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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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
NASA NTRS1982

Steam chugging analysis in single-vent vapor injection

A complete cycle of the periodic steam chugging phenomenon is analyed. Steam velocity and pressure variations in the vent are described by one-dimensional conservation equations. This is coupled either to the water slug model when water is in the vent, or, the infinite pool spherical bubble model at the vent exit during bubble growth. An isolated spherical bubble model is used for computing the collapse pressures. Comparisons of the model predictions with the UCLA 1/12-scale and the Japan 1/6-scale data indicate that the vent-pipe model predicts the vent-clearing times and the bubble growth times well. In addition, the predicted maximum chugging heights compared well with those measured in the Japan data. On bubble collapse pressures, the comparison with the spherical bubble model predictions is only fair. The model generally overpredicts the magnitude of the spikes. On examining the effects of pool subcooling and steam mass flux, general agreement is found between the predicted trends and those measured.

Lee, C. K. B.↗
NASA NTRS2012

The Mars Science Laboratory (MSL) MMRTG In-Flight: A Power Update

The MSL Multi-Mission Radioisotope Thermoelectric Generator (MMRTG) was fueled on October 28, 2008 by the Department Of Energy (DOE) in preparation for a late 2009 launch. Shortly after, the MSL launch was delayed approximately 2 years until 2011. The fueled MMRTG was placed in storage to await the new date for liftoff. Occasional measurements of the MMRTG's power output were taken and compared with power predictions that pre-dated fueling. An error in the predictive models was quickly recognized and remedied. The predictions, while improved, carried significant uncertainty. This uncertainty did not deter the launch of MSL, but did alter the planned mission on the surface of Mars. Once launched, the MSL spacecraft provided a hi-fidelity telemetry stream measuring the generator's electrical and thermal performance. These data were used to update the predictive models and a new prediction of the performance of the MMRTG on the surface of Mars was run just before Entry, Descent, and Landing (EDL) at Mars. The MSL MMRTG is working extremely well, providing power above predictions and operating within its flight allowable temperature limits. The generator was producing approximately 114 W at the beginning of the surface mission. This paper will elaborate on power modeling for the MSL MMRTG along with a review of some of the data recorded from the MSL cruise to Mars, EDL, and the early days of the surface mission.

thermoelectric generator↗
DOE OSTI2021

Improved Understanding of Coupled Water and Carbon Cycle Processes through Machine Learning Approaches

Focal Area(s): This white paper addresses how explainable machine learning (ML) algorithms can improve insights gained from complex data (Focal Area 3). We will also address how ML approaches related to sensor compression and low energy AI hardware can be used for efficient data acquisition (Focal Area 1). Science Challenge: We will focus on the coupled water cycle and carbon cycle processes in terrestrial ecosystems and terrestrial-aquatic interfaces. Thus, our approaches will be centered around several data-model integration challenges indicated in EESD strategic plans for two science focus areas: Terrestrial Ecosystem Science and Hydrobiogeochemistry. We can use AI to correct systematic model errors due to biases in either data or model structure/processes. Error patterns in model predictions usually vary by region, model, season etc. But there are systematic patterns in them. e.g., soil moisture tends to be overestimated in the arid western continental United States underestimated in wetter eastern USA; some land surface models tend to underestimate moisture in wet seasons and overestimate in dry seasons. Moreover, the consequences of extreme events (e.g., drought, extreme flooding, storm surges associated with tropical storms, hurricanes) on carbon cycle processes are not well represented in ecosystem and Earth system models. Redox-sensitive processes (e.g., rapid oxidation/reduction of iron and other redox-sensitive elements in soil microsites subjected to fluctuated hydrology) in terrestrial-aquatic interfaces further challenge model predictions of hot-spots (and hot-moments) due to poor understanding of underlying mechanisms. Thus, AI/ML approaches can be used to learn patterns in the data and model errors and use them to build model equations and correct process-based model errors.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2013

Community-wide Validation of Geospace Model Ground Magnetic Field Perturbation Predictions to Support Model Transition to Operations

In this paper we continue the community-wide rigorous modern space weather model validation efforts carried out within GEM, CEDAR and SHINE programs. In this particular effort, in coordination among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), modelers, and science community, we focus on studying the models' capability to reproduce observed ground magnetic field fluctuations, which are closely related to geomagnetically induced current phenomenon. One of the primary motivations of the work is to support NOAA SWPC in their selection of the next numerical model that will be transitioned into operations. Six geomagnetic events and 12 geomagnetic observatories were selected for validation.While modeled and observed magnetic field time series are available for all 12 stations, the primary metrics analysis is based on six stations that were selected to represent the high-latitude and mid-latitude locations. Events-based analysis and the corresponding contingency tables were built for each event and each station. The elements in the contingency table were then used to calculate Probability of Detection (POD), Probability of False Detection (POFD) and Heidke Skill Score (HSS) for rigorous quantification of the models' performance. In this paper the summary results of the metrics analyses are reported in terms of POD, POFD and HSS. More detailed analyses can be carried out using the event by event contingency tables provided as an online appendix. An online interface built at CCMC and described in the supporting information is also available for more detailed time series analyses.

MAGNETIC↗
NASA NTRS2016

Community-Wide Validation of Geospace Model Local K-Index Predictions to Support Model Transition to Operations

We present the latest result of a community-wide space weather model validation effort coordinated among the Community Coordinated Modeling Center (CCMC), NOAA Space Weather Prediction Center (SWPC), model developers, and the broader science community. Validation of geospace models is a critical activity for both building confidence in the science results produced by the models and in assessing the suitability of the models for transition to operations. Indeed, a primary motivation of this work is supporting NOAA/SWPCs effort to select a model or models to be transitioned into operations. Our validation efforts focus on the ability of the models to reproduce a regional index of geomagnetic disturbance, the local K-index. Our analysis includes six events representing a range of geomagnetic activity conditions and six geomagnetic observatories representing midlatitude and high-latitude locations. Contingency tables, skill scores, and distribution metrics are used for the quantitative analysis of model performance. We consider model performance on an event-by-event basis, aggregated over events, at specific station locations, and separated into high-latitude and midlatitude domains. A summary of results is presented in this report, and an online tool for detailed analysis is available at the CCMC.

Glocer, A.↗
DOE OSTI2017

Assessment of an atmospheric transport model for annual inverse estimates of California greenhouse gas emissions

Abstract Atmospheric inverse estimates of gas emissions depend on transport model predictions, hence driving a need to assess uncertainties in the transport model. In this study we assess the uncertainty in WRF‐STILT (Weather Research and Forecasting and Stochastic Time‐Inverted Lagrangian Transport) model predictions using a combination of meteorological and carbon monoxide (CO) measurements. WRF configurations were selected to minimize meteorological biases using meteorological measurements of winds and boundary layer depths from surface stations and radar wind profiler sites across California. We compare model predictions with CO measurements from four tower sites in California from June 2013 through May 2014 to assess the seasonal biases and random errors in predicted CO mixing ratios. In general, the seasonal mean biases in boundary layer wind speed (< ~ 0.5 m/s), direction (< ~ 15°), and boundary layer height (< ~ 200 m) were small. However, random errors were large (~1.5–3.0 m/s for wind speed, ~ 40–60° for wind direction, and ~ 300–500 m for boundary layer height). Regression analysis of predicted and measured CO yielded near‐unity slopes (i.e., within 1.0 ± 0.20) for the majority of sites and seasons, though a subset of sites and seasons exhibit larger (~30%) uncertainty, particularly when weak winds combined with complex terrain in the South Central Valley of California. Looking across sites and seasons, these results suggest that WRF‐STILT simulations are sufficient to estimate emissions of CO to up to 15% on annual time scales across California.

Bagley, Justin E.↗
DOE OSTI2018

A Mathematical Model for Prediction of Carbon Concentration During RH Refining Process

A mathematical model was developed to predict the carbon concentration during RH refining process. Three reaction sites such as the free surface, inner sites of the molten steel in vacuum chamber, and the bubble surface were considered. The decarburization at inner sites of the molten steel in vacuum chamber was dominant at the initial stage, and then subsequently, it was dominated by the free surface and bubble surface. The decarburization rate constant was also evaluated.

36 MATERIALS SCIENCE↗
NASA NTRS2005

Solar Wind Fluctuations and Their Consequences on the Magnetosphere

Efforts have been made to extract the physical meaning of each term in our prediction model of the Dst index using the solar wind as the only input. The work has been published Journal of Geophysical Research (Temerin and Li, 21002). We found different terms in the model representing different current in the magnetospheric system and each current has different rise and decay times, with the symmetric ring current the slowest, then the partial ring current, then the tail current. We also have been trying to understand the physical meaning of the diffusion coefficient used in our prediction model of relativistic electron fluxes at geostationary orbit. The model reproduced the observations of MeV electron flux variations well, the diffusion coefficient had be assumed only die to local magnetic field fluctuations, leading to its 10th power dependence on the L. We have studied the theoretical derivation of the diffusion coefficient and we believe that the effect electric field fluctuations at smaller L could become more significant. We have expanded our previous radiation belt electron prediction model, which predicted MeV electron geosynchronous orbit based on solar wind measurements, to predict MeV electrons inside geosynchronous orbit. The model results are compared with measurements from Polar/CEPPAD. Prediction efficiencies of 0.56 and 0.54, respectively, at L=6 and L=4, have been achieved over the entire year of 1998. This work wa reported at 2003 Fall AGU and has been accepted for publication in Space Weather (Barker et al., 2005). We also have used simultaneous measurements of the upstream solar wind and of energetic electrons at geosynchronous orbit to analyze the response of electrons over a very wide energy range, 50 keV-6MeV, to solar wind variations. Enhancements of energetic electron fluxes over this whole energy range are modulated by the solar wind speed and the polarity of the interplanetary magnetic field (IMF). The solar wind speed seems to be a dominant controlling parameter for electrons of all energy. This work has been published in Space Weather (Li et al., 2005).

Li, Xin-Lin↗
DOE OSTI2019

The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production

Machine learning is frequently applied in the wind energy industry to build statistical models of wind farm power production using atmospheric data as input. In the field of wind power forecasting, in particular, there has been substantial research into finding the best-performing learning algorithms that improve model predictions. Overlooked in the literature, however, is the influence of atmospheric turbulence and stability measurements in improving model predictions. It has been well-established through observations and physical models that these effects can have considerable influence on wind farm power production; yet consideration of these effects in statistical models is almost entirely absent from the literature. In this work, we examine the impact of atmospheric turbulence and stability inputs on statistical model predictions of wind farm power output. Hourly observations from a wind farm in the Pacific Northwest United States located in very complex terrain are used. Five common learning algorithms and nine atmospheric variables are considered, five of which represent some measure of turbulence or stability. Here, we find a considerable improvement in hourly power predictions when some measure of turbulence or stability is included in the model. In particular, turbulent kinetic energy was found to be the most important variable apart from wind speed and more important than wind direction, pressure, and temperature. By contrast, the choice of learning algorithm is shown to be relatively less important in improving predictions. Based on this work, we recommend that turbulence and stability variables become standard inputs into statistical models of wind farm power production.

17 WIND ENERGY↗
NASA NTRS1989

Results of inphase axial-torsional fatigue experiments on 304 stainless steel

A series of axial-torsional, inphase, strain-controlled, low-cycle fatigue tests were performed at room temperature on tubular specimens of 304 stainless steel. The program was conducted in cooperation with the task group on multiaxial fatigue research of ASTM committee E-09. The objective was to quantify the variability in multiaxial test results among several laboratories. Only included is data which was generated at the NASA Lewis Research Center's High Temperature Fatigue and Structures Laboratory. The experimental equipment and procedures used are described. The tubular specimens were polished on the outer surface to aid in the use of a cellulose film surface replication technique for crack detection. However, cracking initiated predominantly on the internal surface for all specimens. Honing of the bore of the tubular specimens lessened but did not entirely eliminate this problem. The observed fatigue lives are compared with lives calculated from three multiaxial life models. Constants for the life prediction models were obtained from uniaxial and torsional tests performed on the same heat of material. The observed fatigue lives agreed with calculated lives to within a factor of two for all but one of the life prediction models.

Bonacuse, Peter J.↗
DOE OSTI2022

A Time-Varying Model for Predicting Formaldehyde Emission Rates in Homes

Recent studies have succeeded in relating emissions of various volatile organic compounds to material mass diffusion transfer using detailed empirical characteristics of each of the individual emitting materials. While significant, the resulting models are often scenario specific and/or require a host of individual component parameters to estimate emission rates. This study developed an approach to estimate aggregated emissions rates based on a wide number of field measurements. We used a multi-parameter regression model based on previous mass transfer models to predict formaldehyde emission rate for a whole dwelling using field-measured, time-resolved formaldehyde concentrations, air exchange rates, and indoor environmental parameters in 63 California single-family houses built between 2011 and 2017. The resulting model provides time-varying formaldehyde emission rates, normalized by floor area, for each study home, assuming a well-mixed mass balance transport model of the home, and a well-mixed layer transport model of indoor surfaces. The surface layer model asserts an equilibrium concentration within the surface layer of the emitted materials that is a function of temperature and RH; the dwelling ventilation rate serves as a surrogate for indoor concentration. We also developed a more generic emission model that is suitable for broad prediction of emission for a population of buildings. This model is also based on measurements aggregated from 27 homes from the same study. We showed that errors in predicting household formaldehyde concentrations using this approach were substantially less than those using a traditional constant emission rate model, despite requiring less unique building information.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1974

A model and predictive scale of passenger ride discomfort

A model to define the interrelationship of the various factors (vibratory and nonvibratory) important to passenger comfort, in realistic transport vehicle vibration environments was developed. The model represents: (1) a framework for the investigation of comfort within diverse transportation vehicles; (2) a mechanism for the development of a scale of comfort; (3) a mechanism through which design criteria can be obtained for improving the rideability of current and future transportation vehicles; and (4) a tool for obtaining information for the maximization of passenger ride quality, based upon sociological and psychological information. The application of the model is based upon the computational steps necessary for derivation of the comfort scale. The emphasis within the scale is upon the summation of comfort units; the summation being obtained through the use of appropriately determined factors, both within and between axes.

Dempsey, T. K.↗
NASA NTRS1989

The use of models to predict potential contamination aboard orbital vehicles

A model of fungal growth on air-exposed, nonnutritive solid surfaces, developed for utilization aboard orbital vehicles is presented. A unique feature of this testable model is that the development of a fungal mycelium can facilitate its own growth by condensation of water vapor from its environment directly onto fungal hyphae. The fungal growth rate is limited by the rate of supply of volatile nutrients and fungal biomass is limited by either the supply of nonvolatile nutrients or by metabolic loss processes. The model discussed is structurally simple, but its dynamics can be quite complex. Biofilm accumulation can vary from a simple linear increase to sustained exponential growth, depending on the values of the environmental variable and model parameters. The results of the model are consistent with data from aquatic biofilm studies, insofar as the two types of systems are comparable. It is shown that the model presented is experimentally testable and provides a platform for the interpretation of observational data that may be directly relevant to the question of growth of organisms aboard the proposed Space Station.

Boraas, Martin E.↗
NASA NTRS1998

Hindlimb unloading of growing rats: a model for predicting skeletal changes during space flight

A model that uses hindlimb unloading of rats was developed to study the consequences of skeletal unloading and reloading as occurs during and following space flight. Studies using the model were initiated two decades ago and further developed at National Aeronautics and Space Administration (NASA)-Ames Research Center. The model mimics some aspects of exposure to microgravity by removing weightbearing loads from the hindquarters and producing a cephalic fluid shift. Unlike space flight, the forelimbs remain loaded in the model, providing a useful internal control to distinguish between the local and systemic effects of hindlimb unloading. Rats that are hindlimb unloaded by tail traction gain weight at the same rate as pairfed controls, and glucocorticoid levels are not different from controls, suggesting that systemic stress is minimal. Unloaded bones display reductions in cancellous osteoblast number, cancellous mineral apposition rate, trabecular bone volume, cortical periosteal mineralization rate, total bone mass, calcium content, and maturation of bone mineral relative to controls. Subsequent studies reveal that these changes also occur in rats exposed to space flight. In hindlimb unloaded rats, bone formation rates and masses of unloaded bones decline relative to controls, while loaded bones do not change despite a transient reduction in serum 1,25-dihydroxyvitamin D (1,25D) concentrations. Studies using the model to evaluate potential countermeasures show that 1,25D, growth hormone, dietary calcium, alendronate, and muscle stimulation modify, but do not completely correct, the suppression of bone growth caused by unloading, whereas continuous infusion of transforming growth factor-beta2 or insulin-like growth factor-1 appears to protect against some of the bone changes caused by unloading. These results emphasize the importance of local as opposed to systemic factors in the skeletal response to unloading, and reveal the pivotal role that osteoblasts play in the response to gravitational loading. The hindlimb unloading model provides a unique opportunity to evaluate in detail the physiological and cellular mechanisms of the skeletal response to weightbearing loads, and has proven to be an effective model for space flight.

Non-NASA Center↗
DOE OSTI2026

Comparison of structurally diverse simulation models for prediction of epidemic outcomes caused by a long-distance dispersed pathogen

Long-distance dispersal (LDD) pathogens pose substantial challenges for epidemic control due to their ability to generate new infection foci at great distances. While various modeling approaches have been developed to understand and manage such outbreaks, little work has compared how models of different structures behave under shared conditions. Here, in this study, we compare four structurally distinct epidemiological models — EPIMUL, GEMF, PoPS, and Warwick — each adapted to simulate the spread of wheat stripe rust (WSR), a wind-dispersed LDD pathogen, under identical epidemiological parameters and dispersal kernel. Using data from a controlled field experiment, we evaluate the ability of each model to replicate disease prevalence under nine intervention scenarios that vary in timing and culling area. While the models differ substantially in design — ranging from spatial grid-based to network-based and raster-based frameworks — the shared dispersal kernel allowed for close alignment in their predictions. All models accurately captured general epidemic trends, particularly the strong effect of early intervention on disease suppression. We qualitatively compared their behavioral responses across scenarios and also evaluated an ensemble prediction by averaging across model outputs. Our findings highlight how integrating shared epidemiological components into distinct modeling frameworks can improve consistency and accuracy, while reinforcing the importance of early culling in managing LDD pathogen outbreaks.

Dispersal kernel↗