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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
NASA NTRS2016

Making Connections: Where STEM Learning and Earth Science Data Services Meet

STEM (Science, Technology, Engineering, Mathematics) learning is most effective when students are encouraged to see the connections between science, technology and real world problems. Helping to make these connections has become an increasingly important aspect of Earth Science data research. The Global Hydrology Resource Center (GHRC), one of NASA's 12 EOSDIS (Earth Observing System Data Information System) data centers, has developed a new type of documentation called the micro article to facilitate making connections between data and Earth science research problems.

Micro articles↗
NASA NTRS2018

Spreading Sequence System for Full Connectivity Relay Network

Fully connected uplink and downlink fully connected relay network systems using pseudo-noise spreading and despreading sequences subjected to maximizing the signal-to-interference-plus-noise ratio. The relay network systems comprise one or more transmitting units, relays, and receiving units connected via a communication network. The transmitting units, relays, and receiving units each may include a computer for performing the methods and steps described herein and transceivers for transmitting and/or receiving signals. The computer encodes and/or decodes communication signals via optimum adaptive PN sequences found by employing Cholesky decompositions and singular value decompositions (SVD). The PN sequences employ channel state information (CSI) to more effectively and more securely computing the optimal sequences.

Kwon, Hyuck M.↗
NASA NTRS2019

Predicting Well-Connected SEP Events from Observations of Solar EUVs and Energetic Protons

This study shows a quantitative assessment of the use of Extreme Ultraviolet (EUV) observations in the prediction of Solar Energetic Proton (SEP) events. The UMASEP scheme (Space Weather, 9, S07003, 2011; 13, 2015, 807-819) forecasts the occurrence and the intensity of the first hours of SEP events. in order to predict well-connected events, this scheme correlates Solar Soft X-rays (SXR) with differential proton fluxes of the GOES satellites. In this study, we explore the use of the EUV time history from GOES-EUVS and SDO-AIA instruments in the UMASEP scheme. This study presents the results of the prediction of the occurrence of well-connected >10 MeV SEP events, for the period from May 2010 to December 2017, in terms of Probability of Detection (POD), False Alarm Ratio (FAR), Crticial Success Index (CSI), and the average and median of the warning times. The UMASEP/EUV-based models were calibrated using GOES and SDO data from May 2010 to October 2014, and validated using out-of-sample SDO data from November 2014 to December 2017. The best results were obtained by those models that used EUV data in the range 50-340 angstroms. We conclude that the UMASEP/EUV-based models yield similar or better POD results, and similar or worse FAR results, than those of the current real-time UMSEP/SXR-based model. The reason for the higher POD of the UMASEP/EUV-based models in the range of 50-340 angstroms was due to the high percentage of successful predictions of well-connected SEP events associated with 10 MeV SEP events, improves the overall performance, obtaining a POD of 92.9% (39/42) compared with 81% (34/42) of the current tool, and a slightly worse FAR of 31.6% (18/57) compared with 29.2% (14/58) of the current tool.

Nunez, Marlon↗
DOE OSTI2020

Method and system for routing based on a predicted connectivity quality

A system and method for routing based on a predicted connectivity quality is disclosed. The method includes receiving, by a controller, map information corresponding to a geographical area. The method also includes receiving, by the controller, wireless connectivity information corresponding to the geographical area. The method also includes generating output data of at least one route between a starting location and a destination location within the geographical area. The generating is based on the wireless connectivity information.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Field Study of Grid-connected Heat Pump Water Heaters in the Southeast U.S.: The Next Right Thing

The integration of grid-connected functionality and advanced control algorithms into heat pump water heaters (HPWH) offers the capability to shift load with minimal customer impact. This capability provides a flexible grid resource to utilities, while the increased energy efficiency of HPWHs offers customers a lower electric bill. The combined utility/customer value from grid-connected HPWHs is compelling in the Southeast U.S. where residential electric water heating is prevalent and utility load management is common. This paper presents the results of a HPWH load shifting study conducted in Central Florida using the CTA-2045 standard. Building upon previous research in the Pacific Northwest, this study consisted of approximately 45 occupied homes equipped with HPWHs undergoing load shifting strategies weekly for over a year. Curtailment durations ranged from three to five hours in the morning, and four to five hours in the evening to coincide with high-value periods for utility coincident load for system-wide electric demand reductions. During the morning and afternoon, a one- or two-hour load-up event preceded curtailment. Baseline data were collected across varied Florida weather in which no load shifting events were implemented. Results from traditional load shifting strategies were analyzed across seasons and used to devise unique load shifting approaches to increase renewable energy use during periods of high solar energy generation. Regional impacts are forecasted for large-scale implementation of strategies. Lessons learned and recommendations are also provided for how utilities, manufacturers, and regional planners can maximize load shifting benefits from grid-connected HPWHs.

Heat pump water heaters, residential building, loa↗
DOE OSTI2024

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗
DOE OSTI2021

A general theory of avian migratory connectivity

Birds exhibit a remarkable array of seasonal migrations. Despite much research describing migratory behaviour, the underlying forces driving how a species’ breeding and wintering populations redistribute each year, that is, migratory connectivity, remain largely unknown. Here, we test the hypothesis that birds migrate in a way that minimises energy expenditure while considering intraspecific competition for energy acquisition, by developing a modelling framework that simulates an optimal redistribution of individuals between breeding and wintering areas. Using 25 species across the Americas, we find that the model accurately predicts empirical migration patterns, and thus offers a general explanation for migratory connectivity based on first ecological and energetic principles. Our model provides a strong basis for exploring additional processes underlying the ecology and evolution of migration, but also a framework for predicting how migration impacts local adaptation across seasons and how environmental change may affect population dynamics in migratory species.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Uncertain role of clouds in shaping summertime atmosphere-sea ice connections in reanalyses and CMIP6 models

Downwelling longwave radiation (DLR) driven by the atmospheric and cloud conditions in the troposphere is suggested to be a dominant factor to determine the summertime net surface energy budget over the Arctic Ocean and thus plays a key role to shape the September sea ice. We use reanalyses and the self-organizing map (SOM) method to distinguish CMIP6 model performance in replicating the observed strong atmosphere-DLR connection. We find all models can reasonably simulate the linkage between key atmosphere variables and the clear sky DLR but behave differently in replicating the atmosphere-DLR connection due to cloud forcing. In ERA5 and strongly coupled models, tropospheric high pressure is associated with decreased clouds in the mid- and high-levels and increased clouds near the surface. This out-of-phase structure indicates that DLR cloud forcing is nearly neutral, making the clear sky DLR more important to bridge JJA circulation to late-summer sea ice. In MERRA-2 and weakly coupled models, tropospheric clouds display a vertically homogeneous reduction; the cloud DLR is thus strongly reduced due to the cooling effect, which partially cancels out the clear sky DLR and makes the total DLR less efficient to translate circulation forcing to sea ice. The differences of cloud vertical distribution in CMIP6 appear to be differentiated by circulation related relative humidity. Therefore, a better understanding of the discrepancy of different reanalyses and remote sensing products is critical to comprehensively evaluate simulated interactions among circulation, clouds, sea ice and energy budget at the surface in summer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Application of remote sensing precipitation data and the $\mathrm{CONNECT}$ algorithm to investigate spatiotemporal variations of heavy precipitation: Case study of major floods across Iran (Spring 2019)

In recent years, the number of floods following unprecedented rainfall events have increased in Iran during early spring (March 21st to April 20th, referred to in Iran as the month of “Farvadin”). While numerous studies have addressed changes in climate extremes and precipitation trends at different temporal scales from daily to annual across the country, analyses of short-duration and heavy precipitation, especially during recent years, are rarely considered. Furthermore, most studies investigate the variations in extremes and total precipitation using a limited number of synoptic weather stations across Iran. Here this study assesses the variations in heavy precipitation (precipitation with intensities greater than or equal to 3 mm/3 h) at 0.04° spatial and 3-hourly temporal resolution during the month of Farvardin. In addition, the effect of atmospheric river conditions over Iran and their possible link to intensifying heavy precipitation is explored. For this purpose, the CONNected-objECT (CONNECT) algorithm is applied on a precipitation dataset, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS), and an Integrated Water Vapor Transport (IVT) dataset from the NASA Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2). The results suggest that the increase in the number of floods in recent years is related to the increase in the intensity and volume of heavy precipitation events, although the frequency and duration of heavy precipitation events have not changed significantly. Furthermore, the results show that atmospheric river conditions over the country are present during the same window as each year’s most extreme events. It is found that 8 out of 13 of the largest ARs over Iran come from moisture plumes with pathways over the African and Red Sea.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Pore connectivity influences mass transport in natural rocks: Pore structure, gas diffusion and batch sorption studies

For this work, six rocks (one granodiorite, one limestone, two chalks, one mudstone, and one dolostone) with different extents of heterogeneity at six different particle sizes (from 75 to 8000 μm) were studied to describe the effects of pore connectivity on mass transport. The methods applied were (i) porosity measurement of granular rocks, (ii) analyses of gas-phase diffusive transport in a bed of packed particles, along with a solid quartz method at these six particle sizes being developed to identify the contribution of intraparticle diffusion, and (iii) batch sorption tests of multiple ions (anions and cations) with subsequent analyses of inductively coupled plasma-mass spectrometry. Granular porosity measurement results reveal that with decreasing particle sizes, the effective porosities for the “heterogenous” group of rocks (Grimsel granodiorite and Edwards limestone) increase, whereas the porosities of another “homogeneous” group (two Israel chalk samples, Japan mudstone, and Wyoming dolostone) remain constant. Gas diffusion results show that the intraparticle gas diffusion coefficient among these two sample groups, varying in the magnitude of 10 -8 to 10 -6 m 2 /s, are not directly correlated to the porosity differences. Moreover, the batch sorption work displays a different affinity of rocks for various tracers. For Grimsel granodiorite, Japan mudstone, and Wyoming dolostone, the adsorption capacity of Sm 3+ and Eu 3+ increases as the particle size decreases. In general, this integrated research of grain size distribution, granular rock porosity, intraparticle diffusivity, and ionic sorption capacity gives insights into the pore connectivity effect on both physical and chemical transport behaviors for different lithologies and/or different particle sizes.

58 GEOSCIENCES↗
DOE OSTI2022

Energy and mobility impacts of connected autonomous vehicles with co-optimization of speed and powertrain on mixed vehicle platoons

Intersections are known to be traffic bottlenecks where a significant amount of energy consumption could be caused due to deceleration/acceleration in the presence of red signals. With an increased level of connectivity and automation of intelligent transportation systems, connected autonomous vehicles (CAVs) are expected to be able to proactively adjust their driving strategies subject to constraints imposed by the predicted future traffic. As a result, many potential benefits can be achieved, such as improved energy efficiency, enhanced traffic safety, among many others. Notably, the way CAVs are controlled affects the following legacy vehicles (LVs) due to complex traffic dynamics. Here, we are particularly interested in studying the energy and mobility impact of CAVs with an improved traffic prediction method on mixed vehicle platoons at various market penetration rates. Leveraging traffic prediction, CAVs are controlled with co-optimization of their speed and gear position. Specifically, a traffic prediction framework in a rolling horizon fashion is employed based upon a modified Payne–Whitham (PW) model capable of handling mixed traffic consisting of CAVs and LVs. The prediction error of the modified PW model is reduced by 53.62% compared to that of the standard PW model under test scenarios. According to the predicted traffic conditions, speed and gear position of CAVs are co-optimized with the primary goal of minimizing energy consumption when driving on a signalized arterial. The energy benefits achieved by CAVs and the impact of CAVs on LVs behind are studied comprehensively for mixed vehicle platoons. The lead LV follows a real-world speed profile collected on TH-55 in Minnesota. Numerical results show that energy benefits achieved by the vehicle platoon range from 2% to 16%, and a 1% to 5% reduction in travel time for LVs behind CAVs is also observed, at different penetration rates of CAVs in various traffic scenarios. Furthermore, it is observed that CAVs using the proposed eco-driving approach appear to have a positive impact on the LVs behind in terms of energy consumption, regardless of the driving styles of the LVs ahead.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2024

Exchange–correlation entropy from the generalized thermal adiabatic connection

Warm dense matter is a highly energetic phase characterized by strong correlations, thermal effects, and quantum mechanical electrons. Thermal density functional theory is commonly used in simulations of this challenging phase, driving the development of temperature-dependent approximations to the exchange–correlation free energy. Approaches using the adiabatic connection formula are well known at zero temperature and have been recently leveraged at non-zero temperatures as well. In this work, a generalized thermal adiabatic connection (GTAC) formula is proposed, introducing a fictitious temperature parameter. This allows extraction of the exchange–correlation entropy S XC using simulated interaction strength scaling. Here, this procedure uses a Hellmann–Feynman approach to express the exchange–correlation entropy in terms of a temperature- and interaction strength-dependent exchange–correlation potential energy. In addition, analysis of S XC as a function of interaction strength suggests new forms for approximations, and GTAC itself offers a new framework for exploring both the exact and approximate interplay of temperature, density, and interaction strength across a wide range of conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Time–connectivity superposition and the gel/glass duality of weak colloidal gels

Colloidal gels result from the aggregation of Brownian particles suspended in a solvent. Gelation is induced by attractive interactions between individual particles that drive the formation of clusters, which in turn aggregate to form a space-spanning structure. We study this process in aluminosilicate colloidal gels through time-resolved structural and mechanical spectroscopy. Using the time–connectivity superposition principle a series of rapidly acquired linear viscoelastic spectra, measured throughout the gelation process by applying an exponential chirp protocol, are rescaled onto a universal master curve that spans over eight orders of magnitude in reduced frequency. This analysis reveals that the underlying relaxation time spectrum of the colloidal gel is symmetric in time with power-law tails characterized by a single exponent that is set at the gel point. The microstructural mechanical network has a dual character; at short length scales and fast times it appears glassy, whereas at longer times and larger scales it is gel-like. These results can be captured by a simple three-parameter constitutive model and demonstrate that the microstructure of a mature colloidal gel bears the residual skeleton of the original sample-spanning network that is created at the gel point. Our conclusions are confirmed by applying the same technique to another well-known colloidal gel system composed of attractive silica nanoparticles. The results illustrate the power of the time–connectivity superposition principle for this class of soft glassy materials and provide a compact description for the dichotomous viscoelastic nature of weak colloidal gels.

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Nonlinear Direct Power Controller for a Three-Phase Grid-Connected Inverter with Online Parameter Update for PV Application

This work proposes a nonlinear direct power controller for a three-phase grid-connected inverter for photovoltaic application. Grid-connected converters for solar applications require advanced control architectures to ensure optimal operation and extraction of maximum power from the photovoltaic arrays. Therefore, to ensure this, a nonlinear power controller architecture has been proposed to extract the maximum power from the solar arrays and to achieve the most optimal operation. To further improve the performance, a parameter update scheme has been utilized which is based on model reference adaptive system. The latter helps to accomplish not only good dynamic response but also excellent tracking performance. The overall control come estimation architecture is verified based on computer simulations via MATLAB/Simulink and PLECS domain and various important case study results are presented.

direct power control↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗
DOE OSTI2021

Distributed Maneuver Planning With Connected and Automated Vehicles for Boosting Traffic Efficiency

Connected and automated vehicles (CAVs) have the potential to improve traffic throughput and achieve a more efficient utilization of the available roadway infrastructure. They also have the potential to reduce energy consumption through traffic motion harmonization, even when operating in mixed traffic with other human-driven vehicles. The key to realizing these potentials are coordinated control schemes that can be implemented in a distributed manner with the CAVs. In this paper, we propose a distributed predictive control framework that features a two-dimensional maneuver planner incorporating explicit coordination constraints between connected vehicles operating in mixed traffic at various penetration levels. Here, the framework includes a distributed implementation of a reference speed assigner that estimates local traffic speed from on-board measurements and communicated information. We present an extensive evaluation of the proposed framework in traffic micro-simulations at various CAV penetrations from traffic flow, energy use, and lane utilization points of view. Results are compared to a baseline scenario with no CAVs, as well as, a benchmark one-dimensional planner.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Unsupervised Image-Based Classification of Corrosion Severity in Automobile Engine Connecting Rods

Corrosion in engine connecting rods is a critical issue in the automotive industry, potentially leading to catastrophic engine failure, monetary losses, and safety hazards. The labor shortage in the industry further emphasizes the need for fast, accurate, and automated corrosion detection methods to ensure appropriate surface treatments can be applied to restore component integrity. We present an unsupervised image-based framework for classifying corrosion severity in automobile engine connecting rods using short-wave infrared (SWIR) and telecentric grayscale imaging. We employ the structural similarity index measure (SSIM) as a dissimilarity metric and the k-medians clustering algorithm for classification. Our algorithm achieves an overall accuracy of 80.64% for SWIR images, with 100% accuracy in classifying highly corroded samples. For grayscale images, the method attains an overall accuracy of 77.42%, with 90.91% accuracy for highly corroded samples. The method’s ability to work with different imaging modalities and its high accuracy in identifying severe corrosion cases make it a promising tool for automated corrosion assessment in the automotive industry, potentially improving efficiency and safety in engine component maintenance.

42 ENGINEERING↗
DOE OSTI2020

An Authentication Vulnerability Assessment of Connected Lighting Systems

Emerging connected lighting systems (CLS) that incorporate distributed intelligence, network interfaces, and sensors can become data-collection platforms that enable a wide range of valuable new capabilities as well as greater energy savings in buildings and cities. However, CLS technology is currently at an early stage of development, and its increased connectivity introduces cybersecurity risks that are new to the lighting industry and that must be addressed for successful integration with other systems. While a number of existing frameworks, guidelines, and tests for evaluating cybersecurity vulnerability may apply to CLS in whole or in part, there is currently no mandatory requirement for cybersecurity testing or certification. The lighting industry, including technology developers and specification organizations, is currently evaluating the suitability of existing frameworks and guidelines for CLS. To support these efforts, Pacific Northwest National Laboratory (PNNL) is conducting a series of studies intended to educate lighting industry stakeholders on specific cybersecurity practices and characterize their implementation in commercially available CLS with varying system architectures, network-communication technologies, and degrees of maturity. This study demonstrates that tests for authentication vulnerabilities can be developed with objective pass/fail criteria, therby facilitating comparisons between CLS. Based on the limited results of this study, it appears that the CLS that are being brought to market have varying levels of authentication vulnerability. It is hoped that these evaluations will support and perhaps accelerate industry discussions on the risks of specific security vulnerabilities, what vulnerabilities should be addressed by in-development of future lighting-specific best practices, and whether any such practices should be included in voluntary lighting standards.

42 ENGINEERING↗