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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 325 records · Page 18
All sourcesNASA NTRSDOE OSTI
NASA NTRS1995

Impact of the ongoing Amazonian deforestation on local precipitation: A GCM simulation study

Numerical simulation experiments were conducted to delineate the influence of in situ deforestation data on episodic rainfall by comparing two ensembles of five 5-day integrations performed with a recent version of the Goddard Laboratory for Atmospheres General Circulation Model (GCM) that has a simple biosphere model (SiB). The first set, called control cases, used the standard SiB vegetation cover (comprising 12 biomes) and assumed a fully forested Amazonia, while the second set, called deforestation cases, distinguished the partially deforested regions of Amazonia as savanna. Except for this difference, all other initial and prescribed boundary conditions were kept identical in both sets of integrations. The differential analyses of these five cases show the following local effects of deforestation. (1) A discernible decrease in evapotranspiration of about 0.80 mm/d (roughly 18%) that is quite robust in the averages for 1-, 2-, and 5-day forecasts. (2) A decrease in precipitation of about 1.18 mm/d (roughly 8%) that begins to emerge even in 1-2 day averages and exhibits complex evolution that extends downstream with the winds. (3) A significant decrease in the surface drag force (as a consequence of reduced surface roughness of deforested regions) that, in turn, affects the dynamical structure of moisture convergence and circulation. The surface winds increase significantly during the first day, and thereafter the increase is well maintained even in the 2- and 5-day averages.

Walker, G. K.↗
NASA NTRS2009

Water Vapor Measurements by Howard University Raman Lidar during the WAVES 2006 Campaign

Retrieval of water vapor mixing ratio using the Howard University Raman Lidar is presented with emphasis on three aspects: i) performance of the lidar against collocated radiosondes and Raman lidar, ii) investigation of the atmospheric state variables when poor agreement between lidar and radiosondes values occurred and iii) a comparison with satellite-based measurements. The measurements were acquired during the Water Vapor Validation Experiment Sondes/Satellites 2006 field campaign. Ensemble averaging of water vapor mixing ratio data from ten night-time comparisons with Vaisala RS92 radiosondes shows on average an agreement within 10 % up to approx. 8 km. A similar analysis of lidar-to-lidar data of over 700 profiles revealed an agreement to within 20 % over the first 7 km (10 % below 4 km). A grid analysis, defined in the temperature - relative humidity space, was developed to characterize the lidar - radiosonde agreement and quantitatively localizes regions of strong and weak correlations as a function of altitude, temperature or relative humidity. Three main regions of weak correlation emerge: i) regions of low relative humidity and low temperature, ii) moderate relative humidity at low temperatures and iii) low relative humidity at moderate temperatures. Comparison of Atmospheric InfraRed Sounder and Tropospheric Emission Sounder satellites retrievals of moisture with that of Howard University Raman Lidar showed a general agreement in the trend but the formers miss a lot of the details in atmospheric structure due to their low resolution. A relative difference of about 20 % is usually found between lidar and satellites measurements.

Adam, M.↗
NASA NTRS2011

Effect of Cross-Correlation on Geomagnetic Forecast Accuracies

Surface geomagnetic observation can determine up to degree L = 14 time-varying spherical harmonic coefficients of the poloidal magnetic field. Assimilation of these coefficients to numerical dynamo simulation could help us understand better the dynamical processes in the Earth's outer core, and to provide more accurate forecast of geomagnetic secular variations (SV). In our previous assimilation studies, only the poloidal magnetic field in the core is corrected by the observations in the analysis. Unobservable core state variables (the toroidal magnetic field and the core velocity field) are corrected via the dynamical equations of the geodynamo. Our assimilation experiments show that the assimilated core state converges near the CMB, implying that the dynamo state is strongly constrained by surface geomagnetic observations, and is pulled closer to the truth by the data. We are now carrying out an ensemble of assimilation runs with 1000 years of geomagnetic and archeo/paleo magnetic record. In these runs the cross correlation between the toroidal and the poloidal magnetic fields is incorporated into the analysis. This correlation is derived from the physical boundary conditions of the toroidal field at the core-mantle boundary (CMB). The assimilation results are then compared with those of the ensemble runs without the cross-correlation, aiming at understanding two fundamental issues: the effect of the crosscorrelation on (1) the convergence of the core state, and (2) the SV prediction accuracies. The constrained dynamo solutions will provide valuable insights on interpreting the observed SV, e.g. the near-equator magnetic flux patches, the core-mantle interactions, and possibly other geodynamic observables.

Kuang, Weijia↗
NASA NTRS2012

A Reconciled Estimate of Ice-Sheet Mass Balance

We combined an ensemble of satellite altimetry, interferometry, and gravimetry data sets using common geographical regions, time intervals, and models of surface mass balance and glacial isostatic adjustment to estimate the mass balance of Earth's polar ice sheets. We find that there is good agreement between different satellite methods-especially in Greenland and West Antarctica-and that combining satellite data sets leads to greater certainty. Between 1992 and 2011, the ice sheets of Greenland, East Antarctica, West Antarctica, and the Antarctic Peninsula changed in mass by -142 plus or minus 49, +14 plus or minus 43, -65 plus or minus 26, and -20 plus or minus 14 gigatonnes year(sup −1), respectively. Since 1992, the polar ice sheets have contributed, on average, 0.59 plus or minus 0.20 millimeter year(sup −1) to the rate of global sea-level rise.

mass balance↗
NASA NTRS2017

Aerosol EnKF at GMAO

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASAs Global Modeling and Assimilation Office(GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to an ensemble square root filter(EnSRF; Whitaker and Hamill (2002)) type of scheme.We present a summary of our preliminary results of the assimilation of column integrated aerosol observations (Aerosol Optical Depth; AOD) using an Ensemble Square Root Filters (EnSRF) scheme and the ensemble members produced routinely by the meteorological assimilation.

MERRA-2 EnSRF↗
NASA NTRS2015

Visualization and Quantification of Rotor Tip Vortices in Helicopter Flows

Helicopter aeromechanics encompasses a highly vortical flow field. The vortices generated at each blade tip contain unsteady, complex, three-dimensional structures, which interact with each other, other blades, the fuselage and various components of the helicopter. It is crucial to understand vortex kinematics and their subsequent dynamic evolution. Much research has been devoted to the understanding of helicopter vortex dynamics, including a number of experimental studies.1-6 In May 2010 Particle Image Velocimetry (PIV) measurements of a full-scale UH-60A rotor were acquired in the National Full-Scale Aerodynamics Complex (NFAC) 40- by 80-Foot Wind Tunnel.1 These measurements were taken at a plane just downstream of the advancing blade in the vicinity of the blade tip—the so-called PIV plane. The resulting PIV data were then processed using an ensemble-average approach to create graphical representations of the vortical wake velocity and vorticity fields, which, in turn, have enhanced the understanding of rotorcraft vortical wake flow field physics and have provided a more detailed validation of vortical wake computer simulations.7 A common approach used to analyze flow field features is to compute and plot color contour maps of various scalar quantities such as pressure, velocity magnitude and vorticity magnitude. For example, the color map of the vorticity magnitude is typically used to determine vortical flow structure. With this approach the vortex core may appear larger or smaller, depending on the contour levels that are selected. Thus, the resulting visualization is sensitive to user-specified contour levels. For vortex core radius measurements, it is more accurate to calculate the vortex core radius using the cross-flow velocity profile across the vortex core. The task of extracting the cross-flow velocity profile can be time consuming with existing tools since the user needs to manually select the core center then specify sampling points along the profile axis. The task becomes even more challenging when the associated grid system uses AMR (Adaptive Mesh Refinement) where the profile axis could span multiple grid blocks. There are a number of existing techniques for profiling of vortex core attributes;8-9 however, these techniques are not fully automatic in that the user still needs to select the vortex core center to compute the cross-flow velocity profile. The present study introduces a new color map scheme that is based on the vortex core radius, which is fully automatic and does not require user intervention. Analysis and visualization of blade tip vortices on the PIV plane using the proposed new color map scheme are described in Section II. The new approach is evaluated using two case studies, which are described in Section III. The paper ends with a summary in Section IV.

Quantification↗
NASA NTRS2019

Parameters Inference and Model Reduction for the Single-Particle Model of Li Ion Cells

The Single-Particle Model (SPM) of Li ion cell is a computationally efficient model for simulating Li ion cell for weak to moderate currents. The model depends n a number of parameters describing the geometry and material properties of a cell components. In order to apply the model to simulating a cell, the best-fit parametric values have to be inferred from a constant discharge data. We report our efforts to determine the best-fit set for 18650 LP batteries. We found that rather than being best-fit by a particular point in the parametric space the data is fit equally well by an ensemble of points clustering about an effective multidimensional manifold in the parametric space. This property of the SPM is known to be shared by a multitude of the so-called "sloppy models" of complex systems, characterized by a few stiff directions in the parametric space, in which the predicted behavior varies significantly, and a number of sloppy directions in which the behavior doesn't change appreciably. Only the stiff parameters combinations are identifiable. Geometrical features of the BFM give insights to possible reduction of the SPM to a model having fewer sloppy parameters. We have constructed a hierarchy of such models. The fully reduced model depends on only stiff effective parameters which are identifiable and can be used for characterization of the battery's state of health.

Khasin, Michael↗
NASA NTRSDate not supplied

Comparison of Wind-Tunnel and Flight Unsteady Pressure Environments for the Ares I-X Flight Test Vehicle

Wind-tunnel testing of highly-instrumented rigid models is the current standard for the estimation of unsteady environments acting on a launch vehicle during ascent. However, uncertainties are present in how well these constant condition wind-tunnel data model the unsteady environments for a full-scale launch vehicle during accelerating flight. The Ares I-X wind-tunnel and flight tests provided rich data sets for comparative analysis that have been used to develop answers to the question of tunnel-to-flight validity and uncertainty. In this paper, additional comparisons are presented for the unsteady pressure data acquired during wind-tunnel and flight testing to describe current methods that are used to evaluate preflight predictions. Utilizing these methods to generate ensembles of flight-comparable realizations from wind-tunnel data is shown to yield fluctuating magnitudes and spectra that compare well with flight data for the Ares I-X flight test vehicle throughout the transonic flight regime.

buffet↗
NASA NTRSDate not supplied

Comparison of Wind-Tunnel and Flight Unsteady Pressure Environments for the Ares I-X Flight Test Vehicle

Wind-tunnel testing of highly-instrumented rigid models is the current standard for the estimation of unsteady environments acting on a launch vehicle during ascent. However, uncertainties are present in how well these constant condition wind-tunnel data model the unsteady environments for a full-scale launch vehicle during accelerating flight. The Ares I-X wind-tunnel and flight tests provided rich data sets for comparative analysis that have been used to develop answers to the question of tunnel-to-flight validity and uncertainty. In this paper, additional comparisons are presented for the unsteady pressure data acquired during wind-tunnel and flight testing to describe current methods that are used to evaluate preflight predictions. Utilizing these methods to generate ensembles of flight-comparable realizations from wind-tunnel data is shown to yield fluctuating magnitudes and spectra that compare well with flight data for the Ares I-X flight test vehicle throughout the transonic flight regime.

buffet↗
DOE OSTI2026

CHESS 2025: Waveform LiDAR data from NEON AOP surveys

This dataset provides Level 1 (L1) full-waveform light detection and ranging (LiDAR) data collected for the 2025 Colorado Headwaters Ecological Spectroscopy Study (CHESS). These data were acquired to enable characterization of vegetation structure and other three-dimensional features of the land surface, and to evaluate structural changes that may have occurred between a prior LiDAR acquisition in 2018 and the 2025 overflight. Waveform LiDAR data can provide more detailed information about objects on the ground than discrete point clouds typically do, and they are often used for granular target segmentation and characterization of subcanopy vegetation. The data were acquired over three study domains in the Upper Gunnison river basin: the upper East River watershed (CRBU); Almont Triangle and Taylor Canyon (ALMO); and Upper Taylor River watershed (UPTA) between 2025-06-13 and 2025-07-15. LiDAR data were acquired using the Optech Galaxy Prime Airborne LiDAR Terrain Mapper onboard the National Ecological Observatory Network (NEON) Airborne Observation Platform (AOP). These are the primary waveform LiDAR data delivered by NEON and are provided per flightline in compressed Pulsewaves format, an open-source binary file standard. A Pulsewaves object comprises a two files: a pulse (.pls) file, which stores the geographic origin, outgoing vector, and metadata for every laser pulse emitted by the scanner, and a wave file (.wvs), which stores the sequential amplitude samples of the outgoing pulse and the returning signals. The files are published here in their compressed forms (.plz, .wvz). All waveform data were processed following the theoretical workflow described in the NEON L0-to-L1 Waveform LiDAR Algorithm Theoretical Basis Document (Krause and Goulden 2022a); however, the Pulsewaves output format differs from a legacy format described in that document. Waveform amplitude samples are recorded at 1 nanosecond intervals. All coordinates are provided in meters. Horizontal coordinates are referenced in Universal Transverse Mercator (UTM) zone 13N and the World Geodetic System (WGS) 1984 ensemble datum. Elevations are referenced to Geoid12A. Waveform data for the UPTA survey area were collected without incident and the published records are complete. However, both the ALMO and CRBU collections experienced issues that resulted in incomplete data for those areas. On collection day 2018-06-16 a hardware failure caused the waveform digitizer to lose data from the eastern edge of the ALMO site (Figure 22). The waveform data for flightlines 2–20 could not be extracted from the digitizer, and the data proved unrecoverable. As a result, a portion of the site does not have coverage with waveform data. Although no hardware failure was observed during collection over the CRBU area, final waveform files generated by vendor software contained only ~25% of the expected number of return pulses. After discovery, NEON initiated troubleshooting with the vendor. The root cause of the data ablation had not been identified at the time of publication. Additional data will be published in an update to this package if further recovery proves successful. CHESS Project Description: The Colorado Headwaters Ecological Spectroscopy Study (CHESS) comprised a multi-week airborne remote sensing and field observation campaign in the Upper Gunnison Basin, Colorado, conducted in June and July of 2025. Airborne remote sensing was conducted by the National Ecological Observatory Network Airborne Observation Platform (NEON AOP), concurrent with a field campaign run by the Rocky Mountain Biological Laboratory (RMBL), the Lawrence Berkeley National Laboratory (LBNL) and SLAC National Accelerator Laboratory Watershed Function Science Focus Area (SFA), and NASA-JPL (Jet Propulsion Laboratory) Earth Surface Mineral Dust Source Investigation (EMIT) program. Between June 10 and July 18, 2025, the NEON AOP flight team collected high-resolution aerial imaging spectroscopy and Light Detection and Ranging (LiDAR) data over three domains: the Upper East River (CRBU), Almont Triangle (ALMO), and the Upper Taylor Basin (UPTA). In coordination with the flights, a field campaign acquired ground-truth observations, including observations of vegetation composition, foliar traits, forest demography, and subsurface properties in 18 core sampling areas within the domains. Additional surface water observations were taken at over 380 point locations. All CHESS campaign datasets can be found within the CHESS ESS-DIVE data portal: https://data.ess-dive.lbl.gov/portals/chess. Funding Acknowledgement: Field and remote-sensing data acquisition was performed under a grant from the National Aeronautics and Space Administration (80NSSC24K1005). This work was also supported by the Watershed Function Science Focus Area at Lawrence Berkeley National Laboratory funded by the US Department of Energy, Office of Science, Biological and Environmental Research under Contract No. DE-AC02-05CH11231.

2018 NEON and 2025 CHESS Campaigns↗
NASA NTRS1988

High altitude atmospheric modeling

Five empirical models were compared with 13 data sets, including both atmospheric drag-based data and mass spectrometer data. The most recently published model, MSIS-86, was found to be the best model overall with an accuracy around 15 percent. The excellent overall agreement of the mass spectrometer-based MSIS models with the drag data, including both the older data from orbital decay and the newer accelerometer data, suggests that the absolute calibration of the (ensemble of) mass spectrometers and the assumed drag coefficient in the atomic oxygen regime are consistent to 5 percent. This study illustrates a number of reasons for the current accuracy limit such as calibration accuracy and unmodeled trends. Nevertheless, the largest variations in total density in the thermosphere are accounted for, to a very high degree, by existing models. The greatest potential for improvements is in areas where we still have insufficient data (like the lower thermosphere or exosphere), where there are disagreements in technique (such as the exosphere) which can be resolved, or wherever generally more accurate measurements become available.

Hedin, Alan E.↗
NASA NTRS1979

Principal component analysis of solar flares in the soft X-ray flux

The paper considers principal component analysis of solar flares in the soft X-ray flux, a technique for extracting the salient features from a mass of data. The method applies particularly to the analysis of nonstationary ensembles, and its computations require the evaluation of eigenvalues of matrices. The Eispack matrix eigen system routines were used to analyze full-disk proportional-counter data from the X-ray event analyzer which was part of the Skylab experiment. Empirical orthogonal functions were derived for events in the soft X-ray spectrum between 2.5 and 20 A during different time periods, indicating that about 90% of the cumulative power of each analyzed flare is contained in the largest eigenvector. The first two largest eigenvectors are sufficient for an empirical curve fit through the raw data and a characterization of solar flares in the soft X-ray flux, and power spectra of two largest eigenvectors reveal a reported periodicity of about 5 min.

Teuber, D. L.↗
DOE OSTI2026

Sensitivity Analysis of Drivers Water Shortage in the Los Angeles Region During Drought

The code and detailed step-by-step instructions for generating the model output data, processing results, and analysis and plotting are provided at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. The PyArtes model is a python adaptation of the Artes model. PyArtes uses many of the same input data and optimization model architecture as Artes. Documentation for the PyArtes model is provided in the Supplement to the paper. The primary data product are simulated monthly water shortages for indoor and outdoor demand under a large ensemble of drought scenarios (>13,000). The droughts are hypothetical and are not based on historical time series data of supply sources - though historical data did help inform ranges explored for supply parameters. Demands are informed by recent 2017-2021 water supply data. Demands used for the model can be accessed at https://github.com/IMMM-SFA/Ferencz_et_al_2026_ER_Water. Simulations resolve demand for over 90 water providers in the study region. The results report 36 months of water shortage data for each indoor and outdoor demand node. The study also developed a multilayer perceptron (MLP) neural network trained on a subset of the simulated shortage ensemble to emulate worst annual water shortage for a given set of parameter multipliers -- provided the parameter values fall within the ranges sampled in the ensemble. Emulated water shortages for synthetic ensembles are in the MLP-generated shortages folder. The MLP model was used to generate larger ensembles to support Sobol analysis that would have been extremely computationally expensive to simulate. Datasets provided in this repository*: Simulated shortages. These results are used for the analysis for Figures 5, 8, and 9 in the paper, and also to train the MLP emulator. .zip file containing outputs for the 13,312 scenario ensemble. Separate .csv files for indoor and outdoor shortage for each scenario. Rows = demand ids (~100), Columns = months (36) Units = acre-feet/month of shortage (shortage = monthly demand - supply). 1 acft = 1233.48 m^3 .csv files of aggregated shortages derived from the 13,312 ensemble Rows = scenarios (13,312), Columns = demand ids (~100) Units = acre-feet/year (either worst annual shortage or total shortage over the 3-year drought) .csv file of the parameter multipliers scenarios for the ensemble .csv file of the parameter ranges and baseline values the multipliers were applied to MLP-generated shortages. These results are used for Figures 4, 6, and 7 in the paper. mwd higher folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results Emulated shortages. Rows = scenarios, columns = demand ids, units acft Sobol results. Rows = demand ids, columns Sobol (S1, ST, or 95% confidence interval) value for each parameter mwd lower folder: scenario ensembles, emulated worst year total shortages (acft), and Sobol results same organization as mwd higher MLP performance: performance metrics (R^2, RMSE, BIAS, MAPE) for the testing subset (20% or 2,662 scenarios) and simulated vs emulated worst year shortage (acre-feet/year) for every demand node, MWD wholesale regions, and the entire study region (LAC). Supporting data for figures. Figure plotting scripts in the associated GitHub repo. These files support analysis and visualization. Geospatial Data used for plotting simulated water shortages and Sobol results. Dictionary of full names for demand nodes in the model and estimates of water supply by source type informed by Artes input files and California Urban Water Management Planning data: https://water.ca.gov/Programs/Water-Use-And-Efficiency/Urban-Water-Use-Efficiency/Urban-Water-Management-Plans *Readme files provided for each folder.

drought↗
DOE OSTI2025

Quantifying Groundwater Response and Uncertainty in Beaver‐Influenced Mountainous Floodplains Using Machine Learning‐Based Model Calibration

Abstract Beavers ( Castor canadensis ) alter river corridor hydrology by creating ponds and inundating floodplains, and thereby improving surface water storage. However, the impact of inundation on groundwater, particularly in mountainous alluvial floodplains with permeable gravel/cobble layers overlain by a soil layer, remains uncertain. Numerical modeling across various floodplain structures considers topographic and sediment complexity and multidirectional flow, linking inundation to groundwater response. This study develops a model‐data integration workflow to address uncertainty in groundwater response to beaver‐induced inundations in a mountainous alluvial floodplain in the Upper Colorado River Basin. Uncertain factors include seasonal hydrologic dynamics, hydraulic conductivities, floodplain structures, and meteorological forcings. We employed an ensemble of groundwater models, based on geophysical and hydrologic data, with machine learning‐based calibration using a neural density estimator. This allowed us to quantify the vertical flux from the soil layer to the permeable gravel bed, the down‐valley underflow within the gravel bed, and their ratios. Results show a significant increase in the vertical flux relative to down‐valley underflow, from 2 during dry pond periods to 20 during wet periods, serving as an analogy for conditions without and with beaver ponds. The study highlights the influence of floodplain structure on groundwater storage, water balance, and water quality impacted by beaver ponds. A thick gravel bed layer, with a large down‐valley underflow, minimizes the effect of beaver‐induced inundation on water quality. We emphasize the need for field‐scale measurements of floodplain structure and improved characterization of evapotranspiration changes to reduce uncertainty in groundwater response. Plain Language Summary Beavers change the flow of water in river corridors by creating ponds, expanding wetlands, and flooding floodplains. This increases surface water area, promotes plant growth, and enhances biodiversity. However, the impact of this flooding on groundwater flow is not well understood, especially in mountainous areas with gravel layers where water moves easily beneath soil. In this study, we used numerical modeling to investigate how beaver ponds influence groundwater in a mountainous floodplain of the Upper Colorado River Basin. We adapted a machine learning method to validate our numerical models using multiple field data sets. Our findings show that beaver ponds significantly increase vertical water flow from the soil to the gravel during wet periods, compared to when the ponds are fully drained. The study also highlights the importance of floodplain structure in controlling both water flow in gravel layers along the river direction and vertical flow from the soil to the gravel with the presence of beavers. To reduce uncertainty in groundwater response, we emphasize the need for more field‐scale measurements of floodplain structure, hydraulic properties, and evapotranspiration changes. Key Points Floodplain structures and hydraulic conductivities are important for groundwater response with beaver ponds in mountainous floodplains Large down‐valley underflow in permeability‐stratified floodplains reduces beaver‐induced impacts on groundwater storage and water quality Machine learning‐based model calibration methods are effective for estimating posterior distributions of groundwater model parameters

Wang, Lijing↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2001

Input Decimated Ensembles

Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers' performance levels high is an important area of research. In this article, we explore input decimation (ID), a method which selects feature subsets for their ability to discriminate among the classes and uses them to decouple the base classifiers. We provide a summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated ensembles (IDEs) outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and features created using principal components analysis, on a wide range of domains.

Tumer, Kagan↗
DOE OSTI2025

Development of Machine Learning Algorithm for Pebble Bed Modular Reactor Misuse Detection

The objective of this work was to develop a machine learning ensemble that could assist pebble bed reactor verification by evaluating whether a given pebble circulating through a PBR was normal or anomalous using gamma spectroscopy measurements from a notional PBR burnup measurement system. Using a PBR reference design, data sets of synthetic gamma spectra representative of BUMS measurements of normal and anomalous pebbles that may be used to produce special fissile material were generated to train and test an ML anomaly detection ensemble on two reference scenarios – substitution of normal pebbles with target pebbles for production of Pu or 233 U. The ML ensemble correctly identified all anomalous pebbles in the testing data set, and while perfect ensemble performance is normally indicative of overfitting, it was concluded that significantly lower photon intensity of target pebbles produced distinctly less intense photon spectra to where perfect ensemble performance was expected.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2012

Impact of Satellite Atmospheric Motion Vectors In the GMAO GEOS-5 Global Data Assimilation System

The WMO and THORPEX co-sponsored fifth Workshop on the Impact of Various Observing Systems on Numerical Weather Prediction will be organized by the Expert Team on the Evolution of the Global Observing System in Sedona, Arizona, United States, from 22 to 25 May 2012. Participants are expected to come from all the major NWP centres which are active in the area of impact studies. The workshop will be conducted in English. As for the first four workshops it is planned to produce a workshop report to be published as a WMO Technical Report that will include the papers submitted by the participants. The previous four workshops in this series took place in Geneva {April 1997), Toulouse (March 2000), Alpbach (March 2004) and Geneva (May 2008). Results from Observing System Experiments (OSEs), both with global and regional aspects were presented and conclusions were drawn concerning the contributions of the various components of the observing system to the large scale forecast skill at short and medium range (Workshop Proceedings were published as WMO World Weather Watch Technical Reports TD No. 868, 1034, 1228 and 1450). Since then, some significant changes and developments have affected the global observing system and more efforts have been devoted to meso-scale observing and assimilation systems. There has also been a trend toward using techniques other than OSEs to document data impact, such as adjoint-based sensitivity to observations or ensemble-based sensitivity. Field experiments have been carried out, in particular through the THORPEX project, and the use of targeted data has been assessed.

Gelaro, Ronald↗