Impact of Changing Inlet Modes in Ski Face Masks on Adolescent Skiing: A Finite Element Analysis Based on Head Models

Abstract

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO₂ concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future.

1. Introduction

The successful hosting of the 2022 Beijing Winter Olympics has had a significant impact, and its primary legacy has been to promote a greater understanding of skiing culture among residents in the Asia-Pacific region, thereby sparking a wave of mass participation in skiing. The skiing economy suffered a severe blow in 2020 due to the impact of the COVID-19 pandemic during the preparation and execution of the Winter Olympics. However, with China’s economic recovery, the attendance rate of skiing tourists in the Asia-Pacific region increased (by 17% by April 2022) [1]. China, as a major country driving the growth of the skiing economy in the Asia-Pacific region, has become a focal point of research. This is due to two main reasons: (1) The number of skiers increased from 10.3 million to 23.45 million during the 2022 Beijing Winter Olympics, making it the only rapidly growing emerging skiing market globally, ranking among the top three and becoming one of the most promising target markets for many skiing destinations. (2) The consumption capacity, tourism experience, and emphasis on cultivating children’s interests and hobbies among the Chinese population are undergoing rapid changes. The rise of the skiing economy in China is thanks to the emerging skiing market, characterized by 80% of consumers at the beginner level [2] and a gradual trend towards younger demographics. Moreover, skiing is regarded by most people as an entertainment activity rather than a leisure activity, making a comfortable experience while skiing indispensable [3].

In the development of the skiing culture worldwide, there has been an increasing emphasis on fostering skiing among children and youth. Research indicates that children and adolescents make up 13–27% of the 200 million skiers worldwide [4], and in 2020, skiing was included in the Youth Olympic Games by the International Ski Mountaineering Federation (ISMF) [5]. This once again highlights the importance of promoting skiing among children and youth. During the 2022 Beijing Winter Olympics, China issued the “Opinions on Promoting the Development of Ice and Snow Sports with the 2022 Beijing Winter Olympics as an Opportunity”, which called for widespread promotion of ice and snow sports among youth [6]. The “2021 Report on Chinese Youth Participation in Ice and Snow Sports” mentioned that 46.3% of adolescents had started paying attention to ice and snow information, 48.9% had gradually deepened their understanding of ice and snow sports, and 36.8% had expressed a desire to participate in ice and snow sports [7]. Furthermore, skiing is a full-body exercise that promotes comprehensive physical development in children and enhances cardiovascular endurance. Katarzyna Roczniak et al. also found that adolescents who engage in outdoor activities such as skiing for extended periods exhibit higher exercise efficiency compared to their peers and are more likely to acquire new athletic skills [8].

However, skiing is inherently risky, and skiers typically wear protective gear at the onset of the activity to prevent or mitigate potential hazards encountered during skiing. Brown et al. conducted a study revealing a high overall probability of head injuries [9], with adolescents being seven times more prone to such injuries compared to adults [10]. Head protection and safety measures include helmets, face guards, and goggles.

In terms of safety, outdoor ski resorts maintain temperatures below 0 °C throughout the year. Engaging in outdoor skiing activities can cause the body temperature to drop below normal levels, resulting in fatigue and an increased risk of frostbite [11]. Frostbite caused by skiing is typically classified as first-degree frostbite, with the most commonly affected areas being the ears and face [12]. Prolonged exposure of the skin to outdoor conditions can also trigger allergic urticaria. In terms of usage, the face protection and helmet are separate entities, with the helmet encompassing the ski face protection. There is limited research on integrated hard face protection with helmets, with only one design of reference value, the RUROC RG1-DX adult ski helmet (Figure 1). However, this helmet primarily caters to the European market, and its design dimensions do not align with the characteristics of the Asian population, resulting in poor comfort; there is also a lack of adaptation to the skeletal features of the adolescent population.

The existing ski face protection designs, which aim to provide wind resistance and reduce facial injuries, often cover the skier’s mouth and nose. To ensure comfort, most designs use soft, fiber-based materials. However, this design approach limits the face protection’s ability to absorb and remove substances, resulting in decreased breathability over time [13]. This presents several challenges for skiers: (1) Moisture from normal exhalation can clog the gaps in the fiber medium, increasing breathing resistance. Research shows that when breathing encounters resistance, the body compensates by increasing the respiratory rate, tidal volume, heart rate, and other mechanisms to obtain more oxygen [14], leading to an increased respiratory burden. (2) As the skiing duration lengthens in cold environments, the moisture before the face protection can condense and freeze (condensation), adhering to the skier’s face [14,15,16], exacerbating the risk of frostbite. (3) The exhaled airflow cannot be efficiently expelled, causing breathing difficulties and preventing skiers from obtaining sufficient fresh air. Repeated inhalation of CO₂ can lead to adverse effects such as headaches, hypoxia, and skin damage [17,18,19].

These results indicate that most soft face protection made of fiber materials, apart from having poor breathability, can also provide a negative skiing experience and even impact the adolescent skiers’ physical health with prolonged use. In China, a market with great potential for beginner skiing [1], ensuring a better skiing experience for adolescent skiers is crucial for the sustainable development of the skiing sport.

Based on the limitations of the aforementioned research and the need for further research, this study aimed to explore the application of computational fluid dynamics in addressing human factors related to respiration during physical activity and fill the gap in research on enhancing facial protection for adolescent skiers, which should contribute to the sustainability of adolescent skiing education. This article proposes a design approach that combines a hard face protection made of ABS plastic with a ski helmet, by redesigning the material and shape of the face protection based on skiing characteristics. On the one hand, a hard face protection, compared to the traditional soft face protection, will not adhere to the face and cause frostbite even in the presence of condensation. On the other hand, wearing a hard face protection can provide greater protection to the face. The smooth expulsion of exhaled airflow in enclosed spaces and reducing barriers to respiration were the key focuses of this research. Accordingly, after controlling the method of air intake and creating a detailed 3D model of the face, computational fluid dynamics (CFD) simulations were used to analyze the airflow inside the face protection under different intake methods. The research objective was to achieve a reduction in breathing difficulties and the occurrence of ice formation on the faces of adolescents during skiing, thereby enhancing the skiing experience. This research took the site environment of the Chongli Ski Resort as the main reference. Located in Beijing, the Chongli is among China’s high-quality ski resorts [20] and served as a primary venue for the Winter Olympics. Its standardized site environment provides significant reference value.

2. Ski Face Protection Shape Design and Model-Making

Based on the characteristics of ski helmets and the usage environment, the area of vortex generation, which determines the optimality of the design, is a decisive factor in determining whether the exhaled airflow can be rapidly discharged without colliding with the incoming airflow and accumulating inside the helmet face protection cavity [21]. The main factors affecting the smooth expulsion of exhaled airflow in the helmet face protection cavity are the intake method, breathing rate, and airflow cavity shape. Therefore, this study explored the most suitable design by changing the air intake method, as shown in Figure 2.

Due to the limited variety of integrated ski face protection methods, the selection of the intake method was made by referring to the intake methods of integrated motorcycle helmets. Although there are various styles of motorcycle helmets, the intake methods of face protection can be broadly classified into three types: front intake (type I), side intake (type II), and combined front and side intake (type III), as shown in

Table 1. Air intake mode division of face protection based on locomotive all-inclusive helmet.

Brand of Helmet/Helmet Picture		Description of Face Protection Features in Helmet (Type, Air Intake Mode)	Intake Type
Scorpion Exo	FASEED	All-inclusive helmet, front air intake	Type I
Bell	Fogy	All-inclusive helmet, side air intake	Type II
BELL-BROOZER	Fogy	All-inclusive helmet, side and front air intake	Type III

.

In order to accurately capture the airflow characteristics generated during exhalation inside the ski face protection, the cavity formed between the face and the hard face protection was closed-modeled using Rhino7.0 and Solidworks2023 software to create an internal flow field, as shown in Figure 2. During the movement in the flow field, exhalation becomes the main airflow, which collides with the walls of the cavity, causing a change in airflow direction. Therefore, the shape of the cavity in the middle of the face, especially the inclusion of the nasal wings and tip, is crucial. It is known that the angle and shape of the nose also affect the characteristics of airflow transport, especially when the angle is different, which directly affects the direction of the jet. When the jet angle is greater than the horizontal direction, the airflow is transported upward. To more effectively and accurately investigate the influence of the intake method on the airflow in the cavity, this study established a head model based on the data of head and facial dimensions of male adolescents aged 15–17 years according to GB/T 26160-2010 “Head and Face Dimensions of Chinese Minors” [22]. Special attention was given to the nasal dimensions and fine modeling, as shown in Figure 3.

As shown in

Table 2. Specific dimensions of nose precision modeling.

Name	Definition	Size (mm)/Angle (°)
Nasal length	Distance from nasal root point to nasal tip point	45 mm
Nasal depth	The nasion-to-nasal tip distance	12.8 mm
Nasal breadth	Distance between two alars	38 mm
Nasal height	Distance from the root point to the inferior nasal point	49.5 mm
Anterior nostril opening angle/diffusion angle	As shown in Figure 3c	64.5°/18.4°
Nose opening angle/diffusion angle	As shown in Figure 3d	73.5°/26.5°

, the measurements of nasal length, nasal depth, nasal breadth, and nasal height in this study were consistent with the data measured by Feng [23]. The nasal aperture anterior angle, nasal aperture lateral angle, and nasal aperture area of 56 mm² were within the range of data measured by Jitendra [24].

3. Method Numerical Values

The purpose of this study was to improve the comfort of children’s skiing by exploring the airflow generated during movement and investigating the optimal face protection shape. To achieve this, an experimental setup needed to be constructed to test and evaluate face protection. However, this method faced certain obstacles, such as the requirement for relatively closed testing conditions within the face protection, which increased the difficulty of setting up the experimental environment. In comparison to experimental measurements, computational fluid dynamics (CFD) offers greater detail, higher flexibility, a lower cost, and the ability to easily perform parameter analysis to examine the effects of specific parameters throughout the flow domain [25,26]. Therefore, in this study, the commercial solver ANSYS FLUENT 2020R2 was primarily used for CFD simulation analysis of the designed internal flow domain.

This study utilized computational fluid dynamics (CFD) technology to improve the design of ski masks for adolescents. The specific steps were as follows:

(1)

Optimization of design objectives: During teenage skiing, the air entering through the front air intake accelerates the rapid expulsion of exhaled gas, maintaining the breathability of the face shield and ensuring breathing comfort.

(2)

Parameter design: Under the premise of the same air intake area, the position of the front opening of the face shield was selected as the optimization parameter, with each opening area spaced at 1 cm² in the chin area. Three different air intake methods were designed, as shown in Table 1.

(3)

Simulation and analysis: A CFD software was used to establish a flow model of the mask and internal air. After dividing the computational domain and mesh, boundary conditions were set according to actual skiing conditions, utilizing the k-ε turbulent model. The analysis included temperature and velocity fields of the airflow.

(4)

Comparative results: In the CO₂ concentration detection, at the Type II outlet, the gas’s average mass fraction of CO₂ was 17,200 ppm, and at the Type III outlet, it was 23,156 ppm, indicating a 26% increase in concentration. Furthermore, the total vortex ring area in the Type II cavity was the smallest among the three design options at 38.45 cm².

3.1. Computational Domain and Grid

The model was constructed using the computer-aided software Solidworks. In the flow domain, the airflow was generated through the front intake and exhaled gas from the nose, which flowed out through the side exhaust of the helmet, as shown in Figure 2. The initial dimensions of the cavity were 153.5 mm in length, 178.4 mm in width, 31 mm in height, and a thickness of 28.7 mm in the middle of the face, as shown in Figure 2. To simulate the exhaled airflow through the nostrils, two elliptical boundary surfaces with an area of 0.56 cm² each were used [25]. The front intake consisted of two elliptical boundary surfaces with an area of 1 cm² and a spacing of 0.5 cm.

Meshing was performed using a hybrid mesh composed of tetrahedral and hexahedral elements to fill the space. The main intake and exhaust areas were locally refined, with a maximum size of 0.9 × 10⁻³ m. The maximum grid size on the face shield determined the level of detail in capturing flow characteristics, and for small-sized fluid simulation grids inside the face shield, the maximum grid size did not exceed 1 × 10⁻³ m [26]. The cell layer consisted of six layers. Next, the model was run in a pre-computed steady-state simulation, and the ratio of the minimum resolved length scale to the turbulence integral length scale was checked to ensure that 80% of the turbulent kinetic energy was resolved. Finally, a total of 4.1 × 10⁶ grid cells were determined for the flow domain.

3.2. Boundary Conditions

Under atmospheric pressure, exhaled air from humans forms a laminar jet stream [21,27]. The gas setting parameters reference the findings of Hana Salati et al., specifically as follows: temperature of 36 °C (309.15 K), velocity of 1.85 m/s, carbon dioxide mass fraction of 36,000 ppm, and relative humidity of 95% [21,28]. These flow characteristics are applied to the two elliptical inlet boundary surfaces at the nose. The exhaled airflow has significant differences in temperature and velocity compared to the airflow entering the cavity. This temperature difference generates a buoyancy effect within the vortex ring, causing the airflow to tend to move unevenly downward within the cavity and then flow to the sides after contacting the walls. The characteristics of the airflow entering through the front intake depend on the ski resort environment. In this study, the airflow characteristics were determined based on the environment at Wanlong Ski Resort in Hebei Province, China, with a temperature of −8 °C (265.15 K) [29], a carbon dioxide mass fraction of 285 ppm, and air as the gas. Regarding the velocity setting, we found that the air permeability of the intake depends on the different helmet designs and materials. Excessive velocity can affect the insulation performance, so a uniform velocity of 0.5 m/s was set.

3.3. Flow Solver

The airflow generated inside the helmet cavity is known to be turbulent. In this study, the SST k-ω model was used to simulate the turbulent flow. This model is commonly used in Reynolds-averaged Navier–Stokes (RANS) models and has shown a good performance in predicting and capturing the flow characteristics of the face shield and the surrounding airflow [26,30,31]. The incompressible flow equations, which describe the morphology and momentum conservation, are expressed as follows:

$${\displaystyle \frac{\partial }{\partial {{\displaystyle x}}_i}}\left({\overline{u}}_i\right)=0$$

(1)

$${\displaystyle \frac{\partial }{\partial t}}\left(p{\overline{u}}_i\right)+{\displaystyle \frac{\partial }{\partial x_j}}\left(p{\overline{u}}_i{\overline{u}}_j\right)={\displaystyle \frac{\partial \overline{p}}{\partial x_i}}+{\displaystyle \frac{\partial {\sigma }_{ij}}{\partial x_j}}+{\displaystyle \frac{\partial {\tau }_{ij}}{\partial x_j}}$$

(2)

where u is the velocity vector, ρ is the fluid density, p is the pressure, and τ is the stress tensor caused by molecular viscosity.

In terms of the time step setting, a comparison was made between a time step of 0.01 s and 0.002 s, resulting in only a 4.3% difference in wind speed. Additionally, Zhongjian Jia et al. mentioned in [26] that the simulation results for fluid concentration showed only slight differences between time steps of 0.01 s and 0.005 s. Therefore, a time step of 0.008 s was chosen for this study. The SIMPLE method was used for pressure–velocity coupling in the solution process. A second-order bounded scheme was employed to solve the convective term in the momentum equation. The gradient calculation was performed using the least squares cell-based method, with default under-relaxation factors.

4. Results and Discussion

Using fiber material for ski face masks during skiing can result in significant temperature differences between the extreme temperatures of the outdoor snowfield and the exhaled airflow, leading to a higher risk of facial frostbite and a decrease in the protective performance of the gear if effective intervention measures are not taken. While many studies have analyzed the force performance of protective gear after collisions in terms of facial protection during sports activities, the thermal comfort of the facial area within the gear, such as full-face helmets, remains understudied.

In accordance with the aforementioned conditions, we simulated a scenario where an underage male head model wears different types of face masks with different intake methods and exhales during skiing. By parameterizing the simulation results, we compared the distribution of CO₂, vortex generation, airflow velocity, and temperature within the face mask cavity. From the perspective of human factor engineering, we evaluated the protective effectiveness of three design approaches on the facial area. Additionally, we found that existing studies that combine computational fluid dynamics (CFD) with human factor engineering often present simulation results using software like Tecplot360 in the form of images, with language descriptions and summaries of the image results [32,33]. This research approach lacks accuracy, as CFD results are often complex and variable. Therefore, in this study, we further enhanced the accuracy of the research results by post-processing the simulation results through parameterization.

4.1. Distribution of CO₂ Concentration

The exhaled CO₂ level was measured at 3600 ppm, and prolonged inhalation of excessive CO₂ during skiing can lead to an increased respiratory rate and metabolic stress. To observe the discharge and flow of exhaled CO₂ gas within the cavity more intuitively, the distribution of airflow within the entire cavity was charted, as shown in Figure 4. The angle between the nostrils and the cheeks is 23.52°, as shown in Figure 3c, and the exhaled airflow forms a downward jet. Different intake methods have different effects on the exhaled airflow. In the case of forward intake, where the intake hole is located below the cavity, 2.57 cm away from the nostrils, the exhaled CO₂ with a mass fraction of 36,000 ppm encounters the incoming air with a mass fraction of 385 ppm, causing the gas with a high CO₂ concentration (>28,000 ppm) to concentrate in the upper–middle part of the cavity (central region), resulting in a significant concentration gradient difference in CO₂ concentration in the upper and lower parts of the cavity. We also observed that the discharge efficiency of high-concentration CO₂ gas is poor.

In the case of a Type II CO₂ distribution, as shown in Figure 4, the exhaled high-concentration CO₂ gas hits the bottom of the cavity, changes direction, and flows towards the sides, blending with the air entering from the sides, and collectively flowing towards the outlet. At this time, the average mass fraction of gas in the middle part of the cavity is 22,153 ppm, and the average mass fraction of gas at the outlet is 17,200 ppm. When observing Type III, we found that the exhaled gas changes its flow direction towards the sides due to the incoming air from the front in the middle part of the cavity. However, due to the number of side openings, the average mass fraction of gas at the outlet is 23,156 ppm. This indicates that Type III has a slightly poorer discharge efficiency for high-concentration CO₂ gas compared to Type II.

4.2. Vorticity Detection and Distribution

The airflow characteristics in this study were turbulent, formed by a continuous energy input in a strong dissipative system, where the presence of vortices determines the rate of energy dissipation. In a turbulent flow system, there are vortices of different sizes. In other words, the vorticity is inversely proportional to the rate of energy dissipation; the more vorticity there is, the longer the airflow stays in the cavity, resulting in a higher rate of dissipation and a slower discharge velocity. In this study, there were significant differences in temperature and velocity between the exhaled airflow and the airflow entering the cavity, with a maximum temperature difference of 44℃ and a maximum velocity difference of 1.8 m/s. These differences caused the vortices to generate buoyancy, leading the airflow to unevenly move downward in the cavity and then flow sideways upon encountering the wall, which increased the residence time of the airflow in the cavity. Through experimental simulations, we found that by changing the position of the intake opening for the incoming air, while keeping the breathing conditions constant, the area of vortex rings generated in the cavity was affected, thus reducing the accumulation of CO₂ inside the cavity.

In the ergonomic research, we designed the face shield based on the standard head model of underage males, taking into account the facial shape characteristics and dimensions, which resulted in irregular cavity shapes. Additionally, vortices are mostly in 3D forms, making it difficult to parameterize them. Therefore, in order to obtain a more intuitive and comprehensive understanding of the distribution of vortices in the cavity, we selected five cross-sections within the cavity, as shown in Figure 5, with an interval of 0.01 m between each cross-section. On each cross-section, the vortices’ cross-sectional shape is displayed, allowing for a clear understanding of the distribution of strong and weak vortices, as well as the cross-sectional area of the vortices.

When studying the vortex distribution characteristics of different designs, this study used the Ω criterion to identify vortices in the cavity. Liu [34] proposed that compared to the commonly used Q criterion method, the advantage of this method is that it can capture both strong and weak vortices, particularly those with Ω = 0.52. Moreover, it can capture many typical vortex structures very well, and this method is not sensitive to the variations in R. This is because the threshold is normalized to the range of 0–1 [35]. The calculation method for the Ω criterion is as follows, where $A$ is the symmetric part, $B$ is the antisymmetric part, and ε is a small positive number used to avoid division by zero.

$$\Omega ={\displaystyle \frac{{{\displaystyle ‖B‖}}_F^2}{{{\displaystyle ‖A‖}}_F^2+{{\displaystyle ‖B‖}}_F^2+\epsilon }}$$

(3)

Table 3. Vorticity display and flow field display of different intake modes at different sections.

Slice Position	Type I	Type II	Type III
Overall airflow velocity
slice 1
slice 2
slice 3
slice 4
slice 5

describes the distribution and evolution of vortices caused by three different design schemes. Through observation, it can be seen that vortices are inversely proportional to the flow velocity. The regions with higher vortex intensities are mainly concentrated in the middle of the cavity (slice 3 and slice 4), as shown in Figure 5, due to the location of the intake opening, where the incoming and exhaled gases intersect and are more likely to form vortices. However, with different intake methods, significant differences can be observed in the area and formation of the generated vortices.

By comparing the flow characteristics and vortex structures generated by the three different design schemes, it was confirmed that the flow characteristics in the cavity can be controlled and are determined by different intake methods. Here, Type II had a better control effect on the vortices generated in the cavity, resulting in the smallest vortex area.

To further determine the influence of the intake opening position on the flow characteristics, the vortex formation conditions of different design schemes on five cross-sections were compared. Overall, it can be observed that vortices were mainly concentrated in the lower part of the cavity (slice 3, slice 4, slice 5). This is because the exhaled airflow acts as a jet, intersecting with the incoming low-speed airflow, which slows the flow velocity and causes the airflow to change direction, resulting in vortices. Type III generates the largest vortex area, mainly concentrated in slice 3 and slice 4. This is because there are two intake openings in different directions. The exhaled airflow encounters the incoming airflow at slice 3, causing a portion of the airflow to change direction and flow to both sides, forming strong vortices after colliding with the walls. The center of the vortex (x = −0.08, y = −0.031, z = −0.036) has an Ω value of 0.86. Another portion of the airflow flows vertically downwards, with the velocity decreasing to 0.72 m/s. After colliding with the lower wall of the cavity, it undergoes bifurcation and extends weakly rotating flows to both sides of the outlet. This is also the main reason why the average velocity at the outlet of Type III is lower than that of Type II. The total vortex ring area in the Type II cavity is the smallest among the three design schemes, measuring 38.45 cm². As shown in Figure 6, this is mainly because Type II has a lateral intake, and the exhaled jet flows vertically downwards without being influenced by the incoming low-speed air, resulting in less velocity loss and a lower possibility of generating low-speed rotating flows. Moreover, from the cross-sectional images of slice 2 and slice 4, it can be observed that the vortex area and the number of strong vortices in the middle part of the cross-section are smaller than those of Type I and Type III. Therefore, the forward intake method is considered to directly reduce the velocity of the exhaled airflow, and when the exhaled airflow flows vertically downwards, it separates, slowly flows along the inner wall of the cavity towards the outlet, and increases the probability of vortex formation upon colliding with the cavity.

4.3. Variation in Velocity and Temperature Data in a Cavity

To further investigate the characteristics of airflow in the cavity, we marked 30 points on each cross-section, totaling 90 marked points. Due to the irregular shape of the cavity, we only ensured that the x-coordinate of the marked points on three cross-sections was consistent. The specific locations of the marked points on slice 3 are shown in Figure 7a. The front intake opening is located at [±0.004, ±0.004] on the x-axis, which is also the distance between two eyeballs and includes the nose and mouth, as shown in Figure 7b. This area is considered the middle of the face, which is more sensitive and a key protective area. Therefore, this study focused on exploring the changes in fluid velocity and temperature from the middle of the face.

Figure 8a shows the temperature distribution of the airflow in the cavity from left to right during exhalation while skiing. The data of the marked points represent the average values of five cross-sections, providing a more intuitive representation of the trend of flow in the cavity. From the curves, we can see that the temperature change in the cavity is strongly correlated with the position of the intake opening. Starting from x = ±0.04, the temperature increases for type II and type III, while it decreases for type I. Although the temperature change in type I is relatively gentle and the temperature difference in the entire cavity is small, the average temperature in the cavity is higher than that of type I and type II, reaching a maximum of 29.55 °C. However, as cold air enters from the middle of the face and intersects with the exhaled airflow, the temperature significantly decreases in the [±0.04, ±0.04] region, with an average temperature of 29.2 °C. This phenomenon may lead to a continuous decrease in temperature as the breathing rate intensifies during later stages of exercise, which is not conducive to insulation in the middle of the face during long periods of skiing. Similarly, type III also faces the issue of cold air entering from the front, resulting in an average temperature of 27.35 °C in the [±0.04, ±0.04] region. Finally, due to the lateral intake method, type II has an average temperature of 27.46 °C in the cavity, but the average temperature in the middle of the face is 35.35 °C, resulting in less heat loss for the exhaled gas (36 °C). Compared to the other two design schemes, type II may provide better insulation in the middle of the face.

Figure 8b shows the variation in airflow velocity for children from left to right in the cavity during exhalation while skiing. The velocity change trends for the three design schemes are similar, with average velocities of 0.5 m/s, 0.53 m/s, and 0.54 m/s, indicating that different design schemes have a minimal impact on the flow velocity in region B of the cavity. However, this does not necessarily imply that it does not affect gas discharge. At the junction between region A and region B, as shown in Figure 7b, where the cavity suddenly narrows to 0.01 m, the airflow is accelerated. Additionally, different design schemes result in different vortex formations at the junction. Among them, type II generates a strong vortex (Ω ≥ 0.9) with the smallest vortex area. As shown in Figure 8c, type II accelerates the airflow reaching region B at the outlet, with a maximum velocity of 0.78 m/s. The parameterization of average velocity and temperature at the outlet determines, to some extent, the discharge of exhaled gas and the temperature loss in the cavity. Taking type I as an example, the front intake opening is located directly in front of the face and far from the outlet, resulting in the highest average temperature (28.3 °C) and the slowest average velocity (0.67 m/s) at the outlet. On the other hand, in type II, the intake opening is located on the cheek, closer to the outlet. Therefore, when the exhaled jet hits the bottom of the cavity and changes direction, some of the airflow flows towards the outlet, intersecting with the airflow entering from the side and flowing towards the outlet. In this process, the energy consumption of turbulence is minimized, and the air entering from the side not only provides fresh air but also merges with the exhaled airflow, resulting in an increased flow velocity of 0.78 m/s. Based on this study’s inference, the side intake design of type II reduces the possibility of turbulent fragmentation in different directions, as evidenced by the minimal vortex area in the vorticity results. Therefore, we believe that type II can better improve the generation of vorticity in the cavity and enhance the smoothness of airflow during exhalation.

5. Conclusions

This study investigated the thermal comfort of young skiers using hard face shields and evaluated the characteristics of airflow during exhalation using computational fluid dynamics (CFD). The results were compared in terms of fluid velocity, temperature, CO₂ concentration, and vorticity distribution. The conclusions are as follows: (1) Different intake positions can affect and control the flow characteristics of the mask’s airflow. (2) When the intake port is located in the middle of the mask (type I, type III), this leads to a decrease in temperature here, affecting the insulation performance of the skiing mask. (3) The incoming air from the front intersects with the exhaled airflow, creating entrainment flow, which can cause turbulent flow and increased vorticity generation, leading to a decrease in discharge velocity. (4) Different intake methods have no significant impact on the average flow velocity within the entire cavity. (5) The side intake method (type II) can enhance the insulation performance of the face shield and ensure the rapid discharge of exhaled CO₂ gas produced by young skiers, ensuring the safety of the face shield and improving the comfort and overall experience during skiing.

This study demonstrates the feasibility of using CFD methods in ergonomic research and design. Through this technological innovation, we have not only optimized the design of ski face masks but also provided substantial support for the development of adolescent skiing. Through this research, we hope to further promote the development of physical education, enhance the overall quality of adolescents’ skiing, and thus offer more possibilities for their future. Similarly, in the field of human factor engineering, computational fluid dynamics methods have many other potential applications. For example, CFD can be used for studying virus permeability, the interaction between respiratory devices and ventilation systems, and the airflow resistance of protective respiratory devices, along with other applications in fields such as exploring the comfort of AR or VR devices. Therefore, our experiment has important guiding significance for ergonomic research on other forms of personal protective equipment.

However, our study also has some limitations. Firstly, we focused primarily on the discharge process of exhaled gas as the primary research objective. To achieve this, we made assumptions about the airflow velocity at the intake port and ignored the particle size distribution and mass of exhaled droplets. Secondly, the size of the model was constructed using only the head dimensions of the 50th percentile, which means that the results cannot be generalized to the head shapes of all Chinese adolescents. In the future, we can consider experimenting with different head model dimensions, ages, and even races. Finally, facial protection during skiing also needs to consider the impact resistance of protective gear, and this study only focused on thermal comfort. Overcoming these issues will be the primary focus of future work. Nonetheless, despite the areas for further improvement mentioned above, the current research results demonstrate the feasibility of hard face shields for skiing and specifically indicate that full-face helmets with side intake designs can enhance the performance of protective gear.