Performance evaluation of personalized ventilation personalized exhaust (PV PE) system in air conditioned healthcare settings 8

Chapter 8: Evaluation of energy saving implication using the most optimal PV-PE configuration 8.1 Introduction All the experiments conducted in Chapters to of this thesis have flow rates of PE fixed at 10 l/s and 20 l/s. In this chapter, the Computational Fluid Dynamics (CFD) models are used to explore other lower PE air flow rates and determine the energy saving implication of using the most optimal PV-PE (Top-PE) configuration. The results from the CFD study could provide more information on the system and better visualization than the experimental study, such as the tracking of the pathlines of PV air and transportation of pollutants in the room. 8.2 Validation of CFD simulations 8.2.1 The geometrical model The two manikins, the desk, the MV supply terminals and ceiling exhausts, the DV supply outlet, the RMP PV ATD, and Top-PE are built into the model with Gambit as shown in Figure 8.1. The room size and numerical objects are kept the same as in the Indoor Environmental Chamber in the experimental study. The geometry of the manikin model is similar to the real manikin, with more than 1000 faces on its surface in sitting position. The computational thermal manikin is obtained by Gao and Niu (2004) using 3D laser scanning technique, which is same as the one used in the preliminary studies. The two numerical manikins are seated face to face in front of a table in the middle of the room. Figure 8.1 shows that the RMP terminal is above and in front of the manikin head and the Top-PE are two small cylinders above the manikin’s heads. The other objects such as the equipment to support the manikin system, the table to place the equipment, the chairs, the PV pipes and the legs of tables, are not included in the CFD model. Figure. 8.1 General view of the modelled geometry 8.2.2 The turbulence model It is always difficult to simulate theindoor air flow because of the flow complexity and instability, and due to the extremely complex geometry of the manikin body, it is more challenging to predict the airflow rate. Therefore, appropriate turbulence method and the discretization methods are carefully selected. In this study, the ANSYS FLUENT 14.0 is selected as the simulation platform. Generally, there are three approaches for turbulent flow prediction: Direct Numerical Simulation (DNS), Large Eddy Simulation (LES) and Reynolds-Averaged Navier-Stokes (RANS). They have a lot of differences in terms of theoretical ideas, computational cost, accuracy and area of application. DNS computes a turbulent flow by directly solving the highly reliable Navier-Stokes equation without approximations. DNS resolves the whole range of spatial and temporal scales of the turbulence, from the smallest dissipative scales (Kolmogorov scales) to the integral scale. As a result, DNS requires a very fine grid resolution to capture the smallest eddies in the turbulent flow. In addition, the DNS method requires very small time steps, which makes the simulation extremely long. The theoretical basis of the LES method is the fact that large eddies of turbulent flows depend on the geometry while the smaller scales are more universal (Kolmogorov 1941) and the hypothesis that the turbulent motion can be separated into large-eddies and small-eddies such that the separation between the two does not have a significant impact on the large-eddies (Deardorff, 1970). LES is precise in predicting a lot of turbulence flows; however, the computing cost is still remarkable given the fast development of computer capacity and speed. The RANS approach calculates statistically averaged (Reynolds-averaged) variables for both steady-state and dynamic flows and simulates turbulence fluctuation effect on the mean airflow by using different turbulence models. Despite the challenges associated with turbulence modelling, the RANS approach has become very popular in modelling airflows in enclosed environment. Therefore, RANS approach is chosen in this study to simulate the indoor air flows in the studied Indoor Environmental Chamber ventilated with the combined ventilation systems. The RANS models consist of two types: RANS Eddy-Viscosity models and RANS Reynolds Stress models. The former include zero-equation models, one-equation models, two-equation models such as k-ε models and k-ω models, and multiequation models. The k-εmodels are very popular, especially the standard k-εmodel, the RNG k-ε model and the Realizable k-ε model. The standard k-epsilon model is adopted to simulate the indoor air flow patterns. In previous numerical research, Gao and Niu (2004) and Pantelic (2010) have used this model to simulate indoor air flow based on the assumption that this model is capable of simulating convective heat transfer of buoyancy-driven air flow as long as a reasonable value of Y+ is achieved. The total number of cells is 6402744. Trial-and-error was necessary for proper characterization of the boundary layers because the non-dimensional wall distance for a wall-bounded flow Y+ can only be calculated after solving the flow field. Two layers of uniform boundary layer cells were placed around the manikin’s body with size of 0.0022 m and enhanced wall treatment was applied. The value Y+ was approximately within 0.5-2 for most part across the body surface and around to for small part at face area. This is acceptable since in practice, up to 4-5 is considered acceptable as it is still inside the viscous sub layer. Figure. 8.2 Contours of wall Y+ 8.2.3 Boundary conditions Another important factor is the boundary conditions since it may affect the convergence as well as the correctness of results, especially the boundary conditions of the ventilation openings in this study. The turbulence intensity can be achieved from the experiments. The hydraulic diameter can be calculated from the shape of the opening. However, the air diffusers are often complex due to the complex geometry including curved surfaces, perforated plates, guided rails and other components. The PV ATD, MV supply diffuser, DV supply air diffusers involved in this study are all complex including two perforated openings and one four way diffuser, which has been introduced in Chapter 3. Most frequently used methods for openings include: the basic method, the momentum method, the box method and the prescribed velocity method. For the four-way ceiling supply air diffuser, a simplified model (Cheong et al, 2001), as shown in Figure 8.3, is able to predict the air flow pattern accurately. Figure 8.3. Models for the four way air diffuser The boundary conditions at the re-circulated four way air diffuser are shown in Table 8.1. Table 8.1: Boundary conditions for MV supply diffusers Location Four way air diffuser Four way air diffuser Four way air diffuser Four way air diffuser Velocity components (m/s) x y z Boundary type -1.821 3.17 Velocity inlet 3.17 -1.821 Velocity inlet -1.821 -3.17 Velocity inlet -3.17 -1.821 Velocity inlet For RMP PV ATD and DV diffuser, a simplified box method is used to set the boundary conditions. According to theory, as shown in Figure 8.4, the air flow region out of perforated diffuser can be divided into three parts, the core zone (length x1), the mixing zone (length x2) and the well-mixed zone (length x3). The air flow velocity could be taken as uniform across a cross section in the well-mixed zone. Based on the theory, the total length of the core zone and the mixing zone is about 40 times of the hole diameter on the perforated panel. Hence, in the simulation, the opening panel of the RMP (hole diameter: mm) is set in a new position 200 mm in front of its original position. For the semi-circular DV diffuser, it is set as the surface of another semi-circle, which has the same axis as the original one and a 200 mm larger radius in the CFD model. Figure 8.4 Air flow region out of perforated diffuser [Source: Li and Zhao (2009)] Other detail boundary conditions are listed in Table 8.2 Table 8.2: Boundary conditions Turbulence model Numerical Schemes Standard k–epsilon model Upwind second-order difference; PRESTO forpressure Mixing ventilation inlet Velocity inlet; Temperature = 23 °C; I = 10% Room air exhaust Pressure outlet; Gauge pressure=0 Pa Personalized exhaust Pressure outlet Room wall, floor and ceiling Adiabatic wall Manikin body T =34°C Mouth RMP air terminal device Flow rate (L) = 8.4 l/min; Turbulence Intensity (I) = 0.5%; Hydraulic diameter (D) = 0.013 m Velocity inlet; I =10%; D =0.1 m Chair Adiabatic wall 8.3 Model validation Since the top-PE performs better according to the results of experiments, it is selected as the most optimal PV-PE configuration for evaluating the implication for energy saving. In the model validation exercise, both top-PE and shoulder-PE are employed. Figure 8.5 shows the sketch of the measured case and photo of the experiment room. Figure 8.5 Sketch of the measured case and photo of the experiment room. (a) Sketch of the measured Top-PE case (b) Sketch of the measured Shoulder-PE case (c) Photo of the experiment room The inputs of boundary conditions in CFD simulation are all taken from the experiments. The concentrations of exhaled air at the mouth of the Numerical Infected Manikin as well as at the mouth of the Numerical Healthy Manikin are the output used for calculating the iF. The simulated and experimental cases are listed in Table 8.3. The three cases are selected because they consist of the two extreme flow rates (0 l/s and 20 l/s) and both the two PE types. Table 8.3 Model validation cases Case Ventilation mode PV air flow rate (l/s) PE type PE flow rate (l/s) Mixing ventilation Top-PE Mixing ventilation Top-PE 20 Mixing ventilation Shoulder-PE 20 Comparison of simulated and measured Intake Fraction (iF) and Personalized Exposure Effectiveness are shown in Figure 8.6. It proves that the simulated concentration of exhaled air at the mouth of healthy manikin correlates well with the measured ones, and the value is quite close to the experimental data. 6.00E-­‐03   Intake  fraction   5.00E-­‐03   4.00E-­‐03   Simulated   3.00E-­‐03   Measured   2.00E-­‐03   1.00E-­‐03   0.00E+00   Case  1   Case  2   Case  3   0.4   0.35   0.3   PEE   0.25   0.2   Simulated   0.15   Measured   0.1   0.05     Case  1   Case  2   Case  3   Figure 8.6 Comparison of iF and PEE between measured and simulated cases 8.4 Parametric Variations and Results In order to exhaust the exhaled contaminated air right around the infected person as much as possible, a higher flow rate of PE could achieve better results. However, considering the energy usage, a lower flow rate of PE is always preferred. In this case, lower flow rate from l/s to l/s is simulated using the CFD model. l/s of PE is also modelled for the cases that PE is not used. The results are compared with the experiments of PE at 10 l/s. In this simulation, only top-PE is considered since it is more efficient than the shoulder-PE. Both MV and DV are also considered. Table 8.4: Detailed parametric variations studied PE for Infected Person Top-PE PE flow rate (l/s) PV flow rate for Healthy Person 0 5 The results of iF are shown in Figure 8.7 and Figure 8.8. From these two Figures, for the same flow rate for IP, the case with PV for HP has a slightly lower iF than the case without PV. This indicates that the PV is helpful in reducing the amount of exposure to the exhaled air, especially at lower flow rates of PE. With the increase of PE flow rate up to more than l/s, the advantage of PV is not so significant. With MV, with the increase of flow rate from l/s to l/s, the reduction of iF is small. From l/s to l/s, l/s to l/s as well as l/s to 10 l/s, there is a larger drop of iF. Both the two lines follow the same trend. With DV, at low flow rate of PE, the decrease of iF is not obvious from l/s to l/s compared with the higher flow rates. A more pronounced reduction is observed from flow rates from of l/s to l/s and from l/s to l/s. Figure 8.7 iF with MV Figure 8.8 iF with DV 8.5 Discussion The general pattern observed in the plot is as follows: there is an initial significant drop in iF when the flow rate is increased from l/s. This is followed by a region where no significant improvement in iF is noticed. The iF improves significantly when the flow rate crosses a certain threshold. According to the trend of iF changes in Figures 8.7 and 8.8, a higher flow rate is required with DV to achieve the same iF. The flow rate to be applied in the real healthcare settings can be chosen according to the target of the iF. With MV, the increase of flow rate from l/s to l/s cannot lead to a much further reduction in iF, thus a lower flow rate is preferable if the target of iF is relative high. From ls/ to 10 l/s, the increase of flow rate can result in a quick drop of iF. Thus, a higher flow rate is preferred if a low iF is required. With DV, the increase of flow rate will achieve more reduction in iF. From l/s to l/s, the slope is relative gentle, especially for the line with PV used for HP. From l/s to 10 l/s, the slope is steeper. Therefore, it is worthy increasing the flow rate from l/s onwards if a much lower iF is targeted. The second point of energy saving implication of the PE system is that with a higher flow rate (eg: 10 l/s), it can achieve a better infection control than using PV alone. Furthermore, with the higher flow rate, the PV does not help much in terms of further reducing the exposure of the HP to the exhaled contaminant. This implies that comparing between using PV alone and using PE alone, the latter gives better results and the energy consumption of PE can be compensated for not using the PV system. Thirdly, further energy saving could be achievedwhen the fans are equipped with occupancy sensors. This enables the exhaust motor and fans to be switched on or off automatically when the Infected Person enters or leaves the room. However, the energy saved might be insignificant. It might be only substantial when the presence time of an Infected Person is short.   [...]... not help much in terms of further reducing the exposure of the HP to the exhaled contaminant This implies that comparing between using PV alone and using PE alone, the latter gives better results and the energy consumption of PE can be compensated for not using the PV system Thirdly, further energy saving could be achievedwhen the fans are equipped with occupancy sensors This enables the exhaust motor... target of the iF With MV, the increase of flow rate from 4 l/s to 7 l/s cannot lead to a much further reduction in iF, thus a lower flow rate is preferable if the target of iF is relative high From 7 ls/ to 10 l/s, the increase of flow rate can result in a quick drop of iF Thus, a higher flow rate is preferred if a low iF is required With DV, the increase of flow rate will achieve more reduction in iF... is relative gentle, especially for the line with PV used for HP From 6 l/s to 10 l/s, the slope is steeper Therefore, it is worthy increasing the flow rate from 6 l/s onwards if a much lower iF is targeted The second point of energy saving implication of the PE system is that with a higher flow rate (eg: 10 l/s), it can achieve a better infection control than using PV alone Furthermore, with the higher... achievedwhen the fans are equipped with occupancy sensors This enables the exhaust motor and fans to be switched on or off automatically when the Infected Person enters or leaves the room However, the energy saved might be insignificant It might be only substantial when the presence time of an Infected Person is short   . Chapter 8: Evaluation of energy saving implication using the most optimal PV-PE configuration 8. 1 Introduction All the experiments conducted in Chapters 5 to 7 of this thesis have flow rates of. of PV air and transportation of pollutants in the room. 8. 2 Validation of CFD simulations 8. 2.1 The geometrical model The two manikins, the desk, the MV supply terminals and ceiling exhausts,. scanning technique, which is same as the one used in the preliminary studies. The two numerical manikins are seated face to face in front of a table in the middle of the room. Figure 8. 1