Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation.
Abstract not provided.
Zhang, Yijia↗
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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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Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults
Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event data with appropriate labeling, an unsupervised approach is highly valuable. This approach enables using event data without labeling, which is far easier to obtain. This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data is used to train the CVAE that identifies different faults as different clusters in the latent space. The clusters are then used as the foundation model to categorize the real-world data.
Alam, Maksudul↗
Irradiance Monitoring for Bifacial PV Systems’ Performance and Capacity Testing
Not Available
14 SOLAR ENERGY↗
Design and development of optical modules for the BUTTON-30 detector
BUTTON-30 is a neutrino detector demonstrator located in the STFC Boulby underground facility in the north-east of England. The main goal of the project is to deploy and test the performance of the gadolinium-loaded water-based liquid scintillator for neutrino detection in an underground environment. This will pave the way for a future large-volume neutrino observatory that can also perform remote monitoring of nuclear reactors for nonproliferation. This paper describes the design and construction of the watertight optical modules of the experiment.
COMSOL simulations↗
Baselining Activity and Stability of ORR Catalysts and Electrodes for Proton Exchange Membrane Fuel Cells for Heavy-Duty Applications
Activity and stability of electrodes with Pt and PtCo alloy catalysts supported on high surface area carbon, hereafter to as a-Pt/C and d-PtCo/C, were evaluated for heavy-duty applications. Both catalysts had nearly identical Pt loading (50-wt% Pt on carbon and 0.25 mg Pt cm −2 ) and had undergone thermal treatment to stabilize them by growing the average particle size to 4–5 nm. Both were subjected to 90,000 (90 k) standard accelerated stress tests (AST) cycles consisting of 0.6–0.95 V square wave potentials, 3-s hold at upper and lower potential limits in H 2 /N 2 at 1.5 atm, 80 °C and 100% RH. Test protocols were developed to monitor the performance losses and characterize them in terms of activity for the oxygen reduction reaction (ORR), oxygen transport in the electrode and proton transport in the membrane and cathode catalyst layer. Despite the nearly double initial ORR activity, the PtCo/C electrode degraded faster due to the leaching of Co from the catalyst that had started even before the imposition of the AST potential cycles. Commensurate with Co leaching, Co poisoning of ionomer is responsible for the inferior performance of d-PtCo/C electrode at high current densities both before and after AST.
30 DIRECT ENERGY CONVERSION↗
Neck Muscle Coactivation Response to Varied Levels of Mental Workload During Simulated Flight Tasks
Objective To evaluate neck muscle coactivation across different levels of mental workload during simulated flight tasks. Background Neck pain (NP) is highly prevalent among military aviators. Given the complex nature within the flight environment, mental workload may be a risk factor for NP. This may induce higher levels of neck muscle coactivity, which over time may accelerate fatigue, increase neck discomfort, and affect flight task performance. Method Three counterbalanced mental workload conditions represented by simulated flight tasks modulated by interstimulus frequency and complexity were investigated using the Modifiable Multitasking Environment (ModME). The primary measure was a neck coactivation index to describe the neuromuscular effort of the neck muscles as a system. Additional measures included perceived workload (NASA TLX), subjective discomfort, and task performance. Participants ( n = 60; 30M, 30F) performed three test conditions over 1 hr each while seated in a simulated seating environment. Results Neck coactivation indices (CoA) and subjective neck discomfort corresponded with increasing level of mental workload. Average CoAs for low, medium, and high workloads were: .0278(SD = .0232), .0286(SD = .0231), and .0295(SD = .0228), respectively. NASA TLX mental, temporal, effort, and overall scores also increased with the level of mental workload assigned. For ModME task performance, the overall performance score, monitoring accuracy, and resource management accuracy decreased while reaction times increased with the increasing level of mental workload. Communication accuracy was lowest with the low mental workload but had higher reaction times relative to increasing workload. Conclusion Mental workload affects neck muscle coactivation during combinations of simulated flight tasks within a simulated helicopter seating environment. Application The results of this study provide insights into the physical response to mental workload. With increasing multisensory modalities within the work environment, these insights may assist the consideration of physical effects from cognitive factors.
Behavioral Sciences↗
Carbon dioxide (CO2) flux, amplicon sequencing and liquid chromatography–mass spectrometry (LC-MS) data for pyrogenic organic matter (PYOM) amended soil incubations in lab, Madison, WI, 2021-22
This dataset comprises soil-pyrogenic organic matter (PyOM) incubation experiments conducted in a laboratory setting. The focus was on measuring carbon dioxide (CO2) flux emitted in the headspace of incubation jars over a one-month period. The aim was to estimate carbon mineralization from two different carbon fractions in PyOM, as well as carbon mineralization from soil organic carbon (SOC). Bacterial and fungal community profiles were tracked at various time points during the incubation to observe changes in the overall community and specific responders to PyOM. LC-MS analysis of soil samples was also performed to monitor changes in the soil's chemical composition.The provided dataset includes processed CO2 flux measurements obtained from the Picarro-multiplexer measurement setup for each sample incubation jar. This data is available in the "CO2Flux.csv" file. Additionally, the dataset includes CO2 flux data partitioned to estimate mean carbon mineralization from the two PyOM carbon fractions, bulk PyOM, and SOC. The cumulative carbon mineralization data is provided in "cml_respired.csv," while the rate of carbon mineralization data is in "rate_respired.csv." The file "cml_respired_lastcyc.csv" contains cumulative carbon mineralization data for the PyOM carbon fractions, bulk PyOM, and SOC throughout the entire incubation period.For the LC-MS analysis, the raw data can be found in the "20220721_NZ_PeakHeights_FinalAnalysis.csv" file, while sample metadata is provided in "20220721_NZpilot_Metadata.csv". Processed LC-MS data is available in the file "20220721_NZ_FINAL_msdat_sub3.csv". Lastly, the dataset includes the relative abundance data for bacterial genera that exhibited a significant positive response to the addition of PyOM produced at 350 degrees Celsius, which can be found in "resp_genera_350.csv".
54 ENVIRONMENTAL SCIENCES↗
AmeriFlux US-xCL NEON LBJ National Grassland (CLBJ)
This is the AmeriFlux version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ). Site Description - The LBJ Grasslands is 16,800 acres of land managed by the US Forest Service under the US Department of Agriculture. There is a rich legacy of land use, ranging back to the mid-19th century. Currently, LBJ Grasslands are used for recreation and hunting, livestock grazing, and fossil fuel extraction. Ongoing ecological monitoring is performed at the site, along with prescribed burning.
Network), NEON (National Ecological Observatory↗
AmeriFlux FLUXNET-1F US-xCL NEON LBJ National Grassland (CLBJ)
This is the AmeriFlux Management Project (AMP) created FLUXNET-1F version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ). This is the FLUXNET version of the carbon flux data for the site US-xCL NEON LBJ National Grassland (CLBJ) produced by applying the standard ONEFlux (1F) software. Site Description - The LBJ Grasslands is 16,800 acres of land managed by the US Forest Service under the US Department of Agriculture. There is a rich legacy of land use, ranging back to the mid-19th century. Currently, LBJ Grasslands are used for recreation and hunting, livestock grazing, and fossil fuel extraction. Ongoing ecological monitoring is performed at the site, along with prescribed burning.
Network), NEON (National Ecological Observatory↗
CORRTEX Analysis Techniques
Continuous Reflectometry for Radius versus Time Experiments, or CORRTEX, is a diagnostic that was developed during nuclear testing to measure the distance at which rock walls were disrupted by an explosive device as a function of the time at which disruption occurred. Distances recorded are dynamic measurements of cable length as the shockwaves from an explosive event crush the experiment cables, reducing their usable length. Applications of the CORRTEX diagnostic have been expanded in recent times to include instrumentation of the explosive device itself, to record phenomena such as the speed of the detonation wave in the explosive charge and the crushing or disassembly of material in the charge housing. The diagnostic has likewise been used to monitor explosive performance in rock blasting and oil well hole-clearing activities.
42 ENGINEERING↗
Earned Value Management Systems for Operations Activities
An earned value management system (EVMS), which monitors contractor performance, is a requirement for program and project management for all major acquisitions by the United States Federal Government with development effort: (i.e., an asset requiring management attention because of its importance to an agency’s mission; high development, operating, or maintenance costs; high risk and/or high return). As an area less explored in earned value management (EVM) practices, this paper will survey the application of an EVMS for operations activities defined as: (1) Non-capital asset activities that are projects (or project - like) with definable start and end dates, with discrete scopes of work, and measurable accomplishments; as well as (2) Routine or recurring facility or environmental operations. This paper will examine the use of an EVMS to evaluate performance of operations and maintenance activities required once construction of a capital asset is complete and being used as intended. Such activities include upgrades and maintenance in order for capital assets to meet their mission function over a life-cycle (through repair, replacement, etc.). This paper will provide background on this topic from the perspective of the Department of Energy (DOE) Environmental Management (EM) Program. In addition, it will provide material from a panel discussion provided by a group of experts from the October 2019 Office of Environmental Management Project Management Workshop, as well as material from subsequent research on this topic.
97 MATHEMATICS AND COMPUTING↗
Using Monitoring Data to Improve HPC Performance via Network-Data-Driven Allocation.
Abstract not provided.
Zhang, Yijia↗
Application of Heat Transfer Enhancement (HTE) System for Improved Efficiency of Power Plant Condensers
The mission of the National Energy Technology Laboratory (NETL), a U.S. laboratory under the Department of Energy, is to drive innovation and deliver solutions for an environmentally sustainable and prosperous energy future. Through the U.S. Department of Energy (DOE)/Fossil Energy’s (FE) Crosscutting Research Program, NETL funded Interphase Materials to develop and demonstrate a technology to improve power plant condenser efficiency. From 2018 through 2021, Interphase Materials developed THERMOPHASE, an advanced material applied to the condenser during plant operation to increase efficiency and lower fuel consumption, CO 2 emissions, and water withdrawal. THERMOPHASE was evaluated in controlled environments where improvements to heat transfer and a reduction in fouling were observed. THERMOPHASE was also applied to the main condenser of the Longview Power plant and changes to the plant performance were monitored. After two years following application of THERMOPHASE, a reduction in condenser back pressure of 0.26 ± 0.13 inHg was observed resulting in an estimated $3.35M in fuel savings, 136 million lbs. of decreased CO 2 emissions, and 1,287 million gallons reduced water withdrawal.
01 COAL, LIGNITE, AND PEAT↗
Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams
Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility’s control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.
97 MATHEMATICS AND COMPUTING↗
Explosion Detection Using Smartphones: Ensemble Learning with the Smartphone High-Explosive Audio Recordings Dataset and the ESC-50 Dataset
Explosion monitoring is performed by infrasound and seismoacoustic sensor networks that are distributed globally, regionally, and locally. However, these networks are unevenly and sparsely distributed, especially at the local scale, as maintaining and deploying networks is costly. With increasing interest in smaller-yield explosions, the need for more dense networks has increased. To address this issue, we propose using smartphone sensors for explosion detection as they are cost-effective and easy to deploy. Although there are studies using smartphone sensors for explosion detection, the field is still in its infancy and new technologies need to be developed. We applied a machine learning model for explosion detection using smartphone microphones. The data used were from the Smartphone High-explosive Audio Recordings Dataset (SHAReD), a collection of 326 waveforms from 70 high-explosive (HE) events recorded on smartphones, and the ESC-50 dataset, a benchmarking dataset commonly used for environmental sound classification. Two machine learning models were trained and combined into an ensemble model for explosion detection. The resulting ensemble model classified audio signals as either “explosion”, “ambient”, or “other” with true positive rates (recall) greater than 96% for all three categories.
45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
Optimizing Deep Geothermal Drilling for Energy Sustainability in the Appalachian Basin
This study investigates the geological and geomechanical characteristics of the MIP 1S geothermal well in the Appalachian Basin to optimize drilling and address the wellbore stability issues encountered. Data from well logs, sidewall core analysis, and injection tests were used to derive elastic and rock strength properties, as well as stress and pore pressure profiles. A robust 1D-geomechanical model was developed and validated, correlating strongly with wellbore instability observations. This revealed significant wellbore breakout, widening the diameter from 12 ¼ inches to over 16 inches. Advanced technologies like Cerebro Force™ In-Bit Sensing were used to monitor drilling performance with high accuracy. This technology tracks critical metrics such as bit acceleration, vibration in the x, y, and z directions, Gyro RPM, stick-slip indicators, and bending on the bit. Cerebro Force™ readings identified hole drag caused by poor hole conditions, including friction between the drill string and wellbore walls and the presence of cuttings or debris. This led to higher torque and weight on bit (WOB) readings at the surface compared to downhole measurements, affecting drilling efficiency and wellbore stability. Optimal drilling parameters for future deep geothermal wells were determined based on these findings.
Environmental Sciences & Ecology↗
Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations
This article presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin, perform condition monitoring, or assess loads for dedicated control strategies. The mechanical model is built using a Rayleigh–Ritz approach and a set of joint coordinates. We present a general method and illustrate it using a 2-degrees-of-freedom (DOF) model of a wind turbine and using rotor speed, generator torque, pitch, and tower-top acceleration as measurement signals. The different components of the model are tested individually. The overall method is evaluated by computing the errors in estimated tower-bottom-equivalent moment from a set of simulations. From this preliminary study, it appears that the tower-bottom-equivalent moment is obtained with about 10 % accuracy. The limitation of the model and the required steps forward are discussed.
17 WIND ENERGY↗
Monitoring and prediction of nervous functions in space.
Monitoring human performance during manned orbital flight for assessment of central nervous system function, noting animal studies during simulated stresses of space flight
FLIGHT SIMULATION↗