**1. Introduction**

Ventilators are a type of equipment used to promote air flow [1]. They are classified into two categories: power and natural ventilators [2]. Natural ventilators, also known as unpowered ventilators, are equipment that use the pressure difference between the inside and outside of a building to drive the air flow inside the building [3,4]. Natural ventilators can considerably reduce building energy consumption [5]. Therefore, investigations into optimizing natural ventilators are necessary [6,7]. Gonzalez et al. studied the ventilation performance of a natural ventilator under free flow conditions, and established its mathematical model [8]. Kang et al. determined the relationship of air velocity with ventilators [9]. Kim et al. found that the speed and ventilation volume of natural ventilators are positively correlated with the ambient wind speed [10]. Research results have proven that the optimization of ventilators can improve air quality within a room [11,12].

Studies on the performance optimization of ventilators are important for building ventilation, which were implemented earlier in Europe and America [13–16]. Building openings and various ventilation equipment were the main focus of studies on natural building ventilation [17], and ventilators can effectively improve indoor air quality [18]. Santamouris [19] investigated the relationship of air flow speed and indoor carbon dioxide concentration in classrooms with intermittent natural ventilation. Gan simulated the numerical of the indoor environment [20]. These research results have encouraged further study of air flow, which were also the basis of our study [21,22].

**Citation:** Li, M.; Qiang, Y.; Wang, X.; Shi, W.; Zhou, Y.; Yi, L. Effect of Wind Speed on the Natural Ventilation and Smoke Exhaust Performance of an Optimized Unpowered Ventilator. *Fire* **2022**, *5*, 18. https://doi.org/ 10.3390/fire5010018

Academic Editor: Dahai Qi

Received: 20 December 2021 Accepted: 22 January 2022 Published: 28 January 2022

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**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). *fire*

At present, most studies on the ventilation performance of natural ventilators have been conducted through CFD simulation [23–25]. Varela-Boydo and Moya, using CFD, test 28 different inlet extension designs [26]. The results showed that the majority of designs increase air induction to the building by reducing the ascending currents from inside the inlet opening to the outside of the towers to a minimum. However, some of the tested designs formed vortexes inside the inlet openings, which prevented air induction. These results enrich the ventilation theory of natural ventilators, and provide methods to improve the volume of ventilators [27]. signs increase air induction to the building by reducing the ascending currents from inside the inlet opening to the outside of the towers to a minimum. However, some of the tested designs formed vortexes inside the inlet openings, which prevented air induction. These results enrich the ventilation theory of natural ventilators, and provide methods to improve the volume of ventilators [27]. However, few scholars have studied the combination of ventilation performance and fan blades. As such, in this study, we investigated the effects of combinations of natural test 28 different inlet extension designs [26]. The results showed that the majority of designs increase air induction to the building by reducing the ascending currents from inside the inlet opening to the outside of the towers to a minimum. However, some of the tested designs formed vortexes inside the inlet openings, which prevented air induction. These results enrich the ventilation theory of natural ventilators, and provide methods to improve the volume of ventilators [27]. However, few scholars have studied the combination of ventilation performance and fan blades. As such, in this study, we investigated the effects of combinations of natural ventilator and axial fan blades on ventilation performance. The design of the combination

numerical of the indoor environment [20]. These research results have encouraged further

At present, most studies on the ventilation performance of natural ventilators have been conducted through CFD simulation [23–25]. Varela-Boydo and Moya, using CFD,

numerical of the indoor environment [20]. These research results have encouraged further

At present, most studies on the ventilation performance of natural ventilators have been conducted through CFD simulation [23–25]. Varela-Boydo and Moya, using CFD, test 28 different inlet extension designs [26]. The results showed that the majority of de-

However, few scholars have studied the combination of ventilation performance and fan blades. As such, in this study, we investigated the effects of combinations of natural ventilator and axial fan blades on ventilation performance. The design of the combination is shown in Figure 1. ventilator and axial fan blades on ventilation performance. The design of the combination is shown in Figure 1. is shown in Figure 1.

*Fire* **2022**, *5*, x FOR PEER REVIEW 2 of 22

*Fire* **2022**, *5*, x FOR PEER REVIEW 2 of 22

study of air flow, which were also the basis of our study [21,22].

study of air flow, which were also the basis of our study [21,22].

**Figure 1.** Natural ventilator before and after optimization. **Figure 1.** Natural ventilator before and after optimization.

**Figure 1.** Natural ventilator before and after optimization. Since the 1950s, scholars have researched fan blades [28]. Beiler found that forward fan blades can improve the ventilation performance of a fan [29]. Hossain optimized the design of aircraft fans [29]. Farrall and Simmons simulated and analyzed the fan of an aircraft [30]. Elgowainy et al. found that the number of fan blades has an important impact on the static pressure of a fan [31]. Yang et al. found that forward fan blades can reduce the pressure loss of axial flow fans, improving fan efficiency [32]. Moreover, Vad et al. found that forward-type fan blades can significantly improve the flow field distribution Since the 1950s, scholars have researched fan blades [28]. Beiler found that forward fan blades can improve the ventilation performance of a fan [29]. Hossain optimized the design of aircraft fans [29]. Farrall and Simmons simulated and analyzed the fan of an aircraft [30]. Elgowainy et al. found that the number of fan blades has an important impact on the static pressure of a fan [31]. Yang et al. found that forward fan blades can reduce the pressure loss of axial flow fans, improving fan efficiency [32]. Moreover, Vad et al. found that forward-type fan blades can significantly improve the flow field distribution at the tip of the fan blade, reducing energy loss, as shown in Figures 2 and 3 [33]. The influences of fan blade parameters, such as the number of fan blades, inclination angle, and bending direction, on fan ventilation performance have been investigated. Since the 1950s, scholars have researched fan blades [28]. Beiler found that forward fan blades can improve the ventilation performance of a fan [29]. Hossain optimized the design of aircraft fans [29]. Farrall and Simmons simulated and analyzed the fan of an aircraft [30]. Elgowainy et al. found that the number of fan blades has an important impact on the static pressure of a fan [31]. Yang et al. found that forward fan blades can reduce the pressure loss of axial flow fans, improving fan efficiency [32]. Moreover, Vad et al. found that forward-type fan blades can significantly improve the flow field distribution at the tip of the fan blade, reducing energy loss, as shown in Figures 2 and 3 [33]. The influences of fan blade parameters, such as the number of fan blades, inclination angle, and bending direction, on fan ventilation performance have been investigated.

at the tip of the fan blade, reducing energy loss, as shown in Figures 2 and 3 [33]. The

**Figure 2. Figure 2.**  Photo of forward curved fan blades. Photo of forward curved fan blades.

**Figure 2.** Photo of forward curved fan blades.

**Figure 3.** Photo of backward curved fan blades. **Figure 3.** Photo of backward curved fan blades.

In our newly designed natural ventilator (swirl natural ventilator), we used different types of fan blades. We compared and analyzed the influence of design parameters, such as the number of axial fan blades, inclination angle, and bending direction, on ventilation volume and ventilation efficiency. Many research results have been published regarding the optimization of natural ventilators by improving its ventilation performance [34,35], but studies on the use of optimized natural ventilators for smoke exhaust in the case of fire are limited [36]. Therefore, after the ventilation performance of this ventilator was improved through experiments, the proposed ventilator can be installed in buildings or tunnels. As such, when a fire occurs, the ventilator can quickly exhaust the smoke and reduce the temperature in the environment as much as possible, without consuming energy. In our newly designed natural ventilator (swirl natural ventilator), we used different types of fan blades. We compared and analyzed the influence of design parameters, such as the number of axial fan blades, inclination angle, and bending direction, on ventilation volume and ventilation efficiency. Many research results have been published regarding the optimization of natural ventilators by improving its ventilation performance [34,35], but studies on the use of optimized natural ventilators for smoke exhaust in the case of fire are limited [36]. Therefore, after the ventilation performance of this ventilator was improved through experiments, the proposed ventilator can be installed in buildings or tunnels. As such, when a fire occurs, the ventilator can quickly exhaust the smoke and reduce the temperature in the environment as much as possible, without consuming energy.

Our ventilator design considerably improves the ventilation volume of natural ventilators, thus enhancing the smoke exhaust performance in building or tunnel fires, and reducing the energy consumption of the building. Our ventilator design considerably improves the ventilation volume of natural ventilators, thus enhancing the smoke exhaust performance in building or tunnel fires, and reducing the energy consumption of the building.

### **2. Materials and Methods 2. Materials and Methods**

### *2.1. First Experiment 2.1. First Experiment*

The first experiment focused on the axial fan blade setting. In this experiment, we used a swirling natural ventilator, as shown in Figure 1. The first experiment focused on the axial fan blade setting. In this experiment, we used a swirling natural ventilator, as shown in Figure 1.

### 2.1.1. Experimental Model 2.1.1. Experimental Model

The rotating shaft of the ventilator was connected to the motor. The axial fan blades and ventilator were coaxial during operation. In order to control the speed of the ventilator, a small adjustable motor (YVF-6314, Shenyang Weishida, Figure 4) was selected to drive the natural ventilator to rotate. The speed of the motor was controlled by a frequency modulator, as shown in Figure 5. The experiment was conducted using a controlled variable method. After the power The rotating shaft of the ventilator was connected to the motor. The axial fan blades and ventilator were coaxial during operation. In order to control the speed of the ventilator, a small adjustable motor (YVF-6314, Shenyang Weishida, Figure 4) was selected to drive the natural ventilator to rotate. The speed of the motor was controlled by a frequency modulator, as shown in Figure 5. *Fire* **2022**, *5*, x FOR PEER REVIEW 4 of 22 of the cyclone ventilator was 600 mm, and the ventilator was fixed on the top of the air duct model by a flange and setscrews.

**Figure 4.** Photo of YVF-6314 adjustable motor. **Figure 4.** Photo of YVF-6314 adjustable motor.

**Figure 5.** Photo of frequency modulator.

**Figure 6.** Photo of QM-600 swirl natural ventilator.

**Figure 4.** Photo of YVF-6314 adjustable motor.

duct model by a flange and setscrews.

*Fire* **2022**, *5*, x FOR PEER REVIEW 4 of 22

duct model by a flange and setscrews.

**Figure 5.** Photo of frequency modulator. **Figure 5.** Photo of frequency modulator. **Figure 4.** Photo of YVF-6314 adjustable motor.

The experiment was conducted using a controlled variable method. After the power was turned on, we ensured the ventilator reached the predetermined speed by adjusting the frequency modulator. A hot wire anemometer (AR866, Hong Kong Xima, measurement error of ±3% ± 0.1 d) was calibrated to minimize the influence of external environmental factors.

of the cyclone ventilator was 600 mm, and the ventilator was fixed on the top of the air

of the cyclone ventilator was 600 mm, and the ventilator was fixed on the top of the air

The speed of the natural ventilator (SW6234C, Guangzhou Suwei, measurement error of ±0.05% ± 1 d) was controlled by a tachometer to ensure the stable running of the natural ventilator.

During the experiments, the heat pressure and wind speed were controlled. This experiment was conducted in an air duct model. The model was composed of a stainless steel pipe that was 1.8 m long and 0.8 m in diameter. To reduce air leakage, we used tin foil to seal the gap between the air duct model and the natural ventilator. Considering the size of the air duct model, we selected a QM-600 swirl natural ventilator as the research object, as shown in Figure 6, and its parameters as shown in Table 1. The bottom diameter of the cyclone ventilator was 600 mm, and the ventilator was fixed on the top of the air duct model by a flange and setscrews. **Figure 5.** Photo of frequency modulator.

**Figure 6.** Photo of QM-600 swirl natural ventilator.

**Figure 6.** Photo of QM-600 swirl natural ventilator. **Figure 6.** Photo of QM-600 swirl natural ventilator.


**Table 1.** Parameters of QM-600 swirl natural ventilator.

Considering the sizes of the air duct model and the natural ventilator, we selected a group of axial flow fan blades with a radius of 30 cm for the study. To reduce the resistance, we selected an axial flow fan composed of plastic. During the experiment, the air duct was fixed 15 cm away from the bottom of the cyclone ventilator by aluminum discs and screws, as shown in Figure 7. Considering the sizes of the air duct model and the natural ventilator, we selected a group of axial flow fan blades with a radius of 30 cm for the study. To reduce the resistance, we selected an axial flow fan composed of plastic. During the experiment, the air duct was fixed 15 cm away from the bottom of the cyclone ventilator by aluminum discs and screws, as shown in Figure 7.

**Figure 7.** Performance optimization platform of a QM-600 swirl natural ventilator. **Figure 7.** Performance optimization platform of a QM-600 swirl natural ventilator.

### 2.1.2. Data Measurement 2.1.2. Data Measurement

When the fluid flowed in the duct model, the velocity and pressure distribution in the same cross-section of the duct model was uneven due to fluid viscosity. Therefore, the same measurement section of the air duct model was divided into several parts with the same area, and the velocities at characteristic points of each part were measured. The velocities of the characteristic points were used as the average velocity of the part, we used a hot wire anemometer to measure the wind speed. When the fluid flowed in the duct model, the velocity and pressure distribution in the same cross-section of the duct model was uneven due to fluid viscosity. Therefore, the same measurement section of the air duct model was divided into several parts with the same area, and the velocities at characteristic points of each part were measured. The velocities of the characteristic points were used as the average velocity of the part, we used a hot wire anemometer to measure the wind speed.

According to the size of the air duct model, five testing sections were set in the vertical direction in our experiments. The intervals of these measured sections were 0.2, 0.2, 0.4, 0.4, and 0.2 m, and eight testing points were set on each testing section: *r*1–*r*8. According to the size of the air duct model, five testing sections were set in the vertical direction in our experiments. The intervals of these measured sections were 0.2, 0.2, 0.4, 0.4, and 0.2 m, and eight testing points were set on each testing section: *r*1–*r*8.

The air volume in the air duct was calculated according to Equation (1). As the air duct model in this study was a circular tube, we selected the middle rectangle method to arrange the testing points [17]. The air volume in the air duct was calculated according to Equation (1). As the air duct model in this study was a circular tube, we selected the middle rectangle method to arrange the testing points [17].

$$q\_{\upsilon} = \sum\_{i=1}^{i=n} F\_{\bar{t}} u\_i = \frac{A}{n} \sum\_{i=1}^{i=n} u\_i \tag{1}$$

where *A* is the cross-sectional area of air duct model, m2; *Fi* is the area of each part, m2; *ui* is the velocity of the *i*th characteristic point, m/s; and *qv* is the average air volume of pipeline, m3/s. The cross-section radius of the pipe was assumed to be *r* and the cross-section was where *A* is the cross-sectional area of air duct model, m<sup>2</sup> ; *F<sup>i</sup>* is the area of each part, m<sup>2</sup> ; *ui* is the velocity of the *i*th characteristic point, m/s; and *q<sup>v</sup>* is the average air volume of pipeline, m3/s.

divided into *n* concentric rings with equal area, as shown in Figure 8. The radius of the

The cross-section radius of the pipe was assumed to be *r* and the cross-section was divided into *n* concentric rings with equal area, as shown in Figure 8. The radius of the characteristic circles, *r*1, *r*3, *r*5, and *r*7, was calculated according to Equation (2), and the radius of the characteristic circles, *r*2, *r*4, *r*6, and *r*8, was calculated according to Equation (3).

$$r\_{2i-1} = R\sqrt{\frac{2i-1}{2n}}\tag{2}$$

$$r\_{2i} = R\sqrt{\frac{2i}{2n}}\tag{3}$$

where *R* is the radius of air duct model, m; *r* is the radius of the characteristic circles in the air duct model, m; *n* is the number of characteristic circles in the air duct model; and *i* is the order of the characteristic circles in the air duct model. characteristic circles, *r*1, *r*3, *r*5, and *r*7, was calculated according to Equation (2), and the radius of the characteristic circles, *r*2, *r*4, *r*6, and *r*8, was calculated according to Equation (3).

**Figure 8.** Arrangement of testing points in the air duct model (vertical view). **Figure 8.** Arrangement of testing points in the air duct model (vertical view).

2 1 2 1 2 *<sup>i</sup> <sup>i</sup> r R n* <sup>−</sup> <sup>−</sup> <sup>=</sup> (2) 2 2 2 *<sup>i</sup> <sup>i</sup> r R <sup>n</sup>* <sup>=</sup> (3) where *R* is the radius of air duct model, m; *r* is the radius of the characteristic circles in the air duct model, m; *n* is the number of characteristic circles in the air duct model; and *i* is In the air duct model, we set five testing sections from the top to the bottom. As shown in Figure 9, the distance between testing section 1 and the top was 0.2 m; that between testing sections 1 and 2 was 0.2 m; that between sections 2 and 3, sections 3 and 4, and sections 4 and 5 was 0.4 m; and the distance between section 5 and the bottom was 0.2 m. In each testing section, a total of 8 testing points were arranged. The positions of these testing points were *r*8, *r*6, *r*4, *r*2, *r*1, *r*3, *r*5, and *r*<sup>7</sup> from left to right, as shown in Figure 8. The radii of the characteristic circles were 14.1, 20, 24.5, 28.3, 31.6, 34.6, 37.4, and 40 cm for *r*1–*r*8, respectively.

the order of the characteristic circles in the air duct model. In the air duct model, we set five testing sections from the top to the bottom. As shown in Figure 9, the distance between testing section 1 and the top was 0.2 m; that between testing sections 1 and 2 was 0.2 m; that between sections 2 and 3, sections 3 and 4, and sections 4 and 5 was 0.4 m; and the distance between section 5 and the bottom was 0.2 m. In each testing section, a total of 8 testing points were arranged. The positions of these testing points were *r*8, *r*6, *r*4, *r*2, *r*1, *r*3, *r*5, and *r*7 from left to right, as shown in Figure 8. The radii of the characteristic circles were 14.1, 20, 24.5, 28.3, 31.6, 34.6, 37.4, and 40 cm Because the position of the natural ventilator was high and the rotating speed of the ventilator was fast, it was difficult to measure the speed of the ventilator. Therefore, we selected a tachometer to measure the ventilator speed. Before the experiment, we pasted a square reflective sticker with a side length of 1 cm on the middle of the outer edge of the ventilator impeller, and we placed the tachometer at an equally high position on the experimental frame with a horizontal distance of 10 cm from the ventilator. In the experiment, when the infrared emission port of the tachometer was facing the center of the natural ventilator, the rotational speed of the natural ventilator could be measured.

Because the position of the natural ventilator was high and the rotating speed of the ventilator was fast, it was difficult to measure the speed of the ventilator. Therefore, we selected a tachometer to measure the ventilator speed. Before the experiment, we pasted a square reflective sticker with a side length of 1 cm on the middle of the outer edge of the ventilator impeller, and we placed the tachometer at an equally high position on the experimental frame with a horizontal distance of 10 cm from the ventilator. In the experi-

ural ventilator, the rotational speed of the natural ventilator could be measured.

for *r*1–*r*8, respectively.

**Figure 9.** Arrangement of testing points in the air duct model (side view). **Figure 9.** Arrangement of testing points in the air duct model (side view). **Figure 9.** Arrangement of testing points in the air duct model (side view).

### *2.2. Second Experiment 2.2. Second Experiment*

*2.2. Second Experiment*  When the same wind speed was applied to the ventilator (before and after optimization), the ventilator speed changed, so the first experiment could not specifically be used to calculate the change in ventilation efficiency. Therefore, we needed to confirm the rela-When the same wind speed was applied to the ventilator (before and after optimization), the ventilator speed changed, so the first experiment could not specifically be used to calculate the change in ventilation efficiency. Therefore, we needed to confirm the relationship between different wind speeds and ventilation speed. When the same wind speed was applied to the ventilator (before and after optimization), the ventilator speed changed, so the first experiment could not specifically be used to calculate the change in ventilation efficiency. Therefore, we needed to confirm the relationship between different wind speeds and ventilation speed.

tionship between different wind speeds and ventilation speed. A fan (ZG-2) was used to simulate different wind speed effects. ZG-2 is depicted in A fan (ZG-2) was used to simulate different wind speed effects. ZG-2 is depicted in Figure 10 and its parameters are listed in Table 2. A fan (ZG-2) was used to simulate different wind speed effects. ZG-2 is depicted in Figure 10 and its parameters are listed in Table 2.

**Figure 10.** Photo of ZG-2 fan. **Figure 10.** Photo of ZG-2 fan.

**Table 2.** The parameters of ZG fan-2. **Table 2.** The parameters of ZG fan-2.

2.2.1. Experimental Model


Zg-2 1.5 50 1000 350 2800 380

The axial fan was placed to the left of the ventilator, which formed the platform for the second experiment. We used the same natural ventilator speed measurement method

**(rpm) Voltage (V)** 

### 2.2.1. Experimental Model *Fire* **2022**, *5*, x FOR PEER REVIEW 8 of 22 in the second experiment as in the first experiment, as shown in Figure 9. The wind speed

The axial fan was placed to the left of the ventilator, which formed the platform for the second experiment. We used the same natural ventilator speed measurement method in the second experiment as in the first experiment, as shown in Figure 9. The wind speed of the fan was calculated by Equation (5). The experimental setting is shown in Figure 11. in the second experiment as in the first experiment, as shown in Figure 9. The wind speed of the fan was calculated by Equation (5). The experimental setting is shown in Figure 11. of the fan was calculated by Equation (5). The experimental setting is shown in Figure 11.

*Fire* **2022**, *5*, x FOR PEER REVIEW 8 of 22

**Figure 11.** Experimental platform for the fan system. **Figure 11.** Experimental platform for the fan system. 2.2.2. Data Measurement

### 2.2.2. Data Measurement 2.2.2. Data Measurement In the experiments, we needed to measure wind speed. However, the wind speed

In the experiments, we needed to measure wind speed. However, the wind speed was not easy to measure. Because the wind near the outlet of the fan was relatively strong, and the different wind speeds led to an irregular distribution of wind speed within the outlet of the fan, we needed to set a rectifier grid to eliminate eddy currents and to ensure a stable flow. The length of the air duct in the rectification section L1 was 1.0 m. The rectifying plate was located in the middle of the air duct in the rectification section, and the In the experiments, we needed to measure wind speed. However, the wind speed was not easy to measure. Because the wind near the outlet of the fan was relatively strong, and the different wind speeds led to an irregular distribution of wind speed within the outlet of the fan, we needed to set a rectifier grid to eliminate eddy currents and to ensure a stable flow. The length of the air duct in the rectification section L1 was 1.0 m. The rectifying plate was located in the middle of the air duct in the rectification section, and the length L2 was 0.2 m. was not easy to measure. Because the wind near the outlet of the fan was relatively strong, and the different wind speeds led to an irregular distribution of wind speed within the outlet of the fan, we needed to set a rectifier grid to eliminate eddy currents and to ensure a stable flow. The length of the air duct in the rectification section L1 was 1.0 m. The rectifying plate was located in the middle of the air duct in the rectification section, and the length L2 was 0.2 m.

length L2 was 0.2 m. The rectifier in Figure 12 has a honeycomb structure, and was used on the inlet pipe of blower to balance air flow. Its functions by making the fluid smoothly pass through the fan blade, reducing air flow disturbances to the blade and the pressure loss caused by the rotation of air flow. The rectifier in Figure 12 has a honeycomb structure, and was used on the inlet pipe of blower to balance air flow. Its functions by making the fluid smoothly pass through the fan blade, reducing air flow disturbances to the blade and the pressure loss caused by the rotation of air flow. The rectifier in Figure 12 has a honeycomb structure, and was used on the inlet pipe of blower to balance air flow. Its functions by making the fluid smoothly pass through the fan blade, reducing air flow disturbances to the blade and the pressure loss caused by the rotation of air flow.

**Figure 12.** Schematic diagram of the rectifier. **Figure 12.** Schematic diagram of the rectifier. **Figure 12.** Schematic diagram of the rectifier.

The amount of air flowing out of the outlet in the axial flow fan decayed with increasing air supply distance. Therefore, we calculated the ambient wind speed according to the velocity attenuation equation. The minimum ambient wind speed, which drove the natural ventilator, was determined by several experiments on the amount of air ventilated by the fan. An anemometer was used to measure the wind speed at the fan outlet through The amount of air flowing out of the outlet in the axial flow fan decayed with increasing air supply distance. Therefore, we calculated the ambient wind speed according to the velocity attenuation equation. The minimum ambient wind speed, which drove the natural ventilator, was determined by several experiments on the amount of air ventilated by The amount of air flowing out of the outlet in the axial flow fan decayed with increasing air supply distance. Therefore, we calculated the ambient wind speed according to the velocity attenuation equation. The minimum ambient wind speed, which drove the natural ventilator, was determined by several experiments on the amount of air ventilated by the fan. An anemometer was used to measure the wind speed at the fan outlet through the

the fan. An anemometer was used to measure the wind speed at the fan outlet through the middle rectangle method. Then, the relationship between the ventilation speed and

the middle rectangle method. Then, the relationship between the ventilation speed and

the ambient wind speed could be obtained.

the ambient wind speed could be obtained.

middle rectangle method. Then, the relationship between the ventilation speed and the ambient wind speed could be obtained.

1

*Fire* **2022**, *5*, x FOR PEER REVIEW 9 of 22

$$\frac{v\_{\chi}}{v\_{0}} = K \left(\frac{b}{\varkappa}\right)^{\frac{1}{2}}\tag{4}$$

where *v<sup>x</sup>* is the wind speed at *x* distance from the fan, m/s; *v*<sup>0</sup> is the wind speed at the fan outlet, m/s; *K* is the proportionality coefficient in the equation, and its value is 2.35; and *b* is the diameter of the fan, m. where *vx* is the wind speed at *x* distance from the fan, m/s; *v*0 is the wind speed at the fan outlet, m/s; *K* is the proportionality coefficient in the equation, and its value is 2.35; and *b* is the diameter of the fan, m.

### *2.3. Third Experiment 2.3. Third Experiment*

### 2.3.1. Experimental Model 2.3.1. Experimental Model

The aim of this third experiment was to compare the smoke exhaust performance of the ventilator before and after optimization. We studied smoke exhaust by analyzing the variation in temperature in the air duct of the natural ventilator. During a fire, the faster the temperature decreases in a natural ventilator's air duct, the better its smoke exhaust performance. Therefore, in this experiment, two oil pans (15 × 15 × 4 cm and 20 × 20 × 4 cm) were placed at the bottom of the natural ventilator, and alcohol was poured into it as a fire source. After the alcohol was ignited, the burning metal box simulated a fire. The temperature variation inside the air duct with different fire scales was measured in the experiments. The experimental device is shown in Figure 13. The aim of this third experiment was to compare the smoke exhaust performance of the ventilator before and after optimization. We studied smoke exhaust by analyzing the variation in temperature in the air duct of the natural ventilator. During a fire, the faster the temperature decreases in a natural ventilator's air duct, the better its smoke exhaust performance. Therefore, in this experiment, two oil pans (15 × 15 × 4 cm and 20 × 20 × 4 cm) were placed at the bottom of the natural ventilator, and alcohol was poured into it as a fire source. After the alcohol was ignited, the burning metal box simulated a fire. The temperature variation inside the air duct with different fire scales was measured in the experiments. The experimental device is shown in Figure 13.

**Figure 13.** Photo of fire experiment. **Figure 13.** Photo of fire experiment.

### 2.3.2. Data Measurement 2.3.2. Data Measurement

K-type thermocouples were used for the temperature measurement in the experiments. There were four testing sections in total, and five temperature testing points were symmetrically arranged on each testing section, as shown in Figure 14. K-type thermocouples were used for the temperature measurement in the experiments. There were four testing sections in total, and five temperature testing points were symmetrically arranged on each testing section, as shown in Figure 14.

To ensure the accuracy of data measurement, an intelligent temperature acquisition system was used. The measurement error of the system was less than 3%, and the measured data could be stored in real time. To ensure the accuracy of data measurement, an intelligent temperature acquisition system was used. The measurement error of the system was less than 3%, and the measured data could be stored in real time.

**Figure 14.** Layout diagram of temperature testing points. **Figure 14.** Layout diagram of temperature testing points.
