A novel framework for cost-effectively reconstructing the global flow field by super-resolution

2021 ◽  
Vol 33 (9) ◽  
pp. 095105
Author(s):  
Longyan Wang ◽  
Zhaohui Luo ◽  
Jian Xu ◽  
Wei Luo ◽  
Jianping Yuan
Keyword(s):  
Flow Field ◽  
Super Resolution ◽  
Global Flow

scholarly journals On the global flow-field dynamics around an airfoil

2019 ◽  
Vol 213 ◽  
pp. 02089 ◽  
Author(s):  
Václav Uruba ◽  
Pavel Procházka ◽  
Vladislav Skála
Keyword(s):  
Flow Field ◽  
Global Flow ◽  
Time Resolved ◽  
Piv Technique

The global flow-field around an airfoil will be studied using time-resolved PIV technique. Interactions between pressure and suction sides flows will be studied in detail. Spanwise structures dynamics is to be analysed.

Flow in Automotive Alternators

1996 ◽  
Vol 210 (4) ◽  
pp. 283-292 ◽  
Author(s):  
T A Brungart ◽  
G A Meyer ◽  
G C Lauchle
Keyword(s):  
Flow Field ◽  
Flow Visualization ◽  
Laser Doppler Velocimetry ◽  
Rotational Velocity ◽  
Reynolds Numbers ◽  
Global Flow ◽  
Injection Flow ◽  
Visual Access ◽  
Clear Plastic ◽  
Automotive Alternator

A study of the flow field in and around an automotive alternator is described. The approach taken is to test the alternator in water at equivalent in-air Reynolds numbers in order to reduce the rotational velocity by a factor of approximately 15 and provide an environment ideal for flow visualization. The casing and stator were constructed from clear plastic in order to provide visual access inside the alternator. Dye injection flow visualization is used to assess qualitatively the global flow field. Laser Doppler velocimetry is used to perform a detailed, quantitative study of the alternator external flow field. Results from the flow field study are presented and modifications necessary for improved cooling and aerodynamic performance are identified and demonstrated.

CFD Driven Analysis of a Multi-Port Pressure Probe for Real Engine Testing

2017 ◽  
Author(s):  
Aude Lahalle ◽  
Fabrizio Fontaneto ◽  
Tony Arts
Keyword(s):  
Flow Field ◽  
Temperature Sensor ◽  
High Performance ◽  
Turbulence Models ◽  
Aircraft Engine ◽  
Pressure Probe ◽  
Global Flow ◽  
Sensor Location ◽  
Flow Topology ◽  
Engine Testing

During the development phase of an aircraft engine, extensive ground and flight instrumentation testing is performed. The performance of the instrumentation is quantified in terms of Recovery Factor (RF). Experimental data only provide measurement at the sensor location, but no information is available for the flow field taking place in and around the probe. The scope of the present paper is to numerically investigate the flow field taking place inside and around a three Kiel heads rake. The study will focus on the description of the flow field and aims at highlighting the mechanisms affecting the RF. Each head has an elliptical casing and contains a pressure sensor (pneumatic pressure tap) and a temperature sensor (thermocouple). Steady-state RANS simulations have been realized for two different Mach numbers (M = 0.30 and M = 0.65), providing a complete mesh and turbulence models sensitivity analysis. The simulated global flow field demonstrated that regardless of the Mach number, the flow topology is very similar in all cases. The Kiel heads investigation highlighted a well guided flow field confirming the sensors’ high performance.

scholarly journals Super-resolution reconstruction of turbulent flows with machine learning

2019 ◽  
Vol 870 ◽  
pp. 106-120 ◽  
Author(s):  
Kai Fukami ◽  
Koji Fukagata ◽  
Kunihiko Taira
Keyword(s):  
Machine Learning ◽  
Flow Field ◽  
Turbulent Flow ◽  
Turbulent Flows ◽  
Field Data ◽  
Super Resolution ◽  
Two Dimensional ◽  
Learning Models ◽  
Resolution Analysis ◽  
Machine Learning Models

We use machine learning to perform super-resolution analysis of grossly under-resolved turbulent flow field data to reconstruct the high-resolution flow field. Two machine learning models are developed, namely, the convolutional neural network (CNN) and the hybrid downsampled skip-connection/multi-scale (DSC/MS) models. These machine learning models are applied to a two-dimensional cylinder wake as a preliminary test and show remarkable ability to reconstruct laminar flow from low-resolution flow field data. We further assess the performance of these models for two-dimensional homogeneous turbulence. The CNN and DSC/MS models are found to reconstruct turbulent flows from extremely coarse flow field images with remarkable accuracy. For the turbulent flow problem, the machine-leaning-based super-resolution analysis can greatly enhance the spatial resolution with as little as 50 training snapshot data, holding great potential to reveal subgrid-scale physics of complex turbulent flows. With the growing availability of flow field data from high-fidelity simulations and experiments, the present approach motivates the development of effective super-resolution models for a variety of fluid flows.

The flow field due to a body in impulsive motion

1996 ◽  
Vol 325 ◽  
pp. 79-111 ◽  
Author(s):  
Renwei Mei ◽  
Christopher J. Lawrence
Keyword(s):  
Finite Difference ◽  
Flow Field ◽  
Transition Zone ◽  
Bluff Body ◽  
The Body ◽  
Matched Asymptotic Expansion ◽  
Volume Flux ◽  
Global Flow ◽  
Unsteady Wake ◽  
Long Time

An asymptotic analysis for the long-time unsteady laminar far wake of a bluff body due to a step change in its travelling velocity from U1 to U2 is presented. For U1 [ges ] 0 and U2 > 0, the laminar wake consists of a new wake of volume flux Q2 corresponding to the current velocity U2, an old wake of volume flux Q1 corresponding to the original velocity U1, and a transition zone that connects these two wakes. The transition zone acts as a sink (or a source) of volume flux (Q2 – Q1) and is moving away from the body at speed U2. Streamwise diffusion is negligible in the new and old wakes but a matched asymptotic expansion that retains the streamwise diffusion is required to determine the vorticity transport in the transition zone. A source of volume flux Q2 located near the body needs to be superposed on the unsteady wake to form the global flow field around the body. The asymptotic predictions for the unsteady wake velocity, unsteady wake vorticity, and the global flow field around the body agree well with finite difference solutions for flow over a sphere at finite Reynolds numbers. The long-time unsteady flow structures due to a sudden stop (U2 = 0) and an impulsive reverse (U1U2 < 0) of the body are analysed in detail based on the asymptotic solutions for the unsteady wakes and the finite difference solutions. The elucidation of the long-time behaviour of such unsteady flows provides a framework for understanding the long-time particle dynamics at finite Reynolds number.

scholarly journals Data-driven super-resolution reconstruction of supersonic flow field by convolutional neural networks

AIP Advances ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 065321
Author(s):  
Chen Kong ◽  
Juntao Chang ◽  
Ziao Wang ◽  
Yunfei Li ◽  
Wen Bao
Keyword(s):  
Neural Networks ◽  
Supersonic Flow ◽  
Flow Field ◽  
Convolutional Neural Networks ◽  
Super Resolution ◽  
Data Driven
Sign in / Sign up

Export Citation Format

Share Document