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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Implementation of ATLAS Distributed Computing monitoring dashboards using InfluxDB and Grafana

For the last 10 years, the ATLAS Distributed Computing project has based its monitoring infrastructure on a set of custom designed dashboards provided by CERN. This system functioned very well for LHC Runs 1 and 2, but its maintenance has progressively become more difficult and the conditions for Run 3, starting in 2021, will be even more demanding; hence a more standard code base and more automatic operations are needed. A new infrastructure has been provided by CERN, based on InfluxDB as the data store and Grafana as the display environment. ATLAS has adapted and further developed its monitoring tools to use this infrastructure for data and workflow management monitoring and accounting dashboards, expanding the range of previous possibilities with the aim to achieve a single, simpler, environment for all monitoring applications. This document describes these tools and the data flows for monitoring and accounting.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

TomoPyUI : a user-friendly tool for rapid tomography alignment and reconstruction

The management and processing of synchrotron and neutron computed tomography data can be a complex, labor-intensive and unstructured process. Users devote substantial time to both manually processing their data ( i.e. organizing data/metadata, applying image filters etc. ) and waiting for the computation of iterative alignment and reconstruction algorithms to finish. In this work, we present a solution to these problems: TomoPyUI , a user interface for the well known tomography data processing package TomoPy . This highly visual Python software package guides the user through the tomography processing pipeline from data import, preprocessing, alignment and finally to 3D volume reconstruction. The TomoPyUI systematic intermediate data and metadata storage system improves organization, and the inspection and manipulation tools (built within the application) help to avoid interrupted workflows. Notably, TomoPyUI operates entirely within a Jupyter environment. Herein, we provide a summary of these key features of TomoPyUI , along with an overview of the tomography processing pipeline, a discussion of the landscape of existing tomography processing software and the purpose of TomoPyUI , and a demonstration of its capabilities for real tomography data collected at SSRL beamline 6-2c.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2014

ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing: ActiveSpaces: Exploring dynamic code deployment for extreme scale data processing

Managing the large volumes of data produced by emerging scientific and engineering simulations running on leadership-class resources has become a critical challenge. The data have to be extracted off the computing nodes and transported to consumer nodes so that it can be processed, analyzed, visualized, archived, and so on. Several recent research efforts have addressed data-related challenges at different levels. One attractive approach is to offload expensive input/output operations to a smaller set of dedicated computing nodes known as a staging area. However, even using this approach, the data still have to be moved from the staging area to consumer nodes for processing, which continues to be a bottleneck. In this paper, we investigate an alternate approach, namely moving the data-processing code to the staging area instead of moving the data to the data-processing code. Specifically, we describe the ActiveSpaces framework, which provides (1) programming support for defining the data-processing routines to be downloaded to the staging area and (2) runtime mechanisms for transporting codes associated with these routines to the staging area, executing the routines on the nodes that are part of the staging area, and returning the results. We also present an experimental performance evaluation of ActiveSpaces using applications running on the Cray XT5 at Oak Ridge National Laboratory. Finally, we use a coupled fusion application workflow to explore the trade-offs between transporting data and transporting the code required for data processing during coupling, and we characterize sweet spots for each option.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

High Performance Computing Peak Shaving for Microreactor Operation

There are multiple nuclear microreactors currently under development that are designed to provide autonomous power for as many as ten or more years without refueling and are designed to power high performance computing (HPC) datacenters. But the load-follow speeds for a nuclear microreactor will be much slower than grid power and slower than the power variance typical of a HPC system. HPC datacenters experience peak power load variance driven by several factors ranging from the operation of cooling systems to remove heat from the servers to supporting a wide range of user application workflows and architectures each with different power signatures. One mechanism to support the limited load-follow of a microreactor is peak shaving where an energy storage mechanism is used to shed peak load and reduce significant power variance. This work explores peak electrical load shaving using uninterruptible power supply (UPS) systems designed for HPC support in the context of peak shaving when operating using a nuclear microreactor with a load-follow limited to 10% of load per minute. Using a self contained HPC datacenter complete with stand-alone cooling system and provisioned with an x86 cluster, an ARM cluster, and a graphics processing unit (GPU) cluster, peak shaving for microreactor operation using the UPS battery backup is explored while running two classes of typical HPC user applications. HPC architecture suitability for microreactor operation under this type of peak shaving is examined.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

PyOECP: A flexible open-source software library for estimating and modeling the complex permittivity based on the open-ended coaxial probe (OECP) technique

Here, we present PyOECP, a Python-based flexible open-source software for estimating and modeling the complex permittivity obtained from the open-ended coaxial probe (OECP) technique. The transformation of the measured reflection coefficient to complex permittivity is performed based on three different methods. The software library contains the dielectric spectra of common reference liquids, which can be used to transform the reflection coefficient into the dielectric spectra. Several Python routines that are commonly employed (e.g., SciPy and NumPy) in the field of science and engineering are required only so that the users can alter the software structure depending on their needs. The modeling algorithm exploits the Markov Chain Monte Carlo method for the data regression. The discrete relaxation models can be built by a proper combination of well-known relaxation models. In addition to these models, electrode polarization, a typical measurement artifact for interpreting dielectric spectra, can be incorporated into the modeling algorithm. A continuous relaxation model, which solves the Fredholm integral equation of the first kind (a mathematically ill-posed problem), is also included. This open-source software enables users to freely adjust the physical parameters to obtain physical insight into their materials under test and will be consistently updated for more accurate measurement and interpretation of dielectric spectra in an automated manner. This work describes the theoretical and mathematical background of the software, lays out the workflow, and validates the software functionality based on both synthetic and empirical data included in the software.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

NNSA/CEA Workflow Workshop Report 2021

Researchers from three NNSA labs plus CEA recently met for a half-day workshop on scientific and engineering workflows in March of 2021. Tools, projects and use cases from each institution were described. A wide range of unique capabilities and requirements were represented. The workshop highlights the fact that the CEA/NNSA workflows space mirrors the broader open science workflows community, with multiple technologies under development in a number of science domains and under a number of funding streams, with differing capabilities and focuses. Despite the number of tools, presentations and discussions have shown that these tools cover specific mission spaces at the four labs, each with distinctive capabilities that do not completely overlap with each other. We believe there is a strong interest in the short term for sharing lessons learned and collaborating on benchmarks and site evaluation of projects. This report, prepared by the NNSA/CEA Workflows Working Group, briefly summarizes the presentations in the areas of domain specific workflows, end user environments, data management, job and resource management, and infrastructure, and then identifies six broad areas for potential collaboration. A key finding is that users could benefit from greater interoperability, compatibility, and composability of the workflow technologies under development and that point-to-point collaboration opportunities should be identified to explore these aspects.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Fulcrum House – Towards a Sustainable Composite Architecture

In the last decades, the accessibility to digital fabrication technologies positioned composites as an affordable, structurally efficient and low-maintenance alternative to traditional mineral-based and high-energy materials. Of special interest are carbon nanotubes (CNT) and carbon foam (CFoam) from methane and coal pyrolysis respectively, as an environmentally friendly substitute to glass and carbon fiber. This paper presents the design of a principled single-family house, as a first case study to identify the constraints of such a material system, including technology gap, techno-economic and life cycle assessment, as well as building code requirements. In turn, the investigation proposes a suitable design-to-fabrication workflow, which potentially could bring CNT and CFoam materials to the construction of buildings.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2015

SU-F-BRB-07: A Plan Comparison Tool to Ensure Robustness and Deliverability in Online-Adaptive Radiotherapy

Purpose: To develop a plan comparison tool that will ensure robustness and deliverability through analysis of baseline and online-adaptive radiotherapy plans using similarity metrics. Methods: The ViewRay MRIdian treatment planning system allows export of a plan file that contains plan and delivery information. A software tool was developed to read and compare two plans, providing information and metrics to assess their similarity. In addition to performing direct comparisons (e.g. demographics, ROI volumes, number of segments, total beam-on time), the tool computes and presents histograms of derived metrics (e.g. step-and-shoot segment field sizes, segment average leaf gaps). Such metrics were investigated for their ability to predict that an online-adapted plan reasonably similar to a baseline plan where deliverability has already been established. Results: In the realm of online-adaptive planning, comparing ROI volumes offers a sanity check to verify observations found during contouring. Beyond ROI analysis, it has been found that simply editing contours and re-optimizing to adapt treatment can produce a delivery that is substantially different than the baseline plan (e.g. number of segments increased by 31%), with no changes in optimization parameters and only minor changes in anatomy. Currently the tool can quickly identify large omissions or deviations from baseline expectations. As our online-adaptive patient population increases, we will continue to develop and refine quantitative acceptance criteria for adapted plans and relate them historical delivery QA measurements. Conclusion: The plan comparison tool is in clinical use and reports a wide range of comparison metrics, illustrating key differences between two plans. This independent check is accomplished in seconds and can be performed in parallel to other tasks in the online-adaptive workflow. Current use prevents large planning or delivery errors from occurring, and ongoing refinements will lead to increased assurance of plan quality.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Predicting target vessel location on robot-assisted coronary artery bypass graft using CT to ultrasound registration

Purpose: Although robot-assisted coronary artery bypass grafting (RA-CABG) has gained more acceptance worldwide, its success still depends on the surgeon's experience and expertise, and the conversion rate to full sternotomy is in the order of 15%-25%. One of the reasons for conversion is poor pre-operative planning, which is based solely on pre-operative computed tomography (CT) images. In this paper, the authors propose a technique to estimate the global peri-operative displacement of the heart and to predict the intra-operative target vessel location, validated via both an in vitro and a clinical study. Methods: As the peri-operative heart migration during RA-CABG has never been reported in the literatures, a simple in vitro validation study was conducted using a heart phantom. To mimic the clinical workflow, a pre-operative CT as well as peri-operative ultrasound images at three different stages in the procedure (Stage{sub 0}--following intubation; Stage{sub 1}--following lung deflation; and Stage{sub 2}--following thoracic insufflation) were acquired during the experiment. Following image acquisition, a rigid-body registration using iterative closest point algorithm with the robust estimator was employed to map the pre-operative stage to each of the peri-operative ones, to estimate the heart migration and predict the peri-operative target vessel location. Moreover, a clinical validation of this technique was conducted using offline patient data, where a Monte Carlo simulation was used to overcome the limitations arising due to the invisibility of the target vessel in the peri-operative ultrasound images. Results: For the in vitro study, the computed target registration error (TRE) at Stage{sub 0}, Stage{sub 1}, and Stage{sub 2} was 2.1, 3.3, and 2.6 mm, respectively. According to the offline clinical validation study, the maximum TRE at the left anterior descending (LAD) coronary artery was 4.1 mm at Stage{sub 0}, 5.1 mm at Stage{sub 1}, and 3.4 mm at Stage{sub 2}. Conclusions: The authors proposed a method to measure and validate peri-operative shifts of the heart during RA-CABG. In vitro and clinical validation studies were conducted and yielded a TRE in the order of 5 mm for all cases. As the desired clinical accuracy imposed by this procedure is on the order of one intercostal space (10-15 mm), our technique suits the clinical requirements. The authors therefore believe this technique has the potential to improve the pre-operative planning by updating peri-operative migration patterns of the heart and, consequently, will lead to reduced conversion to conventional open thoracic procedures.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Managing a Cray supercomputer as a git branch

Summary Modern software engineering/DevOps techniques applied to Cray XC Series systems management enable higher fidelity translation from test to production environments, reduce administration costs by avoiding duplicate efforts, and increase reliability. This can be done by using a git repository to track and manage all system configurations on the SMW(s) (System Management Workstation) and then adapt a gitflow‐like development methodology. Using git improves change management with peer review of changes, identification of who made a change, knowing why a change was made, facilitating easier reversion of bad changes, and enables a workflow using the ideas from gitflow with individual branches for preparing, maintaining, and recording releases. Using branches enables multiple contributors to work on different types of changes without getting unexpected changes from another feature area. We will describe a process similar to that used at NERSC (National Energy Research Scientific Computing Center) to extract/abstract configuration data, manage it using git, and then re‐apply changes to the SMW(s).

Jacobsen, Douglas M.↗
DOE OSTI2023

Electron-phonon coupling from GW perturbation theory: Practical workflow combining BerkeleyGW, ABINIT, and EPW

Here we present a workflow of practical calculations of electron-phonon (e-ph) coupling with many-electron correlation effects included using the GW perturbation theory (GWPT). This workflow combines BerkeleyGW, ABINIT, and EPW software packages to enable accurate e-ph calculations at the GW self-energy level, going beyond standard calculations based on density functional theory (DFT) and density-functional perturbation theory (DFPT). This workflow begins with DFT and DFPT calculations (ABINIT) as starting point, followed by GW and GWPT calculations (BerkeleyGW) for the quasiparticle band structures and e-ph matrix elements on coarse electron k- and phonon q-grids, which are then interpolated to finer grids through Wannier interpolation (EPW) for computations of various e-ph coupling determined physical quantities such as the electron self-energies or solutions of anisotropic Eliashberg equations, among others. A gauge-recovering symmetry unfolding technique is developed to reduce the computational cost of GWPT (as well as DFPT) while fulfilling the gauge consistency requirement for Wannier interpolation.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2025

Evolution of the ATLAS TDAQ online software framework towards Phase-II upgrade: Use of Kubernetes as an orchestrator of the ATLAS Event Filter computing farm

The ATLAS experiment at the LHC at CERN continuously evolves its TDAQ system to meet the challenges of new physics goals and technological advancements. As ATLAS prepares for the Phase-II Run 4 of the LHC, significant enhancements in the TDAQ Controls and Configuration (TDAQ-CC) tools have been designed to ensure efficient data collection, processing, and management. This abstract presents the evolution of ATLAS TDAQ-CC system leading up to Phase-II Run 4. As part of the evolution towards Phase-II, Kubernetes has been chosen to orchestrate the Event Filter (EF) farm. By leveraging Kubernetes, ATLAS can dynamically allocate computing resources, scale processing capacity in response to changing data taking conditions and ensure high availability of data processing services. The integration of the Kubernetes with the TDAQ Run Control framework enables perfect synchronisation between the experiment’s data acquisition components and the computing infrastructure. We will discuss the architectural considerations and implementation challenges involved in Kubernetes integration with the ATLAS TDAQ-CC system. We will highlight the benefits of using Kubernetes as an EF farm orchestrator, including improved resource utilization, enhanced fault tolerance, and simplified deployment and management of data processing workflows. In addition, we will report on the extensive testing of Kubernetes that was conducted using a farm of 2500 servers within the experiment data taking environment, demonstrating its scalability and robustness in handling the demands of the ATLAS TDAQ system for Phase-II. The adoption of Kubernetes represents a significant step forward in the evolution of ATLAS TDAQ-CC system, aligning with industry best practices in container orchestration.

Corso Radu, Alina [Univ. of California, Irvine, CA↗
NASA NTRS2005

Health Management Applications for International Space Station

Traditional mission and vehicle management involves teams of highly trained specialists monitoring vehicle status and crew activities, responding rapidly to any anomalies encountered during operations. These teams work from the Mission Control Center and have access to engineering support teams with specialized expertise in International Space Station (ISS) subsystems. Integrated System Health Management (ISHM) applications can significantly augment these capabilities by providing enhanced monitoring, prognostic and diagnostic tools for critical decision support and mission management. The Intelligent Systems Division of NASA Ames Research Center is developing many prototype applications using model-based reasoning, data mining and simulation, working with Mission Control through the ISHM Testbed and Prototypes Project. This paper will briefly describe information technology that supports current mission management practice, and will extend this to a vision for future mission control workflow incorporating new ISHM applications. It will describe ISHM applications currently under development at NASA and will define technical approaches for implementing our vision of future human exploration mission management incorporating artificial intelligence and distributed web service architectures using specific examples. Several prototypes are under development, each highlighting a different computational approach. The ISStrider application allows in-depth analysis of Caution and Warning (C&W) events by correlating real-time telemetry with the logical fault trees used to define off-nominal events. The application uses live telemetry data and the Livingstone diagnostic inference engine to display the specific parameters and fault trees that generated the C&W event, allowing a flight controller to identify the root cause of the event from thousands of possibilities by simply navigating animated fault tree models on their workstation. SimStation models the functional power flow for the ISS Electrical Power System and can predict power balance for nominal and off-nominal conditions. SimStation uses realtime telemetry data to keep detailed computational physics models synchronized with actual ISS power system state. In the event of failure, the application can then rapidly diagnose root cause, predict future resource levels and even correlate technical documents relevant to the specific failure. These advanced computational models will allow better insight and more precise control of ISS subsystems, increasing safety margins by speeding up anomaly resolution and reducing,engineering team effort and cost. This technology will make operating ISS more efficient and is directly applicable to next-generation exploration missions and Crew Exploration Vehicles.

Alena, Richard↗
NASA NTRS2016

IN13B-1660: Analytics and Visualization Pipelines for Big Data on the NASA Earth Exchange (NEX) and OpenNEX

We are developing capabilities for an integrated petabyte-scale Earth science collaborative analysis and visualization environment. The ultimate goal is to deploy this environment within the NASA Earth Exchange (NEX) and OpenNEX in order to enhance existing science data production pipelines in both high-performance computing (HPC) and cloud environments. Bridging of HPC and cloud is a fairly new concept under active research and this system significantly enhances the ability of the scientific community to accelerate analysis and visualization of Earth science data from NASA missions, model outputs and other sources. We have developed a web-based system that seamlessly interfaces with both high-performance computing (HPC) and cloud environments, providing tools that enable science teams to develop and deploy large-scale analysis, visualization and QA pipelines of both the production process and the data products, and enable sharing results with the community. Our project is developed in several stages each addressing separate challenge - workflow integration, parallel execution in either cloud or HPC environments and big-data analytics or visualization. This work benefits a number of existing and upcoming projects supported by NEX, such as the Web Enabled Landsat Data (WELD), where we are developing a new QA pipeline for the 25PB system.

visualization↗
DOE OSTI2025

A Combined Computational and Mathematical Analysis of Interconnect Fatigue Potential in Photovoltaic Modules

A finite element model of a 60-cell monocrystalline silicon glass-polymer photovoltaic module was simulated with ±1.0 kPa and ±2.4 kPa loads applied to the glass to calculate the deformation under load. Cell-to-cell displacements were used to approximate interconnect strain and stress. A mathematical fatigue cycle life relation was fitted to data for the interconnect material (copper), to generate a life prediction at each interconnect location based on the local stress means, reversal extents, and amplitudes. Interconnect stress was found to be significantly asymmetric about zero despite symmetric positive and negative module loads due to laminate thickness offsets about the neutral plane and the effects of module framing. Cycle life results indicated that interconnect fatigue failure was unlikely to occur over a 30-year lifetime of conservative wind and snow load cycles since the typical cell design feature of leaving some unconstrained length between the cell edge and first solder pad increases the effective gauge length and decreases the stress levels below the material endurance limit. Follow-up analyses found that 3.6 mm and 6.4 mm were the minimum unconstrained lengths required to survive the assumed lifetime of wind and snow cycles, respectively, confirming that typical industrial module constructions with 8–15 mm unconstrained lengths should survive conservatively. Notably, large magnitude, low-cycle snow loading was consistently the limiting factor requiring a longer unconstrained interconnect length. Finally, insights and workflows from this study inform module interconnection design limits for survival against mechanical fatigue in deployment environments.

14 SOLAR ENERGY↗
DOE OSTI2023

Integrating Artificial Intelligence into Science Gateways

Science gateways are altering the manner in which people interact with high performance computing (HPC) by providing a web browser based interface to advanced computing platforms. In particular, science gateways lower the barrier to using HPC by simplifying the process of submitting workloads to such systems and by offloading the efforts required to use HPC to the maintainers of the system. While science gateways decrease the time-to-science that comes with using such advanced systems, progress can still be made in improving the user's experience. In this paper we explore two strategies for integrating artificial intelligence tools commonly found in non-HPC service workflows: voice activated assistants and chatbots. Since August 2021, the HPC group at Idaho National Laboratory answers an average of 581 support tickets per month of which a large percentage could be addressed via these two strategies. This work defines the key capabilities that an HPC voice activated assistant and chatbot would need to address for a userbase consisting of largely non-expert users as well as a design for integration into the Open OnDemand science gateway.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Interlaced Characterization and Calibration (ICC) for Improved Computational Simulation Credibility

Accurate material characterization and model calibration are pivotal for simulations used for high-consequence engineering decisions. Current characterization and calibration methods (1) use simplified test specimen geometries and global data, (2) cannot guarantee that sufficient characterization data is collected for a specific model of interest, (3) provide only mean parameter values with no uncertainty quantification, and (4) are sequential, inflexible, and time-consuming. This work developed a new paradigm—coined Interlaced Characterization and Calibration (ICC)—which drives forward the state-of-the-art in model calibration by bringing together recent advancements into one improved workflow. The ICC paradigm (1) employs tools to efficiently use full-field data to calibrate high-fidelity material models, (2) aligns the data needed with the data collected by adopting an optimal experimental design protocol, (3) provides uncertainty metrics on the calibrated model parameters, and (4) incorporates these advances into a quasi real-time feedback loop. The ICC framework was validated synthetically with both low-fidelity and high-fidelity simulations paired with several different elastoplastic material models, and was also demonstrated experimentally with an aluminum 6061 cruciform exemplar specimen. Results showed that the ICC framework—in which Bayesian optimal experimental design actively guided the experiment— resulted in calibrations with similar or better accuracy than predetermined experiments based on subject matter expertise. Moreover, the ICC framework produced a complete model calibration— with quantified uncertainties on model parameters—in 1 week, a 5 - 10× increase in efficiency over traditional approaches. Thus, the ICC paradigm improves both the calibration process and quality, by (1) improving efficiency, which increases agility of solid mechanics modeling and enables utilization of computational simulation (CompSim) at earlier stages of the design cycle and (2) providing quantified, and in some cases reduced, parameter uncertainties, which increases confidence in model predictions and supports credible decision making.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2016

GC31G-1182: Opennex, a Private-Public Partnership in Support of the National Climate Assessment

The NASA Earth Exchange (NEX) is a collaborative computing platform that has been developed with the objective of bringing scientists together with the software tools, massive global datasets, and supercomputing resources necessary to accelerate research in Earth systems science and global change. NEX is funded as an enabling tool for sustaining the national climate assessment. Over the past five years, researchers have used the NEX platform and produced a number of data sets highly relevant to the National Climate Assessment. These include high-resolution climate projections using different downscaling techniques and trends in historical climate from satellite data. To enable a broader community in exploiting the above datasets, the NEX team partnered with public cloud providers to create the OpenNEX platform. OpenNEX provides ready access to NEX data holdings on a number of public cloud platforms along with pertinent analysis tools and workflows in the form of Machine Images and Docker Containers, lectures and tutorials by experts. We will showcase some of the applications of OpenNEX data and tools by the community on Amazon Web Services, Google Cloud and the NEX Sandbox.

datasets↗