Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “network impacts”

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 1,495 records · Page 83
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Empowering Women in Biomedical Informatics: Pivoting for Success in the Post-Pandemic World

The COVID-19 pandemic and the isolation measures implemented to contain its spread have exacerbated pre-existing barriers to advancement for women in biomedical informatics. In 2020, the Women in AMIA (WIA) Networking, Mentoring, and Lifecycle Committee administered a survey to identify resources and needs to assist women in informatics. Part of this survey identified several factors impacted by the pandemic: 1) work-life boundaries disappeared and caring for family while working from home became increasingly challenging; 2) isolation, stress and safety of well-being was heightened during the pandemic impacting physical and mental health; 3) diverse working environment and modality brought unforeseen challenges in collaborations and unified communications. These challenges present an opportunity for promoting career development for women professionals in biomedical informatics. This WIA-sponsored panel brings together leaders in the field to highlight their personal experiences and strategies to support women professionals.

Dua, Prerna↗
DOE OSTI2018

Modeling ocean, rail, and truck transportation flows to support policy analysis

Freight transportation represents about 9.5% of GDP in the U.S., it is responsible for about 8% of greenhouse gas emissions, and supports the import and export of about 3.6 trillion in international trade. It is therefore important that the national freight transportation system is designed and operated efficiently. Hence, this paper develops a mathematical model to estimate international and domestic freight flows across ocean, rail, and truck modes, which can be used to study the impacts of changes in our infrastructure, as well as the imposition of new user fees and changes in operating policies. The model integrates a user equilibrium-based logit argument for path selection with a system optimal argument for rail network operations. This leads to the development of a unique solution procedure that is demonstrated in a large-scale analysis focused on all intercity freight and U.S export/import containerized freight. The model results are compared with the reported flow volumes. Here, the model is applied to two case studies: a disruption of the seaports of Los Angeles and Long Beach (LA and LB) similar to the impacts that would be felt in an earthquake; and implementation of new user fees at the California ports.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRSDate not supplied

Integrating Low-Cost Sensor Systems and Networks to Enhance Air Quality Applications

Recent advances in low-cost air quality sensor systems are rapidly increasing the accessibility of air quality information around the world. At the same time, there are many technical challenges to appropriately and effectively using of the information they provide. A key opportunity to increase the applicability and actionability of low-cost sensor data is to use these devices at network scale and combine their information with insights from other air quality data sources such as numerical models and satellite remote sensing. When the limitations of low-cost sensors are understood and acknowledged, and appropriate complementary data are used to overcome these limitations, low cost sensors can support a variety of applications such as improving location-specific air quality forecasting and estimation, quantifying local source impacts, identifying air quality disparities, assessing the benefits of mitigation actions, and promoting community engagement with air quality issues. This presentation will first provide an overview and summarize key findings from a recently released World Meteorological Organization report discussing how networks of low-cost air quality sensor systems can be integrated to effectively support such applications. The presentation will also give a brief overview of an ongoing NASA-funded effort to create accessible tools for integrating multiple sources of air quality information, including global forecasting models, satellite remote sensing data, and in-situ information from both regulatory and low-cost monitors.

Carl Malings↗
DOE OSTI2016

Solar Training Network Requirements & Benefits

The white house recently announced the launch of the Solar Training Network, a new U.S. Department of Energy Sunshot initiative funded program. This $2.1 Million program will run two years and is designed to connect solar trainers, solar employers, and solar job seekers in order to help meet the labor needs of the booming solar industry. The program aims to create a dedicated employment pipeline of skilled solar workers to help employers find skilled solar employees when and where they need them. We are actively recruiting employer partners, including solar installation companies, financing firms, manufacturers, and more. This presentation will cover all of the requirements and benefits of employer partnership as well as the positive impact the program is expected to have on our partners' talent acquisition efforts.

14 SOLAR ENERGY↗
NASA NTRS1990

Real-time data system: Incorporating new technology in mission critical environments

If the Space Station Freedom is to remain viable over its 30-year life span, it must be able to incorporate new information systems technologies. These technologies are necessary to enhance mission effectiveness and to enable new NASA missions, such as supporting the Lunar-Mars Initiative. Hi-definition television (HDTV), neural nets, model-based reasoning, advanced languages, CPU designs, and computer networking standards are areas which have been forecasted to make major strides in the next 30 years. A major challenge to NASA is to bring these technologies online without compromising mission safety. In past programs, NASA managers have been understandably reluctant to rely on new technologies for mission critical activities until they are proven in noncritical areas. NASA must develop strategies to allow inflight confidence building and migration of technologies into the trusted tool base. NASA has successfully met this challenge and developed a winning strategy in the Space Shuttle Mission Control Center. This facility, which is clearly among NASA's most critical, is based on 1970's mainframe architecture. Changes to the mainframe are very expensive due to the extensive testing required to prove that changes do not have unanticipated impact on critical processes. Systematic improvement efforts in this facility have been delayed due to this 'risk to change.' In the real-time data system (RTDS) we have introduced a network of engineering computer workstations which run in parallel to the mainframe system. These workstations are located next to flight controller operating positions in mission control and, in some cases, the display units are mounted in the traditional mainframe consoles. This system incorporates several major improvements over the mainframe consoles including automated fault detection by real-time expert systems and color graphic animated schematics of subsystems driven by real-time telemetry. The workstations have the capability of recording telemetry data and providing 'instant replay' for flight controllers. RTDS also provides unique graphics animated by real-time telemetry such as workstation emulation of the shuttle's flight instruments and displays of the remote manipulator system (RMS) position. These systems have been used successfully as prime operational tools since STS-26 and have supported seven shuttle missions.

Muratore, John F.↗
DOE OSTI2017

National Plug-In Electric Vehicle Infrastructure Analysis

This document describes a study conducted by the National Renewable Energy Laboratory quantifying the charging station infrastructure required to serve the growing U.S. fleet of plug-in electric vehicles (PEVs). PEV sales, which include plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), have surged recently. Most PEV charging occurs at home, but widespread PEV adoption will require the development of a national network of non-residential charging stations. Installation of these stations strategically would maximize the economic viability of early stations while enabling efficient network growth as the PEV market matures. This document describes what effective co-evolution of the PEV fleet and charging infrastructure might look like under a range of scenarios. To develop the roadmap, NREL analyzed PEV charging requirements along interstate corridors and within urban and rural communities. The results suggest that a few hundred corridor fast-charging stations could enable long-distance BEV travel between U.S. cities. Compared to interstate corridors, urban and rural communities are expected to have significantly larger charging infrastructure requirements. About 8,000 fast-charging stations would be required to provide a minimum level of coverage nationwide. In an expanding PEV market, the total number of non-residential charging outlets or 'plugs' required to meet demand ranges from around 100,000 to more than 1.2 million. Understanding what drives this large range in capacity requirements is critical. For example, whether consumers prefer long-range or short-range PEVs has a larger effect on plug requirements than does the total number of PEVs on the road. The relative success of PHEVs versus BEVs also has a major impact, as does the number of PHEVs that charge away from home. This study shows how important it is to understand consumer preferences and driving behaviors when planning charging networks.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

National Plug-In Electric Vehicle Infrastructure Analysis

This presentation describes a study conducted by the National Renewable Energy Laboratory quantifying the charging station infrastructure required to serve the growing U.S. fleet of plug-in electric vehicles (PEVs). PEV sales, which include plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), have surged recently. Most PEV charging occurs at home, but widespread PEV adoption will require the development of a national network of non-residential charging stations. Installation of these stations strategically would maximize the economic viability of early stations while enabling efficient network growth as the PEV market matures. This document describes what effective co-evolution of the PEV fleet and charging infrastructure might look like under a range of scenarios. To develop the roadmap, NREL analyzed PEV charging requirements along interstate corridors and within urban and rural communities. The results suggest that a few hundred corridor fast-charging stations could enable long-distance BEV travel between U.S. cities. Compared to interstate corridors, urban and rural communities are expected to have significantly larger charging infrastructure requirements. About 8,000 fast-charging stations would be required to provide a minimum level of coverage nationwide. In an expanding PEV market, the total number of non-residential charging outlets or 'plugs' required to meet demand ranges from around 100,000 to more than 1.2 million. Understanding what drives this large range in capacity requirements is critical. For example, whether consumers prefer long-range or short-range PEVs has a larger effect on plug requirements than does the total number of PEVs on the road. The relative success of PHEVs versus BEVs also has a major impact, as does the number of PHEVs that charge away from home. This study shows how important it is to understand consumer preferences and driving behaviors when planning charging networks.

25 ENERGY STORAGE↗
NASA NTRSDate not supplied

Quantifying Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
NASA NTRSDate not supplied

Quantifying the Effects of Departure and Flight Time Uncertainty on Urban Air Mobility Operations

Demand capacity balancing is a key mechanism for maintaining safe and efficient Urban Air Mobility (UAM) operations. However, uncertainties such as departure delays and flight time variation may reduce the effectiveness of algorithms used for balancing and detrimentally impact the safety and efficiency of UAM operations. In this paper, the effects of these uncertainties on UAM operations are quantified by modeling a distribution of departure and flight time errors. A route network in the Dallas/Fort Worth metropolitan area was used to simulate traffic demand with and without uncertainty. Simulations were conducted with three main models of uncertainty – first uncertainty in departure time delay resulting in late takeoffs, second with uncertainty in flight times in addition to departure delays, and finally, uncertainty in departure times that cause either late or early takeoffs. Each of these simulations were performed using varied standard deviations to fully understand the effects of uncertainties. Results from these simulations were compared to a baseline simulation using the same parameters, but without any uncertainty. The results suggest that both safety and efficiency are significantly impacted by uncertainty even with relatively low uncertainty introduced. These results work towards quantifying the effects of uncertainty in flight scheduling for UAM. They will also aid in the further development of the demand capacity balancing algorithms for UAM operations and associated air traffic management.

Urban Air Mobility↗
DOE OSTI2024

Modeling the Impacts of Material Properties on Oscillatory Neuron Behavior

In this study, neuromorphic computing, which mimics the functions of biological brains, offers improvements in both latency and energy efficiency over typical von Neumann computing architectures. Spiking neural networks can be especially power-efficient because they encode information temporally and can use more sparse electrical inputs. Here, we study the design of volatile memristors (variable resistors with memory) for neuronal devices, with particular consideration toward the feasibility of all-on-chip oscillation using built-in capacitance. We use circuit simulations to model the behavior of oscillator neurons with a range of realistic material properties. We find that energy inputs increase with insulating-phase resistivity, thermal conductivity, and device aspect ratio. However, we also find that the minimum capacitance needed for oscillation decreases with increasing insulating-phase resistivity, which opposes the constraints for power efficiency. Based on published data on NbO 2 , VO 2 , and EuNiO 3 , we find that existing materials can be engineered for all-on-chip spiking using their parasitic capacitance.

36 MATERIALS SCIENCE↗
DOE OSTI2021

AFLEET: ASSESS THE IMPACTS OF CONVENTIONAL AND ALTERNATIVE FUEL VEHICLES

The Alternative Fuel Life-Cycle Environmental and Economic Transportation (AFLEET) tool, developed by researchers at Argonne National Laboratory for the U.S. Department of Energy (DOE) Clean Cities coalition network, estimates the environmental and economic costs and benefits of alternative fuel and advanced vehicles. Using simple inputs, AFLEET lets fleet managers estimate and compare petroleum use, greenhouse gas emissions (GHGs), air pollutant emissions, and cost of ownership for conventional and alternative fuel vehicles (AFVs).

Burnham, Andrew↗
DOE OSTI2015

Jumpstarting commercial-scale CO 2 capture and storage with ethylene production and enhanced oil recovery in the US Gulf

CO 2 capture, utilization, and storage (CCUS) technology has yet to be widely deployed at a commercial scale despite multiple high-profile demonstration projects. We suggest that developing a large-scale, visible, and financially viable CCUS network could potentially overcome many barriers to deployment and jumpstart commercial-scale CCUS. To date, substantial effort has focused on technology development to reduce the costs of CO 2 capture from coal-fired power plants. Here, we propose that near-term investment could focus on implementing CO 2 capture on facilities that produce high-value chemicals/products. These facilities can absorb the expected impact of the marginal increase in the cost of production on the price of their product, due to the addition of CO 2 capture, more than coal-fired power plants. A financially viable demonstration of a large-scale CCUS network requires offsetting the costs of CO 2 capture by using the CO 2 as an input to the production of market-viable products. As a result, we demonstrate this alternative development path with the example of an integrated CCUS system where CO 2 is captured from ethylene producers and used for enhanced oil recovery in the U.S. Gulf Coast region.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2015

Travel Patterns And Characteristics Of Transit Users In New York State

This research is a detailed examination of the travel behaviors and patterns of transit users within New York State (NYS), primarily based on travel data provided by the National Household Travel Survey (NHTS) in 2009 and the associated Add-on sample households purchased by the New York State Department of Transportation (NYSDOT). Other data sources analyzed in this study include: NYS General Transit Feed Specification (GTFS) to assist in analyzing spatial relationships for access to transit and the creation of Transit Shed geographic areas of 1, 2.5, and 5 miles from transit stop locations, LandScan population database to understand transit coverage, and Census Bureau s American Community Survey (ACS) data to examine general transit patterns and trends in NYS over time. The majority of analyses performed in this research aimed at identifying transit trip locations, understanding differences in transit usage by traveler demographics, as well as producing trip/mode-specific summary statistics including travel distance, trip duration, time of trip, and travel purpose of transit trips made by NYS residents, while also analyzing regional differences and unique travel characteristics and patterns. The analysis was divided into two aggregated geographic regions: New York Metropolitan Transportation Council (NYMTC) and NYS minus NYMTC (Rest of NYS). The inclusion of NYMTC in all analysis would likely produce misleading conclusions for other regions in NYS. TRANSIT COVERAGE The NYS transit network has significant coverage in terms of transit stop locations across the state s population. Out of the 19.3 million NYS population in 2011, about 15.3 million (or 79%) resided within the 1-mile transit shed. This NYS population transit coverage increased to 16.9 million (or 88%) when a 2.5-mile transit shed was considered; and raised to 17.7 million (or 92%) when the 5-mile transit shed was applied. KEY FINDINGS Based on 2009 NHTS data, about 40% of NYMTC households used transit as their means of transportation on any typical day; while only 4% of households located elsewhere in NYS did the same. Regardless of geographic regions, 45% of the transit users came from households with income under $40,000, while 20% of transit users were from the top income group of $100,000 plus households. Travel made by NYMTC transit users were nearly twice as likely to be for work trips as compared to their counterpart non-transit users. Transit users in NYS generally made more trips but with shorter distances; they also drove less, which resulted in fewer miles. Furthermore, NYS transit users spent more time on each trip than their counterpart non-transit users. Because of the intensity of transit network services within NYMTC, 88% of the households reside within the 1-mile transit shed. Outside the NYMTC, however, only 54% of the region s households are located within the same distance. Impact to vehicle ownership was clearly evidenced. Nearly all people from zero-vehicle households in NYMTC lived within a 1-mile radius of transit stops. Elsewhere in NYS, 74% of residents from zero-vehicle households resided within the 1-mile transit shed. Close proximity to transit has a significant impact on increasing transit uses. Transit mode share, as a main mode, was higher for NYS residents that lived within the 1-mile transit shed than others. Based on ACS data, over the period from 2005 to 2013, the total number of NYMTC workers increased more than 9%, while transit commuting grew at a higher rate of more than 15% during the same period. REMARKS Note that transit use in areas outside the NYMTC region generally is not common, resulting in a smaller sample size of transit users in the Rest of NYS region. Caution should be exercised for statistics produced based on small sample sizes that tend to be less precise (i.e., with a larger margin of error). Furthermore, standardized transit network data were not available prior to 2005; comparable analyses using 2001 NHTS therefore was not feasible. As a result, this study focused on examining travel behaviors of transit users using 2009 NHTS data only.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

Evaluating Energy Efficiency Opportunities from Connected and Automated Vehicle Deployments Coupled with Shared Mobility in California

Connected and Automated Vehicles (CAVs) can be considered to be a disruptive transportation technology, with the potential to significantly improve overall transportation system efficiency; however, CAVs may increase induce vehicle miles traveled (VMT) and bring on greater energy consumption. Further, shared mobility is another disruptive transportation event that is reshaping our travel patterns. The primary goal of this project was to extensively collect data from vehicles and associated infrastructure equipped with CAV technologies from both real-world experiments and simulation studies mainly deployed in California, and develop a comprehensive framework for evaluating energy efficiency opportunities from large-scale (e.g., statewide) introduction of CAVs and a wide deployment of shared mobility systems in a variety of scenarios. To quantify the combined impact of CAV and shared mobility on travel behavior, traffic performance, and energy efficiency, a unique mesoscopic simulation-based model was developed for mobility and energy efficiency evaluation considering these disruptive transportation technologies. As a complement to existing studies on nationwide evaluation of CAVs’ energy impacts, this project was focused on data collection efforts and CAV applications under congested traffic environments that are frequently experienced on a massive scale across the major metropolitan areas in California. Extensive real-world data collection supplemented with simulation studies were conducted to cover a variety of CAV and shared mobility scenarios, particularly on scenarios less-explored in the existing research. Another key component of this project was to consider the interaction between different CAV technologies and shared mobility models, and the compound effect on energy efficiency. A comprehensive modeling suite was developed to quantify the impact of new mobility technologies on travel behavior and traffic performance. The developed modeling framework includes an energy intensity module, mode choice module and activity generation module that are integrated into an agent-based BEAM simulation platform to perform impact analysis based on a variety of scenarios. In addition, the RouteE model has been upgraded to incorporate the impact of CAVs on traffic flow, VMT and energy intensity, using micro-simulation data collected from both freeways and urban arterials. A novel fundamental influencing factor (FIF) mode choice model was developed to link CAV and shared mobility components with travel behaviors, and adapted into the BEAM-centered model framework. A statewide energy inventory was constructed under various CAV technology deployment scenarios by incorporating datasets and models for predicting vehicle market share and vehicle usage, which are tightly associated with the penetration of shared mobility systems. Based applying this modeling suite to a calibrated network in Riverside California, it was found that cooperative automated driving in general will improve mobility, but automated vehicles, even when deployed in a shared autonomous fleet, will likely bring an increase of VMT (up to 36%) due to mode shifts and deadheading. Ride-hailing vehicles typically have better energy efficiency and a higher share of electric vehicles, which helps offset the negative impact from VMT increases when estimating the system-level energy consumption. In general, simulation results show a 6% increase in energy consumption for the scenarios with an increasing shift to ride-hailing modes. The statewide analysis based on the National Household Travel Survey (NHTS) sample data is consistent with the findings from the Riverside network and validate the developed clustering-prediction modeling methodology. The outcomes from this project will help close the knowledge gap on recognizing the potential performance and energy impacts of a broad deployment of CAV and shared mobility technologies across a wide range of roadway infrastructure with varying levels of congestion. Results from this project: 1) will support policymakers in steering CAV development and deployment towards an energy favorable direction; 2) reduce uncertainties in estimating energy saving opportunities from new mobility technologies and services; 3) increase the confidence of CAV technology investors both on the infrastructure side (i.e., transportation agencies) and on the vehicle side (i.e., OEMs); and 4) expedite the deployment of energy-efficient CAV and shared mobility applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

NRAP-Open-IAM: Generic Aquifer Component Development and Testing

The Generic Aquifer Model calculates the concentrations of dissolved salt and dissolved CO 2 surrounding a leaking legacy well. The Generic Aquifer model can also estimate the size of an “impact plume” where concentration changes exceed user-specified thresholds. The model is a component of NRAP-Open-IAM, an open-source Integrated Assessment Model (IAM) developed by the National Risk Assessment Partnership (NRAP) to perform risk assessment for geologic CO 2 storage. The input parameters were selected to cover a wide range of groundwater aquifers and leakage rates. The generic aquifer model was developed using a generative adversarial deep learning network, trained using a large synthetic dataset of STOMP multiphase flow simulations. The deep learning model predictions of dissolved salt and dissolved CO 2 in the aquifer compare well to the original STOMP simulation results. The extent of aquifer impacted by leaking CO 2 or brine is calculated using a user-defined mass fraction threshold. The aquifer impact volumes calculated based on STOMP simulation results compare well to those calculated based on the deep learning model. In a provided python script, gridded observation results from the generic aquifer component of NRAP-Open-IAM are converted to HDF5 format files for monitoring design with the DREAM code.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Avian Activity Classification Using Recurrent Networks to Fuse Videos with Metadata on Imbalanced Datasets

Activity classification plays a crucial role in various real-life scenarios involving both humans and animals. There is an increasing need for precise activity classification focused on avian-solar interactions, as the usage of solar energy facilities, such as photovoltaic array power stations, has been observed to impact bird species richness, behavior, and activity. However, there has been no work to develop an automated system to monitor and classify these avian-solar interactions. All current methods rely on human observers, which is time and human resources costly and subject to errors related to searcher efficiency. With the recent success of Deep Learning models in activity classification problems, this paper develops a recurrent neural network-based model to automatically classify six avian activities around solar energy facilities. Our proposed model integrates critical feature engineering metadata with video frame data, enabling improved learning and more accurate activity classification. Furthermore, we address the challenge of data imbalance during training and demonstrate the efficacy of our model in detecting and classifying different activities within video tracks. Additionally, we analyze the saliency/backpropagation map of the trained proposed model and validate its decision-making rationale.

Avian activity classification; bidirectional LSTM;↗
DOE OSTI2017

Study of Optical Properties of Polymeric Materials Subjected to Degradation

Results of a study on polyethylene, polyvinyl chloride, latex and polypropylene films by photocolorimetry are described. The polymeric films were subjected to destructive environmental factors as ozone, ultra-violet irradiation and γ-radiation. It is shown that these factors can lead to a reduction or an increase of transparency. This fact is due to the mechanisms of crosslinking, crystallization and destruction of macromolecules. Correlation between the photocolorimetry data and distributions of relaxation times of a nuclear magnetic resonance of protons in different sites of the polymeric network received earlier is established.

36 MATERIALS SCIENCE↗