10 0。058 0。034 Healthcare

50 0。274 0。163 Building

100 (baseline) 0。538 0。326

200 1。018 0。643 38 18。92 17。81

400 1。766 1。166 27 19。45 19。10

16 (baseline) 20。16 20。52

4 20。79 22。09

For this study, following PNNL’s method [4], the single doors for each case are used as the baseline for the calculations of the air infiltration/energy reductions obtained from vestibule door (Eqs。 (3) and (4))。

Reduction  = SingleDoor  − VestibuleDoor (3)

Reduction

%Reduction = SingleDoor (4)

The overall door coefficients, C, used for all the methods in this paper, are based on the flow coefficient, CA, obtained from Yuill [5] for single and vestibule doors (Eq。 (2))。 The door coefficients depend on the door usage frequency as well as the door size。 In this paper, 100 Ph door usage frequencies and 2 m × 2。4 m door size are used as the baseline of comparison (Table 6 shows C for a 2 m × 2。4 m door at different door usage values)。 The pressure differences, 6P, across the doors presented in Table 7 are calculated based on the Rp values obtained from ASHRAE [8]; where 6P = Rp2。 The pressure differences presented in Table 7 are only used in the Design and Seasonal Design Methods and they depend on the building height and outdoor temperature。

In total, more than 100 EnergyPlus energy simulations and 50 CONTAM airflow simulations have been conducted for this   study。

3。 Results and discussion

3。1。 Experimental  validation

The comparison between the experimental and theoretical results [5] are presented in Fig。 2。 In the negative pressure difference range, the difference between the extrapolated Yuill’s infiltration curve for the single door and the experimental results is 5。9% proving the accuracy for Yuill’s model [5]。 It is important to note however that, the average overall calculated difference between the experimental results and the infiltration curve obtained from Yuill’s [5] model is 3。48%。 This indicates that in the negative pres- sure region a higher difference can be observed。 In addition, the average calculated flow coefficient, CD, for the experimental results

−7 21。44 23。52

−18 22。18 25。30

−29 23。04 27。35

    −40 24。01 29。16

is 0。62, which is similar to that obtained by Yuill [5]。 However the CD in the positive pressure region was calculated to be 0。60 and in the negative pressure region range it was calculated to be 0。67。 This indicated that the airflow in the chamber can experience slightly different resistance based on the direction of the flow through the door。 The average error for the pressure difference measurements was calculated to ± 1。3% or ± 0。21 Pa。 The average bias error for the air flow measurements (for all the experiments conducted) was calculated to be ± 9% or ± 0。02 m3/s (based on tests conducted on the air tightness of the chamber and the error range of the equip- ment used)。 Finally based on the small differences calculated, the experimental data presented in Fig。 2 provide a validation for the flow coefficients of the single door case reported in Yuill’s study in the negative as well as the positive flow directions  [5]。摘要：建筑物门入口是商业建筑空气渗透和能源流失的一个主要来源。先前的研究已计算门入口的空气渗透和基于压力因素的前厅节能潜力的简化方法。然而，估算压差和穿过门的合成渗透速率也可以验证在不同流动条件下使用的气流系数。在本文中，一项实验研究被用来验证对于一个在渗透和漏出条件下完全打开的单扇门的气流系数。此研究呈现了对于两个参考建筑模型来说通过单个自动和前厅门的建模空气渗透的四种方法：两种方法使用了压力因素，另两种则基于气流模拟。能源模拟是从每个方法获得气流渗透率然后进行使用。结果表明，设计方法与模拟方法相比估高了通过门的压差、空气渗透率以及前庭的储存潜力。总之，与更广泛使用的设计方法相比，发现气流模拟提供了更真实的压差和通过门入口的渗透率。文献综述