We present two technologies for speeding up co-simulations under the FMI standards. By smoothing the input signals inside each FMU, the internal integrator may avoid re-initialization. This can significantly reduce the number of model and Jacobian evaluations. To further help the integrator we also propose a predictor compensation technique tailored to the input smoother. The main benefit of our technologies is the ease-of-use, requiring no model manipulations, nor any special co-simulation master algorithms. The technologies are implemented in Dymola~2025x and validated with both an academic mechanical model as well as thermo-fluid examples where we can observe performance gains with factor up to 100, and often around 5-10. One of these thermo-fluid examples is used in the \emph{OpenSCALING} research project to generate training data for constructing surrogate models, for which the input smoothing is especially important to speed up the dataset creation.

The global expansion of data center construction is fueled by the rising demand for AI applications. These energy-intensive facilities face increasing pressure to operate efficiently due to the European Efficiency Directive (ENER, 2024), for instance. Minimizing their environmental impact is therefore critical. To address this challenge, the System Simulation team in Danfoss Climate Solutions and TLK Energy are focusing in this joint presentation on innovative heat recovery approaches in modern, liquid-cooled data centers as a key strategy for energy-efficient operation. Using a holistic model of the overall system from liquid cooled server racks via the cooling loop to chillers, dry coolers and heat recovery including heat consumers, we can determine the system efficiency covering all interactions of the sub-systems for fully transient conditions. The backbone of the system models are the validated models of the relevant components from Danfoss’ portfolio. By setting the simulation results in relation to the needed CAPEX we are going to draw a picture of an optimized system setup for the presented use case.