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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Measurement Adequacy for Monitoring Data Center Oscillations

Artificial intelligence (AI) training data centers with periodic load profiles can induce sustained grid oscillations across a wide frequency range, making accurate monitoring essential for reliable power system operation. This report evaluates the adequacy of existing measurement systems for monitoring such oscillations, focusing on phasor measurement units (PMUs) and point-on-wave (POW) measurement systems. The analysis shows that while PMUs are highly effective for monitoring low-frequency electromechanical oscillations, they have inherent limitations in accurately representing higher-frequency oscillations due to constraints imposed by reporting rates and the bandwidth of phasor estimation filters. Even when configured with higher reporting rates, the filtering inherent in the phasor estimation process can significantly attenuate oscillation magnitudes, potentially leading to underestimation of oscillatory behavior. This has important implications for compliance and performance monitoring of large loads. To address the limitations associated with PMU-based monitoring, the report examines the use of high-resolution POW measurements, which can capture oscillations across a broader frequency range. However, continuous POW monitoring introduces practical challenges related to large data volumes, communication bandwidth, and real-time data processing. For this reason, the report also discusses emerging approaches that use POW measurements as a complementary capability alongside PMUs to improve observability of oscillations from large data center loads.

47 OTHER INSTRUMENTATION↗
NASA NTRS1978

Human operator identification model and related computer programs

Four computer programs which provide computational assistance in the analysis of man/machine systems are reported. The programs are: (1) Modified Transfer Function Program (TF); (2) Time Varying Response Program (TVSR); (3) Optimal Simulation Program (TVOPT); and (4) Linear Identification Program (SCIDNT). The TV program converts the time domain state variable system representative to frequency domain transfer function system representation. The TVSR program computes time histories of the input/output responses of the human operator model. The TVOPT program is an optimal simulation program and is similar to TVSR in that it produces time histories of system states associated with an operator in the loop system. The differences between the two programs are presented. The SCIDNT program is an open loop identification code which operates on the simulated data from TVOPT (or TVSR) or real operator data from motion simulators.

Kessler, K. M.↗
NASA NTRS1979

On the equivalence between semiempirical fracture analyses and R-curves

The relationship between several semiempirical fracture analyses (SEFA) and the R-curve concept of fracture mechanics is examined. The conditions for equivalence between a SEFA and an R-curve are derived. A hypothetical material is employed to study the relationship analytically. Equivalent R-curves are developed for real materials using data from the literature. For each SEFA there is an equivalent R-curve whose magnitude and shape are determined by the SEFA formulation and its empirical parameters. If the R-curve is indeed unique then the various empirical parameters cannot be constant, and vice versa. However, for one SEFA the differences are small enough that they may be within the range of normal data scatter for real materials.

Orange, T. W.↗
NASA NTRS1980

A relation between semiempirical fracture analyses and R-curves

The relations between several semiempirical fracture analyses (SEFA) and the R-curve concept of fracture mechanics are examined and the conditions for equivalence between a SEFA and an R-curve are derived. A hypothetical material is employed to study the relation analytically. Equivalent R-curves are developed for several real materials using data from the literature. For each SEFA there is an equivalent R-curve whose magnitude and shape are determined by the SEFA formulation and its empirical parameters. If the R-curve is indeed unique, then the various empirical parameters cannot be constant, and vice versa. However, for one SEFA the differences are small enough that they may be within the range of normal data scatter for real materials.

Orange, T. W.↗
NASA NTRS1980

On the equivalence between semiempirical fracture analyses and R-curves

The relationship between the R-curves and semiempirical fracture analyses (SEFA) is investigated theoretically using a hypothetical material. Equivalent R-curves (ERC) are developed for real materials using data from the literature. It is shown that for each SEFA there is an ERC whose magnitude and shape are determined by the SEFA formulation and its empirical parameters. The ERC is equivalent in that it predicts exactly the same relationship between the fracture stress and the initial crack length (residual strength) as the SEFA. If the effective R-curve is unique, then the various empirical parameters cannot be constant, and vice versa. However, for one of the SEFA examined, Newman's SEFA, parameter variations are small enough to be within the range of normal data scatter for real materials.

Orange, T. W.↗
NASA NTRS1985

User data management

The primary objective is to identify, develop, and demonstrate key data management technologies to support user access to Space Station data. To accomplish this objective, there are several technical challenges which must be addressed. The first is how to provide routine customer access to high volume, dynamic and distributed data bases. This access will encompass the functions of mission and payload planning and operations, data processing and analysis, and data archive and distribution. Secondly, there must be some analysis of architectures for handling high volume data streams like those expected from the Space Station. This analysis will examine the use of packetized versus non-packetized data formats, modular expansion capabilities, real-time versus non-real-time data processing, and the interfaces and architecture required to support telescience operations. The task will also determine benchmarks of performance capabilities for technology operations, such as varied data base structures, data access procedures, distributed data base design, and data base machines. Information is provided here in outline form.

Source record↗
NASA NTRS1990

Two Tethered Balloon Systems

Systems take meteorological measurements for variety of research projects. Report describes work done by NASA Langley Research Center in atmospheric research using tethered balloon systems composed of commercially available equipment. Two separate tethered balloon systems described in report have payloads and configurations tailored to requirements of specific projects. Each system capable of measuring atmospheric parameter or species in situ and then telemetering this data in real time to ground station. Meteorological data and concentration of ozone typically measured. Indicates instrumented tethered balloon systems have distinct advantages over other systems for gathering data on troposphere.

Youngbluth, Otto↗
NASA NTRS1994

Long duration biaxial cylinder testing of balloon grade polyethylene films with emphasis on automated data acquisition and control

Characterization of a 2-dimensional material constitutive model in the practical biaxial state for engineering utility of balloon envelope structural analysis is achievable with laboratory cylinder tests. Hoop (circumferential, and longitudinal strains are time-wise measured on axially loaded pressurized cylinders. Pre-programmed ramped loading changes as well as precise long term pressure control are achieved through cascaded proportional, integral derivative (PID) flow control loops that are part of an integrated data acquisition and process control system. Real time data reduction and analysis allow monitoring of unattended tests that have durations of up to a month. Measured and controlled parameters and variables are discussed; date analysis techniques are reviewed.

Martone, Michael↗
NASA NTRS2018

Using ADOPT Algorithm and Operational Data to Discover Precursors to Aviation Adverse Events

The US National Airspace System (NAS) is making its transition to the NextGen system and assuring safety is one of the top priorities in NextGen. At present, safety is managed reactively (correct after occurrence of an unsafe event). While this strategy works for current operations, it may soon become ineffective for future airspace designs and high density operations. There is a need for proactive management of safety risks by identifying hidden and "unknown" risks and evaluating the impacts on future operations. To this end, NASA Ames has developed data mining algorithms that finds anomalies and precursors (high-risk states) to safety issues in the NAS. In this paper, we describe a recently developed algorithm called ADOPT that analyzes large volumes of data and automatically identifies precursors from real world data. Precursors help in detecting safety risks early so that the operator can mitigate the risk in time. In addition, precursors also help identify causal factors and help predict the safety incident. The ADOPT algorithm scales well to large data sets and to multidimensional time series, reduce analyst time significantly, quantify multiple safety risks giving a holistic view of safety among other benefits. This paper details the algorithm and includes several case studies to demonstrate its application to discover the "known" and "unknown" safety precursors in aviation operation.

aviation safet↗
NASA NTRSDate not supplied

Using Artificial Intelligence and Machine Learning to Enhance Mission Design and Operations of the Habitable Worlds Observatory (HWO)

One key aspect in the development of HWO is the early deployment of artificial intelligence (AI) and machine learning (ML) to enhance mission science and operations. Our subtask group is part of the HWO AI/ML working group and focuses on AI and ML for mission operations. Our task group seeks to educate other HWO working groups about AI and ML capabilities for mission operations, investigate how to bridge technology gaps, and enable new capabilities particularly in the areas of observational scheduling, instrument health monitoring, and downlink operations. We focus on mission tasking / scheduling both for mission analysis in development and operations. AI and ML for mission scheduling includes: tools to support proposal calls and review, ensuring fairness in calls for proposals, community peer reviews and ease workloads, as well as in-flight and ground software development (e.g., using natural language processing (NLP) to support process automation from requirements). AI and ML for the mission’s development and operations include 1) anomaly detection and prediction (from onboard and ground based tools) to monitor the spacecraft’s health, 2) ground-based automated scheduling for mission operations including long-term and short-term planning and maintenance, and 3) flight system flexible execution (as flight proven for Spitzer and JWST) to enable robust execution despite execution variations, and 4) data analysis for prioritization (e.g., real-time data evaluation leading to autonomous actions and adjustments, high-priority identification, onboard data compression, etc.). Incorporation of ML and AI will enable HWO to address the major science questions related to exoplanet characterization, general astrophysics, and solar system exploration and also extend the boundaries of space mission technologies.

Mark Moussa↗
DOE OSTI2011

Error minimizing algorithms for nearest eighbor classifiers

Stack Filters define a large class of discrete nonlinear filter first introd uced in image and signal processing for noise removal. In recent years we have suggested their application to classification problems, and investigated their relationship to other types of discrete classifiers such as Decision Trees. In this paper we focus on a continuous domain version of Stack Filter Classifiers which we call Ordered Hypothesis Machines (OHM), and investigate their relationship to Nearest Neighbor classifiers. We show that OHM classifiers provide a novel framework in which to train Nearest Neighbor type classifiers by minimizing empirical error based loss functions. We use the framework to investigate a new cost sensitive loss function that allows us to train a Nearest Neighbor type classifier for low false alarm rate applications. We report results on both synthetic data and real-world image data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Existing Whole-House Solutions Case Study: Bay Ridge Gardens - Mixed Humid Affordable Multifamily Housing Deep Energy Retrofit, Annapolis, Maryland

Under this project, the BA-PIRC research team evaluated the installation, measured performance, and cost-effectiveness of efficiency upgrade measures for a tenant-in-place deep energy retrofit at the Bay Ridge multifamily development in Annapolis, Maryland. The design and construction phase of the Bay Ridge project was completed in August 2012. This case study summarizes system commissioning, short-term test results, utility bill data analysis, and analysis of real-time data collected over a one-year period after the retrofit was complete.

BA-PIRC↗
DOE OSTI2017

System for estimating fatigue damage

In one aspect, a system for estimating fatigue damage in a riser string is provided. The system includes a plurality of accelerometers which can be deployed along a riser string and a communications link to transmit accelerometer data from the plurality of accelerometers to one or more data processors in real time. With data from a limited number of accelerometers located at sensor locations, the system estimates an optimized current profile along the entire length of the riser including riser locations where no accelerometer is present. The optimized current profile is then used to estimate damage rates to individual riser components and to update a total accumulated damage to individual riser components. The number of sensor locations is small relative to the length of a deepwater riser string, and a riser string several miles long can be reliably monitored along its entire length by fewer than twenty sensor locations.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Improving Solar PV Degradation Prediction Certainty: PREDICTS2 (Final Technical Report)

The rate and type of in-service PV performance degradation vary for different module designs, vintages, and operating climates. The current IEC 61215 module qualification testing standard can be used to identify design and manufacturing flaws that may lead to early life failures, but it was not intended to accurately predict degradation rates in the first 5 to 10 years of a PV plant’s lifetime, nor long-term performance, under varied operating conditions. Correlations between accelerated aging data and real-world degradation data are needed to better predict wear-out degradation in response to environmental stressors, such as elevated temperature, temperature cycling, moisture, wind load, and solar radiation. The PREDICTS2 project aged 36 pristine-condition ‘spare’ modules from a commercial PV power plant in the U.S. Southwest using the IEC 61215 and Qualification Plus (Qual Plus) testing standards. The study is unique in its ability to compare performance degradation data for fielded modules operating outdoors for 6 years in a commercial plant environmental with degradation data for modules of the same make, model and vintage that have been aged artificially in an indoor laboratory. Changes in individual module current-voltage (I-V) curve data were measured before and after the artificial aging process and compared to I-V curve data for the fielded modules. This project advanced the industry’s understanding of how environmental stressors affect performance degradation and established relationships between degradation mechanisms and changes to module electrical parameters and power loss. Improved certainty in degradation rates is expected to be the greatest source of LCOE reduction resulting from this project. Reducing the spread in the degradation rate probability distribution lowers project risk, which translates to lower project financing costs.

14 SOLAR ENERGY↗
DOE OSTI2015

Parallel Tensor Compression for Large-Scale Scientific Data.

As parallel computing trends towards the exascale, scientific data produced by high-fidelity simulations are growing increasingly massive. For instance, a simulation on a three-dimensional spatial grid with 512 points per dimension that tracks 64 variables per grid point for 128 time steps yields 8 TB of data. By viewing the data as a dense five way tensor, we can compute a Tucker decomposition to find inherent low-dimensional multilinear structure, achieving compression ratios of up to 10000 on real-world data sets with negligible loss in accuracy. So that we can operate on such massive data, we present the first-ever distributed memory parallel implementation for the Tucker decomposition, whose key computations correspond to parallel linear algebra operations, albeit with nonstandard data layouts. Our approach specifies a data distribution for tensors that avoids any tensor data redistribution, either locally or in parallel. We provide accompanying analysis of the computation and communication costs of the algorithms. To demonstrate the compression and accuracy of the method, we apply our approach to real-world data sets from combustion science simulations. We also provide detailed performance results, including parallel performance in both weak and strong scaling experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗