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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 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Dichotomy between freshwater and heat flux effects on oceanic conveyor belt stability and global climate

The Atlantic meridional overturning circulation is an important global-scale oceanic circulation, and its changes may be responsible for past abrupt climate change events. By using two versions of a coupled climate model, here we show that the stability of this circulation depends not only on the background climate, but also on the type of primary external forcing: freshwater vs. greenhouse gases. When freshwater forcing is dominant, hysteresis of this circulation (an abrupt collapse/reactivation) becomes possible only under simulated glacial conditions with closed Bering Strait. Under present day and future conditions, both freshwater and greenhouse gas forcings could collapse this circulation, but only greenhouse gas forcing produced a bi-stable equilibrium state comparable to abrupt climate change. Our results demonstrate that the Bering Strait status (open vs. closed) may facilitate or prohibit the existence of this circulation’s hysteresis, irrespective of the background climate conditions, but is directly related to the primary forcing.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quasilinear modeling of heat flux from microtearing turbulence

Microtearing (MT) turbulence affects plasma confinement and profile evolution in many different magnetic-confinement scenarios, ranging from tokamak core plasmas to the H-mode pedestal and from spherical tokamaks to reversed-field pinches. Thus, an urgent need exists to design and test reduced models of MT turbulence and transport. Here, a heuristic quasilinear model is composed and verified by comparing its predictions against nonlinear MT simulations. It is found to yield good predictions of magnetic flutter transport as key parameters are varied: the collision frequency, the electron temperature gradient, and the normalized plasma pressure.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Hybrid Improved Empirical Mode Decomposition and Artificial Neural Network Model for the Prediction of Critical Heat Flux (CHF)

Three Hybrid artificial neural network (ANN) models namely radial basis function (RBF), generalized regression neural networks (GRNN), and multi-layer perceptron (MLP) combined with empirical mode decomposition (EMD) are developed for CHF predictive modelling using CHF experimental databases.First, the original experimental inputs data series are decomposed into several intrinsic mode functions (IMFs) and one residual by EMD, whose components are divided into high, medium and low components. The performance parameters of the hybrid models indicates that the root mean square error (RMSE) are 0.8831, 0.6522, and 0.4149; the mean absolute error (MAE) are 0.6697, 0.4636, and 0.1935. The values of the R-square of the developed prediction approach utilizing EMD-RBF, EMD-GRNN, and EMD-MLP models are 0.8553, 0.9302, and 0.9818, and the index of agreement are 0.9464, 0.9700, and 0.9894., The value of the R-square and the index of agreement of the proposed models are much higher than those of the simple models .The Pearson's test results show that the association strength between the measured and the predicted values of the proposed model EMD-MLP is the strongest. These results show the following: (a) compared with other related, recent studies, the prediction accuracy of the hybrid model EMD- MLP proposed in this research is the best hybrid model; (b) the proposed hybrid model (EMD-MLP) attains superior performance compared with simple models.

Djeddou, Messaoud↗