Probabilistic Flow Regime Map Modeling of Two-Phase Flow

The purpose of this investigation is to develop models for two-phase heat transfer, void fraction, and pressure drop, three key design parameters, in single, smooth, horizontal tubes using a common probabilistic two-phase flow regime basis. Probabilistic two-phase flow maps are experimentally developed for R134a at 25 ??C, 35 ??C, and 50 ??C, R410A at 25 ??C, mass fluxes from 100 to 600 kg/m2-s, qualities from 0 to 1 in 8.00 mm, 5.43 mm, 3.90 mm, and 1.74 mm I.D. horizontal, smooth, adiabatic tubes in order to extend probabilistic two-phase flow map modeling to single tubes. An automated flow visualization technique, utilizing image recognition software and a new optical method, is developed to classify the flow regimes present in approximately one million captured images. The probabilistic two-phase flow maps developed are represented as continuous functions and generalized based on physical parameters. Condensation heat transfer, void fraction, and pressure drop models are developed for single tubes utilizing the generalized flow regime map developed. The condensation heat transfer model is compared to experimentally obtained condensation data of R134a at 25 ??C in 8.915 mm diameter smooth copper tube with mass fluxes ranging from 100 to 300 kg/m2-s and a full quality range. The condensation heat transfer, void fraction, and pressure drop models developed are also compared to data found in the literature for a wide range of tube sizes, refrigerants, and flow conditions.Air Conditioning and Refrigeration Project 18

A new photobioreactor for continuous microalgal production in hatcheries based on external-loop airlift and swirling flow

This study deals with the scale of a new photobioreactor for continuous microalgal production in hatcheries. The combination of the state-of-art with the constraints inherent to hatcheries has turned the design into a closed, artificially illuminated and external-loop airlift configuration based on a succession of elementary modules, each one being composed of two transparent vertical interconnected columns. The liquid circulation is ensured pneumatically (air injections) with respect to a swirling motion (tangential inlets). A single module of the whole photobioreactor was built-up to investigate how parameters, such as air sparger type, gas flow rate, tangential inlet, column radius and height can influence radiative transfer, hydrodynamics, mass transfer and biological performances. The volumetric productivities were predicted by modeling radiative transfer and growth of Isochrysis affinis galbana (clone Tahiti). The hydrodynamics of the liquid phase was modeled in terms of global flow behavior (circulation and mixing times, Péclet number) and of swirling motion decay along the column (Particle Image Velocimetry). The aeration performances were determined by overall volumetric mass transfer measurements. Continuous cultures of Isochrysis affinis galbana (clone Tahiti) were run in two geometrical configurations, generating either an axial or a swirling flow. Lastly, the definitive options of design are presented as well as a 120 Liter prototype, currently implemented in a French mollusk hatchery and commercialized

EV-FlowNet: Self-Supervised Optical Flow Estimation for Event-based Cameras

Event-based cameras have shown great promise in a variety of situations where frame based cameras suffer, such as high speed motions and high dynamic range scenes. However, developing algorithms for event measurements requires a new class of hand crafted algorithms. Deep learning has shown great success in providing model free solutions to many problems in the vision community, but existing networks have been developed with frame based images in mind, and there does not exist the wealth of labeled data for events as there does for images for supervised training. To these points, we present EV-FlowNet, a novel self-supervised deep learning pipeline for optical flow estimation for event based cameras. In particular, we introduce an image based representation of a given event stream, which is fed into a self-supervised neural network as the sole input. The corresponding grayscale images captured from the same camera at the same time as the events are then used as a supervisory signal to provide a loss function at training time, given the estimated flow from the network. We show that the resulting network is able to accurately predict optical flow from events only in a variety of different scenes, with performance competitive to image based networks. This method not only allows for accurate estimation of dense optical flow, but also provides a framework for the transfer of other self-supervised methods to the event-based domain.Comment: 9 pages, 5 figures, 1 table. Accompanying video: https://youtu.be/eMHZBSoq0sE. Dataset: https://daniilidis-group.github.io/mvsec/, Robotics: Science and Systems 201

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Turbulent heat transfer in spacer-filled channels: Experimental and computational study and selection of turbulence models

Heat transfer in spacer-filled channels of the kind used in Membrane Distillation was studied in the Reynolds number range 100–2000, encompassing both steady laminar and early-turbulent flow conditions. Experimental data, including distributions of the local heat transfer coefficient h, were obtained by Liquid Crystal Thermography and Digital Image Processing. Alternative turbulence models, both of first order (k-ε, RNG k-ε, k-ω, BSL k-ω, SST k-ω) and of second order (LRR RS, SSG RS, ω RS, BSL RS), were tested for their ability to predict measured distributions and mean values of h. The best agreement with the experimental results was provided by first-order ω-based models able to resolve the viscous/conductive sublayer, while all other models, and particularly ε-based models using wall functions, yielded disappointing predictions

CFD modelling of two-phase stirred bioreaction systems by segregated solution of the Euler–Euler model

An advanced study of a bioreactor system involving a Navier–Stokes based model has been accomplished. The model allows a more realistic impeller induced flow image to be combined with the Monod bioreaction kinetics reported previously. The time-course of gluconic acid production by Aspergillus niger strain is simulated at kinetic conditions proposed in the literature. The simulation is based on (1) a stepwise solution strategy resolving first the fluid flow field, further imposing oxygen mass transfer and bioreaction with subsequent analysis of flow interactions, and (2) a segregated solution of the model replacing the multiple iterations per grid cell with single iterations. The numerical results are compared with experimental data for the bioreaction dynamics and show satisfactory agreement. The model is used for assessment of the viscosity effect upon the bioreactor performance. A 10-fold viscosity rise results in 2-fold decrease of KLa and 25% decrease of the specific gluconic acid production rate. The model allows better understanding of the mechanism of the important bioprocess

REGION SPECIFIC WAVELET COMPRESSION FOR 4K SURVEILLANCE IMAGES

For successful transmission of massively sequenced images during 4K surveillance operations large amount of data transfer cost high bandwidth, latency and delay of information transfer. Thus, there lies a need for real-time compression of this image sequences. In this study we present a region specific approach for wavelet based image compression to enable management of huge chunks of information flow by transforming Harr wavelets in hierarchical order.  Â

SIFT Flow: Dense Correspondence across Scenes and its Applications

While image alignment has been studied in different areas of computer vision for decades, aligning images depicting different scenes remains a challenging problem. Analogous to optical flow where an image is aligned to its temporally adjacent frame, we propose SIFT flow, a method to align an image to its nearest neighbors in a large image corpus containing a variety of scenes. The SIFT flow algorithm consists of matching densely sampled, pixel-wise SIFT features between two images, while preserving spatial discontinuities. The SIFT features allow robust matching across different scene/object appearances, whereas the discontinuity-preserving spatial model allows matching of objects located at different parts of the scene. Experiments show that the proposed approach robustly aligns complex scene pairs containing significant spatial differences. Based on SIFT flow, we propose an alignment-based large database framework for image analysis and synthesis, where image information is transferred from the nearest neighbors to a query image according to the dense scene correspondence. This framework is demonstrated through concrete applications, such as motion field prediction from a single image, motion synthesis via object transfer, satellite image registration and face recognition

S2WAT: Image Style Transfer via Hierarchical Vision Transformer using Strips Window Attention

This paper presents a new hierarchical vision Transformer for image style transfer, called Strips Window Attention Transformer (S2WAT), which serves as an encoder of encoder-transfer-decoder architecture. With hierarchical features, S2WAT can leverage proven techniques in other fields of computer vision, such as feature pyramid networks (FPN) or U-Net, to image style transfer in future works. However, the existing window-based Transformers will cause a problem that the stylized images will be grid-like when introduced into image style transfer directly. To solve this problem, we propose S2WAT whose representation is computed with Strips Window Attention (SpW Attention). The SpW Attention can integrate both local information and long-range dependencies in horizontal and vertical directions by a novel feature fusion scheme named Attn Merge. Qualitative and quantitative experiments demonstrate that S2WAT achieves comparable performance to state-of-the-art CNN-based, Flow-based, and Transformer-based approaches. The code and models are available at https://github.com/AlienZhang1996/S2WAT