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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Design of machine learning models with domain experts for automated sensor selection for energy fault detection

Data-driven techniques that extract insights from sensor data reduce the cost of improving system energy performance through fault detection and system health monitoring. To lower cost barriers to widespread deployment, a methodology is proposed that takes advantage of existing sensor data, encodes expert knowledge about the application system to create ‘virtual sensors’ and applies statistical and mathematical methods to reduce the time required for manual configurations. The approach combines sensor data points with encoded expert knowledge that is generic to the application system but independent of a particular deployment, thereby reducing the need to tailor to individual deployments. Additionally, this paper not only presents a method that detects faults from measured energy data, but also (1) describes an engagement method with experts in the energy system domain to identify data, (2) integrates domain knowledge with the data, (3) automatically selects from among the large pool of potential input data, and (4) uses machine learning to automatically build a data-driven fault detection model. Demonstration on a commercial building chiller plant shows that only a small number of virtual sensors is necessary for fault detection with high accuracy rates. This corresponds to the use of only five out of 52 original sensor data points features. With as few as four features, classification F1 scores exceed 90% on the training set and 80% on the testing set. The results are implementable and realizable using off-the-shelf tools. The goal is to design with domain experts an energy monitoring system that can be configured once and then widely deployed with little additional cost or effort.

47 OTHER INSTRUMENTATION↗
DOE OSTI2016

India Commercial Buildings Data Framework: A Summary of Potential Use Cases

This report details a potential set of use cases for India’s Commercial Buildings Data Framework. The use cases are aimed at enabling data-driven, evidence-based policy making and at transforming the market for energy efficiency in the building sector by facilitating the adoption of (1) superior energy-efficient building design and operation and maintenance practices, and (2) better specification and procurement of end-use equipment and systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
NASA NTRS2004

Prioritizing Scientific Data for Transmission

A software system has been developed for prioritizing newly acquired geological data onboard a planetary rover. The system has been designed to enable efficient use of limited communication resources by transmitting the data likely to have the most scientific value. This software operates onboard a rover by analyzing collected data, identifying potential scientific targets, and then using that information to prioritize data for transmission to Earth. Currently, the system is focused on the analysis of acquired images, although the general techniques are applicable to a wide range of data modalities. Image prioritization is performed using two main steps. In the first step, the software detects features of interest from each image. In its current application, the system is focused on visual properties of rocks. Thus, rocks are located in each image and rock properties, such as shape, texture, and albedo, are extracted from the identified rocks. In the second step, the features extracted from a group of images are used to prioritize the images using three different methods: (1) identification of key target signature (finding specific rock features the scientist has identified as important), (2) novelty detection (finding rocks we haven t seen before), and (3) representative rock sampling (finding the most average sample of each rock type). These methods use techniques such as K-means unsupervised clustering and a discrimination-based kernel classifier to rank images based on their interest level.

Castano, Rebecca↗
DOE OSTI2024

Homomorphic Encryption for Electrical Metering Aggregation: Protecting the Privacy of Building Tenants

Electrical meters are devices that measure consumer electricity usage. The data collected by these meters is necessary for utility billing and electrical grid management but can also be used to assess the environmental impact of buildings. Prior research has found that unprotected metering data could potentially be used to infer some information about the behaviors of building tenants by detecting changes in electricity usage. For example, a period of low electricity usage could suggest that the tenants are not in the building. As smart metering becomes more common, there is a growing need for data privacy protections for metering data that do not negatively impact the quality and availability of data used for energy management and billing applications. To identify potential solutions, we developed a Python-based data aggregation platform to analyze the potential efficacy of privacy-enhancing technologies for energy metering applications. This platform aggregates groups of metering sites into virtual buildings, which could potentially detach changes in electrical activity from individual tenants, making it more difficult to track the activity of a specific tenant. To further protect data during analysis, this project utilizes homomorphic encryption as part of its initial approach. Homomorphic encryption offers a means of protecting energy consumption data while permitting mathematical operations to be performed without the need to know the data contents. This allows for data to be processed into usable statistics without revealing energy consumption information. A series of homomorphic encryption libraries were evaluated to determine their applicability and limitations in the context of metering data. The use of these techniques may help to reassure consumers and encourage further adoption of smart grid infrastructure.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2017

Testing predictions of the quantum landscape multiverse 1: the Starobinsky inflationary potential

The 2015 Planck data release has placed tight constraints on the allowed class of inflationary models. The current data favors concave downwards inflationary potentials while offering interesting hints on possible deviations from the standard picture of CMB perturbations. We here test the predictions of the theory of the origin of the universe from the landscape multiverse, against the most recent Planck data, for the case of concave downwards inflationary potentials, such as the Starobinsky model of inflation. By considering the quantum entanglement correction of the multiverse, we can place a lower limit on the local 'SUSY breaking' scale b > 1.2 × 10{sup 7} GeV at 95% c.l. from Planck TT+lowTEB. We find that this limit is consistent with the range for b that allows the landscape multiverse to explain a serie of anomalies present in the current data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
NASA NTRS1983

LANDSAT 4 investigations of thematic mapper and multispectral scanner applications

Digital data analyses suggest the potential for TM data to provide improved land cover information with the mid-IR band being especially useful. Coefficients of variations for major land categories were greater for MSS data channels than for TM channels. Interpretation of black and white images of the six reflective TM bands indicates a strong interpreter preference for bands 5 and 7 for making distinctions in most of the classification categories that were addressed; however, a strong case can be made for a color composite containing visible, near-IR, and mid-IR spectral regions when distinguishing vegetation. A transformation from each red, green, blue color space into hue, intensity, and saturation space has potential for enhanced interpretability of TM color composite images. A perspective view transformation was demonstrated that could be useful for presently registered layers of spatial data in an oblique view format.

Lauer, D. T.↗
NASA NTRS2008

Potential of VIIRS Time Series Data for Aiding the USDA Forest Service Early Warning System for Forest Health Threats: A Gypsy Moth Defoliation Case Study

The Healthy Forest Restoration Act of 2003 mandated that a national forest threat Early Warning System (EWS) be developed. The USFS (USDA Forest Service) is currently building this EWS. NASA is helping the USFS to integrate remotely sensed data into the EWS, including MODIS data for monitoring forest disturbance at broad regional scales. This RPC experiment assesses the potential of VIIRS (Visible/Infrared Imager/Radiometer Suite) and MODIS (Moderate Resolution Imaging Spectroradiometer) data for contribution to the EWS. In doing so, the RPC project employed multitemporal simulated VIIRS and MODIS data for detecting and monitoring forest defoliation from the non-native Eurasian gypsy moth (Lymantria despar). Gypsy moth is an invasive species threatening eastern U.S. hardwood forests. It is one of eight major forest insect threats listed in the Healthy Forest Restoration Act of 2003. This RPC experiment is relevant to several nationally important mapping applications, including carbon management, ecological forecasting, coastal management, and disaster management

Spruce, Joseph P.↗
NASA NTRS1976

A new sequential decoder for the DSN telemetry subsystem

A sequential decoder was implemented in the DSS telemetry subsystem for the DSN MARK 3 data system implementation. This decoder performs the same decoding function as the data decoder assembly performs in the telemetry and command data handling subsystem. However, the new decoder is much faster, allowing potentially high data rates in the future.

Wilcher, J. H.↗
NASA NTRSDate not supplied

Inception of a Spaceflight-specific Mouse to Human Expression Profiling Translation Model

Rodents are foundational model organisms often utilized due to their seemingly analogous morphologies and biological responses to humans. However, recent studies have demonstrated that murine model data are limited in their applicability, particularly in inflammatory disease. In space studies, accurately predicting human response from mouse data is critical due to extreme limiting factors in both rodent and human spaceflight research. With successful prediction, spaceflight ailments can be predicted and prevented while respecting the constraints of the spaceflight industry and minimizing danger to humans. To do so, novel methodologies must be developed that predict human response from murine data after considering biological differences between rodents and humans in spaceflight. After considering terrestrial models, we determined that a spaceflight-based expression profiting translation tool should be created to accurately capture predictions of human gene expression in spaceflight from mouse data. To prepare to build this model, we organized known human spaceflight risks, chose analog human diseases as training data categories, then identified existing RNASeq disease datasets from GEO as potential training data. In addition, we classified existing Genelab mouse differential gene expression datasets for use as experimental data.

Translation↗
DOE OSTI2018

Data summary for the evaluation of potential surface collapse at the BRISTOL underground nuclear event

This report provides data for the evaluation of potential surface collapse above an underground nuclear event executed in Yucca Flat back in 1991. This work is being done in support of the Source Physics Experiment Program. Safety decisions must be made before a potential surface crater area can be reentered for any work. Our statements on cavity collapse and surface crater formation are provided as input into the safety decision process. They also do not address possible radiation dangers that may be present. LLNL subject matter experts who have been active in the underground testing program at NNSS (formerly NTS) reviewed these data. Both classified and unclassified data are available for review; various amounts of information are accessible, depending on the age of the test. Lack of data can hamper evaluation and introduce uncertainty; we make no attempt to quantify this uncertainty.

58 GEOSCIENCES↗
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.↗
NASA NTRS1978

Comparison of surface and satellite gravity data

Satellite derived potential coefficients (GEM 9) are compared to terrestrial gravity data by degree in terms of coefficient differences and in terms of mean anomaly differences. The root mean square undulation difference (to degree 20) was 9 + or - m and the anomaly difference was + or - 7 mgals with GEM 9 commission errors of + or - 1.7 m and + or - 3.8 mgals. The standard deviations of the GEM 9 implied undulations increased from + or - 4 cm at degree 2 to + or - 53 cm at degree 20. The corresponding values implied by a recent (June 1978) terrestrial 5 deg field were + or - 2.53 m and + or - 0.38 m (at degree 20). Comparisons between Geos-3 altimeter derived anomalies and 1 deg X 1 Deg terrestrial data showed that + or - 6-8 mgals is a reasonable accuracy estimate for the altimeter derived anomalies. Anomalies derived from satellite to satellite tracking data indicate an accuracy of about + or - 6 mgals for the recovery of 5 deg equal area blocks.

Rapp, R. H.↗
DOE OSTI2024

PSInet: a new global water potential network

Abstract Given the pressing challenges posed by climate change, it is crucial to develop a deeper understanding of the impacts of escalating drought and heat stress on terrestrial ecosystems and the vital services they offer. Soil and plant water potential play a pivotal role in governing the dynamics of water within ecosystems and exert direct control over plant function and mortality risk during periods of ecological stress. However, existing observations of water potential suffer from significant limitations, including their sporadic and discontinuous nature, inconsistent representation of relevant spatio-temporal scales and numerous methodological challenges. These limitations hinder the comprehensive and synthetic research needed to enhance our conceptual understanding and predictive models of plant function and survival under limited moisture availability. In this article, we present PSInet (PSI—for the Greek letter Ψ used to denote water potential), a novel collaborative network of researchers and data, designed to bridge the current critical information gap in water potential data. The primary objectives of PSInet are as follows. (i) Establishing the first openly accessible global database for time series of plant and soil water potential measurements, while providing important linkages with other relevant observation networks. (ii) Fostering an inclusive and diverse collaborative environment for all scientists studying water potential in various stages of their careers. (iii) Standardizing methodologies, processing and interpretation of water potential data through the engagement of a global community of scientists, facilitated by the dissemination of standardized protocols, best practices and early career training opportunities. (iv) Facilitating the use of the PSInet database for synthesizing knowledge and addressing prominent gaps in our understanding of plants’ physiological responses to various environmental stressors. The PSInet initiative is integral to meeting the fundamental research challenge of discerning which plant species will thrive and which will be vulnerable in a world undergoing rapid warming and increasing aridification.

Forestry↗
DOE OSTI2018

A large-scale evaluation of automated metadata inference approaches on sensors from air handling units

Building automation systems provide abundant sensor data to enable the potential of using data analytics to, among other things, improve the energy efficiency of the building. However, deployment of these applications for buildings, such as, fault detection and diagnosis (FDD) on multiple buildings remains a challenge due to the non-trivial efforts of organizing, managing and extracting metadata associated with sensors (e.g., information about their location, function, etc.), which is required by applications. One of the reasons leading to the problem is that varying conventions, acronyms, and standards are used to define this metadata. To better understand the nature of the problem, as well as the performance and scalability of existing solutions, in this report we implement and test 6 different time-series based metadata inference approaches on sensors from 614 air handling units (AHU) instrumented in 35 building sites accounting for more than 400 buildings distributed across United States of America. We infer 12 types of sensors and actuators in AHUs required by a rule-based FDD application: AHU performance and assessment rules (APAR). Our results show that: (1) the average performance of these approaches in terms of accuracy is similar across building sites, though there is significant variance; (2) the expected accuracy of classifying the type of points required by APAR for a new unseen building is, on average, 75%; (3) the performance of the model does not decrease as long as training data and testing data are extracted from adjacent months.

42 ENGINEERING↗
DOE OSTI2022

Sharing is caring: An extensive analysis of parameter-based transfer learning for the prediction of building thermal dynamics

In recent years deep neural networks have been proposed as a lightweight data-driven model to capture high-dimensional, nonlinear physical processes to predict building thermal responses. However, the need of a large amount of data for the training process of deep neural networks clashes with the potential limited data availability in most existing or new buildings. Transfer learning aims to enhance the performance of a target learner exploiting knowledge from related and similar environments. This study conducted a suite of experiments that leveraged 250 data-driven models based on a synthetic dataset of a building archetype to study the influence of data availability, energy efficiency level, occupancy and climate for the transfer process of thermal dynamics. The performance of the transfer learning process was compared against a classical machine learning approach. Here, the results suggest that building thermal dynamics can be effectively transferred under the same climatic conditions, increasing performance when dealing with different occupancy schedules, efficiency levels and low data availability. Furthermore, the paper compares the performance of both transfer learning and machine learning approaches in an online fashion, to support the implementation in real-world deployment.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Beyond optimization—supervised learning applications in relativistic laser-plasma experiments

We explore the applications of machine learning techniques in relativistic laser-plasma experiments beyond optimization purposes. We predict the beam charge of electrons produced in a laser wakefield accelerator given the laser wavefront change caused by a deformable mirror. Machine learning enables feature analysis beyond merely searching for an optimal beam charge, showing that specific aberrations in the laser wavefront are favored in generating higher beam charges. Supervised learning models allow characterizing the measured data quality as well as recognizing irreproducible data and potential outliers. Furthermore, we also include virtual measurement errors in the experimental data to examine the model robustness under these conditions. This work demonstrates how machine learning methods can benefit data analysis and physics interpretation in a highly nonlinear problem of relativistic laser-plasma interaction.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Data-driven discovery of coordinates and governing equations

The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Progress toward a universal biomedical data translator

Clinical, biomedical, and translational science has reached an inflection point in the breadth and diversity of available data and the potential impact of such data to improve human health and well-being. However, the data are often siloed, disorganized, and not broadly accessible due to discipline-specific differences in terminology and representation. To address these challenges, the Biomedical Data Translator Consortium has developed and tested a pilot knowledge graph-based “Translator” system capable of integrating existing biomedical data sets and “translating” those data into insights intended to augment human reasoning and accelerate translational science. Having demonstrated feasibility of the Translator system, the Translator program has since moved into development, and the Translator Consortium has made significant progress in the research, design, and implementation of an operational system. Herein, we describe the current system’s architecture, performance, and quality of results. We apply Translator to several real-world use cases developed in collaboration with subject-matter experts. Finally, we discuss the scientific and technical features of Translator and compare those features to other state-of-the-art, biomedical graph-based question-answering systems.

60 APPLIED LIFE SCIENCES↗