Modeling of Biofilms in Industrial Applications

posted Mar 19, 2011, 2:10 PM by Giacomo Aletti   [ updated Mar 20, 2011, 7:51 AM ]

Teacher: Ioanna Vasiliadou, University Carlos III Madrid


Biofilms consisted of bacterial populations or other microorganisms can be used in many different industrial applications. For instance, bacterial biofilms can be successfully applied to industrial wastewater biological treatment. The oil/biodiesel production, using oleaginous fungal biofilms represents another industrial application. Thus, the development of mathematical models capable of describing the processes considered in mixed-culture biofilms is of great importance. The mathematical models may be used as a guide in designing and assessing the conditions under which biofilm industrial applications can be optimized. In this project the mathematical modelling of biofilms’ behavior, considering processes such as biofilm growth, biomass detachment, and nutrient diffusion in the biofilm, will be discussed. The numerical solution of the models’ equations and the comparison of theoretical predictions with real experimental data will be conducted with a commercial numerical code, a computer program for the identification and simulation of aquatic systems.

Introduction to Data Assimilation

posted Mar 19, 2011, 2:09 PM by Giacomo Aletti   [ updated Jul 1, 2011, 6:30 AM by Ecmi Milano ]

Teacher: Tuomo Kauranne, Technical University of Lappeenranta


Data assimilation is the process of optimally combining measurements or observations with the predictions of a mathematical model of the phenomenon under study. Data assimilation is used in, for example, weather and climate forecasting, where we make an optimal compromise between observations from ground stations, satellite measurements, radiosondes and so on on the one hand, and a weather forecast by an atmospheric model from some past time on the other hand. One of the most important methods of data assimilation is Kalman filtering. This course gives a brief, hands-on introduction to data assimilation with Matlab, using simple chemical reactions as an example.

Detection and control of immersed objects

posted Mar 19, 2011, 2:08 PM by Giacomo Aletti   [ updated Apr 18, 2011, 1:53 AM by Ecmi Milano ]

Teacher: Ana Leonor Silvestre, Technical University of Lisbon


Consider a rigid body immersed in a viscous incompressible fluid.  We begin this course with a brief analysis of the Stokes equations,  which will be used to model the fluid flow.  Then,  motivated by applications in Naval Engineering and Military Engineering,  for instance,  we will address the following issues:
1. Boundary control problems:  find the boundary values on the surface of the rigid body,  in given control spaces,  in order to  (i)  steer the solid into a prescribed velocity field;  (ii)  self-propel the body with maximal efficiency.
2. Inverse obstacle problem:  determine the location and shape of the immersed object by means of stress measurements.

Multivariate statistics for Microelectronics

posted Mar 19, 2011, 2:07 PM by Giacomo Aletti   [ updated Apr 12, 2011, 4:46 AM ]

Teachers: A.Micheletti, Universita' degli Studi di Milano


In this course we will introduce some methods of multivariate and functional statistics useful to perform data analyses, and in particular regressions, when the data are interpreted as realizations of random functions. Such problems arise frequently when the variables measured in an experiment are either random vectors, or functions or fields, and we are interested in finding an analitical expression for the mean function or operator which links two groups of such variables, called the regressors and the response. This is the case e.g. when dealing with some physical quantities measured on electronic devices in microelectronics, like the electrical field, its intensity, the mobility of the electrons, etc. When the dimension of the device is very small (nanoscale), the random location of the single atoms of dopants is relevant to determine the spatial structure of such fields and the identification of an analitic relationship between the location of the atoms and the functions describing such fields would be quite useful to accelerate the procedure of simulation and optimization of electronic devices. 

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