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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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DOE OSTI2024

ATAT: Astronomical Transformer for time series and Tabular data

Context. The advent of next-generation survey instruments, such as theVera C. RubinObservatory and its Legacy Survey of Space and Time (LSST), is opening a window for new research in time-domain astronomy. The Extended LSST Astronomical Time-Series Classification Challenge (ELAsTiCC) was created to test the capacity of brokers to deal with a simulated LSST stream. Aims. Our aim is to develop a next-generation model for the classification of variable astronomical objects. We describe ATAT, the Astronomical Transformer for time series And Tabular data, a classification model conceived by the ALeRCE alert broker to classify light curves from next-generation alert streams. ATAT was tested in production during the first round of the ELAsTiCC campaigns. Methods. ATAT consists of two transformer models that encode light curves and features using novel time modulation and quantile feature tokenizer mechanisms, respectively. ATAT was trained on different combinations of light curves, metadata, and features calculated over the light curves. We compare ATAT against the current ALeRCE classifier, a balanced hierarchical random forest (BHRF) trained on human-engineered features derived from light curves and metadata. Results. When trained on light curves and metadata, ATAT achieves a macro F1 score of 82.9 ± 0.4 in 20 classes, outperforming the BHRF model trained on 429 features, which achieves a macro F1 score of 79.4 ± 0.1. Conclusions. The use of transformer multimodal architectures, combining light curves and tabular data, opens new possibilities for classifying alerts from a new generation of large etendue telescopes, such as theVera C. RubinObservatory, in real-world brokering scenarios.

Astronomy & Astrophysics↗

DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real-world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5-based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

algorithm validation↗

DOE OSTI2021

Vanderbilt Alumni Hall

This dataset includes processed and raw data from the Vanderbilt Alumni Hall Building (VAH) located on the University of Vanderbilt Campus in Nashville, TN. VAH is a mixed use commercial building (LEED Gold Certified) that consists of classrooms, meeting rooms, office rooms, conference rooms, an exercise room, and others. This dataset spans one year of time-series data from 2019 and includes measurements corresponding to whole building electrical power, thermal power, zone level thermal power, set-point, indoor temperature, humidity, water and air side supply and return temperatures and flow rates as CSV files. In addition to the measurements, a metadata .json file, and a .ttl file to visualize the data as per BRICK schema are also included.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

NASA NTRS2006

Symbolic Constraint Maintenance Grid

Version 3.1 of Symbolic Constraint Maintenance Grid (SCMG) is a software system that provides a general conceptual framework for utilizing pre-existing programming techniques to perform symbolic transformations of data. SCMG also provides a language (and an associated communication method and protocol) for representing constraints on the original non-symbolic data. SCMG provides a facility for exchanging information between numeric and symbolic components without knowing the details of the components themselves. In essence, it integrates symbolic software tools (for diagnosis, prognosis, and planning) with non-artificial-intelligence software. SCMG executes a process of symbolic summarization and monitoring of continuous time series data that are being abstractly represented as symbolic templates of information exchange. This summarization process enables such symbolic- reasoning computing systems as artificial- intelligence planning systems to evaluate the significance and effects of channels of data more efficiently than would otherwise be possible. As a result of the increased efficiency in representation, reasoning software can monitor more channels and is thus able to perform monitoring and control functions more effectively.

James, Mark↗

NASA NTRS1974

Rhythmic variation in heart rate and respiration rate during space flight - Apollo 15

As part of the operational biomedical monitoring for Apollo manned missions, ECG and respiration rate are telemetered at selected intervals to mission control. The data were collected as part of this monitoring program. These data were evaluated for circadian and ultradian rhythmicity because of their uniqueness. The ability to detect and quantitate biorhythms in living systems during space flight is an important aspect of evaluating hypotheses concerning the underlying mechanisms of these phenomena. Circadian variation in heart rate during space flight is demonstrated here. In analyzing generated time series data it has been found that period discrimination is much better than the theoretical limit.

Rummel, J. A.↗

NASA NTRS2018

A Reconstruction of Global Hydroclimate and Dynamical Variables Over the Common Era

Hydroclimate extremes critically affect human and natural systems, but there remain many unanswered questions about their causes and how to interpret their dynamics in the past and in climate change projections. These uncertainties are due, in part, to the lack of long-term, spatially resolved hydroclimate reconstructions and information on the underlying physical drivers for many regions. Here we present the first global reconstructions of hydroclimate and associated climate dynamical variables over the past two thousand years. We use a data assimilation approach tailored to reconstruct hydroclimate that optimally combines 2,978 paleoclimate proxy-data time series with the physical constraints of an atmosphere?ocean climate model. The global reconstructions are annually or seasonally resolved and include two spatiotemporal drought indices, near-surface air temperature, an index of North Atlantic variability, the location of the intertropical convergence zone, and monthly Nino indices. This database, called the Paleo Hydrodynamics Data Assimilation product (PHYDA), will provide a critical new platform for investigating the causes of past climate variability and extremes, while informing interpretations of future hydroclimate projections.

Natural hazards↗

DOE OSTI2022

Tsdat: An Open-Source Data Standardization Framework for Marine Energy and Beyond: Preprint

Many organizations are tasked with the collection and processing of large quantities of data from various measurement devices. Data reported from these sources are often not interoperable with datasets and software used by analysts and other organizations in the same field, introducing barriers for collaboration on large-scale projects. This poses a particular problem for cross-device comparisons and machine learning applications. To address these challenges, the open source Time-Series Data Pipelines (Tsdat) software was developed by a joint collaboration between Pacific Northwest National Laboratory, the National Renewable Energy Laboratory, and Sandia National Laboratories to facilitate collaboration and accelerate advancements in the Marine Energy domain through the development of an open-source ecosystem of tools. This paper will describe the Tsdat software and the data standards within which the framework operates. A beta version of the framework has been released and is currently being used by several projects in marine energy, wind energy, and building energy systems.

big data↗

NASA NTRS2014

Seasonal Parameterizations of the Tau-Omega Model Using the ComRAD Ground-Based SMAP Simulator

NASA's Soil Moisture Active Passive (SMAP) mission is scheduled for launch in November 2014. In the prelaunch time frame, the SMAP team has focused on improving retrieval algorithms for the various SMAP baseline data products. The SMAP passive-only soil moisture product depends on accurate parameterization of the tau-omega model to achieve the required accuracy in soil moisture retrieval. During a field experiment (APEX12) conducted in the summer of 2012 under dry conditions in Maryland, the Combined Radar/Radiometer (ComRAD) truck-based SMAP simulator collected active/passive microwave time series data at the SMAP incident angle of 40 degrees over corn and soybeans throughout the crop growth cycle. A similar experiment was conducted only over corn in 2002 under normal moist conditions. Data from these two experiments will be analyzed and compared to evaluate how changes in vegetation conditions throughout the growing season in both a drought and normal year can affect parameterizations in the tau-omega model for more accurate soil moisture retrieval.

SMAP↗

DOE OSTI2021

Inception Based Deep Convolutional Neural Network for Remaining Useful Life Estimation of Turbofan Engines

Accurate estimation of the remaining useful life (RUL) is a key component of condition based maintenance (CBM) and prognosis and health management (PHM). Data-based models for the estimation of RUL are of particular interest because expert knowledge of systems is not always available and physical modeling is often not feasible. In this paper, a deep convolutional neural network (CNN) architecture is investigated for its ability to estimate the RUL of turbofan engines. The input to the model is a window of time series data collected from the engine under test. Inputting raw sensor data allows features to be learned instead of manually determined. To incorporate the ability to detect features of differing lengths, inception modules are used in the neural network architecture. The model is trained and tested using the new Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) data set and high prognosis accuracy was achieved. The developed model was used in the 2021 PHM Society Data Challenge and received second place, further validating its ability to accurately estimate RUL.

DeVol, Nathaniel↗

DOE OSTI2020

Numerical Validation of an Algorithm for Combined Soiling and Degradation Analysis of Photovoltaic Systems

We describe and demonstrate an open-source algorithm for simultaneously quantifying degradation and soiling of photovoltaic (PV) systems from energy-production time series data. The new analysis is based on year-on-year degradation rate analysis combined with stochastic rate and recovery soiling analysis. The algorithm is designed to fit into the workflow provided by RdTools, a Python module maintained by NREL and collaboratively developed with the community, which provides a framework and functions for degradation and loss-factor analysis of PV field data. We demonstrate the method on numerically simulated PV data sets and show that it reduces the root-mean-square error of the P50 degradation rate estimate when soiling is present.

14 SOLAR ENERGY↗

NASA NTRS2011

Semi-Supervised Novelty Detection with Adaptive Eigenbases, and Application to Radio Transients

We present a semi-supervised online method for novelty detection and evaluate its performance for radio astronomy time series data. Our approach uses adaptive eigenbases to combine 1) prior knowledge about uninteresting signals with 2) online estimation of the current data properties to enable highly sensitive and precise detection of novel signals. We apply the method to the problem of detecting fast transient radio anomalies and compare it to current alternative algorithms. Tests based on observations from the Parkes Multibeam Survey show both effective detection of interesting rare events and robustness to known false alarm anomalies.

anomaly detection↗

NASA NTRS2020

Global Distribution and 14-Year Changes in Erythemal Irradiance, UV Atmospheric Transmission, and Total Column Ozone 2005 – 2018 Estimated from OMI and EPIC Observations

Satellite data from the Ozone Measuring Instrument (OMI) and Earth Polychromatic Imaging Camera (EPIC) are used to study changes and global distribution of UV erythemal irradiance in mW/m(exp 2) E(ζ,φ,z,t) and the dimensionless UV index (E/25 mWm(exp 2)) over the Earth’s surface as a function of latitude ζ, longitude φ, altitude z, and time t. OMI time series data starting in January 2005 to December 2018 are used to estimate 14-year changes in erythemal irradiance, total column ozone TCO3, scene reflectivity LER, and aerosol absorption τA at specific land sites in the Northern and Southern Hemispheres using publicly available OMI data. Estimates of changes in atmospheric transmission ΔCτ(ζ,φ,z,t) derived from OMI measured cloud and haze reflectivity show an increase between 60°S to 45°S almost no average 14-year change from 55°S to 30°N but show an increase from 35°N to 55°N. This implies increased solar insolation at higher latitudes that suggests positive feedback for global warming. Changes in aerosol absorption of UV irradiance ΔCA(ζ,φ,z,t) derived from changes in τA are mostly in the equatorial region and at higher southern latitudes resulting in correlated changes in E. TCO3 derived from OMI data has increased at a rate of 2% per decade for the latitudes between 60°S to 10°N changing to a decrease of 1% per decade between 40°N to 60°N. The result is an average decrease in E(ζ,φ,z,t) at a rate of 2% per decade in the Southern Hemisphere and an increase between 40°N to 60°N. For some specific sites (latitudes from 55°S to 45°N) there has been little or no change in E(ζ,φ,z,t) for the period 2005 – 2018. Nearly half the sites show strong correlation effects of both short- and long-term cloud and absorbing aerosol change as well as anticorrelation with total column ozone change ΔTCO3. Synoptic EPIC data from the sunlit Earth are used to derive ozone and reflectivity needed for global images of the distribution of E(ζ,φ,z,t) from sunrise to sunset centered on the Americas, Europe-Africa, and Asia. EPIC data are used to show the latitudinal distribution of E(ζ,φ,z,t) from the equator to 75° for specific longitudes. Dangerously high amounts of erythemal irradiance (12 < UV index < 18) are found for many low latitude and high-altitude sites (e.g., San Pedro, Chile (2.45 km), La Paz, Bolivia (3.78 km). Lower UV indices at some equatorial or high-altitude sites (e.g., Quito, Ecuador) are moderated by the presence of persistent cloud effects. High UVI levels (UVI > 6) are also found at most mid-latitude sites during the summer months.

Ozone Measuring Instrument (OMI) and Earth Polychr↗

NASA NTRS1995

C-Band Backscatter Measurements of Winter Sea-Ice in the Weddell Sea, Antarctica

During the 1992 Winter Weddell Gyre Study, a C-band scatterometer was used from the German ice-breaker R/V Polarstern to obtain detailed shipborne measurement scans of Antarctic sea-ice. The frequency-modulated continuous-wave (FM-CW) radar operated at 4-3 GHz and acquired like- (VV) and cross polarization (HV) data at a variety of incidence angles (10-75 deg). Calibrated backscatter data were recorded for several ice types as the icebreaker crossed the Weddell Sea and detailed measurements were made of corresponding snow and sea-ice characteristics at each measurement site, together with meteorological information, radiation budget and oceanographic data. The primary scattering contributions under cold winter conditions arise from the air/snow and snow/ice interfaces. Observations indicate so e similarities with Arctic sea-ice scattering signatures, although the main difference is generally lower mean backscattering coefficients in the Weddell Sea. This is due to the younger mean ice age and thickness, and correspondingly higher mean salinities. In particular, smooth white ice found in 1992 in divergent areas within the Weddell Gyre ice pack was generally extremely smooth and undeformed. Comparisons of field scatterometer data with calibrated 20-26 deg incidence ERS-1 radar image data show close correspondence, and indicate that rough Antarctic first-year and older second-year ice forms do not produce as distinctively different scattering signatures as observed in the Arctic. Thick deformed first-year and second-year ice on the other hand are clearly discriminated from younger undeformed ice. thereby allowing successful separation of thick and thin ice. Time-series data also indicate that C-band is sensitive to changes in snow and ice conditions resulting from atmospheric and oceanographic forcing and the local heat flux environment. Variations of several dB in 45 deg incidence backscatter occur in response to a combination of thermally-regulated parameters including sea-ice brine volume, snow and ice complex dielectric properties, and snow physical properties.

Drinkwater, M. R.↗

DOE OSTI2022

Application of machine learning approaches in the analysis of mass absorption cross-section of black carbon aerosols: Aerosol composition dependencies and sensitivity analyses

Physics-based models typically require an in-depth understanding of a phenomenon and assumptions of the underlying process(es), which are often hard to obtain in practice, whereas data-driven machine learning models learn the structure and patterns in the training data without any prior theoretical assumptions and then use inference to develop useful predictions. A novel machine learning-based algorithm has been previously developed for the prediction of black carbon mass absorption cross-section (MAC BC ) and applied to a variety of different atmospheric environments. In contrast to light scattering theories which require assumptions about the underlying physics, this algorithm uses time-series data of aerosol properties to estimate the temporally-varying MAC BC at 870 nm. Here, we analyze our algorithm and discuss the influence of aerosol optical properties (such as Ångström exponents and single scattering albedo) and chemical composition on the model outputs and the associated accuracy. Additionally, we conduct sensitivity analyses on our models to understand how the predictions change in response to different sets of input variables. Our support vector machine (SVM) for regression model is the least sensitive to variations in the input variables, although all models tend to exhibit a degradation to their accuracy when scattering Ångström exponents are less than one.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

LBNL Fault Detection and Diagnostics Datasets

These datasets can be used to evaluate and benchmark the performance accuracy of Fault Detection and Diagnostics (FDD) algorithms or tools. It contains operational data from simulation, laboratory experiments, and field measurements from real buildings for seven HVAC systems/equipment (rooftop unit, single-duct air handler unit, dual-duct air handler unit, variable air volume box, fan coil unit, chiller plant, and boiler plant). Each dataset includes a .pdf file to document key information necessary to understand the content and scope, multiple csv files containing all the time-series data for faults at different severity levels and one fault-free case, and a ttl file to visualize the data according to BRICK schema. The dataset was created by LBNL, PNNL, NREL, ORNL and Drexel University.

AC↗

NASA NTRS2008

Post-Flight Estimation of Motion of Space Structures: Part 2

A computer program related to the one described in the immediately preceding article estimates the relative position of two space structures that are hinged to each other. The input to the program consists of time-series data on distances, measured by two range finders at different positions on one structure, to a corner-cube retroreflector on the other structure. Given a Cartesian (x,y,z) coordinate system and the known x coordinate of the retroreflector relative to the y,z plane that contains the range finders, the program estimates the y and z coordinates of the retroreflector. The estimation process involves solving for the y,z coordinates of the intersection between (1) the y,z plane that contains the retroreflector and (2) spheres, centered on the range finders, having radii equal to the measured distances. In general, there are two such solutions and the program chooses the one consistent with the design of the structures. The program implements a Kalman filter. The output of the program is a time series of estimates of the relative position of the structures.

Brugarolas, Paul↗

DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Data-Driven Approach for Uncertainty Propagation and Reachability Analysis in Dynamical Systems

In this paper, we propose a data-driven approach for uncertainty propagation and reachability analysis in a dynamical system. The proposed approach relies on the linear lifting of a nonlinear system using linear Perron-Frobenius (P-F) and Koopman operators. Here, the uncertainty can be characterized in terms of the moments of a probability density function. We demonstrate how the P-F and Koopman operators are used for propagating the moments. Time-series data is used for the finite-dimensional approximation of the linear operators, thereby enabling data-driven approach for moment propagation. Simulation results are presented to demonstrate the effectiveness of the proposed method.

42 ENGINEERING↗