FORMING CANONICAL 8D DATA CLOUDS TO EXPLORE THE TRANSIENT DYNAMICS OF PHYSICAL STIFFENED PLATES: A MACHINE LEARNING APPROACH FOR COMPLEX MECHANICAL STRUCTURES

摘要

This work presents a data-driven explorative study of the physics of the dynamics of a physical structure of complicated geometry. The geometric complexity of the physical system renders the typical single sensor acceleration signal quite complicated for a physics interpretation. We need the spatial dimension to resolve the single sensory signal over its entire time horizon. Thus we are introducing the spatial dimension by the canonical eight-dimensional data cloud (Canonical 8D-Data Cloud) concept to build methods to explore the impact-induced free dynamics of physical complex mechanical structures. The complex structure in this study is a large scale aluminum alloy plate stiffened by a frame made of T-section beams. The Canonical 8D-Data Cloud is identified with the simultaneous acceleration measurements by eight piezoelectric sensors equally spaced and attached on the periphery of a circular material curve drawn on the uniform surface of the stiffened plate. The Data Cloud approach leads to a systematic exploration-discovery-quantification of uncertainty in this physical complex structure. It is found that considerable uncertainty is stemming from the sensitivity of transient dynamics on the parameters of space-time localized force pulses, the latter being used as a means to diagnose the presence of structural anomalies. The Data Cloud approach leads to aspects of machine learning such as reduced dynamics analytics of big sensory data by means of heavenly machine-assisted computations to carry out the unparalleled data reduction analysis enabled by the Advanced Proper Orthogonal Decomposition Transform. Emphasized is the connection between the characteristic geometric features of high-dimensional datasets as a whole, the Data Cloud, and the modal physics of the dynamics.