In the current state-of-the-art modeling tools for simulation, it is common to describe system behavior symbolically using mixed continuous and discrete differential-algebraic equations, so called hybrid DAEs. To correctly resolve higher index problems, hybrid systems and to efficiently use ODE solvers, a matching and sorting problem has to be solved, commonly referred to as causalization. Typically multidimensional equations and variables are scalarized, which leads to excessive build time and generated code size in the case of large systems.

An algorithm will be presented, that preserves array structures as much as possible while still solving the problem of causalization in scalar fashion. Test results carried out in the OpenModelica tool show a reduction in build time of one/two orders of magnitude and of a factor two/three in the simulation run time for models of the ScalableTestSuite library.

This paper provides a detailed analysis of the reasons behind the poor simulation performance observed when mass flow rates become very small, commonly referred to as zero mass flow issues. By using simple example models, we effectively demonstrate the underlying causes of these simulation performance issues. We highlight various contributing factors that play a significant role in exacerbating the problem.

Furthermore, we propose and examine countermeasures to mitigate these challenges. These countermeasures include modifications to the model itself, utilization of available settings in simulation tools, and adjustments to the solver. By implementing and evaluating these countermeasures, we illustrate their impact on improving simulation performance in scenarios involving low mass flow rates.

Machine learning models improve the speed and quality of physical models. However, they require a large amount of data, which is often difficult and costly to acquire. Predicting thermal comfort, for example, requires a controlled environment, with participants presenting various characteristics (age, gender, ...). This paper proposes a method for hybridizing real data with simulated data for thermal comfort prediction. The simulations are performed using Modelica Language. A benchmarking study is realized to compare different machine learning methods. Obtained results look very promising with an F1 score of 0.999 obtained using the random forest model.