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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 1,261 records · Page 70
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Steady-State Plant Model to Predict Hydroden Levels in Power Plant Components

The National Renewable Energy Laboratory (NREL) and Acciona Energy North America developed a full-plant steady-state computational model that estimates levels of hydrogen in parabolic trough power plant components. The model estimated dissolved hydrogen concentrations in the circulating heat transfer fluid (HTF), and corresponding partial pressures within each component. Additionally for collector field receivers, the model estimated hydrogen pressure in the receiver annuli. The model was developed to estimate long-term equilibrium hydrogen levels in power plant components, and to predict the benefit of hydrogen mitigation strategies for commercial power plants. Specifically, the model predicted reductions in hydrogen levels within the circulating HTF that result from purging hydrogen from the power plant expansion tanks at a specified target rate. Our model predicted hydrogen partial pressures from 8.3 mbar to 9.6 mbar in the power plant components when no mitigation treatment was employed at the expansion tanks. Hydrogen pressures in the receiver annuli were 8.3 to 8.4 mbar. When hydrogen partial pressure was reduced to 0.001 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.001 mbar to 0.02 mbar. When hydrogen partial pressure was reduced to 0.3 mbar in the expansion tanks, hydrogen pressures in the receiver annuli fell to a range of 0.25 mbar to 0.28 mbar. Our results show that controlling hydrogen partial pressure in the expansion tanks allows us to reduce and maintain hydrogen pressures in the receiver annuli to any practical level.

14 SOLAR ENERGY↗
NASA NTRS2016

A Continuum Damage Mechanics Model to Predict Kink-Band Propagation Using Deformation Gradient Tensor Decomposition

A new model is proposed that represents the kinematics of kink-band formation and propagation within the framework of a mesoscale continuum damage mechanics (CDM) model. The model uses the recently proposed deformation gradient decomposition approach to represent a kink band as a displacement jump via a cohesive interface that is embedded in an elastic bulk material. The model is capable of representing the combination of matrix failure in the frame of a misaligned fiber and instability due to shear nonlinearity. In contrast to conventional linear or bilinear strain softening laws used in most mesoscale CDM models for longitudinal compression, the constitutive response of the proposed model includes features predicted by detailed micromechanical models. These features include: 1) the rotational kinematics of the kink band, 2) an instability when the peak load is reached, and 3) a nonzero plateau stress under large strains.

Bergan, Andrew C.↗
NASA NTRS1986

Development of low Reynolds number two equation turbulence models for predicting external heat transfer on turbine blades

A research effort was underway to study the use of two equation low Reynolds number turbulence models in predicting gas side heat transfer on turbine blades. The major objectives of this work are basicly threefold: study the predictive capabilities of two equation low Reynolds number turbulence models under the conditions characteristic of modern gas turbine blades; explore potential improvements to the models themselves as well as to the specification of initial conditions; and provide a comparison of the predictions of these models with the experimental data from a broad range of recently available turbine cascade experiments. The problems associated with predicting the boundary layer transition from laminar to turbulent flow are emphasized, as this may be the most serious deficiency of current modeling techniques. The results and conclusions of the first two phases are briefly described.

Patankar, Suhas V.↗
NASA NTRS1988

A model to predict the removal of oxygen from air using a zirconia solid electrolyte membrane

A finite difference mathematical model has been developed to predict the removal of oxygen from air using a zirconia separation cell. The model predicts the electrical and mass transfer processes in circular disk cells with either axial or radial current flow in the electrodes and in tubular cells with axial current flow in the electrodes. Representative results are presented and discussed.

Marner, W. J.↗
NASA NTRSDate not supplied

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
NASA NTRS2023

Aero-Engines AI - A Machine-Learning App for Aircraft Engine Concepts Assessment

Effective deployment of machine-learning (ML) models could drive a high level of efficiency in aircraft engine conceptual design. Aero-Engines AI is a user-friendly app that has been created to deploy trained machine-learning (ML) models to assess aircraft engine concepts. It was created using tkinter, a GUI (graphical user interface) module that is built into the standard Python library. Employing tkinter greatly facilitates the sharing of ML application as an executable file which can be run on Windows machines (without the need to have Python or any library installed). The app gets user input for a turbofan design, preprocesses the input data, and deploys trained ML models to predict turbofan thrust specific fuel consumption (TSFC), engine weight, core size, and turbomachinery stage-counts. The ML predictive models were built by employing supervised deep-learning and K-nearest neighbor regression algorithms to study patterns in an existing open-source database of production and research turbofan engines. They were trained, cross-validated, and tested in Keras, an open-source neural networks API (application programming interface) written in Python, with TensorFlow (Google open-source artificial intelligence library) serving as the backend engine. The smooth deployment of these ML models using the app shows that Aero-Engines AI is an easy-touse and a time-saving tool for aircraft engine design-space exploration during the conceptual design stage. Current version of the app focuses on the performance prediction of conventional turbofans. However, the scope of the app can easily be easily expanded to include other engine types (such as turboshaft and hybrid-electric systems) after their ML models are developed. Overall, the use of a machine-learning app for aircraft engine concept assessment represents a promising area of development in aircraft engine conceptual design.

machine learning↗
DOE OSTI2019

Predicting Sooting Tendencies from Chemical Structure with Experimental and Theoretical Insight

Particulate matter (PM) emissions from internal combustion engines negatively impact public health and global climate. These problems are exacerbated by newer gasoline direct injection engines, which are more fuel-efficient, but also produce more soot than traditional spark ignited engines. Reducing soot formation is therefore of paramount importance in the development of new fuels. A fuel's sooting tendency is a quantitative parameter that describes the sooting behavior of a pure compound or fuel mixture. The yield sooting index (YSI), developed by McEnally and Pfefferle, accurately measures sooting tendencies using small sample quantities. Using an experimental sooting tendency database, we have developed a predictive model for sooting behavior from a quantitative structure-activity relationship (QSAR). It was developed so that input molecules are first decomposed into individual carbon-type fragments for which the sooting tendency contribution can be assigned based on a Bayesian linear regression against the experimental database. The model's predictive accuracy is comparable to its training performance using leave-one-out cross-validation. We have used this model to provide quantitative insights into the effects of chemical structure on soot formation, but excitingly, we have also been able to readily identify the presence of more complicated kinetic sooting mechanisms for structures which are extreme outliers. Oxygenated aromatics can be produced readily from biomass as renewable sources and oxygenated aromatics with very similar structures tend to have a much lower sooting tendency, for example methoxybenzene (anisole, 107), 2-methylphenol (m-cresol, 103), 2-ethylphenol (120), 3-ethylphenol (138) and 1-phenylethanol (142). Thus, the presence of just one oxygen atom in an aromatic compound can drastically alter the reaction pathways leading to soot precursors. We have applied density functional theory (DFT) calculations and flow reactor experiments to examine how oxygenation alters reaction pathways in a combustion environment. This study has allowed us to gain understanding on how the location of an oxygenated functional group influences soot formation. Our work provides a blueprint for the design of oxygenated fuels from biomass, which minimize the production of soot in low oxygen environments.

09 BIOMASS FUELS↗
DOE OSTI2016

Using Topic Modeling and Text Embeddings to Predict Deleted Tweets

Predictive models for tweet deletion have been a relatively unexplored area of Twitter-related computational research. We first approach the deletion of tweets as a spam detection problem, applying a small set of handcrafted features to improve upon the current state-of-the- art in predicting deleted tweets. Next, we apply our approach to a dataset of deleted tweets that better reflects the current deletion rate. Since tweets are deleted for reasons beyond just the presence of spam, we apply topic modeling and text embeddings in order to capture the semantic content of tweets that can lead to tweet deletion. Our goal is to create an effective model that has a low-dimensional feature space and is also language-independent. A lean model would be computationally advantageous processing high-volumes of Twitter data, which can reach 9,885 tweets per second. Our results show that a small set of spam-related features combined with word topics and character-level text embeddings provide the best f1 when trained with a random forest model. The highest precision of the deleted tweet class is achieved by a modification of paragraph2vec to capture author identity.

Potash, Peter J.↗
DOE OSTI2019

Characterizing the length-dependence of DNA nanotube end-to-end joining rates

DNA nanotechnology provides a route towards the synthesis of custom nano-structured materials and circuits through hierarchical assembly processes. Although predictive kinetic models are being developed for the assembly of DNA nanostructures from small monomeric components, a general model for the hierarchical assembly of DNA nanostructures remains elusive. DNA tile nanotubes provide an ideal model system for the study of hierarchical assembly via end-to-end joining. In this study, we experimentally characterize the length-dependence of the end-to-end joining rate of DNA tile nanotubes. We then test the ability of three different models of polymer end-to-end joining to reproduce experimentally measured changes in nanotube lengths during a joining reaction using an ODE model for nanotube joining. All three models predict physically realistic joining rates that are consistent with prior measurements, with a length-independent end-to-end joining rate model providing the best fit to the experimental data. A length-independent constant joining rate is consistent with other DNA self-assembly processes across a broad range of length scales and also suggests how tractable models for hierarchical DNA nanostructure could be developed.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

TPSAS-NF1676L-29139-DND

The Arctic has been warming at a rate outpacing globally-averaged warming by 2-3 times, a phenomenon known as "Arctic Amplification" (AA). AA is evident in observations of Arctic surface temperature over the last century as well as in model predictions. The accuracy of model predictions is crucial to determine our response to climate change, as Arctic changes have consequences for the atmospheric circulation, sea level rise, and the carbon cycle. Unfortunately, models show a wide range of possible futures and intermodel spread in projections of Arctic temperature is larger than for any other region: a 2°C global temperature increase- the limit set by the Paris Climate Agreement- would lead to an Arctic temperature change between 3-7°C according to Coupled Model Intercomparison 5 (CMIP5) models. This intermodel spread represents a key deficiency in the understanding of Arctic feedback processes and how changes in various physical parameters (e.g. sea ice cover, clouds, heat transport and surface fluxes) affect the temperature response. Using a process-oriented decomposition of the simulate Arctic surface energy budget, we attribute the largest contribution to the CMIP5 intermodel spead in projected Arctic warming to the Barents-Kara Sea region. Our results reveal that the increasing seasonal fluxing of ocean heat content is a significant part of the intermodel spread in AA and relates strongly to change in surface turbulent fluxes. Our results indicate that if we are to significantly reduce the intermodel spread in projected AA we must focus on understanding the behavior and physical processes that drive surface energy flows in the B-K sea region.

Patrick Taylor↗
NASA NTRS2008

Modeling and Prediction of Fan Noise

Fan noise is a significant contributor to the total noise signature of a modern high bypass ratio aircraft engine and with the advent of ultra high bypass ratio engines like the geared turbofan, it is likely to remain so in the future. As such, accurate modeling and prediction of the basic characteristics of fan noise are necessary ingredients in designing quieter aircraft engines in order to ensure compliance with ever more stringent aviation noise regulations. In this paper, results from a comprehensive study aimed at establishing the utility of current tools for modeling and predicting fan noise will be summarized. It should be emphasized that these tools exemplify present state of the practice and embody what is currently used at NASA and Industry for predicting fan noise. The ability of these tools to model and predict fan noise is assessed against a set of benchmark fan noise databases obtained for a range of representative fan cycles and operating conditions. Detailed comparisons between the predicted and measured narrowband spectral and directivity characteristics of fan nose will be presented in the full paper. General conclusions regarding the utility of current tools and recommendations for future improvements will also be given.

Envia, Ed↗
DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Evaluation of WRF-Solar Cloud Forecast Using the NSRDB: Preprint

Cloud forecast is a crucial component in predicting solar irradiance from numerical weather prediction (NWP) models. Assessing cloud properties from the NWP models requires significant work due to the need for high-quality data, spatial analysis covering model extent, and detailed analysis of model performance for different types of clouds. This study presents an evaluation of the WRF-Solar cloud forecast using the National Solar Radiation Database (NSRDB). We propose an evaluation framework applied to a single model prediction as well as ensemble-based forecasts. Various cloud detection metrics are calculated when comparing with the satellite-derived dataset. The mismatched clouds from the WRF-Solar model are quantified using nine cloud types classified by cloud top height and cloud optical depth. The results based on the WRF-Solar forecasts covering the entire U.S. for the full year of 2018 shows mismatched cloud frequency in the range of 8% - 46% for thick and high-level (deep convective) to thin and low-level (cumulus) clouds.

cloud mask forecast↗
DOE OSTI2019

Enhancing materials property prediction by leveraging computational and experimental data using deep transfer learning

Abstract The current predictive modeling techniques applied to Density Functional Theory (DFT) computations have helped accelerate the process of materials discovery by providing significantly faster methods to scan materials candidates, thereby reducing the search space for future DFT computations and experiments. However, in addition to prediction error against DFT-computed properties, such predictive models also inherit the DFT-computation discrepancies against experimentally measured properties. To address this challenge, we demonstrate that using deep transfer learning, existing large DFT-computational data sets (such as the Open Quantum Materials Database (OQMD)) can be leveraged together with other smaller DFT-computed data sets as well as available experimental observations to build robust prediction models. We build a highly accurate model for predicting formation energy of materials from their compositions; using an experimental data set of $$1,643$$ 1 , 643 observations, the proposed approach yields a mean absolute error (MAE) of $$0.07$$ 0.07 eV/atom, which is significantly better than existing machine learning (ML) prediction modeling based on DFT computations and is comparable to the MAE of DFT-computation itself.

36 MATERIALS SCIENCE↗
NASA NTRS1980

A comparison of radiative transfer models for predicting the microwave emission from soils

Two general types of numerical models for predicting microwave emission from soils are compared-coherent and noncoherent. In the former, radiation in the soil is treated coherently, and the boundary conditions on the electric fields across the layer boundaries are used to calculate the radiation intensity. In the latter, the radiation is assumed to be noncoherent, and the intensities of the radiation are considered directly. The results of the two approaches may be different because of the effects of interference, which can cause the transmitted intensity at the surface (i.e., emissivity) to be sometimes higher and sometimes lower for the coherent case than for the noncoherent case, depending on the relative phases of reflected fields from the lower layers. This coupling between soil layers in the coherent models leads to greater soil moisture sampling depths observed with this type of model, and is the major difference that is found between the two types of models. In noncoherent models, the emissivity is determined by the dielectric constraint at the air/soil interface. The subsequent differences in the results are functions of both the frequency of the radiation being considered and the steepness of the moisture gradient near the surface. The calculations were performed at frequencies of 1.4 and 19.4 GHz and for two sets of soil profiles. Little difference was observed between the models at 19.4 GHz; and only at the lower frequency were differences apparent because of the greater soil moisture sampling depth at this frequency.

Schmugge, T. J.↗
DOE OSTI2020

Modified Isotherm Modeling to Predict Cs Exchange with Crystalline Silicotitanate in Tank Waste Simulants

The U.S. Department of Energy is working to expedite processing of Hanford tank waste supernate at the Hanford Waste Treatment and Immobilization Plant. To support this goal, Washington River Protection Solutions is designing a Tank Side Cesium Removal (TSCR) system for suspended solids and cesium (Cs/ 137 Cs) removal from Hanford tank waste supernate. The ion exchange media selected for Cs removal at TSCR is crystalline silicotitanate (CST) that is manufactured in a nearly spherical form by Honeywell UOP LLC (UOP; Des Plaines, IL) as product IONSIV® R9140-B (Na form). The Zheng Anthony Miller (ZAM) isotherm model (Zheng et al. 1997) is a multicomponent ion exchange model used to predict the exchange of Group I metals onto CST. The ZAM isotherm has historically been used to predict Cs distribution values from Hanford and Savannah River Site (SRS) tank waste simulants. Figure S.1 summarizes model predictions from the ZAM isotherm that indicate poor prediction of Cs distribution values for simple and complex simulants with the engineered form of CST, IONSIV® R9140-B, and IONSIV® R9120-B where the solid line indicates a perfect fit by the model. The dotted lines indicate ±20% error. Batch contact testing with Hanford tank waste complex and simple simulants was used in conjunction with SRS simulants to experimentally determine Cs distribution values using a modification to the original isotherm model. The experimentally determined maximum Cs capacity for IONSIV® R9140-B CST in both the simple and complex matrices was found to be 0.53±0.3 mmoles Cs/g of CST. This value is not drastically different from the maximum Cs capacity of 0.58 mmoles Cs/g TAM-5 reported by Zheng et al. (1997). However, it is important to note that TAM-5 (commercially IONSIV® IE-910) is a powder. Hamm et al. (2002) determined that a dilution factor was needed to account for the Zr(OH)2 binder in the engineered form of CST. Hamm et al. determined that a dilution factor of 0.68 was appropriate to account for binder contribution and correct overprediction of Cs exchange on the engineered form of CST in ZAM calculations. This reduced the total capacity from 0.58 mmol/g with TAM-5 to 0.39 mmol/g for the engineered form of CST (Hamm et al. 2002). Despite substituting the experimentally determined maximum Cs capacity of 0.55 mmoles Cs/g for the literature-reported capacity of 0.39 mmoles Cs/g, it was determined that additional modifications to the model’s equilibrium rate constants were necessary in refining the isotherm model. The modified model overpredicted K+ uptake by the CST when compared to digested CST results described by Campbell et al. (2019). The modified model was further revised to omit three of the five K+ exchange equilibrium reactions described by ZAM to reduce the additional K+ loading seen by the model. Figure S.2 summarizes the revised model isotherm predictions plotted against measured Kd values.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Numerical convergence and validation of the DIMP inverse particle transport model

The data integration with modeled predictions (DIMP) model is a promising inverse radiation transport method for solving the special nuclear material (SNM) holdup problem. Unlike previous methods, DIMP is a completely passive nondestructive assay technique that requires no initial assumptions regarding the source distribution or active measurement time. DIMP predicts the most probable source location and distribution through Bayesian inference and quasi-Newtonian optimization of predicted detector re-sponses (using the adjoint transport solution) with measured responses. DIMP performs well with for-ward hemispherical collimation and unshielded measurements, but several considerations are required when using narrow-view collimated detectors. DIMP converged well to the correct source distribution as the number of synthetic responses increased. DIMP also performed well for the first experimental validation exercise after applying a collimation factor, and sufficiently reducing the source search vol-ume's extent to prevent the optimizer from getting stuck in local minima. DIMP's simple point detector response function (DRF) is being improved to address coplanar false positive/negative responses, and an angular DRF is being considered for integration with the next version of DIMP to account for highly collimated responses. Overall, DIMP shows promise for solving the SNM holdup inverse problem, especially once an improved optimization algorithm is implemented.

42 ENGINEERING↗
NASA NTRS2015

A Vertically Flow-Following, Icosahedral Grid Model for Medium-Range and Seasonal Prediction. Part 1: Model Description

A hydrostatic global weather prediction model based on an icosahedral horizontal grid and a hybrid terrain following/ isentropic vertical coordinate is described. The model is an extension to three spatial dimensions of a previously developed, icosahedral, shallow-water model featuring user-selectable horizontal resolution and employing indirect addressing techniques. The vertical grid is adaptive to maximize the portion of the atmosphere mapped into the isentropic coordinate subdomain. The model, best described as a stacked shallow-water model, is being tested extensively on real-time medium-range forecasts to ready it for possible inclusion in operational multimodel ensembles for medium-range to seasonal prediction.

mathematical models↗