Dynamics

This dissertation focuses on the development of data-driven and physics-based modeling for two distinct significant structural engineering applications: time-varying response variables estimation and unwanted lateral vibration control. In the first part, I propose a machine learning (ML)-based surrogate modeling to directly predict dynamic responses over an entire mechanical system during operations. Any mechanical system design, as well as structural health monitoring systems, require transient vibration analysis. However, traditional methods and modeling calculations are time- and resource-consuming. The use of ML approaches is particularly promising in scientific and engineering challenges containing processes that are not completely understood, or where it is computationally infeasible to run numerical or analytical models at desired resolutions in space and time. In this research, an ML-based surrogate for the FEA approach is developed to forecast the time-varying response, i.e., displacement of a two-dimensional truss structure. Various ML regression algorithms including decision trees and deep neural networks are developed to predict movement over a truss structure, and their efficiencies are investigated. ML algorithms have been combined with FEA in preliminary attempts to address issues in static mechanical systems.

In the past few years, the development of complex dynamical networks or systems has stimulated great interest in the study of the principles and mechanisms underlying the Internet of things (IoT). IoT is envisioned as an intelligent network infrastructure with a vast number of ubiquitous smart devices present in diverse application domains and have already improved many aspects of daily life. Many overtly futuristic IoT applications acquire data gathered via distributed sensors that can be uniquely identified, localized, and communicated with, i.e., the support of sensor networks. Soft-sensing models are in demand to support IoT applications to achieve the maximal exploitation of transforming the information of measurements into more useful knowledge, which plays essential roles in condition monitoring, quality prediction, smooth control, and many other essential aspects of complex dynamical systems. This in turn calls for innovative soft-sensing models that account for scalability, heterogeneity, adaptivity, and robustness to unpredictable uncertainties. The advent of big data, the advantages of ever-evolving deep learning (DL) techniques (where models use multiple layers to extract multi-levels of feature representations progressively), as well as ever-increasing processing power in hardware, has triggered a proliferation of research that applies DL to soft-sensing models. However, many critical questions need to be further investigated in the deep learning-based soft-sensing.

How one behaves after interacting with a friend may not be the same as before
the interaction began What factors a ect the formation of social interactions
between people and, once formed, how do social interactions leave lasting changes on
individual behavior? In this dissertation, a thorough review and conceptual synthesis
is provided Major features of coordination dynamics are demonstrated with
examples from both the intrapersonal and interpersonal coordination literature that
are interpreted via a conceptual scheme, the causal loops of coordination dynamics
An empirical, behavioral study of interpersonal coordination was conducted to
determine which spontaneous patterns of coordination formed and whether a remnant
of the interaction ensued ("social memory") To assess social memory in dyads, the
behavior preceding and following episodes of interaction was compared In the
experiment, pairs of people sat facing one another and made continuous flexion-extension finger movements while a window acted as a shutter to control
whether partners saw each other's movements Thus, vision ("social contact") allowed
spontaneous information exchange between partners through observation Each trial consisted of three successive intervals lasting twenty seconds: without social contact
("me and you"), with social contact ("us"), and again without ("me and you")
During social contact, a variety of patterns was observed ranging from phase coupling
to transient or absent collective behavior Individuals also entered and exited social
coordination differently In support of social memory, compared to before social
contact, after contact ended participants tended to remain near each other's
movement frequency Furthermore, the greater the stability of coupling, the more
similar the partners' post-interactional frequencies were Proposing that the
persistence of behavior in the absence of information exchange was the result of prior
frequency adaptation, a mathematical model of human movement was implemented
with Haken-Kelso-Bunz oscillators that reproduced the experimental findings, even
individual dyadic patterns Parametric manipulations revealed multiple routes to
persistence of behavior via the interplay of adaptation and other HKB model
parameters The experimental results, the model, and their interpretation form the
basis of a proposal for future research and possible therapeutic applications

A 6-Degree Of Freedom (DOF) numeric model and computer simulation along with the 1/10th scale physical model of the Rapidly Deployable Stable Platform (RDSP) are being developed at Florida Atlantic University in response to military needs for ocean platforms with improved sea keeping characteristics. The RDSP is a self deployable spar platform with two distinct modes of operation enabling long distance transit and superior seakeeping. The focus of this research is the development of a Dynamic Position (DP) and motion mitigation system for the RDSP. This will be accomplished though the validation of the mathematical simulation, development of a novel propulsion system, and implementation of a PID controller. The result of this research is an assessment of the response characteristics of the RDSP that quantifies the performance of the propulsion system coupled with active control providing a solid basis for further controller development and operational testing.