Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “energy use behavior”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Cluster analysis of occupancy schedules in residential buildings in the United States

The energy performance of residential buildings significantly depends on the building occupants’ behavior, which can be highly variable. When the heating, ventilation and air conditioning (HVAC) system is controlled based on the presence or absence of occupants in a building, occupant behavior is of even further importance to its energy performance. In current practice, building energy simulation tools generally use a single occupancy profile to represent the building’s occupancy schedule, the schedule of which is considered to be the same, regardless of the type of household being modeled. Thus, there is significant potential for improvement to allow for more flexibility and accuracy in calculation of occupancy. The objective of this study is to assess the variations in the typical types of occupancy schedules followed by the U.S. population using cluster analysis. American Time Use Survey data, which statically represents the overall U.S. population’s activities, across 12 years (2006–2017), is used. The ATUS data is segregated into smaller groups based on age and weekday/weekend, then divided into activities that are considered “at home” and “away from home”, which are mapped to the presence or non-presence of occupants in the home. Cluster analysis is then used to identify common types of occupancy schedule patterns for each age group. Three main types of patterns are obtained from cluster analysis for each age group, which together represent approximately 88% of people in the United States. The output of the cluster analysis is further analyzed to evaluate the variation in characteristics, including the number of times leaving home, time of day when leaving the home, and the timespan of absence from the home. The results of this study provide detailed insights on how typical occupants in the United States spend their time in residential spaces which can be used to create occupancy profiles for residential buildings. Finally, these occupancy profiles could be utilized inform an assessment of the energy use impact of occupancy-based controls of energy consuming systems and technologies.

42 ENGINEERING↗
DOE OSTI2026

Determining Stability Margins in Adiabatic Superconducting Magnets with 3-D Finite Element Analysis

Superconducting magnets play a key role in the development of experiments at Fermilab; understanding the operating stability of these can allow us to utilize more potent magnets for future experiments (like the proposed Muon Collider), optimize the design of magnets in more immediate experiments (like Mu2e), and research the future use of more exotic materials (like high-temperature superconductors). This summer, I developed a 3-D parametric FEA program in ANSYS Mechanical APDL that simulates quench in superconducting magnets, and I also developed a parametric MATLAB program that predicts thermal behavior in magnet quench using the MIITS method. These programs can provide useful quenching parameters (like minimum quench energy and normal zone propagation velocity) for different cases of quench, leading to the previously mentioned objective of magnet design optimization. To test the programs, preliminary cases were run and the data produced was compared and analyzed. The results of these analyses, as well as the program operating methods, are discussed in this project.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Development and application of a uranium mononitride (UN) potential: Thermomechanical properties and Xe diffusion

We report that atomic-scale modeling of thermophysical and defect properties of uranium mononitride (UN) plays an important role in establishing a better understanding and improved models of UN fuel performance. Having an accurate interatomic potential is crucial for generating reliable data at finite temperatures using molecular dynamic simulations. We report a new interatomic potential for UN, based on a combination of many-body and pairwise interactions, a simple form that we later show could be easily adapted to include Xe-U and Xe-N interactions, i.e., generating a U-N-Xe interatomic potential. The potential was fitted to experimental thermal expansion and single crystal elastic constants, as well as Frenkel, Schottky, anti-Schottky, and antisite pair reaction energies from density functional theory (DFT) calculations. Using the potential, we successfully reproduced experimental lattice parameters, thermal expansion, single crystal elastic constants, and temperature dependent heat capacity. The potential also performs reasonably well in reproducing the energy of the aforementioned stoichiometric defect reactions and defect migration barriers calculated using DFT. However, the potential underestimates the energy difference between the tetrahedral and dumbbell uranium interstitials, and a more complex potential form might be needed to overcome this issue. The potential was also used to predict UN single crystal elastic constants and elastic properties at different temperatures, showing that UN becomes softer and more compressible with increasing temperature. We also compare our potential against literature data from previous empirical potentials, demonstrating similar or better behavior depending on the property of interest. To enable the simulation of Xe in UN a Buckingham potential has been fitted to DFT-derived Xe incorporation energies. The potential was then used to determine the activation energy for Xe diffusion due to various Xe-containing defects, with {XeU : VU} exhibiting the lowest activation energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Estimating Travel Energy Consumption Uncertainty Based on Inferred Travel Mode and Sensed Travel Length

To properly inform transport policy and infrastructure changes, transportation related metrics need both measured values and uncertainties of those values. Travel monitoring smartphone apps can record people's travel behavior, but trip data quality is limited by sensor errors, user labeling rates and the accuracy of inference algorithms used for travel diary creation. We discuss the use of phone app recorded travel diary data to estimate energy consumption, and propose the use of propagation of variance to find error bars for such estimates. We define energy consumption for one trip as trip length times the energy intensity per distance unit of the travel mode used. We characterize trip length errors with relative error and inferred trip mode errors with confusion matrix columns. The resulting variances of each measurement are then propagated to the final calculated energy consumption. We tested our uncertainty methods on a dataset that used phone app data combined with prompted recall, consisting of 92,234 labeled trips for over 500,000 miles. Accounting for uncertainty using expected energy intensities and variance propagation gives a dataset-wide aggregate energy consumption percent error of about 8%, within one standard deviation from the truth. Future work could involve applying similar methods to other travel diary based metrics.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2020

HIDES: Hybrid Intrusion Detector for Energy Systems

The establishment of a resilient electric grid accompanied by a secure communications network is an ongoing battle as advanced persistent threats continue to exploit existing vulnerabilities in legacy supervisory control and data acquisition system (SCADA) infrastructure. Traditional intrusion detection systems (IDSs) lack consistent performance because of the continuously evolving attack surface of SCADA systems. These shortcomings can be overcome by integrating logical system behavior, protocol-specific knowledge, and data-based learning to develop a comprehensive IDS solution. In this paper, we present a Hybrid Intrusion Detector for Energy Systems by integrating a network-based IDS, state-of-the-art machine learning-based IDS, and model-based IDS to detect unknown and stealthy cyberattacks targeting the SCADA networks. The proposed IDS uses synchrophasor measurements and cyber logs to learn patterns of different scenarios based on spatiotemporal behaviors of power systems. As a proof of concept, we implement and validate the proposed IDS by leveraging resources available at the National Renewable Energy Laboratory's Energy Systems Integration Facility test bed. Experimental results show promising performance in detecting cyberattacks while providing realtime visualization of power system measurements and cyber logs.

machine-learning intrusion detection system↗
DOE OSTI2020

A numerical-homogenization based phase-field fracture modeling of linear elastic heterogeneous porous media

Most porous media, such as geomaterials and biomaterials are highly heterogeneous in nature, and they contain large variations of microscopic pore structures, such as pore sizes, pore distribution, and pore shapes. The oscillation of microscopic structures is a substantial challenge in theoretical characterization and is usually ignored in continuous modeling. However, mechanical behavior of porous media such as deformation and failure, are essentially impacted by the microscopic heterogeneity which needs to be considered in modeling a porous media. Here, this research proposes a numerical modeling framework with a capability to investigate the effect of microscopic heterogeneity on the macroscopic fracture behavior in porous media by using a numerical homogenization technique, combined with the phase-field fracture modeling method. This numerical modeling strategy computes a homogenized elasticity tensor based on microscopic heterogeneous pore structures heterogenous porous domain by solving boundary value problems at microscopic domain. The strain energy and subsequent propagation of macroscopic fractures will be updated using homogenized stiffness information. Using this numerical scheme, the microscopic pore structure’s impact on the fracture behavior through the homogenized elastic tensor will be taken into account. This multiscale technique is benchmarked against classical problems. Finally, the results highlight the importance of the underlying pore structure and reveal that both fracture strength and propagation path can be influenced by the microscopic heterogeneity.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2021

The action-integral and energy to explode short gold wires in ambient air

This paper is about electrically induced explosions of short (1 mm) and thin (38 μm diameter) gold bridgewires in ambient air and contrasts this behavior with that observed for longer wires in a vacuum used in applications such as Z-pinch systems. The action-integral (the time integral of the wire current squared) and energy-to-burst are measured using high current discharge systems. It is found that in contrast to some reports, the action-integral to burst is not a constant for a specific wire geometry and material but, instead, is a function of the rate of current change in the wire around the time of burst and the related metric of the current flux at burst. Furthermore, it is found that the accuracy of the constant action-integral to burst approximation is dependent on the discharge system used. Short gold wires such as these have relevancy to exploding bridgewire detonators.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Beyond curvature-driven grain growth: Insights from fully anisotropic Monte Carlo Potts simulations

Grain boundary (GB) motion away from the center of curvature, termed anti-curvature behavior, has recently been observed in 3D experiments but is not predicted by classical grain growth theory. In this study, we investigate this behavior using a novel, fully anisotropic Monte Carlo Potts (MCP) model that incorporates both misorientation and inclination dependencies of GB energy. We perform 3D grain growth simulations with isotropic and anisotropic GB energies to explore the relationship between GB velocity and curvature. Contrary to the classical relation that velocity is a product of reduced mobility and mean curvature, we observe no consistent correlation between velocity and mean curvature for individual GBs, even under isotropic conditions, though there is correlation between the average velocity and curvature for some cases. The 3D simulations exhibit frequent anti-curvature motion regardless of GB energy anisotropy including with isotropic GBs, though larger curvatures occur with anisotropic GB energy functions that promote low-energy GBs. In 2D simulations, anti-curvature behavior only occurs with the anisotropic functions that favor low-energy GBs. This difference between 2D and 3D results suggests that anti-curvature behavior results in part from the increased freedom of motion intrinsic in 3D GB networks. Furthermore, our results support recent experimental observations that demonstrate that simple curvature-driven models are insufficient for describing GB migration in polycrystals.

Anti-curvature↗
DOE OSTI2021

Reconstructing quintessence

We present an Effective Field Theory based reconstruction of quintessence models of dark energy directly from cosmological data. We show that current cosmological data possess enough constraining power to test several quintessence model properties for redshifts z ϵ [0, 1.5] with no assumptions about the behavior of the scalar field potential. We use measurements of the cosmic microwave background, supernovae distances, and the clustering and lensing of galaxies to constrain the evolution of the dark energy equation of state, Swampland Conjectures, the shape of the scalar field reconstructed potential, and the structure of its phase space. The standard cosmological model still remains favored by data and, within quintessence models, deviations from its expansion history are bounded to be below the 10% level at 95% confidence at any redshift below z = 1.5.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Comparison of Heterogeneous Hydroformylation of Ethylene and Propylene over RhCo 3 /MCM-41 Catalysts

The reaction mechanisms of heterogenous hydroformylation of ethylene and propylene were compared at 413-453 K using RhCo 3 /MCM-41 as catalysts. The reaction rates of propylene for both hydroformylation and the undesired side reaction of hydrogenation were found to be about one order of magnitude lower than those for ethylene in flow reactor studies. The difference in the kinetic behavior between ethylene and propylene was investigated by measuring the reaction orders and apparent activation energies, and these macro-kinetic observables were analyzed using the degree of rate control (DRC) method. In situ diffuse reflectance infrared Fourier transform spectroscopy (DRIFTS) experiments were performed to characterize the surface intermediates formed during the reactions. When the reactant was changed from ethylene to propylene, the IR peak corresponding to adsorbed CO exhibited a significant increase, while the IR peaks of alkyl group decreased in magnitude. Combined with the DRIFTS results, DRC analysis indicates that the first step of olefin hydroformylation, the formation of an alkyl group on the catalyst surface, plays a key role in the difference between ethylene and propylene. This step is kinetically non-relevant when ethylene is the reactant, but it is one of the rate-controlling steps for propylene. Furthermore, the low concentration of the adsorbed propyl group, which is a common intermediate shared by both hydroformylation and hydrogenation of propylene, decreases the rates of both reaction pathways as compared to ethylene.

10 SYNTHETIC FUELS↗
DOE OSTI2020

Linking low and high temperature NO oxidation mechanisms over Brønsted acidic chabazite to dynamic changes of the active site

The oxidation of NO over Brønsted acid sites in chabazite (CHA) zeolites shows an atypical temperature dependence; at low temperature the apparent activation energy is negative, but it becomes positive as the temperature exceeds a transition temperature. To explain this behavior we used density functional theory and statistical mechanics to investigate high and low temperature mechanisms for this reaction and propose a dynamic active site change in response to temperature variation. Our simulations show that the apparent activation barrier in the low temperature regime is more negative over Brønsted acidic CHA as compared to the siliceous zeolite framework. This effect is attributed to further enthalpic stabilization of the transition states by physical interaction with the H-CHA Brønsted acid sites. At elevated temperature, our calculations support both the existence and the significant catalytic role of NO+ in providing a modified active site. The temperature dependent transformation of the active site from H-CHA to NO-CHA sites may occur via two plausible ion-exchange mechanisms that define a transition temperature for the reaction. This transition temperature can be tuned by incorporating different trivalent metal atoms (B, Al, Ga or In) within the CHA framework. We found the lowest transition temperature for H-[In] CHA and H-[Ga]CHA. Here, the ability to control the dynamic response of the active site and the associated switch between low and high temperature mechanism with negative and positive apparent activation energy, respectively, is of fundamental interest for the design of zeolite catalysts operating in the presence of NO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Comparison of plug flow and multi-node stratified tank modeling approaches regarding computational efficiency and accuracy

Residential water heaters contain water stratified by temperature-driven density differences. This implies that a water tank can reach a state in which the top and bottom sections have different temperatures, unless mixing happens. A high degree of thermal stratification can improve the efficiency of some water heaters, by saving the amount of energy required for the heat-up process. Studies of stratification became popular in the 1970s and it remains an active research topic today. The research has led to the development of different models and techniques to better predict and define a stratified tanks behavior. By comparing these models and techniques used previously to describe thermal stratification, the phenomenon could be better understood, exploited, and used to increase efficiency and thermal energy capacity in modern water tanks. From the existing models, we found the one-dimensional standard plug-flow and a multi node model to be appropriate for analyzing the processes of the heat up and cool-down in a water tank. These two models are based on energy balances. This work involved comparing the accuracy and computational effort needed to implement these models. To assess accuracy, we compared both types of existing models to experimental data (also collected in this work) which included a heat up process using an external heat pump. This external process included a layering process that has an eddy diffusivity at five times the rate of thermal diffusion. For this project, we implemented the models in MATLAB, the multi-paradigm numerical computing environment. We quantified model accuracy using the root mean squared error between modeled data and experimental data for six measured tank temperatures. Comparing the accuracy and the computational time taken to run the simulation provides a method to contrast the performance of each model and a way to rate it. The multi node model was run using from 6 to 96 spatial nodes; the plug flow model was run using 1 to 0.001 º C temperature bin sizes. Additionally, timesteps were varied from 4 to 236 s. The results quantify the tradeoff between accuracy and computational time, providing guidance for simulations to intelligently select the best model type and simulation parameters. This research can be used to validate the pre-existing models and possibly improve the modern water tank.

Bulnes, Fernando Karg↗
DOE OSTI2024

A Behavior Tree Approach for Battery-Aware Inspection of Large Structures Using Drones

Electric multi-rotor drones have been used to inspect several structures, including large buildings and dams. In these inspections, energy consumption is a concern. To prevent the drone from running out of battery, commercial drones usually come back to their home position when the battery level reaches a minimum threshold. The pilots then need to replace the battery and use their own experience to restart the inspection mission approximately from where it ended before the drone returned home. Instead of relying on the human operator, in this paper, we automate this process using behavior trees, which is an effective way to perform autonomous mission control and supervision. By integrating battery management strategies into a behavior tree framework, this paper demonstrates the drone’s adaptive and resilient decision-making when confronted with limited power constraints. We implemented our methodology using a commercial drone and tested the proposed ideas in a photogrammetry-based inspection task.

42 ENGINEERING↗
DOE OSTI2022

Fragmentation of faceted crystalline wires

The reduction of surface energy is a well-known driving force for the fragmentation of nanowires. Fragmentation can limit the service life of those small low-dimensional building blocks or can be controlled to induce beneficial shape changes. For isotropic surface energy, fragmentation is analogous to the classic Rayleigh-Plateau instability of liquid jets. However, commonly synthesized crystalline nanowires have strongly anisotropic surface energies and exhibit facets coinciding with cusps in the Wulff plot. Depending on growth orientation, different fragmentation behaviors have been seen in nanowires. Using phase-field simulations, we show that fragmentation of faceted nanowires with cubic crystal symmetry may occur by a finite-amplitude nonlinear instability, as opposed to a Rayleigh-Plateau-like linear instability, depending on nanowire growth orientation. Here, we carry out a weakly nonlinear analysis based on sharp interface theory to characterize the faceted nanowire shape corresponding to a surface-energy saddle point. The analysis predicts that the minimum amplitude of a periodic shape perturbation to trigger fragmentation increases with cusp strength but decreases inversely proportionally to the perturbation wavelength for long wavelengths, in good quantitative agreement with phase-field predictions. The results provide the theoretical foundation to predict nanowire stability as a function of length and surface energy in diverse applications.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Shock compression behavior of stainless steel 316L octet-truss lattice structures

Lattice structures offer desirable mechanical properties for applications of energy absorption and impact mitigation but limited research has been carried out on their shock compression behavior. In this work, the shock compression behavior of stainless steel 316L (SS316L) octet-truss lattice structures was investigated through experimental techniques and numerical simulations. Plate impact experiments with high-speed imaging were conducted at impact velocities of 270 – 390 m/s on lattice specimens with 5x5x10 unit cell geometries additively manufactured (AM) using direct metal laser sintering. High-speed imaging together with digital image correlation was used to extract full-field measurements and define a two-wave structure consisting of an elastic wave and planar compaction (shock) wave which propagated along the impact direction. A linear shock velocity versus particle velocity relation was found to approximate the measurements with a unit slope and a linear fit constant equal to the crushing speed. Furthermore, the shock velocity versus particle velocity relation, full-field measurements, and elastic limit together with the Eulerian form of the Rankine-Hugoniot jump conditions were used to find relations for the stress and internal energy behind the shock. Stress behind the shock increased with relative density and particle velocity, and specific internal energy converged to a single curve similar to that of bulk AM SS316L. Explicit finite element analysis using the Johnson-Cook constitutive model demonstrated similar shock behavior observed in experiments and a linear shock velocity versus particle velocity relation and corresponding Hugoniot calculations were found to be in agreement with experimental results. Numerical simulations confirmed negligible effects of exterior versus interior measurements and further validated the application of one-dimensional shock theory.

42 ENGINEERING↗
DOE OSTI2025

Utilization of Carbon Supply Chain Wastes and Byproducts to Manufacture Graphite for Energy Storage Applications

This project explored how coal and waste coal can be transformed into high-value graphite used in batteries for electric vehicles and power grids. A manufacturing technique that converts coal into carbon foam and subsequently graphite was developed and refined to improve the thermal and mechanical properties of the resulting materials. Cost-effectiveness and environmental impacts were also evaluated. Coal-derived graphite, especially when processed at high temperatures, was shown to perform comparably to commercial graphite in battery tests. Advanced computer simulations were used to model battery behavior and predict long-term performance, demonstrating that coal-based graphite has the potential to support electric vehicle deployment while reducing reliance on imported materials. This work offers a promising pathway for cleaner energy storage solutions and creates economic opportunities for coal-reliant communities by generating new uses for mining byproducts.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Evaluating the Structural Response of Amphiphilic Monolayers to Environmental Stimuli

Amphiphilic monolayers composed of end groups with distinct polar and nonpolar functional groups offer rapid and reversible interfacial adaptation in response to environmental stimuli such as a change in interfacial medium polarity. We have synthesized and characterized a suite of monolayers with functional groups of competing polarity designed to reconfigure their interfacial chemical composition in response to solvent polarity. In these films, the end group is designed to be able to reorient and expose the functional groups that minimize the interfacial free energy between the film and the environment. Using a combination of spectroscopic, computational, and wettability studies, we have investigated the responsive interfacial behavior of different end groups upon exposure to environments with varying polarities. Contact angle measurements across a series of polar and dispersive probe liquids reveal trends that reflect the underlying molecular flexibility and composition. Vibrational sum frequency generation (SFG) spectroscopy and atomistic molecular dynamics (MD) simulations confirm solvent-driven reorientation of the end groups, with restructuring observed at the interface. To quantify these effects, we have developed a surface energy calculation model that incorporates solvent-induced surface rearrangements into the estimations. Our findings reveal a strong dependence of surface energy and switching behavior on the length and flexibility of the functionalities in the end group, which affects the exposure of certain interfacial compositions under different solvents. These results offer new insights into the design of adaptive monolayers and provide a framework for evaluating solvent-responsive surfaces.

functional groups↗