Settling Dynamics of Spherical Particles in Fracking Fluids

3rd place, 2016 Denman Undergraduate Research ForumHydraulic fracturing is an enhanced oil and gas recovery method that involves pumping fluids and proppants into shale fractures. Fracking fluid is formulated to keep proppants suspended for a desired time. The flow of fracking fluid and the settling dynamics of proppants are of great interest in fracking wells. This research aims to add fiber suspensions to fracking fluid system to reduce the amount of chemical additives without compromising performance. In this research, fracking fluid samples are rheologically characterized, settling rates of rigid, dense spherical particles in fracking fluids are measured. Experimental conditions are reproduced using COMSOL Multiphysics. Simulation results are compared against experimental data and other modeling attempts. The fracking fluid samples are homogenous mixtures of deionized water, 0.5 wt% guar, <0.1% sodium tetraborate and chopped fiber. Chopped fiber investigated nylon fiber with diameters of 25 and 38 microns and length of 6 and 10 millimeters and glass fiber with diameters of 50 microns and length of 3 and 6 millimeters. Fiber concentrations range from 0 to 15 nL3, where n is number of fiber per milliliter of fracking fluid. Results show that by fitting fracking fluids with Cross Model and using COMSOL laminar flow simulation, settling rates of proppants in fracking fluids can be accurately predicted. It is also observed both experimentally and computationally that addition of fiber reduces particle settling rate by more than 50%.A three-year embargo was granted for this item.Academic Major: Chemical Engineerin

Deep Learning Face Attributes in the Wild

Predicting face attributes in the wild is challenging due to complex face variations. We propose a novel deep learning framework for attribute prediction in the wild. It cascades two CNNs, LNet and ANet, which are fine-tuned jointly with attribute tags, but pre-trained differently. LNet is pre-trained by massive general object categories for face localization, while ANet is pre-trained by massive face identities for attribute prediction. This framework not only outperforms the state-of-the-art with a large margin, but also reveals valuable facts on learning face representation. (1) It shows how the performances of face localization (LNet) and attribute prediction (ANet) can be improved by different pre-training strategies. (2) It reveals that although the filters of LNet are fine-tuned only with image-level attribute tags, their response maps over entire images have strong indication of face locations. This fact enables training LNet for face localization with only image-level annotations, but without face bounding boxes or landmarks, which are required by all attribute recognition works. (3) It also demonstrates that the high-level hidden neurons of ANet automatically discover semantic concepts after pre-training with massive face identities, and such concepts are significantly enriched after fine-tuning with attribute tags. Each attribute can be well explained with a sparse linear combination of these concepts.Comment: To appear in International Conference on Computer Vision (ICCV) 201