Advanced Double skin facades by means of flexible control of air flow and solar shading (thesis, or thesis + internship) - REINVENT project
Research Groups TEBE (http://www.tebe.polito.it/)
Description The thesis is aimed to evaluate the performance and to design and implement a model based control for an innovative Double Skin Facade by means of flexible control of airflow and solar shading, in the framework of an industrial project with partners. Within the project we have already a full scale prototype of the facade, mounted on an outdoor experimental facility of the Energy Dept. at Politecnico di Torino.
The thesis will start with a literature review on double skin facades, working physical principles and technologies, with a focus on rule based and model based control strategies, to control the integrated solar shading and airflow in the cavity of the double skin. In parallel the student will develop skills related to building performance simulation of double skin facade by means of EnergyPlus and lumped parameter models (developed within the research group).
One of the main and final activity, would be to test a control based on a physical model of the double skin facade (already developed by the research group) on the real scale facade mock-up, by means of sensors and monitoring systems. In this activity he will be supporting and it will be helped by the reseachers of the research group. The models developed for the model-based control will be used to evaluate the performance of the double skin facade in different possible boundary conditions.
Required skills Knowledge of Building Physics fundamentals and advanced concepts (at least 2 building physics courses completed during the BSc and MSc degree); willingness and attitude for literature review and modelling work (including scripting in Matlab or Python); Excellent Excel skills; elements of Python or Matlab programming (or willingness to learn some); Use of Energyplus or equivalent building energy performance simulation tools. Attitude and willingness for experimental work.
Deadline 05/01/2023 PROPONI LA TUA CANDIDATURA