Characterizing the effects of turbulence on sediment oxygen and sediment nitrate demand using flow-through reactors

Onondaga Lake has received the municipal effluent and industrial waste from the city of Syracuse for more than a century. Historically, 75 metric tons of mercury were discharged to the lake by chlor-alkali facilities. These legacy deposits of mercury now exist primarily in the lake sediments. Under anoxic conditions, methylmercury is produced in the sediments and can be released to the overlying water. Natural sedimentation processes are continuously burying the mercury deeper into the sediments. Eventually, the mercury will be buried to a depth where it no longer has an impact on the overlying water. In the interim, electron acceptor amendment systems can be installed to retard these chemical releases while the lake naturally recovers. Electron acceptor amendment systems are designed to meet the sediment oxygen demand in the sediment and maintain manageable hypolimnion oxygen concentrations. Historically, designs of these systems have been under designed resulting in failure. This stems from a mischaracterization of the sediment oxygen demand. Turbulence at the sediment water interface has been shown to impact sediment oxygen demand. The turbulence introduced by the electron amendment system can thus increase the sediment oxygen demand, resulting in system failure if turbulence is not factored into the design. Sediment cores were gathered and operated to steady state under several well characterized turbulence conditions. The relationship between sediment oxygen/nitrate demand and turbulence was then quantified and plotted. A maximum demand was exhibited at or above a fluid velocity of 2.0 mm•s-1. Below this velocity, demand decreased rapidly with fluid velocity as zero velocity was approached. Similar relationships were displayed by both oxygen and nitrate cores.

POSE ESTIMATION AND 3D RECONSTRUCTION USING SENSOR FUSION

A camera maps 3-dimensional (3D) world space to a 2-dimensional (2D) image space. In the process it loses the depth information, i.e., the distance from the camera focal point to the imaged objects. It is impossible to recover this information from a single image. However, by using two or more images from different viewing angles this information can be recovered, which in turn can be used to obtain the pose (position and orientation) of the camera. Using this pose, a 3D reconstruction of imaged objects in the world can be computed. Numerous algorithms have been proposed and implemented to solve the above problem; these algorithms are commonly called Structure from Motion (SfM). State-of-the-art SfM techniques have been shown to give promising results. However, unlike a Global Positioning System (GPS) or an Inertial Measurement Unit (IMU) which directly give the position and orientation respectively, the camera system estimates it after implementing SfM as mentioned above. This makes the pose obtained from a camera highly sensitive to the images captured and other effects, such as low lighting conditions, poor focus or improper viewing angles. In some applications, for example, an Unmanned Aerial Vehicle (UAV) inspecting a bridge or a robot mapping an environment using Simultaneous Localization and Mapping (SLAM), it is often difficult to capture images with ideal conditions. This report examines the use of SfM methods in such applications and the role of combining multiple sensors, viz., sensor fusion, to achieve more accurate and usable position and reconstruction information. This project investigates the role of sensor fusion in accurately estimating the pose of a camera for the application of 3D reconstruction of a scene. The first set of experiments is conducted in a motion capture room. These results are assumed as ground truth in order to evaluate the strengths and weaknesses of each sensor and to map their coordinate systems. Then a number of scenarios are targeted where SfM fails. The pose estimates obtained from SfM are replaced by those obtained from other sensors and the 3D reconstruction is completed. Quantitative and qualitative comparisons are made between the 3D reconstruction obtained by using only a camera versus that obtained by using the camera along with a LIDAR and/or an IMU. Additionally, the project also works towards the performance issue faced while handling large data sets of high-resolution images by implementing the system on the Superior high performance computing cluster at Michigan Technological University.