Directional Airflow for HVAC Systems

Directional airflow has been utilized to enable targeted air conditioning in cars and airplanes for many years, where the occupants could adjust the direction of flow. In the building sector however, HVAC systems are usually equipped with stationary diffusors that can only supply the air either in the form of diffusion or with fixed direction to the room in which they have been installed. In the present thesis, the possibility of adopting directional airflow in lieu of the conventional uniform diffusors has been investigated. The potential benefits of such a modification in control capabilities of the HVAC system in terms of improvements in the overall occupant thermal comfort and energy consumption of the HVAC system have been investigated via a simulation study and an experimental study. In the simulation study, an average of 59% per cycle reduction was achieved in the energy consumption. The reduction in the required duration of airflow (proportional to energy consumption) in the experimental study was 64% per cycle. The feasibility of autonomous control of the directional airflow, has been studied in a simulation experiment by utilizing the Reinforcement Learning algorithm which is an artificial intelligence approach that facilitates autonomous control in unknown environments. In order to demonstrate the feasibility of enabling the existing HVAC systems to control the direction of airflow, a device (called active diffusor) was designed and prototyped. The active diffusor successfully replaced the existing uniform diffusor and was able to effectively target the occupant positions by accurately directing the airflow jet to the desired positions.

Name thesis:degree_name

M.S.

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Civil Engineering

Department dc:contributor.department

Civil and Environmental Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2019

Author dc:creator

• Abedi, Milad

Chair dc:contributor.committeechair

• Jazizadeh, Farrokh

Committee members dc:contributor.committeemember

• Huang, Bert
• Tokekar, Pratap

• HVAC
• Thermal Comfort
• Reinforcement Learning
• Building Energy Consumption

Statement dc:rights

• Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States

Licence dc:rights.uri

• http://creativecommons.org/licenses/by-nc-nd/3.0/us/

Language dc:language.iso

en_US

Handle dc:identifier.uri

http://hdl.handle.net/10919/88524

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/88524