Heating demand Dutch stock topic removed

education/msctopics/index.md

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-## Heating energy demand of the Dutch building stock with the help of the 3D BAG 2.0
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-The [3D BAG 2.0](https://3dbag.nl/en/viewer), a dataset containing LoD2 geometries of buildings of the whole Netherlands, has been recently released by the 3D Geoinformation group. The scope of this thesis is to test the suitability of the 3D BAG to estimate the energy demand of residential buildings. In this thesis, the energy demand computation will be based on the quasi-static energy-balance method and will implemement a Python-based subset of the specifications defined by the recently introduced Dutch norm [NTA8800](https://www.gebouwenergieprestatie.nl/).
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-![](img/3dbag_nta8800_mini.jpg){:width="800px"}
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-The thesis is a collaboration between the 3DGeoinformation group and the [Building Energy Epidemiology](https://www.tudelft.nl/bk/over-faculteit/afdelingen/management-in-the-built-environment/organisatie/leerstoelen/building-energy-epidemiology) group at TU Delft. Attendance of elective course [GEO5014](https://studiegids.tudelft.nl/a101_displayCourse.do?course_id=60400) in Q5 (September 2022) is highly recommended, as many relevant topics needed for this thesis will be covered.
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-You will programm in Python and (a bit) in PL/pgSQL.
-Before picking the topic, please contact us!
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-**Contact:** [Giorgio Agugiaro](https://3d.bk.tudelft.nl/gagugiaro/) (3D Geoinformation Group)
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 ## Optimizing building mesh designs for computational fluid dynamics using machine learning 
 
 Since one of the major burdens when performing computational fluid dynamic simulations (CFD) is to set up a good mesh, improving the current capabilities to mesh automatically complex geometries would have a large impact for the computational fluid dynamics community. This task becomes really essential when geometries are complex, such as high resolution level of detail buildings, and severals hundreds of simulations need to be run to quantify uncertainties.