The proposed project is related to analysis and optimization of a system of potential interest to oil and gas industry in terms of oil-spill remediation. A fixed wing aircraft can be retrofitted with an oil dispersant system suitable for fast response in case of large oil-spills. This oil dispersant system is to satisfy certain requirements from the safety and efficiency perspectives. In other words, the forces developed on the dispersant system while deployed in flight are required to have minimal effect on the airframe of the aircraft.
Lightweight Composite Consoles for Aerospace Applications: Carbon fiber composites used in aerospace applications are subjected to harsh environments while in service. In particular, structural components are often exposed to oils, fuels and hydraulic fluids. Over time, these contaminants may affect the material properties of the composites and thus, the mechanical behavior of the components. This project will investigate the effect of various contaminants and combined stress environments on composite materials used in aerospace applications.
The effective planning of resources when scheduling maintenance tasks and repair jobs is an enormous challenge, especially for heavy industries such as aerospace and transportation manufacturers. In such industries, because of the product complexity and variety, not to mention continuous technological improvements, a broad range of maintenance tasks and high-performance services should be done over the course of a year to guarantee the safety and reliability of the products.
Teams in crisis management operate in uncertainty and time pressure conditions, which severely constrain team performance. Systems capable of detecting critical levels of cognitive functioning could help teams to adapt better to the situation they face by allowing an intelligent re-allocation of tasks across agents. Traditionally, adaptive systems are based on the operator behavioral response (such as performance).
Weight reduction has been one of major driving forces of research and innovation in automotive industries. Changing from the conventional Laser overlap welding to edge welding could save 50% of total weight of joint flanges of workpiece. Also, edge welding can significantly improve the welded joint mechanical properties. However, edge welding is challenging since the thin laser beam needs to be guided on the joint constantly within tight boundaries. For this reason, project of vision-guided robotics for laser/MIG welding seam tracking is proposed.
The proposed research project is intended to develop a software solution (middleware) that will work in conjunction with a remote communication device (i.e. ability to communicate via satellite and/or cellular networks) to greatly reduce the cost and complexity of monitoring in-field sensors including wireless sensors and critical industrial equipment from a centrally located office environment. The initial target market for this technology is the oil and gas sector within North America with target applications of environmental and critical infrastructure monitoring.
Robotic sensor networks (RSN) are increasingly applied to Critical Infrastructure Protection (CIP). In such an application, a RSN is deployed to safe-guard some critical infrastructure (e.g., building, pipeline, etc.) in a secure and reliable fashion. By actively considering risk as a major driving force, this project aims at deciding on the optimal configuration of an ensemble of robotic nodes, their data processing and information extraction mechanisms and the ways in which they can reconfigure themselves in order to respond to emerging threats and risks in the environment.
Unmanned Aerial Vehicles (UAVs) are gaining significant amount of attention with research institutes and industry. They are entering into the domain of civil applications such as searchand-rescue, and urban policing. Making robust UAVs that can take off the ground, fly, and safely land on rough terrains autonomously remains a challenge.
In aviation industry a large flow of data including thousands of parameters are registered by FDRs (Flight Data Recorders). The objective of this project is to use this big data to improve the efficiency and safety of flights. The data is collected and segmented from the raw datasets and then proper data cleaning methods are used to preprocess data. Then, by the help of analytical models we define a baseline for different registered parameters and compare individual flights against the baseline to detect anomalies.