This paper presents a computational study of the combined effects of variable geometry and asymmetry in the legs of thermocouples of thermoelectric modules used in solar thermoelectric generators (STEGs). Six different models were considered for the thermocouples in each module, namely: rectangular-rectangular legs, rectangular-trapezoidal legs, rectangular-X legs, trapezoidal-trapezoidal legs, trapezoidal-X legs, and X-X legs. Simulations of the six different modules under the same heat flux was carried out in ANSYS 2020 R2 software. Temperature and voltage distributions were obtained for each model and the results indicate significant variations due to the utilization of varying leg geometries. Results show that the X-X leg module generated the highest temperature gradient and electric voltage. In comparison, a temperature gradient and electric voltage of 297 K and 16 V, respectively were achieved with the X-X leg module as against 182 K and 8.4 V, respectively, achieved in a conventional rectangular leg module. This suggests a 63.2% and 90.5% increase in the temperature gradient and electric voltage of the conventional TE module. Therefore, this study demonstrates that X geometry gives the best performance for thermoelectric modules and STEGs.
Heating, Ventilation and Air Conditioning (HVAC) control and energy usage management has been identified as a promising way of improving building energy efficiency and thus contributing to solving the energy challenges of the world. However, a critical aspect of reducing HVAC energy usage is fault detection and diagnosis (FDD). Several studies have been conducted on FDD in HVAC systems with less focus on residential HVAC systems. One reason identified for this reduced attention with residential HVAC is that state-of-the-art FDD tools greatly depend on data available through the building automation system (BAS), and this detailed data is not typically available in the residential sector. Meanwhile, using sensors for developing FDD-enabled HVAC systems is not cost-efficient due to the cost associated with required sensors compared to the energy savings realized, thus making residential FDD less attractive. However, studies have shown that faults cause an additional 20.7TWh of energy consumption from residential HVACs across the US, annually. Thus, this paper gives a critical review of various studies that have been done on FDD in residential HVAC systems and proposes a data analytical approach, which if actualized, could reduce the sensor requirements for FDD in residential HVAC, thus addressing the cost barrier.
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