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Parameter sensitivity analysis is a core activity to assess the robustness of dynamical models with regard to unreliable parameters. This becomes critical for nonlinear models with many parameters subject to large uncertainties. In such contexts too often numerical experimentation is required due to the lack of analytic expressions for the derivatives of state variables with respect to parameters. A naive sweeping of the full parameter space is usually not an option due to combinatorial explosion. In this work we present OMSens, an open platform to assess the sensitivity of Modelica models tailored to work with OpenModelica. OMSens uses different methods to sensitivity analysis including an approach based on derivate-free non-linear optimization. This is a new approach not previously used in Modelica tools which provides important advantages such as robustness and applicability to models for which the derivatives of state variables don't exist or are not available. We tested OMSens with a Modelica version of World3, a large nonlinear socio-economic model. OMSens was effective to pinpoint a nonintuitive subset of parameters that, when perturbed within small ranges, yield strong changes on key state variables.

This paper is concerned with the tearing method according to Francois Cellier. Tearing is used to reduce the dimension of algebraic loops, which inevitably arise in the modelling of scientific systems using differential-algebraic equations, as far as possible to achieve an efficient simulation. However, the original tearing method according to Cellier is not suitable for the application in practice, since restrictions on the solvability of equations for variables, and other features, which appear in reality, are not considered. In this work, different changes to the method are introduced and tested, which make it possible to use Cellier Tearing in practice. In addition, the modeller can influence the selection of tearing variables. Modifications of the integrated heuristic are presented, whose efficiency is statistically evaluated at the end of this work. With these changes and the new heuristics, Cellier's method becomes a very suitable way to optimize the efficiency of simulation in practice.