Benny Raphael

The inhaled air quality in the breathing zone is strongly influenced by flow interactions around occupants. Personalized Ventilation (PV) aims to supply conditioned outdoor air directly to the occupants' breathing zone and thus improves the inhaled air quality. In this research, a Personalized Exhaust (PE) system is developed, which has two local exhaust devices installed with the chair, just above the shoulder level. Such a system placed in front of a PV system will introduce more PV air into the breathing zone of a seated person as well as exhausting part of the exhaled air from the free convective flow. This study investigates how the performance of a PV system will be affected after being integrated with the PE system. Experiments were conducted in an environmental chamber in Mixing Ventilation or Displacement Ventilation mode for the background air-conditioning system. A breathing thermal manikin was placed in the PE integrated chair in front of the PV air terminal device (ATD) to simulate a seated person in an office environment. The manikin was moved longitudinally away from the PV ATD as well as in an arc to 12 different locations. The performance of the PV-PE system at the 12 different locations was tested with regard to its ability in pulling the PV air towards a seated person moving within a small area in front of the workstation. Findings imply that the use of the combined PV-PE system for a seated person could provide more outdoor air than a PV system alone. © 2014 Elsevier Ltd.

A great challenge associated with urban growth is to design for energy efficient and healthy built environments. Exploiting the potential for natural ventilation in buildings might improve pedestrian comfort and lower cooling loads, particularly in warm and tropical climates. As a result, predicting wind behavior around naturally ventilated buildings has become important and one of the most common prediction approaches is computational fluid dynamics (CFD) simulation. While accurate wind prediction is essential, simulation is complex and predictions are often inconsistent with field measurements. Discrepancies are due to the large uncertainties associated with modeling assumptions, as well as the high spatial and temporal climatic variability that influences sensor data. This paper proposes metrics to estimate the expected predictive performance of sensor configurations and assesses their usefulness in improving simulation predictions. The evaluations are based on the premise that measurement data are best used for falsifying model instances whose predictions are inconsistent with the data. The potential of the predictive performance metrics is demonstrated using full-scale high-rise buildings in Singapore. The metrics are applied to assess previously proposed sensor configurations. Results show that the performance metrics successfully evaluate the robustness of sensor configurations with respect to reducing uncertainty of wind predictions at other unmeasured locations.

Warm climates pose challenges to building energy consumption and pedestrian comfort. Knowledge of the wind flow around buildings can help address these issues through improving natural ventilation, energy use, and outdoor thermal comfort. Computational fluid dynamics (CFD) simulations are widely used to predict wind flow around buildings, despite the large discrepancies that often occur between model predictions and actual measurements. Wind speed and direction exhibit a high degree of variability that adds uncertainties in modeling and measurements. Although some studies focus on methods to evaluate and minimize modeling uncertainties, sensor placement has been mostly based on subjective judgment and intuition; no systematic methodology is available to identify optimal sensor locations prior to field measurement. This work proposes a methodology for systematic sensor placement for situations when no measurement data are available and knowledge of the wind environment around buildings is limited. Sequential sensor placement algorithms and criteria are used to identify sensor configurations based on CFD simulation predictions at plausible locations. Optimal sensor configurations are compared for their ability to improve wind speed predictions at another location where no measurements are taken. The methodology is applied to two full-scale building systems of varying size. Results show that the methodology can be applied prior to field measurement to identify optimal configurations of a limited number of sensors that improve wind speed predictions at unmeasured locations.

In healthcare centers, healthcare workers would not know if patients are infected or not until the infected person is diagnosed and moved to an isolation room; therefore, the effectiveness of ventilation systems in removing the exhaled infectious air in the consultation room becomes important. To prevent the transmission of exhaled air, two types of personalized exhaust devices were explored: toppersonalized exhaust and shoulder-personalized exhaust. Three different arrangements with 30 cases between 2manikins were studied experimentally using tracer gas. The intake fraction was compared with and without the personalized exhaust at the end of three different exposure durations: 10, 20, and 30 min. The results show that after applying either top-personalized exhaust or shoulderpersonalized exhaust, the intake fraction is significantly decreased. With the higher flow rate of 20 L/s of personalized exhaust, the intake fractions at 30 min are found to be even lower than the intake fractions at 10 min without personalized exhaust. This implies that despite a longer exposure time, if the exposure amount is reduced, the infection risk is observed to be lower.

Good prediction of the behavior of wind around buildings improves designs for natural ventilation in warm climates. However wind modeling is complex, predictions are often inaccurate due to the large uncertainties in parameter values. The goal of this work is to enhance wind prediction around buildings using measurements through implementing a multiple-model system-identification approach. The success of system-identification approaches depends directly upon the location and number of sensors. Therefore, this research proposes a methodology for optimal sensor configuration based on hierarchical sensor placement involving calculations of prediction-value joint entropy. Computational Fluid Dynamics (CFD) models are generated to create a discrete population of possible wind-flow predictions, which are then used to identify optimal sensor locations. Optimal sensor configurations are revealed using the proposed methodology and considering the effect of systematic and spatially distributed modeling errors, as well as the common information between sensor locations. The methodology is applied to a full-scale case study and optimum configurations are evaluated for their ability to falsify models and improve predictions at locations where no measurements have been taken. It is concluded that a sensor placement strategy using joint entropy is able to lead to predictions of wind characteristics around buildings and capture short-term wind variability more effectively than sequential strategies, which maximize entropy.