Power engineering applications are often characterised by a large number of influencing variables whose relationships to each other are unknown and complex. Experimental modelling uses a mixture of experimental data collection methods and statistical or regression modelling techniques to gather information about the process under study. The experimental part is performed on a dedicated measurement station, often designed and built for the purpose of the experimental model. In the past, we have performed many successful implementations of purpose-built measurement stations that provide a wealth of experimental information about the process. Using the experimental data, we were able to find out the underlying relationships and minimise variables such as energy consumption, drying time, fibre length, etc.
Experimental models have been successfully used in the past for improving fibre generation distribution in mineral wool processes (https://doi.org/10.5545/sv-jme.2020.6557), household appliance operation ( https://doi.org/10.1080/07373937.2018.1502778 ), cavitation modelling (https://doi.org/10.1016/j.ijmultiphaseflow.2021.103700) , drying processes (https://doi.org/10.5545/sv-jme.2020.6717), free surface flow modelling (https://doi.org/10.1016/j.euromechflu.2020.02.001), etc.
