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Bahri Uzunoglu
Associate Professor of Energy Technology
Uppsala University, Division of Electricity
Angstrom Laboratory
Lägerhyddsvägen 1, 524 Uppsala
bahriuzunoglu@computationalrenewables.com
Cell: (734)-606585
Phone: (498)-299195

RESEARCH INTEREST AND PUBLICATIONS

Reduced Modelling, Data Mining, Inverse Modelling, Numerical Analysis, Numerical linear algebra, Numerical approximations to differential equations, ODES and PDES, Control and Optimal Control Theory, Data assimilation, Kalman Filter and Numerical Weather Prediction, Hydrodynamic Stability

APPROXIMATION OF LARGE-SCALE DYNAMICAL SYSTEMS FOR STOCHASTIC MODELLING, ESTIMATION AND CONTROL.

In today's technological world, physical as well as artificial processes are described mainly by mathematical models. The weather forecast, a physical problem or power systems (VLSI), wind turbines, marine renewable energy systems, active and passive flow control and mathematical finance, artificial problems, constitute examples of such processes of stochastic models, estimation and control. Furthermore, these are dynamical systems as their future depends on their past evolution. In this framework of mathematical models, there is an ever-increasing need for improved accuracy, which leads to models of high complexity.

The basic motivation for system approximation is the need for simplified models of dynamical systems, which capture the main features of the original complex model. This need arises from limited computational, accuracy, and storage capabilities. This simplified model is then used in place of the original complex model, for either simulation or control.

In the former case, simulation, one seeks to predict the system behavior. However, often simulation of the full model is not feasible. Consequently, an appropriate simplification of this model is necessary, resulting in simulation with reduced computational complexity. In particular, discretization in problems that arise from dynamical partial differential equations (PDEs) which evolve in three spatial dimensions can easily lead to 1 million equations. In such cases, reduced simulation models are essential for the quality and timeliness of the prediction. Other methods for accelerating the simulation time exist, like parallelization of the corresponding algorithm.

Publications at the hyperlink

Thesis Supervisions in Wind Renewable Energy Research

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