Directional Airflow for HVAC Systems
| dc.contributor.author | Abedi, Milad | en |
| dc.contributor.committeechair | Jazizadeh, Farrokh | en |
| dc.contributor.committeemember | Huang, Bert | en |
| dc.contributor.committeemember | Tokekar, Pratap | en |
| dc.contributor.department | Civil and Environmental Engineering | en |
| dc.date.accessioned | 2019-03-25T16:43:03Z | en |
| dc.date.available | 2019-03-25T16:43:03Z | en |
| dc.date.issued | 2019 | en |
| dc.description.abstract | 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. | en |
| dc.description.abstractgeneral | The notion of adjustable direction of airflow has been used in the car industry and airplanes for decades, enabling the users to manually adjust the direction of airflow to their satisfaction. However, in the building the introduction of the incoming airflow to the environment of the room is achieved either by non-adjustable uniform diffusors, aiming to condition the air in the environment in a homogeneous manner. In the present thesis, the possibility of adopting directional airflow in place 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 on average. 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. | en |
| dc.description.degree | M.S. | en |
| dc.format.medium | ETD | en |
| dc.identifier.uri | http://hdl.handle.net/10919/88524 | en |
| dc.language.iso | en_US | en |
| dc.publisher | Virginia Tech | en |
| dc.rights | Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States | en |
| dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/us/ | en |
| dc.subject | HVAC | en |
| dc.subject | Thermal Comfort | en |
| dc.subject | Reinforcement Learning | en |
| dc.subject | Building Energy Consumption | en |
| dc.title | Directional Airflow for HVAC Systems | en |
| dc.type | Thesis | en |
| thesis.degree.discipline | Civil Engineering | en |
| thesis.degree.grantor | Virginia Polytechnic Institute and State University | en |
| thesis.degree.level | masters | en |
| thesis.degree.name | M.S. | en |