Wind Environment Adaptability and Parametric Simulation of Tujia Sanheyuan Courtyard Dwellings in Southeastern Chongqing, China

Abstract

In the context of the energy crisis and the urgency of passive design in contemporary architecture, this study focuses on the Tujia-style Sanheyuan in southeastern Chongqing, China, which is highly adaptable to local climatic conditions. Using field surveys, architectural mapping, computational fluid dynamics numerical simulations, and multi-parameter comparative analysis, this study systematically explores the relationship between the geometric form of the Sanheyuan and its courtyard ventilation performance. Based on the Tujia construction scale modulus, this study summarizes the basic prototype of the Sanheyuan, analyzes the selection paths of its three sets of construction parameters, and constructs 48 typical courtyard models for wind environment simulation. By introducing five evaluation indicators—wind speed uniformity coefficient, proportion of strong wind zone area, proportion of calm wind zone area, and unit area wind rate—this study comprehensively assesses the impact of Sanheyuan design parameters on courtyard wind environment adaptability. This study concludes that specific spatial design parameters of the Tujia-style Sanheyuan significantly influence wind environment adaptability, offering quantitative guidance for climate-responsive and culturally informed architectural design. This study found that the optimal side room width-to-depth ratio is [1.00, 0.86, 0.83]; the optimal ridge height-to-stilt height ratio is [4.29, 8.00, 2.96]; and the optimal building footprint-to-side room area ratio is [3.01, 5.06, 4.75].

1. Introduction

Tujia Sanheyuan, a typical southwestern mountainous ethnic architecture, is widely distributed in the hilly and canyon areas of the Wuling Mountain Area [1,2,3]. Its spatial configuration and structural characteristics reflect a comprehensive response to and accumulated adaptation to the environment of the Wuling Mountain Area. The main house is centrally located as the core unit, while the wing rooms are often built on terrain with elevation differences, forming a stilted frame structure with a “level roof, uneven ground” characteristic. The layout is a “One Main with Two Wings” U-shaped layout that faces inward and is open. This maintains the privacy of the living space while creating a microclimate that allows air and light to flow through. Overall, the spatial configuration, structural form, and construction characteristics of the Tujia Sanheyuan are closely related to the “high temperature, high humidity, heavy rainfall, and low wind speed” environment in which it is situated, adapting to the complex topography and effectively enhancing the building’s ventilation and dehumidification performance. This reflects the construction strategies and ecological wisdom gradually formed by local ancestors through long-term climate adaptation. The spatial configuration, structural form, and construction characteristics of the Tujia Sanheyuan are directly and profoundly influenced by the wind environment, which is a critical factor in regulating the balance of humidity and heat and guiding natural ventilation [4]. The spatial layout, structural form, and construction characteristics of the Tujia-style Sanheyuan (a traditional courtyard house) have a direct and profound impact [5,6,7]. The Tujia-style Sanheyuan can be considered a typical example of passive energy-efficient architectural design, characterized by its effective adaptation to regional wind environments and its effective utilization of the microclimate. Against this backdrop, this study attempts to explore the wind environment adaptability of the Sanheyuan by employing field surveys, architectural surveying, CFD numerical simulations, and multi-parameter comparative analysis methods [8,9]. It investigates the relationship between the geometric form of the Sanheyuan and courtyard ventilation performance, thus assessing the impact of the design parameters of the Sanheyuan on the adaptability of the courtyard’s wind environment. This study has three main research objectives: 1. To provide technical support for the conservation and renovation design of vernacular architectural heritage by collecting data and analyzing the geometric forms of the Tujia-style Sanheyuan in southeastern Chongqing, China, and summarizing the basic types of Sanheyuan along with three sets of design parameters. 2. Based on the local construction scale modulus, to clarify the selection logic of discontinuous design parameters employed during the construction of the Tujia-style Sanheyuan, and to summarize the cultural characteristics of its construction, thereby providing data support for the study of its wind environment adaptability design parameters. 3. Through CFD numerical simulation combining multi-factor evaluation indicators, to analyze the impact of different construction parameters on the wind environment of Tujia-style Sanheyuan, in order to guide the climate adaptability design of new rural dwellings in beautiful villages and provide a historical reference for contemporary architectural space wind environment adaptability design.

2. Literature Review

Research methods for studying the wind environment of traditional dwellings include field measurements, wind tunnel experiments, and numerical simulations [8,9]. Cutting-edge studies in the field primarily rely on numerical simulations, often combined with field measurements or wind tunnel experiments, for comprehensive analysis [10,11,12,13,14,15,16]. Oke (1988) [17] and Blocken (2015) [18] have systematically studied the interaction between buildings and the wind environment in complex mountainous or urban terrains using CFD and wind tunnel simulations. Their work has provided important modeling methods and theoretical foundations for the microclimate adaptability of traditional settlement buildings [17,18,19]. Using quantitative field measurements and simulation analysis with relevant software, this research conducts temperature and humidity testing and wind environment simulations on traditional dwelling courtyards of different spatial scales, clarifying the regional climate adaptability patterns of traditional courtyards [20]. Professor Edward Ng and his team have thoroughly explored the sustainability issues of traditional villages in the mountainous regions of southwest China. This study emphasizes the crucial role of the wind environment in architectural design and proposes a sustainability evaluation framework suitable for mountain villages [21]. By analyzing the interaction between local buildings and the natural environment, this study provides theoretical support and practical guidance for improving the village wind environment and enhancing building sustainability. Naboni, Reiter, and Allegrini have conducted research on the wind environment adaptability of traditional dwellings in different climate zones [22,23,24]. These studies primarily focus on aspects such as the architectural design of buildings, courtyard space proportions, and settlement layouts in temperate, tropical, and mountainous regions. By selecting typical case studies, they conducted numerical simulation analyses and used CFD 2024 R1 software to model the relationships between various architectural parameters and natural ventilation effects. Consequently, they proposed adaptive optimization strategies and climate-specific design recommendations, enhancing the wind environment performance and microclimate comfort of traditional dwellings [25,26].

Overall, wind environment studies of traditional courtyards have mostly focused on typical case analyses. However, research on the wind environment adaptability of the geometric forms of Tujia-style Sanheyuan in the southeastern Chongqing region of China—particularly those with a large sample size and specific regional focus—remains insufficiently in-depth. Additionally, wind environment research on traditional courtyard buildings generally revolves around continuous design parameters under modern modular systems, without considering the discontinuous selection of design parameters based on the traditional local construction scale. A comprehensive comparative study of multiple discontinuous construction parameters, based on the Tujia construction scale modulus in southeastern Chongqing, remains lacking. Moreover, the evaluation indicators for traditional courtyard wind environment adaptability are relatively singular and do not consider a multi-factor comprehensive evaluation. Furthermore, research into the mechanisms of wind environment adaptability has not been sufficiently in-depth.

Therefore, this study aims to address the following questions: 1. Based on field surveys and architectural mapping, collect data on the geometric forms of Tujia-style Sanheyuan in southeastern Chongqing, China, and conduct a type analysis. Through a comparative analysis of multiple samples and various spatial types, what is the basic form of Tujia-style Sanheyuan? Moreover, how can the three sets of construction parameters for Tujia-style Sanheyuan be determined? 2. Based on the local construction scale modulus of Tujia people in southeastern Chongqing, what is the selection path for the three sets of discontinuous construction parameters in traditional courtyards? What are the corresponding CFD simulation results for these three sets of discontinuous construction parameters? By integrating multi-factor evaluation indicators—such as wind zone area ratio, wind speed uniformity coefficient, and unit area wind rate—what is the impact of the range of values for the three sets of construction parameters on the wind environment of Tujia-style Sanheyuan? Through CFD simulation analysis, what is the underlying mechanism of wind environment adaptability for the Tujia-style Sanheyuan? How can this be interpreted from the perspective of the regional ethnic architectural culture? Finally, summarize the wind environment adaptability design parameters for Tujia-style Sanheyuan in southeastern Chongqing and the wind environment adaptability patterns based on historical accumulation and evolution. The research framework is illustrated in Figure 1.

3. Materials and Methods

3.1. Research Area

The southeastern Chongqing region is in the middle of the Wuling Mountain Range, in the southeast of Chongqing. The land is composed of a mix of mid–low mountains and canyon basins, with many variations in elevation (Figure 2). The national standard Building Climate Zoning Standard GB50178-93 [27] states that this area, which includes the counties of Qianjiang, Youyang, Xiushan, Pengshui, Shizhu, and Wulong, has a “Hot Summer and Cold Winter” climate. Overall, the region is characterized by “high temperature, high humidity, rainy, and low wind speed”.

This climatic feature demonstrates significant stability, based on long-term observational data from the China Meteorological Administration (1991–2020). In terms of temperature characteristics, the annual average temperature remains stable in the range of 14.0 ± 0.4 °C, with a summer average temperature of 28.0 ± 1.2 °C and a winter average temperature of 4.0 ± 0.8 °C. The probability of extreme high temperatures reaching 38 °C and low temperatures dropping to −5 °C is less than 5%. In terms of precipitation, the annual rainfall is 1350 ± 150 mm, with the rainy season from May to September accounting for approximately 70% of the total precipitation, and the interannual variation coefficient is only 11%. The wind environment also demonstrates stability, with an annual average wind speed of 1.5 ± 0.3 m/s. The dominant westerly winds (270 ± 15°) occur with a frequency of 63% throughout the year. The climate stability is largely attributed to the unique topographic modulation provided by the Wuling Mountains. The elevation gradient (300–1200 m) creates a temperature difference of less than 2 °C per 100 m, effectively buffering regional temperature fluctuations, making the climate much more stable than that in the plains. Additionally, the canyon terrain significantly reduces the wind speed variation coefficient to 0.21, which is much lower than the typical value greater than 0.4 in plain. Furthermore, the karst groundwater system, along with over 60% forest cover, works together to stabilize the relative humidity, maintaining it within the range of 80 ± 5%.

Comprehensive studies indicate that, despite the uncertainty of global climate change, the southeastern Chongqing region has maintained a high level of stability in its core climate parameters over the past 70 years owing to the dual influence of its terrain and ecosystem (with a variation coefficient of <10%). This unusual climate places a lot of stress on vernacular architecture when it comes to ventilation, controlling temperature and humidity, and arranging space. The Tujia Sanheyuan has developed a unique passive environmental regulation strategy over a long period to adapt to the hot and humid natural environment. This demonstrates deep regional wisdom and ecological adaptability.

3.2. Field Investigation and Data Analysis

3.2.1. Field Investigation

Traditional Tujia Sanheyuan courtyards in the southeastern Chongqing region were selected for the field investigation. To ensure the representativeness of the research samples, the sample selection covered all five administrative regions in southeastern Chongqing. The surveyed Sanheyuan samples follow the basic “one main house and two side rooms” form, but due to differences in regional environment, socio-economic conditions, user groups, and functions, the Sanheyuan spaces exhibit variations in enclosure ratio, aspect ratio, and orientation. Additionally, both traditional dwellings and modern renovation cases were considered. The Tujia Sanheyuan courtyards in Qianjiang District are represented by the No. 8 residence of Fengtai Village, Zhang’s residence in Qiaoliang Village, and a certain residence in Fengtai Village; those in Shizhu Tujia Autonomous County are represented by the No. 1 residence in Xiangxi Village, Liu’s residence in Huanglong Village, and Chen’s residence in Xincheng Village; and those in Youyang Tujia and Miao Autonomous County are represented by Li’s courtyard in Qifen Village and Liu’s residence in Dahekou Village, among others. The field investigation included real-life photographs and measured drawings, as shown in Figure 3 and Figure 4.

This study examines the adaptability of courtyard spaces to the high-temperature, high-humidity, rainy, and low-wind-speed climate of southeastern Chongqing using a three-section spatial survey. The courtyard space is usually open and acts as a bridge between the building and the indoor and outdoor spaces. The opening usually faces the main wind direction or the direction that receives good sunlight. This not only allows for good natural convection and chimney effect ventilation in the courtyard, but it also keeps the heat load from getting too high from direct sunlight. The ground floor of the wing rooms on both sides often features semi-open corridors, which help regulate the microclimate. The wooden framework, combined with the stilted building form, not only adapts to the mountainous terrain but also improves the thermal-driven ventilation efficiency under indoor–outdoor temperature differences, creating a comfortable courtyard space in a hot and humid environment. To determine the wind speed and wind direction boundary conditions required for CFD simulation, field wind environment monitoring was conducted in July 2024 in the study area using typical Sanheyuan samples selected for the investigation. The monitoring points were set at a height of 1.5 m in the open courtyard area. A portable anemometer was used for continuous observations, resulting in a 10 min average wind speed of 1.6 m/s. The dominant wind direction was from the west (270°, from west to east), with high wind direction stability. However, field investigations revealed that traditional Tujia construction often considers Feng Shui principles, daily comfort, and psychological needs when selecting the site and orientation. This typically results in the dominant wind direction forming a crosswind feature, blowing from the left side of the courtyard to the right side (with the building layout facing southwest). This empirically dominant wind direction demonstrates high adaptability and universality in practical life. Therefore, in this study, the empirical wind direction is set as the default simulation wind direction in the CFD simulation to better reflect the real wind environment of the traditional Sanheyuan.

3.2.2. Data Analysis

Based on field interviews and a literature review, the local construction measurement modules used in the Three Gorges Region of the Yangtze River Basin are examined. One Chinese foot is approximately equal to 29.8 cm, with characteristic local construction measurement modules such as the Five-Feet, Zhang’s Rod, Curved Ruler, and Luban Ruler (Figure 5).

The facade width of Zhang’s Rod, marked by the Yabai Measurement Method, often includes the lucky numbers “one”, “two”, “six”, “eight”, and “nine”. These numbers complement the Cunbai with the lucky stars of “six” and “eight”, forming a balanced and ideal measurement system. Based on the aforementioned field investigation, the basic construction parameters of vernacular courtyards in the Three Gorges Basin were summarized and selected (Figure 5). The main house height considered in this study is 5.6 and 5.26 m; the hall width is 3.94, 4.60, and 5.26 m; the main house depth is 4.0, 5.6, and 7.94 m; the roof slope range is from 1/5 to 1/6, meaning the ratio of the vertical roof height to the main house depth is 0.5–0.6, with a 0.01 variation; the number of main house columns is 3, 5, and 7. Based on the aforementioned basic construction parameters, the range of the three sets of vernacular courtyard construction parameters was derived. After filtering through the local construction measurement modules, three sets of construction parameters considered in this study were obtained: wing room width-to-depth ratio, ridge height-to-stilt height ratio of the wing rooms, and building footprint-to-wing room area ratio (Figure 6).

Based on the aforementioned construction parameter data of the Sanheyuan, different models were derived by changing the wing room width-to-depth ratio. The models were categorized into three groups based on the number of columns. There are 6 five-column vernacular courtyard models, numbered A1–A6, 6 seven-column Sanheyuan models, numbered A7–A12, and 9 nine-column Sanheyuan models, numbered A13–A21, for a total of 21 models. Similarly, by changing the ridge height-to-stilt height ratio of the wing rooms, the models were divided into three groups based on the number of columns.

There are 2 five-column Sanheyuan models, numbered B1–B2, 3 seven-column Sanheyuan models, numbered B3–B5, and 4 nine-column Sanheyuan models, numbered B6–B9, for a total of 9 models. Finally, by changing the building footprint-to-stilt area ratio, the models were categorized into three groups based on the number of columns. There are 6 five-column Sanheyuan models, numbered C1–C6, 6 seven-column Sanheyuan models, numbered C7–C12, and 6 nine-column Sanheyuan models, numbered C13–C18, for a total of 18 models. In total, 48 Sanheyuan models were simulated in this study, categorized into 3 major groups, with each major group further divided into 9 smaller groups. The specific breakdown is described in detail in Section 3. All 48 models were selected after filtering through local construction measurement modules. The number of models in the 9 smaller groups is not the same, ranging from 2 to 6, with the exact number related to the local construction measurement modules.

Overall, the 48 models developed in this study follow the traditional construction scale of the Tujia people in southeastern Chongqing and are regionally representative in terms of design parameters. The model dimensions comply with the local “pressure scale white” taboo system, with key dimensions such as the main house width being 3.94 m (1 zhang 3 chi 2 cun, corresponding to the “Liu Bai” auspicious star) and 4.60 m (1 zhang 5 chi 4 cun, corresponding to the “Ba Bai” auspicious star), avoiding the use of inauspicious measurements like “three” and “four”. The roof slope is strictly controlled within the range of 0.5 to 0.6 (from 5:1 to 6:1), with a measurement error of less than 3%. Additionally, in terms of the structural system, the selected models cover four typical column-grid systems from the field survey. Among them, the 5-column ground-supported models (A1–A6, B1–B2, and C1–C6) represent the basic form, accounting for 44% of the total sample; the 7-column ground-supported models (A7–A12, B3–B5, and C7–C12) adapt to the need for expansion, accounting for 31%; and the 9-column ground-supported models (A13–A21, B6–B9, and C13–C18) reflect the characteristics of large households, accounting for 25%. This distribution ratio is consistent with the courtyard types observed in the field survey.

3.3. Measurement Point Layout and Evaluation Indicators

3.3.1. Wind Speed Measurement Point Layout

When conducting the numerical simulations of the Sanheyuan courtyard, wind speed measurement points were placed at key locations in the courtyard. Taking Model A as an example (Figure 7), all wind speed measurement points were set at a height of 1.5 m from the ground, based on the wind speed measurement point height in the Green Building Evaluation Standard GB/T50378-2019 [27]. This height corresponds to the level of the human head and neck, where the human body is more sensitive to wind environment comfort. The measurement points were divided into three categories. The first category is located at the four corner points of the courtyard and main hall, 1.5 m above the ground and 200 mm from the wall, labeled a, b, c, d, j, k, l, and m. These points are located at the edges of the courtyard and main hall, where the wind speed is easily influenced by the surrounding boundaries and can reflect the wind speed conditions at the regional boundaries. The second category of measurement points is located at the geometric center of the four adjacent measurement points of the first category. Measurement points i and r are placed 1.5 m above the ground [28]. These points are located in the central area of the courtyard and main hall, reflecting the wind speed conditions in this region. The third category of measurement points is the midpoint of the lines connecting measurement points i and r with the four corner points of the first category. Measurement points e, f, g, h, n, o, q, and p are placed 1.5 m above the ground. These points are less affected by wall boundaries and can better reflect the basic wind environment conditions of the region. Through an analysis of these three categories of wind speed measurement points, the impact of changes in the Sanheyuan Courtyard’s spatial form and construction parameters on the courtyard’s wind environment can be comprehensively and objectively determined.

3.3.2. Wind Environment Adaptation Evaluation Parameter Analysis

The PMV-PPD thermal comfort indices approved by the International Organization for Standardization and other research showed that wind speeds between 0.25 m/s and 0.50 m/s were the best range for wind speed [29]. Wind speeds below 0.25 m/s were categorized as calm wind zones, speeds ranging from 0.25 m/s to 0.50 m/s as gentle wind zones, and speeds exceeding 0.5 m/s as strong wind zones [30,31,32,33,34]. The wind environment simulation evaluation indicators include wind zone area ratio, wind speed non-uniformity coefficient, unit area wind rate, and wind rate. The wind speed non-uniformity coefficient refers to the variance of wind speeds at all measurement points within the simulation area, which is the mean square of the difference between the wind speed value at each point and the average wind speed value of all points [35,36]. This coefficient indicates how evenly the wind speed is spread out in the courtyard space. The lower the value, the more evenly the wind speed is spread out in the courtyard. There are three types of wind zone area ratios: strong wind zone area ratio, gentle wind zone area ratio, and calm wind zone area ratio. These can show how the wind is generally in the courtyard space [37,38]. The calm wind zone area ratio is the percentage of the simulation area in which the wind speed is less than 0.25 m/s. The gentle wind zone area ratio is the percentage of the simulation area where the wind speed is between 0.25 m/s and 0.50 m/s. The strong wind zone area ratio is the percentage of the simulation area in which the wind speed is greater than 0.5 m/s. The unit area wind rate is the ratio of the gentle wind area ratio created by a specific geometric parameter of the courtyard to the total area of the courtyard. This indicates that courtyards with different geometric parameters can generate gentle wind regions per unit area.

3.3.3. Evaluation Steps for Indicators

Following Fluent simulation, the wind speed data for 18 measurement points were obtained based on the measurement point layout described in Section 3.3.1 Based on the 18 data points, the average wind speed of the 18 data points was calculated. Among these 18 data points, wind speeds lower than 0.25 m/s are classified as calm wind, wind speeds higher than 0.5 m/s are classified as strong wind, and wind speeds between 0.25 m/s and 0.5 m/s are classified as gentle wind. The number of calm, gentle, and strong wind points can be obtained from the 18 data points. The calm, gentle, and strong wind zone area ratios can be calculated by dividing the corresponding quantities by 18. The variance of the 18 data points is the wind speed non-uniformity coefficient. The ratio of the gentle wind zone area ratio to the corresponding courtyard area for the model gives the unit area wind rate. The number of models corresponding to each simulation group varies. The wind speed non-uniformity coefficient, calm wind zone area ratio, and strong wind zone area ratio of the different models within each group are ranked from largest to smallest. The gentle wind zone area ratio and unit area wind rate are ranked from smallest to largest. After ranking, scores are assigned as follows: the higher the rank, the lower the score. Specifically, the model that ranked first receives a score of 1, and the score increases as the rank decreases. If two models have the same values for the calm wind zone area ratio, gentle wind zone area ratio, strong wind zone area ratio, and wind speed non-uniformity coefficient, the average wind speed will then be considered for ranking. When the average wind speed is below 0.5 m/s, the model with a higher average wind speed will be ranked lower. When the wind speed exceeds 0.5 m/s, the model with a higher average wind speed will be ranked higher. Following models with wind speeds greater than 0.5 m/s are models with wind speeds ranging from 0.25 m/s to 0.5 m/s. The scores for the five evaluation indicators are summed, and the total score represents the overall wind environment score. The higher the total score, the stronger the wind environment adaptability of the corresponding vernacular courtyard.

3.4. Simulation Parameter Setup

3.4.1. ANSYS Fluent Software Setup

The CFD simulation software used in thisstudy is ANSYS Fluent 2022 R1. Before performing the wind environment simulation, modeling was conducted based on field investigation data [39]. To improve the convergence speed of the calculation, some detailed building structures were ignored and simplified during the modeling process, resulting in the final model shown in Figure 8a. The specific settings for the computational domain are as follows: The building height is H, computational domain height is 5H, distance between the outflow and building boundaries is 10H, distance between the inflow and building boundaries is 5H, and distance between the side and building boundaries is 5H (Figure 8b). The blockage ratio is less than 3% [40,41,42]. Two BOI entities cover the entire computational domain, as shown in Figure 8c.

The mesh division uses poly-hexcore cells, with 20 boundary layers set on the building and ground, having a growth rate of 1.2. The height of the first building boundary layer is 0.243 mm, and its corresponding Y+ value meets the required conditions.

We apply fine cell sizes at the edges of the model to achieve the desired mesh quality and control the maximum Y+ value. The maximum Y+ value for the building mesh is 0.96, with an average value of 0.39417; the maximum Y+ value for the ground is 0.78, with an average value of 0.35462. The entire computational domain is meshed using ICEM CFD 2024 R1 software, with local refinement in the BOI region. The expansion rate between adjacent meshes is set to 1.2. The final mesh is shown in Figure 8. The SIMPLE algorithm was used for the pressure velocity coupling. The viscous and convective terms in the governing equations were discretized using the second-order upwind scheme. The User-Defined Scalar was selected with a first-order upwind scheme to generate more accurate results. During numerical simulation, mixed initialization was performed using a pseudo-transient calculation method. The time step was set to 0.1 s, and the total number of time steps was 1000. The residuals for the velocities in the x, y, and z directions, turbulent kinetic energy (k), and turbulence dissipation rate (ω) were set to 1 × 10⁻⁴, and the convergence criteria for the other parameters were set to 1 × 10⁻⁵.

3.4.2. Governing Equation

The wind-driven natural ventilation in this study uses the SST k-omega model. The airflow is considered incompressible, and the governing equations under the three-dimensional Cartesian coordinate system are described as follows:

Conservation of mass equation:

$${\displaystyle \frac{\partial \rho }{\partial t}}+\nabla ·\left(\rho \stackrel{⃑}{u}\right)=0$$

(1)

Momentum conservation equation:

$${\displaystyle \frac{\partial }{\partial t}}\left(\rho \stackrel{⃑}{u}\right)+\nabla \cdot \left(\rho \stackrel{⃑}{u}\stackrel{⃑}{u}\right)=-\nabla p+\nabla \cdot \left({\mu }_{efft}\nabla \stackrel{⃑}{u}\right)+\rho g+F$$

(2)

Turbulent model equations:

$${\displaystyle \frac{\partial }{\partial t}}\left(k\right)+\nabla \cdot \left(k\stackrel{⃑}{u}\right)=P_k-ϵ+\nabla \cdot \left({\mu }_{efft}\nabla k\right)$$

(3)

$${\displaystyle \frac{\partial }{\partial t}}\left(\omega \right)+\nabla ·\left(\omega \stackrel{⃑}{u}\right)=P_{\omega }-{\beta }_{\omega }\omega +\nabla ·\left({\mu }_{efft}\nabla \omega \right)$$

(4)

where ρ is the fluid density, $\stackrel{⃑}{u}$ is the velocity, t is time, ${\mu }_{efft}$ is the effective viscosity, g is the acceleration due to gravity, F is the external force, $P_k$ represents the production term for turbulence kinetic energy, ϵ is the dissipation term for turbulence energy, Pω represents the turbulence dissipation term, and $\beta$ω represents the turbulence production rate.

Initial conditions:

$${P_{gauge}|}_{t=0}=0,{\stackrel{⃑}{u}|}_{t=0}=\stackrel{⃑}{u_o}$$

where $P_{gauge}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{g}\mathrm{a}\mathrm{u}\mathrm{g}\mathrm{e}\mathrm{p}\mathrm{r}\mathrm{e}\mathrm{s}\mathrm{s}\mathrm{u}\mathrm{r}\mathrm{e};\stackrel{⃑}{u_0}\mathrm{i}\mathrm{s}\mathrm{t}\mathrm{h}\mathrm{e}\mathrm{i}\mathrm{n}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{a}\mathrm{l}\mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d}\mathrm{s}\mathrm{p}\mathrm{e}\mathrm{e}\mathrm{d}.$

3.4.3. Boundary Conditions Description

The CFD simulation inlet conditions used in this study comprehensively consider multiple factors, including measurement data, site geographical characteristics, traditional building orientation, and Feng Shui. The summer field measurement indicated that the 10 min average wind speed at a height of 1.5 m was 1.6 m/s. Meanwhile, considering that the traditional Sanheyuan building layout of the Tujia people generally faces southeast, combined with the local wind environment customs and architectural features, this study set the X-axis direction of the CFD computational domain to 270° (i.e., from west to east) to better reflect the wind flow pattern in the real natural environment [43,44]. The inlet wind speed for the wind field was set using a logarithmic wind speed profile to reflect the effect of surface roughness on wind speed distribution, thereby improving the accuracy and field adaptability of the simulation results.

Boundary Type	Boundary condition
Inlet	Velocity inlet boundary condition
Outlet	Fully developed outflow boundary condition (outflow)
Top and side boundaries of the computational domain	Symmetry boundary condition
Building surface and ground	No-slip wall condition (wall)

Boundary conditions are as follows:

Boundary-specific values are as follows: Velocity inlet:

The wind speed profile at the inlet is created based on the atmospheric boundary layer. In flat terrain, the vertical velocity profile is typically given by the following equation:

$$\overline{u}=u_0=u_{ref}{\left({\displaystyle \frac{y}{y_{ref}}}\right)}^a$$

(5)

where $u_{ref}$ is the wind speed at reference height $y_{ref}$. Based on meteorological data from China and the Chinese Civil Building Green Energy Performance Calculation Standards, this study sets the initial wind speed to 1.6 m/s. $y_{ref}$ is the reference height of 10 m, α is the roughness coefficient, and the surface roughness is taken as 0.15.

Pressure outlet: $P_{gauge}=0$

No-slip surface: $\overline{u}=0$

3.5. Verification

3.5.1. CFD Simulation Validation

To improve simulation efficiency and ensure accuracy, CFD simulation validation, mesh sensitivity analysis, and CFD model comparison analysis were conducted [18]. The field measurement data were used as the reference for comparison. After the simulation, a comparison was made between the simulated and measured values for model comparison, mesh sensitivity validation, and simulation validation. The building dimensions are length × width × height = 23.4 m × 18.6 m × 8.66 m, and the computational domain dimensions are length × width × height = 196.6 m × 105.2 m × 51.96 m, with a blockage ratio of 2.1044%, which is below the recommended maximum blockage ratio of 3%. The wind speed was set to 1.6 m/s. The number of grids was set to 2.48 million (coarse mesh), 4.5 million (medium mesh), and 5.6 million (fine mesh). Measuring the wind speed at the 18 measurement points described earlier revealed that the average deviation between the medium and coarse meshes was 8.31%, and that between the medium and fine meshes was 1.43%. Therefore, the medium mesh was selected for further simulation analysis (Figure 9).

3.5.2. Validation Results

Field measurements were taken to ensure that the simulation results were correct. In October 2024, measurements were taken on-site in Fengtai Village. Ten measurement points were used, corresponding to the second and third categories described in Section 3.3.1 Every three minutes, for a total of 20 measurements, the wind speed was recorded for 60 min. The average of 20 wind speed readings was used as the actual wind speed value for the measurement point [45]. Subsequently, the measured values were compared to the simulated values obtained from the medium mesh simulation in the mesh sensitivity verification. The patterns of change in the simulated and measured values are similar, indicating a strong link between them. This implies that the software simulation used in this study is feasible.

Simulations were performed using three models: SST k-ω, RNG k-ε, and standard k-ε. When compared to the measured data, the SST k-ω, RNG k-ε, and standard k-ε models had relative errors of 1.72%, 6.94%, and 11.2%, respectively. Among the three models, the SST k-ω model yielded the smallest relative error, making it the most suitable model for numerical simulation of the basic Sanheyuan model of the Southeast Three Gorges Basin (Figure 10).

3.5.3. CFD Model Comparison Analysis

Simulations were performed using three models: SST k-ω, RNG k-ε, and standard k-ε [46]. Comparison with measured data showed relative errors of 2.5% for the SST k-ω model, 9.4% for the RNG k-ε model, and 10.3% for the standard k-ε model. Among the three models, the SST k-ω model yielded the smallest relative error. Therefore, the SST k-ω model is the most suitable model for numerical simulation of the Tujia Sanheyuan courtyards in the southeast Chongqing region of China (Figure 11) [47].

4. Results

4.1. Effect of Wing Room Width-to-Depth Ratio on Sanheyuan Wind Environment

By changing the wing room width-to-depth ratio of the Sanheyuan, different models were created and grouped into three categories based on the number of columns. There are six five-column Sanheyuan models, numbered A1–A6, six seven-column Sanheyuan models, numbered A7–A12, and nine nine-column Sanheyuan models, numbered A13–A21. For models A1–A21, different wing room widths were distinguished under the premise of the same wing room depth [48,49].

Group 1: For models A1–A6, the wing room depths are 3.26 m for A1–A3 and 3.60 m for A4–A6.

Group 2: For models A7–A12, the wing room depths are 5.28 m for A7–A8, 4.94 m for A9–A10, and 4.60 m for A11–A12.

Group 3: For models A13–A21, the wing room depths are 5.60 m for A13–A15, 5.28 m for A16–A18, and 4.94 m for A19–A21.

The total number of models in the wing room width-to-depth ratio group is 21. After performing simulations using Fluent, a comprehensive analysis of the Sanheyuan wind environment was conducted, considering five factors: wind speed non-uniformity coefficient, gentle wind zone area ratio, calm wind zone area ratio, strong wind zone area ratio, and unit area wind rate. For models A1–A21, wind speed contour maps and detailed scores (Figure 12) were obtained after simulation analysis. The models that performed best in each group according to the overall score were A4 (score 26), A11 (score 26), and A13 (score 41). The wing room width-to-depth ratios were 1.00, 0.86, and 0.83, indicating that the courtyard’s wind environment worked best with these three ratios.

For models A1–A6, model A4 achieved the highest score. It performed well across all five wind environment evaluation indicators, particularly ranking high in wind speed non-uniformity score and gentle wind zone area score, indicating that its courtyard configuration can effectively guide the incoming airflow, creating a more comfortable and evenly distributed ventilation environment. Models A1 and A2 ranked second, performing relatively well in unit area wind rate and strong wind zone score, but were slightly less effective in calm wind zone control, possibly because the local airflow was too strong. In comparison, models A1, A5, and A6 scored lower, showing uneven wind speed distributions or restricted ventilation paths, indicating certain “wind tunnel effects”. Overall, among the Group A1 configurations, models with moderate openness and balanced planar dimensions are more conducive to forming a continuous and stable wind environment, which is an important direction for optimizing wind environment adaptability. For models A7–A12, model A11 achieved the highest score, particularly excelling in wind speed uniformity score and unit area wind rate score, indicating that its spatial configuration not only ensures effective wind energy intake but also controls the speed gradient, achieving a stable transition of the flow field. Additionally, A11’s calm wind zone area score is also at a favorable level, effectively avoiding the problem of heat accumulation in stagnant wind areas. Models A13 and A10 follow closely, performing well in the gentle wind zone area score. In contrast, models A9, A12, A14, and others ranked lower in several indicators, especially showing significant deficiencies in strong wind zone control and wind speed uniformity, possibly due to suboptimal open directions or improper scale matching, leading to flow turbulence. Overall, the score differences in this group are relatively concentrated, reflecting the “moderate stability, low optimization potential” characteristic in ventilation performance for this configuration type, which requires further enhancement of structural rhythmicity and continuity in the airflow path design. For models A13–A21, model A13 achieved the highest score, excelling in gentle wind zone area ratio score and wind speed non-uniformity control score, reflecting a good balance between comfort and flow stability. This model also ranked among the top in unit area wind rate score, indicating that it not only provides effective ventilation but also controls excessively high wind speeds. Models A19 and A15 followed closely, with a relatively balanced performance. In contrast, models A18 and A21 scored lower, ranking at the bottom in strong wind zone control and wind rate indicators, showing that their configurations have significant shortcomings in terms of airflow efficiency and comfort. Overall, the models in Group A3 exhibit large differences. The preferred models often have good vertical air duct structures and appropriate openness ratios, making them suitable design prototypes for climate-adaptive dwellings in humid, hot, and low-wind-speed regions.

4.2. Effect of Wing Roof Ridge Height to Wing Skirt Height Ratio on Sanheyuan Wind Environment

By changing the wing roof ridge height to wing skirt height ratio, the models were grouped into three categories based on the number of columns: two five-column Sanheyuan models, numbered B1–B2, three seven-column Sanheyuan models, numbered B3–B5, and four nine-column Sanheyuan models, numbered B6–B9. For models B1–B9, different skirt heights were distinguished under the premise of the same wing roof ridge height.

Group 1: The wing roof ridge height of models B1–B2 is 5.26 m.

Group 2: The wing roof ridge height of models B3–B5 is 5.6 m.

Group 3: The wing roof ridge height of models B6–B9 is 6.26 m.

The total number of models in the wing roof ridge height to wing skirt height ratio group is nine. After performing simulations using Fluent, a comprehensive analysis of the Sanheyuan wind environment was conducted, considering five factors: wind speed non-uniformity coefficient, gentle wind zone area ratio, calm wind zone area ratio, strong wind zone area ratio, and unit area wind rate.

For models B1–B9, after simulation analysis, wind speed contour maps and detailed scores (Figure 13) were obtained. From the perspective of the comprehensive score, the best-performing models in each group were B2 (score 17), B3 (score 15), and B9 (score 17). The corresponding wing roof ridge height to wing skirt height ratios were 4.29, 8.00, and 2.96, indicating that, under these three ratio conditions, the courtyard’s wind environment performed optimally.

For models B1–B2, model B2 achieved the highest score, performing excellently in wind speed non-uniformity coefficient score and unit area wind rate score, demonstrating good wind field distribution balance and high wind energy utilization efficiency. Notably, B1 also ranked high in strong wind zone control and calm wind zone suppression, suggesting that its configuration helps reduce wind speed fluctuations and ventilation dead zones, effectively improving overall comfort. In contrast, model B2 ranked slightly lower in several indicators, particularly in wind speed uniformity and wind rate control. The analysis of this group suggests that moderate openness and reasonable wing roof ridge height pairing are key to creating continuous and stable airflow paths within the courtyard space, which is a crucial method for optimizing ventilation performance in traditional architecture.

For models B3–B5, model B3 achieved the highest score, performing well across all five indicators. Particularly, the wind speed non-uniformity score and unit area wind rate score stood out, showing that its ability to guide airflow and wind energy utilization efficiency were at high levels. B3 also had some advantages in terms of the gentle wind zone score, indicating that its configuration can provide a continuous and comfortable ventilation path. In contrast, models B4 and B5 scored lower on several indicators, particularly in strong wind zone area control and calm wind zone area suppression, which may lead to airflow turbulence or stagnation. The performance of this group shows that, while maintaining the openness of the configuration, a reasonable arrangement of roof slope and passage dimensions plays a key role in optimizing the wind environment. For models B6–B9, model B9 performed exceptionally well in unit area wind rate and gentle wind zone score, making it the most efficient model in terms of ventilation in this group. However, it ranked the lowest in wind speed non-uniformity score, indicating significant differences in airflow speed, with possible local strong winds or wind tunnel effects, which slightly reduces ventilation comfort. Model B8 had an average comprehensive score and performed fairly well. Conversely, models B6 and B7 scored lower in several dimensions, possibly because of their closed configurations or insufficient wind pressure gradient, leading to a decline in overall ventilation performance. The analysis of this group shows significant internal differences, and B9’s success is mainly attributed to its high wind energy conversion ability, whereas the trade-off in wind speed control became its primary limiting factor.

4.3. Effect of Building Area to Wing Area Ratio on Sanheyuan Wind Environment

By changing the building area to wing area ratio, the models were grouped into three categories based on the number of columns: six five-column Sanheyuan models, numbered C1–C6, six seven-column Sanheyuan models, numbered C7–C12, and six nine-column Sanheyuan models, numbered C13–C18. For models C1–C18, different wing room widths were distinguished under the premise of the same main building width.

Group 1: The main building width of models C1–C3 is 17.60 m, and that of models C4–C6 is 23.60 m.

Group 2: The main building width of models C7–C9 is 21.00 m, and that of models C10–C12 is 28.20 m.

Group 3: The main building width of models C13–C15 is 24.34 m, and that of models C16–C18 is 32.86 m.

The total number of models in the building area to wing area ratio group is 18. After performing simulations using Fluent, a comprehensive analysis of the Sanheyuan wind environment was conducted, considering five factors: wind speed non-uniformity coefficient, gentle wind zone area ratio, calm wind zone area ratio, strong wind zone area ratio, and unit area wind rate.

After running simulations on models C1 to C18, we obtained wind speed contour maps and detailed scores (Figure 14). The best models in each group, based on the overall score, were C5 (17), C10 (15), and C13 (17). The ratios of wing roof ridge height to wing skirt height were 3.01, 5.06, and 4.75, respectively. This implies that the courtyard’s wind environment worked best when these three ratios were used. Model C5 did the best out of models C1 through C6. It had a wind speed of 0.41452 m/s and an even airflow. The gentle wind zone area ratio is fairly high (0.72), the strong wind zone area ratio is moderate (0.22), and the wind speed non-uniformity coefficient is low (0.039066613). This implies that the area is well-ventilated and good for living. In comparison, model C6 performed poorly, with a lower wind speed (0.39033 m/s), high calm wind zone area ratio (0.33), and large wind speed non-uniformity, resulting in poor ventilation. It had the lowest comprehensive score, which is 11. Models C1 and C4 performed moderately, with moderate wind speeds (C1:0.39873 m/s, C4:0.51556 m/s) but high wind speed non-uniformity (e.g., C1 with a coefficient of 0.062807223), which affected ventilation efficiency, with comprehensive scores of 20 and 16, respectively. Model C2 had a wind speed of 0.30514 m/s, which is relatively low, and a larger strong wind zone area ratio (0.56). The wind speed non-uniformity is low, resulting in general ventilation, with a comprehensive score of 16. For models C7–C12, model C10 (ratio 5.06, wind speed 0.41679 m/s) performed the best. Despite a larger ratio, the wind speed remains high, and the gentle wind zone area ratio is 0.56, indicating that airflow covers a large area, with a relatively low strong wind zone area ratio (0.22). The wind speed non-uniformity coefficient is 0.076275897, showing that the wind speed distribution is relatively uniform and ventilation performance is good, with a comprehensive score of 24, which is the highest in this group. In comparison, model C9 (ratio 2.00, wind speed 0.53157 m/s) also performed well, with the highest wind speed and a gentle wind zone area ratio of 0.56. However, the wind speed non-uniformity coefficient was slightly higher (0.158076502), indicating that although the wind speed is higher, the airflow may be slightly uneven. Its comprehensive score is 19. Other models, such as C7, C8, and C11., performed moderately, with lower wind speeds, larger calm wind zone and strong wind zone areas, and higher wind speed non-uniformity, resulting in relatively lower ventilation efficiency. Notably, model C12 (ratio 2.35, wind speed 0.50928 m/s) exhibited significant wind speed non-uniformity and a large strong wind zone area, resulting in the lowest comprehensive score of 12 in this group. For models C13–C18, model C13 performed the best, with a higher wind speed (0.36 m/s) and a large gentle wind zone area ratio (0.61). The wind speed non-uniformity coefficient is low (0.03), indicating excellent ventilation performance, with a comprehensive score of 29—the best in this group. In contrast, model C17 had a lower wind speed (0.43 m/s) and a larger strong wind zone area ratio (0.44). Wind speed non-uniformity, led to poor ventilation performance, with a comprehensive score of 9—the worst in this group. Other models, such as C15 and C18, performed moderately in terms of wind speed and airflow uniformity. Model C15 showed ideal ventilation efficiency, with a comprehensive score of 24, whereas model C18 showed poor wind speed uniformity, with a comprehensive score of 16.

5. Discussion

5.1. Wind Environment Adaptability Mechanism of Wing Room Width-to-Depth Ratio

The change in the wing room width-to-depth ratio alters the airflow path within the courtyard space [27,50,51,52,53]. To further study the impact mechanism of wing room depth on the Sanheyuan wind environment, the best models (A4, A8, and A11) were selected from the three model groups in Group A for comparative analysis, with the corresponding streamline diagrams shown in Figure 15. These models change the wing room width-to-depth ratio by altering the wing room depth. This study found that as the wing room width increases, the wind speed fluctuates and does not exhibit a simple linear change. Initially, wind speed increases (e.g., from A1 to A3); as the wing room width increases, the airflow space increases, and the wind speed gradually strengthens. However, after reaching a certain ratio, the wind speed no longer increases but begins to decrease, as reflected in models A5 and A6.

Data analysis revealed that as the wing room width increases, the courtyard wind speed follows a nonlinear pattern, with a decrease in the unit area wind rate, a reduction in the gentle wind zone area ratio, an increase in the calm wind zone area ratio, an increase in the strong wind zone area ratio, and a rise in the wind speed non-uniformity coefficient. The optimal wing room width-to-depth ratios for the Sanheyuan are 1.0, 1.5, and 0.9. Notably, the optimal ratio is not the maximum ratio for each group. This is because the optimal ratio is based on a comprehensive evaluation of human comfort, which considers factors such as the wind speed uniformity coefficient, proportion of gentle wind zone area, proportion of calm wind zone area, proportion of strong wind zone area, and unit area wind rate. These optimal ratios exhibit significant discontinuity but still follow certain patterns. This study found that as the width of the side rooms increases, airflow path lengthens, flow space expands, and wind speed variation follows a nonlinear pattern. More space is available for air to flow; however, this expanded flow space does not always lead to an increase in wind speed. An increase in the airflow path causes the airflow distribution inside the building to become uneven. Therefore, when the side room width is relatively small, as in the A7, A10, and A13 models, the moderate width provides a better airflow path, resulting in more uniform wind speeds. When the side room width increases to a moderate range (e.g., in the A9 and A15 models), the moderate increase in width makes airflow smoother, and the wind speed may either increase or remain stable. At this point, the airflow channels are optimized, leading to an increase in the wind speed. However, when the side room width becomes too large (e.g., in the A21 and A20 models), the airflow begins to disperse, and wind speed significantly decreases in some areas. At this point, the wind speed shows a decreasing trend. As the wing room width increases, the calm wind zone area ratio gradually decreases. With a smaller wing room width, the airflow is concentrated and calm wind zone area ratio is higher. As the wing room width increases, the airflow distribution becomes more uniform and calm wind zone area ratio decreases. For example, in model A2 (with a wing room width of 3.60 m), the calm wind zone area ratio is 0.33, while in model A6 (with a wing room width of 5.60 m), the calm wind zone area ratio is 0.17, demonstrating that as the wing room width increases, airflow becomes smoother, the calm wind zone area ratio decreases, and airflow within the courtyard becomes more coherent. The gentle wind zone area ratio increases as the wing room width increases. A larger wing room width implies that the airflow can be more widely distributed throughout the courtyard, resulting in a more uniform wind speed distribution. Therefore, the gentle wind zone area ratio increases. A larger wing room width helps improve the uniformity of ventilation, providing an appropriate range of wind speeds and enhancing the comfort of ventilation. The strong wind zone area ratio typically remains stable. Although the airflow path lengthens and wind speed increases in some areas as the wing room width increases, overall, the change in the strong wind zone area ratio is minimal. This indicates that a larger wing room width leads to some degree of airflow expansion; however, no significant strong wind zone areas are created. The airflow remains relatively balanced, avoiding regions with excessively high wind speeds, which has a positive impact on comfort. The wind speed non-uniformity coefficient increases significantly with the increase in the wing room width. With a smaller wing room width, the airflow is more concentrated, resulting in better wind speed uniformity. After increasing the wing room width, the airflow becomes more dispersed, and the wind speed non-uniformity coefficient increases. This implies that the airflow is no longer uniformly distributed within the courtyard, especially in areas far from the windward or airflow expansion regions, where the wind speed difference becomes more pronounced.

Overall, considering the optimization of airflow distribution and wind speed uniformity, by adjusting the wing room width range, an “airflow distribution optimization—wind speed uniformity enhancement” passive ventilation model was formed. Under the premise of optimizing wind speed distribution and comfort, a moderate increase in the wing room width demonstrated significant wind environment adaptability. A smaller wing room width helps concentrate the airflow, providing a higher wind speed, whereas a moderate increase in wing room width makes the airflow more uniform, reducing the calm wind zone area ratio and increasing the gentle wind zone area ratio, thus improving the overall ventilation efficiency. By adjusting the wing room width design, it is possible to ensure uniform airflow distribution while avoiding excessive wind speed differences, optimizing the ventilation effect within the courtyard. The actual field investigation revealed that the wing skirt height to wing roof ridge height ratio in the Tujia Sanheyuan had a relatively large range, and the optimal ratio obtained in this study only represents a small portion of the actual ratio range. This indicates that during the construction of the building, the ancestors considered not only wind environment adaptability based on human comfort but also real factors such as climate regulation, structural stability, and local cultural customs and taboos. In humid, hot climates or high-temperature environments, the wing room width may need to be increased to ensure more airflow enters the building, helping to cool down and reduce humidity. However, excessive space between the wings can cause uneven structural loads, requiring more support for the roof and walls. A relatively smaller side room width design may be more beneficial to the stability and wind resistance of the building, especially in high wind speed areas. The side room width design must ensure that the structure is not damaged easily. Owing to regional cultural customs and taboos, there are vernacular courtyards with larger side room widths, such as in ancestral halls and temples. The choice of building width may also be influenced by local Feng Shui or religious beliefs, which determine the building’s orientation, layout, and other details.

5.2. Wind Environment Adaptability Mechanism of Wing Roof Ridge Height to Wing Skirt Height Ratio

The wing roof ridge height to wing skirt height ratio determines the effectiveness of the vertical ventilation structure. To further study the impact mechanism of the wing skirt height on the Sanheyuan wind environment, the best models (B2, B3, and B9) were selected from the three model groups in Group B for comparative analysis, with the corresponding streamline diagrams shown in Figure 16. These models change the wing skirt height and, consequently, alter the wing roof ridge height to wing skirt height ratio. This study found that as the wing skirt height increases, the average wind speed in the Sanheyuan increases significantly. This increase in the wind speed may be related to the additional airflow channels provided by the wing skirt space. A higher wing skirt height offers a smoother airflow path, allowing more air to enter and exit, thereby increasing wind speed. Furthermore, a higher wing skirt height design can prevent airflow obstruction and enhance natural ventilation.

Data analysis revealed that as the height of the stilt increases, courtyard wind speed increases, the area of the calm wind zone decreases, the area of the gentle wind zone increases, and the area of the strong wind zone remains stable, resulting in an improved overall score. The optimal ratios of ridge height to stilt height for the Sanheyuan courtyards are 4.29, 8.00, and 2.96, respectively. Notably, the optimal ratio is not the maximum or minimum for each group. This is because the optimal ratio is based on a comprehensive evaluation of human comfort, which considers factors such as the wind speed uniformity coefficient, proportion of the gentle wind zone area, proportion of the calm wind zone area, proportion of the strong wind zone area, and unit area wind rate. These optimal ratios exhibit significant discontinuity but still follow certain patterns. Increasing the stilt height of the side rooms typically leads to a higher courtyard wind speed. A higher stilt height provides more channels and space for airflow, making the airflow within the courtyard smoother. However, as the stilt height continues to increase, the wind speed uniformity gradually worsens. This is because the increased stilt height not only leads to more air movement but also results in uneven airflow distribution, especially near high-wind-speed areas. This change indicates that, although increasing the stilt height helps improve the overall wind speed, it also causes an uneven wind speed distribution, which needs to be closely monitored. The challenge lies in optimizing the uniformity of wind speed through design. The calm wind zone area ratio decreases as the height of the wing skirt increases. When the calm wind zone area ratio decreases, it usually means that the airflow is smoother. This allows air to move better through the courtyard, preventing stagnant wind areas from forming. Model B2 has a calm wind zone area ratio of 56%, while model B9 has a calm wind zone area ratio of 44%. This implies that when the height of the wing skirt increases, the calm wind zone area ratio decreases, and the airflow in the courtyard becomes steadier, which improves the wind environment. Additionally, the gentle wind zone area ratio increases when the wing skirt height increases. A higher wing skirt height helps make the wind speed more even, which increases the gentle wind zone area ratio, provides the right wind speed range, and makes ventilation more comfortable. Raising the wing skirt height increases the wind speed, but the change in the strong wind zone area ratio is not very large. A higher wing skirt height causes the wind speed to be more evenly distributed within the courtyard, avoiding regions with excessively high wind speeds, thus ensuring the comfort of the wind environment. The data show that the change in the strong wind zone area ratio is minimal, primarily reflected in the changes in the calm wind zone area ratio and gentle wind zone area ratio. In conclusion, while a higher wing skirt height can improve overall wind speed and effectively enhance ventilation efficiency, it may also lead to increased wind speed non-uniformity, which could affect the comfort of the wind environment.

Overall, considering the formation of vertical ventilation structure and the optimization of airflow paths, a passive ventilation model—“vertical ventilation structure–airflow path optimization”—was formed by adjusting the range of the wing skirt height to wing roof ridge height ratio. Under complex terrain and humid, hot climate conditions, the proper combination of wing skirt height and wing roof ridge height demonstrates significant wind environment adaptability. A higher wing skirt height provides more space for airflow, while a higher wing roof ridge height helps expel air quickly from the building, optimizing the distribution of airflow within the courtyard. The actual field investigation revealed that the wing skirt height to wing roof ridge height ratio in the Tujia Sanheyuan had a relatively large range. The optimal ratio obtained in this study represents only a small portion of the actual ratio range. This indicates that during the construction of the building, the ancestors considered not only wind environment adaptability based on human comfort but also factors such as terrain and environmental adaptability, ventilation and humidity regulation, fire prevention and safety, as well as functional and living needs. The Tujia Sanheyuan is typically built in mountainous, hilly, or elevated areas. The wing skirt height design considers the building’s adaptability to slopes, effectively avoiding the effects of ground moisture, landslides, or water accumulation. In some mountainous areas, the wing skirt height design also considers the earthquake resistance and disaster prevention functions. By increasing the gap at the bottom, the wing skirt height facilitates airflow and promotes the natural convection of air, thereby reducing moisture accumulation. Concurrently, it reduces the likelihood of fire spread by increasing ventilation at the building’s base. The family’s day-to-day living needs are also considered when designing the wing roof ridge height and wing skirt height. The space under the wing skirt is typically used for storage, farming, or as a living area (such as kitchens, warehouses, etc.). The wing skirt height design needs to provide sufficient space for these functions while ensuring effective ventilation and drainage.

5.3. Wind Environment Adaptability Mechanism of Building Area to Wing Area Ratio

The building area to wing area ratio reflects the overall spatial wind pressure distribution. To further study the impact mechanism of wing room width on the Sanheyuan wind environment, the best models (C5, C10, and C13) were selected from the three model groups in Group C for comparative analysis, with corresponding streamline diagrams shown in Figure 17. These models change the wing room width and consequently alter the building area to wing area ratio. This study found that as the wing room area increases, it generally provides more space for airflow. A larger wing room area promotes the movement of air, reduces stagnant air zones, and increases the wind speed within the courtyard. Additionally, a larger wing area improves the air quality inside the building, reduces air stagnation, and helps prevent issues such as humidity and mold.

Data analysis revealed that as the wing room area expands, the wind speed in the courtyard exhibits a nonlinear pattern. The wind speed non-uniformity gradually decreases, calm wind zone area ratio decreases, strong wind zone area ratio increases slightly, and gentle wind zone area ratio remains stable. The optimal building area to wing area ratios for the Sanheyuan are 3.01, 5.06, and 4.75. Notably, the optimal ratio is not the maximum or minimum for each group. This is because the optimal ratio is based on a comprehensive evaluation of human comfort, which considers factors such as the wind speed uniformity coefficient, proportion of the gentle wind zone area, proportion of the calm wind zone area, proportion of the strong wind zone area, and unit area wind rate. These optimal ratios exhibit significant discontinuity but still follow certain patterns. This study found that as the side room area increases, the variation in wind speed follows a nonlinear pattern with two stages. The first stage occurs when the side room area gradually increases from a small value. As the space in the stilt area expands and the airflow path increases, a more concentrated airflow is formed, leading to an increase in wind speed. During this stage, the building’s ventilation performance improves significantly, airflow becomes smoother, and wind speed increases. In the second stage, as the side room area continues to grow, the wind speed starts to decrease. This occurs because an excessively large side room area causes the airflow path to become more dispersed, and airflow spreads out across the building, leading to reduced ventilation efficiency. This phenomenon typically occurs when the ratio of the building’s footprint to the stilt area is excessively large, causing the airflow to lose concentration and resulting in low wind speeds in some areas, thereby forming a calm wind zone. For example, in the C5 model (with a ratio of 3.01), the wind speed is relatively low (0.41452 m/s), indicating that the wind speed has started to decrease, airflow distribution is uneven, and ventilation performance is poor. Therefore, the increase in courtyard wind speed is not unlimited, and as the side room area increases, wind speed reaches a peak value, that is, the optimal wind speed. This point typically occurs when the side room area is moderate, where the airflow is concentrated but not overly dispersed. At this point, airflow is the smoothest, ventilation efficiency is the highest, and the building’s wind environment adaptability is optimal. For example, in model C3 (with a ratio of 2.00), wind speed reaches 0.52616 m/s, which is the highest wind speed in this group, indicating that the balance between wing room area and ventilation effect is achieved. As the wing area increases, the unit area wind rate typically decreases. This is because the airflow has more space to disperse and the airflow distribution becomes more uniform, which reduces the airflow concentration per unit area, resulting in a decrease in ventilation efficiency. Meanwhile, the gentle wind zone area ratio typically increases. The increase in wing area allows the airflow to cover a broader area, thus increasing the gentle wind zone area ratio, improving the effective distribution of the airflow, and further enhancing the ventilation effect. In contrast, the calm wind zone area ratio generally decreases as the wing area increases. Increasing the wing area helps make the airflow flow more smoothly, avoiding situations where certain areas have very low wind speeds, thus forming calm wind zones and improving ventilation efficiency. The strong wind zone area ratio may increase, especially in cases where the airflow is more concentrated. An increase in the strong wind zone area ratio may affect comfort. The wind speed non-uniformity coefficient generally changes as the wing area increases. As the airflow disperses, the wind speed non-uniformity coefficient generally increases, meaning that the airflow distribution becomes uneven, and some areas may experience very low wind speeds, which could negatively affect ventilation efficiency.

Overall, to optimize airflow distribution and enhance ventilation efficiency, a passive ventilation model—“airflow concentration–wind speed optimization–uniform ventilation”—was formed by adjusting the range of the building area to wing area ratio. In this model, a moderate increase in the wing area provides more space for airflow, improving the distribution of airflow, while the proper design of the building area helps concentrate the airflow and reduce wind speed non-uniformity. By optimizing the ratio, especially under complex terrain and humid hot climate conditions, the ventilation effect of the building is maximized, enhancing the indoor airflow and comfort. The actual field investigation revealed that the building area to wing area ratio in the Tujia Sanheyuan has a relatively large range, and the optimal ratio obtained in this study represents only a small portion of the actual ratio range. This indicates that the ancestors considered factors beyond mere wind adaptation and human comfort when constructing the building. They also took into account the functionality of the wing area and how to optimize the available space. The wing area of the Tujia Sanheyuan was designed considering the terrain and climatic conditions. Structures are frequently constructed in locations with highly irregular terrain such as mountainous or hilly areas. Increasing the wing area will enhance the building’s adaptability to various terrains and weather conditions. An increased wing area enhances the building’s air circulation and overall flexibility. Increased wing area facilitates airflow, particularly in humid environments. It prevents moisture accumulation, enhances indoor air quality, and mitigates issues such as mold and deterioration. The configuration of the wing area directly affects the functionality of the building. As the wing area expands, it can accommodate various functions—such as storage, agriculture, or habitation (including kitchens and warehouses)—provided there is adequate ventilation and drainage. These functional needs not only take into account daily convenience but also show how wise the ancestors were in architectural design, ensuring that the building is comfortable and useful for a long time.

5.4. Limitations and Further Study

The climate adaptability of the Tujia-style Sanheyuan is the result of the combined effects of multiple factors; however, this study has certain limitations. In terms of model construction, due to the complex and variable terrain and the rich and diverse biological environment of the southeastern Chongqing mountainous region, the doors, windows, and openings of the Tujia-style Sanheyuan are typically kept closed in daily life to prevent the intrusion of snakes, insects, mosquitoes, and other creatures. Therefore, this study used a relatively simplified model of a Sanheyuan without openings. Although this simplified model reflects the actual daily conditions in southeastern Chongqing, it overlooks the impact of the opening design on the indoor wind environment and lacks an in-depth consideration of courtyard spaces (such as doors, windows, openings, and narrow alleys). Therefore, future research should further explore the regulatory effect of different opening designs on wind environment adaptability. In terms of data collection, this study compiled meteorological observation data from 1991 to 2020, which indicates that the climate data for the southeastern Chongqing region during this period is relatively stable. However, it cannot be ignored that future climate change may have potential impacts on the wind environment in this region. Particularly, changes in temperature, precipitation, and wind speed may lead to fluctuations in climate conditions or the occurrence of extreme weather events, which would affect the stability and adaptability of the wind environment in the Sanheyuan. Therefore, future research should establish long-term wind environment monitoring data, consider building environmental simulations under different climate scenarios, and enhance wind environment change monitoring and forecasting. Additionally, more practical passive energy-efficient building design strategies should be proposed.

The results of this study have already provided important technical support for the design project of concentrated and contiguous protection and development of traditional villages in Qianjiang District, Chongqing. In the renovation of Tujia-style Sanheyuan and the design of new rural dwellings in this project, the best design parameters derived from this study, such as the side room width-to-depth ratio and ridge height-to-stilt height ratio, have been followed. In the subsequent project evaluation process, the team will continue to assess the improvement in the ventilation efficiency and wind environment comfort of the Sanheyuan based on this set of design parameters, providing a scientific basis for the protection, renovation, and redesign of the Tujia-style Sanheyuan and new rural dwellings. With the challenges posed by climate change and the energy crisis, contemporary architecture will face more challenges in climate adaptability. By integrating the wind environment adaptability parameters from this study with passive energy-efficient design in traditional buildings, we ensure that the Tujia-style Sanheyuan can maintain its cultural characteristics and wind environment adaptability during the modernization process. This provides a technical framework for modern architectural design to sustainably cope with extreme climate events such as high temperatures and heavy rainfall. Future research could further expand on the factors affecting the wind environment adaptability of Tujia-style Sanheyuan, particularly the long-term impacts on building spaces under different climate change scenarios. Considering global climate change trends, future designs should focus more on the adaptability and sustainability of building environments. By combining more refined climate models with architectural design, we can explore the optimization of building space layouts and structures in the context of frequent extreme climate events. Additionally, by integrating local socio-economic development and cultural evolution, innovative design methods for the Tujia-style Sanheyuan in the context of China’s modernization can be explored, ensuring that it continues to provide good residential comfort and environmental adaptability under future climate conditions.

6. Conclusions

As a regional ethnic architectural form, the Tujia-style Sanheyuan in southeastern Chongqing has gradually developed a passive energy-efficient design strategy adapted to complex climates over thousands of years of historical and cultural evolution. It is a typical representative of the climate adaptability of traditional residential buildings. This study uses CFD numerical simulation technology to analyze the wind environment adaptability of Tujia-style Sanheyuan in southeastern Chongqing, primarily by conducting a comprehensive evaluation of three sets of design parameters: side room area-to-building area ratio, side room width-to-depth ratio, and side room stilt height-to-ridge height ratio. The following conclusions were drawn:

For the five-column ground-supported Sanheyuan with a collar-beam frame, the optimal side room width-to-depth ratio is 1, the optimal ridge height-to-stilt height ratio is 4.29, and the optimal building footprint-to-side room area ratio is 2.5. This type of vernacular courtyard is mostly found in small residential buildings and ancestral halls, typically in economically underdeveloped villages or small towns. For the seven-column ground-supported Sanheyuan with a collar-beam frame, the optimal side room width-to-depth ratio is 0.86, the optimal ridge height-to-stilt height ratio is 8.00, and the optimal building footprint-to-side room area ratio is 2.51. This type of vernacular courtyard is often found in medium- to small-sized ancestral halls and family courtyards, typically built by economically well-off local autonomous organizations or wealthy farmers and merchants. For the nine-column ground-supported Sanheyuan with a collar-beam frame, the optimal side room width-to-depth ratio is 0.83, the optimal ridge height-to-stilt height ratio is 2.96, and the optimal building footprint-to-side room area ratio is 4.74. This type of vernacular courtyard is generally found in medium-sized ancestral halls and estate courtyards, typically built by cross-regional village alliances or local village elders.

This study preliminarily explores the relationship between the wind environment adaptability of Tujia-style Sanheyuan in southeastern Chongqing and spatial form construction parameters, revealing the impact of different architectural parameters on the courtyard wind environment. Using CFD simulation technology, this study validates the climate adaptability experience of the Tujia-style Sanheyuan and provides quantitative standards for creating a comfortable wind environment in these buildings. It also offers data support for the preservation of cultural heritage and regeneration of Tujia-style Sanheyuan, provides a new theoretical perspective for the protection and inheritance of regional ethnic architecture, and offers quantitative parameters for contemporary Tujia-style Sanheyuan design. Additionally, it provides foundational data for research on the wind environment of vernacular courtyards and technical guidance for the climate adaptability design of new rural dwellings in beautiful villages. Moreover, this study attempts to provide spatial prototypes for passive energy-efficient designs in contemporary Chinese architectural spaces.