Mechanical & Aerospace Engineering

Biography

Biography: Ioannis T Georgiou

Abstract

The typical aerospace and ocean platform is a quite complicated structural system interacting with the environment and the installed propulsion and energy conversion machinery. Th e full order dynamics response of such a complex system is coupled and nonlinear and in local critical areas exhibits multi-physics interactions (solid-fl uid, solid-thermal, solid-thermal-electromagnetic). Th e full order multi-physics interaction renders the interpretation of a sensory information quite diffi cult for early-stage damage diagnostics. The realistic full order dynamics could be in contrast to the reduced order dynamics used in a classical model-based analysis. Full order dynamics should be subjected to a reduction process for obvious reasons. Given the fact that modern information technology has revolutionized the design-monitoring of the aerospace and ocean platform, rapid generation of datasets for the full order dynamics can occur on a routine basis via the following mechanical engineering practices: (1) the use of high fi delity computational models in design and (2) the use of a dense network of high performance sensors (accelerometers, fi ber optics, strain gauges). Th e pivotal observation is that the connection between the coupled dynamics and the spatial features of the complex structure is carried implicitly in the raw datasets. Th ese space-time datasets contain the essential features of the dynamics of the complex structure and defi nitely should form the basis for a pure data-driven analysis in analogy to the classical model-driven analysis. Th e pivotal point to start is to view the dataset as a geometric object embedded in the hyperspace of observations. Th e cloud formed by the space-time dataset processes necessarily stationary geometric features. Th is intrinsic properties-referred to as POD modes-of the cloud can be identifi ed exactly by the powerful proper orthogonal decomposition or projection data processing procedure. We have advanced the proper orthogonal decomposition for scalar fi elds to compute the POD modes of nonlinear coupled multi-dimensional fi elds in structural dynamics by using as the prototype the fi nite element simulations of the coupled dynamics of nonlinearly elastic rods and shells. Th ese advanced POD tools were used to explore the full order dynamics of quite complicated structures (sandwich structures, ship frames, fl exible machinery mechanics) (Project-PYTHAGORAS). Th e advanced POD tools were used to investigate the experimental dynamics-for advanced diagnostics-of a range of technology important physical complex structures with local critical areas (Project-IMS-PB-DIAGNOSIS). Th e systematic research establishes the fact that advanced proper orthogonal decomposition tools off er an unparalleled procedure to exploit in depth big datasets produced during the design and subsequent vibrations-based structural-machinery monitoring of aerospace and ocean systems. Th e POD-based geometric modal analysis is data-driven and independent of the geometric features of the structural system. Given the powerful geometric modal-like properties of the POD Transform, big datasets of the full order dynamics of complex structural systems are reduced into multiscale orthogonal resolutions. Th e classical modal analysis cannot operate on the dataset level as the POD does. An advanced POD is the ideal multiscale decomposition tool.