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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

STNS01-44 BEE – FY22-1: Enhanced BEE Client [Slide]

BEE provides a portable, modular, HPC-focused workflow engine capable of managing containerized applications at scale. In FY22 BEE is completing enhancements and refinements that will complete the major development work of the workflow system. The first major milestone is the development of a graphical client. The second milestone will be the ability for BEE to automatically restart checkpointed tasks. The final milestone for FY22 will be the ability to launch and manage multiple simultaneous workflows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

WIRE: Resource-efficient Scaling with Online Prediction for DAG-based Workflows

This paper introduces WIRE that manages resources for the DAG-based workflows on IaaS clouds. WIRE predicts and plans resources over the MAPE (Monitor-Analyze-Plan-Execute) loops to: 1) Estimate task performance with online data, 2) Conduct simulations to predict the upcoming loads based on online estimates and workflow DAGs, 3) Apply a resource-steering policy to size cloud instance pools for the maximal parallelism that is consistent with low cost. We implement WIRE on Pegasus WMS/HTCondor and evaluate its performance on the ExoGENI network cloud. The results show that WIRE attains low resource cost with the performance that is typically within a factor of two of optimal.

Xie, Bing↗
NASA NTRS2015

Streamling the Change Management with Business Rules

Will discuss how their organization is trying to streamline workflows and the change management process with business rules. In looking for ways to make things more efficient and save money one way is to reduce the work the workflow task approvers have to do when reviewing affected items. Will share the technical details of the business rules, how to implement them, how to speed up the development process by using the API to demonstrate the rules in action.

Basic Rules↗
DOE OSTI2016

SU-F-T-245: The Investigation of Failure Mode and Effects Analysis and PDCA for the Radiotherapy Risk Reduction

Purpose: To optimize the clinical processes of radiotherapy and to reduce the radiotherapy risks by implementing the powerful risk management tools of failure mode and effects analysis(FMEA) and PDCA(plan-do-check-act). Methods: A multidiciplinary QA(Quality Assurance) team from our department consisting of oncologists, physicists, dosimetrists, therapists and administrator was established and an entire workflow QA process management using FMEA and PDCA tools was implemented for the whole treatment process. After the primary process tree was created, the failure modes and Risk priority numbers(RPNs) were determined by each member, and then the RPNs were averaged after team discussion. Results: 3 of 9 failure modes with RPN above 100 in the practice were identified in the first PDCA cycle, which were further analyzed to investigate the RPNs: including of patient registration error, prescription error and treating wrong patient. New process controls reduced the occurrence, or detectability scores from the top 3 failure modes. Two important corrective actions reduced the highest RPNs from 300 to 50, and the error rate of radiotherapy decreased remarkably. Conclusion: FMEA and PDCA are helpful in identifying potential problems in the radiotherapy process, which was proven to improve the safety, quality and efficiency of radiation therapy in our department. The implementation of the FMEA approach may improve the understanding of the overall process of radiotherapy while may identify potential flaws in the whole process. Further more, repeating the PDCA cycle can bring us closer to the goal: higher safety and accuracy radiotherapy.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2015

The Open Data Repositorys Data Publisher

Data management and data publication are becoming increasingly important components of researcher's workflows. The complexity of managing data, publishing data online, and archiving data has not decreased significantly even as computing access and power has greatly increased. The Open Data Repository's Data Publisher software strives to make data archiving, management, and publication a standard part of a researcher's workflow using simple, web-based tools and commodity server hardware. The publication engine allows for uploading, searching, and display of data with graphing capabilities and downloadable files. Access is controlled through a robust permissions system that can control publication at the field level and can be granted to the general public or protected so that only registered users at various permission levels receive access. Data Publisher also allows researchers to subscribe to meta-data standards through a plugin system, embargo data publication at their discretion, and collaborate with other researchers through various levels of data sharing. As the software matures, semantic data standards will be implemented to facilitate machine reading of data and each database will provide a REST application programming interface for programmatic access. Additionally, a citation system will allow snapshots of any data set to be archived and cited for publication while the data itself can remain living and continuously evolve beyond the snapshot date. The software runs on a traditional LAMP (Linux, Apache, MySQL, PHP) server and is available on GitHub (http://github.com/opendatarepository) under a GPLv2 open source license. The goal of the Open Data Repository is to lower the cost and training barrier to entry so that any researcher can easily publish their data and ensure it is archived for posterity.

Astrobiology data↗
NASA NTRSDate not supplied

From the Knowledge-based Digital Platform (KbDP) Concept for Advanced Air Mobility Research to a Preliminary Prototype

Advanced Air Mobility (AAM) encompasses a range of innovative operational and technological changes to aviation (electric aircraft, increasingly automated aircraft, increasingly automated airspace operations, etc.) that are transforming aviation’s role in everyday movement of people and goods. There are multiple associated concepts and use cases for AAM, all interrelated, including small Unmanned Aircraft System (UAS) Traffic Management (UTM), Upper-Class E Traffic Management (ETM), Extensible Traffic Management (xTM), Regional Air Mobility (RAM), and Urban Air Mobility (UAM). These AAM operations must integrate with traditional Air Traffic Management (ATM) operations, as well as non-aviation modes of transportation and logistics. National Aeronautics and Space Administration (NASA) is spearheading an innovative digital engineering approach to integrate, communicate, and facilitate the research of multi-modal transportation systems. The Knowledge-based Digital Platform (KbDP) is a concept being developed that ties the workflows of Project Managers (PM), Principal Investigators (PI), and System Engineers together across organizational boundaries. It does so through the management of an information database defined by mathematical, data science, and system engineering principles. Machine Learning (ML) algorithms play a key role in this concept by extracting meaningful knowledge from the information database, which the human user leverages to greatly improve the efficiency and effectiveness of their research. Expected benefits of this concept include improved technology transfers from research to production, improved research portfolio investments, and research outcomes that are more integrated with all aspects of the multi-modal transportation problem. The preliminary KbDP prototype has been realized using UAM as a pathfinder use case and developed by a team of system engineer, software developer, data scientist, and interns.

Systems Engineering↗
DOE OSTI2021

ExaWorks: Workflows for Exascale

Exascale computers will offer transformative capabilities to combine data-driven and learning-based approaches with traditional simulation applications to accelerate scientific discovery and insight. These software combinations and integrations, however, are difficult to achieve due to challenges of coordination and deployment of heterogeneous software components on diverse and massive platforms. We present the ExaWorks project, which can address many of these challenges: ExaWorks is leading a co-design process to create a workflow Software Development Toolkit (SDK) consisting of a wide range of workflow management tools that can be composed and interoperate through common interfaces. We describe the initial set of tools and interfaces supported by the SDK, efforts to make them easier to apply to complex science challenges, and examples of their application to exemplar cases. Furthermore, we discuss how our project is working with the workflows community, large computing facilities as well as HPC platform vendors to sustainably address the requirements of workflows at the exascale.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Science Capsule: Towards Sharing and Reproducibility of Scientific Workflows

Workflows are increasingly processing large volumes of data from scientific instruments, experiments and sensors. These workflows often consist of complex data processing and analysis steps that might include a diverse ecosystem of tools and also often involve human-in-the-loop steps. Sharing and reproducing these workflows with collaborators and the larger community is critical but hard to do without the entire context of the workflow including user notes and execution environment. In this paper, we describe Science Capsule, which is a framework to capture, share, and reproduce scientific workflows. Science Capsule captures, manages and represents both computational and human elements of a workflow. It automatically captures and processes events associated with the execution and data life cycle of workflows, and lets users add other types and forms of scientific artifacts. Science Capsule also allows users to create `workflow snapshots' that keep track of the different versions of a workflow and their lineage, allowing scientists to incrementally share and extend workflows between users. Our results show that Science Capsule is capable of processing and organizing events in near real-time for high-throughput experimental and data analysis workflows without incurring any significant performance overheads.

Ghoshal, Devarshi↗
DOE OSTI2017

Echo

This document is a white paper marketing proposal for Echo™ is a data analysis platform designed for efficient, robust, and scalable creation and execution of complex workflows. Echo’s analysis management system refers to the ability to track, understand, and reproduce workflows used for arriving at results and decisions. Echo improves on traditional scripted data analysis in MATLAB, Python, R, and other languages to allow analysts to make better use of their time. Additionally, the Echo platform provides a powerful data management and curation solution allowing analysts to quickly find, access, and consume datasets. After two years of development and a first release in early 2016, Echo is now available for use with many data types in a wide range of application domains. Echo provides tools that allow users to focus on data analysis and decisions with confidence that results are reported accurately.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

SU-F-P-03: Management of Time to Treatment Inititation: Case for An Electronic Whiteboard

Purpose: To determine if data mining of an electronic whiteboard improves the management of the Time to Treatment Initiation (TTI) in radiation oncology. Methods: An electronic whiteboard designed to help in managing the planning workflow and improves communication regarding patient planning progress was used to record the dates at which each phase of the planning process began or completed. These are CT Sim date, Plan Start, Physician Review, Physicist Review, Approval for Treatment Delivery, Setup or Verification of Simulation. Results: During clinical implementation, the electronic whiteboard was able to fulfill its primary objective of providing a transparent account of the planning progress of each patient. Peer pressure also meant that individual tasks, such as contouring, were easily brought to the attention of the responsible party and prioritized accordingly. Data mining to analyze the electronic whiteboard per patient (figure 1), per diagnosis (figure 2), per treatment modality (figure 3), per physician (figure 4), per planner (figure 5), etc., added another sophisticated tool in the management of Time to Treatment Initiation without compromising quality of the plans being generated. A longer than necessary time between CT Sim and Plan Start can be discussed among the members of the treatment team as an indication of inadequate/outdated CT Simulator, Contouring Tools, Image Fusion Tools, Other Imaging Studies (MRI, PET/CT) performed, etc. The same for the Plan Start to Physician Review where an extended time than expected may be due unrealistic planning goals, limited planning system features, etc. Conclusion: An Electronic Whiteboard in radiation oncology is not only helping with organizing planning workflow, it is also a potent tool that can be used to reduce the Time to Treatment Initiation by providing the clinic with hard data about the duration of each phase treatment planning as a function of different variable affecting the planning process. The work is supported by the Global Medical Physics Institute.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

MO-D-213-01: Workflow Monitoring for a High Volume Radiation Oncology Center

Purpose: Implement a center wide communication system that increases interdepartmental transparency and accountability while decreasing redundant work and treatment delays by actively monitoring treatment planning workflow. Methods: Intake Management System (IMS), a program developed by ProCure Treatment Centers Inc., is a multi-function database that stores treatment planning process information. It was devised to work with the oncology information system (Mosaiq) to streamline interdepartmental workflow.Each step in the treatment planning process is visually represented and timelines for completion of individual tasks are established within the software. The currently active step of each patient’s planning process is highlighted either red or green according to whether the initially allocated amount of time has passed for the given process. This information is displayed as a Treatment Planning Process Monitor (TPPM), which is shown on screens in the relevant departments throughout the center. This display also includes the individuals who are responsible for each task.IMS is driven by Mosaiq’s quality checklist (QCL) functionality. Each step in the workflow is initiated by a Mosaiq user sending the responsible party a QCL assignment. IMS is connected to Mosaiq and the sending or completing of a QCL updates the associated field in the TPPM to the appropriate status. Results: Approximately one patient a week is identified during the workflow process as needing to have his/her treatment start date modified or resources re-allocated to address the most urgent cases. Being able to identify a realistic timeline for planning each patient and having multiple departments communicate their limitations and time constraints allows for quality plans to be developed and implemented without overburdening any one department. Conclusion: Monitoring the progression of the treatment planning process has increased transparency between departments, which enables efficient communication. Having built-in timelines allows easy prioritization of tasks and resources and facilitates effective time management.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Workflow-based automatic processing for Internet of Floating Things crowdsourced data

Data from sensors incorporated into mobile devices, such as networked navigational sensors, can be used to capture detailed environmental information. We describe here a workflow and framework for using sensors on boats to construct unique new datasets of underwater topography (bathymetry). Starting with a large number of measurements of position, depth, etc., obtained from such an Internet of Floating Things, we illustrate how, with a specialized protocol, data can be communicated to cloud resources, even when using delayed, intermittent, or disconnected networks. We then propose a method for automatic sensor calibration based on a novel reputation approach. Sampled depth data are interpolated efficiently on a cloud computing platform in order to provide a continuously updated bathymetric database. Our prototype implementation uses the FACE-IT Galaxy workflow engine to manage network communication and exploits the computational power of GPGPUs in a virtualized cloud environment, working with a CUDA-parallel algorithm, for efficient data processing. We report on an initial evaluation involving data from a sailing vessel in Italian coastal waters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Hybrid Programming for Near-Term Quantum Computing Systems

Recent computations involving quantum processing units (QPUs) have demonstrated a series of challenges inherent to hybrid classical-quantum programming, compilation, execution, and verification and validation. Despite considerable progress, system-level noise, limited low-level instructions sets, remote access models, and an overall lack of portability and classical integration presents near-term programming challenges that must be overcome in order to enable reliable scientific quantum computing and support robust hardware benchmarking. In this work, we draw on our experience in programming QPUs to identify common concerns and challenges, and detail best practices for mitigating these challenges within the current hybrid classical-quantum computing paradigm. Following this discussion, we introduce the XACC quantum compilation and execution framework as a hardware and language independent solution that addresses many of these hybrid programming challenges. XACC supports extensible methodologies for managing a variety of programming, compilation, and execution concerns across the increasingly diverse set of QPUs. We use recent nuclear physics simulations to illustrate how the framework mitigates programming, compilation, and execution challenges and manages the complex workflow present in QPU-enhanced scientific applications. Finally, we codify the resulting hybrid scientific computing workflow in order to identify key areas requiring future improvement.

Mccaskey, Alex↗