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NASA NTRSDate not supplied

Exploring Radiation, Water and Energy Closure in Key Deep Tropical Regions Over the Last 3 Decades

The closure of global energy and water budgets at various time scales depends heavily upon the on the processes of water and energy exchange in the deep tropics. A key process observed is the change in convection regimes from disorganized convection to organized deep convection. During these periods an imbalance in key tropical regions is observed of water budget terms between observations and atmospheric reanalysis systems, implying that processes are not simulated well. For instance, periods of imbalance in the water budget may be at least partly attributed to precipitation estimates due to the transition between cloud and precipitation systems in which ice cloud affects microwave radar estimates (Lietmann-Niimi et al. 2024, submitted). A recent paper by Hsaio et al (2023, pre-published version) found that the transition process is characterized by wind shear and longwave cloud radiative feedback signatures as well. This is just one example how these processes also affect the radiative fluxes and thus the energy budget at the TOA. In this presentation, we assess the net radiative budget and components in the deep tropics by comparing multiple data products (GEWEX SRB Rel4IP, CERES EBAF/SYN1deg Ed4.2, ISSCP FH, etc.) and describing the temporal variability of energy balance terms across the deep tropics for the period from 1988 to near present in six key oceanic regions. These six key tropical oceanic regions are West Indian Ocean, the south Indian Ocean east, tropical western, central and eastern Pacific Ocean, and Tropical Atlantic Ocean. In these regions we assess the sensitivity of the fluxes as a function of fluctuations in cloud types in response to various larger scale atmospheric processes (e.g. El Niño, Indian Ocean Dipole). We assess this variability of the radiative flux anomalies (including the net TOA, surface and the atmospheric divergence fluxes) against water vapor divergence, cloud properties (include ISCCP “Weather States, Tselioudis et al. 2021) and larger scale wind shear to assess the uncertainties of energy closure in these regions. These are contrasted against the modeled radiative fluxes from ERA-5 and MERRA-2. Ocean buoy measurements of radiative fluxes are utilized to help assess data radiative flux uncertainty over the nearly 40 year period. In general, the variability for overlap periods of these various data products agrees well, but there are significant differences in the net fluxes that vary according to the rendering of surface, atmospheric, aerosol and cloud properties. These differences also affect closure terms and inferences on systematic uncertainties within the observational estimates. We conclude with recommendations for continuing work

Radiation budget↗

NASA NTRS1993

Non-invasive optical detection of glucose in cell culture nutrient medium

The objective of the proposed research was to begin the development of a non-invasive optical sensor for measuring glucose concentration in the output medium of cell cultures grown in a unique NASA bioreactor referred to as an integrated rotating-wall vessel (IRWV). The input, a bovine serum based nutrient media, has a known glucose concentration. The cells within the bioreactor digest a portion of the glucose. Thus, the non-invasive optical sensor is needed to monitor the decrease in glucose due to cellular consumption since the critical parameters for sustained cellular productivity are glucose and pH. Previous glucose sensing techniques have used chemical reactions to quantify the glucose concentration. Chemical reactions, however, cannot provide for continuous, real time, non-invasive measurement as is required in this application. Our effort while in the fellowship program was focused on the design, optical setup, and testing of one bench top prototype non-invasive optical sensor using a mid-infrared absorption spectroscopy technique. Glucose has a fundamental vibrational absorption peak in the mid-infrared wavelength range at 9.6 micron. Preliminary absorption data using a CO2 laser were collected at this wavelength for water based glucose solutions at different concentrations and one bovine serum based nutrient medium (GTSF) with added glucose. The results showed near linear absorption responses for the glucose-in-water data with resolutions as high at 108 mg/dl and as low as 10 mg/dl. The nutrient medium had a resolution of 291 mg/dl. The variability of the results was due mainly to thermal and polarization drifts of the laser while the decrease in sensitivity to glucose in the nutrient medium was expected due to the increase in the number of confounders present in the nutrient medium. A multispectral approach needs to be used to compensate for these confounders. The CO2 laser used for these studies was wavelength tunable (9.2 to 10.8 micrometers), however, it was to unstable across wavelengths to test the multispectral approach. From this research, further NASA support was obtained to continue the work throughout the year in which a more stable light source will be used at smaller, near-infrared, wavelengths. It is anticipated that a more compact, non-invasive, optical glucose sensor will be realized which can be used with a bioreactor on future space shuttle missions. It is also anticipated that a multispectral optical sensor may be used to determine the concentration of other molecules needed within the NASA bioreactor, such as fructose and galactose.

Cote, Gerald L.↗

NASA NTRS1993

Variability of prediction of maximal oxygen concumption on the cycle ergometer using standard equations

Several investigations within the Exercise Countermeasures Project at the NASA Johnson Space Center focused on the assessment of maximum oxygen consumption (VO2(sub max)) within the Astronaut Corps pre- and postspace flight. Investigations during the Apollo era suggested that there was a significant decrease in postflight VO2(sub max) when compared to preflight values, and current studies have documented that this trend continues in the Space Shuttle era. It is generally accepted and was confirmed in our laboratory that VO2(sub max) can be predicted from submaximal measures taken during graded exercise tests on the cycle ergometer with respect to populations. However, previous work had not examined the effect of day-to-day variations in the physiologic responses that might alter these predictions for individuals. Stability of individual submaximal data over serial tests is important so that predicted changes in VO2(sub max) are reflective of actual VO2(sub max) changes. Therefore, the purpose of this investigation was to determine which of the accepted equations to predict VO2(sub max) would be less affected by normal daily physiologic changes.

Greenisen, Michael C.↗

NASA NTRS2003

Three-Gorge Reservoir: A 'Controlled Experiment' for Calibration/Validation of Time-Variable Gravity Signals Detected from Space

With the advances of measurements, modern space geodesy has become a new type of remote sensing for the Earth dynamics, especially for mass transports in the geophysical fluids on large spatial scales. A case in point is the space gravity mission GRACE (Gravity Recovery And Climate Experiment) which has been in orbit collecting gravity data since early 2002. The data promise to be able to detect changes of water mass equivalent to sub-cm thickness on spatial scale of several hundred km every month or so. China s Three-Gorge Reservoir has already started the process of water impoundment in phases. By 2009,40 km3 of water will be stored behind one of the world s highest dams and spanning a section of middle Yangtze River about 600 km in length. For the GRACE observations, the Three-Gorge Reservoir would represent a geophysical controlled experiment , one that offers a unique opportunity to do detailed geophysical studies. -- Assuming a complete documentation of the water level and history of the water impoundment process and aided with a continual monitoring of the lithospheric loading response (such as in area gravity and deformation), one has at hand basically a classical forwardinverse modeling problem of surface loading, where the input and certain output are known. The invisible portion of the impounded water, i.e. underground storage, poses either added values as an observable or a complication as an unknown to be modeled. Wang (2000) has studied the possible loading effects on a local scale; we here aim for larger spatial scales upwards from several hundred km, with emphasis on the time-variable gravity signals that can be detected by GRACE and follow-on missions. Results using the Green s function approach on the PREM elastic Earth model indicate the geoid height variations reaching several millimeters on wavelengths of about a thousand kilometers. The corresponding vertical deformations have amplitude of a few centimeters. In terms of long-wavelength spherical harmonics, the induced geoid height variations are very close to the accuracy of GRACE- recoverable gravity field, while the low-degree (2 to 5) harmonics should be detectable. With a large regional time-variable gravity signal, the Three-Gorge experiment can serve as a useful calibration/verification for GRACE (including the elastic loading effects), and future gravity missions (especially for visco-elastic yielding as well as underground water variations).

Chao, Benjamin F.↗

DOE OSTI2022

“Gray Zone” Simulations Using a Three-Dimensional Planetary Boundary Layer Parameterization in the Weather Research and Forecasting Model

Abstract Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Δ x ) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Δ x ≈ 100–1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional mesoscale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. Significance Statement The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface–atmosphere interaction studies.

Juliano, Timothy W.↗

NASA NTRS2013

Gesture-Based Robot Control with Variable Autonomy from the JPL Biosleeve

This paper presents a new gesture-based human interface for natural robot control. Detailed activity of the user's hand and arm is acquired via a novel device, called the BioSleeve, which packages dry-contact surface electromyography (EMG) and an inertial measurement unit (IMU) into a sleeve worn on the forearm. The BioSleeve's accompanying algorithms can reliably decode as many as sixteen discrete hand gestures and estimate the continuous orientation of the forearm. These gestures and positions are mapped to robot commands that, to varying degrees, integrate with the robot's perception of its environment and its ability to complete tasks autonomously. This flexible approach enables, for example, supervisory point-to-goal commands, virtual joystick for guarded teleoperation, and high degree of freedom mimicked manipulation, all from a single device. The BioSleeve is meant for portable field use; unlike other gesture recognition systems, use of the BioSleeve for robot control is invariant to lighting conditions, occlusions, and the human-robot spatial relationship and does not encumber the user's hands. The BioSleeve control approach has been implemented on three robot types, and we present proof-of-principle demonstrations with mobile ground robots, manipulation robots, and prosthetic hands.

BioSleeve↗

DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗

NASA NTRS1989

Aeroassist flight experiment aerodynamics and aerothermodynamics

The problem is to determine the transitional flow aerodynamics and aerothermodynamics, including the base flow characteristics, of the Aeroassist Flight Experiment (AFE). The justification for the computational fluid dynamic (CFD) Application stems from MSFC's system integration responsibility for the AFE. To insure that the AFE objectives are met, MSFC must understand the limitations and uncertainties of the design data. Perhaps the only method capable of handling the complex physics of the rarefied high energy AFE trajectory is Bird's Direct Simulation Monte Carlo (DSMC) technique. The 3-D code used in this analysis is applicable only to the AFE geometry. It uses the Variable Hard Sphere (VHS) collision model and five specie chemistry model available from Langley Research Center. The code is benchmarked against the AFE flight data and used as an Aeroassisted Space Transfer Vehicle (ASTV) design tool. The code is being used to understand the AFE flow field and verify or modify existing design data. Continued application to lower altitudes is testing the capability of the Numerical Aerodynamic Simulation Facility (NASF) to handle 3-D DSMC and its practicality as an ASTV/AFE design tool.

Brewer, Edwin B.↗

NASA NTRS1979

Maximum Likelihood Factor Analysis of the Effects of Chronic Centrifugation on the Structural Development of the Musculoskeletal System of the Rat

At the age of 30 days female Sprague-Dawley rats were placed on a 3.66 m radius centrifuge and subsequently exposed almost continuously for 810 days to either 2.76 or 4.15 G. An age-matched control group of rats was raised near the centrifuge facility at earth gravity. Three further control groups of rats were obtained from the animal colony and sacrificed at the age of 34, 72 and 102 days. A total of 16 variables were simultaneously factor analyzed by maximum-likelihood extraction routine and the factor loadings presented after-rotation to simple structure by a varimax rotation routine. The variables include the G-load, age, body mass, femoral length and cross-sectional area, inner and outer radii, density and strength at the mid-length of the femur, dry weight of gluteus medius, semimenbranosus and triceps surae muscles. Factor analyses on A) all controls, B) all controls and the 2.76 G group, and C) all controls and centrifuged animals, produced highly similar loading structures of three common factors which accounted for 74%, 68% and 68%. respectively, of the total variance. The 3 factors were interpreted as: 1. An age and size factor which stimulates the growth in length and diameter and increases the density and strength of the femur. This factor is positively correlated with G-load but is also active in the control animals living at earth gravity. 2. A growth inhibition factor which acts on body size, femoral length and on both the outer and inner radius at mid-length of the femur. This factor is intensified by centrifugation.

Amtmann, E.↗

NASA NTRS2004

Integrated Risk Management Within NASA Programs/Projects

As NASA Project Risk Management activities continue to evolve, the need to successfully integrate risk management processes across the life cycle, between functional disciplines, stakeholders, various management policies, and within cost, schedule and performance requirements/constraints become more evident and important. Today's programs and projects are complex undertakings that include a myriad of processes, tools, techniques, management arrangements and other variables all of which must function together in order to achieve mission success. The perception and impact of risk may vary significantly among stakeholders and may influence decisions that may have unintended consequences on the project during a future phase of the life cycle. In these cases, risks may be unintentionally and/or arbitrarily transferred to others without the benefit of a comprehensive systemic risk assessment. Integrating risk across people, processes, and project requirements/constraints serves to enhance decisions, strengthen communication pathways, and reinforce the ability of the project team to identify and manage risks across the broad spectrum of project management responsibilities. The ability to identify risks in all areas of project management increases the likelihood a project will identify significant issues before they become problems and allows projects to make effective and efficient use of shrinking resources. By getting a total team integrated risk effort, applying a disciplined and rigorous process, along with understanding project requirements/constraints provides the opportunity for more effective risk management. Applying an integrated approach to risk management makes it possible to do a better job at balancing safety, cost, schedule, operational performance and other elements of risk. This paper will examine how people, processes, and project requirements/constraints can be integrated across the project lifecycle for better risk management and ultimately improve the chances for mission success.

Connley, Warren↗

NASA NTRS2017

Progress Report on the Airborne Composition Standard Variable Name and Time Series Working Groups of the 2017 ESDSWG

The role of NASA's Earth Science Data Systems Working Groups (ESDSWG) is to make recommendations relevant to NASA's Earth science data systems from users' experiences and community insight. Each group works independently, focusing on a unique topic. Progress of two of the 2017 Working Groups will be presented. In a single airborne field campaign, there can be several different instruments and techniques that measure the same parameter on one or more aircraft platforms. Many of these same parameters are measured during different airborne campaigns using similar or different instruments and techniques. The Airborne Composition Standard Variable Name Working Group is working to create a list of variable standard names that can be used across all airborne field campaigns in order to assist in the transition to the ICARTT Version 2.0 file format. The overall goal is to enhance the usability of ICARTT files and the search ability of airborne field campaign data. The Time Series Working Group (TSWG) is a continuation of the 2015 and 2016 Time Series Working Groups. In 2015, we started TSWG with the intention of exploring the new OGC (Open Geospatial Consortium) WaterML 2 standards as a means for encoding point-based time series data from NASA satellites. In this working group, we realized that WaterML 2 might not be the best solution for this type of data, for a number of reasons. Our discussion with experts from other agencies, who have worked on similar issues, identified several challenges that we would need to address. As a result, we made the recommendation to study the new TimeseriesML 1.0 standard of OGC as a potential NASA time series standard. The 2016 TSWG examined closely the TimeseriesML 1.0 and, in coordination with the OGC TimeseriesML Standards Working Group, identified certain gaps in TimeseriesML 1.0 that would need to be addressed for the standard to be applicable to NASA time series data. An engineering report was drafted based on the OGC Engineering Report template, describing recommended changes to TimeseriesML 1.0, in the form of use cases. In 2017, we are conducting interoperability experiments to implement the use cases and demonstrate the feasibility and suitability of these modifications for NASA and related user communities. The results will be incorporated into the existing draft engineering report.

OGC (Open Geospatial Consortium) WaterML 2 standar↗

NASA NTRS2020

Watershed Modeling with Remotely Sensed Big Data: MODIS Leaf Area Index Improves Hydrology and Water Quality Predictions

Traditional watershed modeling often overlooks the role of vegetation dynamics. There is also little quantitative evidence to suggest that increased physical realism of vegetation dynamics in process-based models improves hydrology and water quality predictions simultaneously. In this study, we applied a modified Soil and Water Assessment Tool (SWAT) to quantify the extent of improvements that the assimilation of remotely sensed Leaf Area Index (LAI) would convey to streamflow, soil moisture, and nitrate load simulations across a 16,860 km2 agricultural watershedin the midwestern United States. We modified the SWAT source code to automatically override the model’s built-in semiempirical LAI with spatially distributed and temporally continuous estimates from Moderate Resolution Imaging Spectroradiometer (MODIS). Compared to a “basic” traditional model with limited spatial information, our LAI assimilation model (i) significantly improved daily streamflow simulations during medium-to-low flow conditions, (ii) provided realistic spatial distributions of growing season soil moisture, and (iii) substantially reproduced the long-term observed variability of daily nitrate loads. Further analysis revealed that the overestimation or underestimation of LAI imparted a proportional cascading effect on how the model partitions hydrologic fluxes and nutrient pools. As such, assimilation of MODIS LAI data corrected the model’sLAI overestimation tendency, which led to a proportionally increased rootzone soil moisture and decreased plant nitrogen uptake. With these new findings, our study fills the existing knowledge gap regarding vegetation dynamics in watershed modeling and confirms that assimilation of MODIS LAI data in watershed models can effectively improve both hydrology and water quality predictions.

Adnan Rajib↗

NASA NTRS2020

Intensive disc-reverberation mapping of Fairall 9: first year of Swift and LCO monitoring

We present results of time-series analysis of the first year of the Fairall 9 intensive disc-reverberation campaign. We used Swift and the Las Cumbres Observatory global telescope network to continuously monitor Fairall 9 from X-rays to near-infrared at a daily to subdaily cadence. The cross-correlation function between bands provides evidence for a lag spectrum consistent with the τ ∝ λ^(4/3) scaling expected for an optically thick, geometrically thin blackbody accretion disc. Decomposing the flux into constant and variable components, the variable component’s spectral energy distribution is slightly steeper than the standard accretion disc prediction. We find evidence at the Balmer edge in both the lag and flux spectra for an additional bound-free continuum contribution that may arise from reprocessing in the broad-line region. The inferred driving light curve suggests two distinct components, a rapidly variable (<4 d) component arising from X-ray reprocessing, and a more slowly varying (>100 d) component with an opposite lag to the reverberation signal.

J V Hernández Santisteban↗

NASA NTRSDate not supplied

Simulating Micrometeoroid Bombardment on Mercury: Identifying New Space Weathering Features in the Laboratory

Airless bodies are continually exposed to the harsh interplanetary space environment, causing the alteration of their surfaces through a process known as space weathering (SW). SW is dominated by solar wind irradiation and micrometeoroid bombardment, which together alter the spectral, microstructural, and chemical characteristics of grains on the surface of airless bodies. The effects of SW are variable and depend on the heliocentric distance and the initial composition of the planetary surface, among other factors. On the Moon and S- type asteroids, it is well established that SW darkens and reddens the surface of these bodies and attenuates absorption bands across the Vis-NIR wavelengths. These spectral effects are largely the result of the production of metallic Fe nanoparticles (npFe). However, Mercury’s SW environment is unique compared to the Moon and S-type asteroids. Mercury is a geochemical endmember, with a surface composition low in Fe (<2 wt.%) and enriched in volatile components, including regions of the surface hypothesized to contain up to 4 wt.% graphite in the low reflectance material (LRM). In fact, the presence of graphite on Mercury has been hypothesized to act as a reducing agent for silicates during SW to produce Si-bearing Fe-rich metal. In addition, its location in the solar system exposes Mercury to an extreme SW environment, with the surface of the planet experiencing an intense solar wind flux and higher flux and velocity of micrometeoroid impactors compared to the Moon and S-type asteroids. The effects that such harsh SW has on materials with the unique compositional characteristics of Mercury are not well constrained. To better understand SW at Mercury, we must investigate these processes in the laboratory. Here, we present the results of our analyses of the spectral, microstructural and chemical characteristics of Mercury analog samples irradiated by pulsed laser to simulate the short duration, high temperature events associated with micrometeoroid impacts.

Nick Bott↗

NASA NTRS1938

Correction of Profile-Drag Results from Variable-Density Tunnel and the Effect on the Choice of Wing-Section Thickness

Profile-drag coefficients published from tests in the N.A.C.A. variable-density tunnel (Technical Reports Nos. 460, 537, 586, and 610, references 1 to 4) have tended to appear high as compared with results from the N.A.C.A. full-scale tunnel (Technical Report No. 530, reference 5) and from foreign sources (references 6 to 8). Such discrepancies were considered in Technical Report No. 586, and corrections for turbulence and tip effects were derived that tended to reduce the profile-drag coefficients, particularly for the thicker airfoils. The corrected profile-drag coefficients, designated by the lower-case symbol cdo as contrasted with the older CDO, have been employed in the airfoil reports published since Technical Report No. 460, but even these corrected results continued to appear high, particularly for the thicker sections. The important practical result is that a smaller increase of drag with airfoil thickness is indicated, which may be of primary importance to the airplane designer in choosing the optimum airfoil sections for actual wings. Further investigations of this subject were, of course, undertaken, one of the most important being an investigation of three symmetrical sections N.A.C A. 0009, 0012, and 0018 under conditions of low turbulence in the full-scale tunnel. Preliminary results from this investigation also indicate a smaller increase in drag with airfoil thickness than the results from the variable-density tunnel. Furthermore, comparative tests made in the two tunnels by applying strings to the surface of the N.A.C.A. 0012 airfoil to move the transition point to a predetermined position indicated that the effective reynolds Number concept would account approximately for the drag as affected by the position of transition from laminar to turbulent flow in the boundary layer.

Jacobs, Eastman N.↗

NASA NTRS1986

Application of solar max ACRIM data to analyze solar-driven climatic variability on Earth

Langley, in the late nineteenth century, attempted to measure solar irradiance over an extended period of time in order to detect changes. The problem with this and other early attempts was that ground based measurements are not sufficiently accurate to measure solar irradiance fluctuations, which are on the order of 0.1%. It was not until the Active Cavity Radiometer Irradiance Monitor (ACRIM) experiment on the NASA Solar Maximum Mission (SMM) was launched in 1980 that continuous data with precision 0.1% was available.

Hoffert, M. I.↗

DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (3 rd Annual Report)

Nuclear power plants collect and store large volumes of heterogeneous data from various components and systems. With recent advances in machine learning (ML) techniques, these data can be leveraged to develop diagnostic and short-term forecasting models to better predict future equipment condition. Maintenance operations can then be planned in advance whenever degraded performance is predicted, thus resulting in fewer unplanned outages and the optimization of maintenance activities. This enables lower maintenance costs and improves the overall economics of nuclear power. This report primarily focuses on developing a short-term forecasting process that leverages a feature selection process to distill large volumes of heterogeneous data and predict specific equipment parameters. A variety of feature selection methods, including Shapley Additive Explanations (SHAP) and variance inflation factor (VIF), were used to select the optimal features as inputs for three ML methods: long short-term memory (LSTM) networks, support vector regression (SVR), and random forest (RF). Each combination of model and input features was used to predict a pump bearing temperature both 1 and 24 hours in advance, based on actual plant system data. The optimal inputs for the LSTM and SVR were selected using the SHAP values, while the optimal input for the RF consisted solely of the response variable itself. Each model produced similar 1-hour-ahead predictions, with root mean square errors (RMSEs) of roughly 0.006. For the 24-hour-ahead predictions, differences could be seen between LSTM, SVR, and RF, as reflected by model performances of 0.036 ± 0.014, 0.0026 ± 0, and 0.063 ± 0.004 RMSE, respectively. As big data and continuous online monitoring become more widely available, the proposed feature selection process can be used for many applications beyond the prediction of process parameters within nuclear infrastructure. This report summarizes the Fiscal Year 2021 research progress encompassing the (1) data cleaning and feature selection necessary for ML applications; (2) development of short-term forecasting models to predict future plant process parameters for both single and multiple time steps ahead; and (3) validation of the feature selection methods and short-term forecasting models given new data from different systems.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗