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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 415 records · Page 23
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Assessment of Charging Infrastructure for Plug-in Electric Vehicles at Marine Corps Base Camp Lejeune. Task 3

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for the U.S. Department of Energy’s advanced vehicle testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (Intertek) to conduct several U.S. Department of Defense-based studies to identify potential U.S. Department of Defense transportation systems that are strong candidates for introduction or expansion of plug-in electric vehicles (PEVs). Task 1 consisted of a survey of the non-tactical fleet of vehicles at Marine Corps Base Camp Lejeune to begin the review of vehicle mission assignments and types of vehicles in service. Task 2 selected vehicles for further monitoring and involved identifying daily operational characteristics of these select vehicles. Data logging of vehicle movements was initiated in order to characterize the vehicle’s mission. The Task 3 vehicle utilization report provided results of the data analysis and observations related to the replacement of current vehicles with PEVs. Finally, this report provides an assessment of charging infrastructure required to support the suggested PEV replacements. Intertek acknowledges the support of Idaho National Laboratory, Marine Corps headquarters, and Marine Corps Base Camp Lejeune Fleet management and personnel for participation in this study. Intertek is pleased to provide this report and is encouraged by enthusiasm and support from Marine Corps Base Camp Lejeune personnel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

Development and Testing of a First-of-a-Kind Process to Support Commissioning a New Hazard Category 2 Nuclear Facility - 16573

The Department of Energy's Department of Environmental Management faces the complex task of cleaning up waste from nuclear weapons production. Often, the clean-up requires novel and untested technologies and approaches. In these cases, it is always necessary to provide some measure of assurance that the design engineers will be able to produce a design that will function in the means intended. Too many times we have seen first of a kind nuclear processing technologies put forth as commercially ready, only to be found insufficiently mature once attempted to be readied for operation. As now required by the DOE's Technology Readiness Assessment Process, it is necessary to demonstrate technologies in a similar environment to the current application and tested with a range of bounding simulants, or actual waste materials, if possible. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Developing Mars-Based Clinical Scenarios for an Earth Independent Medical Operations (EIMO) – Based Decision Support Service

As crewed missions move beyond Low-Earth Orbit, pre-mission planning cannot fully buy down the medical risks of exploration-class missions. Martian missions, where increased hazards exist, (such as long-duration spaceflight, surface-level EVA operations, and communications delays) will require a paradigm shift in the structure of a medical system. An Earth-Independent Medical Operations-based Medical System (EIMO-MS) will need to optimize four critical domains to help provide medical care: utilization of Pre-Mission Planning, augmentation of Acute and Prolonged Medical Decision Making, automated tracking of Resource Management, and assistance in Task Load Balance. The ideal EIMO-MS will be able to accomplish this goal by having an interactive, adaptable interface that will be able to provide real-time medical services. It must respond based on the level of crewmember training, medical situation, and available medical and non-medical resources. To showcase the capabilities and requirements of such a sophisticated automated MS, a series of clinical scenarios of escalating complexity were developed with clinical and systems engineering input. These scenarios describe in clinical detail what a theoretical future medical system, enhanced with multiple information streams (such as a medical database, an AI-based Decision Support System, real-time monitoring, enhanced in-situ laboratory imaging, etc.) can achieve in conjunction with a trained and experienced crew. Scenarios are comprised of: a context section including objectives and applicable spaceflight environment, a highlighted assumptions section, a clinical narrative section, and a systems engineering activity diagram demonstrating the integrated Medical System (MS). The “swim lanes” of the activity diagram act as the logistical core of each scenario and show how the MS will interact with the crew, ground support, and other in-flight systems. The Design Reference Mission that is used for the scenarios is based on existing reference mission profiles [1] with a projected 30-sol stay on the Martian surface. Scenarios span the spectrum from planned evaluations, minor medical care, urgent care, surgical guidance, critical and expectant management, and behavioral health care. Mission complexity will exponentially increase during deep space and Mars exploration-class missions, and medical support for these missions will likewise need to increase in autonomy and adaptability. The integrated system that will support these missions will need to provide assistance in a variety of anticipated and unforeseen scenarios. These medical scenarios, guided by clinician input, are initial steps in crafting the requirements for an EIMO-based medical system. By working in a systems engineering framework, requirements and capabilities can be extracted and mapped while maintaining a clinical core.

Prashant Parmar↗
DOE OSTI2025

MRCI Subtask 2.4/2.5: Regional/Subregional Analysis and Risk Assessment Final Technical Summary Report

The objective of the Midwest Regional Carbon Initiative (MRCI) project is to implement a collaborative Regional Initiative (RI) to accelerate the deployment of carbon capture, and storage (CCS) in the Midwest-Northeastern quadrant of the United States. This report is a Technical Summary report describing work performed on Tasks 2.4 (Conducting Regional/Subregional Analysis) and 2.5 (Assessing and Managing Risk) during the MRCI project. In Task 2.4, detailed numerical reservoir simulation models were developed for selected carbon storage (CS) systems identified under Task 2.1 (Battelle, 2021a) in the MRCI study area. The objective of Task 2.4 is to demonstrate a dynamic modeling methodology for evaluating the suitability of the (selected) CS systems in the MRCI region for hosting a commercial-scale storage project. In this study, CO2 injectivity was evaluated for different CS systems with an annual injection rate of 1 million metric tonnes (MMT) of CO2 considered as the minimum requirement for a commercial scale project. The objective of Task 2.5 is to assess key risks associated with storing CO2 in the different CS systems across the MRCI region and to demonstrate a method(s) for assessing these risks that may be used by developers of future CO2 storage projects in the region. The risk analysis was limited to evaluating two types of leakage risks (i.e., wellbore leakage, flow across unfractured caprock) at three modeled sites considered in Task 2.4.

MRCI,Report,Summary,Technical Challenges,dynamic m↗
NASA NTRS1982

Results of the automated power systems management /APSM/ program and future technology implementation

The APSM program was initiated in 1975. The purpose of this program was to develop and demonstrate the technology and benefits of autonomous operation of planetary spacecraft power systems to meet the projected requirements of future missions. Development of the APSM program was based on implementing a selected set of autonomous functions in a state-of-the-art breadboard power system. A distributed microcomputer system was developed to implement the functions. Several critical programmatic elements were identified as necessary to implement autonomous functions. These elements, including proper skill combination, well defined autonomous functions, and management of the software design and development task, were found to be more significant than hardware management. The incorporation of APSM technology in future space programs is also discussed.

Bridgeforth, A. O.↗
NASA NTRS1996

Fast Track Study

The NASA Fast Track Study supports the efforts of a Special Study Group (SSG) made up of members of the Advanced Project Management Class number 23 (APM-23) that met at the Wallops Island Management Education Center from April 28 - May 8, 1996. Members of the Class expressed interest to Mr. Vem Weyers in having an input to the NASA Policy Document (NPD) 7120.4, that will replace NASA Management Institute (NMI) 7120.4, and the NASA Program/Project Management Guide. The APM-23 SSG was tasked with assisting in development of NASA policy on managing Fast Track Projects, defined as small projects under $150 million and completed within three years. 'Me approach of the APM-23 SSG was to gather data on successful projects working in a 'Better, Faster, Cheaper' environment, within and outside of NASA and develop the Fast Track Project section of the NASA Program/Project Management Guide. Fourteen interviews and four other data gathering efforts were conducted by the SSG, and 16 were conducted by Strategic Resources, Inc. (SRI), including five interviews at the Jet Propulsion Laboratory (JPL) and one at the Applied Physics Laboratory (APL). The interviews were compiled and analyzed for techniques and approaches commonly used to meet severe cost and schedule constraints.

Source record↗
DOE OSTI2018

Nonnegative tensor factorization for contaminant source identification

Unsupervised Machine Learning (ML) is becoming increasingly popular for solving various types of data analytics problems including feature extraction, blind source separation, exploratory analyses, model diagnostics, etc. In this work, we have developed a new unsupervised ML method based on Nonnegative Tensor Factorization (NTF) for identification of the original groundwater types (including contaminant sources) present in geochemical mixtures observed in an aquifer. Frequently, groundwater types with different geochemical signatures are related to different background and/or contamination sources. The characterization of groundwater mixing processes is a challenging but very important task critical for any environmental management project aiming to characterize the fate and transport of contaminants in the subsurface and perform contaminant remediation. This task typically requires solving complex inverse models representing groundwater flow and geochemical transport in the aquifer, where the inverse analysis accounts for available site data. Usually, the model is calibrated against the available data characterizing the spatial and temporal distribution of the observed geochemical types. Numerous different geochemical constituents and processes may need to be simulated in these models which further complicates the analyses. Additionally, the application of inverse methods may introduce biases in the analyses through the assumptions made in the model development process. Here, we substitute the model inversion with unsupervised ML analysis. The ML analysis does not make any assumptions about underlying physical and geochemical processes occurring in the aquifer. Our ML methodology, called NTFk, is capable of identifying (1) the unknown number of groundwater types (contaminant sources) present in the aquifer, (2) the original geochemical concentrations (signatures) of these groundwater types and (3) spatial and temporal dynamics in the mixing of these groundwater types. These results are obtained only from the measured geochemical data without any additional site information. In general, the NTFk methodology allows for interpretation of large high-dimensional datasets representing diverse spatial and temporal components such as state variables and velocities. NTFk has been tested on synthetic and real-world site three-dimensional datasets. Finally, the NTFk algorithm is designed to work with geochemical data represented in the form of concentrations, ratios (of two constituents; for example, isotope ratios), and delta notations (standard normalized stable isotope ratios).

58 GEOSCIENCES↗
DOE OSTI2015

Radiological mapping of facilities under dismantling process by digital autoradiography - 15230

because safety standards have changed. In that context, radioactive waste management is a challenging task faced by existing nuclear power countries. Therefore, characterization is essential to classify wastes and to reduce costs. In many facilities, after the removal of glove boxes, fume cupboards and so on, the remaining materials on the floor, walls, and ceiling must be characterized. Gamma emitters can be detected remotely by commercial devices. However, analytical techniques to detect alpha and beta emitters in situ are generally not well suited for large area investigations. The Digital Autoradiography (DA) technique, initially dedicated to research in biology, was re-routed for final state characterization before facility demolition. It is based on the exposure of radio-sensitive phosphor screens on the materials to characterize. Phosphor screens, after a scanning process, provide a picture of the radioactivity, and are reusable several tens of times. This analytical technique is sensitive to all types of radioactivity, requires neither operators nor electricity during acquisition process, and produces only very few wastes. DA has already been tested in different facilities to obtain semi-quantitative values of C-14 and H-3 activities. Large amount of data gathered to cover the whole surface to be characterized. A Geographic Information System (GIS) associated with geostatistical method has been used to provide Radiological Mapping. This method to interpret Digital Autoradiography results has been proved very efficient to prepare remediation project. (authors)

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Implementation Approach for Electric Vehicles at Marine Corps Base Camp Lejeune. Task 4

Battelle Energy Alliance, LLC, managing and operating contractor for the U.S. Department of Energy’s Idaho National Laboratory, is the lead laboratory for U.S. Department of Energy Advanced Vehicle Testing. Battelle Energy Alliance, LLC contracted with Intertek Testing Services, North America (Intertek) to conduct several U.S. Department of Defense base studies to identify potential U.S. Department of Defense transportation systems that are strong candidates for introduction or expansion of plug-in electric vehicles (PEVs). This study is focused on the Marine Corps Base Camp Lejeune (MCBCL) located in North Carolina. Task 1 consisted of a survey of the non-tactical fleet of vehicles at MCBCL to begin the review of vehicle mission assignments and types of vehicles in service. In Task 2, daily operational characteristics of vehicles were identified to select vehicles for further monitoring and attachment of data loggers. Task 3 recorded vehicle movements in order to characterize the vehicles’ missions. The results of the data analysis and observations were provided. Individual observations of the selected vehicles provided the basis for recommendations related to PEV adoption, i.e., whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively PEVs) can fulfill the mission requirements. It also provided the basis for recommendations related to placement of PEV charging infrastructure. This report focuses on an implementation plan for the near-term adoption of PEVs into the MCBCL fleet. Intertek acknowledges the support of Idaho National Laboratory, Marine Corps headquarters, and Marine Corps Base Camp Lejeune fleet management and personnel for participation in this study. Intertek is pleased to provide this report and is encouraged by enthusiasm and support from MCBCL personnel.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2018

Scanning A uger spectromicroscopy using the S cope F oundry software platform

We have developed an open‐source, Python‐based software platform called ScopeFoundry for performing complex, multidimensional microscopy experiments that integrate diverse types of experimental hardware. ScopeFoundry streamlines experiment development by providing a modular environment where hardware controls and measurement schemes can be arranged and customized. Important software tasks such as data management and user interface generation are automated. Using this platform, we have implemented scanning Auger spectromicroscopy on a modified commercial surface analysis system. Spectromicroscopy produces multidimensional datasets with complete spectra at each image pixel, allowing for detailed analysis of material phases where Auger peaks may change in shape and position. Analysis of the surface phases of an advanced electron emission dispenser cathode is presented as an example. We describe the general ScopeFoundry platform, its application to Auger spectromicroscopy, and ways it can be applied to advance surface analysis and microscopy.

Durham, Daniel B.↗
DOE OSTI2018

Automatic Halo Management for the Uintah GPU-Heterogeneous Asynchronous Many-Task Runtime

The Uintah computational framework is used for the parallel solution of partial differential equations on adaptive mesh refinement grids using modern supercomputers. Uintah is structured with an application layer and a separate runtime system. Uintah is based on a distributed directed acyclic graph of computational tasks, with a task scheduler that efficiently schedules and executes these tasks on both CPU cores and on-node accelerators. The runtime system identifies task dependencies, creates a task graph prior to the execution of these tasks, automatically generates MPI message tags, and automatically performs halo transfers for simulation variables. Automating halo transfers in a heterogeneous environment poses significant challenges when tasks compute within a few milliseconds, as runtime overhead affects wall time execution, or when simulation variables require large halos spanning most or all of the computational domain, as task dependencies become expensive to process. These challenges are magnified at production scale when application developers require each compute node perform thousands of different halo transfers among thousands simulation variables. The principal contribution of this work is to (1) identify and address inefficiencies that arise when mapping tasks onto the GPU in the presence of automated halo transfers, (2) implement new schemes to reduce runtime system overhead, (3) minimize application developer involvement with the runtime, and (4) show overhead reduction results from these improvements.

Concurrency↗
DOE OSTI2020

Automatic log analysis with NLP for the CMS workflow handling

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.

Layer, Lukas↗
DOE OSTI2017

CMS event processing multi-core efficiency status

In 2015, CMS was the first LHC experiment to begin using a multi-threaded framework for doing event processing. This new framework utilizes Intel’s Thread Building Block library to manage concurrency via a task based processing model. During the 2015 LHC run period, CMS only ran reconstruction jobs using multiple threads because only those jobs were sufficiently thread efficient. Recent work now allows simulation and digitization to be thread efficient. In addition, during 2015 the multi-threaded framework could run events in parallel but could only use one thread per event. Work done in 2016 now allows multiple threads to be used while processing one event. In this presentation we will show how these recent changes have improved CMS’s overall threading and memory efficiency and we will discuss work to be done to further increase those efficiencies.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Exploring Tradeoffs in Federated Learning on Serverless Computing Architectures

Federated learning is driving the development of new techniques to efficiently and securely use data across multiple sites while using diverse resources. One of these techniques is the use of the serverless computing paradigm to abstract away resource specific configurations, allowing federated learning across heterogeneous environments. However, deploying federated learning across edge resources, the cloud, and traditional HPC sites will require specialized approaches in order to best account for the weaknesses and strengths of each resource. In this work, we explore the new tradeoffs presented by managing a federated learning task across heterogeneous resources and demonstrate these tradeoffs with experiments using a serverless federated learning framework.

Baughman, Matt↗
DOE OSTI2019

Maximizing the Benefits of Volt/VAR Optimization in the Presence of Community Energy Storage

This paper reports on the need for coordinating smart grid volt/volt-ampere reactive (VAR) control (VVC) and community energy storage (CES) to maximize the benefits of both approaches. It addresses a key application of volt/VAR optimization (VVO): the conservation of voltage reduction (CVR). The lack of coordination among network controls, such as VVO, and asset controls at the community battery storage system can lead to detrimental effects on grid operation. A smart grid-enabled VVC uses the VVO to perform the CVR operation. VVO can perform numerous tasks for advanced distribution management systems with coordination of the system operator. To achieve the optimal VVC solution, we use a particle swarm optimization driven VVO method. The test system has been simulated for two scenarios-during light loading and high loading condition-with and without the CES system. In both scenarios, the operation of CVR and CES have been performed considering different case studies. The outcomes of the simulated results demonstrate that significant improvement in peak load-demand reduction is achieved by the proposed VVC and CES system with reduced system losses.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

BEE - FY20 P6-3: Release BEEWorkflowManager, BEETaskManager, and client application 2.3.6.01 – LANL ATDM ST / STNS01-4 P6 Milestone Completion Documentation

Release BEEWorkflowManager, BEETaskManager, and client software. The BEEWorkflowManager daemon runs on the HPC cluster login node. It accepts workflows submitted by the BEE client. These workflows are specified using the Common Workflow Language (CWL) standard. The BEEWorkflowManager loads workflows into the Neo4j graph database to create the workflow directed acyclic graph (DAG), and submits the workflow tasks to the BEETaskManager for execution. The BEEWorkflowManager records the state of the workflow and its tasks, and communicates this state to the BEE client. The BEEWorkflowManager will start, pause, and cancel a running workflow and its tasks at the command of the BEE client. The BEETaskManager daemon runs on the HPC cluster login node. It accepts tasks from the BEEWorkflowManager, turns those tasks into HPC resource manager jobs (e.g. a slurm job script), and submits the job to the cluster resource manager. The BEETaskManager then tracks the status of the job (pending, running, complete) and updates the BEEWorkflowManager. The BEETaskManager will also cancel a queued or running job when commanded to do so by the BEEWorkflowManager. The first release of the BEETaskManager will support the Slurm resource manager and the Charliecloud linux container runtime.

97 MATHEMATICS AND COMPUTING↗