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At least 163 records · Page 9

All sources NASA NTRS DOE OSTI

DOE OSTI2021

Effects of 5-Ion Beam Irradiation and Hindlimb Unloading on Metabolic Pathways in Plasma and Brain of Behaviorally Tested WAG/Rij Rats

A limitation of simulated space radiation studies is that radiation exposure is not the only environmental challenge astronauts face during missions. Therefore, we characterized behavioral and cognitive performance of male WAG/Rij rats 3 months after sham-irradiation or total body irradiation with a simplified 5-ion mixed beam exposure in the absence or presence of simulated weightlessness using hindlimb unloading (HU) alone. Six months following behavioral and cognitive testing or 9 months following sham-irradiation or total body irradiation, plasma and brain tissues (hippocampus and cortex) were processed to determine whether the behavioral and cognitive effects were associated with long-term alterations in metabolic pathways in plasma and brain. Sham HU, but not irradiated HU, rats were impaired in spatial habituation learning. Rats irradiated with 1.5 Gy showed increased depressive-like behaviors. This was seen in the absence but not presence of HU. Thus, HU has differential effects in sham-irradiated and irradiated animals and specific behavioral measures are associated with plasma levels of distinct metabolites 6 months later. The combined effects of HU and radiation on metabolic pathways in plasma and brain illustrate the complex interaction of environmental stressors and highlights the importance of assessing these interactions.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2023

Anomalous behavior detection by an artificial intelligence-enabled system with multiple correlated sensors

Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

Serebryakov, Sergey↗

DOE OSTI2023

Aggregate residential demand flexibility behavior: A novel assessment framework

Residential demand flexibility (DF) could save the U.S. electric grid up to 10 GW of peak demand while supporting increased amounts of renewable generation. However, less than 40% of the estimated DF peak reduction capacity is currently realized, and less than 8% of American households are enrolled. These low participation rates are combined with high rates of "overriding" a DF event and eventual opt-outs among enrolled customers. There is still not a comprehensive understanding of the drivers of DF participation and occupant interaction with DF program signals. We, therefore, present a novel survey processing framework to assess occupant DF-relevant behavior from the American Time Use Survey (ATUS). Using the framework, we summarize both the extensive and intensive behavior of more than 200,000 ATUS respondents (2003-2018 data) and provide insights on the DF-relevant behavior of residential occupants, which is generally overlooked in the literature. Here we use the framework to identify the activity priorities of residential occupants in the United States during different DF-relevant periods (critical peak, peak, and off-peak). These preferred activities capture overlooked routine behaviors that could be barriers to DF participation, if ignored, and might explain the high levels of overrides often exhibited by participants of demand response.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2023

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↗

DOE OSTI2022

A guideline to document occupant behavior models for advanced building controls

The availability of computational power, and a wealth of data from sensors have boosted the development of model-based predictive control for smart and effective control of advanced buildings in the last decade. More recently occupant-behavior models have been developed for including people in the building control loops. However, while important objectives of scientific research are reproducibility and replicability of results, not all information is available from published documents. Therefore, the aim of this paper is to propose a guideline for a thorough and standardized occupant-behavior model documentation. For that purpose, the literature screening for the existing occupant behavior models in building control was conducted, and the occupant behavior modeling processes were studied to extract practices and gaps for each of the following phases: problem statement, data collection, and preprocessing, model development, model evaluation, and model implementation. Here, the literature screening pointed out that the current state-of-the-art on model documentation shows little unification, which poses a particular burden for the model application and replication in field studies. In addition to the standardized model documentation, this work presented a model-evaluation schema that enabled benchmarking of different models in field settings as well as the recommendations on how OB models are integrated with the building system.

Building control↗

DOE OSTI2022

Revealing the interplay between “intelligent behavior” and surface reconstruction of non-precious metal doped SrTiO 3 catalysts during methane combustion

The impact of surface reconstruction of a model perovskite, SrTiO 3 (STO), on CH 4 activation for combustion and oxidative coupling was previously revealed that the reaction rate was proportional to the creation of Srterminated step sites. Doped perovskites (SrTi 1-x M x O 3 , M=metal dopant) present yet another form of reconstruction throughout the surface and the bulk, where the metal dopant can migrate in and out of the perovskite lattice, also known as "intelligent behavior". In this work, understanding the interplay between perovskite surface reconstruction (surface termination) and the "intelligent behavior" is tackled for the first time, and the catalytic consequences are probed with CH 4 combustion as a model reaction. A set of experimental techniques, including XRD, Raman spectroscopy, X-ray adsorption spectroscopy, kinetic measurements, as well as DFT calculations were used to understand the catalytic behavior of the reconstructed surfaces of Ni and Cu-doped STO for methane combustion. Here, we found that during methane oxidation, the diffusion of Ni and Cu into the lattice due to the "intelligent behavior" is accompanied by Sr enrichment on the surface of the perovskite. This Srenrichment process is reversible when Cu or Ni species exsolute as clusters/nanoparticles upon H 2 treatment. Such a surface reconstruction is found to greatly impact the catalytic activity of doped perovskites towards methane combustion.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Think before you throw! An analysis of behavioral interventions targeting PET bottle recycling in the United States

The United States generates 42 Mt of plastic waste each year and is one of the biggest contributors to ocean plastic waste. Consequently, plastic has become synonymous with the linear economy, and many scholars are studying and proposing circular economy solutions to mitigate plastic pollution. Recycling has received much attention from both social sciences and engineering as a circular economy strategy, but no study has yet quantified how behavioral interventions could asymmetrically affect different populations. Here, this study combines agent-based modeling, material flow analysis, system dynamics, and life cycle assessment to assess the effect of four behavioral interventions on the collection rates of polyethylene terephthalate bottle waste, displaced virgin plastic manufacturing, and avoided greenhouse gas (GHG) emissions. Results show that, while behavioral interventions would require about 300–900 GJ of additional energy at end-of-life due to improved collection rates, they would avoid about 500–700 thousand metric tons of GHG emissions. Results also illustrate the importance of habits in disposal behaviors and show that different forms of interventions can be better adapted to particular social contexts than others. While the circular economy and its application to plastic waste should certainly not be restricted to recycling, this study demonstrates that improved collection rates and recycling technologies can contribute to reducing the amount of plastic waste polluting our oceans.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2022

Explosion behavior investigation and safety assessment of large-format lithium-ion pouch cells

We report large-format lithium-ion (Li-ion) batteries with high energy density for electric vehicles are prone to thermal runaway (or even explosion) under abusive conditions. In this study, overcharge induced explosion behaviors of large-format Li-ion pouch cells with Li[Ni 0.8 Co 0.1 Mn 0.1 ]O 2 cathode at different current rates (C-rates) (0.5C, 1C, 2C) were investigated. The explosion characteristics of the cells were elucidated by discussing the evolution of the cell voltage, the surface temperature and the shock wave pressure. Generally, the whole overcharge process could be divided into four stages according to the evolution of several key parameters and the overcharge behaviors; the overcharge C-rate has a great influence on cells’ thermal behaviors. The experimental results showed that the thermal runaway process of Li-ion cells caused by overcharging consisted of two kinds of explosions, physical explosion and chemical explosion. The existence of observable negative pressure zone in the pressure curves indicated that the Li-ion cells are not a self-supplying oxygen system during the explosion. Further, the explosion dynamics parameters were matched. An explosion TNT-equivalent conversion strategy that depended on the pressure of the shock wave was utilized to evaluate the released energy and its hazards. In addition, with respect to the overcharge of Li-ion pouch cells, a safety assessment method and a safety management method were proposed based on the explosion behaviors. From the perspective of battery safety, this study is of great significance for the safety design of Li-ion cells and can provide guidance for engineers to optimize the safety function of battery packs.

25 ENERGY STORAGE↗

DOE OSTI2024

Observation and Modeling of Dynamic Fracture Behaviors of Battery Cell Under Impact Loading Using Enhanced Representative Volume Element Concept

The burgeoning electric automobile industry has increased interest in battery safety. Battery cells experience significant mechanical stress during operation, including the impact of accidents and vibrations from driving. The potential for thermal runaway reactions in battery cells raises safety concerns. Although numerous researchers have defined the dynamic behavior of battery cells and proposed numerical models to describe it, few studies have focused on the high-strain rate mechanical impact phase correlated with the onset of fracture. In this study, we describe the dynamic behavior of pouch battery cells and propose a modeling method to study their mechanical failure under impact situations. Impact tests are conducted at various velocities and heights. To overcome numerical issues commonly encountered under rapid deformation scenarios, a new finite element model is developed based on the representative volume element model. The proposed approach efficiently simulates continuous crack propagation and brittleness behavior during impact by permitting the individual behavior of the cell components. Therefore, engineers can reliably design safer electric vehicle battery cells by measuring the properties of the cell components.

ENERGY STORAGE↗

DOE OSTI2020

Rheological behavior and fiber spinning of polyacrylonitrile (PAN)/Carbon nanotube (CNT) dispersions at high CNT loading

Rheological studies play an important role in polymer processing including fiber spinning. In the current work, rheological behavior has been studied for polyacrylonitrile (PAN)/carbon nanotube (CNT) dispersions in dimethyl formamide (DMF), where CNT loading is as high as 15 wt% of the total solids (polymer + CNT). The presence of CNTs increased the elastic-like and shear thinning behavior of the dispersions. The terminal slope of the log G' versus log G" curve, which was used to characterize the homogeneity of solution or dispersion, is dependent on the CNT concentration but independent of temperature between 0 and 60 °C. PAN/CNT-10 (90 wt% PAN +10 wt% CNT) and PAN/CNT-15 were gel spun at different conditions, with small diameter capillary at high shear rate and large diameter capillary at relatively low shear rate. Additionally, for PAN/CNT-10, fiber spinning with lower molecular weight polymer showed increased continuous jetting time when compared to spinning with higher molecular weight polymer. In addition, the rheological behavior of PAN/cellulose nanocrystal (CNC) dispersion with 20–60 wt% CNC was also compared to that of the PAN/CNT dispersions. PAN/CNC and PAN/CNT share similarities in both rheological behavior and fiber spinning at high filler loading.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Conflicting Information and Compliance With COVID-19 Behavioral Recommendations

The prevalence of COVID-19 is shaped by behavioral responses to recommendations and warnings. Available information on the disease determines the population’s perception of danger and thus its behavior; this information changes dynamically, and different sources may report conflicting information. We study the feedback between disease, information, and stay-at-home behavior using a hybrid agent-based-system dynamics model that incorporates evolving trust in sources of information. We use this model to investigate how divergent reporting and conflicting information can alter the trajectory of a public health crisis. The model shows that divergent reporting not only alters disease prevalence over time, but also increases polarization of the population’s behaviors and trust in different sources of information.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Behavioral and neuroinflammatory changes caused by glyphosate: Base herbicide in mice offspring

Abstract Background Glyphosate is a pesticide considered of low toxicity, but scientific evidences show it can be harmful to health. This study aimed to evaluate the toxicity in mice offspring exposed to glyphosate‐based herbicide (GBH) during the intrauterine period. Methods Female matrices received glyphosate 0.3 mg/kg daily per oral throughout the gestational period, which was variable between 18 and 22 days. From the 25th until the 28th days post‐birth, mice offspring were subjected to behavioral tests, and the prefrontal cortex was processed for immunohistochemical analysis. Results Two significant behavioral changes were observed: anxiety in the GLIF0.3 group, increase in the behavior burying marbles in the marble‐burying test and hyperactivity, expressed by the significant increase of the crossing number in the open field test. The increased microglia, TNF‐alpha, and astrocyte expression were also observed in the prefrontal cortex of offspring treated with GLIF0.3. Conclusion Exposure to GBH during mice intrauterine development induces hyperactive and anxious behavior, evidencing neuroinflammation.

de Castro Vieira Carneiro, Carla Larissa↗

DOE OSTI2022

Resident and transient coyotes exhibit differential patterns of movement behavior across heterogeneous landscapes in the southeastern United States

Coyotes (Canis latrans) are a highly adaptable canid species whose behavioral plasticity has allowed them to persist in a wide array of habitats throughout North America. As generalists, coyotes can alter movement patterns and change territorial strategies between residency (high site fidelity) and transiency (low site fidelity) to maximize fitness. Uncertainty remains about resident and transient coyote movement patterns and habitat use because research has reached conflicting conclusions regarding patterns of habitat use by both groups. We quantified effects of habitat on resident and transient coyote movement behavior using first passage time (FPT) analysis, which assesses recursive movement along an individual's movement path to delineate where they exhibit area-restricted search (ARS) behaviors relative to habitat attributes. We quantified monthly movement rates for 171 coyotes (76 residents and 53 transients) and then used estimated FPT values in generalized linear mixed models to quantify monthly habitat use for resident and transient coyotes. Transients had greater movement rates than residents across all months except January. Resident FPT values were positively correlated with agricultural land cover during fall and winter, but negatively correlated with agriculture during spring. Resident FPT values were also negatively correlated with developed habitats during May–August, deciduous land cover during June–August, and wetlands during September–January except November. FPT values of transient coyotes were positively correlated with developed areas throughout much of the year and near wetlands during July–September. Transient FPT values were negatively correlated with agriculture during all months except June and July. High FPT values (ARS behavior) of residents and transients were generally correlated with greater densities of edge habitat. Although we observed high individual variation in space use, our study found substantive differences in habitat use between residents and transients, providing further evidence that complexity and plasticity of coyote habitat use is influenced by territorial strategy.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

A constitutive structural parameter c b for the work hardening behavior of additively manufactured Ti-6Al-4V

The mechanical behavior of Ti-6Al-4V produced by additive manufacturing processes is assessed as based on a model derived from the Kocks–Mecking relationship. A constitutive parameter c b is derived from a linear Kocks–Mecking relationship for the microstructure that is characteristic of the work hardening behavior. The formulation for c b is determined by considering the plastic strain between the strengths at the proportional limit and the plastic instability. In this way, the model accommodates the variation in work hardening behavior observed when evaluating material as produced and tested along different orientations. The modeling approach is presented and evaluated for the case of Ti-6Al-4V additively manufactured materials as tested under quasi-static uniaxial tension. It is found that different test specimen orientations, along with postbuild heat treatments, produce a change in the microstructure and plasticity behavior which can be accounted for in the corresponding change of the c b values.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Automated Experiments of Local Non–Linear Behavior in Ferroelectric Materials

An automated experiment in multimodal imaging to probe structural, chemical, and functional behaviors in complex materials and elucidate the dominant physical mechanisms that control device function is developed and implemented. Here, the emergence of non-linear electromechanical responses in piezoresponse force microscopy (PFM) is explored. Non-linear responses in PFM can originate from multiple mechanisms, including intrinsic material responses often controlled by domain structure, surface topography that affects the mechanical phenomena at the tip-surface junction, and the presence of surface contaminants. Using an automated experiment to probe the origins of non-linear behavior in ferroelectric lead titanate (PTO) and ferroelectric Al 0.93 B 0.07 N films, it is found that PTO shows asymmetric nonlinear behavior across a/c domain walls and a broadened high nonlinear response region around c/c domain walls. In contrast, for Al 0.93 B 0.07 N, well-poled regions show high linear piezoelectric responses, when paired with low non-linear responses regions that are multidomain show low linear responses and high nonlinear responses. It is shown that formulating dissimilar exploration strategies in deep kernel learning as alternative hypotheses allows for establishing the preponderant physical mechanisms behind the non-linear behaviors, suggesting that automated experiments can potentially discern between competing physical mechanisms. Furthermore, this technique can also be extended to electron, probe, and chemical imaging.

36 MATERIALS SCIENCE↗

DOE OSTI2026

A Data-Driven Method for Modeling Creep-Fatigue Stress- Strain Behavior Using Neural ODEs

In this paper, we introduce a data-driven machine learning approach for modeling one-dimensional stress–strain behavior under cyclic loading, utilizing experimental data from the nickel-based Alloy 617. The study employs uniaxial creep–fatigue test data acquired under various loading histories and compares two distinct neural network-based ODE models. The first model, known as the black-box model, comprehensively describes the strain–stress relationship using a Neural ODE equation. To interpret this black-box model, we apply the Sparse Identification of Nonlinear Dynamical Systems (SINDy) technique, transforming the black-box model into an equation-based model using symbolic regression. The second model, the Neural flow rule model, incorporates Hooke’s Law for the linear elastic component, with the nonlinear part characterized by a Neural ODE. Both models are trained with experimental data to accurately reflect the observed stress–strain behavior. We conduct a detailed comparison with the standard Chaboche model, which includes three back stresses. Our results demonstrate that the neural network-based ODE models precisely capture the experimental creep–fatigue mechanical behavior, exceeding the standard Chaboche model’s accuracy. Furthermore, an interpretable model derived from the black-box neural ODE model through symbolic regression achieves accuracy comparable to the Chaboche model, enhancing its interpretability. The results highlight the potential of neural network-based ODE models to depict complex creep–fatigue behavior, eliminating the necessity for experts to define a specific, material-focused model form.

creep-fatigue↗

DOE OSTI2025

Enhancing occupant behavior representation for interoperability between building information modeling and building energy modeling

Building Performance Simulation (BPS) has been adopted as an essential tool for designing, operating, and retrofitting buildings to optimize energy efficiency throughout the building life cycle. The Green Building XML (gbXML) schema facilitates seamless data exchange between Building Information Modeling (BIM) and Building Energy Modeling (BEM) software tools. However, limited occupant behavior (OB) representation in BIM often leads to inconsistent and inaccurate energy simulation in BEM software. This paper presents 154 systematic enhancements to the existing occupant behavior XML (obXML) schema v1.3.4, initially developed for standardizing OB representation for BEM, to address existing limitations and improve interoperability with BIM models. The enhancements encompass improved integration with BIM models through extended building representations and system operations, expanded support for advanced OB models with additional environmental parameters and mathematical capabilities, and implementation of a standardized model documentation framework. To facilitate seamless data transformation between gbXML and obXML schemas, we developed a publicly available gb-obXML Schema Converter. Three case studies demonstrate the enhanced schema’s capabilities: representation of building information using a two-story office building model, documentation of a window operation behavior model, and validation of the schema converter’s functionality. The enhanced obXML schema v1.4 enables sophisticated modeling of occupant-building interactions while maintaining consistency with industry-standard BIM schemas. The standardized documentation framework facilitates reproducibility and knowledge sharing in the OB research community, while the schema converter automates the integration of building information into OB simulation workflows. These enhancements establish a foundation for more accurate building performance simulation by supporting sophisticated representation of occupant behavior within the BIM-to-BEM simulation workflows.

Chung, Jihoon↗

DOE OSTI2021

An electronic origin to the oscillatory segregation behavior in Ni-Cr and other BCC defects in FCC metals

Ni-Cr alloys exhibit oscillatory segregation behaviors near low index surfaces, in which the preferred segregation species changes from Ni in the first layer to Cr in the second layer. In many dilute-alloy systems, this oscillatory pattern is attributed to the elastic release of stresses in the local lattice around the segregating solute or impurity atom. These stresses are mostly thought to originate from mismatches in the atomic size of the solute and host atoms. In Ni-Cr alloys, however, an appreciable mismatch in atomic size is not present, leading to questions about the origins of the oscillatory behavior in this alloy. Therefore, using density functional theory, we have modeled the segregation of a single Cr atom in the and surfaces of FCC Ni, an alloy which exhibits this oscillatory behavior. Using Bader charge analysis, we show that the negative energy correlates directly with the amount of charge on the Cr atom. As Ni atoms strip valence charge from the Cr, the Cr contracts slightly in size. The greatest contraction and highest positive charge for the Cr occurs when it is in the second layer of the surface where the system exhibits the oscillating negative segregation energy. We then find that this behavior persists in other alloy systems (Ag-Nb, Cu-Cr, Pt-Nb, and Pt-V), which exhibit similar atomic radii and electronegativity differences between host and solute to Ni-Cr. These represent alloys in which the host metal exhibits an FCC ground-state structure while the solute metal exhibits a BCC ground-state structure.

36 MATERIALS SCIENCE↗