Title: Integrative physiology in Modelica
Authors: Jiří Kofránek, Tomas Kulhanek, Marek Matejak, Filip Ježek and Jan Šilar
Abstract:The integrative model of human physiology connects individual physiological subsystems into a single unit. They are very large (contain thousands of variables) and represent a formalized description of interconnected physiological regulations. The issue of formalization of physiological systems became part of a series of international projects (e.g. the worldwide program "PHYSIOME", or the European program "VIRTUAL PHYSIOLOGICAL HUMAN"). The development of large-scale models of human physiology was facilitated by a new generation (i.e. equation-based) simulation environment, especially by Modelica language. These models can be used to explain the causal relations of the pathogenesis of many diseases, they can be applied in the evaluation of clinical trials and they can also be used in the core of sophisticated medical simulators.
Links: Full paper
Title: Sound Source Extension Library for Modelica
Authors: Johann Emhofer, Raimund Zitzenbacher and Christoph Reichl
Abstract:Transient thermodynamic models in Modelica are widely used for energetic simulations of machines and systems which are located nearby people. Nevertheless, so far no libraries exist which consider the noise of such machines in the simulations. The Sound Source Extension library (SSElib) proposed in this work, should close this gap. With the aid of the SSElib, acoustic characteristics can be added to existing Modelica models (e.g. to a compressor model). The acoustic characteristic added to the existing model is frequency dependent in the one-octave band and could be further depend on an input parameter like the rotational speed of a compressor. With the inclusion of sound sources into energetic models, the sound behavior of machines can be considered and control strategies can be optimized to lower the noise of machines.
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Title: Towards Medical Cyber-Physical Systems: Modelica and FMI based Online Parameter Identification of the Cardiovascular System
Authors: Jonas Gesenhues, Marc Hein, Maike Ketelhut, Thivaharan Albin and Dirk Abel
Abstract:This paper presents a concept for online parameter identification intended to be used within cardiovascular research labs and hospitals of the future featuring a data network of medical sensors. It is based on iterative nonlinear optimization using a moving horizon scheme and object-oriented Modelica models. Special FMUs have been developed to interface the optimization module and the sensor hardware. The concept is demonstrated on an exemplary application of identifying the parameters of a model for the systemic circulation. Unlike classical online parameter identification methods, this concept allows for quickly implementing changes of the underlying model.
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