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All sourcesNASA NTRSDOE OSTI
NASA NTRS1958

The Effects of Longitudinal Control-System Dynamics on Pilot Opinion and Response Characteristics as Determined from Flight Tests and from Ground Simulator Studies

The results of a fixed-base simulator study of the effects of variable longitudinal control-system dynamics on pilot opinion are presented and compared with flight-test data. The control-system variables considered in this investigation included stick force per g, time constant, and dead-band, or stabilizer breakout force. In general, the fairly good correlation between flight and simulator results for two pilots demonstrates the validity of fixed-base simulator studies which are designed to complement and supplement flight studies and serve as a guide in control-system preliminary design. However, in the investigation of certain problem areas (e.g., sensitive control-system configurations associated with pilot- induced oscillations in flight), fixed-base simulator results did not predict the occurrence of an instability, although the pilots noted the system was extremely sensitive and unsatisfactory. If it is desired to predict pilot-induced-oscillation tendencies, tests in moving-base simulators may be required. It was found possible to represent the human pilot by a linear pilot analog for the tracking task assumed in the present study. The criterion used to adjust the pilot analog was the root-mean-square tracking error of one of the human pilots on the fixed-base simulator. Matching the tracking error of the pilot analog to that of the human pilot gave an approximation to the variation of human-pilot behavior over a range of control-system dynamics. Results of the pilot-analog study indicated that both for optimized control-system dynamics (for poor airplane dynamics) and for a region of good airplane dynamics, the pilot response characteristics are approximately the same.

Sadoff, Melvin↗
NASA NTRS1995

Interaction of the terrestrial and atmospheric hydrological cycles in the context of the North American southwest summer monsoon

Work under this grant has used information on precipitation and water vapor fluxes in the area of the Mexican Monsoon to analyze the regional precipitation climatology, to understand the nature of water vapor transport during the monsoon using model and observational data, and to analyze the ability of the TRMM remote sensing algorithm to characterize precipitation. An algorithm for estimating daily surface rain volumes from hourly GOES infrared images was developed and compared to radar data. Estimates were usually within a factor of two, but different linear relations between satellite reflectances and rainfall rate were obtained for each day, storm type and storm development stage. This result suggests that using TRMM sensors to calibrate other satellite IR will need to be a complex process taking into account all three of the above factors. Another study, this one of the space-time variability of the Mexican Monsoon, indicate that TRMM will have a difficult time, over the course of its expected three year lifetime, identifying the diurnal cycle of precipitation over monsoon region. Even when considering monthly rainfalls, projected satellite estimates of August rainfall show a root mean square error of 38 percent. A related examination of spatial variability of mean monthly rainfall using a novel method for removing the effects of elevation from gridded gauge data, show wide variation from a satellite-based rainfall estimates for the same time and space resolution. One issue addressed by our research, relating to the basic character of the monsoon circulation, is the determination of the source region for moisture. The monthly maps produced from our study of monsoon variability show the presence of two rainfall maxima in the analysis normalized to sea level, one in south-central Arizona associated with the Mexican monsoon maximum and one in southeastern New Mexico associated with the Gulf of Mexico. From the point of view of vertically-integrated fluxes and flux divergence of water vapor from ECMWF data, most moisture at upper levels arrives from the Gulf of Mexico, while low level moisture comes from the northern Gulf of California. Composites of ECMWF analyses for wet and dry periods (classified by rain gauge data) show that both regimes show low level moisture arriving from northern and central Gulf of California. Above 700 MB, moisture comes from both source regions and the Sierra Madre Occidental. During wet periods a longer fetch through the moist air mass above western Mexico results in a greater moisture flux into the Sonoran Desert region, while there is less moisture from the Gulf of Mexico both above and below 700 mb. Work on the grant subcontract at the University of Colorado concentrated on the development of a technique useful to TRMM combining visible, infrared and passive microwave data for measuring precipitation. Two established techniques using either visible or infrared data applied over the US Southwest correlated with gauges at the 0.58 to 0.70 level. The application of some established passive microwave techniques were less successful for a variety of reason, including problems in both the gauge and satellite data quality, sampling problems and weaknesses inherent in the algorithms themselves. A more promising solution for accurate rainfall estimation was explored using visible and infrared data to perform a cloud classification, which when combined with information about the background (e.g. Iand/ocean), was used to select the most appropriate microwave algorithm from a suite of possibilities.

Dickinson, Robert E.↗
DOE OSTI2023

Empirical Study of the Effect of Thermal Loading on the Heating Efficiency of Variable-Speed Air Source Heat Pumps

Heating buildings with air source heat pumps (ASHPs) has the potential to save energy compared to utilizing conventional heat sources. Accurate understanding of the efficiency of ASHPs is important to maximize the energy savings. While it is well understood that, in general, ASHP efficiency decreases with decreasing outdoor temperature, it is not well understood how the ASHP efficiency changes with different levels of thermal loading, even though it is an important consideration for sizing and controlling ASHPs. The goal of this study was to create an empirical model of the ASHP efficiency as a function of two independent variables–outside temperature and level of thermal loading. Four ductless mini-split ASHPs were evaluated in a cold chamber where the temperature (representing the outdoor temperature) was varied over a wide range. For each temperature, the ASHP performance data were collected at several levels of thermal loading. The data for all four ASHPs were combined and approximated with an analytical function that can be used as a general model for the ASHP steady-state efficiency as a function of the outside temperature and level of thermal loading. To the knowledge of the authors, no such empirical model that is solely based on third-party test data has been published before. While limitations exist, the model can be used to help guide future selection and operation of ASHPs.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

Danovo Energy Solution's presented its paper named: Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events at the 2026 Georgia Tech Fault & Disturbance Analysis Conference. The full paper can be found at OSTI ID# 3169150 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danova Energy Solutions]↗
DOE OSTI2026

Feature-Based PMU Event Classification under Variable PMU Participation and Overlapping Events

This paper is the basis for a presentation help at the 2026 Georgia Tech Fault & Disturbance Analysis Conference, which can be found at OSTI # 3168287 Paper Abstract—Phasor Measurement Units (PMUs) stream time synchronized, high-resolution measurements from the grid, enabling data-driven techniques for event detection and classification. Accurate event classification improves grid reliability and stability. Events can be detected by varying numbers of PMUs and exhibit different durations depending on the event type. This variability challenges standard classifiers that require uniform input sizes. Moreover, multiple events may coincide, which increases classification complexity. Standard classifiers assign each instance to the class with the highest predicted probability, whereas overlapping events may exhibit comparable probabilities across multiple classes. In this study, to handle data size variability, we extract a wide range of time–frequency domain features from all available PMUs for each event into a fixed-length vector, facilitating the application of standard machine learning classifiers, including Random Forest, XGBoost, LightGBM, Support Vector Machine, and Multilayer Perceptron. To account for overlapping events, a probabilistic post-processing step is applied. For a given data instance, if multiple predicted class probabilities exceed 30% and the differences between them are less than 10%, the event is assigned to multiple classes. Experiments using real-world PMU data demonstrate that the Random Forest and XGBoost models achieve the highest accuracy, while the proposed post-processing method yields perfect classification performance on external unseen test sets.

Nematirad, Reza [Danovo Energy Solutions]↗
DOE OSTI2025

Application of Atmospheric Gases and Particulate Matter to the Assessment of Urban Heat Island

Background: Urban heat island (UHI), where built areas are warmer compared to non-urban regions, increases human related diseases and mortality. A key challenge in UHI analysis is the designation of sites as urban or suburban/rural; however, the growing complexity of green spaces in urban areas and the predominance of the transportation sector in nonurban areas creates a dilemma for distinct delineation. Objectives: This study aims to utilize the variability of atmospheric components such as particulate matter (PM), inorganic gases, and volatile organic compounds (VOCs) as direct tracers of the degree of urbanization for ground-based measurements to fully comprehend UHI in convoluted regions with indistinct delineation of urban and nonurban environments. Methods: Atmospheric gases and aerosols were used as direct tracers of urbanization for UHI analysis. Inorganic gases and particulate matter were monitored in two sites in a southeastern US city with varying degrees of urbanization. VOCs were analyzed using a proton transfer reaction time-of-flight mass spectrometer. Results: The more-urbanized site exhibited warmer night conditions and elevated total oxidant levels, leading to the formation of nanometer-sized particles. Machine learning analysis revealed similar atmospheric pollutant profiles for both sites, suggesting comparable sources and variability. Biogenic VOCs were enhanced at the less-urbanized site; however, levels of anthropogenic aromatic VOCs were comparable for both sites. A comprehensive mass spectra analysis revealed distinct molecular backbones per site that further affirmed the applicability of VOCs as indicators of urbanization. Conclusion: This study concludes that VOCs provide more direct and accurate information than typical inorganic gases and PM parameters for characterizing the degree of urbanization. Further exploration of VOCs can enhance our understanding of UHI dynamics and its interaction with vegetation in urban green spaces.

Air quality sensor↗
NASA NTRS2016

Evaluation of the Sensitivity of the Amazonian Diurnal Cycle to Convective Intensity in Reanalyses

Model parameterizations of tropical deep convection are unable to reproduce the observed diurnal and spatial variability of convection in the Amazon, which contributes to climatological biases in the water cycle and energy budget. Convective intensity regimes are defined using percentiles of daily minimum 3-hourly averaged outgoing longwave radiation (OLR) from Clouds and the Earth's Radiant Energy System (CERES). This study compares the observed spatial variability of convective diurnal cycle statistics for each regime to MERRA-2 and ERA-Interim (ERA) reanalysis data sets. Composite diurnal cycle statistics are computed for daytime hours (06:00-21:00 local time) in the wet season (December-January-February). MERRA-2 matches observations more closely than ERA for domain averaged composite diurnal statistics-specifically precipitation. However, ERA reproduces mesoscale features of OLR and precipitation phase associated with topography and the propagation of the coastal squall line. Both reanalysis models are shown to underestimate extreme convection.

Itterly, Kyle F.↗
NASA NTRS1975

A variable conductance heat pipe flight experiment - Performance in space

The Ames Heat Pipe Experiment (AHPE) is a variable conductance heat pipe/radiator system which was launched aboard the OAO-C spacecraft in August, 1972. All available flight data was reviewed and those from a few orbits were selected for correlation with predictions from an analytical model of the system. The principal conclusion of this study is that gas controlled variable conductance heat pipes can perform reliably for long time periods in the space environment and can effectively provide temperature stabilization for spacecraft electronics. Furthermore, the performance of such systems can be adequately predicted using existing analysis tools.

Wanous, D. J.↗
NASA NTRS1987

The exotic helium variable PG 1346 + 082

The results of an extensive study of the blue object PG 1346 + 082, which is both a photometric and a spectroscopic variable, are presented. The system spans the B magnitude range 13.6-17.2, is brighter than m(pg) of about 14.0 roughly 74 percent of the time, and has a photometric quasi-period of four to five days. The rapid photometric flickering at minimum light is identified as the signature of mass transfer in a binary sytsem, and the He I emission at minimum light as indicating the presence of an accretion disk. The inability to detect high-energy X-rays, the absence of the high-excitation lines of He II in the optical spectrum, and the complete absence of hydrogen in the spectrum show that PG 1346 + 082 is an interacting binary white dwarf system. This identification lends strong support to the interacting twin-degenerate interpretation for AM CVn. The outbursts of the system may be similar in physical origin to dwarf novae outbursts.

Wood, M. A.↗
NASA NTRS1995

Integrated Logistics Support Analysis of the International Space Station Alpha, Background and Summary of Mathematical Modeling and Failure Density Distributions Pertaining to Maintenance Time Dependent Parameters

The process of predicting the values of maintenance time dependent variable parameters such as mean time between failures (MTBF) over time must be one that will not in turn introduce uncontrolled deviation in the results of the ILS analysis such as life cycle costs, spares calculation, etc. A minor deviation in the values of the maintenance time dependent variable parameters such as MTBF over time will have a significant impact on the logistics resources demands, International Space Station availability and maintenance support costs. There are two types of parameters in the logistics and maintenance world: a. Fixed; b. Variable Fixed parameters, such as cost per man hour, are relatively easy to predict and forecast. These parameters normally follow a linear path and they do not change randomly. However, the variable parameters subject to the study in this report such as MTBF do not follow a linear path and they normally fall within the distribution curves which are discussed in this publication. The very challenging task then becomes the utilization of statistical techniques to accurately forecast the future non-linear time dependent variable arisings and events with a high confidence level. This, in turn, shall translate in tremendous cost savings and improved availability all around.

Sepehry-Fard, F.↗
NASA NTRS2003

Aircraft Noise Perception Study in Brazil: A Perspective on Airport Sustainable Growth and Environmental Awareness

Aircraft noise perception is related to several variables that are tangible and objective, such as the number of operations, flight schedules. Other variables, instead, are more subjective, such as preferences. However, although their elusiveness, they contribute to determine the individuals' perception of this type of externality. Despite the fact that the complaints related to aeronautical noise have been registered since the decade of 50, it has been observed that the perception of noise seems to have grown, especially since the 80's. It has been argued that this change in noise perception has its roots on the accelerated expansion of air traffic. But, it is necessary to point out the important role played on modeling preferences, by the growing environmental conscience and the higher welfare and quality of life standards and expectations. In that context, the main objective of this paper is to study the aeronautical noise perception in the neighborhoods of the Aeroporto Internacional de Sao Paulo - AISP (the biggest airport of South America). Specifically, it analyzes the relationship between aircraft noise perception and social class, which is expected to be positive. Since noise perception is an intangible variable, this study chose as a proxy the value losses of residential properties, caused by aeronautical noise. The variable social class has been measured utilizing average per capita income of the population who live nearby the airport. The comparison of both, the lowest and the highest social class suggests that the relationship between social class and noise perception is positive in the AISP region. Moreover, it was observed that all social classes are very susceptible to aircraft noise annoyance. In fact, the magnitude of the noise perception proxy for both social classes -the residential value losses- was found to be comparable to levels encountered in developed countries.

deArantesGomesEller, Rogerio↗
DOE OSTI2020

Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017)

The study assesses the long-term spatial and temporal solar resource variability in America using the 20-year National Renewable Energy Laboratory's (NREL's) National Solar Radiation Database (NSRDB). Specifically, the coefficient of variation (COV) is used to analyze the spatial and temporal (interannual and seasonal) variability. Further, both spatial and temporal long-term variability are analyzed using the Köppen-Geiger climate classification. The temporal variability is found that, on average, the continental United States (CONUS) COV reaches up to 5% for global horizontal irradiance (GHI) and 10% for direct normal irradiance (DNI), and that the NSRDB domain's COV is roughly twice that of CONUS. For the seasonal variability analysis, the winter months are found to exhibit higher COV than the other seasons. In particular, December exhibits the highest variability, reaching on average 30% for DNI and 20% for GHI over various areas. On the other hand, the summer months demonstrate significantly lower variability, reaching only less than 20% for DNI and 10% for GHI, on average. Similarly, the spatial variability is analyzed by comparing each pixel to its neighbors. The long-term spatial variability is found to increase with the number of neighboring pixels being considered, which is equivalent to an increase in distance (within a 100-km x 100-km square grid). As expected, the DNI spatial variability is higher than that of GHI. Moreover, the annual solar irradiance anomalies are found to reach ±25% for both GHI and DNI (and even exceed those value in some instances) during each year of the 20-year period.

14 SOLAR ENERGY↗
DOE OSTI2023

Predicting Postoperative Injury and Military Discharge Status After Knee Surgery in the US Army

Background: Researchers have assessed postoperative injury or disability predictors in the military setting but typically focused on 1 type of surgical procedure at a time, used relatively small sample sizes, or investigated mixed cohorts with civilian populations. Purpose: To identify the relationship between baseline variables and injury incidence or military discharge status in US Army soldiers after knee surgery. Study Design: Case-control study; Level of evidence, 3. Methods: Data were obtained from a repository containing personnel, performance, and medical records for all active-duty US Army soldiers. Multivariate logistic regressions were used to estimate the effects of numerous variables on postoperative injury or on medical discharge. Variable selection and model validation were conducted using the k-fold method. Results: A total of 7567 soldiers underwent knee surgery between 2017 and 2019. Meniscal procedures were the most common type of surgery (39%), and approximately 71% of the cohort had a postoperative injury. Significant predictors for sustaining a postoperative injury included having a previous nonknee injury (odds ratio [OR], 1.5), female sex (OR, 1.3), and Black race (OR, 1.2). Within 4 years after surgery, 17% of soldiers were discharged from the military because of knee-related disability. Significant predictors for discharge from duty included enlisted rank (OR, 2.3), recent fitness test failure (OR, 1.9), number of previous knee surgeries (OR, 1.7), and having a previous nonknee injury (OR, 1.6). Conclusion: After knee surgery, nearly three-fourths of the soldiers in this cohort sustained a postoperative injury and almost one-fifth of soldiers were medically discharged from the military within 4 years. This study identified variables that indicate statistically increased risk for these postoperative outcomes and highlighted potentially modifiable factors.

Orthopedics↗
NASA NTRS1971

A note on independent variables for restricted three-body problems.

In studies of the elliptic restricted three-body problem, the true anomaly of the motion of the primaries is often used as the independent variable. The equations of motion then show invariancy in form from the circular case. It is of interest whether other independent variables exist, such that the invariant form of the equations is maintained. It is found that true anomaly is the only such variable.

Heppenheimer, T. A.↗
DOE OSTI2023

The Peculiar Variable X-Ray Spectrum of the Active Galactic Nucleus PKS 2005–489

PKS 2005-489 is a well-known, bright southern BL Lac object that has been detected up to TeV energies. In a low-flux state it exhibits the expected multiwavelength double-peaked spectrum in the radio- γ -ray band. The high-flux state shows extreme flux variations in the X-ray band with a hardening as well as a peculiar curved feature in the spectrum. Thus far, PKS 2005-489 is the only source to exhibit such a feature. To study the X-ray variability further, we obtained the first hard X-ray spectrum of the source with the Nuclear Spectroscopic Telescope Array. We compare quasi-simultaneous radio, optical, UV, soft and hard X-ray, and γ -ray data of PKS 2005-489 to archival data in order to study its broadband behavior. We find a very consistent quiet state in the spectral energy distribution, with little variation in spectral shape or flux between the 2012 and 2020 data. A possible explanation for the peculiar X-ray spectrum in the flaring state is an additional component in the jet, possibly accelerated via magnetic reconnection, that is not co-spatial to the low-flux state emission region.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

OpenCRUMS USA: An Open Machine Learning Framework for Characterizing Variability in Aerosol Reanalysis Data

Advances in artificial intelligence (AI) have called for exploring how these techniques can be used for exploring patterns in large climate datasets. To that regard, the U.S. Department of Energy AI for Earth System Predictability (AI4ESP) supported a pilot initiative called the Open Classification of Regimes in the Southeast USA (OpenCRUMS USA) project to explore how AI can be used to characterize modes of spatial variability in large climate datasets. For this study, we focus on comparing two methods for characterizing the modes of spatial variability of surface aerosol concentration over the Houston region: empirical orthogonal functions (EOFs) and layerwise relevance propagation (LRP) applied to a convolutional neural network (CNN) classifier. We show that EOF analysis typically attributes spatial variability modes that span all of southeast Texas, prohibiting the attribution of spatial variability to localized regions. However, using LRP on the CNN classifier resolves the explanatory parameters at a finer spatial resolution than EOFs. This allows for the attribution of the spatial variability of surface aerosols to local regions of organic carbon which was not possible using EOFs. In addition, the LRP analysis also suggests that synoptic-scale transport of dust is most prevalent during anticyclonic and pretrough synoptic conditions as categorized by self-organizing maps.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

UNM High Energy Physics: Task B

The High Altitude Water Cherenkov (HAWC) observatory provides a unique window into the "TeV" gamma-ray universe. HAWC provides an important 1 TeV to 1000 TeV high-mass window for indirect searches for dark matter (DM) annihilation or decay. Additional fundamental physics studies benefit from HAWC's sensitivity to variable and/or transient sources. Such transient sources include: gamma ray bursts (GRBs), temporal studies of super massive black holes (specifically Markarian (Mrk) 421 and 501), and most recently searches for gamma-ray counterparts to PeV neutrino sources (observed by IceCube) and gravitational wave sources (observed by LIGO/Virgo). The UNM (Task B) research program includes leadership of the HAWC detector and of HAWC science. During this final reporting period the UNM group includes Professor John Matthews and graduate student Zhixiang Ren with the research goal focused on the development of analysis tools to enhance the sensitivity of HAWC to <1 TeV, gamma rays. This program began in 2016 with an initial focus to work to resolve known problems with the low energy analysis bins in the HAWC data. In 2017 this evolved to the development of new analysis tools to optimize current HAWC <1 TeV sensitivity as well as how HAWC (or a future HAWC-like detector) could be modified to be significantly more sensitive to <1 TeV gamma rays. In these studies Ren lead the development and evaluation of new HAWC <1 TeV analysis tools and Matthews lead the study of possible experimental modifications to HAWC to improve the <1 TeV sensitivity.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS2009

Variability of Marine Aerosol Fine-Mode Fraction and Estimates of Anthropogenic Aerosol Component Over Cloud-Free Oceans from the Moderate Resolution Imaging Spectroradiometer (MODIS)

In this study, we examine seasonal and geographical variability of marine aerosol fine-mode fraction (f(sub m)) and its impacts on deriving the anthropogenic component of aerosol optical depth (tau(sub a)) and direct radiative forcing from multispectral satellite measurements. A proxy of f(sub m), empirically derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 5 data, shows large seasonal and geographical variations that are consistent with the Goddard Chemistry Aerosol Radiation Transport (GOCART) and Global Modeling Initiative (GMI) model simulations. The so-derived seasonally and spatially varying f(sub m) is then implemented into a method of estimating tau(sub a) and direct radiative forcing from the MODIS measurements. It is found that the use of a constant value for fm as in previous studies would have overestimated Ta by about 20% over global ocean, with the overestimation up to 45% in some regions and seasons. The 7-year (2001-2007) global ocean average tau(sub a) is 0.035, with yearly average ranging from 0.031 to 0.039. Future improvement in measurements is needed to better separate anthropogenic aerosol from natural ones and to narrow down the wide range of aerosol direct radiative forcing.

Yu, Hongbin↗