Range hood is a local ventilation device applied widely in residential kitchen for maintaining healthy environment. This study firstly defines the direct capture efficiency (DCE) based on the two-zone model in a confined kitchen space. A mass flux ratio of the secondary captured pollutant to the entrained pollutant from the room zone is proposed for the determination of DCE, where the distribution coefficient is firstly solved, and then its sensitivity analysis on the DCE is carried out. To validate the mass flux ratio and concisely identify the DCE, a virtual purification method that artificially sets the escaped pollutant to zero, is further applied. Compared with the newly developed DCE, the existing indexes, such as contaminant removal efficiency (CRE), total capture efficiency (TCE), fail to differentiate the direct capture from the total capture. Finally, the effects of such factors as makeup airflow pattern, exhaust flow rate, cooking source temperature and the individual occupied/unoccupied on the DCE are fully studied. It is confirmed that different makeup airflow pattern results in distinguished airflow distribution, which makes a significant difference of more than 30% in DCE. Over 50% increase of DCE can be achieved when the exhaust flow rate is increased from 300 to 600 m³/h. About 30% decrease of DCE is observed with the increased cooking source temperature from 100 to 300 ℃, and 10% increase of DCE is appeared in the individual occupied case. This reasonable definition and determination of DCE would help to improve the real capture performance of range hoods.

The effects of different human walking patterns on contaminant dispersion in residential kitchens were investigated through computational fluid dynamics simulation with the dynamic mesh method. A tracer gas experiment was performed to verify the feasibility and accuracy of the simulation method. Flow characteristics induced by human walking were minutely described, and the transient capture efficiency of the range hood was adopted to assess the impact of human walking quantitatively. Human walking parallel to a counter, human walking parallel to a counter manned by another human, and human walking toward a counter were studied. Results showed that the mutual effect of the wake and thermal plume caused contaminant dispersion and decreased the performance of the range hood as the human subject walked beside the counter. Even a standing person operated ahead the counter, the wake would affect the thermal plume in a certain extent. The decrement of capture efficiency approached 0.5 in the most unfavorable situation. Moreover, the coaction of the positive/negative pressure zone and impinging air jet drew the thermal plume to the human body. The fluctuation of capture efficiency in this condition was moderate relative to that for the human walking pattern beside the counter. This research could provide a comprehensive overview of different human walking patterns and their impact on residential kitchens and thereby facilitate the maintenance of kitchen air quality.

Central flues are now commonly adopted in high-rise residential buildings in China for cooking oil fumes (COF) exhaust. Range hoods of all floors are connected to the central shaft, where oil fumes were gathered and exhausted through the outlet at the building roof. As households may cook and use their range hood at random periods, there is great uncertainty of the amount of COF being exhausted. In addition, users can often adjust the exhaust rate of the range hood according to their needs. As a result, thousands of possible operating conditions consisting of distinct combinations of on/off conditions and fan speed occur randomly in the central COF exhaust system, causing the exhaust performance to vary considerably from condition to condition. This work developed a mathematical model for characterizing the operation of the central COF exhaust system in a high-rise residential building as well as its iterative solving method. Full-scale tests coupled with CFD simulation referring to a real 30-floor building were conducted to validate the proposed model. The results show that the model agreed well with the CFD and experimental data under various system operating conditions. Moreover, the Monte-Carlo method was introduced to simulate the random operating characteristics of the system, and a hundred thousand cases corresponding to distinct system operating conditions were sampled and statistically analyzed.

Terrorist attacks through building ventilation systems are becoming an increasing concern. In case pollutants are intentionally released in a building with mechanical ventilation systems, it is critical to localize the source and characterize its releasing curve. Previous inverse modeling studies have adopted the adjoint probability method to identify the source location and used the Tikhonov regularization method to determine the source releasing profile, but the selection of the prediction model and determination of the regularization parameter remain challenging. These limitations can affect the identification accuracy and prolong the computational time required. To address the difficulties in solving the inverse problems, this work proposed a Markov-chain-oriented inverse approach to identify the temporal release rate and location of a pollutant source in buildings with ventilation systems and validated it in an experimental chamber. In the modified Markov chain, the source term was discrete by each time step, and the pollutant distribution was directly calculated with no iterations. The forward Markov chain was reversed to characterize the intermittently releasing profile by introducing the Tikhonov regularization method, while the regularized parameter was determined by an automatic iterative discrepancy method. The source location was further estimated by adopting the Bayes inference. With chamber experiments, the effectiveness of the proposed inverse model was validated, and the impact of the sensor performance, quantity and placement, as well as pollutant releasing curves on the identification accuracy of the source intensity was explicitly discussed. Results showed that the inverse model can identify the intermittent releasing rate efficiently and promptly, and the identification error for pollutant releasing curves with complex waveforms is about 20%.

When a chemical or biological agent is suddenly released into a ventilation system, its dispersion needs to be promptly and accurately detected. In this work, an optimization method for sensors layout in air ductwork was presented. Three optimal objectives were defined, i.e. the minimum detection time, minimum contaminant exposure, and minimum probability of undetected pollution events. Genetic algorithm (GA) method was used to obtain the non-dominated solutions of multi- objectives optimization problem and the global optimal solution was selected among all of the non-dominated solutions by ordering solutions method. Since the biochemical attack occurred in a ventilation system was a random process, two releasing scenarios were proposed, i.e. the uniform and the air volume-based probability distribution. It was found that such a probability distribution affected the results of optimal sensors layout and also resulted in different detect time and different probability of undetected events. It was discussed how the objective functions are being compatible and competitive with each other, and how sensor quantity affect the optimal results and computational load. The impact of changes on other parameters was given, i.e. the deposition coefficient, the air volume distribution and the manual releasing. This work presents an angle of air ductwork design for indoor environment protection and expects to help in realizing the optimized sensor system design for sudden contaminant releasing within ventilation systems.