The Use of the Fluid Flow Program Phoenics in Engineering Design

Introduction

10.1093/oso/9780198719878.003.0001 ◽

2018 ◽

Author(s):

Marcel Escudier

Keyword(s):

Fluid Flow ◽

Engineering Design ◽

Ocean Waves ◽

Basic Design ◽

Natural Phenomena ◽

Turbofan Engine ◽

Expansion Waves ◽

Supersonic Aircraft ◽

Bodily Fluids ◽

Design Calculations

In this chapter the wide array of engineering devices, from the kitchen tap (a valve) to supersonic aircraft, the basic design of which depends upon considerations of the flow of gases and liquids, is shown. Much the same is true of most natural phenomena from the atmosphere and our weather to ocean waves, and the movement of sperm and other bodily fluids. In this textbook a number of the concepts, principles, and procedures which underlie the analysis of any problem involving fluid flow or a fluid at rest are introduced. In this Introduction, examples have been selected for which, by the end of the book, the student should be in a position to make practically useful engineering-design calculations. These include a dam, a rocket motor, a supersonic aerofoil with shock and expansion waves, a turbojet engine, a turbofan engine, and the blading of a gas turbine.

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Using Physics-Informed Generative Adversarial Networks to Perform Super-Resolution for Multiphase Fluid Simulations

10.1115/detc2021-66719 ◽

2021 ◽

Author(s):

Matthew Li ◽

Christopher McComb

Keyword(s):

Machine Learning ◽

Fluid Flow ◽

Engineering Design ◽

Computational Cost ◽

Super Resolution ◽

Generative Adversarial Networks ◽

Generative Adversarial Network ◽

Turbulent Fluid Flow ◽

Turbulent Fluid ◽

Multiphase Fluid

Abstract Computational Fluid Dynamics (CFD) simulations are useful to the field of engineering design as they provide deep insights on product or system performance without the need to construct and test physical prototypes. However, they can be very computationally intensive to run. Machine learning methods have been shown to reconstruct high-resolution single-phase turbulent fluid flow simulations from low-resolution inputs. This offers a potential avenue towards alleviating computational cost in iterative engineering design applications. However, little work thus far has explored the application of machine learning image super-resolution methods to multiphase fluid flow (which is important for important for emerging fields such as marine hydrokinetic energy conversion). In this work, we apply a modified version of the Super-Resolution Generative Adversarial Network (SRGAN) model to a multiphase turbulent fluid flow problem, specifically to reconstruct fluid phase fraction at a higher resolution. Two models were created in this work, one with a simple physics-constrained loss function and one without, and the results are discussed and analyzed. We found that both models were able to significantly outperform non-machine learning upsampling methods and can preserve an impressive amount of detail and nuance, showing the versatility of the SRGAN model for upsampling fluid simulations. However, the difference in accuracy between the two models is quite minimal. This indicates that, for these contexts studied here, the additional complexity of a physics-informed approach may not be justified.

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Resolution and measurement in the scanning electron microscope

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100127256 ◽

1987 ◽

Vol 45 ◽

pp. 534-537 ◽

Cited By ~ 2

Author(s):

Michael T. Postek

Keyword(s):

Electron Microscope ◽

Scanning Electron Microscope ◽

Engineering Design ◽

Resolving Power ◽

Practical Applications ◽

Instrument Resolution ◽

Accurate Definition ◽

Definition Of ◽

Scanning Electron ◽

Better Than

The term ultimate resolution or resolving power is the very best performance that can be obtained from a scanning electron microscope (SEM) given the optimum instrumental conditions and sample. However, as it relates to SEM users, the conventional definitions of this figure are ambiguous. The numbers quoted for the resolution of an instrument are not only theoretically derived, but are also verified through the direct measurement of images on micrographs. However, the samples commonly used for this purpose are specifically optimized for the measurement of instrument resolution and are most often not typical of the sample used in practical applications.SEM RESOLUTION. Some instruments resolve better than others either due to engineering design or other reasons. There is no definitively accurate definition of how to quantify instrument resolution and its measurement in the SEM.

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