Papers by Daniel Bouskela

Title:	New Equation-based Method for Parameter and State Estimation
Authors:	Luis Corona Mesa-Moles, Erik Henningsson, Daniel Bouskela, Audrey Jardin and Hans Olsson
Abstract:	To get reliable simulation results from a Modelica model it is important to parametrize and initialize the model using the best estimate of the state of the system. Commonly, this state estimation is done by inverse calculation on a square system of equations requiring as many known values as states to be computed. In practice this constraint is an important limitation and, in addition, this method does not provide any information on the uncertainties or confidence level associated to the estimated state. Taking advantage of the mathematical formulation of Modelica equations, this paper presents a new method to cope with the difficulties associated to the inverse calculation method. This approach adapts and extends the framework of data assimilation to provide a fully-integrated Modelica tool, which efficiently can handle every type of state estimation problem for static models. This method has been successfully tested with simple and complex Modelica models. Finally, the Modelica implementation of this technique allows to easily extend it to further applications.
Keywords:	Modelica, parameter estimation, state estimation, model, data assimilation
Paper:	full paper
Bibtex:	@InProceedings{modelica.org:Mesa-Moles:2021, title = "{New Equation-based Method for Parameter and State Estimation}", author = {Luis Corona Mesa-Moles and Erik Henningsson and Daniel Bouskela and Audrey Jardin and Hans Olsson}, pages = {129--139}, doi = {10.3384/ecp21181129}, booktitle = {Proceedings of the 14th International Modelica Conference}, location = {Link\"oping, Sweden}, editor = {Martin Sj\"olund and Lena Buffoni and Adrian Pop and Lennart Ochel}, isbn = {978-91-7929-027-6}, issn = {1650-3740}, month = sep, series = {Link\"oping Electronic Conference Proceedings}, number = {181}, publisher = {Modelica Association and Link\"oping University Electronic Press}, year = {2021} }

Title:	New Method to Perform Data Reconciliation with OpenModelica and ThermoSysPro
Authors:	Daniel Bouskela, Audrey Jardin, Arunkumar Palanisamy, Lennart Ochel and Adrian Pop
Abstract:	Data reconciliation aims at improving the accuracy of measurements by reducing the effect of random errors in the data. This is achieved by introducing redundancies on the measured quantities in the form of constraints based on fundamental physical laws such as mass, momentum and energy balance equations. These constraints are called the auxiliary conditions. Modelica is an equational language that was conceived to express models based on first principle physics for the purpose of behavioral simulation. This paper shows how to reuse such models for the purpose of data reconciliation. The novelty is to automatically extract the auxiliary conditions from the Modelica model. Then the reconciled values are computed using a least square method constrained by the auxiliary conditions, as specified by the VDI 2048 standard. The new method has been implemented in OpenModelica. A simple example built with ThermoSysPro illustrates the method in detail.
Keywords:	data reconciliation, Modelica, model reuse, cyber-physical systems, structural analysis
Paper:	full paper
Bibtex:	@InProceedings{modelica.org:Bouskela:2021, title = "{New Method to Perform Data Reconciliation with OpenModelica and ThermoSysPro}", author = {Daniel Bouskela and Audrey Jardin and Arunkumar Palanisamy and Lennart Ochel and Adrian Pop}, pages = {453--462}, doi = {10.3384/ecp21181453}, booktitle = {Proceedings of the 14th International Modelica Conference}, location = {Link\"oping, Sweden}, editor = {Martin Sj\"olund and Lena Buffoni and Adrian Pop and Lennart Ochel}, isbn = {978-91-7929-027-6}, issn = {1650-3740}, month = sep, series = {Link\"oping Electronic Conference Proceedings}, number = {181}, publisher = {Modelica Association and Link\"oping University Electronic Press}, year = {2021} }