Learning to Interpret Fluid Type Phenomena via Images

Learning to interpret fluid-type phenomena via images is a long-standing challenging problem in computer vision. The problem becomes even more challenging when the fluid medium is highly dynamic and refractive due to its transparent nature. Here, we consider imaging through such refractive fluid media like water and air. For water, we design novel supervised learning-based algorithms to recover its 3D surface as well as the highly distorted underground patterns. For air, we design a state-of-the-art unsupervised learning algorithm to predict the distortion-free image given a short sequence of turbulent images. Specifically, we design a deep neural network that estimates the depth and normal maps of a fluid surface by analyzing the refractive distortion of a reference background pattern. Regarding the recovery of severely downgraded underwater images due to the refractive distortions caused by water surface fluctuations, we present the distortion-guided network (DG-Net) for restoring distortion-free underwater images. The key idea is to use a distortion map to guide network training. The distortion map models the pixel displacement caused by water refraction. Furthermore, we present a novel unsupervised network to recover the latent distortion-free image. The key idea is to model non-rigid distortions as deformable grids. Our network consists of a grid deformer that estimates the distortion field and an image generator that outputs the distortion-free image. By leveraging the positional encoding operator, we can simplify the network structure while maintaining fine spatial details in the recovered images. We also develop a combinational deep neural network that can simultaneously perform recovery of the latent distortion-free image as well as 3D reconstruction of the transparent and dynamic fluid surface. Through extensive experiments on simulated and real captured fluid images, we demonstrate that our proposed deep neural networks outperform the current state-of-the-art on solving specific tasks

Experimental investigation and CFD simulation of slug flow in horizontal channels

For the investigation of stratified two-phase flow, two horizontal channels with rectangular cross-section were built at Forschungszentrum Dresden-Rossendorf (FZD). The channels allow the investigation of air/water co-current flows, especially the slug behaviour, at atmospheric pressure and room temperature. The test-sections are made of acrylic glass, so that optical techniques, like high-speed video observation or particle image velocimetry (PIV), can be applied for measurements. The rectangular cross-section was chosen to provide better observation possibilities. Moreover, dynamic pressure measurements were performed and synchronised with the high-speed camera system. CFD post-test simulations of stratified flows were performed using the code ANSYS CFX. The Euler-Euler two fluid model with the free surface option was applied on grids of minimum 4∙105 control volumes. The turbulence was modelled separately for each phase using the k-ω based shear stress transport (SST) turbulence model. The results compare well in terms of slug formation, velocity, and breaking. The qualitative agreement between calculation and experiment is encouraging and shows that CFD can be a useful tool in studying horizontal two-phase flow. Furthermore, CFD pre-test calculations were done to show the possibility of slug flow generation in a real geometry and at relevant parameters for nuclear reactor safety. The simulation was performed on a flat model representing the hot-leg of the German Konvoi-reactor, with water and saturated steam at 50 bar and 263.9°C. The results of the CFD-calculation show wave generation in the horizontal part of the hot-leg which grow to slugs in the region of the bend

New devices for flow measurements: Hot film and burial wire sensors, infrared imagery, liquid crystal, and piezo-electric model

An experimental program aimed at identifying areas in low speed aerodynamic research where infrared imaging systems can make significant contributions is discussed. Implementing a new technique, a long electrically heated wire was placed across a laminar flow. By measuring the temperature distribution along the wire with the IR imaging camera, the flow behavior was identified

Flame front propagation velocity measurement and in-cylinder combustion reconstruction using POET

The objective of this thesis is to develop an intelligent diagnostic technique POET (Passive Optical Emission Tomography) for the investigation of in cylinder combustion chemiluminescence. As a non-intrusive optical system, the POET system employs 40 fibre optic cables connected to 40 PMTs (Photo Multiplier Tube) to monitor the combustion process and flame front propagation in a modified commercial OHV (Over Head Valve) Pro 206 IC engine. The POET approach overcomes several limitations of present combustion research methods using a combination of fibre optic detection probes, photomultipliers and a tomographic diagnostics. The fibre optic probes are placed on a specially designed cylinder head gasket for non-invasively inserting cylinder. Each independent probe can measure the turbulent chemiluminescence of combustion flame front at up to 20 kHz. The resultant intensities can then be gathered tomographically using MART (Multiplicative Algebraic Reconstruction Technique) software to reconstruct an image of the complete flame-front. The approach is essentially a lensless imaging technique, which has the advantage of not requiring a specialized engine construction with conventional viewing ports to visualize the combustion image. The fibre optic system, through the use of 40, 2m long thermally isolated fibre optic cables can withstand combustion temperatures and is immune from electronic noise, typically generated by the spark plug. The POET system uses a MART tomographic methodology to reconstruct the turbulent combustion process. The data collected has been reconstructed to produce a temporal and spatial image of the combustion flame front. The variations of lame turbulence are monitored by sequences of reconstructed images. Therefore, the POET diagnostic technique reduces the complications of classic flame front propagation measurement systems and successfully demonstrates the in-cylinder combustion process. In this thesis, a series of calibration exercises have been performed to ensure that the photomultipliers of the POET system have sufficient temporal and spatial resolution to quantitatively map the flow velocity turbulence and chemiluminescence of the flame front. In the results, the flame has been analyzed using UV filters and blue filters to monitor the modified natural gas fuel engine. The flame front propagation speed has been evaluated and it is, on average, 12 m/s at 2280 rpm. Sequences of images have been used to illustrate the combustion explosion process at different rpm

Twenty-five years of aerodynamic research with IR imaging: A survey

Infrared imaging used in aerodynamic research evolved during the last 25 years into a rewarding experimental technique for investigation of body-flow viscous interactions, such as heat flux determination and boundary layer transition. The technique of infrared imaging matched well its capability to produce useful results, with the expansion of testing conditions in the entire spectrum of wind tunnels, from hypersonic high-enthalpy facilities to cryogenic transonic wind tunnels. With unique achievements credited to its past, the current trend suggests a change in attitude towards this technique: from the perception as an exotic, project-oriented tool, to the status of a routine experimental procedure