Airflow patterns are essential for heating, ventilation and air conditioning (HVAC) systems. Traditional HVAC systems are predesigned and operated using a fixed airflow pattern. However, the indoor occupancy and heat source always vary and therefore, the fixed flow pattern cannot efficiently maintain the required indoor environment conditions. In this study, a novel Adjustable Fan Network (AFN) for improving airflow pattern manoeuvrability is proposed. It integrates multiple small and adjustable axial fans into an AFN, enabling it to change the airflow pattern based on the actual demand with only one set of equipment. Further, the outflow characteristics of two types of axial fans were measured using a quad-view colour sequence particle streak velocimetry (CSPSV) in a test chamber. The ventilation system was then designed based on typical scenarios. Finally, the performance of the AFN was evaluated under different scenarios using a quad-view CSPSV. Based on the results, it was evident that the AFN can provide a better direct supply of air to the occupied zone under different scenarios. With the growing demand for personal thermal comfort and energy-saving in HVAC systems, the novel AFN system has a great potential to be a highly controllable terminal for demand-oriented ventilation.
D’OcaSHongTLangevinJ.The human dimensions of energy use in buildings: a review. Renew Sustain Energy Rev2018;
81: 731–742.
2.
LinZChowTTTsangCFFongKFChanLS.Stratum ventilation – a potential solution to elevated indoor temperatures. Build Environ2009;
44: 2256–2269.
3.
LimTSSchaeferLKimJTKimG.Energy benefit of the underfloor air distribution system for reducing air-conditioning and heating loads in buildings. Indoor Built Environ2011;
21: 62–70.
4.
ASHRAE. Space air diffusion. In: ASHRAE handbook – fundamentals. Atlanta: ASHRAE, 2014.
5.
NielsenP.Displacement ventilation in a room with low-level diffusers. Indoor Environ Technol1988;
R8836: 1–18.
6.
TuddenhamD.Design considerations for a floor based air conditioning system with modular services units at the new Hong Kong bank headquarters. ASHRAE Trans1985;
91: 387–403.
7.
GulMSPatidarS.Understanding the energy consumption and occupancy of a multi-purpose academic building. Energy Build2015;
87: 155–165.
8.
FangerPO. Human requirements in future air conditioned environments: a search for excellence. In: Proceedings of the 3rd ISHVAC, Shenzhen, 17–19 November 1999, pp. 86–92.
9.
MelikovAKCermakRMajerM.Personalized ventilation: evaluation of different air terminal devices. Energy Build2002;
34: 829–836.
10.
WatanabeSMelikovAKKnudsenGL.Design of an individually controlled system for an optimal thermal microenvironment. Build Environ2010;
45: 549–558.
11.
SchiavonSMelikovAKSekharC.Energy analysis of the personalized ventilation system in hot and humid climates. Energy Build2010;
42: 699–707.
12.
ShaoXLiXMaXLiangC.Multi-mode ventilation: an efficient ventilation strategy for changeable scenarios and energy saving. Build Environ2017;
115: 332–344.
13.
CaoSJRenC.Ventilation control strategy using low-dimensional linear ventilation models and artificial neural network. Build Environ2018;
144: 316–333.
14.
ChenZJiangCXieL.Building occupancy estimation and detection: a review. Energy Build2018;
169: 260–270.
15.
ZhouPHuangGLiZ.Demand-based temperature control of large-scale rooms aided by wireless sensor network: energy saving potential analysis. Energy Build2014;
68: 532–540.
16.
ShaoXLiXMaH.Identification of constant contaminant sources in a test chamber with real sensors. Indoor Built Environ2016;
25: 997–1010.
17.
ShaoXWangKLiX.Rapid prediction of the transient effect of the initial contaminant condition using a limited number of sensors. Indoor Built Environ2019;
28: 322–334.
18.
BenezethYLaurentHEmileBRosenbergerC.Towards a sensor for detecting human presence and characterizing activity. Energy Build2011;
43: 305–314.
19.
LiNCalisGBecerik-GerberB.Measuring and monitoring occupancy with an RFID based system for demand-driven HVAC operations. Autom Constr2012;
24: 89–99.
20.
NgTM.From “Where I am” to “Here I am”: accuracy study on location-based services with iBeacon technology. HKIE Trans2015;
22: 23–31.
21.
Walmsley-EyreLCardell-OliverR. Hierarchical classification of low resolution thermal images for occupancy estimation. In: 2017 IEEE 42nd conference on local computer networks workshops (LCN Workshops), 9 October 2017, pp. 9–17. Singapore: IEEE.
22.
ZouJZhaoQYangWWangF.Occupancy detection in the office by analyzing surveillance videos and its application to building energy conservation. Energy Build2017;
152: 385–398.
23.
FleuretFBerclazJLengagneRFuaP.Multicamera people tracking with a probabilistic occupancy map. IEEE Trans Pattern Anal Mach Intell2008;
30: 267–282.
24.
FengQZhangCLuJCaiHChenZYangYLiFLiX.Source localization in dynamic indoor environments with natural ventilation: an experimental study of a particle swarm optimization-based multi-robot olfaction method. Build Environ2019;
161: 106228.
25.
KongKHChongWTKohVL.Human behaviour-dependent and variable-flow-reversible mechanical ventilation system design in an underground parking facility. Indoor Built Environ2019;
28: 1324–1340.
26.
EricksonVLLinYKamtheABrahmeRSuranaACerpaAESohnMDNarayananS. Energy efficient building environment control strategies using real-time occupancy measurements. In: Proceedings of the first ACM workshop on embedded sensing systems for energy-efficiency in buildings – BuildSys ’09, November, 2009, pp. 19–24. Berkeley: ACM Press.
27.
EricksonVLCerpaAE. Occupancy based demand response HVAC control strategy. In: Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building – BuildSys ’10, 3 November, 2010, pp. 7–12. New York: ACM Press.
28.
AbediMJazizadehFHuangBBattagliaF. Smart HVAC systems – adjustable airflow direction. In: Smith I and Domer B (eds) Advanced computing strategies for engineering. EG-ICE2018. Lecture notes in computer science. Cham: Springer, pp. 193–209.
29.
ZhaiYArensEElsworthKZhangH.Selecting air speeds for cooling at sedentary and non-sedentary office activity levels. Build Environ2017;
122: 247–257.
30.
ZhuYLuoMOuyangQHuangLCaoB.Dynamic characteristics and comfort assessment of airflows in indoor environments: a review. Build Environ2015;
91: 5–14.
31.
GhahramaniAZhangKDuttaKYangZBecerik-GerberB.Energy savings from temperature setpoints and deadband: quantifying the influence of building and system properties on savings. Appl Energy2016;
165: 930–942.
32.
LuoMArensEZhangHGhahramaniAWangZ.Thermal comfort evaluated for combinations of energy-efficient personal heating and cooling devices. Build Environ2018;
143: 206–216.
33.
AlotaibiSChakrounWHabchiCGhaliKGhaddarN.Effectiveness of contaminant confinement in office spaces equipped with ceiling personalized ventilation system. Build Simul2018;
11: 773–786.
34.
LuoMYuJOuyangQCaoBZhuY.Application of dynamic airflows in buildings and its effects on perceived thermal comfort. Indoor Built Environ2018;
27: 1162–1174.
35.
ArensEZhangHPasutWZhaiYHoytTHuangL.Air movement as an energy efficient means toward occupant comfort – final report. Berkeley, CA, USA, https://escholarship.org/uc/item/2d656203 (2013, 12 December 2019).
WangHZhangHHuXLuoMWangGLiXZhuY.Measurement of airflow pattern induced by ceiling fan with quad-view colour sequence particle streak velocimetry. Build Environ2019;
152: 122–134.
39.
WangHLiXShaoXWangBLinY.A colour-sequence enhanced particle streak velocimetry method for air flow measurement in a ventilated space. Build Environ2017;
112: 77–87.
40.
WangHWangGLiX.High-performance color sequence particle streak velocimetry for 3D airflow measurement. Appl Opt2018;
57: 1518–1523.
41.
Anonymous. SAI bubble generator: description and operating instructions.
New York:
Sage, 1988.
42.
AmaiHNovoselacA.Experimental study on air change effectiveness in mixing ventilation. Build Environ2016;
109: 101–111.
43.
Tecplot. User’s manual. Bellevue: Tecplot,2018, pp. 326–328.
44.
BaturinVV.Fundamentals of industrial ventilation.
Oxford:
Pergamon Press, 1972.
45.
NilssonLJ.Air-handling energy efficiency and design practices. Energy Build1995;
22: 1–13.
