Multiscale Surrogate Modeling and Uncertainty Quantification for Periodic Composite Structures

Computational modeling of the structural behavior of continuous fiber composite materials often takes into account the periodicity of the underlying micro-structure. A well established method dealing with the structural behavior of periodic micro-structures is the so- called Asymptotic Expansion Homogenization (AEH). By considering a periodic perturbation of the material displacement, scale bridging functions, also referred to as elastic correctors, can be derived in order to connect the strains at the level of the macro-structure with micro- structural strains. For complicated inhomogeneous micro-structures, the derivation of such functions is usually performed by the numerical solution of a PDE problem - typically with the Finite Element Method. Moreover, when dealing with uncertain micro-structural geometry and material parameters, there is considerable uncertainty introduced in the actual stresses experienced by the materials. Due to the high computational cost of computing the elastic correctors, the choice of a pure Monte-Carlo approach for dealing with the inevitable material and geometric uncertainties is clearly computationally intractable. This problem is even more pronounced when the effect of damage in the micro-scale is considered, where re-evaluation of the micro-structural representative volume element is necessary for every occurring damage. The novelty in this paper is that a non-intrusive surrogate modeling approach is employed with the purpose of directly bridging the macro-scale behavior of the structure with the material behavior in the micro-scale, therefore reducing the number of costly evaluations of corrector functions, allowing for future developments on the incorporation of fatigue or static damage in the analysis of composite structural components.Comment: Appeared in UNCECOMP 201

Iron–sulfur clusters: from metals through mitochondria biogenesis to disease

Iron–sulfur clusters are ubiquitous inorganic co-factors that contribute to a wide range of cell pathways including the maintenance of DNA integrity, regulation of gene expression and protein translation, energy production, and antiviral response. Specifically, the iron–sulfur cluster biogenesis pathways include several proteins dedicated to the maturation of apoproteins in different cell compartments. Given the complexity of the biogenesis process itself, the iron–sulfur research area constitutes a very challenging and interesting field with still many unaddressed questions. Mutations or malfunctions affecting the iron–sulfur biogenesis machinery have been linked with an increasing amount of disorders such as Friedreich’s ataxia and various cardiomyopathies. This review aims to recap the recent discoveries both in the yeast and human iron–sulfur cluster arena, covering recent discoveries from chemistry to disease

Structural health monitoring of an innovative timber building

A main focus in timber construction research is the development of innovative, sustainable and reliable structures. In order to determine the long-term structural behaviour of these novel structures, structural health monitoring is a valuable tool. In the past two years an innovative timber-hybrid pilot building has been conceived, designed and realized at ETH Zürich. The building contains four innovative structural systems, a post-tensioned timber frame, two timber-concrete hybrid floor systems using beech LVL, and a biaxial pure timber floor in beech wood. In order to fully understand the combined structural behaviour of these innovative systems an extensive monitoring system was set up. The dense sensor network was implemented along with the construction progress, in order to also quantify the effects of important construction stages on the structural behaviour (addition of significant loads, addition of stiffening elements, extreme changes in environmental climate, etc.). The installed setup includes 16 load cells, measuring the changes in the post-tension force in the frame, absolute deformation measurements, temperature and relative humidity sensors, as well as measurements of the moisture content of timber. The monitoring campaign is planned to be continued for several years beyond the completion of construction, in order to quantify the long-term behaviour during the use phase of the building

Optimal sensor placement through Bayesian experimental design: effect of measurement error and number of sensors

Sensors networks for the health monitoring of structural systems ought to be designed to render both accurate estimations of the relevant mechanical parameters and an affordable experimental setup. Therefore, the number, type and location of the sensors have to be chosen so that the uncertainties related to the estimated health are minimized. Several deterministic methods based on the sensitivity of measures with respect to the parameters to be tuned are widely used. Despite their low computational cost, these methods do not take into account the uncertainties related to the measurement process. In former studies, a method based on the maximization of the information associated with the available measurements has been proposed and the use of approximate solutions has been extensively discussed. Here we propose a robust numerical procedure to solve the optimization problem: in order to reduce the computational cost of the overall procedure, Polynomial Chaos Expansion and a stochastic optimization method are employed. The method is applied to a flexible plate. First of all, we investigate how the information changes with the number of sensors; then we analyze the effect of choosing different types of sensors (with their relevant accuracy) on the information provided by the structural health monitoring system

Cazas del tesoro (Yincanas) con códigos QR

Las cazas del tesoro presentan una alternativa interesante y lúdica a la hora de trabajar con información directa en la web. Es una manera efectiva de poner en práctica los conocimientos adquiridos en el aula de lenguas, tanto a nivel lingüístico como a nivel cultural. Permiten trabajar diferentes destrezas, así como introducir a nuestros alumnos en el uso de las nuevas tecnologías. Así, al llevar las cazas del tesoro con códigos QR a clase, permitimos un acercamiento enriquecedor y motivador, al tiempo que introducimos los dispositivos móviles en el aula, dándoles un uso efectivo y práctico. Los alumnos aprenden, además, a buscar, analizar y elaborar la información para poder aplicarla en contextos reales

Cost-Benefit Optimization of Sensor Networks for SHM Applications

Structural health monitoring (SHM) is aimed to obtain information about the structural integrity of a system, e.g., via the estimation of its mechanical properties through observations collected with a network of sensors. In the present work, we provide a method to optimally design sensor networks in terms of spatial configuration, number and accuracy of sensors. The utility of the sensor network is quantified through the expected Shannon information gain of the measurements with respect to the parameters to be estimated. At assigned number of sensors to be deployed over the structure, the optimal sensor placement problem is ruled by the objective function computed and maximized by combining surrogate models and stochastic optimization algorithms. For a general case, two formulations are introduced and compared: (i) the maximization of the information obtained through the measurements, given the appropriate constraints (i.e., identifiability, technological and budgetary ones); (ii) the maximization of the utility efficiency, defined as the ratio between the information provided by the sensor network and its cost. The method is applied to a large-scale structural problem, and the outcomes of the two different approaches are discussed

An optimal sensor placement method for SHM based on Bayesian experimental design and polynomial chaos expansion

We present an optimal sensor placement methodology for structural health monitoring (SHM) purposes, relying on a Bayesian experimental design approach. The unknown structural properties, e.g. the residual strength and stiffness, are inferred from data collected through a network of sensors, whose architecture, i.e., type and position may largely affect the accuracy of the monitoring system. In tackling this issue, an optimal network configuration is herein sought by maximizing the expected information gain between prior and posterior probability distributions of the parameters to be estimated. Since the objective function linked to the network topology cannot be analytically computed, a numerical approximation is provided by means of a Monte Carlo analysis, wherein each realization is obtained via finite element modeling. Since the computational burden linked to this procedure often grows infeasible, a Polynomial Chaos Expansion (PCE) approach is adopted for accelerating the computation of the forward problem. The analysis expands over joint samples covering both structural state and design variables, i.e., sensor locations. Via increase of the number of deployed sensors in the network, the optimization procedure soon turns computationally costly due to the curse of dimensionality. To this end, a stochastic optimization method is adopted for accelerating the convergence of the optimization process and thereby the damage detection capability of the SHM system. The proposed method is applied to thin flexible structures, and the resulting optimal sensor configuration is shown. The effects of the number of training samples, the polynomial degree of the approximation expansion and the optimization settings are also discussed

Δυναμικός Προγραμματισμός

Εθνικό Μετσόβιο Πολυτεχνείο--Μεταπτυχιακή Εργασία. Διεπιστημονικό-Διατμηματικό Πρόγραμμα Μεταπτυχιακών Σπουδών (Δ.Π.Μ.Σ.) “Εφαρμοσμένες Μαθηματικές Επιστήμες