1/1/2024 0 Comments Tidal heatingCaught in the Laplace resonance with the Galilean moons Ganymede and Europa, Io is the most tidally heated and volcanically active world in the Solar System. The reason for this appearance is extensive tidal heating in the moon's interior. Aim of this thesis is to improve our understanding of these interconnections (1-3) and to constrain Io's current interior dynamics based on the moon's volcanic activity derived from satellite and Earth-based observations over the last 20 years.Ībstract = "Io's spectacular and unique appearance is characterised by its yellowish surface, colourful lava deposits, and black calderas. Due to the strong dependence on melt, Io's volcanic activity hints at the dynamics beneath the surface and can therefore be used to improve our understanding of the underlying mechanisms. The physical state of Io’s interior, the driving tidal dissipation and heat transport mechanisms are unknown, however, form a strongly interconnected system: 1) Io’s internal temperature and melt distribution are controlled by tidal dissipation and heat loss processes 2) The total amount and pattern of tidal dissipation depend on the rheological properties of Io's interior 3) These rheological properties, in turn, depend on the internal temperature and melt distribution. It is therefore the best place to study fundamental processes important for the early evolution of terrestrial planets, and the habitability of icy satellites and terrestrial exoplanets subject to tidal heating. According to our research, the GitHub software for the modeling is a good candidate for predicting the energy consumption in large building groups, but further research is needed to explore its scalability for several buildings.Io's spectacular and unique appearance is characterised by its yellowish surface, colourful lava deposits, and black calderas. The results were promising in terms of predicting the energy consumption, and one of the scenarios also showed energy saving potential. The software for the modeling is published on GitHub. The methodology was tested in one case study using normal operational data, and the model was applied to analyze the energy savings in two different practical scenarios. We present a methodology to select the best-fitting ML model on the basis of both Bayesian optimization of black-box models for defining hyperparameters and a fivefold cross-validation for the assessment of each model’s predictive performance. An industrial use case to reach large building groups is restricted to using normal operational data in the modeling, and this is one reason for the low utilization of ML in HVAC optimization. Optimizing the heating, ventilation, and air conditioning (HVAC) system to minimize district heating usage in large groups of managed buildings is of the utmost important, and it requires a machine learning (ML) model to predict the energy consumption.
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