Thermal comfort measurement in an indoor environment is essential as it impacts people’s health and productivity. Several indices, such as effective temperature, heat stress index, Oxford index, and wet bulb globe temperature, have been used to measure human thermal comfort quantitatively over the past decades. Among other indices, several international standards, such as ASHRAE 55-2017, ISO7730, EN 16798-1 and EN 16798-2, worldwide accept predicted mean vote (PMV) as an indicator of thermal comfort conditions, as it considers all key environmental and personal factors influencing human thermal comfort. While several devices are available and reported both commercially and in the literature that measures PMV, most of these devices are very bulky and only take environmental parameters in...[ Read more ]

Thermal comfort measurement in an indoor environment is essential as it impacts people’s health and productivity. Several indices, such as effective temperature, heat stress index, Oxford index, and wet bulb globe temperature, have been used to measure human thermal comfort quantitatively over the past decades. Among other indices, several international standards, such as ASHRAE 55-2017, ISO7730, EN 16798-1 and EN 16798-2, worldwide accept predicted mean vote (PMV) as an indicator of thermal comfort conditions, as it considers all key environmental and personal factors influencing human thermal comfort. While several devices are available and reported both commercially and in the literature that measures PMV, most of these devices are very bulky and only take environmental parameters into account. The focus of this thesis is to develop a CMOS compatible MEMS human thermal comfort sensing (HTCS) system based on PMV index and apply it to indoor heating ventilation and air conditioning (HVAC) systems.

First, we developed a PMV based micro HTCS system with multiple MEMS sensors. The system comprised of a wireless multi-sensor (CMOS MEMS air velocity, MEMS RH, and MEMS air temperature) module to measure three environmental parameters and a smartphone App with a novel motion analytics algorithm (for estimation of metabolic rate) and personal factors (clothing insulation) input. While the majority of the reported HTCS systems are bulky and take only environmental parameters into consideration, our developed system utilizes MEMS technology for sensors’ fabrication and measures the essential environmental and human factors involved to compute human thermal comfort.

Then, to reduce both the size and cost of the HTCS system, we developed a single-chip fabrication process for CMOS compatible MEMS temperature, RH, and highly sensitive air velocity sensors. Our multi-sensor chip (MSC) has two merits. First, it utilizes a low-cost 3-mask fabrication process to fabricate temperature, RH, and TMCV sensors on a single chip with a proper packaging layer (parylene C), which acts as both packaging (for temperature and air velocity sensors) and sensing layer (for RH sensor). Second, a partially released thermoresistive calorimetric air velocity (TMCV) sensor with dual pairs of detectors was fabricated to double its sensitivity. The sensors were successfully characterized. The measurement results indicated a maximum sensitivity of 312 mV/(m/s) for the developed TMCV sensor with dual detectors which is almost double compared to a conventional single pair of detectors. Besides, the crosstalk effect among the sensors on MSC was studied, which indicated that TMCV sensor can influence other (RH and temperature) sensors by almost 2 °C to 7 °C.

To minimize the crosstalk, an improved multi-sensor chip (iMSC) was developed. In the improved design, the TMCV sensor was fully released from the backside to minimize its heat transferred to the substrate (where temperature and RH sensors are located). This also resulted in low power operation of the TMCV sensor, which improved its normalized sensitivity from 17.52 μV/(m/s)mW to 354.37 μV/(m/s)mW. Furthermore, the normalized sensitivity of our improved TMCV sensor is much higher than most of the reported thermoresistive flow sensors. Moreover, in the improved design, the crosstalk is minimized to about 0.1 °C.

Finally, to demonstrate the application of PMV for an indoor environment, a PMV based control technique was developed to control the fan coil unit (FCU) of an HVAC system. The technique was successfully tested in a demo room where it achieved optimum thermal comfort at low energy consumption compared to traditional temperature-based control systems. Besides, a PMV based smart personalized ventilation system (sPVS) was developed in this thesis. The system provides optimal thermal comfort based on the PMV index at escalated temperatures by regulating the local air velocity. The system was successfully tested. An excellent thermal comfort ( │PMV│<0.5) was achieved by sPVS by regulating the local air velocity at escalated setpoint temperatures (24.5 °C ~ 27.5 °C) and RH (66% ~ 72%). The sPVS can be used in an indoor environment with high setpoint temperatures to provide better local thermal comfort with reduced energy consumption.