Optimizing Urban Ventilation in Heritage Settings: A Computational Fluid Dynamics and Field Study in Zhao’an Old Town, Fujian

Abstract

This study investigates the optimization of urban ventilation in Zhao’an Old Town, Fujian, through the integration of Computational Fluid Dynamics (CFD) simulations and field measurements. The findings underscore the critical roles of spatial layout indicators, such as the Frontal Area Index (FAI), Opening-to-Facade Ratio (OFR), and Building Volume Density (BVD), in influencing wind flow and thermal performance. The FAI was identified as the most influential factor in shaping airflow, while the OFR and BVD highlighted the importance of open spaces and balanced building density for natural ventilation and thermal comfort. Practical strategies, such as optimizing building orientations, increasing facade permeability, and leveraging natural cooling elements like the Dongxi River, are proposed to address ventilation challenges while preserving the town’s cultural and historical integrity. Unlike previous studies, this research combines CFD simulations with summer field measurements to provide a highly accurate and contextually relevant evaluation of wind flow dynamics in compact urban environments. By systematically analyzing the interplay between urban morphology and ventilation efficiency, this study offers actionable recommendations for improving outdoor comfort in heritage settings. The outcomes serve as a valuable reference for sustainable urban planning, contributing to the development of strategies that balance environmental performance with the preservation of Zhao’an Old Town’s unique cultural heritage.

1. Introduction

The urban heat island (UHI) effect, a phenomenon where urban areas experience significantly higher temperatures than their rural surroundings, has become a critical issue in urban sustainability [1]. Factors such as the high thermal capacity of construction materials, limited vegetation, and obstructed natural ventilation contribute to intensified heat retention in densely built environments [2,3,4]. These conditions not only reduce thermal comfort but also lead to increased energy consumption and pose significant public health risks during extreme heat events. Among various mitigation strategies, urban ventilation has been recognized as a natural, energy-efficient solution for dissipating heat and moisture, making it an essential approach for improving microclimatic conditions in urban environments [5,6,7].

The role of ventilation becomes even more critical in historical urban areas, where traditional architectural layouts often intensify thermal stress due to compact and enclosed spatial configurations. Zhao’an Old Town, located in Fujian Province, offers a representative example of the challenges involved in balancing environmental optimization and cultural preservation. Renowned for its traditional Min-nan architectural style, the town features narrow alleyways, enclosed courtyards, and high enclosure ratios [8]. While these characteristics reflect the region’s rich cultural heritage, they also impede natural airflow, contributing to localized heat accumulation during the hot and humid summer months. Despite the town’s proximity to the Dongxi River, which has the potential to provide cooling effects through natural breezes, the compact urban layout often limits its influence. Understanding how specific spatial configurations affect ventilation is, therefore, essential for developing strategies that enhance environmental performance while preserving heritage values [9,10].

Layout indicators, such as building orientation, enclosure ratios, and alley widths, play a pivotal role in shaping the wind environment in traditional towns [11,12]. For example, buildings aligned perpendicular to prevailing wind directions can enhance ventilation by creating direct airflow pathways, while high enclosure ratios are often associated with reduced wind penetration and stagnant air zones [13,14,15]. The permeability of spatial configurations also influences ventilation, as more permeable layouts allow for greater airflow, improving thermal comfort in outdoor spaces [16,17,18]. These findings highlight the importance of systematically evaluating how specific layout factors affect wind flow dynamics and environmental performance.

Computational Fluid Dynamics (CFD) simulations are widely recognized as a robust and effective tool for analyzing wind environments and optimizing ventilation strategies. By offering detailed insights into airflow dynamics and their interactions with spatial configurations, CFD enables researchers to assess existing ventilation conditions and explore potential improvements [19,20]. In heritage contexts like Zhao’an Old Town, CFD simulations provide a valuable method for evaluating wind environments without altering the original architectural layouts. When integrated with onsite measurements, CFD enhances the accuracy and reliability of the analysis, combining real-world data with predictive modeling. This comprehensive approach not only ensures a better understanding of local wind conditions but also supports the development of practical and context-sensitive ventilation strategies tailored to heritage settings.

Despite the extensive application of CFD in urban wind studies, research focusing on the role of layout indicators in heritage contexts remains limited. To address these challenges, this study investigates how spatial layout indicators influence ventilation and thermal performance in dense heritage settings like Zhao’an Old Town. It seeks to answer the critical research question: How can spatial layout indicators be optimized to improve airflow and mitigate thermal stress in high-density heritage environments? To this end, the research introduces a systematic framework for evaluating the interplay between urban morphology and ventilation dynamics.

This study makes several key contributions. First, it quantitatively assesses the impact of innovative layout indicators, including the Opening-to-Facade Ratio (OFR), Building Volume Density (BVD), and Building Orientation Angle (BOA), on wind flow and thermal performance. These indicators were selected for their theoretical relevance and practical significance in optimizing ventilation efficiency in heritage settings. Second, the integration of summer field measurements with Computational Fluid Dynamics (CFD) simulations ensures high accuracy and contextual relevance, addressing the limitations of previous studies that relied predominantly on theoretical models. Third, the findings provide practical strategies, such as enhancing facade permeability and optimizing building orientation, to alleviate ventilation bottlenecks in traditional urban layouts. Fourth, this study establishes quantitative benchmarks for urban design and restoration projects in heritage areas, offering actionable insights for sustainable environmental improvements.

Zhao’an Old Town, characterized by its compact urban fabric and proximity to the cooling potential of the Dongxi River, serves as a representative case study for addressing ventilation challenges in heritage environments. The findings highlight specific strategies, such as enhancing facade permeability and optimizing building orientation, to improve ventilation efficiency in traditional urban layouts. Additionally, this study establishes quantitative benchmarks for urban design and restoration projects, providing actionable insights for enhancing outdoor comfort in densely built heritage areas. These contributions advance sustainable urban planning practices while supporting efforts to preserve the cultural and historical identity of Zhao’an Old Town.

2. Study Area and Methodology

2.1. Overview of the Study Area

Zhao’an Old Town, located in Zhao’an County at the southern tip of Fujian Province near the Guangdong border, was selected as the study area for the following reasons: (1) Cultural and Architectural Value: The town represents a distinctive blend of Min-nan and Chao-shan cultures, characterized by traditional Min-nan-style architecture. Narrow alleys, enclosed courtyards, and high enclosure ratios define its compact urban layout, reflecting the region’s cultural heritage while posing challenges for natural ventilation; (2) Ventilation Challenges: The high building density and enclosed spatial configurations restrict airflow, particularly during the hot and humid summer months. These conditions contribute to localized thermal discomfort, making Zhao’an Old Town an ideal case for studying the impact of spatial configurations on ventilation performance in heritage settings; and (3) Proximity to Cooling Elements: The Dongxi River runs along the town, offering the potential for natural cooling through moist airflows. However, the dense urban fabric often limits the river’s cooling influence, providing an opportunity to explore the interplay between natural elements and compact urban designs.

To address these challenges, this study employed Smart 3D for point cloud modeling, using over 8000 aerial images to create a detailed, multidimensional reconstruction of the area during the measurement period. The aerial imagery was then used alongside a building model constructed in SketchUp (Figure 1). Measurement points were selected based on specific criteria using AMap, targeting areas with high resident activity, narrow alleyways with high enclosure ratios, and atrium spaces within buildings. A total of 22 points were chosen for wind environment measurements and simulations to analyze how different building layouts affect airflow patterns (Figure 2).

2.2. Selection of Layout Indicators

The evaluation of the wind environment in traditional urban blocks necessitated the careful selection of key layout indicators, each reflecting critical aspects of airflow dynamics, environmental performance, and the unique morphological characteristics of Zhao’an Old Town. These indicators, categorized into building geometry, openings and porosity, sky and wind exposure, and proximity to cooling elements, were chosen for their theoretical relevance and practical applicability in assessing and optimizing ventilation in dense heritage settings. Building Volume Density (BVD), defined as the ratio of cumulative building volume to courtyard area, serves as a critical metric of urban density. High BVD values often restrict airflow, intensifying turbulence and limiting ventilation efficiency, particularly in compact urban environments. The Opening-to-Facade Ratio (OFR), representing the proportion of open facade areas relative to the total facade area, is pivotal in promoting wind penetration and mitigating airflow blockages, a factor of heightened importance in Zhao’an’s semi-enclosed architectural typologies. The Aspect Ratio (AR), calculated as the length-to-width ratio of courtyards, assesses the influence of courtyard geometry on channeling airflow and providing shading, although its role is secondary to indicators such as BVD and the OFR. The Building Orientation Angle (BOA), capturing the alignment of buildings with prevailing wind directions, directly impacts ventilation efficiency by determining the extent of airflow interaction with structural layouts, particularly in narrow alleys and courtyards. Indicators related to sky and wind exposure, such as the Sky View Factor (SVF), measure the openness of urban spaces to the sky, influencing both wind exposure and solar radiation. While the SVF’s correlation with ventilation is weaker compared to other factors, it remains relevant for assessing the overall spatial openness. The Frontal Area Index (FAI) quantifies the extent to which building facades obstruct prevailing winds, with higher FAI values potentially creating stagnation zones that require careful design interventions. Lastly, the Distance to Dongxi River (D) highlights the cooling potential of proximity to the river, where closer distances facilitate natural breezes and improved microclimatic conditions, underscoring the role of water bodies in urban ventilation strategies.

These indicators were selected to balance computational efficiency with the preservation of architectural heritage, ensuring that the study captures the multifaceted dynamics of airflow in Zhao’an Old Town while providing actionable insights for sustainable urban design and conservation. A detailed summary of these layout indicators is presented in Table 1.

2.3. Field Measurements

This study employs a combined approach of field measurements and numerical simulations to comprehensively assess the wind environment in Zhao’an Old Town. Field measurements were conducted from August 15 to September 15, during the summer season, with two daily sessions, a morning session from 10:00 to 12:30 and an afternoon session from 13:00 to 15:30, in order to capture variations in wind conditions throughout the day.

Data collection was carried out at 22 pre-selected measurement points throughout the old town. These points were chosen based on field visits and predefined criteria to ensure a comprehensive representation of the town’s spatial configurations and ventilation challenges. Through on-site surveys and observations, key areas were identified based on their significance to pedestrian comfort and airflow dynamics. Measurement points were distributed across locations with high resident activity, such as public pathways and gathering spaces, to reflect wind conditions that directly affect outdoor comfort. Narrow alleyways with high enclosure ratios, characteristic of the town’s traditional architecture, were included to analyze constrained airflow patterns. Additionally, semi-enclosed courtyards were selected to evaluate how spatial layouts influence ventilation efficiency. This systematic selection process ensured that the measurement points captured the diversity of Zhao’an Old Town’s urban environment.

Wind speed and direction data were recorded using a memory hot-wire anemometer (model AM4214SD), chosen for its high precision and ability to log data over extended periods (Figure 3b). Additionally, temperature and humidity data were recorded using a memory-type thermo-hygrometer (model HT-3007SD) to complement the wind measurements (Figure 3a). Both devices were mounted on a tripod at a standardized height of 1.5 m to represent pedestrian-level conditions [14,30]. To ensure consistent placement and minimize interference, the tripod was positioned at least 1 m away from walls, vegetation, or other obstructions. The orientation of the anemometer was carefully adjusted to ensure accurate wind direction recording, and all setups were aligned to minimize external influences. An example of the field measurement setup is shown in Figure 3c.

At each measurement point, data on wind speed, wind direction, and temperature were recorded at 10 min intervals throughout each session. This approach resulted in 400 individual readings per session at each location, which were subsequently averaged to provide representative values for each parameter. This systematic method ensured accurate and reliable data collection across all 22 measurement points, capturing the diverse spatial configurations and environmental conditions in Zhao’an Old Town.

2.4. CFD Simulation Setup

To simulate the outdoor wind environment in Zhao’an Old Town, a three-dimensional model was constructed based on the actual dimensions of the buildings to ensure an accurate representation of the urban morphology. To improve convergence speed and computational efficiency, reasonable simplifications were applied. Small protrusions and irregularities on building surfaces were omitted, and buildings with simple cubic geometries were represented as regular cubes. These simplifications are justified by the predominantly rectangular enclosure shapes of the buildings in Zhao’an Old Town, which ensure that the key geometric features influencing airflow are preserved. This approach follows established practices in urban wind studies, where simplified geometries are often used to balance computational feasibility and simulation accuracy without significantly affecting overall wind flow predictions [31,32]. Figure 4 shows the Phoenics model used in this simulation.

The standard k-ε turbulence model was employed for the simulation. This model is widely used for urban wind flow analysis as it approximates first-order closed Reynolds stress and provides robust results for predicting airflow in complex urban environments. The PRESTO (Pressure Staggering Option) discretization scheme was adopted to ensure accurate pressure–velocity coupling, while the integrated PARSOL function effectively handled solid boundary interactions. The computational domain was defined following the GB 50736-2012 standard [33] to include a horizontal region with a radius of 5H from the target building’s center (where H represents the building height) and a vertical domain extending to 3H above the highest structure. The inflow boundary condition included a prevailing southeast wind direction (120°) with a wind speed of 2.7 m/s, representing typical summer conditions in Zhao’an Old Town. Outflow boundaries were set to zero static pressure, while the ground surface was assigned a roughness value of 0.22 to reflect the urban terrain. These conditions were derived from the GB 50736-2012 standard and the China Meteorological Data Network.

To enhance computational accuracy, a refined structured mesh was applied, with grid density significantly increased near building surfaces and areas with complex airflow patterns. This localized mesh refinement ensured an accurate representation of the detailed wind flow dynamics near the structures, capturing interactions between airflow and building geometries effectively. The grid independence was verified through iterative testing to ensure consistent results, with numerical convergence achieved after 4000 iterations at a convergence criterion of 10⁻³.

3. Results and Discussion

3.1. CFD Simulation Results

CFD simulations were conducted using Phoenics software at 22 selected measurement points within Zhao’an Old Town to assess the wind environment and explore potential strategies for optimizing ventilation. The analysis was carried out in two stages: a macro-scale simulation of the overall wind environment across the entire site, followed by a detailed micro-scale simulation for each measurement point. This approach allowed for a comprehensive understanding of both the broad wind dynamics and the specific localized variations caused by urban morphology.

Figure 5 provides a general overview of the wind flow patterns across the study area based on the macro-scale simulation with a prevailing southeast wind direction (approximately 2.7 m/s). In contrast, Figure 6 illustrates the wind speed vector distribution at 1.5 m above ground level for each of the 22 selected measurement points, reflecting localized variations in wind conditions. These micro-scale simulations, which were refined using field measurements and meteorological data from nearby weather stations, offer a more precise representation of wind patterns at pedestrian height, crucial for evaluating thermal comfort and health.

3.2. Comparative Analysis of Measured and Simulated Values

To assess the accuracy of the CFD simulations, the wind speed data from 22 measurement points were analyzed and compared between the measured and simulated values, as shown in Figure 7 and Figure 8. The Pearson correlation coefficient (r) between the measured and simulated wind speeds was 0.84502, indicating a strong positive correlation. Additionally, the coefficient of determination (R²) was 0.71406, suggesting that approximately 71.4% of the variability in the measured values can be explained by the simulation model. This indicates a reasonably good agreement between the measured and simulated wind speeds, though some discrepancies were observed.

The results reveal that, in general, the simulated wind speeds were higher than the measured values. This is likely due to the fixed inflow wind speed boundary condition used in the CFD simulation, which may not fully capture the temporal and spatial variability of wind conditions in the real-world environment. For example, at point 1, the measured wind speed was 0.91 m/s, while the simulated value was 0.68 m/s. In contrast, at point 3, the measured wind speed was 1.21 m/s, with a simulated value of 1.66 m/s, showing a closer match between the two. The largest discrepancies were observed at points such as 12 (measured 0.5 m/s) and 22 (measured 0.87 m/s), where the simulated wind speeds were both notably higher than the measured values. These differences are likely due to the simplified assumptions in the CFD model, such as uniform inflow wind speed and idealized building geometries, which do not account for transient factors like localized turbulence, open doors, or varying ventilation conditions. However, such simplifications are standard practice in CFD studies and are necessary to ensure computational feasibility while providing a consistent baseline for wind flow analysis.

Combining field measurements and CFD simulations is a widely accepted approach in urban wind studies. Prior research has demonstrated that this integration allows for validating simulation models against real-world data [34,35], ensuring a balance between computational efficiency and accuracy. Field measurements capture dynamic and transient wind variations, while CFD simulations provide a controlled environment for analyzing broader wind flow trends. This study’s high R² values confirm that the CFD model effectively captures the overall wind speed trends despite localized discrepancies.

The results indicate that Phoenics is a reliable tool for simulating wind environments in complex urban heritage settings, where accurately representing both macroscopic trends and site-specific variations is essential. The integration of field data and CFD simulations provides a robust framework for analyzing urban ventilation dynamics in Zhao’an Old Town. Future studies could refine these models by incorporating dynamic inflow boundary conditions and more detailed microclimatic data to further enhance accuracy.

3.3. Analysis of Layout Indicators on Wind Flow

In this study, the relationship between various layout indicators and wind flow in the Zhao’an Old Town area was quantitatively examined using the Pearson correlation coefficient. The Pearson correlation coefficient (r) was calculated to assess the strength and direction of linear relationships between layout indicators and measured wind speed, using the formula provided in Equation (1) [36]. The coefficient, ranging from −1 to 1, reflects the degree of linear correlation between variables, where positive values indicate direct relationships and negative values denote inverse relationships. The strength of correlation was categorized based on the following scale: strong positive (0.70 to 1.00), moderate positive (0.40 to 0.70), weak positive (0.10 to 0.40), very weak or no correlation (−0.10 to 0.10), weak negative (−0.40 to −0.10), moderate negative (−0.70 to −0.40), and strong negative (−1.00 to −0.70) [37]. The classification is summarized in Table 2, this approach provides a comprehensive understanding of how urban layout elements affect wind flow dynamics in traditional courtyard environments:

(1)${\mathrm{r}}_{\mathrm{x}\mathrm{y}}={\displaystyle \frac{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{y}}\right)}{\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{x}}_{\mathrm{i}}-\overline{\mathrm{x}}\right)}^2}\sqrt{{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\left({\mathrm{y}}_{\mathrm{i}}-\overline{\mathrm{y}}\right)}^2}}}$

The results of the correlation analysis are summarized and visually represented in Figure 9, which compares the Pearson correlation coefficients between various layout indicators and measured wind speed. As shown in Figure 9, different layout indicators exhibit varying degrees of correlation with wind speed, highlighting key factors that influence airflow patterns in the courtyards of Zhao’an Old Town.

Among all indicators, the Frontal Area Index (FAI) exhibits the strongest positive correlation with wind speed (r = 0.81). This suggests that building facades play a critical role in shaping airflow patterns. A higher FAI indicates a greater extent of building surfaces exposed to prevailing winds, which can enhance wind speed in certain areas by increasing airflow interactions. However, this also implies the potential for localized turbulence, requiring careful planning to balance wind acceleration and stability. The Opening-to-Facade Ratio (OFR) follows with a strong positive correlation (r = 0.73). Higher OFR values enable better wind flow through open spaces, reducing blockages and promoting natural ventilation. This finding underscores the importance of integrating open and unobstructed areas in urban design to maximize airflow, especially in densely built heritage settings. The Building Volume Density (BVD) also shows a moderate positive correlation with wind speed (r = 0.59). Higher density increases the likelihood of confined spaces where wind may accelerate due to turbulence, but excessive density can impede airflow in areas with inadequate openings. This highlights the importance of balancing density with sufficient openness to ensure optimal airflow distribution across urban spaces. The Building Orientation Angle (BOA) demonstrates a moderate negative correlation with wind speed (r = −0.69). Misaligned building orientations relative to prevailing wind directions reduce ventilation efficiency. Aligning buildings with dominant wind directions can significantly improve airflow, particularly in narrow alleys and courtyards.

Other indicators, such as the Sky View Factor (SVF) (r = 0.49), have a weaker but still notable positive correlation with wind speed. While the SVF influences wind exposure by reflecting the openness of spaces to the sky, its impact is secondary compared to the FAI and OFR.

In summary, the FAI is identified as the most influential factor affecting wind flow in Zhao’an Old Town, followed by the OFR and BVD. These findings underscore the need to prioritize the optimization of building facade design, increase open spaces, and align building orientations with prevailing winds to enhance ventilation in high-density urban areas. Such strategies can significantly improve airflow while preserving the cultural and architectural integrity of heritage environments like Zhao’an Old Town.

3.4. Analysis of Layout Indicators on Temperature

In this study, the relationship between various urban layout indicators and the temperature within the Zhao’an Old Town area was analyzed using the Pearson correlation coefficient. This statistical method helps quantify the linear relationships between different urban layout factors, such as the Aspect Ratio, Building Volume Density, and open space ratio, and the resulting temperature variations in narrow alleys and semi-enclosed public spaces. These spaces, characteristic of the high-density, traditional urban fabric of Zhao’an Old Town, are subject to specific microclimatic conditions that significantly influence outdoor thermal comfort and energy performance.

The Pearson correlation coefficients were calculated to assess the strength and direction of these relationships, and the results are visualized in Figure 10. The temperature in these environments is strongly influenced by the interaction of building mass, openness, and orientation, as well as the proximity to cooling elements, such as water bodies. The analysis provides insights into how urban layout decisions can directly affect the thermal environment, which is critical for understanding the sustainability and comfort of densely built areas in traditional towns.

Among the indicators, Building Volume Density (BVD) shows the strongest positive correlation with temperature (r = 0.80), indicating that higher building density leads to significant heat retention. Densely built areas restrict airflow, limit natural cooling, and intensify the urban heat island effect. Reducing building density or incorporating open spaces in high-density areas could help mitigate these challenges and improve outdoor thermal comfort. The Frontal Area Index (FAI) and Distance to Dongxi River (D) both exhibit moderate positive correlations with temperature (r = 0.61). High FAI values reflect compact building facades that trap heat, contributing to localized temperature increases. Meanwhile, the correlation between the Distance to Dongxi River (D) and temperature suggests that proximity to the river effectively lowers temperatures, emphasizing the river’s role as a natural cooling element. Areas closer to the Dongxi River benefit from its cooling effect through enhanced airflow and evaporative cooling, demonstrating the importance of leveraging water bodies to improve microclimatic conditions in dense urban environments. The Opening-to-Facade Ratio (OFR) exhibits a moderate positive correlation with temperature (r = 0.51), as more open facades allow better wind flow and cooling, reducing heat accumulation in enclosed spaces. This underscores the importance of maximizing openness in urban designs to enhance ventilation and regulate temperatures. Interestingly, the Building Orientation Angle (BOA) shows a moderate negative correlation with temperature (r = −0.57). Buildings aligned with prevailing wind directions facilitate airflow and reduce heat retention. Optimizing building orientation is particularly effective for improving ventilation in narrow alleys and courtyards, where air stagnation is common.

Other indicators, such as the Aspect Ratio (AR) (r = 0.33), exhibit less pronounced effects on temperature. While elongated courtyards may slightly influence shading and airflow, their impact is secondary to more dominant factors like density and openness. Similarly, the Sky View Factor (SVF) (r = 0.23) shows a weaker correlation, indicating its limited role in mitigating heat compared to structural elements that more directly influence ventilation and temperature dynamics.

In summary, the BVD, FAI, and D are identified as the most influential factors affecting temperature in Zhao’an Old Town. These results highlight the critical need for urban design strategies that reduce building density, increase openness, and optimize building orientation while incorporating and maximizing the cooling benefits of water bodies such as the Dongxi River. These measures are essential for improving thermal comfort and ensuring a balance between environmental performance and heritage preservation.

4. Ventilation Optimization Strategies for Zhao’an Region

Based on the correlation analyses, several key factors influencing wind flow and temperature in Zhao’an Old Town have been identified, which are crucial for developing targeted strategies to optimize the wind environment and mitigate heat accumulation, especially in areas with poor ventilation or high thermal stress.

One of the most critical factors is the Opening-to-Facade Ratio (OFR), which demonstrates a strong positive correlation with both wind speed and temperature. Increasing the OFR by incorporating more open spaces within building layouts is essential for improving airflow and reducing heat buildup in narrow alleys and enclosed public spaces. This can be achieved by introducing more open facades or creating passageways that align with prevailing winds to enhance ventilation. Another significant factor is Building Volume Density (BVD), which was found to have a moderate positive correlation with wind speed but a strong positive correlation with temperature. High BVD restricts airflow and leads to higher temperatures in confined spaces. To improve wind flow and thermal comfort, reducing building density in certain areas or creating airflow corridors would be beneficial, along with integrating more open spaces, such as courtyards or plazas.

The Building Orientation Angle (BOA) also plays a key role, with a moderate positive correlation to wind speed and a moderate negative correlation to temperature. Aligning buildings with the prevailing wind direction, particularly southeast winds during the summer, can significantly enhance ventilation and reduce temperatures. This strategy would be especially effective in areas where wind stagnation and heat accumulation are common. The Sky View Factor (SVF), although it showed a weaker correlation with temperature, suggests that increasing sky exposure by reducing vertical obstructions can indirectly improve wind penetration and mitigate heat buildup in certain spaces. Alongside this, the Frontal Area Index (FAI), which had moderate positive correlations with both wind speed and temperature, indicates that reducing the frontal area exposed to prevailing winds by introducing staggered building layouts or incorporating vegetative windbreaks can help balance airflow and thermal comfort. Additionally, the Distance to Dongxi River (D) shows a moderate positive correlation with temperature, indicating that proximity to the river can slightly reduce temperatures. However, the dense urban environment limits the cooling potential of the river. Enhancing connectivity between urban blocks and the river by creating wind corridors or vegetative pathways could help distribute cooler air into more enclosed spaces.

To optimize the wind environment, areas with a low OFR, high BVD, and unfavorable BOA should be prioritized for improvement. Thermal hotspots, particularly those far from the Dongxi River or blocked by dense buildings, should also be targeted. In terms of simulation-based optimization, CFD models can be used to test various modifications in these areas. For example, increasing the OFR by adding openings in facades, aligning building orientations with prevailing winds, and widening narrow alleyways to create airflow corridors are potential strategies to improve ventilation. Additionally, the introduction of green barriers or vegetative screens could direct wind flow and reduce thermal stress. Removing or lowering obstructive structures that block the cooling effect of the Dongxi River is also an important consideration.

In conclusion, a comprehensive approach that combines these strategies, such as optimizing the Opening-to-Facade Ratio, adjusting building orientations, reducing building density, and enhancing green space, can significantly improve wind flow and thermal comfort in Zhao’an Old Town. By carefully considering these factors in urban planning, it is possible to create more sustainable, comfortable, and livable environments that respect the town’s heritage while improving environmental performance.

5. Conclusions

This study investigates the complex interplay between urban layout indicators, wind flow, and temperature in Zhao’an Old Town, a high-density heritage area characterized by narrow alleys, enclosed courtyards, and semi-enclosed public spaces. By integrating field measurements and Computational Fluid Dynamics (CFD) simulations, this research provides a quantitative evaluation of the microclimatic conditions influenced by urban morphology. The key findings highlight the critical role of building density, openness, and orientation in shaping the wind environment and thermal performance of traditional urban areas.

The Pearson correlation analysis reveals that the Frontal Area Index (FAI) exhibits the strongest positive correlation with wind flow, highlighting its critical role in shaping airflow patterns through building facade design. The Opening-to-Facade Ratio (OFR) follows as a key indicator, emphasizing the importance of open spaces in facilitating natural ventilation. Building Volume Density (BVD) also plays a significant role, demonstrating a strong positive correlation with temperature and underscoring the heat-retaining effects of high-density urban structures. Moderate correlations were observed for the Building Orientation Angle (BOA) and Distance to Dongxi River (D), which further influence airflow and temperature dynamics. These findings underscore the need to optimize facade permeability, reduce building density, and align building orientations with prevailing winds to enhance ventilation and thermal comfort. By integrating strategies to maximize the cooling benefits of natural elements like the Dongxi River, these insights provide a framework for balancing environmental performance with the preservation of cultural and architectural integrity in heritage areas like Zhao’an Old Town.

The combination of CFD simulations with field data provides a robust methodology for assessing urban ventilation, particularly in heritage conservation contexts where the preservation of architectural integrity is paramount. This approach allows for the identification of site-specific challenges and the formulation of context-sensitive solutions that optimize environmental performance while maintaining cultural and historical values.

By elucidating the complex interplay between layout configurations and natural ventilation, this study contributes to a deeper understanding of how spatial design can influence microclimatic conditions in traditional urban environments. Unlike prior studies, this research introduces innovative layout indicators, such as the OFR and BOA, to heritage urban ventilation studies, providing a new framework for evaluating and optimizing wind flow in compact, culturally significant areas. The integration of CFD simulations with comprehensive field measurements further ensures the robustness and applicability of the findings, offering actionable insights that bridge the gap between theoretical modeling and practical urban planning for heritage conservation.

The findings are not limited to Zhao’an Old Town but can serve as a reference for other heritage settings with similar compact morphologies and ventilation challenges. However, their application should be adapted to specific local conditions, including climate, architectural typologies, and cultural values. The proposed interventions, such as enhancing facade permeability and optimizing building orientation, are intended as adaptable principles that stakeholders can tailor to diverse heritage contexts in collaboration with conservation experts and local communities. Future research should focus on refining these strategies, exploring non-invasive and reversible methods, and validating their long-term applicability across different heritage environments.

6. Limitations

This study has several limitations that should be noted. Firstly, field measurements were conducted over a single month in the summer, which limits the ability to capture seasonal variations in wind and temperature. Expanding the measurement period across multiple seasons would provide a more comprehensive understanding of the microclimatic conditions. Secondly, although the 22 measurement points were strategically chosen in high-activity areas, they may not fully represent the diversity of urban environments. Including more measurement locations in quieter or less trafficked parts of the town could provide a broader perspective on wind patterns. Lastly, the CFD simulations involved simplifications, such as idealized building geometries and assumptions about atmospheric conditions, which may introduce some uncertainty in the results. Future studies could improve accuracy by incorporating more detailed local features, such as building materials, vegetation, and microclimatic influences. Advancements in simulation tools, such as real-time computational models or AI-driven simulations, could further enhance precision and applicability in urban ventilation studies.

Finally, this study aims to provide a valuable reference for developing ventilation optimization strategies in heritage conservation contexts while maintaining cultural authenticity. The proposed interventions, including enhancing facade permeability and optimizing building orientation, are intended as adaptable principles rather than prescriptive solutions. These findings are meant to guide stakeholders in tailoring strategies to specific heritage settings through collaboration with conservation experts and local communities. Future research should focus on refining these strategies to align more closely with cultural preservation goals, exploring non-invasive and reversible methods where necessary, and validating their long-term applicability across diverse heritage environments. By integrating environmental improvements with heritage conservation priorities, this study bridges the gap between theoretical insights and practical applications, contributing to sustainable urban planning in culturally significant areas.

Author Contributions

Conceptualization, H.C.; methodology, H.C. and Y.M.; investigation, H.C. and S.Z.; formal analysis, S.Z.; validation, Y.M.; software, T.Y. and Y.M.; visualization, T.Y.; writing—original draft preparation, H.C.; writing—review and editing, S.Z., T.Y. and Y.M. All authors have read and agreed to the published version of the manuscript.

Data Availability Statement

The dataset is available upon request from the authors.

Acknowledgments

The authors would like to express their sincere gratitude to Chang Hsu Fu for his invaluable guidance and constructive feedback, which have greatly enhanced the quality of this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

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Figures and Tables

Figure 1. (a) Site area; (b) SketchUp model.

Figure 2. Locations of 22 measurement points (indicated by red circles) in Zhao’an Old Town.

Figure 3. (a) Memory-type thermo-hygrometer (HT-3007SD); (b) memory-type hot-wire anemometer (AM4214SD); (c) field measurement setup in one test point.

Figure 4. Phoenics model.

Figure 5. Overall wind environment of the site.

Figure 6. Wind speed vector distribution and wind direction at 1.5 m above ground level for individual measurement points in Zhao’an Old Town.

Figure 6. Wind speed vector distribution and wind direction at 1.5 m above ground level for individual measurement points in Zhao’an Old Town.

Figure 6. Wind speed vector distribution and wind direction at 1.5 m above ground level for individual measurement points in Zhao’an Old Town.

Figure 7. Comparison of the measured and simulated wind speed values of 22 points in Zhao’an Old Town.

Figure 8. Linear regression of the wind speed simulated and measured values.

Figure 9. Comparison of Pearson correlation coefficients between layout indicators and measured wind speed.

Figure 10. Comparison of Pearson correlation coefficients between layout indicators and measured temperature.

Table 1

Key layout indicators for evaluating wind environment.

Categories	Indicators	Specific Illustrations
Building Geometry	Aspect Ratio (AR)	$\mathrm{A}\mathrm{R}=\mathrm{L}\mathrm{e}\mathrm{n}\mathrm{g}\mathrm{t}\mathrm{h}/\mathrm{W}\mathrm{i}\mathrm{d}\mathrm{t}\mathrm{h}$The ratio of the courtyard’s length to its width, indicating how the courtyard’s shape affects wind flow and channeling effects [21].
	Building Volume Density (BVD)	$\mathrm{B}\mathrm{V}\mathrm{D}=(\sum \mathrm{B}\mathrm{u}\mathrm{i}\mathrm{l}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{V}\mathrm{o}\mathrm{l}\mathrm{u}\mathrm{m}\mathrm{e} (\mathrm{V}\left)\right)/\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{C}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{t}\mathrm{y}\mathrm{a}\mathrm{r}\mathrm{d} \mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a} \left(\mathrm{A}\right)$The ratio of the total volume of buildings surrounding the courtyard or defined area [22,23].
Openings and Porosity	Opening-to-Facade Ratio (OFR)	$\mathrm{O}\mathrm{F}\mathrm{R}=\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{O}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a} \left(\mathrm{A}\mathrm{O}\right)/\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{I}\mathrm{n}\mathrm{n}\mathrm{e}\mathrm{r} \mathrm{F}\mathrm{a}\mathrm{c}\mathrm{a}\mathrm{d}\mathrm{e} \mathrm{A}\mathrm{r}\mathrm{e}\mathrm{a} \left(\mathrm{A}\mathrm{F}\right)$This ratio represents the extent of openings or gaps in the building’s façade, relative to the total inner facade area [24,25].
	Building Orientation Angle (BOA)	$\mathrm{B}\mathrm{O}\mathrm{A}=\left|\mathrm{P}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d} \mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}-\mathrm{M}\mathrm{a}\mathrm{i}\mathrm{n} \mathrm{o}\mathrm{p}\mathrm{e}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{d}\mathrm{i}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n} \mathrm{a}\mathrm{n}\mathrm{g}\mathrm{l}\mathrm{e}\right|$The angle between the prevailing wind direction and the direction of the main opening of the courtyard [26].
Sky Exposure	Sky View Factor (SVF)	Measured using fisheye photography, SVF quantifies the proportion of visible sky from the courtyard or defined area, influencing wind exposure and ventilation [27].
	Frontal Area Index (FAI)	$\mathrm{F}\mathrm{A}\mathrm{I}=\mathrm{C}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{t}\mathrm{y}\mathrm{a}\mathrm{r}\mathrm{d} \mathrm{o}\mathrm{r} \mathrm{d}\mathrm{e}\mathrm{f}\mathrm{i}\mathrm{n}\mathrm{e}\mathrm{d} \mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a}/(\mathrm{F}\mathrm{r}\mathrm{o}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{l} \mathrm{a}\mathrm{r}\mathrm{e}\mathrm{a} \mathrm{e}\mathrm{x}\mathrm{p}\mathrm{o}\mathrm{s}\mathrm{e}\mathrm{d} \mathrm{t}\mathrm{o} \mathrm{p}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{a}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{n}\mathrm{g} \mathrm{w}\mathrm{i}\mathrm{n}\mathrm{d})$Indicates the extent to which building facades block the prevailing wind [28,29].
Proximity to Cooling Elements	Distance to Dongxi River (D)	The shortest horizontal distance from the courtyard’s center to the Dongxi River’s edge.

Table 2

Correlation coefficient and strength of correlation.

Correlation Coefficient	Strength of Correlation
−1.00 to −0.70	Strong Negative
−0.70 to −0.40	Moderate Negative
−0.40 to −0.10	Weak Negative
−0.10 to 0.10	Very Weak or No Correlation
0.10 to 0.40	Weak Positive
0.40 to 0.70	Moderate Positive
0.70 to 1.00	Strong Positive

References

1. Heisler, G.M.; Brazel, A.J. The urban physical environment: Temperature and urban heat islands. Urban Ecosyst. Ecol.; 2010; 55, pp. 29-56.

2. Asimakopoulos, D.N. Energy and Climate in the Urban Built Environment; Earthscan: Barcelona, Spain, 2001.

3. Tan, J.K.; Belcher, R.N.; Tan, H.T.; Menz, S.; Schroepfer, T. The urban heat island mitigation potential of vegetation depends on local surface type and shade. Urban For. Urban Green.; 2021; 62, 127128. [DOI: https://dx.doi.org/10.1016/j.ufug.2021.127128]

4. Stache, E.E.; Schilperoort, B.B.; Ottelé, M.M.; Jonkers, H.H. Comparative analysis in thermal behaviour of common urban building materials and vegetation and consequences for urban heat island effect. Build. Environ.; 2022; 213, 108489. [DOI: https://dx.doi.org/10.1016/j.buildenv.2021.108489]

5. He, B.-J.; Ding, L.; Prasad, D. Relationships among local-scale urban morphology, urban ventilation, urban heat island and outdoor thermal comfort under sea breeze influence. Sustain. Cities Soc.; 2020; 60, 102289. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102289]

6. Wong, M.S.; Nichol, J.E.; To, P.H.; Wang, J. A simple method for designation of urban ventilation corridors and its application to urban heat island analysis. Build. Environ.; 2010; 45, pp. 1880-1889. [DOI: https://dx.doi.org/10.1016/j.buildenv.2010.02.019]

7. He, B.-J.; Ding, L.; Prasad, D. Urban ventilation and its potential for local warming mitigation: A field experiment in an open low-rise gridiron precinct. Sustain. Cities Soc.; 2020; 55, 102028. [DOI: https://dx.doi.org/10.1016/j.scs.2020.102028]

8. Xu, S.; Zhang, J.; Li, Y. Knowledge-Driven and Diffusion Model-Based Methods for Generating Historical Building Facades: A Case Study of Traditional Minnan Residences in China. Information; 2024; 15, 344. [DOI: https://dx.doi.org/10.3390/info15060344]

9. Peng, Y.; Gao, Z.; Buccolieri, R.; Shen, J.; Ding, W. Urban ventilation of typical residential streets and impact of building form variation. Sustain. Cities Soc.; 2021; 67, 102735. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102735]

10. Sanaieian, H.; Tenpierik, M.; Van Den Linden, K.; Seraj, F.M.; Shemrani, S.M.M. Review of the impact of urban block form on thermal performance, solar access and ventilation. Renew. Sustain. Energy Rev.; 2014; 38, pp. 551-560. [DOI: https://dx.doi.org/10.1016/j.rser.2014.06.007]

11. Li, N.; Zhang, F.; Geng, W.; Li, Z. Research on performance improvement design strategies for urban historic districts and suburban traditional villages in multiple scopes. J. Build. Eng.; 2023; 72, 106342. [DOI: https://dx.doi.org/10.1016/j.jobe.2023.106342]

12. Lyu, Y.; Zhang, L.; Liu, X.; Ma, X. Microclimate-Adaptive Morphological Parametric Design of Streets and Alleys in Traditional Villages. Buildings; 2024; 14, 152. [DOI: https://dx.doi.org/10.3390/buildings14010152]

13. Ai, Z.; Mak, C.M. From street canyon microclimate to indoor environmental quality in naturally ventilated urban buildings: Issues and possibilities for improvement. Build. Environ.; 2015; 94, pp. 489-503. [DOI: https://dx.doi.org/10.1016/j.buildenv.2015.10.008] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32288035]

14. Mittal, H.; Sharma, A.; Gairola, A. A review on the study of urban wind at the pedestrian level around buildings. J. Build. Eng.; 2018; 18, pp. 154-163. [DOI: https://dx.doi.org/10.1016/j.jobe.2018.03.006]

15. You, W.; Gao, Z.; Chen, Z.; Ding, W. Improving residential wind environments by understanding the relationship between building arrangements and outdoor regional ventilation. Atmosphere; 2017; 8, 102. [DOI: https://dx.doi.org/10.3390/atmos8060102]

16. You, W.; Shen, J.; Ding, W. Improving residential building arrangement design by assessing outdoor ventilation efficiency in different regional spaces. Archit. Sci. Rev.; 2018; 61, pp. 202-214. [DOI: https://dx.doi.org/10.1080/00038628.2018.1471388]

17. Ghiaus, C.; Allard, F.; Santamouris, M.; Georgakis, C.; Nicol, F. Urban environment influence on natural ventilation potential. Build. Environ.; 2006; 41, pp. 395-406. [DOI: https://dx.doi.org/10.1016/j.buildenv.2005.02.003]

18. Yuan, C.; Ng, E. Building porosity for better urban ventilation in high-density cities–A computational parametric study. Build. Environ.; 2012; 50, pp. 176-189. [DOI: https://dx.doi.org/10.1016/j.buildenv.2011.10.023]

19. Li, Y.; Nielsen, P.V. CFD and ventilation research. Indoor Air; 2011; 21, pp. 442-453. [DOI: https://dx.doi.org/10.1111/j.1600-0668.2011.00723.x] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/21585552]

20. Jiru, T.E.; Bitsuamlak, G.T. Application of CFD in modelling wind-induced natural ventilation of buildings-A review. Int. J. Vent.; 2010; 9, pp. 131-147. [DOI: https://dx.doi.org/10.1080/14733315.2010.11683875]

21. Memon, R.A.; Leung, D.Y.; Liu, C.-H. Effects of building aspect ratio and wind speed on air temperatures in urban-like street canyons. Build. Environ.; 2010; 45, pp. 176-188. [DOI: https://dx.doi.org/10.1016/j.buildenv.2009.05.015]

22. Cao, Q.; Luan, Q.; Liu, Y.; Wang, R. The effects of 2D and 3D building morphology on urban environments: A multi-scale analysis in the Beijing metropolitan region. Build. Environ.; 2021; 192, 107635. [DOI: https://dx.doi.org/10.1016/j.buildenv.2021.107635]

23. Yin, J.; Zhan, Q.; Xiao, Y.; Wang, T.; Che, E.; Meng, F.; Qian, Y. Correlation between Urban Morphology and Wind Environment in Digital City using GIS and CFD Simulations. Int. J. Online Eng.; 2014; 10, pp. 42-48.

24. Albuquerque, D.P.; O’Sullivan, P.D.; da Graca, G.C. Effect of window geometry on wind driven single sided ventilation through one opening. Energy Build.; 2021; 245, 111060. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111060]

25. Ahmadi, S.; Arfa, F.H.; Seyedian, S.A. Analysis of Rural Heritage House Facades as the Initial Step Towards Their Adaptive Reuse and Renovation: A Case Study of Sixteen Houses in Mazandaran Province, Iran. Buildings; 2024; 14, 1938. [DOI: https://dx.doi.org/10.3390/buildings14071938]

26. Zhu, J.; Feng, J.; Lu, J.; Chen, Y.; Li, W.; Lian, P.; Zhao, X. A review of the influence of courtyard geometry and orientation on microclimate. Build. Environ.; 2023; 236, 110269. [DOI: https://dx.doi.org/10.1016/j.buildenv.2023.110269]

27. Svensson, M.K. Sky view factor analysis–implications for urban air temperature differences. Meteorol. Appl.; 2004; 11, pp. 201-211. [DOI: https://dx.doi.org/10.1017/S1350482704001288]

28. Wong, M.S.; Nichol, J.E. Spatial variability of frontal area index and its relationship with urban heat island intensity. Int. J. Remote Sens.; 2013; 34, pp. 885-896. [DOI: https://dx.doi.org/10.1080/01431161.2012.714509]

29. Li, L.; Zhao, Z.; Wang, H.; Shen, L.; Liu, N.; He, B.-J. Variabilities of land surface temperature and frontal area index based on local climate zone. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens.; 2022; 15, pp. 2166-2174. [DOI: https://dx.doi.org/10.1109/JSTARS.2022.3153958]

30. Blocken, B.; Persoon, J. Pedestrian wind comfort around a large football stadium in an urban environment: CFD simulation, validation and application of the new Dutch wind nuisance standard. J. Wind Eng. Ind. Aerodyn.; 2009; 97, pp. 255-270. [DOI: https://dx.doi.org/10.1016/j.jweia.2009.06.007]

31. Rajput, M.; Augenbroe, G.; Stone, B.; Georgescu, M.; Broadbent, A.; Krayenhoff, S.; Mallen, E. Heat exposure during a power outage: A simulation study of residences across the metro Phoenix area. Energy Build.; 2022; 259, 111605. [DOI: https://dx.doi.org/10.1016/j.enbuild.2021.111605]

32. Liu, J.; Heidarinejad, M.; Gracik, S.; Srebric, J. The impact of exterior surface convective heat transfer coefficients on the building energy consumption in urban neighborhoods with different plan area densities. Energy Build.; 2015; 86, pp. 449-463. [DOI: https://dx.doi.org/10.1016/j.enbuild.2014.10.062]

33. GB 50376-2012 Standard for Design of Heating, Ventilation and Air-Conditioning for Civil Buildings; Ministry of Housing and Urban-Rural Development of the China: Beijing, China, 2012; (In Chinese)

34. Hu, C.-H.; Wang, F. Using a CFD approach for the study of street-level winds in a built-up area. Build. Environ.; 2005; 40, pp. 617-631. [DOI: https://dx.doi.org/10.1016/j.buildenv.2004.08.016]

35. Antoniou, N.; Montazeri, H.; Neophytou, M.; Blocken, B. CFD simulation of urban microclimate: Validation using high-resolution field measurements. Sci. Total Environ.; 2019; 695, 133743. [DOI: https://dx.doi.org/10.1016/j.scitotenv.2019.133743]

36. Sedgwick, P. Pearson’s correlation coefficient. BMJ; 2012; 345, e4483. [DOI: https://dx.doi.org/10.1136/bmj.e4483]

37. Liu, Y.; Xu, Y.; Weng, F.; Zhang, F.; Shu, W. Impacts of urban spatial layout and scale on local climate: A case study in Beijing. Sustain. Cities Soc.; 2021; 68, 102767. [DOI: https://dx.doi.org/10.1016/j.scs.2021.102767]