Modulbeschreibung

Computational science and engineering applied to intelligent energy buildings

Kurzzeichen:
M_TuIT_EVA_1068
ECTS-Credits:
3
Leitidee:

Buildings are responsible for about 40 % of the energy consumption and CO2 emissions. The course develops computational skills in Python for modelling and problem solving of coupled heat transfer with special applications to optimize energy consumption for indoor climate control.

Modulverantwortung:
Scholer Matthias
Standort (angeboten):
Buchs
Modultyp:
Wahlpflicht-Modul für MSE Master of Science in Engineering BB STD_08 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für MSE Master of Science in Engineering BB STD_13 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für MSE Master of Science in Engineering BB STD_16 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für MSE Master of Science in Engineering VZ STD_08 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für MSE Master of Science in Engineering VZ STD_13 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für MSE Master of Science in Engineering VZ STD_16 (BU)(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Wahlpflicht-Modul für Technik und IT MSE_20(Keine Semesterempfehlung)Kategorie:Fachliche Vertiefung (MSE-FachV)
Modulbewertung:
Note von 1 - 6

Leistungsnachweise und deren Gewichtung

Während der Unterrichtsphase:

33.3% Written 1h, w/o documents on 8/12/2023

33.3% Written report of group work due on 6/12/2023

33.3% Oral presentation of group work on 8/12/2023

Bewertungsart:
Note von 1 - 6
Gewichtung:

33.3% Written 1h, w/o documents on 8/12/2023

33.3% Written report of group work due on 6/12/2023

33.3% Oral presentation of group work on 8/12/2023

Bemerkungen:

Inhalte

Angestrebte Lernergebnisse (Abschlusskompetenzen):

Buildings are responsible for about 40 % of the energy consumption and CO2 emissions. The course develops computational skills in Python for modelling and problem solving of coupled heat transfer with special applications to optimize energy consumption for indoor climate control.

Modul- und Lerninhalt:

Face to face

Lecture module 1

·     thermal transfer: conduction, convection, and radiation

 

Lecture module 2

·     continuous and discrete models

·     thermal networks

·     transforming the thermal networks into state-space and transfer functions

·     coupling the models

 

Tutorial 1: Read weather data and calculate solar radiation:

1)    introduction to linear algebra and tools (Python, Numpy, Matplotlib);

2)    use Pyhton for reading (weather) data

3)    calculating the solar load

 

Tutorial 2: Simple wall

1)    physical analysis and mathematical models

2)    discretization of mathematical models

3)    numerical stability

4)    implementation

 

Tutorial 3: Simple building in free-running: controlled natural ventilation

1)    physical analysis and mathematical models

2)    discussion of examples

3)    implementation

 

Tutorial 4: Simple building controlled by an HVAC system

1)    physical analysis and mathematical models

2)    discussion of examples

3)    implementation

 

Accompanied individual mini-project:

Intelligent control of a single zone building

 

Autonomous group project:

Students define their own subject on indoor climate control, for example:

- dynamic insulation,

- dynamic solar protection,

- control of floor-heating and fan coils,

- influence of set-point setback,

- control of intermittently heated buildings,

- model predictive control.