Stochastic dynamic response and optimization of structures with finite elements

Κόκκινος, Οδυσσέας; Kokkinos, Odysseas

dc.contributor.author	Κόκκινος, Οδυσσέας	el
dc.contributor.author	Kokkinos, Odysseas	en
dc.date.accessioned	2016-02-17T12:58:47Z
dc.date.available	2016-02-17T12:58:47Z
dc.date.issued	2016-02-17
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/42023
dc.identifier.uri	http://dx.doi.org/10.26240/heal.ntua.2136
dc.rights	Default License
dc.subject	Dynamic Variability Response Functions	en
dc.subject	Robust Design Optimization	en
dc.subject	Stochastic Dynamic Analysis	en
dc.subject	Sensitivity Analysis	en
dc.subject	Multi Objective Genetic Algorithm	en
dc.subject	Δυναμικές Συναρτήσεις Διακύμανσεις της Απόκρισης	el
dc.subject	Στοχαστική Δυναμική Ανάλυση	el
dc.subject	Εύρωστος Βέλτιστος Σχεδιασμός	el
dc.subject	Πολυαντικειμενικοί Γενετικοί Αλγόριθμοι	el
dc.subject	Ανάλυση Ευαισθησίας	el
dc.title	Stochastic dynamic response and optimization of structures with finite elements	en
heal.type	doctoralThesis
heal.classification	Structural engineering	en
heal.classificationURI	http://zbw.eu/stw/descriptor/18495-6
heal.language	en
heal.access	free
heal.recordProvider	ntua	el
heal.publicationDate	2015-12-15
heal.abstract	Establishing reliable and computationally efficient methodologies in stochastic dynamic analysis is a continuing effort in academic research. The first part of this thesis is emphasizing on developing a methodology that provides an alternative way of analyzing stochastic dynamic systems. More specifically, the concept of Variability Response Functions (VRFs) is extended initially to linear and then to general finite element stochastic systems leading to closed form integral expressions for their dynamic mean and variability response. An integral form for the variance of the dynamic response of stochastic systems is considered, involving a Dynamic VRF (DVRF) and the spectral density function of the stochastic field modeling the uncertain system properties. A finite element method-based fast Monte Carlo simulation procedure is used for the accurate and efficient numerical evaluation of these functions. As in the case of linear stochastic systems under static loads, the independence of the DVRF to the spectral density and the marginal probability density function of the stochastic field modeling the uncertain parameters is assumed. This assumption is here validated with brute-force Monte Carlo simulations. As a further validation of the assumption of independence of the variability response function to the stochastic parameters of the problem, the concept of the generalized variability response function was applied and compared to the steady state dynamic variability response function. The uncertain system property considered is the inverse of the elastic modulus (flexibility). The dynamic mean and variability response functions, once established, can be used to perform sensitivity/parametric analyses with respect to various probabilistic characteristics involved in the problem (i.e., correlation distance, standard deviation) and to establish realizable upper bounds on the dynamic mean and variance of the response, at practically no additional computational cost. They also provide an insight into the mechanisms controlling the dynamic mean and variability system response. The second part of this thesis focuses on proposing an alternative approach on Robust Design Optimization (RDO) implementing the concept of Variability Response Function (VRF). The basic idea is to exploit the VRF independence of the stochastic system parameters, in order to obtain safer optima that depend only on the deterministic parameters of the problem. This way, optimal structural designs are achieved which are optimally insensitive to the worst possible uncertainties, that is to say they are free of the spectral-distribution characteristics of the stochastic fields modeling the uncertainties. This is achieved by setting in addition to the total material cost, the maximum VRF value as an objective function. The advantages of using the proposed methodology over traditional Robust Design Optimization are illustrated through an application to a frame-type structure where it is demonstrated that the designs achieved through classical RDO for a given stochastic field description are not optimal for a variation on the spectral properties of the random field modeling the system uncertainty, while optimal designs obtained with the VRF-based RDO are optimum for the worst case scenario stochastic fields.	en
heal.advisorName	Παπαδόπουλος, Βησσαρίων	el
heal.committeeMemberName	Παπαδόπουλος, Βησσαρίων	el
heal.committeeMemberName	Παπαδρακάκης, Μανόλης	el
heal.committeeMemberName	Σταυρίδης, Λεονίδας	el
heal.committeeMemberName	Σπηλιόπουλος, Κωνσταντίνος	el
heal.committeeMemberName	Προβατίδης, Χριστόφορος	el
heal.committeeMemberName	Λαγαρός, Νικόλαος	el
heal.committeeMemberName	Βαμβάτσικος, Δημήτριος	el
heal.academicPublisher	Εθνικό Μετσόβιο Πολυτεχνείο. Σχολή Πολιτικών Μηχανικών. Εργαστήριο Στατικής και Αντισεισμικών Ερευνών	el
heal.academicPublisherID	ntua
heal.numberOfPages	202 σ.
heal.fullTextAvailability	true