Quantifying the impacts of urban wind sheltering on the building energy consumption

Common building energy modeling approaches do not account for the influence of surrounding neighborhood on the energy consumption patterns. This thesis develops a framework to quantify the neighborhood impact on a building energy consumption based on the local wind flow. The airflow in the neighborhood is predicted using Computational Fluid Dynamics (CFD) in eight principal wind directions. The developed framework in this study benefits from wind multipliers to adjust the wind velocity encountering the target building. The input weather data transfers the adjusted wind velocities to the building energy model. In a case study, the CFD method is validated by comparing with on-site temperature measurements, and the building energy model is calibrated using utilities data. A comparison between using the adjusted and original weather data shows that the building energy consumption and air system heat gain decreased by 5% and 37%, respectively, while the cooling gain increased by 4% annually.

Leveraging Multiple Features for Image Retrieval and Matching

The goal of image retrieval and matching is to find and locate object instances in images from a large-scale image database. While visual features are abundant, how to combine them to improve performance by individual features remains a challenging task. In this work, we focus on leveraging multiple features for accurate and efficient image retrieval and matching. We first propose two graph-based approaches to rerank initially retrieved images for generic image retrieval. In the graph, vertices are images while edges are similarities between image pairs. Our first approach employs a mixture Markov model based on a random walk model on multiple graphs to fuse graphs. We introduce a probabilistic model to compute the importance of each feature for graph fusion under a naive Bayesian formulation, which requires statistics of similarities from a manually labeled dataset containing irrelevant images. To reduce human labeling, we further propose a fully unsupervised reranking algorithm based on a submodular objective function that can be efficiently optimized by greedy algorithm. By maximizing an information gain term over the graph, our submodular function favors a subset of database images that are similar to query images and resemble each other. The function also exploits the rank relationships of images from multiple ranked lists obtained by different features. We then study a more well-defined application, person re-identification, where the database contains labeled images of human bodies captured by multiple cameras. Re-identifications from multiple cameras are regarded as related tasks to exploit shared information. We apply a novel multi-task learning algorithm using both low level features and attributes. A low rank attribute embedding is joint learned within the multi-task learning formulation to embed original binary attributes to a continuous attribute space, where incorrect and incomplete attributes are rectified and recovered. To locate objects in images, we design an object detector based on object proposals and deep convolutional neural networks (CNN) in view of the emergence of deep networks. We improve a Fast RCNN framework and investigate two new strategies to detect objects accurately and efficiently: scale-dependent pooling (SDP) and cascaded rejection classifiers (CRC). The SDP improves detection accuracy by exploiting appropriate convolutional features depending on the scale of input object proposals. The CRC effectively utilizes convolutional features and greatly eliminates negative proposals in a cascaded manner, while maintaining a high recall for true objects. The two strategies together improve the detection accuracy and reduce the computational cost.

SOVLENT REACTIVITY AND INTERFACE EVOLUTION AT MODEL ELECTRODES FOR ENERGY APPLICATIONS

The Li-ion rechargeable battery (LIB) is widely used as an energy storage device, but has significant limitations in battery cycle life and safety. During initial charging, decomposition of the ethylene carbonate (EC)-based electrolytes of the LIB leads to the formation of a passivating layer on the anode known as the solid electrolyte interphase (SEI). The formation of an SEI has great impact on the cycle life and safety of LIB, yet mechanistic aspects of SEI formation are not fully understood. In this dissertation, two surface science model systems have been created under ultra-high vacuum (UHV) to probe the very initial stage of SEI formation at the model carbon anode surfaces of LIB. The first model system, Model System I, is an lithium-carbonate electrolyte/graphite C(0001) system. I have developed a temperature programmed desorption/temperature programmed reaction spectroscopy (TPD/TPRS) instrument as part of my dissertation to study Model System I in quantitative detail. The binding strengths and film growth mechanisms of key electrolyte molecules on model carbon anode surfaces with varying extents of lithiation were measured by TPD. TPRS was further used to track the gases evolved from different reduction products in the early-stage SEI formation. The branching ratio of multiple reaction pathways was quantified for the first time and determined to be 70.% organolithium products vs. 30% inorganic lithium product. The obtained branching ratio provides important information on the distribution of lithium salts that form at the very onset of SEI formation. One of the key reduction products formed from EC in early-stage SEI formation is lithium ethylene dicarbonate (LEDC). Despite intensive studies, the LEDC structure in either the bulk or thin-film (SEI) form is unknown. To enable structural study, pure LEDC was synthesized and subject to synchrotron X-ray diffraction measurements (bulk material) and STM measurements (deposited films). To enable studies of LEDC thin films, Model System II, a lithium ethylene dicarbonate (LEDC)-dimethylformamide (DMF)/Ag(111) system was created by a solution microaerosol deposition technique. Produced films were then imaged by ultra-high vacuum scanning tunneling microscopy (UHV-STM). As a control, the dimethylformamide (DMF)-Ag(111) system was first prepared and its complex 2D phase behavior was mapped out as a function of coverage. The evolution of three distinct monolayer phases of DMF was observed with increasing surface pressure — a 2D gas phase, an ordered DMF phase, and an ordered Ag(DMF)2 complex phase. The addition of LEDC to this mixture, seeded the nucleation of the ordered DMF islands at lower surface pressures (DMF coverages), and was interpreted through nucleation theory. A structural model of the nucleation seed was proposed, and the implication of ionic SEI products, such as LEDC, in early-stage SEI formation was discussed.