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Projects in 2011–2012


DYNAR (Dynamic Network for Aquatic Robots) Collaboration, CGU: Shark Tracking Outreach Program (Joint Engineering/Mathematics)

Advisors
Weiqing Gu
Erin Byrne

The DYNAR Clinic team has developed a self-contained educational activity that uses robotics to introduce high-school students to engineering and related mathematics. The high-school students will build an inexpensive, aquatic, remotely operated vehicle (ROV), then use mathematical techniques to track a target using the ROV.

Team

  • Sydney Hanson (fall)
  • Hannah Kastein (fall)
  • Sarah Warkentin (fall) (Project Manager)
  • Michelle Fenxiao Chen (spring)
  • Kevin Kim (spring)
  • Spencer Tung (spring) (Project Manager)
  • Matthew Richman

E. & J. Gallo Winery: Livingston Cooperage Optimization Model

Advisor
Rachel Levy

E. & J. Gallo Winery is the largest winery in the world. Our project focuses on developing a mathematical model that finds an optimal combination of processing and storage tanks at the Livingston Winery, one of E. & J. Gallo's largest winemaking facilities. In addition to accomodating future grape harvests, these tanks must also meet the transfer and storage requirements of the winemaking process. To solve this problem, our team has developed a computer-based application that will return a cost-optimal tank mix.

Team

  • Kevin Black
  • Keiko Hiranaka (Project Manager)
  • Leon Liu
  • Maksym Taran

Los Alamos National Laboratory: Modeling Cooling System Alternative for LANL's Data Center (Joint Engineering/Mathematics)

Advisors
Patrick Little
Lisette de Pillis

Managing energy consumption is a critical problem in maintaining large data centers. The team developed a mathematical model to quantify the energy consumption for alternative cooling systemss, specifically for Los Alamos National Laboratory (LANL) facilities. This model was created by developing a comprehensive engineering analysis that minimizes the power consumption of the cooling system based on changing heat loads and weather given some set temperature inputs. Model outcomes have been validated against efficiency data provided by LANL and a sensitivity analysis. The model will aid LANL in renovating their cooling system to be more energy efficient.

Team

  • Roxie Bartholomew (fall) (Project Manager)
  • Jaclyn Olmos-Silverman (fall)
  • Michelle DeRienzo (spring)
  • Abby Korth (spring)
  • Mary Sullivan (spring) (Project Manager)
  • Daniel Furlong

Shell International Exploration & Production Inc.: Algorithms to Automate the Drilling Monitoring Process

Advisor
Talithia Williams

In 2002, Shell Oil began monitoring real-time drilling data from offshore rigs in order to detect and respond to potential problems as early as possible. In this project, we aim to design and implement an algorithm that monitors key drilling parameters in real time, automatically detects abnormal behavior, and alerts rig monitors of potential issues. This algorithm is intended to assist rig monitors in detecting deviating trends in drilling data and recognizing impending issues quickly.

Team

  • Emil Guliyev
  • Lindsay Hall (Project Manager)
  • Brandon Wei
  • Rebecca Young