Automated Testbed for Building Systems Components

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For the heat transition and integration of renewable energies, systems with components (heat pumps, heat exchangers, absorbers, collectors, etc.) are required that achieve high efficiencies with low temperature differences following the low-ex principle. To achieve these efficiencies, the behavior of the components must be precisely known. Measurement under laboratory conditions is therefore a central prerequisite for the research and development of such systems. The task of the testbed is to enable this precise measurement of components. This provides data for precise modeling and simulation of not only the static but also the dynamic behavior of the components. These models then feed back into the research of novel systems through hardware-in-the-loop simulation and validation, again using the testbed. Due to insufficiently stable conditions and a lack of control options, the acquisition of measurement data and model validation of this quality is not possible through testing in the context of prototypes and demonstrators outside of a laboratory testbed. The special feature of the testbed is that three media streams are available: 1. a conditioned air stream with high power application for heat and moisture transfer (up to 25 kW), 2. a conditioned liquid stream also with high power transfer (up to 25 kW) and 3. a conditioned air stream for limited power application to simulate a two indoor or outdoor climate (up to 5 kW). The measurements follow three use cases: 1. heat and moisture exchange in building services components, 2. measurement of humid air and air collectors, and 3. measurement of airflow for indoor conditioning. One focus of the unit will be to investigate novel system components based on open absorption and air-to-air systems that integrate heat pumps. This includes the use of solar radiation as a heat source with latent heat transport as humidity and other nanofluid-based methods to establish lower-loss transport principles. In addition, the device will enable experimental research on indoor comfort in the context of such novel systems as well as conventional building services systems. Finally, the device provides opportunities of data acquisition for data-driven prediction models generated by machine learning (ML) that represent complex thermodynamic processes for further simulation and planning in an agile manner following the paradigm of component-based ML.