Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variability prediction”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

Fractal analysis of heart rate dynamics as a predictor of mortality in patients with depressed left ventricular function after acute myocardial infarction. TRACE Investigators. TRAndolapril Cardiac Evaluation

A number of new methods have been recently developed to quantify complex heart rate (HR) dynamics based on nonlinear and fractal analysis, but their value in risk stratification has not been evaluated. This study was designed to determine whether selected new dynamic analysis methods of HR variability predict mortality in patients with depressed left ventricular (LV) function after acute myocardial infarction (AMI). Traditional time- and frequency-domain HR variability indexes along with short-term fractal-like correlation properties of RR intervals (exponent alpha) and power-law scaling (exponent beta) were studied in 159 patients with depressed LV function (ejection fraction <35%) after an AMI. By the end of 4-year follow-up, 72 patients (45%) had died and 87 (55%) were still alive. Short-term scaling exponent alpha (1.07 +/- 0.26 vs 0.90 +/- 0.26, p <0.001) and power-law slope beta (-1.35 +/- 0.23 vs -1.44 +/- 0.25, p <0.05) differed between survivors and those who died, but none of the traditional HR variability measures differed between these groups. Among all analyzed variables, reduced scaling exponent alpha (<0.85) was the best univariable predictor of mortality (relative risk 3.17, 95% confidence interval 1.96 to 5.15, p <0.0001), with positive and negative predictive accuracies of 65% and 86%, respectively. In the multivariable Cox proportional hazards analysis, mortality was independently predicted by the reduced exponent alpha (p <0.001) after adjustment for several clinical variables and LV function. A short-term fractal-like scaling exponent was the most powerful HR variability index in predicting mortality in patients with depressed LV function. Reduction in fractal correlation properties implies more random short-term HR dynamics in patients with increased risk of death after AMI.

Non-NASA Center↗
NASA NTRS2022

Predicting Antarctic Net Snow Accumulation at the Kilometer Scale and Its Impact on Observed Height Changes

Sub-grid-scale processes occurring at or near the surface of an ice sheet have a potentially large impact on local and integrated net accumulation of snow via redistribution and sublimation. Given observational complexity, they are either ignored or parameterized over large-length scales. Here, we train random forest (RF) models to predict variability in net accumulation over the Antarctic Ice Sheet using atmospheric variables and topographic characteristics as predictors at 1 km resolution. Observations of net snow accumulation from both in situ and airborne radar data provide the input observable targets needed to train the RF models. We find that local net accumulation deviates by as much as 172% of the atmospheric model mean. The correlation in space between the predicted net accumulation variability and satellite-derived surface-height change indicates that surface processes operate differently through time, driven largely by the seasonal anomalies in snow accumulation.

Antarctic↗
DOE OSTI2024

Uncertainty quantification in multivariable regression for material property prediction with Bayesian neural networks

With the increased use of data-driven approaches and machine learning-based methods in material science, the importance of reliable uncertainty quantification (UQ) of the predicted variables for informed decision-making cannot be overstated. UQ in material property prediction poses unique challenges, including multi-scale and multi-physics nature of materials, intricate interactions between numerous factors, limited availability of large curated datasets, etc. In this work, we introduce a physics-informed Bayesian Neural Networks (BNNs) approach for UQ, which integrates knowledge from governing laws in materials to guide the models toward physically consistent predictions. To evaluate the approach, we present case studies for predicting the creep rupture life of steel alloys. Experimental validation with three datasets of creep tests demonstrates that this method produces point predictions and uncertainty estimations that are competitive or exceed the performance of conventional UQ methods such as Gaussian Process Regression. Additionally, we evaluate the suitability of employing UQ in an active learning scenario and report competitive performance. The most promising framework for creep life prediction is BNNs based on Markov Chain Monte Carlo approximation of the posterior distribution of network parameters, as it provided more reliable results in comparison to BNNs based on variational inference approximation or related NNs with probabilistic outputs.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Functional Predictor Variables for the Leaching Potential of Arsenic and Selenium from Coal Fly Ash

The release of leachates from intact coal ash impoundments is a concern due to the enrichment and mobilization of toxic elements such as arsenic (As) and selenium (Se). This study aims to explore the intrinsic properties of coal fly ash that correlate with the relative leachability of As and Se. We performed leaching experiments with 52 fly ash samples collected from 15 different U.S. power plants and representing coal feedstocks from the three major domestic regions. We assessed the mobilization potential of As and Se in fly ash based on standardized leaching protocols and performed multivariate and lasso regression analyses to explore correlations of leachable As and Se contents with characteristics such as major element contents, loss on ignition, and pH. The results of regression models indicated that major elements (Fe, Ca, and Al) for a wide range of fly ashes can serve as predictor variables for the leaching potential of As but not for Se. LOI and pH were not important predictive variables in the models. Both regression approaches resulted in relatively strong fits for leachable As (correlation coefficient R 2 = 0.78 for both models) compared to models for leachable Se (R 2 = 0.49). Overall, these results suggest that correlation models combined with on-site elemental analysis with portable analyzers may enable a screening method for leachable As in coal ash.

01 COAL, LIGNITE, AND PEAT↗
NASA NTRS2000

A High Resolution Survey of the Disk of M31

This report describes research activities funded for SAO Proposal P3481-5-95, "Monitoring the Center of M31". Related activities for SAO Proposal P3486- 5-95, "A High-Resolution Survey of the Disk of M31" are also described. The research involved the data analysis and interpretation of eleven separate ROSAT (Roentgen Satellite) HRI (High Resolution Imager) observations of the center and inner disk of M31, obtained between July, 1990 and January, 1997. A log of the individual data sets is given. All proposed observations were successfully carried out by the ROSAT Observatory, and standard data products were successfully generated for each observation. There were two basic thrusts to the research. First, we wished to monitor the x-ray source nearest the nucleus of M31, to search for anti-correlated radio/x-ray variability predicted by theoretical models for the source. We would also be able to assess the degree and range of variability of other x-ray sources in the central bulge of M31 and to estimate rates of transients. Secondly, we wished to survey the entire inner disk of M31, and in particular the region covered by the recent MIT wide-band and narrow-band surveys to identify x-ray counterparts to supernova remnants, OB associations, and young blue stars, in areas in which the ROSAT PSPC survey was confused. Results of each research project are summarized. Papers describing the results in more detail are in preparation.

Primini, Francis A.↗
NASA NTRS1977

Reduced variable technique for predicting grain behavior

The use of reduced variables to account for the time-temperature dependence of the relaxation modulus is a standard and universally adopted technique in the stress analysis of solid propellant grains. Recent work has suggested that the reduced variable technique can also be used to account for the dependence of the relaxation modulus on the crosslink density of an elastomer system. By combining variables, it is now possible to determine the relaxation modulus as a function of both mechanical and chemical parameters over a long period of time. The problems involved, the pitfalls to be avoided, and suggested techniques to be used in acquiring data are discussed.

Lewis, G. W.↗
NASA NTRS2008

Predicting Material Performance in the Space Environment from Laboratory Test Data, Static Design Environments, and Space Weather Models

Qualifying materials for use in the space environment is typically accomplished with laboratory exposures to simulated UV/EUV, atomic oxygen, and charged particle radiation environments with in-situ or subsequent measurements of material properties of interest to the particular application. Choice of environment exposure levels are derived from static design environments intended to represent either mean or extreme conditions that are anticipated to be encountered during a mission. The real space environment however is quite variable. Predictions of the on orbit performance of a material qualified to laboratory environments can be done using information on 'space weather' variations in the real environment. This presentation will first review the variability of space environments of concern for material degradation and then demonstrate techniques for using test data to predict material performance in a variety of space environments from low Earth orbit to interplanetary space using historical measurements and space weather models.

Minow, Josep I.↗
NASA NTRS2002

Using Clustering to Establish Climate Regimes from PCM Output

A multivariate statistical clustering technique--based on the k-means algorithm of Hartigan has been used to extract patterns of climatological significance from 200 years of general circulation model (GCM) output. Originally developed and implemented on a Beowulf-style parallel computer constructed by Hoffman and Hargrove from surplus commodity desktop PCs, the high performance parallel clustering algorithm was previously applied to the derivation of ecoregions from map stacks of 9 and 25 geophysical conditions or variables for the conterminous U.S. at a resolution of 1 sq km. Now applied both across space and through time, the clustering technique yields temporally-varying climate regimes predicted by transient runs of the Parallel Climate Model (PCM). Using a business-as-usual (BAU) scenario and clustering four fields of significance to the global water cycle (surface temperature, precipitation, soil moisture, and snow depth) from 1871 through 2098, the authors' analysis shows an increase in spatial area occupied by the cluster or climate regime which typifies desert regions (i.e., an increase in desertification) and a decrease in the spatial area occupied by the climate regime typifying winter-time high latitude perma-frost regions. The patterns of cluster changes have been analyzed to understand the predicted variability in the water cycle on global and continental scales. In addition, representative climate regimes were determined by taking three 10-year averages of the fields 100 years apart for northern hemisphere winter (December, January, and February) and summer (June, July, and August). The result is global maps of typical seasonal climate regimes for 100 years in the past, for the present, and for 100 years into the future. Using three-dimensional data or phase space representations of these climate regimes (i.e., the cluster centroids), the authors demonstrate the portion of this phase space occupied by the land surface at all points in space and time. Any single spot on the globe will exist in one of these climate regimes at any single point in time. By incrementing time, that same spot will trace out a trajectory or orbit between and among these climate regimes (or atmospheric states) in phase (or state) space. When a geographic region enters a state it never previously visited, a climatic change is said to have occurred. Tracing out the entire trajectory of a single spot on the globe yields a 'manifold' in state space representing the shape of its predicted climate occupancy. This sort of analysis enables a researcher to more easily grasp the multivariate behavior of the climate system.

Oglesby, Robert↗
NASA NTRS2005

Applications of Principled Search Methods in Climate Influences and Mechanisms

Forest and grass fires cause economic losses in the billions of dollars in the U.S. alone. In addition, boreal forests constitute a large carbon store; it has been estimated that, were no burning to occur, an additional 7 gigatons of carbon would be sequestered in boreal soils each century. Effective wildfire suppression requires anticipation of locales and times for which wildfire is most probable, preferably with a two to four week forecast, so that limited resources can be efficiently deployed. The United States Forest Service (USFS), and other experts and agencies have developed several measures of fire risk combining physical principles and expert judgment, and have used them in automated procedures for forecasting fire risk. Forecasting accuracies for some fire risk indices in combination with climate and other variables have been estimated for specific locations, with the value of fire risk index variables assessed by their statistical significance in regressions. In other cases, the MAPSS forecasts [23, 241 for example, forecasting accuracy has been estimated only by simulated data. We describe alternative forecasting methods that predict fire probability by locale and time using statistical or machine learning procedures trained on historical data, and we give comparative assessments of their forecasting accuracy for one fire season year, April- October, 2003, for all U.S. Forest Service lands. Aside from providing an accuracy baseline for other forecasting methods, the results illustrate the interdependence between the statistical significance of prediction variables and the forecasting method used.

Glymour, Clark↗
DOE OSTI2025

A Probabilistic Model for Global EMIC Wave Activity Using Van Allen Probes Observations

Electromagnetic ion cyclotron (EMIC) waves play a key role in radiation belt dynamics through resonant interactions. However, their low occurrence probability, high variability, and spatial intermittency pose challenges for accurate modeling. In this study, we present a machine learning (ML)-based global EMIC wave model built on the entire data set from the Van Allen Probes mission. To capture the distinct statistical characteristics of wave occurrence and amplitude, the model is separated into two modules: an occurrence model trained using ML techniques, and a wave amplitude model sampled from observed probability distributions. The input parameters are limited to real-time or predictable variables to ensure practical applicability. Our model shows strong performance across the entire test set and demonstrates improved predictive capability over a baseline random occurrence model, particularly during quiet geomagnetic conditions. Evaluation during both quiet and active periods confirms the model's ability to represent the clustered and intermittent nature of EMIC wave activity. Furthermore, the model provides global estimates of wave power, enabling integration with radiation belt electron data and showing signatures consistent with wave-induced scattering. We found a good correlation between the global wave activity from the model and relativistic electron observation by Van Allen Probes, regardless of the availability of in situ wave observations. The modular structure of the model also allows for straightforward expansion for additional wave properties, such as wave frequency, which can be modeled independently. This flexible, event-sensitive approach offers a promising framework for data-driven radiation belt simulations and space weather applications.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

The Tropical Basin Interaction Model Intercomparison Project (TBIMIP)

Abstract. Large-scale interaction between the three tropical ocean basins is an area of intense research that is often conducted through experimentation with numerical models. A common problem is that modeling groups use different experimental setups, which makes it difficult to compare results and delineate the role of model biases from differences in experimental setups. To address this issue, an experimental protocol for examining interaction between the tropical basins is introduced. The Tropical Basin Interaction Model Intercomparison Project (TBIMIP) consists of experiments in which sea surface temperatures (SSTs) are prescribed to follow observed values in selected basins. There are two types of experiments. One type, called standard pacemaker, consists of simulations in which SSTs are restored to observations in selected basins during a historical simulation. The other type, called pacemaker hindcast, consists of seasonal hindcast simulations in which SSTs are restored to observations during 12-month forecast periods. TBIMIP is coordinated by the Climate and Ocean – Variability, Predictability, and Change (CLIVAR) Research Focus on Tropical Basin Interaction. The datasets from the model simulations will be made available to the community to facilitate and stimulate research on tropical basin interaction and its role in seasonal-to-decadal variability and climate change.

Richter, Ingo↗
NASA NTRS2011

Evapotranspiration as a Regional Climate Priority: Results from a NASA/USDA Workshop

On April 5 to 7, 2011, the National Aeronautics and Space Administration (NASA) and the United States Department of Agriculture-Agricultural Research Service (USDA-ARS) sponsored a Workshop on Evapotranspiration (ET) in Silver Spring Maryland. The workshop was a response to a recommendation in the 2009-2011 GEO (Group on Earth Observations) Work Plan that a workshop on ET should be held to discuss issues related to ET products and services and the potential for incorporating ET activities into the 2012-2015 GEO Work Plan. The workshop had a regional emphasis, although there were several excellent international and global presentations including one on the GEWEX LANDFLUX project. The different scales of these activities suggests that a framework is needed that can accommodate both regional and global ET activities. Despite limitations with the workshop's scheduling, it attracted 76 experts who contributed informative presentations and insightful discussions. The goals of the workshop involved the exchange of information and ideas and the development of plans for providing more visibility for ET issues. Specific objectives included 1) defining the needs and requirements for evapotranspiration data in weather and climate studies, in natural and agro-ecoystem monitoring, and in water resource management; 2) reviewing the methods used to measure and model evapotranspiration; 3) assessing surface and satellite observation systems required to support ET measurement, modeling and evaluation; 4) assessing the feasibility of developing a proposal for a task on evapotranspiration for the 2012-2015 GEO Work Plan, and 5) exploring the level of support and consensus for developing a strategy for establishing evapotranspiration as an Essential Climate Variable (ECV) within the Global Climate Observing System (GCOS) framework The workshop featured a combination of oral presentations and breakout group sessions focused on the above objectives. There were also poster presentations providing opportunities for one-on-one discussions of ET modeling and measurement techniques. Presentations by users of ET data set the tone for the workshop. In the USA at the national and regional levels water rights issues represent a major opportunity for ET applications. ET data play a major role in estimating water loss due to irrigation, the largest cause of consumptive water loss in the USA, particularly in the West. Irrigation requirements are relatively specific since the needs are clearly defined by the geometry and number of the irrigation systems and can be monitored with high resolution satellite data. There was a strong consensus that land surface temperature (LST) at high resolution is critical for monitoring irrigation. State governments have made commitments to more efficient water management in the western US, but they need full access to improved and more timely ET data and applications to implement this plan. Water managers also reported that in spite of the recent development of. new techniques, the procedures used in some of the water balance calculations in some states are out of date and do not take advantage of new observational and data assimilation systems. The development of ET forecasts for water management is also seen as a priority. Although ET forecasts are currently being produced on an experimental basis these predictions could be improved by considering ET as a dynamic prediction variable in models and by increasing the time resolution of these ET predictions.

Lawford, Richard↗
DOE OSTI2023

A cellular selection identifies elongated flavodoxins that support electron transfer to sulfite reductase

Flavodoxins (Flds) mediate the flux of electrons between oxidoreductases in diverse metabolic pathways. To investigate whether Flds can support electron transfer to a sulfite reductase (SIR) that evolved to couple with a ferredoxin, we evaluated the ability of Flds to transfer electrons from a ferredoxin-NADP reductase (FNR) to a ferredoxin-dependent SIR using growth complementation of an Escherichia coli strain with a sulfur metabolism defect. We show that Flds from cyanobacteria complement this growth defect when coexpressed with an FNR and an SIR that evolved to couple with a plant ferredoxin. When we evaluated the effect of peptide insertion on Fld-mediated electron transfer, we observed a sensitivity to insertions within regions predicted to be proximal to the cofactor and partner binding sites, while a high insertion tolerance was detected within loops distal from the cofactor and within regions of helices and sheets that are proximal to those loops. Bioinformatic analysis showed that natural Fld sequence variability predicts a large fraction of the motifs that tolerate insertion of the octapeptide SGRPGSLS. In conclusion, these results represent the first evidence that Flds can support electron transfer to assimilatory SIRs, and they suggest that the pattern of insertion tolerance is influenced by interactions with oxidoreductase partners.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1990

Systems engineering and integration: Cost estimation and benefits analysis

Space Transportation Avionics hardware and software cost has traditionally been estimated in Phase A and B using cost techniques which predict cost as a function of various cost predictive variables such as weight, lines of code, functions to be performed, quantities of test hardware, quantities of flight hardware, design and development heritage, complexity, etc. The output of such analyses has been life cycle costs, economic benefits and related data. The major objectives of Cost Estimation and Benefits analysis are twofold: (1) to play a role in the evaluation of potential new space transportation avionics technologies, and (2) to benefit from emerging technological innovations. Both aspects of cost estimation and technology are discussed here. The role of cost analysis in the evaluation of potential technologies should be one of offering additional quantitative and qualitative information to aid decision-making. The cost analyses process needs to be fully integrated into the design process in such a way that cost trades, optimizations and sensitivities are understood. Current hardware cost models tend to primarily use weights, functional specifications, quantities, design heritage and complexity as metrics to predict cost. Software models mostly use functionality, volume of code, heritage and complexity as cost descriptive variables. Basic research needs to be initiated to develop metrics more responsive to the trades which are required for future launch vehicle avionics systems. These would include cost estimating capabilities that are sensitive to technological innovations such as improved materials and fabrication processes, computer aided design and manufacturing, self checkout and many others. In addition to basic cost estimating improvements, the process must be sensitive to the fact that no cost estimate can be quoted without also quoting a confidence associated with the estimate. In order to achieve this, better cost risk evaluation techniques are needed as well as improved usage of risk data by decision-makers. More and better ways to display and communicate cost and cost risk to management are required.

Dean, ED↗
NASA NTRS1990

The effect of latent heat release on synoptic-to-planetary wave interactions and its implication for satellite observations: Theoretical modeling

The project objectives are to develop process models to investigate the interaction of planetary and synoptic-scale waves including the effects of latent heat release (precipitation), nonlinear dynamics, physical and boundary-layer processes, and large-scale topography; to determine the importance of latent heat release for temporal variability and time-mean behavior of planetary and synoptic-scale waves; to compare the model results with available observations of planetary and synoptic wave variability; and to assess the implications of the results for monitoring precipitation in oceanic-storm tracks by satellite observing systems. Researchers have utilized two different models for this project: a two-level quasi-geostrophic model to study intraseasonal variability, anomalous circulations and the seasonal cycle, and a 10-level, multi-wave primitive equation model to validate the two-level Q-G model and examine effects of convection, surface processes, and spherical geometry. It explicitly resolves several planetary and synoptic waves and includes specific humidity (as a predicted variable), moist convection, and large-scale precipitation. In the past year researchers have concentrated on experiments with the multi-level primitive equation model. The dynamical part of that model is similar to the spectral model used by the National Meteorological Center for medium-range forecasts. The model includes parameterizations of large-scale condensation and moist convection. To test the validity of results regarding the influence of convective precipitation, researchers can use either one of two different convective schemes in the model, a Kuo convective scheme or a modified Arakawa-Schubert scheme which includes downdrafts. By choosing one or the other scheme, they can evaluate the impact of the convective parameterization on the circulation. In the past year researchers performed a variety of initial-value experiments with the primitive-equation model. Using initial conditions typical of climatological winter conditions, they examined the behavior of synoptic and planetary waves growing in moist and dry environments. Surface conditions were representative of a zonally averaged ocean. They found that moist convection associated with baroclinic wave development was confined to the subtropics.

Branscome, Lee E.↗
NASA NTRS1984

Ocean modelling on the CYBER 205 at GFDL

At the Geophysical Fluid Dynamics Laboratory, research is carried out for the purpose of understanding various aspects of climate, such as its variability, predictability, stability and sensitivity. The atmosphere and oceans are modelled mathematically and their phenomenology studied by computer simulation methods. The present state-of-the-art in the computer simulation of large scale oceans on the CYBER 205 is discussed. While atmospheric modelling differs in some aspects, the basic approach used is similar. The equations of the ocean model are presented along with a short description of the numerical techniques used to find their solution. Computational considerations and a typical solution are presented in section 4.

Cox, M.↗
NASA NTRS1986

Fire control method and analytical model for large liquid hydrocarbon pool fires

The dominate parameter governing the behavior of a liquid hydrocarbon (JP-5) pool fire is wind speed. The most effective method of controlling wind speed in the vicinity of a large circular (10 m dia.) pool fire is a set of concentric screens located outside the perimeter. Because detailed behavior of the pool fire structure within one pool fire diameter is unknown, an analytical model supported by careful experiments is under development. As a first step toward this development, a regional pool fire model was constructed for the no-wind condition consisting of three zones -- liquid fuel, combustion, and plume -- where the predicted variables are mass burning rate and characteristic temperatures of the combustion and plume zones. This zone pool fire model can be modified to incorporate plume bending by wind, radiation absorption by soot particles, and a different ambient air flow entrainment rate. Results from the zone model are given for a pool diameter of 1.3 m and are found to reproduce values in the literature.

Fenton, D. L.↗
NASA NTRS1988

Evolution of the Antarctic polar vortex in spring: Response of a GCM to a prescribed Antarctic ozone hole

The possible effect of the Antartic ozone hole on the evolution of the polar vortex during late winter and spring using a general circulation model (GCM) is examined. The GCM is a version of the NCAR Community Climate Model whose domain extends from the surface to the mesosphere and is similar to that described on Boville and Randel (1986). Ozone is not a predicted variable in the model. A zonally averaged ozone distribution is specified as a function of latitude, pressure and month for the radiation parameterization. Rather that explicitly address reasons for the formation of the ozone hole, researchers postulate its existence and ask what effect it has on the subsequent evolution of the vortex. The evolution of the model when an ozone hole is imposed is then discussed.

Boville, B. A.↗