FMI is increasingly being adopted as a standard for exchanging simulation models within and between organizations. Such models often represent significant investments for the model creator. There is thus a large interest in protecting intellectual property while collaborating and sharing simulation models in the form of FMUs. This paper presents a collection of use cases and issues related to IP protection of model contents, that have been identified in interviews with industrial representatives. The requirements in each use case are described, along with an investigation of how well the use cases can be managed within the current version of the FMI standard, including a proposed extension of the standard.

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Cylinder pressure sensors provide detailed information on the diesel engine combustion process. This paper presents a method to use cylinder-pressure data for prediction of engine emissions by exploiting data-mining techniques. The proposed method uses principal component analysis to reduce the dimension of the cylinder-pressure data, and a neural network to model the nonlinear relationship between the cylinder pressure and emissions. An algorithm is presented for training the neural network to predict cylinder-individual emissions even though the training data only provides cylinder-averaged target data. The algorithm was applied to an experimental data set from a six-cylinder heavy-duty engine, and it is verified that trends in emissions during transient engine operation are captured successfully by the model.

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