Electromagnetic Waves and Fluid Mechanical Theory

Mapping Intimacies ◽

10.31219/osf.io/xar3m ◽

2021 ◽

Author(s):

Nicholas Alexander Scott

Keyword(s):

Neural Network ◽

Wireless Network ◽

Electromagnetic Waves ◽

Stokes Equations ◽

Navier Stokes ◽

Schumann Resonance ◽

Mechanical Theory ◽

Navier Stokes Equations ◽

Stokes Constant

The cause for the Navier-Stokes constant values are in my research because of the Schumann Resonance. “Earth’s Frequency.” It is because of this frequency that there is balance at all in the Navier-Stokes equations. If these frequencies could be quantified, a neural network could even be made containing such EM frequencies of 8 hertz resulting in a possible unknown Network like that of even Ted Nelson’s theorized wireless network, “Xanadu.” With the Navier-Stokes equations proven Ted Nelson’s Xanadu Project could even be created if quantified.

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Transfer learning for deep neural network-based partial differential equations solving

Advances in Aerodynamics ◽

10.1186/s42774-021-00094-7 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Xinhai Chen ◽

Chunye Gong ◽

Qian Wan ◽

Liang Deng ◽

Yunbo Wan ◽

...

Keyword(s):

Neural Network ◽

Partial Differential Equations ◽

Differential Equations ◽

Transfer Learning ◽

Stokes Equations ◽

Surrogate Models ◽

Training Data ◽

Navier Stokes ◽

Partial Differential ◽

Navier Stokes Equations

AbstractDeep neural networks (DNNs) have recently shown great potential in solving partial differential equations (PDEs). The success of neural network-based surrogate models is attributed to their ability to learn a rich set of solution-related features. However, learning DNNs usually involves tedious training iterations to converge and requires a very large number of training data, which hinders the application of these models to complex physical contexts. To address this problem, we propose to apply the transfer learning approach to DNN-based PDE solving tasks. In our work, we create pairs of transfer experiments on Helmholtz and Navier-Stokes equations by constructing subtasks with different source terms and Reynolds numbers. We also conduct a series of experiments to investigate the degree of generality of the features between different equations. Our results demonstrate that despite differences in underlying PDE systems, the transfer methodology can lead to a significant improvement in the accuracy of the predicted solutions and achieve a maximum performance boost of 97.3% on widely used surrogate models.

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Artificial neural network method for solving the Navier–Stokes equations

Neural Computing and Applications ◽

10.1007/s00521-014-1762-2 ◽

2014 ◽

Vol 26 (4) ◽

pp. 765-773 ◽

Cited By ~ 22

Author(s):

M. Baymani ◽

S. Effati ◽

H. Niazmand ◽

A. Kerayechian

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Stokes Equations ◽

Navier Stokes ◽

Navier Stokes Equations ◽

Neural Network Method ◽

Network Method ◽

Artificial Neural Network Method ◽

Artificial Neural

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Remarks on the asymptotic behaviour of solutions to the compressible Navier–Stokes equations in the half-line

Proceedings of the Royal Society of Edinburgh Section A Mathematics ◽

10.1017/s030821050200032x ◽

2002 ◽

Vol 132 (03) ◽

pp. 627

Keyword(s):

Asymptotic Behaviour ◽

Stokes Equations ◽

Half Line ◽

Navier Stokes ◽

Navier Stokes Equations ◽

Asymptotic Behaviour Of Solutions

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Effects of stator splitter blades on aerodynamic performance of a single-stage transonic axial compressor

JOURNAL OF MECHANICAL ENGINEERING AND SCIENCES ◽

10.15282/jmes.14.4.2020.05.0579 ◽

2020 ◽

Vol 14 (4) ◽

pp. 7369-7378

Author(s):

Ky-Quang Pham ◽

Xuan-Truong Le ◽

Cong-Truong Dinh

Keyword(s):

Stokes Equations ◽

Three Dimensional ◽

Axial Compressor ◽

Aerodynamic Performance ◽

Numerical Results ◽

Single Stage ◽

Navier Stokes ◽

Navier Stokes Equations ◽

Operating Stability ◽

Length Coverage

Splitter blades located between stator blades in a single-stage axial compressor were proposed and investigated in this work to find their effects on aerodynamic performance and operating stability. Aerodynamic performance of the compressor was evaluated using three-dimensional Reynolds-averaged Navier-Stokes equations using the k-e turbulence model with a scalable wall function. The numerical results for the typical performance parameters without stator splitter blades were validated in comparison with experimental data. The numerical results of a parametric study using four geometric parameters (chord length, coverage angle, height and position) of the stator splitter blades showed that the operational stability of the single-stage axial compressor enhances remarkably using the stator splitter blades. The splitters were effective in suppressing flow separation in the stator domain of the compressor at near-stall condition which affects considerably the aerodynamic performance of the compressor.

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