A hybrid cooling system which combines natural ventilation with a radiant cooling system for a hot and humid climate was studied. Indirect evaporative cooling was used to produce chilled water at temperatures slightly higher than the dew point. With this hybrid system, the condensation issue on the panel surface of a chilled ceiling was overcome. A computational fluid dynamics (CFD) model was employed to determine the cooling load and the parameters required for thermal comfort analysis for this hybrid system in an office-sized, well-insulated test room. Upon closer investigation, it was found that the thermal comfort by the hybrid system was acceptable only in limited outdoor conditions. Therefore, the hybrid system with a secondary fresh air supply system was suggested. Furthermore, the energy consumptions of conventional all-air, radiant cooling, and hybrid systems including the secondary air supply system were compared under similar thermal comfort conditions. The predicted results indicated that the hybrid system saves up to 77% and 61% of primary energy when compared with all-air and radiant cooling systems, respectively, while maintaining similar thermal comfort.

The newly developed Coupled Vane Compressor (CVC) consists of a unique feature that is a couple of vanes cut through the rotor diametrically. Theoretically, this allows the rotor of any diameter which can accommodate the coupled vanes, to be able to operate CVC. Thus, unlike other rotary compressors, CVC design is mostly free of geometrical constraints imposed on the size of its rotor, making it one of the most compact compressors. Nevertheless, CVC suffers from frictional losses similar to other rotary compressors and therefore, studies of dynamic forces acting in CVC is of great interest. In this paper, dynamic forces acting on its components and frictional losses occurring due to rubbing of various components were studied and analysed. Unlike in other vane compressors, in which frictional losses are mostly due to the rubbing of vane tips against cylinder wall and vane sides against vane slot in the rotor, in CVC, the loss also occurs due to rubbing between the coupled vanes. A CVC prototype of 44 cm³ was developed and the compressor power consumption was experimentally studied in an open-type test circuit using air as an operating fluid. The predicted and measured power consumed by CVC were compared and the predicted results were found to have maximum discrepancy of ±15% with the measured results.

In Singapore’s hot and humid climate, air-conditioning and mechanical ventilation (ACMV) systems account for over 60% of commercial building energy consumption, driving efforts to enhance energy efficiency through predictive control strategies such as model predictive control (MPC) to overcome the limitations of conventional reactive building automation systems. This paper presents a multizone MPC system designed to optimize energy consumption and thermal comfort in a commercial building’s ACMV system in Singapore. The system was implemented in a multi-use test building with real occupancy and a deployment area of approximately 850 m2, partitioned into six learning zones, two office spaces, and three open spaces. The ACMV system serving the deployment area consisted of two primary air-handling units and 16 fan coil units, where chilled water was supplied to the cooling coils, and conditioned air was distributed through motorized diffusers. To facilitate predictive control, data-driven thermal prediction models were developed for each zone using a non-linear autoregressive exogenous network with exogenous inputs trained on historical data and disturbances. Thermal comfort optimization was guided by the predictive mean vote, which was targeted at 0, representing thermal neutrality (as per ASHRAE 55 standards), and constrained within a range of −0.5 – 0.5. Performance comparisons demonstrated that the MPC system achieved over 42% energy savings compared to the original thermostat-based control while enhancing thermal comfort. Despite its advantageous control performances, challenges for large-scale deployment remain, including implementation costs, scalability, and model accuracy. Future work can address these challenges by developing comfort models that leverage existing building sensors.