HPC System Data Pipeline to Enable Meaningful Insights through Analytic-Driven Visualizations.
Abstract not provided.
Schwaller, Benjamin↗
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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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Lighting System Control Data to Improve Design and Operation: Tunable Lighting System Data from NICU Patient Rooms
The advancement of LED and controls technology, computing capacity, and software provides new opportunities for researchers and designers to work together to further optimize spaces for occupant benefit. Here, lighting system control data from five neonatal intensive care unit patient rooms was collected over a 25-week monitoring period and analyzed to better understand occupant response to a tunable lighting system with automatic transitions throughout the day. Lighting systems are very rarely refined after installation based on actual use. Objective data detailing how the lighting system is used by the actual occupants highlights the opportunities for optimization after installation and provides insight for improving the next design. As use of the data becomes more commonplace, it can be leveraged for design recommendations. The collection of the data required no additional cost beyond the time for examining the data. The analysis revealed several clear opportunities for improvement, including adjustments to the default control setting at night, re-labeling of the control stations, and adjustments to the nighttime fade rate. The patient room occupants were active users of the different zones, dimming options, and manual overrides made available by the lighting system.
60 APPLIED LIFE SCIENCES↗
Dynamical Downscaling of Earth System Model Data for Energy System Analysis
Assessing energy resources (e.g., solar, wind, and hydro) under future scenarios requires datasets with sufficient spatial and temporal detail to capture variability and extreme events. While global-scale Earth System Model (ESM) projections are widely used, their coarse resolution limits direct application to regional energy system analyses. Dynamical downscaling offers a robust approach to generate physically consistent, fine-scale datasets that better represent local atmospheric processes impacting energy resources. In this work, we present a two-stage approach for producing high-resolution historical and future projections over the contiguous United States (CONUS). First, we optimize the Weather Research and Forecasting (WRF) model configuration for energy-relevant variables - solar irradiance, wind speed, and precipitation - by conducting ERA5-driven simulations at 8-km and 28-km resolution. Multiple physics schemes and model configurations within the WRF are evaluated against observational datasets including the National Solar Radiation Database (NSRDB), the Parameter-elevation Regressions on Independent Slopes Model (PRISM), and the Stage IV multi-radar/multi-sensor precipitation product for the CONUS domain. Using the best-performing configuration, we dynamically downscale MPI-ESM1-2-HR simulations for 2000-2060 under SSP2-4.5 and SSP5-8.5 scenarios at 4-km spatial and hourly temporal resolution. This presentation will provide a comprehensive analysis of the results from multiple numerical experiments and high-resolution ESM projections. In addition, we will discuss potential applications of our high-resolution datasets within the energy sector and outline future research avenues dedicated to evaluating how extreme weather events influence system performance and resilience.
24 POWER TRANSMISSION AND DISTRIBUTION↗
CEDS v_2021_04_21 Gridded emissions data
Gridded data to be produced from the April release of the CEDS data system. Data is in a format identical to the CMIP6 data on ESGF, but extends to 2019.
O'Rourke, Patrick↗
Downscaled Earth System Model Data for Resilient Energy System Planning
The second-generation Sup3rCC dataset provides high-resolution meteorological data generated through the downscaling of multiple earth system models (ESMs) from the Coupled Model Intercomparison Project Phase 6 (CMIP6). This downscaling is performed through application of a generative machine learning approach called Super-Resolution for Renewable Resource Data (sup3r). This dataset builds on the first-generation Sup3rCC data by applying improved bias correction methods and adding downscaled precipitation to the output variables. In this presentation, we explore the output characteristics of the dataset and various validation analyses. We also present and discuss plans for the integration of this data into power system planning models using a decision-making under deep uncertainty (DMDU) methodology.
97 MATHEMATICS AND COMPUTING↗
Evaluating the risk of data loss due to particle radiation damage in a DNA data storage system
DNA data storage is a potential alternative to magnetic tape for archival storage purposes, promising substantial gains in information density. Critical to the success of DNA as a storage media is an understanding of the role of environmental factors on the longevity of the stored information. In this paper, we evaluate the effect of exposure to ionizing particle radiation, a cause of data loss in traditional magnetic media, on the longevity of data in DNA data storage pools. We develop a mass action kinetics model to estimate the rate of damage accumulation in DNA strands due to neutron interactions with both nucleotides and residual water molecules, then utilize the model to evaluate the effect several design parameters of a typical DNA data storage scheme have on expected data longevity. Finally, we experimentally validate our model by exposing dried DNA samples to different levels of neutron irradiation and analyzing the resulting error profile. Our results show that particle radiation is not a significant contributor to data loss in DNA data storage pools under typical storage conditions.
97 MATHEMATICS AND COMPUTING↗
The CLAS12 Data Acquisition System
The CLAS12 Data Acquisition System was designed and built as part of the CLAS12 detector project in Hall B at Jefferson Laboratory. This article contains a full description of the system, including requirements, design, hardware, and software descriptions, as well as the achieved performance. The associated computing, network, and slow controls systems are also described.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Evaluate data lake design for the accelerator control system
Increasing precision in automation for modern particle accelerators not only creates a requirement to gather data from all devices but also demands scalable and high-performance data infrastructure with the capability of handling vast incoming device data. A well architected data lake is suitable for such a system which integrates real-time data acquisition, transient data caching, and long-term storage. This paper evaluates data lake architecture for an Accelerator Control System (ACS), focusing on two critical components of a data lake, data cache and long-term storage.
Jaikar, Amol [Fermilab]↗
Fermilab PIP-II machine protection system digitized data noise elimination scheme and its FPGA implementation
In Fermilab's PIP-II machine protection system, beam loss signals from various detectors are digitized at 125 MS/s. Noise from both high-frequency sources and low-frequency 60 Hz AC power equipment can contaminate the data. To suppress noise across these ranges—especially 60 Hz and its harmonics, which overlap with beam loss signal frequencies—advanced digital processing beyond standard filtering is required. Several real-time functional blocks were simulated and tested on an FPGA: (1) a dual time-constant discharging integrator filter, (2) a de-ripple baseline extraction and storage block, and (3) a fast-recovery discharging integrator. The nonlinear IIR integrator filter removes high-frequency noise and feeds into the baseline extractor. Upon detecting abrupt beam loss, it switches to a longer time constant to prevent baseline distortion. The de-ripple block calculates a valid baseline by averaging over multiple 60 Hz periods, storing results in a 4096-word FPGA RAM. This baseline is subtracted from raw data before integration by the fast-recovery block, which resets quickly after use. All blocks achieved expected performance and were successfully implemented on a low-cost FPGA.
Wu, Jinyuan [Fermilab]↗
Systems and methods for rapid processing and storage of data
Systems and methods of building massively parallel computing systems using low power computing complexes in accordance with embodiments of the invention are disclosed. A massively parallel computing system in accordance with one embodiment of the invention includes at least one Solid State Blade configured to communicate via a high performance network fabric. In addition, each Solid State Blade includes a processor configured to communicate with a plurality of low power computing complexes interconnected by a router, and each low power computing complex includes at least one general processing core, an accelerator, an I/O interface, and cache memory and is configured to communicate with non-volatile solid state memory.
Stalzer, Mark A.↗
Assurance by Design for Cyber Physical Data-Driven Systems
Currently, Cyber Physical Data-Driven Systems (CPDDS) employ machine learning for the classification, data fusion, and control of our nation’s infrastructure, such as the power grid, transportation networks (e.g., fuel distribution, air traffic control), and DoD long-duration collaborative autonomous platforms including unmanned underwater, ground, surface, space, and aerial systems. Many CPDDSs are system-of-systems that should be designed to communicate over disadvantaged networks. It is important to assure that the CPDDSs are resilient against physical and cyber threats by design. Additionally, their design should tolerate misclassification errors resulting from natural and/or adversarial distribution shifts within their data driven components. The all-domain nature of the problem of assuring the design of CPDDSs requires a multi-disciplinary perspective as outlined in this chapter.
Chikkagoudar, Satish↗
Livewire Data Platform - A Solution for Energy Efficient Mobility Systems (EEMS) Data Sharing
The Livewire Data Platform is a data storage and exchange platform. It provides: a platform, allowing data sharing and discovery by linking disparate data sources to a single website via application programming interfaces (APIs); a community, building partnerships and collaboration rather than competition; a system, allowing shared data to increase in size and complexity as EEMS evolves.
47 OTHER INSTRUMENTATION↗
Big data in nuclear comes in the form of many structures, systems, components, data streams, data types, and more
Big Data in nuclear comes in the form of many structures, systems, components, data streams, data types, and more.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
Helium Leak Test Data for System Evaluation
The test data in the flowing pages below has been created to ensure the maintenance conducted on the current MSHLD systems is satisfactory per ASNT Level III review. This is diagnostic test data only and it is intended to be used as a reference document.
36 MATERIALS SCIENCE↗
An Integrated Indexing and Search Service for Distributed File Systems
Data services such as search, discovery, and management in scalable distributed environments have traditionally been decoupled from the underlying file systems, and are often deployed using external databases and indexing services. However, modern data production rates, looming data movement costs, and the lack of metadata, entail revisiting the decoupled file system-data services design philosophy. In this article, we present TagIt, a scalable data management service framework aimed at scientific datasets, which can be integrated into prevalent distributed file system architectures. A key feature of TagIt is a scalable, distributed metadata indexing framework, which facilitates a flexible tagging capability to support data discovery. Furthermore, the tags can also be associated with an active operator, for pre-processing, filtering, or automatic metadata extraction, which we seamlessly offload to file servers in a load-aware fashion. We have integrated TagIt into two popular distributed file systems, i.e., GlusterFS and CephFS. Our evaluation demonstrates that TagIt can expedite data search operation by up to 10× over the extant decoupled approach.
97 MATHEMATICS AND COMPUTING↗
LDM-148: Rubin Observatory Data Management System Design
The Rubin Observatory LSST Data Management System (DMS) is a set of services employing a variety of software components running on computational and networking infrastructure that combine to deliver science data products to the observatory's users and support observatory operations. This document describes the components, their service instances, and their deployment environments as well as the interfaces among them, the rest of the LSST system, and the outside world.
79 ASTRONOMY AND ASTROPHYSICS↗
Albedo Data for Bifacial PV Systems Update
For use by the PV and financial communities to better estimate the performance and to reduce the risk of bifacial PV systems, data sets of ground albedo and associated meteorological data were developed by using existing measurement network data and data contributed by the PV industry. The data sets include time-series data as well as summary information of tabular monthly and yearly data and plots of monthly and hourly albedo values. Complete information is presented in a user’s guide and data are available for download from NREL’s DuraMAT website.
albedo↗
Site G - Distributed Temperature System / Reviewed Data
This dataset contains fiber optic distributed temperature system (DTS) data deployed on tethered balloon system at AWAKEN site G. The DTS data include time stamp, altitude, and temperature measurements.
17 WIND ENERGY↗