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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

A real time, low cost, data acquisition telecommunication network

The real time, low cost data acquisition communication network described features the use of asynchronous ASCII code transmissions operating at 30 characters per second on the dial-up telephone system. The network features an elaborate, but easily generated error checking system, operating in a line-by-line demand-response mode. The system is readily adaptable for any low speed computer-to-computer data transmissions requiring high reliability in data transmission. The network was developed for the acquisition of prelaunch wind data from the Eastern Test Range at Cape Kennedy.

Schulte, R. R.↗
NASA NTRS1991

Estimating Confidence In Data Via Trend Analysis

Recursive equation defines probability-density function storing trends in noisy or potentially erroneous data. Part of algorithm for analysis of trends in data. Developed to determine reliability of data from sensor used to guide robot, and mathematical approach that it represents proves valuable in other applications. Enables computation of statistic (called "confidence statistic") indicating level of confidence in new datum. Generates confidence statistic high or low, depending on whether new datum consistent or inconsistent with previous trends.

Gutow, David A.↗
DOE OSTI2020

Uncertainty about the Uncertainty [Slides]

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable result in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates, in comparison to the measurement uncertainty. Range of true values depends on the physics of the experiment, while measurement uncertainty depends on the measurement method (properties of the detector not of the experiment). When data from one–of–a kind experiment are analyzed, only the measurement uncertainty is reported. It gives no information about the range, in which the true values of experiment would spread if the experiment was repeated. A frequently used approximation, that if a physical quantity is measured as a function of time, only measurement of this quantity, produces uncertainty is also in some real experiments fare to strong. Example: In reaction history time measurement uncertainty propagated to alpha dominated under certain conditions over the flux measurement uncertainty propagated to alpha. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1985

Development of an unsteady aerodynamics model to improve correlation of computed blade stresses with test data

A reliable rotor aeroelastic analysis operational that correctly predicts the vibration levels for a helicopter is utilized to test various unsteady aerodynamics models with the objective of improving the correlation between test and theory. This analysis called Rotor Aeroelastic Vibration (RAVIB) computer program is based on a frequency domain forced response analysis which utilizes the transfer matrix techniques to model helicopter/rotor dynamic systems of varying degrees of complexity. The results for the AH-1G helicopter rotor were compared with the flight test data during high speed operation and they indicated a reasonably good correlation for the beamwise and chordwise blade bending moments, but for torsional moments the correlation was poor. As a result, a new aerodynamics model based on unstalled synthesized data derived from the large amplitude oscillating airfoil experiments was developed and tested.

Gangwani, S. T.↗
DOE OSTI2026

Bayesian Physics Informed Spatio-Temporal Network for Streamflow Data Imputation

Reliable reconstruction of incomplete streamflow records is critical for improving hydrological forecasting, flood preparedness, and water resource management. However, large observational gaps and uncertainties in governing physical parameters limit the accuracy of traditional statistical and machinelearning imputation frameworks. To address these challenges, we develop a Bayesian Physics-Informed Spatio-Temporal Network (BPI-STNet) that jointly captures spatial and temporal dependencies while enforcing hydrologic consistency through embedded physical constraints. The framework integrates a GraphSAGE-LSTM architecture to model spatial connectivity across gauges and temporal flow dynamics, coupled with a Bayesian update mechanism to estimate uncertain parameters in a simplified water-balance framework. Unlike conventional physics-informed networks that rely on sampling-based posterior estimation, BPI-STNet derives an analytic solution to the inverse problem, allowing closed-form Bayesian updates of uncertain parameters Λ={α,β,k} using Gaussian priors and likelihoods. Applied to daily observations from the Susquehanna River Basin (1980-2022), BPI-STNet achieves substantial improvements over a purely data-driven RGNN baseline, which reduced RMSE by 23 % and MAE by 9 %, and achieving an average NSE values up to 0.96. The results demonstrate that coupling Bayesian inference with physics-informed learning yields physically consistent, uncertainty-aware reconstructions that preserve the temporal persistence and statistical distribution of observed flows. The proposed framework establishes a generalizable paradigm for data-sparse hydrologic systems where both data fidelity and physical interpretability are essential.

Krishnan Kutty Ambika, Anukesh [ORNL] (ORCID:00000↗
NASA NTRS2005

Sensor Data Distribution With Robustness and Reliability: Toward Distributed Components Model

In planetary surface exploration mission, sensor data distribution is required in many aspects, for example, in navigation, scheduling, planning, monitoring, diagnostics, and automation of the field tasks. The challenge is to distribute such data in the robust and reliable way so that we can minimize the errors caused by miscalculations, and misjudgments that based on the error data input in the mission. The ad-hoc wireless network on planetary surface is not constantly connected because of the nature of the rough terrain and lack of permanent establishments on the surface. There are some disconnected moments that the computation nodes will re-associate with different repeaters or access points until connections are reestablished. Such a nature requires our sensor data distribution software robust and reliable with ability to tolerant disconnected moments. This paper presents a distributed components model as a framework to accomplish such tasks. The software is written in Java and utilized the available Java Message Services schema and the Boss implementation. The results of field experimentations show that the model is very effective in completing the tasks.

Alena, Richard L.↗
NASA NTRSDate not supplied

Reliability Growth Modeling and Testing

Reliability growth has been modelled as an exponential decline in the cumulative failure rate that continues indefinitely as long as testing continues. Contrary to this, most reliability growth data show a brief high initial failure rate due to infant mortality followed by a long period of constant low failure rate. A two part failure rate model with an initial exponential decline followed by a constant failure rate usually fits the data and provides a more realistic description of reliability growth. The reliability growth process consists of testing, experiencing failures, finding the failure causes, and redesigning the system to remove them. The cost of reliability growth increases with the number of inherent failure modes and the time needed for them to occur and be removed. The failure modes with the lower failure rates will tend to occur later, as their Mean Time Before Failure (MTBF) is the inverse of the failure rate. Reliability growth testing has diminishing returns, since it takes longer to find and remove the less probable failures.This paper first discusses the reliability bathtub curve and then explains that reliability growth is produced by testing, identifying failure causes, and designing to remove them. A simple model of reliability growth is introduced, with a brief group of early failures followed by a constant failure rate. The cumulative failure rate n(t)/t can decline as rapidly as1/t or t-1butdeclines more slowly if additiona lfailures occur. The 56-failure Crow data seti s used to demonstrate the two-phase model of reliability growth followed by a constant failure rate. 13 additional data sets are modeled, with 9 of the 14 data sets showing reliability growth approximately as n(t)/t =1/t or t-1and substantial final failure rates. The model fits most of the data sets, but 4of the 14 show no reliability growth. The reliability growth period typically includes six failures and extends one-quarter or half the total test time. As reliability growth testing continues, the cumulative failure rate should be tracked to estimate the reliability growth exponent and the final failure rate.

reliability growth modeling↗
NASA NTRSDate not supplied

Modeling Reliability Growth

Reliability growth has been modelled as an exponential decline in the cumulative failure rate that continues indefinitely as long as testing continues. Contrary to this, most reliability growth data show a brief high initial failure rate due to infant mortality followed by a long period of constant low failure rate. A two part failure rate model with an initial exponential decline followed by a constant failure rate usually fits the data and provides a more realistic description of reliability growth. The reliability growth process consists of testing, experiencing failures, finding the failure causes, and redesigning the system to remove them. The cost of reliability growth increases with the number of inherent failure modes and the time needed for them to occur and be removed. The failure modes with the lower failure rates will tend to occur later, as their Mean Time Before Failure (MTBF) is the inverse of the failure rate. Reliability growth testing has diminishing returns, since it takes longer to find and remove the less probable failures.This paper first discusses the reliability bathtub curve and then explains that reliability growth is produced by testing, identifying failure causes, and designing to remove them. A simple model of reliability growth is introduced, with a brief group of early failures followed by a constant failure rate. The cumulative failure rate n(t)/t can decline as rapidly as1/t or t-1butdeclines more slowly if additiona lfailures occur. The 56-failure Crow data seti s used to demonstrate the two-phase model of reliability growth followed by a constant failure rate. 13 additional data sets are modeled, with 9 of the 14 data sets showing reliability growth approximately as n(t)/t =1/t or t-1and substantial final failure rates. The model fits most of the data sets, but 4of the 14 show no reliability growth. The reliability growth period typically includes six failures and extends one-quarter or half the total test time. As reliability growth testing continues, the cumulative failure rate should be tracked to estimate the reliability growth exponent and the final failure rate.

reliability growth modeling↗
DOE OSTI2024

Harnessing the predicted maize pan-interactome for putative gene function prediction and prioritization of candidate genes for important traits

Abstract The recent assembly and annotation of the 26 maize nested association mapping population founder inbreds have enabled large-scale pan-genomic comparative studies. These studies have expanded our understanding of agronomically important traits by integrating pan-transcriptomic data with trait-specific gene candidates from previous association mapping results. In contrast to the availability of pan-transcriptomic data, obtaining reliable protein–protein interaction (PPI) data has remained a challenge due to its high cost and complexity. We generated predicted PPI networks for each of the 26 genomes using the established STRING database. The individual genome-interactomes were then integrated to generate core- and pan-interactomes. We deployed the PPI clustering algorithm ClusterONE to identify numerous PPI clusters that were functionally annotated using gene ontology (GO) functional enrichment, demonstrating a diverse range of enriched GO terms across different clusters. Additional cluster annotations were generated by integrating gene coexpression data and gene description annotations, providing additional useful information. We show that the functionally annotated PPI clusters establish a useful framework for protein function prediction and prioritization of candidate genes of interest. Our study not only provides a comprehensive resource of predicted PPI networks for 26 maize genomes but also offers annotated interactome clusters for predicting protein functions and prioritizing gene candidates. The source code for the Python implementation of the analysis workflow and a standalone web application for accessing the analysis results are available at https://github.com/eporetsky/PanPPI.

Genetics & Heredity↗
NASA NTRS1977

Advanced imaging communication system

Key elements of system are imaging and nonimaging sensors, data compressor/decompressor, interleaved Reed-Solomon block coder, convolutional-encoded/Viterbi-decoded telemetry channel, and Reed-Solomon decoding. Data compression provides efficient representation of sensor data, and channel coding improves reliability of data transmission.

Hilbert, E. E.↗
NASA NTRS2012

NAND flash screening and qualification guideline for space application

All space missions have a need for nonvolatile memory (NVM), which maintains data integrity when unpowered. Types of NVM include PROM, EEPROM, NOR Flash, and NAND Flash. PROMs, NOR Flash, and EEPROMs are good choices for storing smaller file size data such as boot code or FPGA configurations. These products have excellent data retention characteristics and can reliably store data for years, unpowered, without any data corruption. They can also be read many times without disturbing the data. Another application of NVM is storage of science and engineering data, which requires large of amounts of memory. The highest density memories available today are SDRAM and NAND Flash. However, the power required to operate and store data in NAND Flash is far less than SDRAM. Wherever high density and low power is required, NAND Flash is very attractive.

Heidecker, Jason↗
DOE OSTI2020

Measurement Uncertainty in One-Of-A-Kind Event Data Analysis

A golden standard in science is to repeat an experiment a statistically significant number of times, recording data using the same set of detectors and the same data analysis methodology. In such case experimental error includes both the range of true values generated by repetitions of the experiment, and measurement uncertainty caused by the detector. They are independent. It is a huge and too frequently used simplification, to assume that one can measure multiple repetitions of an identical experiment, resulting in identical true experimental value. Repetitions, as similar is it is experimentally achievable, have unavoidable built-in differences resulting in a range of the true values rather than in a single value. When modern, very sensitive and well calibrated measurement systems are used, this range is not negligible, and sometimes dominates over the measurement uncertainty. Range of true values depends on built-in differences in physics of the experiment. Stochastic physical processes result typically in a broader range of true values than non-stochastic processes do. Measurement uncertainty depends on a measurement method (properties of the detector not of the experiment). Modern measurement methods, including digital ones, frequently make the measurement uncertainty very small. When data from one–of –a kind experiment are analyzed, only the measurement uncertainty is reported. It provides no information about the range of true experimental values, neither about reliability of a reported data point. Reliability of a data point is in general independent from its measurement uncertainty. However, in practice reliable measurement methods frequently have high measurement uncertainty, while low reliability methods are applied to limit measurement uncertainty. Comparison of reliable data with high measurement uncertainty to not so reliable data measured with low uncertainty is discussed – in different scenarios different data analysis methods are applicable. Methods for data analysis from an experiment repeated statistically significant number of times are very well developed. They do not require a detailed expertise in physics of an experiment, nor in the properties of the measurement system used, and meaning of the reported uncertainty is well understood in any scientific community. It all changes when data from one-of-a-kind experiment is analyzed. Analyst’s expertise is required both in the physics of the experiment and in all aspects of the measurement system, all possible malfunctions. Data users must remember that only measurement uncertainty is reported from any one-of-a-kind experiment. Theory with simulations may provide estimation of expected built-in differences in the experiment, and by this of expected range of true values for a given experiment; yet measurement uncertainty can never be used in place of the range of true experimental values.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Data-Driven Digital Twin for Reliability Assessment of DC/DC Buck Converter

In commercial applications, the operation of DC/DC converters significantly impacts overall system performance and long-term reliability. This study introduces a data-driven digital twin (DT) approach for estimating critical degradation parameters of DC/DC BUCK converter under steady-state condition. Initially, a circuit-level MATLAB/Simulink digital model (DM C ) is refined against a hardware prototype’s switching model dataset using offline particle swarm optimization. The optimized digital model’s steady-state response is then verified with its average model response while varying the duty and load. Subsequently, degradation profiles are imposed on the inductor, capacitor, MOSFET in the DMC. A large dataset is generated from this model, allowing training, validation, and testing of machine learning (ML) models for component health regression tasks. The proposed method employs random forest ML models, achieving impressive regression results with a squared R value as high as 0.99978 and a root mean square error of 4.2× 10 –6 . The method is further validated on a medium power level DC/DC BUCK prototype with varying load conditions, and includes the analysis of MOSFET’s on-resistance under degradation conditions. This data-driven DT method shows promise for identifying parasitic degradation and ohmic loss parameters, enhancing converter reliability assessments in a non-invasive, generalized, and computationally efficient manner.

14 SOLAR ENERGY↗
DOE OSTI2023

Improving the Quality of Geothermal Data Through Data Standards and Pipelines Within the Geothermal Data Repository: Preprint

For machine learning outputs to be applicable to real world problems, high quality data are needed to ensure high quality results. With the more recent emphasis on machine learning in geothermal, there is an increasing need for greater focus on the quality of the data available for use in these projects. For example, Geothermal Operational Optimization Using Machine Learning (GOOML) utilized large quantities of geothermal power plant operational data to inform power plant operational configurations to maximize power generation. High quality datasets result from dependable sensors or devices collecting data, high frequency of measurements, sufficient data points, adequate metadata, reliable storage of data, and sufficient data curation. Another component that contributes to high quality data is reusability, which can be enhanced through data standardization. Data Standardization creates consistency in formatting and contents of like datasets, lessening preprocessing requirements and ensuring adequate information provided by a given dataset. The Geothermal Data Repository (GDR) aims to help improve data quality through automated data standardization for high-value datasets through the implementation of data pipelines alongside reliable and accessible long-term storage for datasets. As such, the GDR has decided to shift away from recommending the use of Excel-based content models and towards the implementation of automated data pipelines. This takes the burden of data standardization off the user and project team and will increase the availability of standardized geothermal data available through the GDR. A set of recommendations, or a data standard for each data type will exist with each data pipeline in order to advise data collection for maximum usability for future research. This paper serves to describe the GDR's proposed transition towards data standardization through automated data pipelines, to discuss the need for and value of such a shift, and to call for suggestions from the community regarding the most useful data standards and pipelines.

data↗
DOE OSTI2024

Data-driven prediction of scaling and ignition of inertial confinement fusion experiments

Recent advances in inertial confinement fusion (ICF) at the National Ignition Facility (NIF), including ignition and energy gain, are enabled by a close coupling between experiments and high-fidelity simulations. Neither simulations nor experiments can fully constrain the behavior of ICF implosions on their own, meaning pre- and postshot simulation studies must incorporate experimental data to be reliable. Linking past data with simulations to make predictions for upcoming designs and quantifying the uncertainty in those predictions has been an ongoing challenge in ICF research. We have developed a data-driven approach to prediction and uncertainty quantification that combines large ensembles of simulations with Bayesian inference and deep learning. The approach builds a predictive model for the statistical distribution of key performance parameters, which is jointly informed by past experiments and physics simulations. The prediction distribution captures the impact of experimental uncertainty, expert priors, design changes, and shot-to-shot variations. We have used this new capability to predict a 10× increase in ignition probability between Hybrid-E shots driven with 2.05 MJ compared to 1.9 MJ, and validated our predictions against subsequent experiments. We describe our new Bayesian postshot and prediction capabilities, discuss their application to NIF ignition and validate the results, and finally investigate the impact of data sparsity on our prediction results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1981

Real-Time Simulation Computation System

The Real-Time Simulation Computation System, which will provide the flexibility necessary for operation in the research environment at the Ames Research Center is discussed. Designing the system with common subcomponents and using modular construction techniques enhances expandability and maintainability qualities. The 10-MHz series transmission scheme is the basis of the Input/Output Unit System and is the driving force providing the system flexibility. Error checking and detection performed on the transmitted data provide reliability measurements and assurances that accurate data are received at the simulators.

Fetter, J. L.↗
NASA NTRS2019

Hydrological Land Surface Data and Services at NASA GES DISC

NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is one of twelveNASA Earth Observing System (EOS) data centers that process, archive, document, and distributedata from Earth science missions and related projects. The GES DISC hosts a wide range ofremotely-sensed and model data and provides reliable and robust data access and services to usersworldwide. This presentation, focusing on hydrological land surface data, provides a summary tablefor the hydrological data holdings, along with discussions of recent updates to data and data services.

Rui, Hualan↗