Search NASA⌕ Search

SEARCH · Search NASA

Results for “usage”

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 1,009 records · Page 56

RF Environment Data Analysis for UAS Operations

As the usage of radio frequency spectrum and uncrewed aircraft systems continue to grow, the radio frequency environment and its implications to flight safety should be understood. This is important to ensure control and command links as well as data collection links maintain strong communications during flight. NASA System-Wide Safety (SWS) is currently developing standards of flight safety but lacks methods for establishing confidence in the availability of radio frequency space to make a go/no-go decision for drone flights. This study examines information about the radio modules used by SWS and how they interact with the RF environment though analysis of historical spectrum data. This review studies the potential effectiveness of observing the power spread and occupancy methods as they apply to the flight system radios. It has been determined that viewing the frequency and time when the environment is greater than a threshold which indicates potential interference levels may give insight into the patterns of spectrum usage.

Radio Frequency↗

Supporting Hazard Analysis for Wildfire Response Using fmdtools and MIKA

The System Wide Safety (SWS) Safety Demonstrator (SD) Series drives development of an increasingly capable In-Time Aviation Safety Management System (IASMS) focusing on humanitarian applications, starting with wildfire response (SD-1). The goals of this report are to (1) provide an early hazard analysis and mitigation evaluation of wildfire response to support these efforts and (2) provide a demonstration of capabilities of the Fault Model Design Tools (fmdtools) and Manager for Intelligent Knowledge Access (MIKA) tools. fmdtools provides a modeling, simulation, and resiliency analysis framework in which a wildfire response model, the System Modeling and Analysis of Resiliency in Scalable Traffic Management for Emergency Response Operations (SMARt-STEReO), is built. MIKA is an intelligent knowledge manager with several capabilities, including assisting in hazard analysis by extracting and analyzing hazards from historical incident reports. The following topics are covered in the report: Understanding Wildfire Hazard Dynamics. We provide a description and simulated examples of how hazards occur in the SMARt-STEReO model of wildfire response and their effect on its outcome. This provides a common mental model and focuses the analysis presented in the remainder of the report. Wildfire Hazard Identification. MIKA identifies wildfire hazards from three relevant datasets: the ICS-209-PLUS, SAFECOM, and SAFENET. Hazards are manually organized into a taxonomy and MIKA analyzes each hazard’s effects, likelihood, severity, and risk. Evaluating Mitigation Strategies. The SMARt-STEReO wildfire response model built in fmdtools evaluates a subset of identified hazards. Specifically, we simulate the effect of communications faults and equipment faults on operator safety, the effect of changing winds and flammability, and a scenario with multiple ignition points and heavy smoke. Tool Limitations and Usage Considerations. We provide a discussion of appropriate tool use cases as well as limitations and considerations for usage. The tool findings are used to synthesize recommendations for wildfire response operations, which can be captured as part of an IASMS. Key recommendations are as follows: Hazards are identified from a broad spectrum of sources including aircraft subsystems, operational sources, and ground crew operations. Highest risk operational environment hazards identified are Evacuations. The highest risk manned aerial operations hazard categorized is Jumper Operations Mishap. Ground crew hazards that are highest risk are Burns, Cargo Operations Overhead, Dehydration, Entrapment, Falling Objects, Heart Attacks, Heat Exhaustion, Inadequate Training or Certification, Vehicle Breakdown, and Vehicle Collision. Modelled containment failures arise from a mismatch between the difficulty of the firefighting scenario and the capacity (e.g., speed, effectiveness, awareness) of the response. In firefighting scenarios where containment is possible (e.g., because the fire does not spread too quickly), these mismatches can occur because of a change in environmental conditions (e.g., wind, flammability, etc) or because of planning, equipment, or communications faults. Improvements to communications increase the capacity of the firefighting response by reducing the time needed to respond to the fire. While surveillance does not increase this capacity by itself, it increases operator safety by increasing state awareness, enabling firefighters to evade approaching fires. Increasing both has a synergistic effect. In general, these performance and resilience increases generalize over fault scenarios as well as unforeseen changes to circumstances (i.e., wind, aridity, etc.). However, these improvements need to be designed so as not to make the system prone to persistent large-scale communications outages, which can reduce performance.

Hazard analysis↗

Scaling a Smart Sub-metered Electrical Data Solution for the Center

Currently, there is no single Center-level point solution that enables facility systems data to be collected and stored from buildings on-site, on-demand, and through a faceted search capability that would enable the cultivation of deeper insights into incipient facility-related issues that would otherwise be overlooked. Such insights would represent a powerful and very valuable tool to aid in data-driven decision making and informing actionable steps for remediation and resolution of these problems. Access to electrically sub-metered data via hardware upgrades is in the process of being restored for Building N232, Sustainability Base, but this is only the first step. The native software capabilities that were originally part of a proposed Agencywide Smart Center initiative project can unlock the ability to access actionable insights. This work would also nicely complement many initiatives that are actively being pursued, under consideration, or pending submission to this same call at the Center. This project is poised to support an NRSAA with Verdigris Technologies, as well as in support of an Agencywide Smart Center initiative. Monitoring electrical consumption usage at the most granular level in Bldg. N232 will be instrumental in accurately quantifying potential utility costs and investments, and monitoring usage trends, as it is being converted into hoteling spaces, to help. This will also help with determining how the capability can scale more broadly across the Center and Agency at large.

Rodney Alexander Martin↗

IMERG and GPCP Seasonality and Response to Climate and Weather Variability

The Integrated Multi-satellitE Retrievals for GPM (IMERG) and the The Global Precipitation Climatology Project (GPCP) are two of the most popular precipitation products. IMERG is a relatively new dataset that targets the needs primarily of the hydrological community by resolving the hydrological cycle of precipitation at fine temporal (30-minutes) and spatial (10-km) scales. IMERG only recently exceeded the user base of the highly successful, but discontinued in 2019, TRMM Multi-satellite Precipitation Analysis (TMPA). GPCP, on the other hand, has traditionally been strong in the climate research community, and recently has been revised under the framework of NASA's Making Earth System Data records for Use in Research Environments (MEaSUREs) program. Both IMERG and GPCP are similar in the underlying approaches to achieve global coverage, in particular using satellite microwave and infrared observations, and adjusting the precipitation retrieval with rain gauge information. While there is a tendency to use both datasets interchangeably, differences remain and some of them limit the usage of IMERG as a climate data record at this point. By applying Principal Component Analysis, we identify the differences and similarities mode-by-mode, and further the guidance on suggested usage of IMERG as a climate data record. As an example in the attached figure, IMERG vs GPSP differences in the explained variance by the two leading seasonal modes can be identified, mainly in the extreme southern latitudes, and over western boundary currents (Gulfstream and Kuroshio). At the preparation time for this presentation, the new version "07"of IMERG was in the works that may resolve the issues presented here. Nevertheless, our analysis can help to gauge the uncertainties of the studies already done using the currently existing IMERG version "06", and evaluate the improvements in the upcoming version "07".

Andrey Savtchenko↗

Validations of Satellite Ozone Profiles in Austral Spring Using Ozonesonde Measurements in the Jang Bogo Station, Antarctica

Using ozonesonde measurements from 2015 to 2018 at the Jang Bogo station located in the southeastern Antarctic region, we evaluate ozone profiles retrieved from the three satellite measurements that are widely used: Ozone Monitoring Instrument (OMI), Microwave Limb Sounder (MLS), and Ozone Mapping Profiler Suite (OMPS) data. For the fair validation, ozonesonde profiles are smoothed using the weighting function of each satellite retrieval algorithm (i.e., convolution process). Compared with limb-viewing MLS and OMPS ozone profiles, the OMI ozone profiles are relatively less qualified: coarser vertical resolution and larger inter-annual variation. Nevertheless, our validation reveals that the quality of all three satellite ozone profiles looks comparable; In general, difference from ozonesonde profile is ∼1 ppm absolutely, and −20 to 30% relatively at maximum. This quantitative range well corresponds to previous work, meaning that our new validation confirms the reliability of satellite ozone profiles in the southeastern Antarctic region where the measurement data for the validation were not enough. Another interesting feature is the role of a priori ozone profile; Nadir-viewing OMI satellite can have qualified ozone profiles by a proper assumption of a priori ozone profile. Since the performance of limb-viewing ozone profiles is better, however, the careful usage of nadir-viewing ozone profile is still required. We think that the simultaneous usage of multiple satellite ozone profiles can contribute to better understanding of Antarctic ozone characteristics.

Antarctica↗

Visualizing Multi-process CPU Utilization using CUSP

The CPU Utilization Statistics Plotter (CUSP) tool automates the interpretation of detailed CPU Utilization trace data and statistics. It puts you on the cusp of understanding how CPU resources are split among the many parallel components of a software system.CUSP combines time-sampled CPU utilization numbers and Event Log annotations to generate human-readable plots and tables. It automatically splits up large CPU usage log files around interesting events, determines and highlights just the tasks of primary relevance by evaluating their changing contribution to each plot's total CPU usage, automatically eliminates irrelevant tasks, provides context by labeling plots with names and durations of all active commands, and uses consistent color-coding to enable quick visual comparison across multiple plots.CUSP has been used to process CPU Utilization trace logs on the Mars Science Laboratory and the Mars 2020 Rover missions during flight software development and Flight Operations on the Martian surface since December 2013.

Maimone, Mark W↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge and support human space missions. Through artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in space biosciences and engineered astronaut health systems, to enable Earth-independence and mission operations autonomy. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated mission biomonitoring, and 8) a Precision Space Health system. AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the space biology field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics to phenotypic data using an ensemble model to infer causality of rodent liver health disruption, 2) usage of explainable ML to interrogate muscular underpinnings of muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interactions, and 5) a suite of benchmarked open science datasets enabling programmers to identify best algorithms to answer space biology questions.

space biology↗

The Potential of Medical Drones: An Analysis of Current and Future Use Cases

Modern Application of Medical-Based Drone Delivery Drones have been used advantageously by militaries for nearly a century, but their uses in civilian life are still mostly cutting-edge, if not theoretical. After a decade of bold proclamations, Amazon’s “PrimeAir” drone delivery system is still in the stage of “preparing” for deliveries, while the public awaits for start ups like SkyDrop (formerly Flirtey) to follow through on impressive promises. Despite the well-publicized disappointment so far in commercial drone delivery, medical drone delivery has already proven itself practical and cheap in several countries, and it promises to expand in the coming years. Drones are uniquely suited to make valuable and urgent deliveries to remote areas, quickly transporting medical supplies where road transportation is prohibitively slow or not available at all. Drones have been used notably to deliver AEDs for out-of-hospital cardiac arrest, frequently beating first-responders to the scene; to deliver blood when there is none on hand at hospitals; to deliver vaccines to an island nation with little transportation infrastructure; and to respond flexibly to medical emergencies in a war zone. Economics make the delivery of food and other cheap goods by drone unattractive in the near-term, but the value and time-sensitivity of medical deliveries mean that drones are already saving lives in healthcare. “We believe the value of new technology is most valuable where it is clearly needed...that’s why we wanted to focus on drones delivering medicine and not delivering pizzas, ”said one executive of a drone system manufacturer. The immediate prospects for the expansion of medical drone use are many; however, they do not exist without their own drawbacks and challenges. Most obvious is the limited range of current commercially-available drones, most of which are isolated to a perimeter of roughly 18 miles. Technological know-how presents another barrier to integration of medical drones on a larger scale. Reports from the United Nations frequently cite a“skill deficit”—a prohibitively low number of qualified drone operators in low-and moderate-income countries (LMICs). Another perhaps more discreet speed bump in global drone development and usage are the various regulations on drone usage. Drone technology has developed so quickly that many states, out of an excess of caution, have nearly snuffed out the fledgling industry with regulation. There also exist significant concerns over the security of private citizens, the efficacy of medical deliveries, and the costs of drone operation. It is these last three barriers which this study will seek to overcome. Put simply, the prospect for human development in LMICs from drone-based medical delivery is far too great to disregard. As of 2020, 3.4 billion people live in rural communities, containing fewer than 5,000 people/km^2. Often lacking infrastructure, these communities are largely isolated from their more populated, urban counterparts. In drones lies the potential to reshape the geographic and developmental distinctions that divide the global population. This development must, therefore, begin first and foremost with advancement in regional well-being and life expectancy. Life expectancy makes up a key facet of human development. The United Nations relies on it as a key indicator of a state’s health. Lars Kunze of the Dortmund University Department of Economic sex plains this as a matter of physical capital accumulation. The longer people live, the more they save as opposed to spend. The more they save, the more which eventually gets invested in themselves and the community as a whole. In providing medical products via drone, it is the intention of this study to enable communities with the means and incentives for long-run savings and investment for future economic development. Through a close analysis of Vanuatu, Rwanda, Tanzania, and Ukraine—four states where drones are currently used to deliver medical supplies—this study develops a framework that LMICs in general and Mexico and particular can adopt and to use medical drones in difficult-to-reach communities for the sake of long-run human developmental initiatives.

Ryan Teoh↗

Logistics Rates and Assumptions for Future Human Spaceflight Missions Beyond LEO

As NASA prepares for future human spaceflight missions with extended crew duration in destinations beyond low Earth orbit (LEO), the Agency has focused itself on understanding the drivers to sustainably support human life beyond Earth’s atmosphere. Future missions to deep space, the lunar surface, and eventually the Martian surface pose new challenges in ensuring the crew is sufficiently supplied with all necessary materials. To mitigate the risk of not delivering sufficient consumables and logistics for human spaceflight missions, NASA has examined past human space mission data and developed metabolic modeling to determine estimates for the crew consumption rates of fluids, solid consumables, and additional equipment needed. This paper is a compilation of guidelines, rates, and assumptions necessary to evaluate the logistics needs for future human exploration conceptual missions beyond LEO, providing a starting point and resource of information regarding usage rates and overall logistics supply planning for crewed exploration missions. Logistics represent all equipment and supplies not installed as part of the vehicle that are needed to support mission activities. Logistics can be further divided into specific categories, including consumables, maintenance items, spares, utilization, outfitting, as well as any packaging required. This paper will also provide use case examples of logistics needs to support human missions in deep space, including a conceptual lunar surface mission. The paper provides information necessary to calculate the mass and volume of known logistics for conceptual future human exploration missions beyond LEO. The assumptions in the paper are updated versions of previous assumptions made by the Agency and were derived from a number of sources, including International Space Station (ISS) historical usage and resupply rates, the Life Support Baseline Values and Assumptions Document (BVAD) 2022, Human Integration Design Handbook (HIDH) 2014, and data gathered from NASA human spaceflight programs and projects. The primary goal of the paper is to establish a set of consistent reference rates that multiple teams and groups can utilize to conduct logistics analysis and compare cases. This methodology is for initial estimates of conceptual human missions and does not take the place of detailed analysis for programs, nor does it provide requirements for programs.

Human Spaceflight↗

Logistics Rates and Assumptions for Future Human Spaceflight Missions Beyond LEO

As NASA prepares for future human spaceflight missions with extended crew duration in destinations beyond low Earth orbit (LEO), the Agency has focused itself on understanding the drivers to sustainably support human life beyond Earth’s atmosphere. Future missions to deep space, the lunar surface, and eventually the Martian surface pose new challenges in ensuring the crew is sufficiently supplied with all necessary materials. To mitigate the risk of not delivering sufficient consumables and logistics for human spaceflight missions, NASA has examined past human space mission data and developed metabolic modeling to determine estimates for the crew consumption rates of fluids, solid consumables, and additional equipment needed. This paper is a compilation of guidelines, rates, and assumptions necessary to evaluate the logistics needs for future human exploration conceptual missions beyond LEO, providing a starting point and resource of information regarding usage rates and overall logistics supply planning for crewed exploration missions. Logistics represent all equipment and supplies not installed as part of the vehicle that are needed to support mission activities. Logistics can be further divided into specific categories, including consumables, maintenance items, spares, utilization, outfitting, as well as any packaging required. This paper will also provide use case examples of logistics needs to support human missions in deep space, including a conceptual lunar surface mission. The paper provides information necessary to calculate the mass and volume of known logistics for conceptual future human exploration missions beyond LEO. The assumptions in the paper are updated versions of previous assumptions made by the Agency and were derived from a number of sources, including International Space Station (ISS) historical usage and resupply rates, the Life Support Baseline Values and Assumptions Document (BVAD) 2022, Human Integration Design Handbook (HIDH) 2014, and data gathered from NASA human spaceflight programs and projects. The primary goal of the paper is to establish a set of consistent reference rates that multiple teams and groups can utilize to conduct logistics analysis and compare cases. This methodology is for initial estimates of conceptual human missions and does not take the place of detailed analysis for programs, nor does it provide requirements for programs.

Human Spaceflight↗

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗

Literature Review of Microwave Regeneration of CO2 Capture Sorbent Materials

Utilizing liquid amines for air revitalization applications has been of continued interest for many decades. They have the advantage of being regenerable sorbents with long lifetimes and a high acuteness for carbon dioxide. While their usage on Earth for flue gas and submarines has been explored for some time now, they are still being researched for use in microgravity environments. To succeed in a microgravity environment, such as usage on the International Space Station, an air revitalization system that is reliant on liquid amines as its sorbent must have a low Equivalent System Mass (ESM), among many other performance parameters. One way of optimizing this ESM number is through bringing down the systems power consumption with the use of microwave heating of the liquid sorbent. Microwave heating allows the liquid sorbent to be heated in a much more efficient way when compared to conventional types of heating techniques, such as convection or conduction. This paper will explore the physical mechanisms behind microwave heating, and how it could potentially be applied to a liquid amine carbon capture system, laying out any concerns and future constraints that a system designer may encounter.

Tiago Faria Costa↗

Plasma-Based Elemental Analyzer for Crewed Transit and Planetary Habitation

As NASA continues to perform research on the ISS and move towards the Moon and Mars, it is important for astronauts to have a technology capable of determining the composition of solid, liquid, and gas samples. Currently, samples are transported back to Earth for analysis, which may impact results due to the time delay of samples waiting to be returned to Earth as well as potential sample contamination from re-entry, change in environment, and various personnel handling. Therefore, researchers at the Applied Chemistry Lab at KSC have developed a proof-of-concept “Plasma-Based Elemental Analyzer,” which has the capability of testing solid, liquid, and gas samples for identification. Currently, the analysis is only qualitative, with quantitative identification left for future work. This technology is similar to ICP-OES, but differs in instrument size and sample preparation. Both technologies use plasma to ionize samples for light emission. The plasma elemental analyzer aims to be portable and lightweight, require only electrical power, and analyze solid, liquid, and gas samples. It does not use acid digestion or a nebulizer, requires less power than ICP-OES, and is able to operate with minimal to no usage of argon gas. Furthermore, the plasma elemental analyzer operates within low power constraints, which makes it feasible for usage on future missions with higher power constraints. The technology was tested at atmospheric pressure using mostly room air as the carrier gas for plasma production in the proof-of-concept study. The results showed the peak signals of the elements with an associated arbitrary signal intensity that allowed for qualitative identification. Solid samples that were tested included inedible biomass from the Golden Heirloom Cherry Tomato plant and Lunar Highlands Simulants (LHS-1). The liquid samples included elements of interest for the space crop production community. This work showed that a system smaller than the traditional ICP-OES equipment is able to be designed and implemented in future use cases, although it requires further development.

Kenneth Engeling↗

Evaluating Delta-V Dispersions Using Linear Covariance Techniques with Applications to Rendezvous and Docking

One of the attractive advantages of using linear covariance analysis (LinCov) is that it can accurately produce both navigation errors and trajectory dispersions in a fraction of the time comparable to the standard Monte Carlo analysis approach that is traditionally adopted. It also has the capability of generating delta-v dispersions which is perhaps the most influential performance metric since many aspects of a mission design revolve around the anticipated propellant usage. Accurately capturing this key performance criteria using linear covariance techniques has several subtle caveats raising reasonable doubts and a cautious sense of skepticism. This paper outlines multiple approaches of modeling delta-v usage in LinCov, highlights their limitations and advantages, and ultimately compares their corresponding results to the actual Monte Carlo performance. The theory is also extended to reliably account for an arbitrary thruster configuration and the resultant propellant consumptions due to both translational burns and attitude maneuvers. This provides near instant insight into the impacts of thruster layout design and different vehicle mass properties. These derived techniques are applied and verified using an NRHO rendezvous and docking scenario consistent with upcoming NASA Artemis missions for Orion.

Linear Covariance Analysis↗

A First Look at the Impact of Electric Vehicle Charging on the Electric Grid in the EV Project

ECOtality was awarded a grant from the U.S. Department of Energy to lead a large-scale electric vehicle charging infrastructure demonstration, called The EV Project. ECOtality has partnered with Nissan North America, General Motors, the Idaho National Laboratory, and others to deploy and collect data from over 5,000 Nissan LEAFsTM and Chevrolet Volts and over 10,000 charging systems in 18 regions across the United States. This paper summarizes usage of residential charging units in The EV Project, based on data collected through the end of 2011. This information is provided to help analysts assess the impact on the electric grid of early adopter charging of grid-connected electric drive vehicles. A method of data aggregation was developed to summarize charging unit usage by the means of two metrics: charging availability and charging demand. Charging availability is plotted to show the percentage of charging units connected to a vehicle over time. Charging demand is plotted to show charging demand on the electric gird over time. Charging availability for residential charging units is similar in each EV Project region. It is low during the day, steadily increases in evening, and remains high at night. Charging demand, however, varies by region. Two EV Project regions were examined to identify regional differences. In Nashville, where EV Project participants do not have time-of-use electricity rates, demand increases each evening as charging availability increases, starting at about 16:00. Demand peaks in the 20:00 hour on weekdays. In San Francisco, where the majority of EV Project participants have the option of choosing a time-of-use rate plan from their electric utility, demand spikes at 00:00. This coincides with the beginning of the off-peak electricity rate period. Demand peaks at 01:00.

33 ADVANCED PROPULSION SYSTEMS↗

Integrated Testing, Simulation and Analysis of Electric Drive Options for Medium-Duty Parcel Delivery Vehicles: Preprint

The National Renewable Energy Laboratory verified diesel-conventional and diesel-hybrid parcel delivery vehicle models to evaluate petroleum reduction and cost implications of plug-in hybrid gasoline and diesel variants. These variants are run on a field-data-derived design matrix to analyze the effects of drive cycle, distance, battery replacements, battery capacity, and motor power on fuelconsumption and lifetime cost. Two cost scenarios using fuel prices corresponding to forecasted highs for 2011 and 2030 and battery costs per kilowatt-hour representing current and long-term targets compare plug-in hybrid lifetime costs with diesel conventional lifetime costs. Under a future cost scenario of $100/kWh battery energy and $5/gal fuel, plug-in hybrids are cost effective. Assuming acurrent cost of $700/kWh and $3/gal fuel, they rarely recoup the additional motor and battery cost. The results highlight the importance of understanding the application's drive cycle, daily driving distance, and kinetic intensity. For instances in the current-cost scenario where the additional plug-in hybrid cost is regained in fuel savings, the combination of kinetic intensity and dailydistance travelled does not coincide with the usage patterns observed in the field data. If the usage patterns were adjusted, the hybrids could become cost effective.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Plug-In Electric Vehicle Fast Charge Station Operational Analysis with Integrated Renewables: Preprint

The growing, though still nascent, plug-in electric vehicle (PEV) market currently operates primarily via level 1 and level 2 charging in the United States. Fast chargers are still a rarity, but offer a confidence boost to oppose 'range anxiety' in consumers making the transition from conventional vehicles to PEVs. Because relatively no real-world usage of fast chargers at scale exists yet, theNational Renewable Energy Laboratory developed a simulation to help assess fast charging needs based on real-world travel data. This study documents the data, methods, and results of the simulation run for multiple scenarios, varying fleet sizes, and the number of charger ports. The grid impact of this usage is further quantified to assess the opportunity for integration of renewables;specifically, a high frequency of fast charging is found to be in demand during the late afternoons and evenings coinciding with grid peak periods. Proper integration of a solar array and stationary battery thus helps ease the load and reduces the need for new generator construction to meet the demand of a future PEV market.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗