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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Wall diffuser velocity effects on American Shad ( Alosa sapidissima ) inside a fishway entrance channel

Attraction water for fishways is typically introduced through a diffuser inside the entrance channel, often through the floor or wall. In the spring of 2019, this laboratory study examined how 151 adult American Shad (Alosa sapidissima) responded to different gross velocities through a wall diffuser inside a full-scale fishway entrance channel. Two velocity conditions were studied, 0.152 m/s and 0.305 m/s, both without turning vanes inside the auxiliary water channel. The fish were tracked using the passive integrated transponder telemetry technique. The results of the experiments showed no difference in American Shad behavior when exhibited to the low and high velocity treatments. Moreover, shad passed the diffuser in roughly 3 out of every 4 attempts, regardless of the treatment. However, the similarity in shad behavior and passage performance is believed to be more of a result of the similarity in flow fields that resulted from the lack of flow guidance devices inside the auxiliary water channel. Furthermore, these findings therefore highlight the importance of properly maintained flow guidance devices, an often-overlooked component of an auxiliary water system.

13 HYDRO ENERGY↗
DOE OSTI2018

ProtoDESI: First On-Sky Technology Demonstration for the Dark Energy Spectroscopic Instrument

The Dark Energy Spectroscopic Instrument (DESI) is under construction to measure the expansion history of the universe using the baryon acoustic oscillations technique. The spectra of 35 million galaxies and quasars over 14,000 square degrees will be measured during a 5-year survey. A new prime focus corrector for the Mayall telescope at Kitt Peak National Observatory will deliver light to 5,000 individually targeted fiber-fed robotic positioners. The fibers in turn feed ten broadband multi-object spectrographs. We describe the ProtoDESI experiment, that was installed and commissioned on the 4-m Mayall telescope from 2016 August 14 to September 30. ProtoDESI was an on-sky technology demonstration with the goal to reduce technical risks associated with aligning optical fibers with targets using robotic fiber positioners and maintaining the stability required to operate DESI. The ProtoDESI prime focus instrument, consisting of three fiber positioners, illuminated fiducials, and a guide camera, was installed behind the existing Mosaic corrector on the Mayall telescope. A fiber view camera was mounted in the Cassegrain cage of the telescope and provided feedback metrology for positioning the fibers. ProtoDESI also provided a platform for early integration of hardware with the DESI Instrument Control System that controls the subsystems, provides communication with the Telescope Control System, and collects instrument telemetry data. In conclusion, lacking a spectrograph, ProtoDESI monitored the output of the fibers using a fiber photometry camera mounted on the prime focus instrument. ProtoDESI was successful in acquiring targets with the robotically positioned fibers and demonstrated that the DESI guiding requirements can be met.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Migration depth and residence time of juvenile salmonids in the forebays of hydropower dams prior to passage through turbines or juvenile bypass systems: implications for turbine-passage survival

Little is known about the three-dimensional depth distributions in rivers of individually marked fish that are in close proximity to hydropower facilities. Knowledge of the depth distributions of fish approaching dams can be used to understand how vulnerable fish are to injuries such as barotrauma as they pass through dams. To predict the possibility of barotrauma injury caused by pressure changes during turbine passage, it is necessary to understand fish behaviour relative to acclimation depth in dam forebays as they approach turbines. A guiding study was conducted using high-resolution three-dimensional tracking results of salmonids implanted with Juvenile Salmon Acoustic Telemetry System transmitters to investigate the depth distributions of subyearling and yearling Chinook salmon ( Oncorhynchus tshawytscha ) and juvenile steelhead ( Oncorhynchus mykiss ) passing two dams on the Snake River in Washington State. Multiple approaches were evaluated to describe the depth at which fish were acclimated, and statistical analyses were performed on large data sets extracted from ~28 000 individually tagged fish during 2012 and 2013. Our study identified patterns of depth distributions of juvenile salmonids in forebays prior to passage through turbines or juvenile bypass systems. This research indicates that the median depth at which juvenile salmonids approached turbines ranged from 2.8 to 12.2 m, with the depths varying by species/life history, year, location (which dam) and diel period (between day and night). One of the most enlightening findings was the difference in dam passage associated with the diel period. The amount of time that turbine-passed fish spent in the immediate forebay prior to entering the powerhouse was much lower during the night than during the day. This research will allow scientists to understand turbine-passage survival better and enable them to assess more accurately the effects of dam passage on juvenile salmon survival.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Efficacy of Midazolam and Ketamine Combination Therapy over Midazolam Monotherapy against Soman Exposure in Carboxylesterase Knockout Mice

Introduction Chemical warfare nerve agents (CWNAs) inhibit acetylcholinesterase (AChE), which leads to status epilepticus (SE), spontaneous recurrent seizures (SRS) and severe neuropathology when treatment is delayed. In addition to binding to AChE, some organophosphorus (OP) CWNAs such as soman (GD) also inhibit carboxylesterase (CaE), which acts as a bioscavenger and can thus reduce the severity of the toxicity of OP agent exposure. Unlike humans, rodents have plasma CaE activity. The CaE knockout (ES1−/−) mouse specifically lacks plasma CaE and might better model human GD exposure compared to wildtype rodents. Delayed treatment of CWNA‐induced SE with midazolam leads to benzodiazepine‐refractory SE. We evaluated combination therapy of midazolam and the NMDA antagonist ketamine in male and female mice for efficacy against soman‐induced lethality, SE and epileptogenesis. Methods Mice implanted with telemetry transmitters for electroencephalography (EEG) seizure identification were exposed to 82 μg/kg GD (~4LD 50 ) and treated with an admix (ip) of atropine sulfate (4 mg/kg) and HI‐6 (50 mg/kg) 1 min after exposure, and with midazolam (3 mg/kg; ip) alone or in combination with ketamine (30 mg/kg, ip) at 40 min after seizure onset. Mice were continuously recorded to evaluate initial seizure duration and SRS and then euthanized 2 weeks after exposure for neuropathology assessment. Results Delayed treatment with midazolam resulted in poor survival, was unable to rapidly terminate behavioral or EEG seizure activity and did not prevent the development of SRS or neuronal loss following GD exposure. Combination therapy of midazolam and ketamine resulted in improved outcome, including an increase in survival from 30% to 78% in female and from 57% to 77% in males compared to midazolam monotherapy. In addition, the incidence of epileptogenesis was lower in mice treated with ketamine and midazolam dual therapy compared to midazolam monotherapy. Conclusions This study demonstrates that delayed treatment of SE with midazolam is not fully protective against the GD‐induced epileptogenesis and neuropathology, exemplifying the need for adjunct treatment to midazolam to prevent or reduce effects of GD‐induced SE. Ketamine combination with midazolam improves outcome. Support or Funding Information Research was supported by the CounterACT Program, NIH OD, and the NINDS (Grant 1R21NS103820‐01 to LA Lumley‐Lange) and by DTRA‐JSTO. Erica Kundrick, Katie Walker, and Sean O'Brien were supported in part by an appointment to the Research Participation Program for the U.S. Army Medical Research and Materiel Command administered by the Oak Ridge Institute for Science and Education through an agreement between the U.S. Department of Energy and U.S. Army Medical Research and Materiel Command. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

Marrero‐Rosado, Brenda↗
DOE OSTI2019

Efficacy of Naloxone as a Treatment for an Acute Exposure to Aerosolized Carfentanil

Carfentanil (CRF) is a powerful synthetic opioid that is approximately 100 times more potent than fentanyl and 10,000 times more potent than morphine. CRF originally was intended to be used as a sedative for big game animals in a veterinary setting, but is becoming increasingly common in the illicit drug trade. The increased use of CRF by people has put strain on first responders, who need to administer increased doses of naloxone (Naracan®, Evzio®), an FDA‐approved opioid antagonist, to counteract the effects. First responders may also worry about the potential of self‐exposure through inhalation or dermal contact with CRF. We set out to study the effectiveness of naloxone against a potentially lethal dose of inhaled CRF in male ferrets. Ferrets were implanted with telemetry devices to study cardiac parameters and exposed to aerosolized CRF in a whole‐body plethysmography chamber to record respiratory parameters. We observed profound depression of respiration which resulted in the animals becoming apneic 23% ± 9% of the time during exposure. We also observed cardiac abnormalities, in the form of premature junctional contractions (PJCs), in the ferrets during the apneic periods. At our acute exposure dose, which was lethal in 9% of our animals, ferrets were unresponsive and incapacitated for 105.6 ± 20.0 minutes following the end of exposure. Naloxone, when administered intramuscularly at human equivalent doses ranging from 4 to 40 mg, significantly reduced the time that ferrets were incapacitated following exposure. Although we saw no significant difference in the reduction of time that the animals were incapacitated between the treatment groups (average times between treatment and revival ranged from 6.5 to 9.7 minutes), higher doses of naloxone did revive a portion of the ferrets quicker than the lower doses (some as soon as 1.5–3.5 minutes post‐treatment). We also observed that naloxone was able to significantly reduce the amount of apneic episodes in CRF‐exposed ferrets and resolved the observed cardiac PJCs. Our results suggest that naloxone is a viable treatment against the effects of a potentially lethal dose of inhaled CRF in ferrets. These data have the potential to inform and aid treatment strategies for an acute inhalation exposure to synthetic opioids. Support or Funding Information Opinions, interpretations, conclusions, and recommendations are those of the author and are not necessarily endorsed by the US Army. This research complied with the Animal Welfare Act and implementing Animal Welfare Regulations and adhered to the principles noted in The Guide for the Care and Use of Laboratory Animals. This research was supported by DTRA RD‐CB. M.R. and N.A.A. were supported in part by an appointment to the Postgraduate Research Participation Program at the US Army Medical Research Institute of Chemical Defense administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the US Department of Energy and USAMRMC. This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal .

McCranor, Bryan J.↗
DOE OSTI2018

Anticonvulsant drug polytherapy stops status epilepticus and prevents neuronal loss in soman‐exposed rats

Introduction When treatment of status epilepticus (SE) is delayed, seizures become self‐sustaining and refractory to benzodiazepine therapy. Chemical warfare nerve agents (CWNAs), such as soman(GD), increase acetylcholine through inhibition of acetylcholine sterase and can lead to SE if seizures are not treated quickly and controlled. Prolonged seizures may lead to extensive neuropathology, spontaneous recurrent seizures and long‐term performance deficits. To identify a better treatment against pharmacoresistant seizures caused by GD exposure, we are using combinations of drugs aimed at reversing the effects of maladaptive receptor trafficking that follows CWNA exposure. Methods Rats were implanted with telemetry transmitters for continuous monitoring of EEG, body temperature and activity. After surgical recovery, rats were exposed to 1.2 LD 50 GD and treated 1 min later with atropine sulfate and the oxime HI‐6 and then 40 min after seizure onset with the anticonvulsant phenobarbital with or without the NMDA antagonist ketamine (PHE/KET), the benzodiazepine midazolam (PHE/MDZ) or a combination of all three drugs (PHE/KET/MDZ).Two weeks after exposure, rat brains were sectioned and stained for neuropathology assessments. Results Triple therapy with PHE/KET/MDZ reduced total time spent in seizures (SE +early recurrent seizures) in the first 72 h and reduced the number of spontaneous recurrent seizures compared to midazolam monotherapy. Tripletherapy also reduced the duration of toxic signs in the hours following GD‐exposure and prevented the development of hyperactivity that occurs in the weeks following exposure compared to GD/MDZ monotherapy. In addition, tripletherapy reduced loss of neurons in the piriform cortex and thalamus compared to GD/MDZ monotherapy. Conclusion Triple therapy with PHE/KET/MDZ hadanticonvulsant, antiepileptic, and neuroprotective effects compared with midazolammonotherapy in GD‐exposed rats. Triple therapy may be a highly effective approach against pharmacoresistant seizures, such as those caused by GD exposure, and may allow for administration of lower doses, resulting in fewer side effects than are often seen with large doses of individual drug therapies. Disclaimer: The views expressed in this abstract are those of the authors and do not reflect the official policy of the Department of the Army, Department of Defense, or the U.S. Government. The experimental protocol was approved by the Animal Care and Use Committee at the United States Army Medical Research Institute of Chemical Defense and all procedures were conducted in accordance with the principles stated in the Guide for the Care and Use of Laboratory Animals and the Animal Welfare Act of 1966 (P.L. 89‐544), as amended. Support or Funding Information KatieWalker, Erica Kundrick and Sean O'Brien were supported in part by an appointment to the Research Participation Program for the U.S. Army Medical Research and Materiel Command administered by the Oak Ridge Institute for Science and Education through an agreement between the U.S. Department of Energy and U.S. Army Medical Research and Materiel Command. This research was supported by the NIH U01 to Dr. Claude Wasterlain.

Stone, Michael↗
DOE OSTI2021

A machine learning degradation model for electrochemical capacitors operated at high temperature

Electrochemical capacitors (ECs) have only recently been considered as an alternative power source for telemetry sensors of drilling equipment for geothermal or oil and gas exploration. The lifecycle analysis and modelling of ECs is underrepresented in literature in comparison to other storage devices e.g. Li-ion batteries. This paper investigates the degradation of ECs when cycled outside the manufacturer-specified operating temperature envelope and proposes a machine learning-based approach for modelling the degradation. Experimental results show that end of life, defined as a 30% decrease in capacitance, occurs at 1,000 cycles when the environmental temperature exceeds the maximum operating temperature by 30%. The life-cycle test data is then used as an input to a Gaussian process regression (GPR) algorithm to predict the capacitance fade trend. The GPR is validated on a total of nine commercial cells from two different manufacturers, achieving an average root mean squared percent error of less than 2% and a mean calibration score of 93% when referenced to a 95% confidence interval. The model can be utilized to determine the EC degradation rate at a range of operating temperature values.

42 ENGINEERING↗
DOE OSTI2023

Autonomy Loops for Monitoring, Operational Data Analytics, Feedback, and Response in HPC Operations

Many High Performance Computing (HPC) facilities have developed and deployed frameworks in support of continuous monitoring and operational data analytics (MODA) to help improve efficiency and throughput. Because of the complexity and scale of systems and workflows and the need for low-latency response to address dynamic circumstances, automated feedback and response have the potential to be more effective than current human-in-the-loop approaches which are laborious and error prone. Progress has been limited, however, by factors such as the lack of infrastructure and feedback hooks, and successful deployment is often site- and case-specific. In this position paper we report on the outcomes and plans from a recent Dagstuhl Seminar, seeking to carve a path for community progress in the development of autonomous feedback loops for MODA, based on the established formalism of similar (MAPE-K) loops in autonomous computing and self-adaptive systems. By defining and developing such loops for significant cases experienced across HPC sites, we seek to extract commonalities and develop conventions that will facilitate interoperability and interchangeability with system hardware, software, and applications across different sites, and will motivate vendors and others to provide telemetry interfaces and feedback hooks to enable community development and pervasive deployment of MODA autonomy loops.

autonomy loops↗
DOE OSTI2023

Intra-hour Solar Irradiance Forecast in Multiple Locations using Deep Transfer Learning

In recent years, solar power system installation imposes several challenges on the operations of local and regional power grids due to the inherent variability of ground-level solar irradiance. This work proposes a novel real-time solar forecast methodology for intra-hour solar irradiance based on deep transfer learning from ground-based sky imager for time horizons ranging from 5-15 min. There are three unique aspects of the proposed methodology: (1) a Deep Learning based algorithm development which is modeled as a classification approach rather than a traditional regression approach; (2) the use of the Transfer Learning technique to show generalization capability, robustness, and portability of baseline model in the newly deployed location where availability of enough data for training is typically scarce, and (3) redefinition of point-based irradiation forecast error estimation technique with a window-based one that is more intuitive and user-friendly. The system is developed using multiple years of irradiance and sky image recording in New Jersey and one-year data from Colorado, USA. The method is validated against ground telemetry from these two locations of diverse geographic and climatic conditions. Results show that the forecasting method proposed in this work is robust and highly accurate (8% MAPE error) for multiple locations deployment.

Deep Learning, Convolution Neural Networks, transf↗
DOE OSTI2026

Energy–Performance Trade-offs in Privacy-Preserving Federated Learning on SmartNIC-Enabled HPC Systems

Federated learning (FL) is increasingly deployed on accelerator-rich high-performance computing (HPC) systems, yet the system-level energy cost of privacy-aware FL remains poorly understood, particularly across heterogeneous networking and server-placement options. We present a measurement-driven study of energy–performance trade-offs for FL on GH200-class nodes across three deployment configurations: CPU-Ethernet, CPU-InfiniBand (RDMA-capable), and a DPU-hosted FL server over InfiniBand using a BlueField-3 SmartNIC/DPU. Using NVIDIA FLARE (NVFLARE), we align node-level power telemetry with per-round timing extracted from NVFLARE logs to quantify time-to-solution (TTS), energy-to-solution (ETS), energy-delay product (EDP), and synchronization behavior for three transformer models (ALBERT, DistilBERT, BERT), trained with and without differential privacy (DP). We find that interconnect choice is the dominant driver of runtime and energy: host-managed InfiniBand consistently reduces communication overhead versus Ethernet, yielding lower TTS/ETS/EDP. In contrast, in our NVFLARE deployment, placing the FL server on the DPU does not consistently match CPU-InfiniBand performance and can be slower—especially for larger models—highlighting that server placement alone is not sufficient to guarantee end-to-end gains. Finally, under our fixed-round protocol, DP increases per-round cost and runtime variance; ETS increases largely in proportion to TTS because average node power remains relatively stable across configurations.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗
DOE OSTI2016

A Nonrelational Data Warehouse for the Analysis of Field and Laboratory Data From Multiple Heterogeneous Photovoltaic Test Sites

A nonrelational, distributed computing, data warehouse, and analytics environment (Energy-CRADLE) was developed for the analysis of field and laboratory data from multiple heterogeneous photovoltaic (PV) test sites. This data informatics and analytics infrastructure was designed to process diverse formats of PV performance data and climatic telemetry time-series data collected from a PV outdoor test network, i.e., the Solar Durability and Lifetime Extension global SunFarm network, as well as point-in-time laboratory spectral and image measurements of PV material samples. Using Hadoop/HBase for the distributed data warehouse, Energy-CRADLE does not have a predefined data table schema, which enables ingestion of data in diverse and changing formats. For easy data ingestion and data retrieval, Energy-CRADLE utilizes Hadoop streaming to enable Python MapReduce and provides a graphical user interface, i.e., py-CRADLE. By developing the Hadoop distributed computing platform and the HBase NoSQL database schema for solar energy, Energy-CRADLE exemplifies an integrated, scalable, secure, and user-friendly data informatics and analytics system for PV researchers. Furthermore, an example of Energy-CRADLE enabled scalable, data-driven, analytics is presented, where machine learning is used for anomaly detection across 2.2 million real-world current-voltage (I–V) curves of PV modules in three distinct Koppen–Geiger climatic zones.

14 SOLAR ENERGY↗
DOE OSTI2023

Glass Additive and Subtractive Manufacturing of a Fiber Optic Rotary Encoder for Downhole Pressure Sensing

This article presents a fiber optic rotary encoder-based pressure sensor for downhole applications. The rotary encoder, which is composed of an encoding pad and an all-glass optical fiber sensing head, converts the rotation angles of a pressure transducer to digital codes. The optical fiber only serves as the telemetry channel to directly transmit the data in digital format such that the environmental temperature influence and the long-time drift are minimized. The glass additive and subtractive manufacturing (ASM) techniques are used to embed the multichannel optical fibers into a bulk-fused silica glass substrate. Because the fused silica glass shares a similar material property to the optical fiber (e.g., thermal expansion coefficient), the optical fiber sensing head is well sealed and fixed even at an elevated temperature. The picosecond laser blackening technique is used to fabricate the high reflectivity contrast encoding pad with high accuracy. Here, the proposed pressure sensor was manufactured and experimentally verified to have a signal-to-noise ratio (SNR) of 23 dB and a linear pressure response with a root-mean-square error (RMSE) of 0.37. During the 400-h 250 °C long-term stability test, there was no digital code variation. In addition, a mathematical model to study the relationships between the sensor’s performances and design parameters was established. According to the model, a 12-bit encoder (0.02% pressure sensitivity) can be achieved with a radius of 20 mm.

02 PETROLEUM↗
DOE OSTI2018

Stochastic correlation analysis to rank the impact of intermittent wind generation on unreliability margins of power systems

Power systems are vulnerable to extreme contingencies (like an outage of a major generating substation or transmission line) that can cause significant generation and load loss and can lead to further cascading outages of other transmission facilities and generators in the system. Power systems operators take time-ahead actions (real time to day ahead) actions based on forecasted data as well as telemetry. However, there is a probability of extreme swings in wind generation that can push the power system that is already close to system boundaries into unreliable states. Increasing penetration on wind generation and uncertainties in short term forecast can move the system into operational states that can violate N-1 reliability criteria. While the power system planning accounts for worst case analyses to mitigate such emergencies, it is necessary to understand the spatial and temporal correlation of load buses with intermittent wind generation. The ranking of wind generators based on stochastic correlation to unreliability margins of load buses provides an insight of system vulnerabilities that can be corrected by appropriate emergency controls. A Monte-Carlo simulation of wind uncertainty forecast is performed to identify the probability distributions of unreliability margins. Ranking of wind generators is performed based on stochastic correlation analysis of the wind power generated and the load losses at the buses. This helps in not only identifying weak buses in the power system but also in identifying the vulnerable wind locations to plan for system reinforcements. Power system modeling and hybrid simulation to calculate unreliability margins are performed using DCAT and demonstrated on the 32-bus Nordic test system.

generators, wind forecasting, wind power generatio↗
DOE OSTI2024

A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale

We present ExaDigiT, an open-source framework for developing comprehensive digital twins of liquid-cooled supercomputers. It integrates three main modules: (1) a resource allocator and power simulator, (2) a transient thermo-fluidic cooling model, and (3) an augmented reality model of the supercomputer and central energy plant. The framework enables the study of "what-if" scenarios, system optimizations, and virtual prototyping of future systems. Using Frontier as a case study, we demonstrate the framework's capabilities by replaying six months of system telemetry for systematic verification and validation. Such a comprehensive analysis of a liquid-cooled exascale supercomputer is the first of its kind. ExaDigiT elucidates complex transient cooling system dynamics, runs synthetic or real workloads, and predicts energy losses due to rectification and voltage conversion. Throughout our paper, we present lessons learned to benefit HPC practitioners developing similar digital twins. We envision the digital twin will be a key enabler for sustainable, energy-efficient supercomputing.

Brewer, Wes↗
DOE OSTI2017

Characterizing cyber tools for monitoring power grid systems: what information is available and who needs it?

Abstract—The power grid is a complex, interconnected system that requires the coordination of multiple entities ensure the reliability of the system. System operators must be aware of the impacts of various natural and man-made contingencies through telemetry from their control systems, and they must also be aware of the integrity and availability of the cyber assets on their systems. A team of researchers from PNNL and INL have conducted a series of observations and interviews at power utilities to identify what information needs to be available for both control room dispatchers and cyber security analysts to maintain situation awareness of cyber security to effectively monitor the cyber assets on utility control systems. The researchers interviewed and observed control room dispatchers and spoke with regional control system engineers and cyber security analysts to define the context in which such information would be used. The results of this work will inform the design of a tool that will enhance cyber situation awareness and help facilitate effective communication between the various roles and organizations with utility and other regional authorities during contingencies. Index Terms—

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

ESnet/JLab FPGA Accelerated Transport

To increase the science rate for high data rates/volumes, Thomas Jefferson National Accelerator Facility (JLab) has partnered with Energy Sciences Network (ESnet) to define an edge to data center traffic shaping / steering transport capability featuring data event aware network shaping and forwarding. The keystone of this ESnet+JLab FPGA Accelerated Transport (EJFAT) is the joint development of an AI/ML directed dynamic compute work Load Balancer (LB) of UDP streamed data. The LB is a suite consisting of a Field Programmable Gate Array (FPGA) executing the dynamically configurable, low fixed latency LB data plane featuring real-time packet redirection and high throughput, and a control plane running on the FPGA host computer that monitors network and compute farm telemetry in order to make dynamic AI/ML guided decisions for destination compute host redirection/load balancing and destination resource provisioning. The LB provides for three-tier horizontal scaling across LB suites, core compute hosts, and CPUs within a host. The LB effectively provides seamless integration of edge/core computing to support direct experimental data processing for immediate use by JLab science programs and others such as the EIC as well as data centers of the future requiring high throughput and low latency for both hot and cooled data for both running experiment data acquisition systems and data center use cases.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Visualizing an Exascale Data Center Digital Twin: Considerations, Challenges and Opportunities

Digital twins are an excellent tool to model, visualize, and simulate complex systems, to understand and optimize their operation. In this work, we present the technical challenges of real-time visualization of a digital twin of the Frontier supercomputer.We show the initial prototype and current state of the twin and highlight technical design challenges of visualizing such a large High Performance Computing (HPC) system. The goal is to understand the use of augmented reality as a primary way to extract information and collaborate on digital twins of complex systems. This leverages the spatio-temporal aspect of a 3D representation of a digital twin, with the ability to view historical and real-time telemetry, triggering simulations of a system state and viewing the results, which can be augmented via dashboards for details. Finally, we discuss considerations and opportunities for augmented reality of digital twins of large-scale, parallel computers.

Maiterth, Matthias↗
DOE OSTI2021

Solving the sample size problem for resource selection functions

Abstract Sample size sufficiency is a critical consideration for estimating resource selection functions (RSFs) from GPS‐based animal telemetry. Cited thresholds for sufficiency include a number of captured animals and as many relocations per animal N as possible. These thresholds render many RSF‐based studies misleading if large sample sizes were truly insufficient, or unpublishable if small sample sizes were sufficient but failed to meet reviewer expectations. We provide the first comprehensive solution for RSF sample size by deriving closed‐form mathematical expressions for the number of animals M and the number of relocations per animal N required for model outputs to a given degree of precision. The sample sizes needed depend on just 3 biologically meaningful quantities: habitat selection strength, variation in individual selection and a novel measure of landscape complexity, which we define rigorously. The mathematical expressions are calculable for any environmental dataset at any spatial scale and are applicable to any study involving resource selection (including sessile organisms). We validate our analytical solutions using globally relevant empirical data including 5,678,623 GPS locations from 511 animals from 10 species (omnivores, carnivores and herbivores living in boreal, temperate and tropical forests, montane woodlands, swamps and Arctic tundra). Our analytic expressions show that the required M and N must decline with increasing selection strength and increasing landscape complexity, and this decline is insensitive to the definition of availability used in the analysis. Our results demonstrate that the most biologically relevant effects on the utilization distribution (i.e. those landscape conditions with the greatest absolute magnitude of resource selection) can often be estimated with much fewer than animals. We identify several critical steps in implementing these equations, including (a) a priori selection of expected model coefficients and (b) regular sampling of background (pseudoabsence) data within a given definition of availability. We discuss possible methods to identify a priori expectations for habitat selection coefficients, effects of scale on RSF estimation and caveats for rare species applications. We argue that these equations should be a mandatory component for all future RSF studies.

Street, Garrett M.↗