Power Management Optimization Platform for High Performance Computing and Data Centers
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32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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Results for “performance monitoring”
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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Design review package for the on-site monitor for solar heating and cooling systems
The performance specification and verification matrix for the on-site monitor (OSM) is presented. The OSM is a portable device which, when connected to the Site Data Acquisition subsystem, allows readout of data on solar heating and cooling operational test sites.
Source record↗
Projected network performance for multiple isotopes using next-generation xenon monitoring systems
Since about 2000 (Bowyer et al., 1998), radioxenon monitoring systems have been under development and testing for the verification of the Comprehensive Nuclear Test-Ban Treaty (CTBT). Operation of the systems since then has resulted in development of a next-generation of systems that are nearly ready for operational deployment. By 2010, the need to screen out civilian sources was well known (Auer et al., 2010; Saey, 2009), and isotopic ratio approaches were soon considered (Kalinowski et al., 2014) to identify specific sources. New generation systems are expected to improve the ability to verify the absence of nuclear tests by using isotopic ratios when multiple isotopes are detected. In this work, thousands of releases were simulated to compute the global detection probability of 131m Xe, 133m Xe, 133 Xe, and 135 Xe at 39 noble gas systems in the International Monitoring System (IMS) for both current and next-generation systems. Three release scenarios are defined at 1 h, 1 d, and 10 d past a 1 kt TNT equivalent 235U explosion event. Multiple cases using from one part in a million to the complete release of the xenon isotopic activity are evaluated for each scenario. Coverage maps and global integrals comparing current and next-generation monitoring systems are presented showing that next-generation noble gas systems will create measurable improvements in the IMS. The global detection probability for 133 Xe is shown to be strong in all scenarios, but only modestly improved by next-generation equipment. However, the detection probability for 131m Xe and 133m Xe increased to about 50% in different scenarios, providing a second detectable isotope for many events. As anticipated from shorter sampling intervals, the expected number of detecting samples roughly doubled and the expected number of detecting stations rose by approximately 50% for all release scenarios. Thus, it might be anticipated that future events would consist of multiple 133 Xe detections and one or more second isotope detections. In conclusion, signals of this nature should increase detection confidence, tighten release location estimates, improve rejection of civilian signals, and lessen the impacts from individual systems being offline for maintenance or repair reasons.
98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
Design and performance of a lead fluoride detector as a luminosity monitor
Not Available
Pérez Benito, R.↗
A Multi-Analysis Approach for Estimating Regional Health Impacts from the 2017 Northern California Wildfires
In the evening of October 8 and early hours of October 9, 2017, high winds in Northern California downed trees and power lines, igniting some of the most devastating wildfires the state had seen, and within hours unhealthy air quality impacted millions of people. We simulated these air quality conditions using fire detection information from the MODIS, VIIRS, and GOES-16 ABI satellite instruments, and applying a set of three WRF–CMAQ simulations, one data fusion, and three machine learning methods. We investigated using the 5-min available GOES-16 fire detection data to simulate timing of fire activity to allocate emissions hourly for the WRF-CMAQ air quality modeling system. Interestingly, this approach did not necessarily improve results compared to the baseline case, which used a default time profile. However, this approach was key to simulating the initial 12-hr explosive fire activity and smoke impacts. The WRF-CMAQ simulations compared well with observational data for the October 8-15 time period and tended to overestimate concentrations October 16-20. To improve these results, we applied three machine learning algorithms. We also had a unique opportunity to evaluate results with temporary monitors deployed specifically for wildfires, and performance was markedly different. For example, at the permanent monitoring locations, the WRF-CMAQ simulations had a Pearson correlation of 0.65, and the data fusion approach improved this (Pearson correlation = 0.95), while at the temporary monitor locations across the WRF-CMAQ, data fusion, and machine learning datasets, the best Pearson correlation was 0.5. The data fusion and machine learning results were biased low and WRF-CMAQ results were biased high. Finally, we applied the optimized PM2.5 exposure estimate in a short-term exposure-response function. Total estimated mortality attributable to PM2.5 exposure during the smoke episode was 83 (95% confidence interval: 0, 196) with 47% of these deaths attributable to wildland fire smoke.
O'Neill, Susan↗
Using spatio-temporal graph neural networks to estimate fleet-wide photovoltaic performance degradation patterns
Accurate estimation of photovoltaic (PV) system performance is crucial for determining its feasibility as a power generation technology and financial asset. PV-based energy solutions offer a viable alternative to traditional energy resources due to their superior Levelized Cost of Energy (LCOE). A significant challenge in assessing the LCOE of PV systems lies in understanding the Performance Loss Rate (PLR) for large fleets of PV systems. Estimating the PLR of PV systems becomes increasingly important in the rapidly growing PV industry. Precise PLR estimation benefits PV users by providing real-time monitoring of PV module performance, while explainable PLR estimation assists PV manufacturers in studying and enhancing the performance of their products. However, traditional PLR estimation methods based on statistical models have notable drawbacks. Firstly, they require user knowledge and decision-making. Secondly, they fail to leverage spatial coherence for fleet-level analysis. Additionally, these methods inherently assume the linearity of degradation, which is not representative of real world degradation. To overcome these challenges, we propose a novel graph deep learning-based decomposition method called the Spatio-Temporal Graph Neural Network for fleet-level PLR estimation (PV-stGNN-PLR). PV-stGNN-PLR decomposes the power timeseries data into aging and fluctuation components, utilizing the aging component to estimate PLR. PV-stGNN-PLR exploits spatial and temporal coherence to derive PLR estimation for all systems in a fleet and imposes flatness and smoothness regularization in loss function to ensure the successful disentanglement between aging and fluctuation. We have evaluated PV-stGNN-PLR on three simulated PV datasets consisting of 100 inverters from 5 sites. Experimental results show that PV-stGNN-PLR obtains a reduction of 33.9% and 35.1% on average in Mean Absolute Percent Error (MAPE) and Euclidean Distance (ED) in PLR degradation pattern estimation compared to the state-of-the-art PLR estimation methods.
14 SOLAR ENERGY↗
Operational support for Upper Atmosphere Research Satellite (UARS) attitude sensors
The Upper Atmosphere Research Satellite (UARS) has several sensors that can provide observations for attitude determination: star trackers, Sun sensors (gimbaled as well as fixed), magnetometers, Earth sensors, and gyroscopes. The accuracy of these observations is important for mission success. Analysts on the Flight Dynamics Facility (FDF) UARS Attitude task monitor these data to evaluate the performance of the sensors taking corrective action when appropriate. Monitoring activities range from examining the data during real-time passes to constructing long-term trend plots. Increasing residuals (differences) between the observed and expected quantities is a prime indicator of sensor problems. Residual increases may be due to alignment shifts and/or degradation in sensor output. Residuals from star tracker data revealed and anomalous behavior that contributes to attitude errors. Compensating for this behavior has significantly reduced the attitude errors. This paper discusses the methods used by the FDF UARS attitude task for maintenance of the attitude sensors, including short- and long-term monitoring, trend analysis, and calibration methods, and presents the results obtained through corrective action.
Lee, M.↗
The Integrated Safety-Critical Advanced Avionics Communication and Control (ISAACC) System Concept: Infrastructure for ISHM
Integrated System Health Management (ISHM) architectures for spacecraft will include hard real-time, critical subsystems and soft real-time monitoring subsystems. Interaction between these subsystems will be necessary and an architecture supporting multiple criticality levels will be required. Demonstration hardware for the Integrated Safety-Critical Advanced Avionics Communication & Control (ISAACC) system has been developed at NASA Marshall Space Flight Center. It is a modular system using a commercially available time-triggered protocol, ?Tp/C, that supports hard real-time distributed control systems independent of the data transmission medium. The protocol is implemented in hardware and provides guaranteed low-latency messaging with inherent fault-tolerance and fault-containment. Interoperability between modules and systems of modules using the TTP/C is guaranteed through definition of messages and the precise message schedule implemented by the master-less Time Division Multiple Access (TDMA) communications protocol. "Plug-and-play" capability for sensors and actuators provides automatically configurable modules supporting sensor recalibration and control algorithm re-tuning without software modification. Modular components of controlled physical system(s) critical to control algorithm tuning, such as pumps or valve components in an engine, can be replaced or upgraded as "plug and play" components without modification to the ISAACC module hardware or software. ISAACC modules can communicate with other vehicle subsystems through time-triggered protocols or other communications protocols implemented over Ethernet, MIL-STD- 1553 and RS-485/422. Other communication bus physical layers and protocols can be included as required. In this way, the ISAACC modules can be part of a system-of-systems in a vehicle with multi-tier subsystems of varying criticality. The goal of the ISAACC architecture development is control and monitoring of safety critical systems of a manned spacecraft. These systems include spacecraft navigation and attitude control, propulsion, automated docking, vehicle health management and life support. ISAACC can integrate local critical subsystem health management with subsystems performing long term health monitoring. The ISAACC system and its relationship to ISHM will be presented.
Gwaltney, David A.↗
Photovoltaic Reliability Performance Model (PVRPM) in Python and the Value of Monitoring.
Abstract not provided.
Lunis, Paul↗
Evaluation of the IMP-16 microprocessor orbit determination system filter
The results of the numerical tests performed in evaluating the interplanetary monitoring platform-16 orbit determination system are presented. The system is capable of performing orbit determination from satellite to satellite tracking data in applications technology satellite range and range rate format. The estimation scheme used is a Kalman filter, sequential (recursive) estimator. Descriptions of the tests performed and tabulations of the numerical results are included.
Shenitz, C. M.↗
Normative Baseline Oculomotor Performance
Future missions to the moon or Mars will require the crew to monitor and assess their health and performance more autonomously, necessitating approaches that are easily useable and interpretable by non-clinicians. The eye-movement-based performance metrics (oculometrics) obtained from NASA-patented technology developed at the Visuomotor Control Laboratory can reliably detect and discriminate the source of mild neural impairment relative to an individual’s baseline visual and sensorimotor performance and can predict performance in manual control tasks. This study aims to develop a database of normative performance that can be used to enable the detection of impairment without a within-subject baseline and to facilitate power analyses for the design of future studies.
eye movements↗
Method, system, and computer-readable medium for determining performance characteristics of an object undergoing one or more arbitrary aging conditions
A method, system, and computer-readable medium are described for characterizing performance loss of an object undergoing an arbitrary aging condition. Baseline aging data may be collected from the object for at least one known baseline aging condition over time, determining baseline multiple sigmoid model parameters from the baseline data, and performance loss of the object may be determined over time through multiple sigmoid model parameters associated with the object undergoing the arbitrary aging condition using a differential deviation-from-baseline approach from the baseline multiple sigmoid model parameters. The system may include an object, monitoring hardware configured to sample performance characteristics of the object, and a processor coupled to the monitoring hardware. The processor is configured to determine performance loss for the arbitrary aging condition from a comparison of the performance characteristics of the object deviating from baseline performance characteristics associated with a baseline aging condition.
97 MATHEMATICS AND COMPUTING↗
Assessment of Condition Monitoring Methods and Technologies for Inservice Inspection and Testing of Nuclear Power Plant Components
This report was prepared for the U.S. Nuclear Regulatory Commission (NRC) to explore the application of advanced technologies toward meeting the current and future regulatory requirements for maintenance and condition monitoring of structures, systems, and components. The advanced technologies considered in this work are advanced sensors and instrumentation, data analytics, machine learning and artificial intelligence (ML/AI), physics-based models, and digital twins (DT). The interest in the application of advanced technologies for condition monitoring in nuclear power plants continues to grow, and current and future licensees are expected to implement advanced technologies as part of their inservice inspection (ISI) and inservice testing (IST) programs. This report delineates the outcomes of an exploratory investigation into the implementation of advanced condition monitoring technologies to address ISI and IST requirements. A thorough review was conducted of the existing regulatory requirements for ISI and IST, along with an analysis of associated industry practices. Additionally, a state-of-the-art assessment was performed on advanced condition monitoring technologies frequently employed in non-nuclear sectors. This research incorporated two nuclear-specific case studies to illustrate the application of these technologies within the current nuclear fleet. The report provides an exhaustive discussion on the technical challenges, considerations, and opportunities associated with the deployment of advanced condition monitoring technologies. The following are key considerations in the application of advanced technologies for the ISI and IST of nuclear power plant components: • Developing adequate verification and validation procedures to confirm the functional and non-functional requirements, • Developing technical capabilities to conduct real-time asset condition monitoring, • Establishing guidance and protocol for modeling and simulation tools to continuously meet regulatory requirements, • Addressing trustworthiness, explainability, and interpretability of ML/AI methods, • Evaluating maintenance activities to maintain an adequate safety margin and avoid undesirable conditions, • Establishing cybersecure condition monitoring programs associated with a computer-based software system, and • Establishing standardized evaluation metrics for advanced condition monitoring programs. Interest in the use of advanced technologies for condition monitoring in ISI and IST programs continues to grow, and the technology is expected to experience rapid and wide industry adoption in the near future. Adoption of advanced technologies for condition monitoring could have novel and unique impacts on regulatory activities associated with ISI and IST programs. The NRC is continuing to explore the regulatory aspects of advanced technologies as part of ISI and IST programs by pursuing additional research in this technical area.
22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
Performance of Off-the-Shelf Technologies for Spacecraft Cabin Atmospheric Major Constituent Monitoring
Monitoring the atmospheric composition of a crewed spacecraft cabin is central to successfully expanding the breadth and depth of first-hand human knowledge and understanding of space. Highly reliable technologies must be identified and developed to monitor atmospheric composition. This will enable crewed space missions that last weeks, months, and eventually years. Atmospheric composition monitoring is a primary component of any environmental control and life support system. Instrumentation employed to monitor atmospheric composition must be inexpensive, simple, and lightweight and provide robust performance. Such a system will ensure an environment that promotes human safety and health, and that the environment can be maintained with a high degree of confidence. Key to this confidence is the capability for any technology to operate autonomously, with little intervention from the crew or mission control personnel. A study has been conducted using technologies that, with further development, may reach these goals.
Tatara, J. D.↗
Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units
Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Analysis of Fault Data Collected from Automated Fault Detection and Diagnostic Products for Packaged Rooftop Units
Rooftop units (RTUs) and other packaged systems are very common in commercial buildings in the U.S., and they often have minimal controls and poor performance. Automated fault detection and diagnostics (AFDD) is a powerful tool that can continuously monitor operating equipment, detect abnormal performance, diagnose problems, and report findings to building operators. AFDD technologies for RTUs have been under development for many years and have recently begun to enter the market in a significant way. There are several AFDD systems available for RTUs that feature a wide range of designs, capabilities, and reporting. Unfortunately, there is little consistency among the AFDD applications and little understanding of the performance and value of these systems. This study presents analysis of AFDD data provided by four companies from over 28,000 RTUs, five building types, and multiple climate zones. The objectives of this investigation were to gain a better understanding of how RTU AFDD systems operate, the types and frequencies of faults identified, and how building operators interact with these systems. The monitoring of a variety of RTUs provides insights into the AFDD monitoring inputs, faults, and diagnostics from which these tools are capable of informing building owners about the status of their HVAC systems.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Nickel cadmium battery operations on-orbit: Trials, tribulations, and success on the Upper Atmosphere Research Satellite
The Upper Atmosphere Research Satellite (UARS), designed, built, integrated, tested, and operated by NASA and Martin Marietta is a low-Earth orbiting, Earth-observing spacecraft which was launched via Space Shuttle Discovery on September 12, 1991 and deployed three days later. The Modular Power Subsystem (MPS) onboard the satellite is equipped with three NASA Standard 50 Ampere-hour (Ah) nickel-cadmium (NiCd) batteries. McDonnell Douglas Electronics Systems Company fabricated the MPS, and batteries from Gates Aerospace Batteries cells. Nominal battery performance was achieved for the first four months of spacecraft operation. First evidence of anomalous battery performance was observed in January 1992, after the first maximum beta angle (low Depth of Discharge) period. Since then, the Flight Operations Team (FOT), under the direction of Goddard Space Flight Center's UARS Project and Space Power Application Branch, has monitored and managed battery performance by adjusting solar array offset angle, conducting periodic deep discharge, and controlling battery recharge ratio. This paper covers a brief overview of the UARS, the FOT's operational battery management, and the observed spacecraft battery performance.
Rao, Gopalakrishna M.↗