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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 253 records · Page 14
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

A Global Building Occupant Behavior Database

This paper introduces a database of 34 field-measured building occupant behavior datasets collected from 15 countries and 39 institutions across 10 climatic zones covering various building types in both commercial and residential sectors. This is a comprehensive global database about building occupant behavior. The database includes occupancy patterns (i.e., presence and people count) and occupant behaviors (i.e., interactions with devices, equipment, and technical systems in buildings). Brick schema models were developed to represent sensor and room metadata information. The database is publicly available, and a website was created for the public to access, query, and download specific datasets or the whole database interactively. The database can help to advance the knowledge and understanding of realistic occupancy patterns and human-building interactions with building systems (e.g., light switching, set-point changes on thermostats, fans on/off, etc.) and envelopes (e.g., window opening/closing). With these more realistic inputs of occupants’ schedules and their interactions with buildings and systems, building designers, energy modelers, and consultants can improve the accuracy of building energy simulation and building load forecasting.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2015

Electric radiation mapping of silver/zinc oxide nanoantennas by using electron holography

In this work, we report the fabrication of self-assembled zinc oxide nanorods grown on pentagonal faces of silver nanowires by using microwaves irradiation. The nanostructures resemble a hierarchal nanoantenna and were used to study the far and near field electrical metal-semiconductor behavior from the electrical radiation pattern resulting from the phase map reconstruction obtained using off-axis electron holography. As a comparison, we use electric numerical approximations methods for a finite number of ZnO nanorods on the Ag nanowires and show that the electric radiation intensities maps match closely the experimental results obtained with electron holography. The time evolution of the radiation pattern as generated from the nanostructure was recorded under in-situ radio frequency signal stimulation, in which the generated electrical source amplitude and frequency were varied from 0 to 5 V and from 1 to 10 MHz, respectively. The phase maps obtained from electron holography show the change in the distribution of the electric radiation pattern for individual nanoantennas. The mapping of this electrical behavior is of the utmost importance to gain a complete understanding for the metal-semiconductor (Ag/ZnO) heterojunction that will help to show the mechanism through which these receiving/transmitting structures behave at nanoscale level.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1991

Variability in total ozone associated with baroclinic waves

One-point regression maps of total ozone formed by regressing the time series of bandpass-filtered geopotential height data have been analyzed against Total Ozone Mapping Spectrometer data. Results obtained reveal a strong signature of baroclinic waves in the ozone variability. The regressed patterns are found to be similar in extent and behavior to the relative vorticity patterns reported by Lim and Wallace (1991).

Mote, Philip W.↗
DOE OSTI2022

Results From Invoking Artificial Neural Networks to Measure Insider Threat Detection & Mitigation

Advances on differentiating between malicious intent and natural “organizational evolution” to explain observed anomalies in operational workplace patterns suggest benefit from evaluating collective behaviors observed in the facilities to improve insider threat detection and mitigation (ITDM). Advances in artificial neural networks (ANN) provide more robust pathways for capturing, analyzing, and collating disparate data signals into quantitative descriptions of operational workplace patterns. In response, a joint study by Sandia National Laboratories and the University of Texas at Austin explored the effectiveness of commercial artificial neural network (ANN) software to improve ITDM. Overall, this research demonstrates the benefit of learning patterns of organizational behaviors, detecting off-normal (or anomalous) deviations from these patterns, and alerting when certain types, frequencies, or quantities of deviations emerge for improving ITDM. Evaluating nearly 33,000 access control data points and over 1,600 intrusion sensor data points collected over a nearly twelve-month period, this study's results demonstrated the ANN could recognize operational patterns at the Nuclear Engineering Teaching Laboratory (NETL) and detect off-normal behaviors—suggesting that ANNs can be used to support a data-analytic approach to ITDM. Several representative experiments were conducted to further evaluate these conclusions, with the resultant insights supporting collective behavior-based analytical approaches to quantitatively describe insider threat detection and mitigation.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2025

Exploring Nonmotorized Travel in New York State Using 2017 National Household Travel Survey Data

This report presents systematic analysis of nonmotorized travel behavior within New York State (NYS) utilizing 2017 National Household Travel Survey data. As walking and biking assume increasingly prominent roles in advancing active transportation objectives, accessible mobility, and sustainable transportation systems, comprehensive understanding of these modal patterns becomes essential for evidence-based policy development and strategic planning initiatives. Recognizing the substantial geographic, demographic, and socioeconomic heterogeneity characterizing NYS—spanning from the concentrated urban fabric of New York City (NYC) to dispersed suburban and rural contexts—this analysis employs the structured "4Ws" analytical framework to examine participant demographics, spatial and temporal distributions, and motivational factors underlying active transportation choices relative to motorized alternatives. This methodological approach captures modal behavior patterns, user characteristics, trip purposes, and temporal variations across the state's diverse contexts. Additionally, the research examines behavioral differences among distinct user classifications, including walk-only and bike-only travelers, while conducting comparative analysis of nonmotorized travel patterns between ALICE (Asset Limited, Income Constrained, Employed) and non-ALICE household categories.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2016

Creating Systems Engineering Products with Executable Models in a Model-Based Engineering Environment

Applying systems engineering across the life-cycle results in a number of products built from interdependent sources of information using different kinds of system level analysis. This paper focuses on leveraging the Executable System Engineering Method (ESEM) which automates requirements verification (e.g. power and mass budget margins and duration analysis of operational modes) using executable SysML models. The particular value proposition is to integrate requirements, and executable behavior and performance models for certain types of system level analysis. The models are created with modeling patterns that involve structural, behavioral and parametric diagrams, and are managed by an open source Model Based Engineering Environment (named OpenMBEE). This paper demonstrates how the ESEM is applied in conjunction with OpenMBEE to create key engineering products (e.g. operational concept document) for the Alignment and Phasing System (APS) within the Thirty Meter Telescope (TMT) project, which is under development by the TMT International Observatory (TIO).

MBSE↗
NASA NTRS1975

Effects of prolonged weightlessness on the swimming pattern of fish aboard Skylab 3

Looping behavior of minnows aboard Skylab 3 is analyzed. Extensive looping patterns were observed at first look on the third day of weightlessness; thereafter, the frequency of the looping episodes diminished until complete adaptation on the twenty-first day, at which time the fish oriented themselves with their backs to the light. The swimming anomaly could be due to (1) absence of continuous bending of sense hairs to a certain extent by gravity, causing the fish to tilt forward in an attempt to increase leverage on the hairs - in the absence of all gravity, tilting is continued into looping (this hypothesis is supported by parabolic flight experiments with partial gravity, in which only tilting was seen); or (2) an attempt by the fish to create a gravitoinertial stimulus by 'centrifuging' its otoliths by looping.

Von Baumgarten, R. J.↗
DOE OSTI2023

Estimate long-term impact on battery degradation by considering electric vehicle real-world end-use factors

Many estimates of battery capacity degradation are based on accelerated lab tests that involve charge-discharge cycles or rely on data or electrochemical modeling. These methods are reasonable for technology benchmarking but rarely consider real-world end-use factors. To address this issue, this study develops the Battery Run-down under Electric Vehicle Operation (BREVO) model. It links the driver's travel pattern to physics-based battery degradation and powertrain energy consumption models. The model simulates the impacts of charging behavior, charging rate, driving patterns, and multiple energy management modules on battery capacity degradation. It finds that, over a 10-year timespan, firstly, for a random driver situated in the New England area, daily direct-current fast charging (60 kW) could lead to up to 22% less battery capacity when compared to daily Level-1 charging (1.8 kW). Second, the battery thermal management system can delay battery degradation by approximately 0.5% in the New England area. Third, warmer ambient temperatures enhance BEV battery usage. The model indicates that the battery capacity in the Los Angeles area is 6% higher than that in the New England area. The BREVO model provides crucial information for consumers and BEV manufacturers on range anxiety, BEV battery design, and decision support of battery warranty.

25 ENERGY STORAGE↗
DOE OSTI2026

Reconstruction of Six-Dimensional Phase Space

A phase space is a mathematical representation of all possible physical states of a system. Particle beams at Fermilab exist within a six-dimensional (6D) phase space defined by three positional components, (x, y, z) and three momentum components, (px, py, pz). To reconstruct this space implies taking measurement data from detectors and mapping out particle behavior using computational methods. The beam detectors, however, are only able to detect spatial distribution among the events of the beam, therefore being limited to positional data. Also, due to the vast number of events in a particle beam, it is extremely difficult to analyze and differentiate every single one’s behavior. However, with Machine Learning (ML), which can distinguish between patterns and map out particle behavior more efficiently. We first used the particle beam software, G4beamline, to simulate a 10,000-event muon beam, adjusting parameters such as initial momentum magnitude (p¬0) and virtual detector position. Using ten virtual detectors, we analyzed p0 values such that minimum 9,990 events were analyzed by every detector. We then input the data from these beam simulations to a C++ program, that randomly selects 100 events, and creates a 2D histogram based on spatial distribution, detector position, and event intensity. This process is repeated 100 times to create 100 histograms per p0 value. These images were then input to a modified ResNet18 Convolutional Neural Network (CNN) for training, and to predict p0 from some unseen set of histograms. The model was accurate when trained on momentum increments of 5 MeV/c and provided with denser training samples around highly variable test values. These results displayed machine learning being able to accurately predict p0 from being trained on different particle behaviors.

Shirlee, Jermain [Fermilab]↗
DOE OSTI2023

Synchronization of Alternative Models in a Supermodel and the Learning of Critical Behavior

“Supermodeling” climate by allowing different models to assimilate data from one another in run time has been shown to give results superior to those of any one model and superior to any weighted average of model outputs. The only free parameters, connection strengths between corresponding variables in each pair of models, are determined using some form of machine learning. It is demonstrated that supermodeling succeeds because near critical states, interscale interactions are important but unresolved processes cannot be effectively represented diagnostically in any single parameterization scheme. In two examples, a pair of toy quasigeostrophic (QG) channel models of the midlatitudes and a pair of ECHAM5 models of the tropical Pacific atmosphere with a common ocean, supermodels dynamically combine parameterization schemes so as to capture criticality, associated critical structures, and the supporting scale interactions. The QG supermodeling scheme extends a previous configuration in which two such models synchronize with intermodel connections only between medium-scale components of the flow; here the connections are trained against a third “real” model. Intermittent blocking patterns characterize the critical behavior thus obtained, even where such patterns are missing in the constituent models. In the ECHAM-based climate supermodel, the corresponding critical structure is the single ITCZ pattern, a pattern that occurs in neither of the constituent models. In conclusion, for supermodels of both types, power spectra indicate enhanced interscale interactions in frequency or energy ranges of physical interest, in agreement with observed data, and supporting a generalized form of the self-organized criticality hypothesis.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Application of materials informatics to vapor-grown carbon nanofiber/vinyl ester nanocomposites through self-organizing maps and clustering techniques

Data mining and knowledge discovery techniques were employed herein to acquire new information on the viscoelastic, flexural, compressive, and tensile properties of vapor-grown carbon nanofiber (VGCNF)/vinyl ester (VE) nanocomposites. Formulation and processing factors (curing environment, presence or absence of dispersing agent, mixing method, VGCNF weight fraction, VGCNF type, high-shear mixing time, and sonication time) and testing temperature were utilized as inputs and the true ultimate strength, true yield strength, engineering elastic modulus, engineering ultimate strength, flexural modulus, flexural strength, storage modulus, loss modulus, and tan delta were selected as outputs. The data mining and knowledge discovery algorithms used in this study include self-organizing maps (SOMs) and clustering techniques. SOMs demonstrated that temperature and tan delta had the most significant effects on the output responses followed by the VGCNF high-shear mixing time, and sonication time. SOMs were also used to produce optimal responses using certain combination(s) of inputs. Fuzzy C-means algorithm (FCM) was also applied to discover patterns in the nanocomposite behavior subsequent to a principal component analysis (PCA), which is a dimensionality reduction technique. Utilizing these techniques, the nanocomposite specimens were separated into different clusters based on the testing temperature (30 °C and 120 °C being the most dominant responses), tan delta, high-shear mixing time, and sonication time. Furthermore, the VGCNF/VE specimens were separated into a cluster based on their viscoelastic responses (storage and loss moduli) at the same temperature. The FCM results indicate that, while all nanocomposite properties in the new framework are essential, the viscoelastic responses of the VGCNF/VE specimens are the most significant. Here this work highlights the utility of data mining and knowledge discovery techniques in the context of materials informatics for the discovery of patterns and trends in the material behavior that are not immediately known.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Automated Environment Generation for Software Model Checking

A key problem in model checking open systems is environment modeling (i.e., representing the behavior of the execution context of the system under analysis). Software systems are fundamentally open since their behavior is dependent on patterns of invocation of system components and values defined outside the system but referenced within the system. Whether reasoning about the behavior of whole programs or about program components, an abstract model of the environment can be essential in enabling sufficiently precise yet tractable verification. In this paper, we describe an approach to generating environments of Java program fragments. This approach integrates formally specified assumptions about environment behavior with sound abstractions of environment implementations to form a model of the environment. The approach is implemented in the Bandera Environment Generator (BEG) which we describe along with our experience using BEG to reason about properties of several non-trivial concurrent Java programs.

Tkachuk, Oksana↗
DOE OSTI2021

Injectable platelet rich fibrin facilitates hair follicle regeneration by promoting human dermal papilla cell proliferation, migration, and trichogenic inductivity

Hair follicle regeneration has been successful in mice but failed in human being for years. Dermal papilla cells, a specialized mesenchymal stem cell derived from dermal papilla within hair follicles, is considered the key cells for hair follicle regeneration function as both regeneration initiator and regulator. Injectable platelet rich fibrin (i-PRF), a novel biomaterial rich in a variety of growth factors and three-dimensional scaffolds, has shown promising effects on tissue regeneration. In this study, we aimed to evaluate the application of i-PRF in human hair follicle regeneration by examining the biological effects of i-PRF on human dermal papilla cells (hDPCs). Biomaterial compatibility, cell viability, proliferation, migration, alkaline phosphatase activity and trichogenic inductivity were assessed after exposing hDPCs to different concentrations of i-PRF extracts. In addition, we investigated the ultrastructure of i-PRF with all cell components filtered. The results revealed that i-PRF possessing excellent biocompatibility and could significantly promote hDPCs proliferation, migration, and trichogenic inductivity. Furthermore, the concentration of i-PRF is able to remarkably influence hDPCs behavior in a dose-dependent pattern. Different concentrations exhibited differential effects on hDPCs behavior. In general, lower concentration promotes cell proliferation better than higher concentration, while higher concentration promotes cell function better reversely. Best concentration for hDPCs in vitro expending is 1% concentration. 20% concentration is optimal for hair follicle regeneration. In summary, our findings concluded that i-PRF facilitates hair follicle regeneration by promoting human dermal papilla cell proliferation, migration, and trichogenic inductivity.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1992

Scheduling message processing for reducing rollback propagation

Traditional checkpointing and rollback recovery techniques for parallel systems have typically assumed the communication pattern is specified by program behavior. In this paper we exploit the property that the communication pattern can often be changed at run-time without affecting program correctness. A scheduling algorithm for message processing and its implementation for reducing rollback propagation are described. The algorithm incorporates a user-transparent prioritized scheme based upon the run-time communication and checkpointing history. Communication trace-driven simulation for several parallel programs written in the Chare Kernel language demonstrates that the probability of rollback propagation can be reduced at the cost of slight additional performance degradation.

Wang, Yi-Min↗
NASA NTRS1999

Sonic Boom Prediction and Minimization of the Douglas Reference OPT5 Configuration

Conventional CFD methods and grids do not yield adequate resolution of the complex shock flow pattern generated by a real aircraft geometry. As a result, a unique grid topology and supersonic flow solver was developed at Northrop Grumman based on the characteristic behavior of supersonic wave patterns emanating from the aircraft. Using this approach, it was possible to compute flow fields with adequate resolution several body lengths below the aircraft. In this region, three-dimensional effects are diminished and conventional two-dimensional modified linear theory (MLT) can be applied to estimate ground pressure signatures or sonic booms. To accommodate real aircraft geometries and alleviate the burdensome grid generation task, an implicit marching multi-block, multi-grid finite-volume Euler code was developed as the basis for the sonic boom prediction methodology. The Thomas two-dimensional extrapolation method is built into the Euler code so that ground signatures can be obtained quickly and efficiently with minimum computational effort suitable to the aircraft design environment. The loudness levels of these signatures can then be determined using a NASA generated noise code. Since the Euler code is a three-dimensional flow field solver, the complete circumferential region below the aircraft is computed. The extrapolation of all this field data from a cylinder of constant radius leads to the definition of the entire boom corridor occurring directly below and off to the side of the aircraft's flight path yielding an estimate for the entire noise "annoyance" corridor in miles as well as its magnitude. An automated multidisciplinary sonic boom design optimization software system was developed during the latter part of HSR Phase 1. Using this system, it was found that sonic boom signatures could be reduced through optimization of a variety of geometric aircraft parameters. This system uses a gradient based nonlinear optimizer as the driver in conjunction with a computationally efficient Euler CFD solver (NIIM3DSB) for computing the three-dimensional near-field characteristics of the aircraft. The intent of the design system is to identify and optimize geometric design variables that have a beneficial impact on the ground sonic boom. The system uses a simple wave drag data format to specify the aircraft geometry. The geometry is internally enhanced and analytic methods are used to generate marching grids suitable for the multi-block Euler solver. The Thomas extrapolation method is integrated into this system, and hence, the aircraft's centerline ground sonic boom signature is also automatically computed for a specified cruise altitude and yields the parameters necessary to evaluate the design function. The entire design system has been automated since the gradient based optimization software requires many flow analyses in order to obtain the required sensitivity derivatives for each design variable in order to converge on an optimal solution. Hence, once the problem is defined which includes defining the objective function and geometric and aerodynamic constraints, the system will automatically regenerate the perturbed geometry, the necessary grids, the Euler solution, and finally the ground sonic boom signature at the request of the optimizer.

Siclari, Michael J.↗
DOE OSTI2024

Forest structural complexity and ignition pattern influence simulated prescribed fire effects

Background: Forest structural characteristics, the burning environment, and the choice of ignition pattern each influence prescribed fire behaviors and resulting fire effects; however, few studies examine the influences and interactions of these factors. Understanding how interactions among these drivers can influence prescribed fire behavior and effects is crucial for executing prescribed fires that can safely and effectively meet management objectives. To analyze the interactions between the fuels complex and ignition patterns, we used FIRETEC, a three-dimensional computational fluid dynamics fire behavior model, to simulate fire behavior and effects across a range of horizontal and vertical forest structural complexities. For each forest structure, we then simulated three different prescribed fires each with a unique ignition pattern: strip-head, dot, and alternating dot. Results: Forest structural complexity and ignition pattern affected the proportions of simulated crown scorch, consumption, and damage for prescribed fires in a dry, fire-prone ecosystem. Prescribed fires in forests with complex canopy structures resulted in increased crown consumption, scorch, and damage compared to less spatially complex forests. The choice of using a strip-head ignition pattern over either a dot or alternating-dot pattern increased the degree of crown foliage scorched and damaged, though did not affect the proportion of crown consumed. We found no evidence of an interaction between forest structural complexity and ignition pattern on canopy fuel consumption, scorch, or damage. Conclusions: We found that forest structure and ignition pattern, two powerful drivers of fire behavior that forest managers can readily account for or even manipulate, can be leveraged to influence fire behavior and the resultant fire effects of prescribed fire. These simulation findings have critical implications for how managers can plan and perform forest thinning and prescribed burn treatments to meet risk management or ecological objectives.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Stretchable and wearable polymeric heaters and strain sensors fabricated using liquid metals

Wearable electronic devices (WEDs) are receiving significant attention because of the increasing interest in soft robotics, electronic skin, and wearable sensors. Liquid metals (LMs) are compelling for WEDs owing to their metallic conductivity, fluidic nature, and low toxicity. For this study, we fabricated stretchable and soft WEDs using LM (eutectic gallium-indium alloy) wires (LMWs) patterned via force wetting through custom-made stencils on an elastic substrate. LMWs can generate thermal energy via Joule heating upon current application and deliver it to the substrate, resulting in wearable polymeric heaters. The LM mixed with carbonyl iron particles (CIPs) can also be patterned while preserving fluidic behavior. The degree of thermal energy generated through the LMWs can be manipulated as a function of the CIP concentration in the LM and geometrical factors of the electrode patterns, i.e., width and length. An elastic film patterned with LMWs attached to the human body exhibits changes in the effective electrical resistance depending on applied strain, demonstrating potential as a wearable strain sensor. This LM utilized WED that can generate thermal energy upon current application through the LMWs and detect the bodily motion has significant potential for application in wearable thermotherapy, electronic skin, and soft sensors.

47 OTHER INSTRUMENTATION↗