Mode I Delamination Fracture Characterization of Polymeric Composites under Elevated Temperature

Delamination is one of the major failure modes seen in the laminated polymeric matrix composite (PMC). Accurate prediction of delamination initiation and propagation is important for the design and analysis of robust composite structures. Existing experimental methodologies that are based on linear elastic fracture mechanics are inadequate to characterize delamination fracture properties under elevated temperature when PMC properties become time-, loading-history, and rate-dependent. A new experimental methodology based on linear viscoelastic fracture theory is developed and verified through finite element analysis and experiments. This new technique determines crack growth curves, such as stress intensity factor vs. crack growth speed and fracture initiation energy vs. crack initiation time, through the experimentally determined J-integral, Js, for a linear viscoelastic double cantilever beam (DCB) specimen. Special test setup is designed and validated for determining accurate Js using just the applied load and the load end rotation angles. This new methodology is then applied to measure the mode I fracture properties of a highly toughened graphite/epoxy composite under various environmental conditions

Cooperative Localization in Mines Using Fingerprinting and Neural Networks

This work is a special investigation in the localization of users in underground and confined areas such as gold mines. It sheds light on the basic approaches that are used nowadays to estimate the position and track users using wireless technology. Localization or Geo-location in confined and underground areas is one of the topics under research in mining labs and industries. The position of personnel and equipments in areas such as mines is of high importance because it improves industrial safety and security. Due to the special nature of underground environments, signals transmitted in a mine gallery suffer severe multipath effects caused by reflection, refraction, diffraction and collision with humid rough surfaces. In such cases and in cases where the signals are blocked due to the non-line of sight (NLOS) regions, traditional localization techniques based on the RSS, AOA and TOA/TDOA lead to high position estimation errors. One of the proposed solutions to such challenging situations is based on extracting the channel impulse response fingerprints with reference to one wireless receiver and using an artificial neural network as the matching algorithm. In this work we study this approach in a multiple access network where multiple access points are present. The diversity of the collected fingerprints allows us to create artificial neural networks that work separately or cooperatively using the same localization technique. In this approach, the received signals by the mobile at various distances are analysed and several components of each signal are extracted accordingly. The channel impulse response found at each position is unique to the position of the receiver. The parameters extracted from the CIR are the received signal strength, mean excess delay, root mean square, maximum excess delay, the number of multipath components, the total power of the received signal, the power of the first arrival and the delay of the first arrival. The use of multiple fingerprints from multiple references not only adds diversity to the set of inputs fed to the neural network but it also enhances the overall concept and makes it applicable in a multi-access environment. Localization is analyzed in the presence of two receivers using several position estimation procedures. The results showed that using two CIRs in a cooperative localization technique gives a position accuracy less than or equal to 1m for 90% of both trained and untrained neural networks. Another way of using cooperative intelligence is by using the time domain including tracking, probabilities and previous positions to the localization system. Estimating new positions based on previous positions recorded in history has a great improvement factor on the accuracy of the localization system where it showed an estimation error of less than 50cm for 90% of training data and 65cm for testing data. The details of those techniques and the estimation errors and graphs are fully presented and they show that using cooperative artificial intelligence in the presence of multiple signatures from different reference points as well as using tracking improves significantly the accuracy, precision, scalability and the overall performance of the localization system

Third International Symposium on Space Mission Operations and Ground Data Systems, part 1

Under the theme of 'Opportunities in Ground Data Systems for High Efficiency Operations of Space Missions,' the SpaceOps '94 symposium included presentations of more than 150 technical papers spanning five topic areas: Mission Management, Operations, Data Management, System Development, and Systems Engineering. The papers focus on improvements in the efficiency, effectiveness, productivity, and quality of data acquisition, ground systems, and mission operations. New technology, techniques, methods, and human systems are discussed. Accomplishments are also reported in the application of information systems to improve data retrieval, reporting, and archiving; the management of human factors; the use of telescience and teleoperations; and the design and implementation of logistics support for mission operations

Fault detection, isolation, and identification for nonlinear systems using a hybrid approach

This thesis presents a novel integrated hybrid approach for fault diagnosis (FD) of nonlinear systems; taking advantage of both system's mathematical model and the adaptive nonlinear approximation capability of computational intelligence techniques. Unlike most FD techniques, the proposed solution simultaneously accomplishes fault detection, isolation, and identification (FDII) within a unified diagnostic module. At the core of this solution are a bank of adaptive neural parameter estimators (NPE) and a set of single-parameterized fault models. The NPEs continuously estimate unknown fault parameters (FP) that are indicators of faults in the system. In view of the availability of full-state measurements, two NPE structures, namely series-parallel and parallel, are developed with their exclusive set of desirable attributes. The parallel scheme is extremely robust to measurement noise and possesses a simpler, yet more solid, fault isolation logic. On the contrary, the series-parallel scheme displays short FD delays and is robust to closed-loop system transients due to changes in control commands. Simple neural network architecture and update laws make both schemes suitable for real-time implementations. A fault tolerant observer (FTO) is then designed to extend the FDII schemes to systems with partial-state measurement. The proposed FTO is a neural state estimator that can estimate unmeasured states even in presence of faults. The estimated and the measured states then comprise the inputs to the FDII schemes. Simulation results for FDII of reaction wheels of a 3-axis stabilized satellite in presence of disturbances and noise demonstrate the effectiveness of the proposed FDII solution under both full and partial-state measurements

Technology transfer process

Meeting: Research and Policy Workshop on Technology Problems for the Caribbean, November 13-December 10, 1983, Runaway Bay, JMTitle supplied by cataloguerItem 31 filed with Module 10Item 20 incompleteLIBHAS: 1-16, 19-22, 24-3