Welcome to Computational Fluid Dynamics (CFD) in Building Ventilation


Description: 

The application of Computational Fluid Dynamics in building ventilation systems is crucial for creating environments that are not only energy - efficient and compliant with regulations but also prioritize occupant health, safety, and comfort. CFD provides a comprehensive understanding of airflow dynamics, enabling engineers and designers to make informed decisions that positively impact the performance of building ventilation systems in the real world.

 This Ph D course delves into the intricacies of Computational Fluid Dynamics ( CFD) as applied to building ventilation. Participan ts will explore advanced numerical methods and s imulation techniques essential for understanding and optimizing airflow within built environments. The course emphasizes practical applications, bridging theoretical concepts with real- world scenarios to enha nce participants' skills in addressing ventilation challenges.

 The course is organized in three days. In the f i rst day, the course gives a n introduction of CFD fundamentals and building ventilation designs by using CFD commonly in practice through different case studies. In the second day, the concepts previously learnt are used to implement concepts from the theory to benchmark study. In the third day, the impact of CFD on IAQ, infection r isk and thermal comfort studies is discussed with through different case studies and exercises.

Day 1 : Introduction to CFD in Ventilation ( Theoretical day)

-          Background and Introduction to CFD principles.

-          Basics of f luid dynamics in the context of building ventilation.

-          Laminar, Transitional and Turbulent f lows in buildings.

-          Application of CFD in building ventilation design

-          The use of Benchmarks for model validation.

Day 2 : CFD techniques in ventilation

-          Problems and possibilities to consider prior to a CFD prediction

-          Building geometry and mesh generation using CFD s imulation software.

-          Boundary conditions as diffusers , people etc.

-          Numerical solver setup.

-          Hands- on workshop: CFD Benchmark study and problem description Practical exercise: https:// www. cfd- benchmarks. com/ Backwardflow/

 Day 3 : Practical Applications

-          CFD for Thermal Comfort, infection r isk and IAQ.

-          Interactive session: Participants present their s imulation results , supplemented with feedbacks and discussion.

-          Validation and verification of CFD simulations through benchmark studies.

-          Discussion on challenges and best practices in CFD for building ventilation.

-          Future trends and advancements in the f ield.

 Participants will gain hands - on experience through practical exercises and real- world case studies, ensuring they leave the course with a comprehensive understanding of applying CFD to optimize building ventilation systems.


Prerequisities:

·         Basic knowledge of thermo- fluid dynamics in buildings

·         Basic knowledge on building ventilation systems

·         Basic knowledge of CFD s imulation tools.

 

Learning objectives:

After the course, participants will be able t o increase their knowledge about the most recent CFD techniques and their applications in the built environment .


Teaching methods:

Teaching will be provided as a mix of lecture presentations, hands - on trainings, simulation exercises and discussions .

 

Criteria for assessment:

Participants will be evaluated through a f inal assignment, which consists in the preparation of a report to be delivered 2 weeks after the end of the course.


Key literature:

  • Nielsen, P. V. ( 2015 ) . Fifty years of CFD for room air distribution, Building and Environment , Volume 91 , September 2015 , Pages 78 - 90 , https://doi.org/10.1016/j.bui ldenv.2015.02.035
  • Li, Y., & Nielsen, P. V. ( 2011 ) . CFD and ventilation research. Indoor air 21 ( 6 ) , 442 - 453 .
  • Cuce, E., Sher, F., Sadiq, H., Cuce, P. M., Guclu, T., & Besir, A. B. ( 2019 ) . Sustainable ventilation strategies in buildings: CFD research. Sustainable Energy Technologies and Assessments, 36 , 100540 .
  • Michael Wetter, Wangda Zuo, Thierry S. Nouidui & Xiufeng Pang . ( 2014 ) . Modelica Buildings l ibrary, Journal of Building Performance Simulation, 7 : 4 , 253 - 270 .
  • Chen, Q. ( 2009 ) . Ventilation performance prediction for buildings: A method overview and recent applications. Building and environment, 44 ( 4 ) , 848 - 858 .
  • CFD benchmark: https://www.cfd-benchmarks.com/
  • Peng, L., Nielsen, P. V., Wang, X., Sadrizadeh, S., Liu, L., & Li, Y. ( 2016 ) . Possible user dependent CFD predictions of t ransitional f low in building ventilation.  Building and Environment, 99 , 130 – 141 . https:// doi. org/ 10 . 1016 / j . buildenv. 2016 . 01 . 014
  • Van Hooff, T., Nielsen, P. V., & Li, Y. ( 2018 ) . Computational f luid dynamics predictions of non- isothermal ventilation f low — How can the user factor be minimized? Indoor Air, 28 ( 6 ) , 866 – 880 . https:// doi. org/ 10 . 1111 / ina. 12492



Organizer:
Prof. Alireza Afshari

Lecturers:

Peter V. Nielsen, Professor ( Aalborg University)

Chen Zhang, Associate Professor ( Aalborg University) 

Haider Latif, Postdoc ( Aalborg University)


ECTS:
3

Time: 

October and November 2024

Place:

Aalborg University ( Copenhagen campus). Online participation will be available.

City:
Copenhagen

Number of seats:
20

Deadline:
TBA

Welcome to Modelica-based simulation of building and district energy systems

Course description:

Due to climate change, more stringent building energy standards are enforced to reduce building primary energy use. This causes a shift towards the use of more advanced and complex heating and cooling systems for the built environment.

Energy simulation programs are powerful tools that have been increasingly used by engineers and researchers for the design, analysis and optimization of energy and district energy systems. However, today’s programs have difficulties to handle the challenges posed by the complexity of future systems, which are expected to integrate renewable energies, thermal storage, and advanced control algorithms.

 

This course aims to present latest developments in modeling and simulation of building and district energy systems based on Modelica modeling language. Modelica is an open-source language that features ease of use, visual design of models with combination of Lego-like predefined blocks, ability to define model libraries with reusable components, and support for modeling and simulation of complex applications involving parts from different engineering domains.

 

The course is organized in three days. In the first day, the course gives a basic introduction of Modelica fundamentals by introducing object-oriented and equation-based modeling. In the second day, the concepts previously learnt are used to develop a building model equipped with a radiator system and an air-based cooling system. In the third day, a district heating system model will be developed by connecting substations, piping network and central plant.

 

 

Day 1: Introduction to Modelica and Dymola

·      What is Modelica?

·      Dymola software tool

·      Basics of the Modelica modeling language

·      Object-oriented and equation-based formulation

·      Modelica libraries

 

Day 2: Hands-on training: Simple house with radiator heating system

·      Solving 1D transient heat conduction

·      Creating a single-room model

·      Building envelope modeling

·      Radiator heating system and controller

 

 

Day 3: Hands-on training: District heating system

·      Substations

·      Piping network

·      Ground losses

·      Central plan

 

 

Prerequisites:

·      Basic knowledge of any programming language

·      Basic knowledge of building simulation programs

·      Knowledge of thermo-fluid dynamics and heat transfer mechanisms in buildings

 

 

Learning objectives:

After the course, participants will be able to use Modelica libraries, create models of building rooms and heating/cooling systems on their own, run simulations, and analyze results.

 

Teaching methods:

Teaching will be provided as a mix of lecture presentations, hands-on trainings, simulation exercises and discussions.

 

Criteria for assessment:

Participants will be evaluated through a final assignment, which consists in the preparation of a report to be delivered 2 weeks after the end of the course.

 

Key literature:

·      Peter Fritzson. (2014). Principles of Object-Oriented Modeling and Simulation with Modelica 3.3: A Cyber-Physical Approach. John Wiley & Sons.

·      Jan Hensen and Roberto Lamberts. (2019). Building Performance Simulation for Design and Operation. Routledge.

·      Michael Wetter, Wangda Zuo, Thierry S. Nouidui & Xiufeng Pang. (2014). Modelica Buildings library, Journal of Building Performance Simulation, 7:4, 253-270.

 

Organizer:

Alireza Afshari, Professor (Aalborg University)

 

Lecturers:

Alessandro Maccarini, Assistant Professor (Aalborg University)

Michael Wetter, Computational Senior Scientist (LBNL, USA)

ECTS:

3.0 ECTS

Time:

21-23 October 2024

Place:

Aalborg University (Copenhagen campus). Online participation will be available.

Max. number of participants:

20

Deadline for registration:

30 September 2024

 

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.


Welcome to Advanced control for building applications

Description: 

Topic, background and motivation for the course:

Despite extensive research and successful implementation of advanced control techniques, like MPC, in other fields, the application of such techniques is still limited in practice in building services engineering. One of the reasons seems to be the lack of knowledge among building service engineers about advanced control methods. There is a growing need for multidisciplinary education on advanced control methods in the built environment.

 Buildings use a large share of total energy use around 35–40% in many countries. In Denmark, buildings account for 40% of the Danish energy use. Building energy-related activities are responsible for the 19% of GHG emissions worldwide. Therefore, it is motivated to investigate the energy saving potential in the building sector. Advanced building control can considerably reduce building energy use. For instance, numerous studies reported that advanced HVAC control can notably reduce energy use and mitigate GHG emissions with average energy savings of 13% to 28%.

The most popular advanced building control solution among the scientific community is Model Predictive Control (MPC) due its proven ability to handle constraints while optimizing the system performance. MPC on the supervisory level can be designed to find energy-efficient or cost-efficient control settings for the local controllers, taking into account the system level characteristics, interactions and comfort constraints. MPC combines building modelling, measurement, disturbance forecasting as well as information from external sources in the optimization formulation in order to find optimal control settings.

Prerequisites:

-        Basic knowledge of a programming language (MATLAB/Python/R)

-        Knowledge of basic control methods, e.g. feedback control loop, PID controllers

-        Basic knowledge of building energy modelling and thermodynamics

-        Basic knowledge of linear algebra

Learning objectives:

This course is intended for PHD students in the built environment and building service engineers, at national and international level, who want to:

-        increase their knowledge about the most recent advanced control techniques and their applications in the built environment

-        learn the theory and practice of Model Predictive Control and MPC problem classes for building applications

-        learn how to formulate an MPC problem for building applications

-        learn how to implement a basic MPC algorithm in a small-scale experimental mock-up

Teaching methods:

Teaching method comprises of lecture presentations by the teachers, simulation exercises with teachers’ supervision and discussion-based experimental demonstration possibly with competition between groups of student. The structure of the course is as follows:

Day 1 (Theoretical)

Lecture 1: A glimpse of control theory

  • Control of dynamical systems: examples of control problems in building application, types of control: model free (PID) and model-based control, open loop, and closed loop control)
  • LTI system: eigen value and vector, stability of the system, controllability
  • Pole placement
  • A MATLAB simulation example from building application for checking the controllability and stabilize the system with pole placement

Lecture 2: Optimal control design

  • LQR control
  • Full-state estimation: observability and Kalman filter design
  • LQG control
  • A MATLAB simulation example from building application for LQR, Kalman filter and LQG control design

Lecture 3: Model Predictive Control

  • General concepts of MPC
  • Modelling (White box - Black box- Grey box)
  • A MATLAB simulation example for Grey box modelling of a case study from a building application

Day 2 (Simulation)

Lecture 1: An MPC design in MATLAB

  • Introduction to CVX toolbox
  • Cost functions formulization and constraints definition
  • Presentation of a real-life case study (SmartVENT project) (Energy flexibility)

Simulation exercise and homework (An example similar to SmartVENT project but simpler, in which the white box model is either modelled in IDA ICE or be one of the SIMULINK models in MATLAB )

Day 3 (Laboratory experiment)

Lecture 1: Introduction on the experimental setup

Experimental exercise:

  • Run the experimental system and be familiar with the it
  • Input-output data collection
  • Identify the system model and simulate the MPC controller
  • Implement the MPC controller on the experimental setup
  • Comparison of a PI control and the MPC control on the system performance

Criteria for assessment:

-        Attendance in all course days as scheduled is required.

-        Report on the simulation results.

-        Presentation of the experimental results provided by each group

Key literature:

-        Jan Drgona et al., All you need to know about model predictive control for building, Annual Reviews in Control, 2020

-        Predictive control with constraints. by Maciejowski

-        L. Wang, Model Predictive Control System Design and Implementation Using MATLAB. Springer, 2009

-        Henrik Madsen, Statistical Modelling of Physical Systems (An introduction to Grey Box modelling)

Organizer:
 Alireza Afshari

Lecturers: Samira Rahnama, Hicham Johra, Mahmood Khatibi

ECTS: 3.0 (28 hours of work load per ECTS)

Time: 2-4 December 2024

Place: Aalborg University

Zip code: 
9220

City: Aalborg

Number of seats: 20

Deadline: 11 October 2024

Important information concerning PhD courses: 
We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.

Sewer Processes - Modeling of sewer microbial and chemical processes

Description: The course provides a basis for up-to-date knowledge and modeling of sewer microbial and chemical processes and shows how this understanding can be applied for design, operation, and maintenance of wastewater collection systems. A central focus of the course is on predicting critical impacts and controlling adverse effects of hydrogen sulfide and other toxic/noxious gases.

The course:

·       Present new modeling tools for the design and operation of sewer networks

·       Establishes sewer processes as a key element in preserving water quality

·       Details the WATS sewer process model

·       Highlights the importance of aerobic, anoxic, and anaerobic processes

The course will introduce experimental methods to quantify wastewater quality in terms of biodegradability and chemical composition. During the course, the participants will get hands-on experience with setting up numerical sewer processes models and with determination of central model parameters.

 

Organizer: Asbjørn Haaning Nielsen & Jes Vollertsen

Time: 7, 8, 9, 10, 11 October 2024

ECTS: 5

Number of seats: 20

Deadline: 16 September 2024

Important information concerning PhD courses:

We have over some time experienced problems with no-shows for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registration.

 For inquiries regarding registration, cancellation, or waiting list, please contact the PhD administration, aauphd@adm.aau.dk