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DOE OSTI2023

Load Profile Inpainting for Missing Load Data Restoration and Baseline Estimation

This paper introduces a Generative Adversarial Nets (GAN) based, Load Profile Inpainting Network (Load-PIN) for restoring missing load data segments and estimating the baseline for a demand response event. The inputs are time series load data before and after the inpainting period together with explanatory variables (e.g., weather data). Here, we propose a Generator structure consisting of a coarse network and a fine-tuning network. The coarse network provides an initial estimation of the data segment in the inpainting period. The fine-tuning network consists of self-attention blocks and gated convolution layers for adjusting the initial estimations. Loss functions are specially designed for the fine-tuning and the discriminator networks to enhance both the point-to-point accuracy and realisticness of the results. We test the Load-PIN on three real-world data sets for two applications: patching missing data and deriving baselines of conservation voltage reduction (CVR) events. We benchmark the performance of Load-PIN with five existing deep-learning methods. Our simulation results show that, compared with the state-of-the-art methods, Load-PIN can handle varying-length missing data events and achieve 15-30% accuracy improvement.

14 SOLAR ENERGY↗

NASA NTRS1977

Digital flight control systems

The design of stable feedback control laws for sampled-data systems with variable rate sampling was investigated. These types of sampled-data systems arise naturally in digital flight control systems which use digital actuators where it is desirable to decrease the number of control computer output commands in order to save wear and tear of the associated equipment. The design of aircraft control systems which are optimally tolerant of sensor and actuator failures was also studied. Detection of the failed sensor or actuator must be resolved and if the estimate of the state is used in the control law, then it is also desirable to have an estimator which will give the optimal state estimate even under the failed conditions.

Caglayan, A. K.↗

NASA NTRS1984

Experiences performing conceptual design optimization of transport aircraft

Optimum Preliminary Design of Transports (OPDOT) is a computer program developed at NASA Langley Research Center for evaluating the impact of new technologies upon transport aircraft. For example, it provides the capability to look at configurations which have been resized to take advantage of active controls and provide and indication of economic sensitivity to its use. Although this tool returns a conceptual design configuration as its output, it does not have the accuracy, in absolute terms, to yield satisfactory point designs for immediate use by aircraft manufacturers. However, the relative accuracy of comparing OPDOT-generated configurations while varying technological assumptions has been demonstrated to be highly reliable. Hence, OPDOT is a useful tool for ascertaining the synergistic benefits of active controls, composite structures, improved engine efficiencies and other advanced technological developments. The approach used by OPDOT is a direct numerical optimization of an economic performance index. A set of independent design variables is iterated, given a set of design constants and data. The design variables include wing geometry, tail geometry, fuselage size, and engine size. This iteration continues until the optimum performance index is found which satisfies all the constraint functions. The analyst interacts with OPDOT by varying the input parameters to either the constraint functions or the design constants. Note that the optimization of aircraft geometry parameters is equivalent to finding the ideal aircraft size, but with more degrees of freedom than classical design procedures will allow.

Arbuckle, P. D.↗

NASA NTRS2003

Application of a data-mining method based on Bayesian networks to lesion-deficit analysis

Although lesion-deficit analysis (LDA) has provided extensive information about structure-function associations in the human brain, LDA has suffered from the difficulties inherent to the analysis of spatial data, i.e., there are many more variables than subjects, and data may be difficult to model using standard distributions, such as the normal distribution. We herein describe a Bayesian method for LDA; this method is based on data-mining techniques that employ Bayesian networks to represent structure-function associations. These methods are computationally tractable, and can represent complex, nonlinear structure-function associations. When applied to the evaluation of data obtained from a study of the psychiatric sequelae of traumatic brain injury in children, this method generates a Bayesian network that demonstrates complex, nonlinear associations among lesions in the left caudate, right globus pallidus, right side of the corpus callosum, right caudate, and left thalamus, and subsequent development of attention-deficit hyperactivity disorder, confirming and extending our previous statistical analysis of these data. Furthermore, analysis of simulated data indicates that methods based on Bayesian networks may be more sensitive and specific for detecting associations among categorical variables than methods based on chi-square and Fisher exact statistics.

NASA Discipline Neuroscience↗

NASA NTRS1998

ROSAT and ASCA Observations of the Seyfert Galaxy 1H0419-577-577, Identified with LB 1727

We discuss the properties of the Seyfert 1.5 galaxy LB 1727 based upon the analysis of two ASCA observations, a two-month Rosat monitoring campaign, and optical data. The target is identified with the HEAO-A1 source 1H0419-577, so it has been observed by ASCA and ROSAT in order to obtain better X-ray variability and spectra data. Only modest (20%) variability is observed within or between ASCA and BeppoSAX observations in the approximately 2 - 10 keV band. However, the soft X-ray flux increased by a factor of 3 over a period of 2 months, while it was monitored daily by the ROSAT HRI instrument. The hard X-ray continuum can be parameterized as a power-law of slope Gamma approximately 1.5 - 1.6 across 0.7 - 11 keV in the rest-frame. We also report the first detection of an iron K(alpha) line in this source, consistent with emission from neutral material. The X-ray spectrum steepens sharply below 0.7 keV yielding a power-law of slope Gamma approximately 3.2. There is no evidence for absorption by neutral material, intrinsic to the nucleus. If the nucleus is unattenuated, then the break energy between the soft-excess and hard component is 0.7+/-0.08 keV. An ionized absorber may produce some turn-up in the spectrum at low energies, but a steepening of the underlying continuum is also required to explain the simultaneous ASCA and HRI data. We cannot rule out the possibility that a significant column of ionized material exists in the line-of-sight, if that is true, then the continuum break-energy can only be constrained to lie within the approximately 0.1 - 0.7 keV band.

Turner, T. J.↗

NASA NTRS1998

ROSAT and ASCA Observations of the Seyfert Galaxy 1H0419-577, Identified with LB 1727

We discuss the properties of the Seyfert 1.5 galaxy LB 1727 based upon the analysis of two ASCA observations, a two-month Rosat monitoring campaign, and optical data. The target is identified with the HEAO-A1 source 1H0419-577, so it has been observed by ASCA and ROSAT in order to obtain better X-ray variability and spectra data. Only modest (20%) variability is observed within or between ASCA and BeppoSAX observations in the approximately 2 - 10 keV band. However, the soft X-ray flux increased by a factor of 3 over a period of 2 months, while it was monitored daily by the ROSAT HRI instrument. The hard X-ray continuum can be parameterized as a power-law of slope Gamma approximately 1.5 - 1.6 across 9.7 - 11 keV in the rest-frame. We also report the first detection of an iron K(alpha) line in this source, consistent with emission from neutral material. The X-ray spectrum steepens sharply below 0.7 keV yielding a power-law of slope Gamma approximately 3.2. There is no evidence for absorption by neutral material, instrinsic to the nucleus. If the nucleus is unattenuated, then the break energy between the soft-excess and hard component is 0.7+/-0.08 keV. An ionized absorber may produce some tum-up in the spectrum at low energies, but a steepening of the underlying continuum is also required to explain the simultaneous ASCA and HRI data. We cannot rule out the possibility that a significant column of ionized material exists in the line-of-sight, if that is true, then the continuum break-energy can only be constrained to lie within the approximately 0.1 - -0.7 keV band.

Turner, T. J.↗

NASA NTRSDate not supplied

Seasonal and Dayurnal Planetary Albedo Variability from Six Years of DSCOVR EPIC Data

Deep Space Climate Observatory (DSCOVR) measurements of Earth’s reflected solar radiation from the Lissajous orbital position near the Lagrangian L1 point provide continuous monitoring of the Earth’s sunlit hemisphere. CERES-based angle models were used to convert the near-hourly reflected radiances of the EPIC images into a climate-style planetary albedo data-point over the sunlit hemisphere. Integration over the sunlit hemispheres averages out the meteorological weather noise, but retains the seasonal and planetary-scale variability. As the Earth rotates, this generates variations in the Earth’s planetary albedo that are precisely aligned in longitude, which constitutes the dayurnal cycle. This dayurnal variability in the planetary albedo arises from planetary-scale changes in cloud radiative properties that can be directly compared to similarly sampled climate GCM output data. Six years of EPIC data have been analyzed, showing characteristic patterns in the seasonal and dayurnal variability of the Earth’s planetary albedo. Much of the seasonal change in planetary albedo is associated with the changing DSCOVR viewing geometry and the change in solar declination. But throughout the year, the highest planetary albedos are observed over the Central Asia (Iraq) longitude, while the lowest planetary albedos occur over the Central Pacific longitude. For these longitudes, the relative seasonal changes in the planetary albedo are slowly varying and anti-correlated. Dayurnal amplitude maxima tend to occur during the July-September timeframe, with April-May and December exhibit distinct minima in the dayurnal amplitude. West Africa and the West Pacific longitudes likewise exhibit anti-correlated seasonal variability, while they also undergo anti-correlated short period oscillations. On the other hand, in nearby longitudes, there are short period spikes in planetary albedo of a few-days duration, as well as longer period oscillations that may range from a week to several months, that generally tend to be correlated.

Planetary Albedo↗

NASA NTRS1988

Eight years of ultraviolet spectra of the variable BL Lacertae object PKS 2155-304

IUE data obtained on the variable BL Lacertae object PKS 2155-304 over an eight-year period are presented and analyzed in a fashion that minimizes the SNR of individual spectra. In general, the ultraviolet continuum can be fitted by a power law model with an average spectral index of roughly 0.89 + or - 0.12(sigma). The observations show that PKS 2155-304 has been variable on a time scale as short as 10 days and by an overall factor of two. The spectral index has changed very little and is uncorrelated with intensity in an absolute sense.

Urry, C. M.↗

DOE OSTI2024

Discovering the Unknowns: A First Step

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.

42 ENGINEERING↗

NASA NTRS2026

Using Satellite Surface Temperature Data to Monitor Urban Heat Island

Exposure to heat is a growing concern nationwide. Temperatures can be elevated in cities compared to surrounding rural areas, referred to as an “urban heat island” (UHI) effect, which is intensified during heat waves. The lack of dense networks of air temperature measurements results in few studies on urban heat. Now, a vast amount of high spatial and temporal satellite data on land surface temperature is available. We identified satellite datasets with the longest surface temperature records but different spatial and temporal resolutions: Landsat (1985-current, biweekly at 60 m and 100 m spatial resolution) and the Moderate Resolution Imaging Spectroradiometer (MODIS) data from the Terra and Aqua satellites (2000-current, daily at 1 km spatial resolution). We investigated how satellites with different spatial and temporal resolutions detect UHI effects differently. We hypothesized that 1) a dataset’s spatial resolution impacts the precision of detected UHIs spatially, since high spatial resolution Landsat data better captures spatial variability in temperature, and 2) daily surface temperature data can detect temporal patterns of UHIs and heatwave frequencies. We analyzed Landsat and MODIS satellite data in the Washington, D.C. and Baltimore region. We found that Landsat describes higher spatial variability of the UHI effect than MODIS data. However, MODIS data shows more consistent seasonal surface temperature patterns than Landsat when compared to in situ air temperature measurements. MODIS data was also able to consistently measure the frequency of heat waves. This study demonstrates the value of NASA satellite data for urban heat and climate change studies.

landsat↗

NASA NTRSDate not supplied

CFD Validation Study of a Hypersonic Cone-Slice-Flap Variable Geometry Configuration

Model validation is the process of determining the degree of accuracy between physical reality and the model. The result of model validation can either be used to improve the model through calibration or quantify the model-form uncertainty. This work focuses on providing the model-form uncertainty through an area metric for a hypersonic cone-slice-flap variable geometry configuration given uncertainty in both the simulation and experimental data. For a variable geometry, performing uncertainty quantification to capture the model-form uncertainty on every configuration is computationally challenging. This work lays out a procedure that can give an accurate representation of the model-form uncertainty using a small number of high-fidelity runs and many low-fidelity runs on multiple configurations. Running this comparison provides a quantifiable measurement for the accuracy of each turbulence model for this type of design. The high-fidelity CFD solver used was VULCAN-CFD and the low-fidelity results came from Cart3D. The experimental data came from the 20-Inch Mach 6 Tunnel located at NASA Langley Research Center. The present work showed that the turbulence simulation overpredicted the drag and lift coefficient, while underpredicting the pitching moment coefficient. The model-form uncertainty estimate resulted up to a 13.6% change in the total uncertainty for the drag coefficient, up to a 57.4% change in total uncertainty for the lift coefficient, and up to a 100% change in total uncertainty for the pitching moment coefficient.

Laura White↗

DOE OSTI2022

Effects of random forest modeling decisions on biogeochemical time series predictions

Abstract Random forests (RF) are an increasingly popular machine learning approach used to model biogeochemical processes in the Earth system. While RF models are robust to many assumptions that complicate deterministic models, there are several important parameterization decisions for appropriate use and optimal model fit. We explored the role that parameter decisions, including training/testing data splitting strategies, variable selection, and hyperparameters play on RF goodness‐of‐fit by constructing models using 1296 unique parameter combinations to predict concentrations of nitrate, a key nutrient for biogeochemical cycling in aquatic ecosystems. Models were built on long‐term, publicly available water quality and meteorology time series collected by the National Estuarine Research Reserve monitoring network for two contrasting ecosystems representing freshwater and brackish estuaries. We found that accounting for temporal dependence when splitting data into training and testing subsets was key for avoiding over‐estimation of model predictive power. In addition, variable selection, the ratio of training to testing data, and to a lesser degree, variables per split and number of trees, were significant parameters for optimizing RF goodness‐of‐fit. We also explored how model parameter decisions influenced interpretation of the relative importance of predictors to the model, and model predictor‐dependent variable relationships, with results suggesting that both data structure and model parameterization influence these factors. Because much of the current RF literature is written for the computational and statistical science communities, the primary goal of this study is to provide guidelines for aquatic scientists new to machine learning to apply RF techniques appropriately to aquatic biogeochemical datasets.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1989

Ignition of nonmetallic materials by impact of high-pressure oxygen. II - Evaluation of repeatability of pneumatic impact test

The gaseous oxygen pneumatic impact test is used to evaluate the suitability of nonmetallic materials for use in high-pressure oxygen systems. The test was evaluated by testing the reactivity of four materials over a range of impact pressures. The evaluation also investigated the effect of valve opening time and other test variables on the frequency of reaction. The variability of the data obtained for each test material was too large for the test method to be capable of distinguishing other than gross differences in the reactivity of materials. No relation was found between this variability and changes in valve opening time and other test variables. The materials studied were found to be unacceptable for use as reference standards. Because of the high variability of the test data, it is recommended that new methods be developed for evaluating the suitability of materials for use in high-pressure oxygen systems.

Schmidt, Naomi↗

NASA NTRS1987

Observations of active galaxies

New information obtained by the EXOSAT satellite on the nature of the X-ray variability in AGNs is discussed. From the EXOSAT data, it is seen that many more AGNs vary on short timescales; in some cases, the character of the time variability is quantitatively and qualitatively different than expected from the flare model of the variability. Data on the two active galaxies NGC5506 and NGC4051, which exemplify these findings, are discussed.

Mushotzky, Richard↗

DOE OSTI2020

Modeling Stochastic Variability in Multiband Time-series Data

In preparation for the era of time-domain astronomy with upcoming large-scale surveys, we propose a state-space representation of a multivariate damped random walk process as a tool to analyze irregularly-spaced multifilter light curves with heteroscedastic measurement errors. We adopt a computationally efficient and scalable Kalman filtering approach to evaluate the likelihood function, leading to maximum O(k 3 n) complexity, where k is the number of available bands and n is the number of unique observation times across the k bands. This is a significant computational advantage over a commonly used univariate Gaussian process that can stack up all multiband light curves in one vector with maximum O(k 3 n 3 ) complexity. Using such efficient likelihood computation, we provide both maximum likelihood estimates and Bayesian posterior samples of the model parameters. Three numerical illustrations are presented: (i) analyzing simulated five-band light curves for a comparison with independent single-band fits; (ii) analyzing five-band light curves of a quasar obtained from the Sloan Digital Sky Survey Stripe 82 to estimate short-term variability and timescale; (iii) analyzing gravitationally lensed g- and r-band light curves of Q0957+561 to infer the time delay. Two R packages, Rdrw and timedelay, are publicly available to fit the proposed models.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Meteorological data from the Billy Bar site at the East River, Colorado

This dataset includes Quality Assessed and Quality Controlled (QA/QC) meteorological data from the Billy Bar field site in the East River Watershed, Colorado in order to inform watershed hydrobiogeochemical processes. The data includes 1-hour aligned time series of Solar Radiation, Wind Speed, Wind Direction, Air Temperature, Relative Humidity, Barometric Pressure, and Precipitation. For each parameter, the QA flags are given: 1 is for extreme (potential abnormal values), and 0 is for values in the expected range. The Rmarkdown document is a stand-alone file/notebook that preserves the text, code, and code results, as well as formatting contained in the original R script. The Data profiling report is a summary and graphical presentation of the data exploration process for data analysis and model building, so that users could focus on understanding data and extracting insights. The report provides a summary of each variable and does data profiling.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1996

Surface and Atmospheric Parameter Retrieval From AVIRIS Data: The Importance of Non-Linear Effects

AVIRIS data represent a new and important approach for the retrieval of atmospheric and surface parameters from optical remote sensing data. Not only as a test for future space systems, but also as an operational airborne remote sensing system, the development of algorithms to retrieve information from AVIRIS data is an important step to these new approaches and capabilities. Many things have been learned since AVIRIS became operational, and the successive technical improvements in the hardware and the more sophisticated calibration techniques employed have increased the quality of the data to the point of almost meeting optimum user requirements. However, the potential capabilities of imaging spectrometry over the standard multispectral techniques have still not been fully demonstrated. Reasons for this are the technical difficulties in handling the data, the critical aspect of calibration for advanced retrieval methods, and the lack of proper models with which to invert the measured AVIRIS radiances in all the spectral channels. To achieve the potential of imaging spectrometry, these issues must be addressed. In this paper, an algorithm to retrieve information about both atmospheric and surface parameters from AVIRIS data, by using model inversion techniques, is described. Emphasis is put on the derivation of the model itself as well as proper inversion techniques, robust to noise in the data and an inadequate ability of the model to describe natural variability in the data. The problem of non-linear effects is addressed, as it has been demonstrated to be a major source of error in the numerical values retrieved by more simple, linear-based approaches. Non-linear effects are especially critical for the retrieval of surface parameters where both scattering and absorption effects are coupled, as well as in the cases of significant multiple-scattering contributions. However, sophisticated modeling approaches can handle such non-linear effects, which are especially important over vegetated surfaces. All the data used in this study were acquired during the 1991 Multisensor Airborne Campaign (MAC-Europe), as part of the European Field Experiment on a Desertification-threatened Area (EFEDA), carried out in Spain in June-July 1991.

Green Robert O.↗

NASA NTRS1995

Gravitropic responses of the Avena coleoptile in space and on clinostats. II. Is reciprocity valid?

Experiments were undertaken to determine if the reciprocity rule is valid for gravitropic responses of oat coleoptiles in the acceleration region below 1 g. The rule predicts that the gravitropic response should be proportional to the product of the applied acceleration and the stimulation time. Seedlings were cultivated on 1 g centrifuges and transferred to test centrifuges to apply a transverse g-stimulation. Since responses occurred in microgravity, the uncertainties about the validity of clinostat simulation of weightlessness was avoided. Plants at two stages of coleoptile development were tested. Plant responses were obtained using time-lapse video recordings that were analyzed after the flight. Stimulus intensities and durations were varied and ranged from 0.1 to 1.0 g and from 2 to 130 min, respectively. For threshold g-doses the reciprocity rule was obeyed. The threshold dose was of the order of 55 g s and 120 g s, respectively, for two groups of plants investigated. Reciprocity was studied also at bending responses which are from just above the detectable level to about 10 degrees. The validity of the rule could not be confirmed for higher g-doses, chiefly because the data were more variable. It was investigated whether the uniformity of the overall response data increased when the gravitropic dose was defined as (gm x t) with m-values different from unity. This was not the case and the reciprocity concept is, therefore, valid also in the hypogravity region. The concept of gravitropic dose, the product of the transverse acceleration and the stimulation time, is also well-defined in the acceleration region studied. With the same hardware, tests were done on earth where responses occurred on clinostats. The results did not contradict the reciprocity rule but scatter in the data was large.

Flight Experiment↗