Remote monitoring of repository integrity using passive seismic arrays

Once radioactive waste is emplaced in the repository, the challenge of monitoring the continued integrity of the excavated openings (e.g., emplacement drifts) escalates tremendously. We envision a seismic monitoring array installed on the surface at Yucca Mountain, which operates automatically to monitor repository opening stability in the long term. The objective is to monitor and validate the structural integrity of the emplacement drifts through identifying and localizing rock falls that could compromise drift access, hinder waste retrievability, and potentially reduce the effective life of waste canisters. Collateral benefits of the system include the ability to address some outstanding uncertainties regarding seismic wave attenuation in the vicinity of the repository, and provision of a tool for security monitoring of the repository in guarding against unauthorized access and entry

Final Report, Next-Generation Mega-Voltage Cargo-Imaging System for Cargo Conainer Inspection, March 2007

The UNLV Research Foundation, as the primary award recipient, teamed with Varian Medical Systems-Security & Inspection Products and the University of Nevada Las Vegas (UNLV) for the purpose of conducting research and engineering related to a "next-generation" mega-voltage imaging (MVCI) system for inspection of cargo in large containers. The procurement and build-out of hardware for the MVCI project has been completed. The K-9 linear accelerator and an optimized X-ray detection system capable of efficiently detecting X-rays emitted from the accelerator after they have passed through the device is under test. The Office of Science financial assistance award has made possible the development of a system utilizing a technology which will have a profound positive impact on the security of U.S. seaports. The proposed project will ultimately result in critical research and development advances for the "next-generation" Linatron X-ray accelerator technology, thereby providing a safe, reliable and efficient fixed and mobile cargo inspection system, which will very significantly increase the fraction of cargo containers undergoing reliable inspection as the enter U.S. ports. Both NNSA/NA-22 and the Department of Homeland Security's Domestic Nuclear Detection Office are collaborating with UNLV and its team to make this technology available as soon as possible

An Intelligent System for Seismic Source Localization

In a stationary sensor situation, locating signals arriving from different directions can be achieved by using multiple sensors placed at different locations. Different methods for locating seismic signals using an array of seismometers have been examined. Matched field processing is a method for source localization whereby model fields are calculated and compared to measured data. 1

A Novel Camera Calibration Algorithm as Part of an HCI System: Experimental Procedure and Results

Camera calibration is an initial step employed in many computer vision applications for the estimation of camera parameters. Along with images of an arbitrary scene, these parameters allow for inference of the scene's metric information. This is a primary reason for camera calibration's significance to computer vision. In this paper, we present a novel approach to solving the camera calibration problem. The method was developed as part of a Human Computer Interaction (HCI) System for the NASA Virtual GloveBox (VGX) Project. Our algorithm is based on the geometric properties of perspective projections and provides a closed form solution for the camera parameters. Its accuracy is evaluated in the context of the NASA VGX, and the results indicate that our algorithm achieves accuracy similar to other calibration methods which are characterized by greater complexity and computational cost. Because of its reliability and wide variety of potential applications, we are confident that our calibration algorithm will be of interest to many

Design of Container Ship Main Engine Waste Heat Recovery Supercritical CO₂ Cycles, Optimum Cycle Selection through Thermo-Economic Optimization with Genetic Algorithm and Its Exergo-Economic and Exergo-Environmental Analysis

In the present study, energy and exergy analyses of a simple supercritical, a split supercritical and a cascade supercritical CO2 cycle are conducted. The bottoming cycles are coupled with the main two-stroke diesel engine of a 6800 TEU container ship. An economic analysis is carried out to calculate the total capital cost of these installations. The functional parameters of these cycles are optimized to minimize the electricity production cost (EPC) using a genetic algorithm. Exergo-economic and exergo-environmental analyses are conducted to calculate the cost of the exergetic streams and various exergo-environmental parameters. A parametric analysis is performed for the optimum bottoming cycle to investigate the impact of ambient conditions on the energetic, exergetic, exergo-economic and exergo-environmental key performance indicators. The theoretical results of the integrated analysis showed that the installation and operation of a waste heat recovery optimized split supercritical CO2 cycle in a 6800 TEU container ship can generate almost 2 MW of additional electric power with a thermal efficiency of 14%, leading to high fuel and CO2 emission savings from auxiliary diesel generators and contributing to economically viable shipping decarbonization

Design of Container Ship Main Engine Waste Heat Recovery Supercritical CO2 Cycles, Optimum Cycle Selection through Thermo-Economic Optimization with Genetic Algorithm and Its Exergo-Economic and Exergo-Environmental Analysis

In the present study, energy and exergy analyses of a simple supercritical, a split supercritical and a cascade supercritical CO2 cycle are conducted. The bottoming cycles are coupled with the main two-stroke diesel engine of a 6800 TEU container ship. An economic analysis is carried out to calculate the total capital cost of these installations. The functional parameters of these cycles are optimized to minimize the electricity production cost (EPC) using a genetic algorithm. Exergo-economic and exergo-environmental analyses are conducted to calculate the cost of the exergetic streams and various exergo-environmental parameters. A parametric analysis is performed for the optimum bottoming cycle to investigate the impact of ambient conditions on the energetic, exergetic, exergo-economic and exergo-environmental key performance indicators. The theoretical results of the integrated analysis showed that the installation and operation of a waste heat recovery optimized split supercritical CO2 cycle in a 6800 TEU container ship can generate almost 2 MW of additional electric power with a thermal efficiency of 14%, leading to high fuel and CO2 emission savings from auxiliary diesel generators and contributing to economically viable shipping decarbonization