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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 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2013

A multi-site resting state fMRI study on the amplitude of low frequency fluctuations in schizophrenia

Background: This multi-site study compares resting state fMRI amplitude of low frequency fluctuations (ALFF) and fractional ALFF (fALFF) between patients with schizophrenia (SZ) and healthy controls (HC). Methods: Eyes-closed resting fMRI scans (5:38 min; n = 306, 146 SZ) were collected from 6 Siemens 3T scanners and one GE 3T scanner. Imaging data were pre-processed using an SPM pipeline. Power in the low frequency band (0.01–0.08 Hz) was calculated both for the original pre-processed data as well as for the pre-processed data after regressing out the six rigid-body motion parameters, mean white matter (WM) and cerebral spinal fluid (CSF) signals. Both original and regressed ALFF and fALFF measures were modeled with site, diagnosis, age, and diagnosis × age interactions. Results: Regressing out motion and non-gray matter signals significantly decreased fALFF throughout the brain as well as ALFF in the cortical edge, but significantly increased ALFF in subcortical regions. Regression had little effect on site, age, and diagnosis effects on ALFF, other than to reduce diagnosis effects in subcortical regions. There were significant effects of site across the brain in all the analyses, largely due to vendor differences. HC showed greater ALFF in the occipital, posterior parietal, and superior temporal lobe, while SZ showed smaller clusters of greater ALFF in the frontal and temporal/insular regions as well as in the caudate, putamen, and hippocampus. HC showed greater fALFF compared with SZ in all regions, though subcortical differences were only significant for original fALFF. Conclusions: SZ show greater eyes-closed resting state low frequency power in frontal cortex, and less power in posterior lobes than do HC; fALFF, however, is lower in SZ than HC throughout the cortex. These effects are robust to multi-site variability. Regressing out physiological noise signals significantly affects both total and fALFF measures, but does not affect the pattern of case/control differences.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

UNH TDP - ADCP Raw Data and Processing Scripts - Fall 2021

This submission contains raw Acoustic Doppler Current Profiler (ADCP) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2021

UNH TDP - IMU Raw Data and Processing Scripts - Fall 2021

This submission contains raw Yost IMU (Inertial Measurement Unit) data and processing scripts associated with MHKDR submission 394 (UNH TDP - Concurrent Measurements of Inflow, Power Performance, and Loads for a Grid-Synchronized Vertical Axis Cross-Flow Turbine Operating in a Tidal Estuary, DOI: 10.15473/1973860) from the University of New Hampshire and Atlantic Marine Energy Center (AMEC) turbine deployment platform. The user is directed to the MHKDR submission 394 for relevant context and detail of this deployment; see link below. The 394_READ_ME file here provides the description from that submission for quick reference. The READ_ME file for this specific instrument from the 394 submission is also available here. This submission contains a zipped folder structure containing raw data in its original format and MATLAB (2019a) processing scripts used to process and manipulate the data into its final form. The final data products are submitted in the 394 submission.

16 TIDAL AND WAVE POWER↗
DOE OSTI2016

Control and data acquisition upgrades for NSTX-U

The extensive NSTX Upgrade (NSTX-U) Project includes major components which allow a doubling of the toroidal field strength to 1 T, of the Neutral Beam heating power to 12 MW, and the plasma current to 2 MA, and substantial structural enhancements to withstand the increased electromagnetic loads. The maximum pulse length will go from 1.5 to 5 s. The larger and more complex forces on the coils will be protected by a Digital Coil Protection System, which requires demanding real-time data input rates, calculations and responses. The amount of conventional digitized data for a given pulse is expected to increase from 2.5 to 5 GB per second of pulse. 2-D Fast Camera data is expected to go from 2.5 GB/pulse to 10, and another 2 GB/pulse is expected from new IR cameras. Our network capacity will be increased by a factor of 10, with 10 Gb/s fibers used for the major trunks. 32-core Linux systems will be used for several functions, including between-shot data processing, MDSplus data serving, between-shot EFIT analysis, real-time processing, and for a new capability, between-shot TRANSP. As a result, improvements to the MDSplus events subsystem will be made through the use of both UDP and TCP/IP based methods and the addition of a dedicated “event server”.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2013

Assimilating irregularly spaced sparsely observed turbulent signals with hierarchical Bayesian reduced stochastic filters

In this paper, we consider a practical filtering approach for assimilating irregularly spaced, sparsely observed turbulent signals through a hierarchical Bayesian reduced stochastic filtering framework. The proposed hierarchical Bayesian approach consists of two steps, blending a data-driven interpolation scheme and the Mean Stochastic Model (MSM) filter. We examine the potential of using the deterministic piecewise linear interpolation scheme and the ordinary kriging scheme in interpolating irregularly spaced raw data to regularly spaced processed data and the importance of dynamical constraint (through MSM) in filtering the processed data on a numerically stiff state estimation problem. In particular, we test this approach on a two-layer quasi-geostrophic model in a two-dimensional domain with a small radius of deformation to mimic ocean turbulence. Our numerical results suggest that the dynamical constraint becomes important when the observation noise variance is large. Second, we find that the filtered estimates with ordinary kriging are superior to those with linear interpolation when observation networks are not too sparse; such robust results are found from numerical simulations with many randomly simulated irregularly spaced observation networks, various observation time intervals, and observation error variances. Third, when the observation network is very sparse, we find that both the kriging and linear interpolations are comparable.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Coolant Pump Predictive Data Analytics from Signatures Generated by the Recursive Short Time Fast Fourier Transform

Although a nuclear reactor is a hostile environment for sensors and signal transmissions, the reactor core is amenable to acoustic communication. An acoustic measurement infrastructure installed at the Advanced Test Reactor (ATR) nozzle trench area records acoustic signals that can capture reactor operating states. The distinct states produce unique signatures that can be identified and tracked using data processing and data analytics. The infrastructure relies on acoustic transmission through ATR in-pile structural components, piping, and coolant that transmit acoustically modified signals generated by the coolant pumps. This paper will discuss results from using the Recursive Short Time Fast Fourier Transform (RSTFFT) technique used to process acoustic signals and provide signatures that are identified and monitored by analytics. The RSTFFT is applied to ATR data to understand the vibration levels and signatures for different operating regimes as displayed by the spectrogram. The combination of coolant pumps for normal and high-power operation generate unique signatures. These acoustic signatures are used to develop machine learning approaches to automatically classify operating regimes. Two machine-learning models, Support Vector Machines and Linear Discriminant Analysis, were developed to classify two event classes. Class 1 is a normal steady-state operation, and Class 2 is any event that is due to start up, shut down, or other actions. Both types of machine learning models had over a 96% prediction accuracy for the two classes. These results lay the foundation for predictive analytic frameworks that can be leveraged by ATR to optimize operations and maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Exploring OpenSNAPI Use Cases and Evolving Requirements [Slides]

Emerging system architectures are rapidly transforming in order to meet shifting requirements. Motivated by expanding data volumes, energy efficiency concerns, and the omnipresent need to improve performance, architectures are increasingly adopting a data-centric approach. At the core of this concept is the goal of minimizing data motion and instead processing data in-situ to the greatest degree possible. Therefore, data-centric designs, in contrast to conventional CPU-centric models, typically distribute compute capabilities throughout the architecture. As part of this paradigm shift, a novel class of devices known as data processing units (DPUs), alongside CPUs and GPUs, are quickly forming a third pillar of data-centric systems. These devices, which include smart network adapters and switches, seek to offload computation on data at the network edge as well as in-flight within the network fabric. The Open Smart Network API (OpenSNAPI) project seeks to develop a unified API for DPU devices. In our previous talks, we introduced the OpenSNAPI project and detailed our investigations regarding the viability of offloading compute intensive kernels to BlueField DPUs. In contrast, in this talk we detail our efforts to offload application-level file I/O to the DPU. We also discuss plans and early efforts to explore in-network compute capabilities. Finally, we describe our observations with respect to the evolving design of OpenSNAPI.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data review processes in the analytical laboratory

These documents contain information on the Analytical laboratory data review processes for the following analytical techniques: - alpha spectrometry - light elements analyses - gamma spectrometry - gas proportional flow counting - HR-ICP-MS - Q-ICP-MS - MC-ICP-MS - ICP OES - liquid scintillation counting

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗
DOE OSTI2021

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗
DOE OSTI2021

WA-Omic_LA.1.0 - Quantitative Lipidomics, Metabolomics, and Sequencing (16S/ITS) Publication Data DOI Package

Corresponding Data Publication: "Rapid remodeling of the soil lipidome in response to a drying-rewetting event." This study reveals specific changes in lipids and metabolites that are indicative of stress adaptation, substrate use, and cellular recovery during soil drying and subsequent rewetting. Drought induced nutrient limitation was reflected in the lipidome and polar metabalome, both of which rapidly shifted (within hours) upon rewet. Reduced nutrient access in dry soil caused the replacement of glycerophospholipids with phosphorus-free lipids and impeded resource-expensive osmolyte accumulation. Elevated levels of ceramides and lipids with long chain polyunsaturated fatty acids, in dry soil suggests that lipids play an important role in fungal drought tolerance. Increasing abundance of bacterial glycerophospholipids and triacylglycerols with fatty acids typical of bacteria and polar metabolites suggest metabolic recovery in representative bacteria once the environmental conditions are conducive for growth. These results underscore the importance of the soil lipidome as a robust indicator of microbial community responses, especially at the short time scales of cell-environment reactions. Data package contents reported here are the first version and contain pre- and post-processed data acquisition and subsequent downstream analysis files using various data source instrument method techniques and Mass Spectroscopy (MS) EMSL capabilities. This publication data package DOI is a comprehensive high-throughput multi-omics data lifecycle collection containing processed data method metadata. Support files include additional data download “Read Me” file containing data descriptor information and data source application ontologies (see data dictionary). Reported data download contents are structured for compliance with project data sharing guidelines, community standards initiatives, and sponsor stakeholder policies supporting FAIR data principles. For increased data availability and interoperability, GC-MS/LC-MS mass spectrometry datasets (Thermo .raw ) were deposited at the MassIVE database repository under the related data accession MSV000086931 and can be accessed by using the API. Statistical data processing software, analysis tools, and data workflows are listed below corresponding to the host repository long-term location.

Amplicon sequencing 16S ITS LC-MS/MS lipidomics mu↗
DOE OSTI2025

Aerial and Processed Model Data Representing As-built Conditions in Coastal Port Arthur, Texas in May 2025

This dataset was collected by the Co-Design Team of the Southeast Texas Urban Integrated Field Lab, a research initiative led by the University of Texas at Austin and funded by the U.S. Department of Energy. The broader project focuses on developing climate-resilient design solutions for the Beaumont–Port Arthur region, with more information available at www.setx-uifl.org. Our team conducted aerial surveys of the Port Arthur coastal neighborhood in May 2025, before the start of construction scheduled for Summer 2026. These pre-construction datasets are designed to facilitate comparative analyses, including pre- and post-construction assessments and simulated inundation scenario evaluations. Aerial images were captured using DroneDeploy autonomous flight systems, with imagery processed through the DroneDeploy engine. All original aerial photographs are provided in JPG format and organized in zipped folders by area. The processed data package includes: 3D surface models Orthomosaics Geospatial and topographic mappings Point clouds For guidance on file contents, structure, and recommended usage, please refer to the included README file.

2D mapping↗
DOE OSTI2020

Feedback on Forty-year Long Clean-up Operations of a Contaminated Soil for Environmental Purpose - 20026

One of the CEA facilities in France is the place of miscellaneous mapping and clean-up operations since the 1980's. The final purpose of the conducted work deals with the environmental remediation of the building. In France, the absence of a regulatory framework for the management of sites polluted by radioactive substances, especially as the lack of release thresholds, has led to develop different approaches of soil radiological characterization. The geostatistical approach is part of them. Preliminary investigations are an unavoidable step prior to clearance and remediation. First, historical analysis enables to define site perimeters to investigate as well as to precisely locate expected contaminated parts. For radioactive pollutants, the media (water, soil, air) and transfer routes participate in the definition of the investigation perimeter and thus also need to be identified through a geological study. At the same time, functional analysis leads to choose the best measurement means which could provide useful information when combined with visual inspection. An equipment well adapted to easy-to-measure radionuclides as gamma emitters, such as gamma probe or on-site gamma spectrometry, is usually selected as a non-destructive assay. Coupled with spatial positions, processed on-site radiological data can be mapped. Data processing consists in mapping and kriging before a geostatistical analysis in order to identify the zones of interest to be targeted. Selected zones are not only linked to high level count rates but also to low level ones so that a pollution-free reference is known in the area. This data processing also enables to categorize waste types according to their origin and contamination levels. An expected volume of different waste then follows as well as the waste characterization equipments. In-depth investigations, e.g. core drillings and samplings, naturally ensue to aim at remediation optimization through the assessment of environmental impact. After clean-up operations, non-destructive assays as well as destructive samplings enable to check the effective clearance. Following this methodology in the present remediation work, results of the first non-destructive assay campaign highlighted a long-lived transuranic radionuclides contamination in the concrete flagstone. Successive non-destructive assays were then performed in the defined zones of interest. Count rates measured with a surface probe were first mapped. The best localization of destructive assays, i.e. core drillings at a 1 meter depth, came from this surface mapping followed by a geostatistical analysis. The concrete flagstone was totally removed. Then, the depth of soil to remove under the flagstone was optimized from the results of radiological activities measured in core drillings samples. A new surface mapping was then drawn after this partial soil excavation. The sand and demolition rubble samples, homogeneously constituted from the flagstone fragments, were analyzed by gamma spectrometry. Meanwhile, waste drums and other containers were also measured by gamma spectrometry and passive neutron measurement devices. All these results have brought miscellaneous pieces of information. In this context, major issues appear in terms of physical constraints such as the premises tininess as well as of soil sampling techniques and in terms of measurement performances assessment and the representativeness of homogeneous samples. The choice of radiological soil and waste characterization devices is described in the light of enhanced performances by keeping in mind radiation protection requirements and a willing to always optimize waste categories (very low level waste) and volume. The improvement of the characterization methods appears through this forty-year long work while sharing the feedback of encountered difficulties. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Myna

The additive manufacturing (AM) community has been developing digital factory tools over the past decade to better leverage the multi-modal process data coming out of the advanced manufacturing process. As a result, numerous databases of additive manufacturing process data exist in the literature and in the archival storage of disparate research groups. While some efforts have been made to create a standard ontology for storing and sharing AM data, in practice a variety of data structures are used to store AM build data, even within a single institution. This causes many problems for maintainability and extensibility when attempting to integrate computational modeling tools with experimental data to either validate models or to provide further insight into results and trends. Myna is a Python-based framework that aims to decrease the effort needed to connect individual computational models to the variety of AM process data that exist in different research groups and institutions. This type of software is sometimes referred to as "middleware" or “glueware,” in that it connects disparate databases and applications into a single computational ecosystem. Instead of maintaining unique interfaces between each application and each database, developers can create a single interface from each application to Myna and thereby gain access to the implemented database connections. Similarly, developing a database connection in Myna provides access to the developed simulation applications. This framework greatly simplifies the maintainability of model applications that rely on experimental data. Using external simulation tools, users will also be able to run pre-configured workflows using the built-in workflow manager. Several examples of input files are provided with Myna for different workflows, including melt pool geometry predictions and detailed melt pool and solidification microstructure predictions.

Knapp, GerryL. [Oak Ridge National Laboratory (ORN↗
DOE OSTI2019

UPS: Unified PMU-Data Storage System to Enhance T+D PMU Data Usability

The emerging distribution-level phasor measurement unit (D-PMU) is expected to play an important role in enhancing distribution system observability and situational-awareness. It is a demanding yet challenging task to develop advanced D-PMU data management and analytics tools to improve D-PMU data usability and further promote D-PMU projects deployment. This paper focuses on D-PMU data processing and storage. It presents a brief review of existing D-PMU data storage systems and points out their limitations on high-performance, flexibility, and scalability. To overcome the limitations, a unified PMU-data storage system (UPS) is proposed. Specifically, a unified I/O interface between storage servers and computing jobs is developed to effectively reduce the overhead of managing various computing jobs and data analytics over multiple storage infrastructures; and PMUCache with PMUCache partitioning and PMUCache replacement algorithms are designed to support in-situ data processing and shared distributed data storage and further serve the computing jobs/queries with different Quality of Service (QoS) requirements. Through a series of experiments, it is demonstrated that UPS achieves high performance on fast and big data processing and storage, and efficiently increases the flexibility and scalability of PMU data management systems.

42 ENGINEERING↗
DOE OSTI2022

Low-Power, Flexible Sensor Arrays with Solderless Board-to-Board Connectors for Monitoring Soil Deformation and Temperature

Landslides are a global and frequent natural hazard, affecting many communities and infrastructure networks. Technological solutions are needed for long-term, large-scale condition monitoring of infrastructure earthworks or natural slopes. However, current instruments for slope stability monitoring are often costly, require a complex installation process and/or data processing schemes, or have poor resolution. Wireless sensor networks comprising low-power, low-cost sensors have been shown to be a crucial part of landslide early warning systems. Here, we present the development of a novel sensing approach that uses linear arrays of three-axis accelerometers for monitoring changes in sensor inclination, and thus the surrounding soil’s deformation. By combining these deformation measurements with depth-resolved temperature measurements, we can link our data to subsurface thermal–hydrological regimes where relevant. In this research, we present a configuration of cascaded I2C sensors that (i) have ultra-low power consumption and (ii) enable an adjustable probe length. From an electromechanical perspective, we developed a novel board-to-board connection method that enables narrow, semi-flexible sensor arrays and a streamlined assembly process. The low-cost connection method relies on a specific FR4 printed circuit board design that allows board-to-board press fitting without using electromechanical components or solder connections. The sensor assembly is placed in a thin, semi-flexible tube (inner diameter 6.35 mm) that is filled with an epoxy compound. The resulting sensor probe is connected to an AA-battery-powered data logger with wireless connectivity. We characterize the system’s electromechanical properties and investigate the accuracy of deformation measurements. Our experiments, performed with probes up to 1.8 m long, demonstrate long-term connector stability, as well as probe mechanical flexibility. Furthermore, our accuracy analysis indicates that deformation measurements can be performed with a 0.390 mm resolution and a 95% confidence interval of ±0.73 mm per meter of probe length. This research shows the suitability of low-cost accelerometer arrays for distributed soil stability monitoring. In comparison with emerging low-cost measurements of surface displacement, our approach provides depth-resolved deformation, which can inform about shallow sliding surfaces.

deformation monitoring↗
DOE OSTI2021

Tidal Energy Resource Characterization, Bottom Lander Measurements, Cook Inlet, AK, 2021

These datasets are from tidal resource characterization measurements collected on the Terrasond High Energy Oceanographic Mooring (THEOM) from 1 July 2021 to 30 August 2021 (60 days) in Cook Inlet, Alaska. The lander was deployed at 60.7207031 N, 151.4294998 W in ~50 m of water. The dataset contains raw and processed data from the following two instruments: 1. A Nortek Signature 500 kHz acoustic Doppler current profiler (ADCP). Data were recorded in 4 Hz in the beam coordinate system from all 5 beams. Processed data has been averaged into 5 minutes bins and converted to the East-North-Up (ENU) coordinate system. 2. A Nortek Vector acoustic Doppler velocimeter (ADV). Data were recorded at 8 Hz in the beam coordinate system. Processed data has been averaged into 5 minutes bins and converted to the Streamwise - Cross-stream - Vertical (Principal) coordinate system. Turbulence statistics were calculated from 5-minute bins, with an FFT length equal to the bin length, and saved in the processed dataset. Data was read and analyzed using the DOLfYN (version 1.0.2) python package and saved in MATLAB (.mat) and netCDF (.nc) file formats. Files containing analyzed data (".b1") were standardized using the TSDAT (version 0.4.2) python package. NetCDF files can be opened using DOLfYN (e.g., `dat = dolfyn.load(''*.nc")`) or the xarray python package (e.g. `dat = xarray.open_dataset("*.nc"). All distances are in meters (e.g., depth, range, etc), and all velocities in m/s. See the DOLfYN documentation linked in the submission, and/or the Nortek documentation for additional details.

16 TIDAL AND WAVE POWER↗
DOE OSTI2024

Portable Acceleration of CMS Computing Workflows with Coprocessors as a Service

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗