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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

A Deep Learning Parameterization for Ozone Dry Deposition Velocities

Abstract The loss of ozone to terrestrial and aquatic systems, known as dry deposition, is a highly uncertain process governed by turbulent transport, interfacial chemistry, and plant physiology. We demonstrate the value of using Deep Neural Networks (DNN) in predicting ozone dry deposition velocities. We find that a feedforward DNN trained on observations from a coniferous forest site (Hyytiälä, Finland) can predict hourly ozone dry deposition velocities at a mixed forest site (Harvard Forest, Massachusetts) more accurately than modern theoretical models, with a reduction in the normalized mean bias (0.05 versus ~0.1). The same DNN model, when driven by assimilated meteorology at 2° × 2.5° spatial resolution, outperforms the Wesely scheme as implemented in the GEOS‐Chem model. With more available training data from other climate and ecological zones, this methodology could yield a generalizable DNN suitable for global models.

Silva, S. J.↗
DOE OSTI2013

Bifurcation analysis and dimension reduction of a predator-prey model for the L-H transition

The L-H transition denotes a shift to an improved confinement state of a toroidal plasma in a fusion reactor. A model of the L-H transition is required to simulate the time dependence of tokamak discharges that include the L-H transition. A 3-ODE predator-prey type model of the L-H transition is investigated with bifurcation theory of dynamical systems. The analysis shows that the model contains three types of transitions: an oscillating transition, a sharp transition with hysteresis, and a smooth transition. The model is recognized as a slow-fast system. A reduced 2-ODE model consisting of the full model restricted to the flow on the critical manifold is found to contain all the same dynamics as the full model. This means that all the dynamics in the system is essentially 2-dimensional, and a minimal model of the L-H transition could be a 2-ODE model.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Reduction-dependent siderophore assimilation in a model pennate diatom

Significance Diatoms can access inorganic iron with remarkable efficiency, but this process is contingent on carbonate ion concentration. As ocean acidification reduces carbonate concentration, inorganic iron uptake may be discouraged in favor of carbonate-independent uptake. We report details of an iron assimilation process that needs no carbonate but requires exogenous compounds produced by cooccurring organisms. We show this process to be critical for diatom growth at high siderophore concentrations, but ineffective at acquiring iron from low-affinity organic chelators or lithogenic particulates. Understanding the caveats associated with iron source preference in diatoms will help predict the impacts of climate change on microbial community structure in high-nitrate low-chlorophyll ecosystems.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

Machine-Learning Analysis of Moisture Carryover in Boiling Water Reactors

Moisture carryover (MCO) is modeled in the General Electric Type-4 boiling water reactor (BWR) using machine-learning methods and data from operating plants. Understanding MCO and the conditions that give rise to an elevated value is important since excessive MCO can damage critical turbine components, can result in elevated dose levels to on-site personnel, and can interfere with late-cycle power management. The analysis of MCO takes into account simplifying reactor symmetries and important geometric dependencies. The plant data are taken from several reactors and were collected over multiple years and multiple fuel cycles. A brief description of the origin of MCO in U.S. BWR plants is given. A machine-learning model is constructed from the data using applicable algorithms and data-reduction techniques. Matching model complexity with available data is one of the more challenging machine-learning tasks. Too many features and too little data will lead to overfitting. The data for each fuel cycle included over 6876 original features, 9 for each fuel bundle. Two approaches are used to reduce the data set into a manageable number of features. The first was an engineering analysis that resulted in the selection of steam quality Q and steam liquid phase velocity V L as the main features driving MCO. Using a Q and a V L for each fuel bundle gives 1528 Q and a V L feature describing the reactor behavior. An analysis of different functional forms of these two variables led to the actual inputs to the neural network model. The second approach involved the use of statistical techniques such as Pearson’s correlation and k-means analysis. The identified groupings of bundles behaved similarly. Treating each grouping as a single feature further reduced the input variable set to a manageable number. Here, a model selection criterion is proposed, and results are presented along with a discussion of related issues.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1974

The use of geomagnetic field models in magnetic surveys

The importance of global geomagnetic field models for the reduction of magnetic surveys is discussed. It is demonstrated that a numerical model with adequate secular variation correction, provides a suitable representation of the regional field. The limitations of the presently available models are reported, with emphasis on the International Geomagnetic Reference Field.

Regan, R. D.↗
NASA NTRS1989

On the control of elastic vehicles - Model simplification and stability robustness

Quantitative criteria are presented for model simplification, or order reduction, such that the reduced order model may be used to synthesize and evaluate a control law, and the stability and stability robustness obtained using the reduced-order model will be preserved when controlling the full-order system. The error introduced due to model simplification is treated as modeling uncertainty, and some of the results from multivariable robustness theory are brought to bear on the model simplification problem. A numerical procedure developed previously is shown to lead to results that meet the necessary criteria. The procedure is applied to reduce the model of a flexible aircraft. Also, the importance of the control law itself, in meeting the modeling criteria, is underscored. An example is included that demonstrates that an apparently robust control law actually amplifies modest modeling errors in the critical frequency region, and leads to undesirable results. The cause of this problem is identified to be associated with the canceling of lightly-damped transmission zeroes in the plant.

Schmidt, David K.↗
NASA NTRS1992

Reduction of Marine Magnetic Data for Modeling the Main Field of the Earth

The marine data set archived at the National Geophysical Data Center (NGDC) consists of shipborne surveys conducted by various institutes worldwide. This data set spans four decades (1953, 1958, 1960-1987), and contains almost 13 million total intensity observations. These are often less than 1 km apart. These typically measure seafloor spreading anomalies with amplitudes of several hundred nanotesla (nT) which, since they originate in the crust, interfere with main field modeling. The source for these short wavelength features are confined within the magnetic crust (i.e., sources above the Curie isotherm). The main field, on the other hand, is of much longer wavelengths and originates within the earth's core. It is desirable to extract the long wavelength information from the marine data set for use in modeling the main field. This can be accomplished by averaging the data along the track. In addition, those data which are measured during periods of magnetic disturbance can be identified and eliminated. Thus, it should be possible to create a data set which has worldwide data distribution, spans several decades, is not contaminated with short wavelengths of the crustal field or with magnetic storm noise, and which is limited enough in size to be manageable for the main field modeling. The along track filtering described above has proved to be an effective means of condensing large numbers of shipborne magnetic data into a manageable and meaningful data set for main field modeling. Its simplicity and ability to adequately handle varying spatial and sampling constraints has outweighed consideration of more sophisticated approaches. This filtering technique also provides the benefits of smoothing out short wavelength crustal anomalies, discarding data recorded during magnetically noisy periods, and assigning reasonable error estimates to be used in the least square modeling. A useful data set now exists which spans 1953-1987.

Baldwin, R. T.↗
DOE OSTI2017

Optimal Sizing of Energy Storage for Community Microgrids Considering Building Thermal Dynamics

This paper proposes an optimization model for the optimal sizing of energy storage in community microgrids considering the building thermal dynamics and customer comfort preference. The proposed model minimizes the annualized cost of the community microgrid, including energy storage investment, purchased energy cost, demand charge, energy storage degradation cost, voluntary load shedding cost and the cost associated with customer discomfort due to room temperature deviation. The decision variables are the power and energy capacity of invested energy storage. In particular, we assume the heating, ventilation and air-conditioning (HVAC) systems can be scheduled intelligently by the microgrid central controller while maintaining the indoor temperature in the comfort range set by customers. For this purpose, the detailed thermal dynamic characteristics of buildings have been integrated into the optimization model. Numerical simulation shows significant cost reduction by the proposed model. The impacts of various costs on the optimal solution are investigated by sensitivity analysis.

Liu, Guodong↗
NASA NTRS2014

Elastomeric Structural Attachment Concepts for Aircraft Flap Noise Reduction - Challenges and Approaches to Hyperelastic Structural Modeling and Analysis

Airframe noise is a significant part of the overall noise of transport aircraft during the approach and landing phases of flight. Airframe noise reduction is currently emphasized under the Environmentally Responsible Aviation (ERA) and Fixed Wing (FW) Project goals of NASA. A promising concept for trailing-edge-flap noise reduction is a flexible structural element or link that connects the side edges of the deployable flap to the adjacent main-wing structure. The proposed solution is distinguished by minimization of the span-wise extent of the structural link, thereby minimizing the aerodynamic load on the link structure at the expense of increased deformation requirement. Development of such a flexible structural link necessitated application of hyperelastic materials, atypical structural configurations and novel interface hardware. The resulting highly-deformable structural concept was termed the FLEXible Side Edge Link (FLEXSEL) concept. Prediction of atypical elastomeric deformation responses from detailed structural analysis was essential for evaluating feasible concepts that met the design constraints. The focus of this paper is to describe the many challenges encountered with hyperelastic finite element modeling and the nonlinear structural analysis of evolving FLEXSEL concepts. Detailed herein is the nonlinear analysis of FLEXSEL concepts that emerged during the project which include solid-section, foamcore, hollow, extended-span and pre-stressed concepts. Coupon-level analysis performed on elastomeric interface joints, which form a part of the FLEXSEL topology development, are also presented.

Sreekantamurthy, Thammaiah↗