages at low free acid concentrations. The cerium compound is most consistent with a linear tetracerium molecule where the Ce – Ce distances repeat at about 4.3 Å out to 16.4 Å. The models were generated by analogy with known crystal structures and compared to high-energy x-ray scattering data. To further evaluate the models, DFT calculations were made, and the equilibrium geometries were compared. The vibrational spectra calculated from those geometries are presented and compared to the experimental results. Magnetization vs. temperature data was collected on the cerium compound, and its behavior was consistent with the proposed model. A geometrical approach to determining the dimensionality and relative positions of the metal ions in these structures is presented.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Messaging for Impact: Behavioral Science-Based Communication Strategies to Advance Energy Efficiency

Currently, five legal authorities require U.S. federal agencies to purchase energy-efficient products. However, compliance with these requirements has consistently been low (~55%). Recent surveys indicate that energy efficiency requirements are not communicated properly within federal agencies and energy-efficiency is not a top priority. In the constantly evolving energy industry, with many solutions for a clean energy future, effective communication methods are vital for widespread adoption of energy efficiency. We have been conducting experiments within the U.S. federal sector that test the effectiveness of messaging strategies developed by using behavioral science principles like loss aversion, framing, and herd behavior. This will identify effective communications methods that encourage energy efficiency adoption. In this study, we measure the effectiveness of different communication strategies by monitoring the web traffic of hyperlinks placed within our digital communications and outreach materials. To determine effective strategies that influence federal buyer behavior, the web traffic from subjects that received the behavioral science-based messaging (treatment group) is compared to that from subjects that received regular messaging (control group) for each of the strategies. This paper provides an overview of the behavioral science principles that we applied, data collection methods, and results of our analysis. Since the lack of effective communication of energy-efficiency policies is also a common problem in the private sector, the messaging strategies developed in this study can be applicable to other large private sector institutions

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Simulating Catalysis with Realistic Pellet Geometries Using Mesoflow: A Case Study of Catalytic Propane Dehydrogenation

We present a case study of catalytic propane dehydrogenation with our open-source multiphysics solver, Mesoflow. The solver was developed to simulate reactive flow coupled to heterogeneous catalytic reactions and deactivation in the context of complex, mesoscale geometry. The method leverages cartesian block-structured adaptive mesh refinement to capture realistic catalyst microstructural features acquired directly from X-ray computed tomography data. A kinetic model for propane dehydrogenation and catalyst deactivation was developed based on temporal analysis of products (TAP) reactor experiments. The TAP reactor experiments allow for precise characterization of intrinsic kinetic reaction steps which are implemented into Mesoflow simulations to model the spatial and temporal evolution of reactants, products, and catalyst active sites. The short-term and long-term deactivation behavior is studied by using XCT data collected from fresh and aged catalyst pellets, which exhibit different microstructural features. This study employs time-splitting algorithms to connect disparate reaction and flow timescales, enabling the simulations to achieve realistic deactivation timescales on the order of minutes while the flow time-scales for small particles (100 microns) are several milliseconds. We also introduce a flexible automated python script that writes the necessary files to construct a Mesoflow simulation from user-created chemical mechanisms. We will also introduce a few new features that are added to Mesoflow such as higher order schemes, implicit chemistry integrators and the ability to run on AMD and NVIDIA graphics-processing-units.

AMReX↗

CASTLE: Conflict Analysis Strategy Testing Laboratory Environment v.1.0.0

SAND2024-01743O The Conflict Analysis Strategy Testing Laboratory Environment (CASTLE) is a software framework that enables and simplifies building a novel, turn-based strategy game in which it can define its own rules, maps, pieces, and interactions. The software is for novice to experienced programmers with some knowledge of Unity3D, a tool used in game production. CASTLE includes a library of common game mechanics used for strategic wargames and traditional board games, such as cards, tokens, dice, and grid maps. It follows design principles popularized by the video game industry and uses singletons for managing portions of the code. CASTLE builds on Unity's component-based design and can respond to engine events during execution. Among the numerous user-friendly features: Build games quickly and cost-effectively Network in real-time and apply data to new games developed on the framework Host multiple participants online Connect rule- or machine learning-based agents to a CASTLE game to serve as opponents or to simulate games Collect data collection from players and in-game behaviors Create a survey to gather demographics or opinions from players Store data locally or save it to an external database through Representational State Transfer (REST) functions CASTLE, which was prototyped using Microsoft Azure, is also designed for easily distributing online games using popular cloud services. The multiplayer functionality includes an agent interface, allowing developers to construct AI players that can substitute for humans in any of the games. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Fabian, Nathan↗

Operational Focused Data Analytics for Optimizing Radiation Portal Monitor-Based Nuclear Smuggling Detection Systems at Global Ports of Entry

The National Nuclear Security Administration’s Office of Nuclear Smuggling Detection and Deterrence has deployed a fleet of radiation portal monitors (RPMs) across the world at global ports of entry including seaports, airports, and land border crossings. These RPMs are integrated into radiation detection systems (RDS) that also include fixed cameras, optical character recognition (OCR) systems, primary scanning systems (e.g., X-ray or gamma-ray), and secondary scanning systems (e.g., spectroscopic radiation portal monitors, portable radiation detection systems). The data from these sensing technologies is collected at the Central Alarm Station (CAS) where servers and computers reside to control and operate the system. Operators utilize the data collected by the CAS and declared cargo information to make decisions on how to respond to an alarm.This work explores the use of CAS-located data, looking at both the sensor data streams and operator inputs, to perform analysis which supports customs and border protection agencies to improve training capability and operational effectiveness. We focus on analyzing site level effectiveness and behavior by rolling up CAS-located data collected from individual occurrences. To-date, more than 15 sites (e.g., seaports, airports, border crossings) have been analyzed in this manner with the goal of understanding system operations to verify effectiveness and recommend potential improvements. This work first aims to provide background information on relevant CAS-located data sources and our current operational system analytics process including example results. After summarizing our current analytic techniques, we discuss how the future data analytics systems can provide key benefits to improving operational performance while minimizing the burden these detection systems place on operators.

Kuhn, Michael↗

Human behavior determinants of exposure to Anopheles vectors of malaria in Sumba, Indonesia

Malaria vector control interventions in Sumba, Indonesia, have not been able to eliminate malaria. Human drivers of exposure to Anopheles bites were investigated as part of a larger clinical trial evaluating the impact of a spatial repellent product on malaria incidence. Human behavioral observations (HBOs) evaluating temporal and spatial presence, sleeping behaviors, and insecticide treated net (ITN) use, were collected parallel to entomological collections—indoor and outdoor human landing catches (HLCs), and house hold surveys. Data demonstrates that mosquito access to humans, enabled by structurally open houses, is evident by the similar entomological landing rates both inside and outside households. The presence of animals inside houses was associated with increased mosquito entry–however, the number of humans present inside houses was not related to increased mosquito landing. Analyzing mosquito landing rates with human behavior data enables the spatial and temporal estimation of exposure to Anopheles bites, accounting for intervention (ITN) presence and usage. Human behavior adjusted exposure to Anopheles bites was found to be highest in the early in the evening, but continued at lower levels throughout the night. Over the night, most exposure (53%) occurred when people were indoors and not under the protection of nets (asleep or awake) followed by exposure outside (44%). Characterized gaps in protection are outdoor exposure as well as exposure indoors–when awake, and when asleep and not using ITNs. Interestingly, in the primary trial, even though there was not a significant impact of the spatial repellent on vector biting rates by themselves (16%), when factoring in human behavior, there was approximately 28% less exposure in the intervention arm than in the placebo arm. The treated arm had less human behavior adjusted bites in all spaces evaluated though there was proportionally higher exposure indoors. This analysis points to the importance of using HBOs both towards understanding gaps in protection as well as how interventions are evaluated. To mitigate ongoing transmission, understanding context specific spatial and temporal exposure based on the interactions of vectors, humans and interventions would be vital for a directed evidence-based control or elimination strategy.

60 APPLIED LIFE SCIENCES↗

The role of driver head pose dynamics and instantaneous driving in safety critical events: Application of computer vision in naturalistic driving

This paper investigates the role of driver behavior especially head pose dynamics in safety–critical events (SCEs). Using a large dataset collected in a naturalistic driving study, this paper analyzes the head pose dynamics and driving behavior in moments leading up to crashes or near-crashes. The study uses advanced computer vision and mixed logit modeling techniques to identify patterns and relationships between drivers’ head pose dynamics and crash involvement. The results suggest that driver-head pose dynamics, especially poses that indicate distraction and movement volatility, are important factors that can contribute to undesirable safety outcomes. Marginal effects show that angular deviation for head pose dynamics indicated by yaw, pitch and roll increase the likelihood of crash intensity by 4.56%, 4.92% and 8.26% respectively. Furthermore, traffic flow and lane changing also contribute to increase in likelihood of crash intensity. These findings provide new insights into pre-crash factors, especially human factors and safety–critical events. The study highlights the importance of considering human factors in designing driver assistance systems and developing safer vehicles. This research contributes by examining naturalistic driving data at the microscopic level with early detection of behaviors that lead to SCEs and provides a basis for future research on automation.

33 ADVANCED PROPULSION SYSTEMS↗

Interpretable Machine Learning for Characterizing Electric Vehicle Charging Behavior: Insights from Real-World Data

As electric vehicle (EV) adoption rises globally, concerns about the impact on aging electrical grids grow, particularly regarding the charging behavior of EV drivers. This study analyzes real-world driving and charging data from Ford battery electric vehicles (BEVs) collected between 2018 and 2019 to develop interpretable models that characterize charging behavior and quantify influencing factors. Prior research has relied on assumptions regarding driver behavior, often overlooking actual charging patterns. By employing generalized linear mixed models (GLMMs), this work offers insights into how various elements, such as next trip distance and state of charge (SOC), influence charging decisions. The dataset comprises over three million park-trip pairs from 1,997 vehicles, revealing that features related to driving behavior significantly dictate charging behavior, while infrastructure and regional factors have lesser impacts. The findings suggest that existing simulation models may oversimplify EV charging behavior assumptions. This work utilizes real-world EV driving and charging data to train interpretable models that describe charging behavior and quantify the factors most associated with how drivers use charging infrastructure. This research underscores the need for interpretable, data-driven methodologies to inform future EV infrastructure planning and grid management.

29 - ENERGY PLANNING, POLICY AND ECONOMY↗