2010 Copyright The publishers will keep this document online on the Internet – or its possible replacement – starting from the date of publication barring exceptional circumstances. The online availability of the document implies permanent permission for anyone to read, to download, or to print out single copies for his/her own use and to use it unchanged for noncommercial research and educational purposes. Subsequent transfers of copyright cannot revoke this permission. All other uses of the document are conditional upon the consent of the copyright owner. The publisher has taken technical and administrative measures to assure authenticity, security and accessibility. According to intellectual property law, the author has the right to be mentioned when his/her work is accessed as described above and to be protected against infringement. For additional information about Linköping University Electronic Press and its procedures for publication and for assurance of document integrity, please refer to its www home page:

This paper presents a simulation framework called UNIF (Unified Numerical Integration Framework) to design numerical integration models, i.e. models mainly ruled by ordinary differential equations. We describe the object-oriented structure of UNIF, which allows to model a system as a hierarchical aggregation of subsystems interacting together, each one having a set of inputs, outputs and integration variables that are involved in a set of equations. The use and the advantages of this framework are illustrated on a full biological model, called GEMINI (Grassland Ecosystem Model with INdividual-centered Interactions), that simulates the life of populations of grassland plants competing for light and soil resources.

The problem of fault diagnosis consists of detecting and isolating faults present in a system. As technical systems become more and more complex and the demands for safety, reliability and environmental friendliness are rising, fault diagnosis is becoming increasingly important. One example is automotive systems, where fault diagnosis is a necessity for low emissions, high safety, high vehicle uptime, and efficient repair and maintenance. One approach to fault diagnosis, providing potentially good performance and in which the need for additional hardware is minimal, is model-based fault diagnosis with residuals. A residual is a signal that is zero when the system under diagnosis is fault-free, and non-zero when particular faults are present in the system. Residuals are typically generated by using a mathematical model of the system and measurements from sensors and actuators. This process is referred to as residual generation. The main contributions in this thesis are two novel methods for residual

ISSN: 1650-36864 th International Workshop on

Modelling and simulation languages are evolving rapidly to support modelling of systems of ever increasing size and complexity. A relatively recent development in the area of physical modelling is the noncausal modelling languages. They support a declarative, highly modular modelling approach, promoting the reuse of components. Modelica is a prime example of this class of languages. However, the mainstream representatives of this class of languages provide limited support for higher-order modelling and structurally dynamic systems. Moreover, the semantics of this class of languages remains a relatively unexplored area. Functional Hybrid Modelling (FHM) is a novel approach to noncausal, hybrid modelling that aims to address these concerns. In this paper, we give a semantics to the discrete part of a simple FHM language expressed in dependent type theory. We use Normalisation by Evaluation to produce a type-preserving and terminating normalisation procedure, the latter property being particularly important for FHM as highly structurally dynamic systems are supported by computing new system configurations during simulation. Furthermore, our implementation has been carefully structured to allow continuous aspects of the semantics to be described separately, in whatever way is deemed appropriate, while retaining the ability to describe precisely how a system evolves in response to discrete events. Categories and Subject Descriptors D.3.2 [Programming Languages]: Language Classifications—applicative (functional) languages, specialized application languages; D.3.1 [Programming