), except for brief excerpts in connection with reviews or scholarly analysis. Use in connection with any form of information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed is forbidden. The use in this publication of trade names, trademarks, service marks, and similar terms, even if they are not identified as such, is not to be taken as an expression of opinion as to whether or not they are subject to proprietary rights.Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com) Preface ScicosLab (http://www.scicoslab.org) is a free open-source software package for scientific computation. ScicosLab includes a fork of Scilab, based on Scilab 4, the modeling and simulation tool Scicos and a number of other toolboxes.Scilab is an interpreted language specifically developed for matrix based numerical computations. It includes hundreds of general purpose and specialized functions for numerical computation, organized in libraries called toolboxes that cover such areas as simulation, optimization, systems and control, and signal processing. These functions reduce considerably the burden of programming for scientific applications.One important ScicosLab toolbox is Scicos. Scicos (http://www.scicos.org) provides a block-diagram graphical editor for the construction and simulation of dynamical systems. Scilab/Scicos is the only open-source alternative to commercial packages for dynamical system modeling and simulation packages such as MATLAB/Simulink and MATRIXx/SystemBuild. Widely used at universities and engineering schools, Scilab/Scicos has also gained ground in industrial environments. ScicosLab is developed and maintained by research groups, in particular, at INRIA 1 and ENPC. 2ScicosLab includes full Scilab and Scicos user's manuals, which are available with search capabilities in a help window. All commands, their syntax, and simple illustrative examples are given. While very useful in finding out the details of a particular command, these manuals do not provide a tutorial on the philosophy of either Scilab or Scicos. Nor do they address how to use several of these commands together in the solution of a technical problem.The objective of this book is to provide a tutorial for the use of Scilab/Scicos with a special emphasis on modeling and simulation tools. While it will provide useful information to experienced users, it is designed to be accessible to beginning users from a variety of disciplines. Students [52] and academic and industrial scientists and engineers should find it useful. The discussion includes some information on modeling and simulation in order to assist the reader in deciding which simulation tools might be most useful to them. Every software environment has its special features, some would say quirks, that experienced users automatically take into account but often prove confusing to beginning users. We have tried to point these out where appropriate.The book is div...
A dynamic model of the settling process in the secondary settler of a wastewater treatment plant is given by a nonlinear scalar conservation law ct + ψ(x, c)x = 0 for the sludge concentration c(t, x), where the flux function ψ(x, c) presents discontinuities. We analyze this PDE with emphasis both on the existence of stationary solutions and on the evolution of the shock corresponding to the rising of a sludge blanket. Theoretical and numerical simulations are compared with real data. A model with two classes of particles in interaction is introduced to take into account the thickening process : it appears to improve the fit with the data. What is more, regulation strategies of the rising of a sludge blanket in case of important water admission to the plant are proposed.
Abstract. For a sequence of dynamic optimization problems, we aim at discussing a notion of consistency over time. This notion can be informally introduced as follows. At the very first time step t 0 , the decision maker formulates an optimization problem that yields optimal decision rules for all the forthcoming time step t 0 , t 1 , . . . , T ; at the next time step t 1 , he is able to formulate a new optimization problem starting at time t 1 that yields a new sequence of optimal decision rules. This process can be continued until final time T is reached. A family of optimization problems formulated in this way is said to be time consistent if the optimal strategies obtained when solving the original problem remain optimal for all subsequent problems. The notion of time consistency, well-known in the field of Economics, has been recently introduced in the context of risk measures, notably by Artzner et al. (2007) and studied in the Stochastic Programming framework by Shapiro (2009) and for Markov Decision Processes (MDP) by Ruszczynski (2009). We here link this notion with the concept of "state variable" in MDP, and show that a significant class of dynamic optimization problems are dynamically consistent, provided that an adequate state variable is chosen.
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