Study on the Influence of Window Size on the Thermal Comfort of Traditional One-Seal Dwellings (Yikeyin) in Kunming Under Natural Wind

Abstract

Under the dual challenges of global energy crisis and climate change, the building sector, as a major carbon emitter consuming 33% of global primary energy, has seen its energy efficiency optimization become a critical pathway towards achieving carbon neutrality goals. The Window-to-Wall Ratio (WWR), serving as a core parameter in building envelope design, directly influences building energy consumption, with its optimized design playing a decisive role in balancing natural daylighting, ventilation efficiency, and thermal comfort. This study focuses on the traditional One-Seal dwellings (Yikeyin) in Kunming, China, establishing a dynamic wind field-thermal environment coupled analysis framework to investigate the impact mechanism of window dimensions (WWR and aspect ratio) on indoor thermal comfort under natural wind conditions in transitional climate zones. Utilizing the Grasshopper platform integrated with Ladybug, Honeybee, and Butterfly plugins, we developed parametric models incorporating Kunming’s Energy Plus Weather meteorological data. EnergyPlus and OpenFOAM were employed, respectively, for building heat-moisture balance calculations and Computational Fluid Dynamic (CFD) simulations, with particular emphasis on analyzing the effects of varying WWR (0.05–0.20) on temperature-humidity, air velocity, and ventilation efficiency during typical winter and summer weeks. Key findings include, (1) in summer, the baseline scenario with WWR = 0.1 achieves a dynamic thermal-humidity balance (20.89–24.27 °C, 65.35–74.22%) through a “air-permeable but non-ventilative” strategy, though wing rooms show humidity-heat accumulation risks; increasing WWR to 0.15–0.2 enhances ventilation efficiency (2–3 times higher air changes) but causes a 4.5% humidity surge; (2) winter conditions with WWR ≥ 0.15 reduce wing room temperatures to 17.32 °C, approaching cold thresholds, while WWR = 0.05 mitigates heat loss but exacerbates humidity accumulation; (3) a symmetrical layout structurally constrains central ventilation, maintaining main halls air changes below one Air Change per Hour (ACH). The study proposes an optimized WWR range of 0.1–0.15 combined with asymmetric window opening strategies, providing quantitative guidance for validating the scientific value of vernacular architectural wisdom in low-energy design.

1. Introduction

1.1. Overview

Global warming induced by climate change is challenging the sustainable development of human society and has triggered a series of negative impacts across economic, environmental, and health domains [1]. Statistical data reveal that since 2014, buildings have accounted for over one-third of global primary energy consumption, with residential sectors contributing more than 65% of this energy use (passive design optimization of low energy buildings in different climates) [2]. This highlights the critical role of optimizing residential building energy efficiency in alleviating global energy pressures and reducing carbon emissions [3]. In this context, passive design strategies have emerged as a research focus in architecture due to their low energy consumption and high environmental adaptability [4]. Traditional vernacular architecture, as exemplary models of climate-responsive design, embodies sustainable wisdom through centuries of adaptation to local climates. Techniques such as natural ventilation, shading, and material optimization demonstrate unique sustainable design principles that offer valuable insights for addressing global energy crises. However, accelerated urbanization has placed many traditional construction techniques at risk of extinction, with their scientific mechanisms remaining insufficiently understood.

As critical components of building envelopes, window openings in vernacular architecture play a pivotal role in passive microclimate regulation [5]. Their dimensional parameters (size, position, and shading) enable precise control of indoor lighting environments and management of thermal-humidity parameters and air quality through natural ventilation, tailored to regional climate variations. Beyond these functions, vernacular window openings effectively regulate solar heat gain (winter collection and summer shading) and minimize thermal loss according to seasonal demands, achieving sustainable performance with minimal energy consumption [6]. Consequently, the Window-to-Wall Ratio (WWR), a key parameter in building envelope design, has become integral to energy consumption analyses. Research on WWR’s impact on building performance has developed into a multidimensional framework encompassing thermal comfort, ventilation efficiency, and daylighting performance, while interacting with other design parameters through complex mechanisms [7]. Extensive studies employing field monitoring and numerical simulations have investigated WWR across diverse climatic conditions [8]. Recent advancements in Computational Fluid Dynamics (CFDs) and building energy simulation (BES) technologies have enabled scholars to explore how dynamic natural wind characteristics modulate WWR effects [9]. However, while existing research has thoroughly examined WWR-building-performance relationships, most studies focus on extreme climate optimization, with transitional climate zones receiving insufficient attention [10]. Furthermore, the synergistic effects between WWR thresholds and courtyard ventilation pathways in compact courtyard dwellings remain underexplored due to spatial complexity.

1.2. Literature Review

Windows in vernacular architecture are not only a core medium for regulating the physical environment but also a crystallization of wisdom rooted in regional culture and climatic adaptation. Their design embodies both functional complexity and dynamic adaptability. The Window-to-Wall Ratio (WWR), a key parameter for studying the relationship between building performance and façade components, is defined as the proportion of window area to the total wall area [11]. By optimizing WWR, it is possible to balance thermal comfort and energy efficiency while meeting daylighting and ventilation requirements [12]. Beyond physical attributes, traditional buildings across the globe utilize diversified WWR designs to demonstrate adaptive solutions shaped by the synergy of human ingenuity and local climates, while also conveying profound cultural connotations embedded in unique ethnic identities. For instance, Nepalese vernacular dwellings integrate low WWR with thick walls to minimize heat loss during winter, with elements like carved wooden window lattices serving as cultural symbols [13]. However, while the definition and calculation methods of WWR possess universal applicability, its impact mechanisms on building performance vary significantly across climatic conditions [14]. Research indicates that WWR directly influences energy consumption and thermal comfort by modulating solar heat gain, natural ventilation efficiency, and the thermal performance of building envelopes [15]. This underscores that WWR optimization requires close integration with regional climatic characteristics [16,17], rather than being treated as an isolated design element.

Existing studies demonstrate that the impact of Window-to-Wall Ratio (WWR) on building performance has evolved into a multidimensional research framework, encompassing three core aspects—thermal comfort, ventilation efficiency, and daylighting performance—while establishing complex interactions with other design parameters [18].

Table 1. The three core research directions and their key components in existing studies.

Research Direction	Common Research Methods	Case Study	Author	Research Tools	Indicators	Result
Thermal Comfort	Field Monitoring + Numerical Simulation	Underground structures in Meymand Village, Iran [27]	Khaksar et al.	Ladybug, Genetic Algorithm (GA) optimization model	Thermal-comfort-related metrics, including indoor air temperature, relative humidity, air velocity	Research findings indicate that building layout parameters (such as length, width, height, orientation, Window-to-Wall Ratio (WWR), and shading depth) significantly influence thermal comfort, with post-optimization annual thermal comfort improved by 31%.
		Ancient timber structures in Northern China [28]	Xu et al.	Testo 425 anemometer, hqzy-1 thermometer, JTR08 hygrometer	Indoor thermal environment parameters, including indoor air temperature, indoor relative humidity, indoor airflow velocity, interior surface temperature, and indoor globe temperature, evaluated according to standard GB/T 50785-2012	Results demonstrate that historic buildings maintain relatively comfortable indoor environments in summer but become excessively cold in winter; key summer comfort strategies include the application of high thermal mass materials, while winter strategies rely on south-facing orientation and maximized Window-to-Wall Ratio (WWR) to harness solar energy.
		Rural residences in Dalian region [29]	Shao et al.	TRNSYS software	Primary focus on indoor temperature, with additional consideration of building envelope thermal performance and spatial layout	Studies reveal that south-facing Window-to-Wall Ratio (WWR) is the most critical factor affecting indoor temperatures, with optimized rural dwellings achieving significant temperature increases without additional active heating, alongside reduced energy consumption and pollutant emissions.
Ventilation Efficiency	CFD simulation + field investigation	Traditional Chinese vernacular dwellings [9]	Zhong et al.	CFD, OpenFOAM, EnergyPlus	Wind projection angle, orientation, wind inclination angle, Window-to-Wall Ratio (WWR), atrium’s top-bottom ratio and width-height ratio	Traditional vernacular dwellings in different regions exhibit significant correlations with local climatic factors. Optimizing these parameters can effectively enhance natural ventilation efficiency and reduce building energy consumption.
		Traditional courtyard houses in Xuzhou, China [30]	Zhang et al.	Ecotect and Phoenics eco-technical software	Building orientation, width-depth ratio, roof slope, courtyard width-depth ratio, Window-to-Wall Ratio (WWR)	Traditional dwellings in Xuzhou achieve optimal climatic adaptability under specific conditions (e.g., main rooms oriented 20° east of due south, width-depth ratio of 2:1, roof slope of 35°), with these optimization strategies effectively enhancing indoor comfort and reducing building energy consumption.
Daylighting Performance	Software simulation + parametric analysis	Traditional residential structures in military settlements of Western Hunan, China [31]	Wu et al.	Ladybug	Average Daylight Factor (ADF), spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE), Daylight Glare Probability (DGP)	Expanding window areas and incorporating transparent tiles can significantly enhance interior illumination, whereas the use of light-colored wallpapers proves ineffective and costly.
		Traditional houses in Guilan region, Iran [32]	Vatankhah et al.	Grasshopper, Ladybug, Honeybee, Octopus	Energy Use Intensity (EUI), spatial Daylight Autonomy (sDA), Annual Sunlight Exposure (ASE)	Building elevation, Window-to-Wall Ratio (WWR), and roof U-value critically affect energy and daylighting performance. Optimization results show the optimal solution as building elevations of 0.3 m and 1.5 m, north-facing WWR of 0.1 and south-facing WWR of 0.5, and roof U-value of 0.2.
		Traditional Miao dwellings in Xiangxi, China [33]	Liu et al.	Three-dimensional laser scanning and UAV oblique photography, Climate Studio	Circadian Stimulus (CS), corneal illuminance (Ecor), Window-to-Wall Ratio (WWR), wall surface reflectance (ρ)	Increasing indoor surface reflectance proves more effective than raising Window-to-Wall Ratio (WWR) for improving circadian stimulus (CS), with optimized results showing that a reflectance of 0.69 at WWR 0.3 significantly enhances CS values.

systematically categorizes the key research directions in current WWR studies, detailing their common methodologies, case studies, and primary conclusions. Specifically, regarding thermal comfort, research reveals that increased WWR typically leads to significant cooling load escalation in summer but may reduce heating demand through additional solar gains in winter [19]. For example, in residential buildings in southern Jiangsu, variations in south-facing WWR most prominently influence heating and air-conditioning energy consumption [20], highlighting the necessity of integrating building orientation into WWR optimization strategies. Furthermore, WWR and building morphology jointly determine natural ventilation efficiency [21]. In hot-humid climates, higher WWR enhances natural ventilation, thereby improving indoor air quality and thermal comfort. However, in cold climates, excessive WWR may exacerbate heat loss, undermining thermal comfort maintenance [22]. Daylighting efficiency represents another critical aspect of WWR’s influence on building performance. Research demonstrates that increased WWR significantly enhances indoor natural illumination levels, reducing both the duration and intensity of artificial lighting use, thereby lowering lighting energy consumption. For instance, in low-latitude regions, optimizing WWR alongside shading designs enables effective control of summer cooling loads while fulfilling daylighting requirements [23]. However, improving daylighting efficiency is not solely dependent on increasing WWR but also requires consideration of factors such as window geometry, placement, and orientation [24]. Beyond the three core research directions mentioned above, existing studies also emphasize the significant impact of interactions between WWR and other design parameters on building performance. The thermal properties of materials constitute one key factor influencing WWR effectiveness. In cold climates, the use of high-performance insulation materials can effectively mitigate heat loss caused by increased WWR, thereby preserving daylighting and ventilation benefits while maintaining optimal thermal comfort [25]. Building orientation also significantly influences WWR effectiveness. South-facing windows can reduce heating demand in winter by maximizing solar energy utilization, while excessive solar radiation during summer may increase cooling loads [26]. Additionally, effective shading strategies can reduce solar heat gain entering indoor spaces during summer, lowering cooling loads, while permitting beneficial solar radiation in winter to enhance indoor temperatures [10].

Recent studies have increasingly focused on the dynamic response mechanisms of the Window-to-Wall Ratio (WWR) under natural wind conditions. In such environments, the impact of WWR on building thermal comfort and energy consumption exhibits complex dynamic characteristics. Research indicates that WWR optimization must be synergistically designed with natural wind properties to maximize ventilation efficiency and energy-saving potential. CFD simulations and experimental validation reveal [34] that enlarging window dimensions and optimizing their positions can increase indoor airflow velocity by sixfold while reducing air temperature by 2.5%, demonstrating that dynamic WWR adjustments significantly enhance natural ventilation efficiency. Similarly, non-adjacent window combinations, compared to adjacent openings, generate more uniform indoor airflow distribution, further proving that coordinated design of WWR and opening layouts critically determines thermal comfort [35]. Furthermore, the dynamic properties of natural wind play a pivotal regulatory role in WWR effectiveness. Quantitative analysis of the power spectral exponent (β-value) of natural wind [36] shows that β-values significantly influence occupants’ thermal comfort perception, with a median β-value of 1.62 under comfortable conditions and 1.10 under discomfort conditions. This finding provides a theoretical foundation for refined WWR design in dynamic wind environments. Additionally, applications of the Reynolds-Averaged Navier-Stokes (RANS) model and k-ω turbulence model elucidate the interaction mechanisms between buoyancy and wind forces [37]: under low wind speeds, buoyancy dominates ventilation, where increased WWR enhances stack-driven airflow; at higher wind speeds, wind forces gradually prevail, necessitating window configuration adjustments to balance wind pressure distribution and avoid localized excessive airflow. However, the dynamic relationship between natural wind and WWR varies markedly across climatic zones. For hot climates like Texas, USA, studies [38] indicate that cross-ventilation strategies combined with high WWR reduce summer indoor temperatures but require dynamic window opening adjustments during transition seasons to prevent overcooling. In contrast, research on vernacular dwellings across six Chinese climatic zones emphasizes optimizing building morphology and interface elements to enhance natural ventilation performance [9].

While existing studies have extensively explored the relationship between Window-to-Wall Ratio (WWR) and building performance, their limitations are increasingly apparent [39]. Most research focuses on extreme climatic zones, with insufficient attention to transitional climates such as subtropical monsoon regions. Taking Kunming as an example, its Köppen climate classification (Cwb, subtropical highland climate) features mild “spring-like” conditions year-round. In such climates, traditional architectural thermal comfort strategies must address both high humidity and low wind speeds during rainy seasons while balancing ventilation and shading in dry seasons—fundamentally differing from the single-objective optimization in extreme climates. However, studies on WWR’s dynamic response mechanisms in such contexts remain scarce [10]. Secondly, existing research predominantly employs static simulation methods, focusing on WWR’s static impacts on energy consumption and thermal comfort, while inadequately quantifying its dynamic interactions with thermal comfort under natural wind conditions. For instance, the thermal buffering effects of vernacular dwellings under natural ventilation dominance and transient wind-speed impacts on indoor temperature-humidity variations have yet to be systematically modeled or analyzed [40]. Furthermore, compact courtyard buildings, with their unique spatial layouts and ventilation pathways, may exhibit special WWR synergies. However, current studies largely concentrate on standalone structures or large courtyards, failing to systematically quantify WWR thresholds or spatial-morphology interactions in small-scale courtyard typologies [2].

1.3. Research Approach of This Study

To address these gaps, this study investigates the traditional One-Seal dwellings (Yikeyin) in Kunming, Southwest China, establishing a coupled dynamic wind-thermal analysis framework through field investigations and numerical simulations to elucidate how window dimensions influence thermal comfort under natural ventilation. Kunming, situated in a subtropical plateau climate zone (Köppen classification Cwb) and renowned as the “Spring City,” presents unique climatic challenges: stable annual temperatures coexist with dynamic seasonal contradictions [41]. The rainy season (May–October) features humidity exceeding 80% with low wind speeds, while the dry season (November–April) requires simultaneous management of ventilation and shading needs. The “Yikeyin” dwellings, characterized by compact courtyard layouts, small inward-facing windows, and rammed earth envelopes, employ a “breathable but non-ventilating” passive regulation strategy. Current research on these dwellings predominantly focuses on historical evolution and sociological aspects, with limited investigation into their architectural performance, particularly regarding natural ventilation systems, thermal properties, and energy efficiency. A systematic analysis of “Yikeyin” performance characteristics holds significant academic and practical value for advancing sustainable architectural design and revitalizing traditional construction wisdom.

This study employs parametric modeling within the Grasshopper platform, integrating Ladybug, Honeybee, and Butterfly plugins to establish a multi-scale simulation framework. Ladybug imports Kunming’s EPW weather data to extract hourly climatic parameters (wind speed, temperature, humidity, and solar radiation) as simulation boundary conditions. Honeybee utilizes EnergyPlus for dynamic thermal-humidity balance calculations, analyzing how WWR (0.05–0.20) and window aspect ratios (vertical, square, and horizontal) influence envelope heat transfer and indoor conditions. Butterfly implements 3D CFD modeling based on RANS turbulence models with k-ε equations and wall functions, quantifying interactions between natural wind fields and window openings. Focusing on typical weeks (6–12 July for summer and 6–12 January for winter), the study employs the SIMPLE algorithm for steady-state flow field iterations, emphasizing coupled effects of transient wind speed and thermal-humidity variations. This parametric framework enables rapid morphological optimization while coupling energy and fluid dynamics simulations, overcoming static evaluation limitations to provide scientific guidance for passive design in dynamic wind environments.

In summary, this research aims to systematically investigate how window dimensions (WWR and aspect ratios) affect thermal comfort and ventilation efficiency in Kunming’s “Yikeyin” dwellings. Integrating field studies with numerical simulations seeks to reveal optimization potentials under natural ventilation while providing scientific foundations for adapting traditional architectural wisdom to contemporary practice. The study contributes to academic theory by enriching building thermal comfort frameworks and offers practical guidance for low-energy design and heritage building retrofitting in transitional climate zones. Specifically, it explores thermal comfort strategies for high-humidity/low-wind rainy seasons and ventilation-shading demands in dry seasons, generating critical data for modernizing traditional architecture. The parametric modeling and multi-scale simulation methodology advance passive design research by introducing dynamic environmental analysis, establishing new approaches for studying traditional buildings in similar climatic regions.

The paper is structured as follows: Section 2 details the research methods and model development, including numerical simulation procedures. Section 3 presents results analyzing thermal comfort and ventilation performance across different window configurations. Section 4 concludes with key findings, discusses research limitations, and proposes future research directions.

2. Materials and Methods

This study aims to thoroughly investigate the influence of window size on the thermal comfort of traditional One-Seal Dwellings (Yikeyin) in Kunming. To ensure the reliability and generalizability of the research findings, a comprehensive methodology combining “on-site investigation and data collection” with “Computational Fluid Dynamics (CFD) numerical simulation” was employed. Real building data and environmental parameters obtained from the on-site investigation provided a fundamental basis for the subsequent establishment and calibration of the CFD model, thereby ensuring the accuracy of the simulation results. Furthermore, CFD numerical simulation overcomes the limitations of traditional on-site measurements, enabling a systematic and detailed analysis of complex indoor airflow patterns and thermal environmental changes under various window size configurations. This ultimately provides scientific evidence and quantitative assessment for optimizing the passive design of traditional dwellings. This multi-faceted approach aims to comprehensively and deeply reveal the impact of window size on thermal comfort, offering data support for sustainable building design.

2.1. Case Study

The “Yikeyin” dwelling, the most representative traditional architectural form in central Yunnan, derives its name from its resemblance to an official seal in overall shape (Figure 1). Based on research by scholars such as Yang Dayu, this residential typology is primarily distributed in central Yunnan, particularly Kunming. Characterized by an enclosed exterior and intricate spatial composition internally, it demonstrates adaptable flexibility to diverse terrains, ethnic groups, family structures, and corresponding socio-economic lifestyles. Key architectural features include a three-bay main house with the central hall (tangwu) flanked by bedrooms; symmetrically arranged two-bay wing rooms (xiangfang) on both sides. The wing rooms, slightly shallower in depth than the secondary bays of the main house, connect tightly with staircases positioned between the front corridors of the main house and the wings, while integrating with the eight-chi (traditional measurement unit) inverted seat (daozuo) at the entrance to enclose a compact courtyard. This prototypical layout, commonly referred to in Kunming as “three bays, four wing rooms, and an eight-chi inverted seat”, coexists with variant types such as the “Half-Yikeyin”, “three bays with two wing rooms”, and “three bays with four wing rooms”.

The Yikeyin dwellings, through their long-term evolutionary development, have gradually formed distinct characteristics: minimal fenestration on exterior façades, small window apertures, and inward-oriented windows facing courtyards. This window configuration ingeniously utilizes courtyards for daylighting (often modulated by corridor eaves for shading) and employs courtyards as airflow mediators to achieve effective natural ventilation and microclimate interaction, thereby enabling passive environmental regulation. Simultaneously, these windows establish visual and spatial connections between interior rooms and the courtyard—the core family activity zone. Beyond practical considerations such as material efficiency and cost-effectiveness inherent to vernacular construction techniques, empirical observations reveal a critical advantage: despite limited window area, these small apertures facilitate gradual air exchange, achieving what locals describe as “air permeation without drafts”. This effect maintains indoor air freshness without noticeable airflow, significantly reducing heat dissipation rates in winter by minimizing thermal loss through windows, thus preserving interior warmth. In summer, it effectively limits outdoor heat and direct solar ingress, reducing solar radiation-induced thermal loads and diminishing active cooling demands.

This study focuses on Village Hebosuo in Kunming, Yunnan Province, China. Located in remote mountainous outskirts far from urban transportation hubs, the village retains relatively pristine traditional features, with well-preserved Yikeyin dwellings (Figure 2). Among the 704 extant traditional dwellings in the village, approximately 78% maintain original earth-wood structures characterized by gently sloped roofs and rammed earth walls that balance wind resistance, thermal insulation, and structural stability. In recent years, urbanization has reduced the permanent population to around 2229 residents, leaving some dwellings vacant and vulnerable to natural decay. Nevertheless, the village’s spatial fabric still vividly reflects traditional construction wisdom and regional adaptability.

Based on field investigations, a representative extant Yikeyin dwelling with the “three bays, four wing rooms, and an eight-chi inverted seat” layout was selected as the study sample. Its windows have a Window-to-Wall Ratio (WWR) ranging between 0.1 and 0.12. The dwelling retains traditional wooden window lattices without modern material modifications, while its intact courtyard and eaves corridors ensure measurable natural ventilation pathways. Climatically, Village Hebosuo falls under a subtropical monsoon climate with an annual average temperature of 14.7 °C. The prevailing wind direction is southwest, with an annual average wind speed of 1.8 m/s [43]. More specifically, winter in Village Hebosuo experiences an average temperature of 13.5 °C, with daily temperatures ranging from a minimum of 8 °C to a maximum of 19 °C, and an average relative humidity of 70.3%. During summer, the average temperature rises to 20 °C, with daily lows of 16 °C and highs of 24 °C, accompanied by an average relative humidity of 76.7%. This climatic profile synergizes with the spatial design of Yikeyin dwellings: the courtyard acts as a core ventilation node, guiding airflow through thermal pressure differentials—air enters via the south-facing main gate, accelerates through the elongated courtyard, and circulates through the main and wing rooms. Meanwhile, the thick rammed-earth walls and compact window apertures effectively mitigate daytime solar heat penetration. The study integrates meteorological and simulation data to analyze how varying WWR influences indoor airflow patterns, with a focus on balancing summer ventilation efficiency and winter wind resistance.

2.2. Methods

To comprehensively understand and quantitatively assess the indoor thermal environment (temperature and humidity) and airflow characteristics (velocity) of Yikeyin dwellings under varying Window-to-Wall Ratios (WWR) in local climatic conditions, this study establishes a coupled computational framework integrating Computational Fluid Dynamic (CFD) and building energy simulations (Figure 3 and Figure 4). The framework is anchored in Rhinoceros 3D modeling software (version 7.0, Robert McNeel & Associates, Seattle, WA, USA) and its versatile visual programming plugin Grasshopper (Robert McNeel & Associates, Seattle, WA, USA), which provides a flexible, parametric node-based programming environment to significantly enhance simulation setup and workflow management efficiency. Within Grasshopper, the study integrates the Ladybug Tools (version 1.4.0, Ladybug Tools LLC, Fairfax, VA, USA) suite, comprising three specialized open-source environmental analysis tools—Ladybug, Honeybee, and Butterfly—forming a complete computational chain spanning geometry creation, environmental data input, complex physical process simulation, and result analysis.

The simulation workflow begins by constructing a precise 3D geometric model of the prototypical Yikeyin dwelling in Rhinoceros. This model is imported into Grasshopper for further refinement to meet geometric input requirements for subsequent simulations. For environmental data, the study employs Ladybug to import and process the Standard EnergyPlus Weather (EPW) file of the building’s location. Ladybug extracts hourly critical meteorological parameters—dry-bulb temperature, humidity, wind speed/direction, and solar radiation—to provide time-varying boundary conditions for simulations. Indoor thermal environment simulations are conducted using the Honeybee plugin, which serves as an interface between the geometric model and the EnergyPlus energy simulation engine. Through Honeybee, detailed thermal properties of building envelope materials and constructions, occupancy schedules, lighting/equipment usage profiles, and natural ventilation opening specifications are defined. Honeybee translates these inputs into EnergyPlus-compatible files and executes thermal-hygric balance calculations to predict time-dependent interior temperature and humidity variations.

Airflow analysis is achieved via the Butterfly plugin, which specializes in CFD simulations for building environments by leveraging the OpenFOAM solver. Using Butterfly, the computational domain encompassing the building model is defined with boundary conditions including inlet wind speed/direction derived from climatic data. For turbulence modeling, a two-equation Reynolds-Averaged Simulation (RAS) model is selected to balance computational efficiency and engineering accuracy. This model solves additional transport equations to close the Reynolds-Averaged Navier-Stokes (RANS) equations, calculating turbulent viscosity or Reynolds stresses to characterize turbulence effects on mean flow. Near-wall flow resolution employs wall function methods, which simulate near-wall physics via empirical relations, reducing mesh density and computational costs. Simulations are executed using OpenFOAM’s simpleFoam solver, which employs the SIMPLE algorithm to iteratively solve coupled mean momentum, continuity, and turbulence equations for steady-state velocity and pressure distributions.

Post-simulation, outputs from EnergyPlus (temperature and humidity) and OpenFOAM (velocity and pressure fields) are processed and visualized through Honeybee and Butterfly components. This includes generating spatial distributions of mean temperature, relative humidity, and airflow velocity to intuitively interpret indoor thermal comfort conditions. The workflow’s efficiency lies in the seamless integration of Ladybug, Honeybee, and Butterfly within Grasshopper, enabling unified coordination of geometric modeling, climatic data, energy simulations, and CFD analysis. This integrated approach provides robust computational support for evaluating the thermal-hygric and natural ventilation performance of Yikeyin dwellings under varying window configurations.

2.3. Research Model Setup of Yikeyin Dwelling

This study establishes a simulation model (Figure 5) based on the prototypical “three bays, four wing rooms, and an eight-chi inverted seat” Yikeyin dwelling in Hebosuo. The model is south-facing with a length of 16.0 m and a width of 14.8 m, featuring a three-bay main house where the central hall (tangwu) is flanked by bedrooms. Symmetrically arranged two-bay wing rooms (xiangfang) on both sides have slightly shallower depths than the secondary bays of the main house, integrating with the entrance to enclose a compact courtyard (Figure 6).

To accurately reflect the building’s baseline physical performance, key simulation parameters were configured within the Honeybee components, selecting default options that best represent the characteristics of a traditional dwelling. The building program was set to Single-Family Detached House to approximate a residential usage pattern. To model the thermal performance of a structure built before modern energy codes, the building vintage was set to DOE Ref Pre-1980, which assumes poor insulation and high infiltration rates, closely mirroring the rammed-earth construction of the Yikeyin. To focus solely on the building envelope’s passive response to the climate, internal heat gains were intentionally excluded; therefore, occupancy, lighting, and equipment loads were all set to zero. This vacant scenario allows for a clear analysis of how the building’s form and window-to-wall ratio influence the indoor environment without the confounding effects of internal loads.

Detailed structural dimensions, material compositions, and thermal performance of the case study model are listed in

Table 2. Structural dimensions, material compositions, and thermal performance of model.

Envelope	Construction	U-Value (W/m2·K)	R-Value (m2·K/W)
Exterior wall	Lime mortar (0.01 m) + Rammed earth/Adobe (0.40 m) + Lime mortar (0.01 m)	1	1
Interior Wall	Lime mortar (0.01 m) + Rammed earth/Adobe (0.30 m) + Lime mortar (0.01 m)	1.52	0.66
Roof	Clay roofing tiles (0.02 m) + Clay-soil screed (0.03 m) + Straw-reinforced clay (0.10 m) + Timber structure (0.05 m)	0.8	1.25
Floor	Floor finish (0.02 m) + Compacted earth/Gravel-soil fill (0.20 m)	2.04	0.49
Ceiling	Timber board finish (0.01 m)	14.08	0.07

.

To accurately validate the applicability and precision of the simulation method concerning the geometric configuration and boundary conditions of the “Yikeyin” dwelling, the research team conducted comprehensive field monitoring from 6 July to 12 July 2024 (for specific monitoring and experimental details, similarly refer to the previous series of articles [35,36]). The monitoring focused on distinct functional zones within the residence, encompassing Rooms 0 to 9. Multiple critical physical environmental parameters, including temperature, relative humidity, and air velocity, were recorded continuously. The field monitoring strictly adhered to building environmental monitoring standards, employing multi-point, continuous long-term monitoring to ensure data comprehensiveness and accuracy. During this period, temperatures fluctuated between 23 °C and 26 °C, while air velocities predominantly remained around 0.06 m/s. Compared with the monitored values, the simulated values all fell within reasonable ranges. This indicates that the simulation method is applicable to this specific spatial context (Figure 7 and Figure 8).

Among all rooms, five spaces—the second-floor central hall and two wing rooms on each side—are equipped with exterior windows (specific dimensions in

Table 3. Window dimensions and locations for 0.1 Window-to-Wall Ratio (WWR).

Window ID	Window Type	Window Orientation	Location	Room No.	Length (m)	Height (m)	Sill Height from Grade (m)
1	Horizontally elongated	South (Facing into the courtyard)	Second floor, central hall	5	3	1.5	4.35
2	Horizontally elongated	West (Facing into the courtyard)	Second floor, right-wing chamber	6	2.4	1.4	3.2
3	Rectangular	West (Facing into the courtyard)	First floor, right-wing chamber	7	1.8	1.4	0.7
4	Rectangular	West (Facing into the courtyard)	First floor, right-wing chamber	7	1.8	1.4	0.7
5	Horizontally elongated	East (Facing into the courtyard)	Second floor, left-wing chamber	8	2.4	1.4	3.2
6	Rectangular	East (Facing into the courtyard)	First floor, left-wing chamber	9	1.8	1.4	0.7
7	Rectangular	East (Facing into the courtyard)	First floor, left-wing chamber	9	1.8	1.4	0.7

), yielding a total Window-to-Wall Ratio (WWR) of 0.1. To investigate the relationship between traditional window sizing and indoor thermal/ventilation performance, three additional WWR variants (0.05, 0.15, and 0.2) were modeled by proportionally scaling these five windows while maintaining identical structural positions, material properties, and thermal parameters. Simulations evaluated the indoor mean temperature, relative humidity, and natural ventilation under summer and winter climatic conditions across four WWR scenarios: 0.05, 0.1, 0.15, and 0.2.

To better reflect realistic occupant behavior, the simulation replaces a static, year-round open-window assumption with a dynamic seasonal schedule. This schedule is based on typical comfort-driven behaviors in Kunming’s transitional climate, where the primary goal is to maximize cooling and dehumidification in summer while prioritizing heat retention in winter. The specific schedules used for the simulations are detailed in

Table 4. Room-specific window opening patterns with time schedules.

Window ID	Summer Opening Pattern (Time)	Winter Opening Pattern (Time)	Location	Room No.
1	Open 24 h	Open: 13:00–14:00	Second floor, central hall	5
2	Open 24 h	Open: 13:00–14:00	Second floor, right-wing chamber	6
3	Open: 08:00–20:00	Open: 12:00–14:00	First floor, right-wing chamber	7
4	Open: 08:00–20:00	Open: 12:00–14:00	First floor, right-wing chamber	7
5	Open 24 h	Open: 13:00–14:00	Second floor, left-wing chamber	8
6	Open: 08:00–20:00	Open: 12:00–14:00	First floor, left-wing chamber	9
7	Open: 08:00–20:00	Open: 12:00–14:00	First floor, left-wing chamber	9

.

2.4. Boundary Condition Configuration

2.4.1. Environmental Parameters

This study utilizes Kunming, Yunnan Province, as the measurement location. The EPW weather file for Kunming (file ID: CHN_YN_Kunming.567780_CSWD.epw) was obtained from the EPW Map website for environmental parameter configuration. Simulation periods were selected as typical weeks generated by the Ladybug plugin: winter period from 6 January (00:00) to 12 January (23:00), and summer period from 6 July (00:00) to 12 July (23:00). Initial values were based on July and January averages, where July had a mean dry-bulb temperature of 20.03 °C (wet-bulb 17 °C), humidity of 81.02%, outdoor wind speed of 2 m/s, and wind direction of 238.53° (0° = true north, predominantly southwest); January had a mean dry-bulb temperature of 8.93 °C (wet-bulb 6 °C), humidity of 64.60%, outdoor wind speed of 2.5 m/s, and wind direction of 235.54°. Wind direction deviated from the building’s primary orientation, and the initial wind field was configured as a gradient wind field. Temperature extremes adopted summer/winter maxima and minima, while humidity extremes utilized diurnal peaks (summer average ~25%, winter ~15%; data sourced from Kunming National Reference Meteorological Station, ID: 56778). Solar radiation values employed 1993–2016 statistical data [44].

2.4.2. Environmental Computational Domain

The computational domain was defined with the inlet boundary positioned at 5d (where d = average building height within the simulation radius) and the outlet boundary at 10d. The domain width was set to 9d, using the simulation diameter d as the base scale (Figure 9).

2.4.3. Mesh Generation Settings

Mesh generation adhered to the principle that the maximum grid size in any dimension should not exceed 1/20 of the corresponding edge length of the base region. Due to differing scales between the background environment and the target model, the background mesh was coarsely configured to enhance computational efficiency, while the target model employed locally refined meshes to improve simulation accuracy.

2.4.4. Computational Model Selection

The simulations utilized a two-equation Reynolds-Averaged Simulation (RAS) turbulence model, specifically the k-ε model combined with wall function methods. Wall functions in RAS simulations serve as empirical or semi-empirical approaches to model near-wall physical properties, avoiding direct resolution of the viscous sublayer and buffer layer with high gradient magnitudes near walls, thereby significantly reducing required grid counts and computational resources. In the two-equation model, the core calculations involve solving two transport equations to determine the modeled turbulent kinetic energy (k) and turbulent dissipation rate (ε). The numerical method employed spatial discretization via the finite volume method and utilized OpenFOAM’s simpleFoam solver, which couples pressure-velocity solutions for incompressible flow within the SIMPLE algorithm framework.

3. Results

This chapter presents the results of a comprehensive analysis evaluating the influence of varying WWR on the indoor thermal comfort of the Yikeyin dwelling. The investigation focuses on two distinct seasonal periods in Kunming: a humid summer week (6–12 July) with a mean temperature of 20.03 °C and 81.02% humidity, and a cool, dry winter week (6–12 January) with a mean temperature of 8.93 °C and 64.60% humidity. The following sections provide a detailed parameter-by-parameter analysis of how different WWR configurations affect indoor environmental conditions, including temperature, humidity, and air movement patterns.

3.1. Analysis of Summer Simulation Results Under Different Window-to-Wall Ratios

3.1.1. Spatial Differentiation of Temperature Distribution (Summer)

Under the baseline scenario (WWR = 0.1), the indoor average temperature ranged between 20.5 and 24.5 °C (Figure 10), with a thermal gradient pattern showing higher temperatures in the central hall compared to wing rooms (

Table 5. Data of mean indoor temperatures for each room at different WWR operating conditions in summer.

Room No.	WWR = 0.05 (°C)	WWR = 0.1 (°C)	WWR = 0.15 (°C)	WWR = 0.2 (°C)
0	24.4	24.3	24.2	24.2
1	24.2	24.0	24.0	24.0
2	24.1	24.0	23.9	23.9
3	24.5	23.7	23.4	22.8
4	24.6	23.8	23.4	22.9
5	24.1	22.1	21.7	21.3
6	23.7	22.3	22.2	22.1
7	22.1	22.0	22.0	22.0
8	23.4	21.5	21.2	20.8
9	22.1	20.9	20.7	20.5

and

Table 6. Visualized simulation outcome of mean indoor temperatures for each room at different WWR operating conditions in summer.

WWR	Average Temperatures per Room	WWR	Average Temperatures per Room
WWR = 0.05	a	WWR = 0.1	b
WWR = 0.15	c	WWR = 0.2	d

). The highest temperature was observed on the left side of the first-floor central hall (Room 0), while the lowest occurred on the first-floor left-wing room. When WWR decreased to 0.05, indoor temperatures rose by 0.3–1.2 °C overall. For instance, the right side of the second-floor central hall (Room 3) reached 24.45 °C at WWR = 0.05, 0.78 °C higher than the baseline, indicating that a smaller WWR may restrict heat exchange efficiency, exacerbating thermal accumulation. Conversely, increasing WWR to 0.15 and 0.2 reduced temperatures by 0.5–1.6 °C and 1.1–2.4 °C, respectively. The first-floor left-wing room (Room 9) cooled to 20.53 °C at WWR = 0.2 (0.36 °C below baseline), but with a 1.33% humidity surge, revealing a comfort paradox of “temperature reduction-humidity increase” that highlights limitations of excessive window expansion in summer’s hot-humid climate.

The finding that Room 7, with a WWR of 0.05, exhibited the lowest mean indoor temperature is the deterministic outcome of the synergistic effect of multiple limiting factors in its building physics environment. The unit is located on the ground floor, with its west-facing fenestration oriented towards a narrow internal courtyard. This layout induces a severe self-shading effect, where the geometric constraints of the courtyard not only drastically limit the window’s Sky View Factor, thereby effectively blocking direct solar radiation, but also significantly attenuate the sky diffuse and ground-reflected radiation components. Coupled with the minimal WWR, which imposes a final restriction on the already attenuated solar radiation flux, and with the absence of any Internal Heat Gains for supplementation, the unit’s Total Solar Gain was consequently the lowest among all analyzed objects. Therefore, its ranking as the lowest in 24 h mean temperatures is an expected outcome consistent with the principles of Building Energy Balance.

3.1.2. Relative Humidity Response Patterns (Summer)

Under baseline WWR = 0.1, relative humidity exhibited a “wing rooms humid—central hall moderate” distribution (Figure 11), with the first-floor left-wing room (Room 9) reaching 74.22%, while central hall areas generally stayed below 70%. At WWR = 0.05, humidity decreased by 1.2–5.8% compared to baseline. For example, humidity in the left side of the first-floor central hall (Room 0) dropped 1.05%, alongside a 0.3 °C temperature rise, partially confirming the trade-off between shading and humidity control. However, this relationship did not explain all zones’ hygrothermal variations—the first-floor right-wing room (Room 7) maintained significantly higher humidity across scenarios, likely due to restricted heat exchange and airflow. At WWR = 0.15 and 0.2, humidity increased by 0.8–3.1% and 1.6–4.5%, respectively, as larger windows enhanced indoor-outdoor air exchange, allowing more humid external air infiltration (

Table 7. Data of indoor relative humidity for each room at different WWR operating conditions in summer.

Room No.	WWR = 0.05 (%)	WWR = 0.1 (%)	WWR = 0.15 (%)	WWR = 0.2 (%)
0	66.2	67.2	67.6	68.1
1	66.3	68.2	68.4	68.8
2	64.9	66.5	67.0	67.7
3	64.8	65.5	66.1	67.4
4	64.9	65.4	65.9	67.2
5	65.4	70.2	71.3	72.7
6	68.6	69.0	68.8	68.7
7	72.4	69.6	69.3	69.0
8	66.6	71.5	72.6	74.0
9	71.3	74.2	74.8	75.5

and

Table 8. Visualized simulation outcome of indoor relative humidity for each room at different WWR operating conditions in summer.

WWR	Relative Humidity per Room	WWR	Relative Humidity per Room
WWR = 0.05	a	WWR = 0.1	b
WWR = 0.15	c	WWR = 0.2	d

). This suggests that enlarging windows beyond certain thresholds may exacerbate humidity issues in wing rooms.

In Figure 11, Rooms 6 and 7, both west-facing, exhibited a high relative humidity under the WWR 0.05 condition. This phenomenon originated from their shared courtyard environment, yet the dominant physical mechanism for each room’s high humidity differed due to their specific architectural details. Specifically, for Room 7 on the ground floor, the dominant factor was its extremely low indoor temperature; the most severe courtyard shading made it the coldest unit, which thermodynamically elevated its relative humidity to the highest point. In contrast, for Room 6 located above it, although its shading was slightly less severe, the dominant factor shifted to its extremely low air exchange rate; the design, which includes one fewer window opening than Room 7, decisively weakens its natural ventilation potential, leading to a far greater capacity to physically ‘trap’ moisture compared to other rooms. Therefore, it can be concluded that the high humidity in Room 7 was primarily “temperature-driven,” whereas in Room 6 it was “ventilation-driven.” Although their causal pathways differ, both ultimately culminate in a high summer humidity risk within this restricted, west-facing courtyard environment, a finding that is highly consistent with the simulation results.

3.1.3. Spatial Heterogeneity of Airflow Velocity Fields (Summer)

Under baseline WWR = 0.1, airflow velocity followed a “courtyard core > central hall > wing rooms” pattern, with indoor averages of 0.025–0.169 m/s. The right side of the second-floor central hall (Room 3) recorded the highest velocity (0.169 m/s) driven by thermal buoyancy, while wing rooms generally stayed below 0.05 m/s. Reducing WWR to 0.05 decreased velocities by 12–60%, likely due to restricted airflow pathways (Figure 12). Anomalously, the central zone of the second-floor central hall (Room 5) reached 0.238 m/s (vs. 0.076 m/s baseline), potentially influenced by local turbulence, warranting further validation. Increasing WWR to 0.15 and 0.2 significantly improved wing room airflow: the first-floor right-wing room (Room 7) reached 0.429 m/s at WWR = 0.2, a 154% increase from baseline, demonstrating enhanced cross-ventilation. However, ground-floor central hall velocities decreased by 15–20% at WWR = 0.2, indicating that excessive window enlargement may disrupt original wind pressure balance, necessitating spatial layout optimization for ventilation pathways (

Table 9. Data of mean indoor air velocity for each room at different WWR operating conditions in summer.

Room No.	WWR = 0.05 (m/s)	WWR = 0.1 (m/s)	WWR = 0.15 (m/s)	WWR = 0.2 (m/s)
0	0.050015	0.042151	0.044026	0.040282
1	0.052039	0.055276	0.051921	0.050758
2	0.04382	0.047961	0.04616	0.039841
3	0.02679	0.115421	0.132598	0.050207
4	0.028029	0.11024	0.122511	0.037791
5	0.237554	0.075904	0.081395	0.562085
6	0.090786	0.029229	0.026216	0.11645
7	0.051368	0.168523	0.194711	0.428506
8	0.103195	0.037201	0.047829	0.055777
9	0.051993	0.0255	0.027256	0.025541

and

Table 10. Visualized simulation outcome of mean indoor air velocity for each room at different WWR operating conditions in summer.

WWR	Average Airflow Velocity per Room (m/s)	WWR	Average Airflow Velocity per Room (m/s)
WWR = 0.05	First Floor Second Floor	WWR = 0.1	First Floor Second Floor
WWR = 0.15	First Floor Second Floor	WWR = 0.2	First Floor Second Floor

).

3.1.4. Threshold Effect of Air Change Efficiency (Summer)

Under the baseline scenario (WWR = 0.1), the air change rate (ACH) ranged from 0 to 10.22 times/h, with wing rooms exhibiting significantly lower ventilation efficiency than the central hall due to their enclosed spatial configuration and restricted airflow. For instance, the first-floor right-wing room (Room 7) recorded only 10.22 ACH. Compared to the baseline, reducing WWR to 0.05 decreased ACH by 40–90%. The central zone of the second-floor central hall (Room 5) showed 0.72 ACH (vs. 6.28 ACH baseline), with some rooms approaching zero ACH, indicating severely limited indoor-outdoor air exchange and compromised air quality (Figure 13). Conversely, increasing WWR to 0.15 and 0.2 enhanced ACH by 1.5–2.5× and 2.5–3.8×, respectively. The first-floor right-wing room (Room 7) reached 30.32 ACH at WWR = 0.2 (197% above baseline), demonstrating that enlarged WWR significantly improves airflow exchange, particularly in wing rooms. However, the left (Room 4) and right (Room 3) sides of the second-floor central hall maintained ACH below 1 time/h even at WWR = 0.2, revealing ventilation suppression caused by traditional symmetrical layouts (

Table 11. Data of the number of indoor air changes for each room at different WWR operating conditions in summer.

Room No.	WWR = 0.05 (Times)	WWR = 0.1 (Times)	WWR = 0.15 (Times)	WWR = 0.2 (Times)
0	1.3474 × 10−7	1.2895 × 10−7	1.2703 × 10−7	1.2262 × 10−7
1	2.4025 × 10−7	2.2597 × 10−7	2.2154 × 10−7	2.1147 × 10−7
2	1.329 × 10−7	1.2557 × 10−7	1.2337 × 10−7	1.1844 × 10−7
3	0	0	0	0
4	0	0	0	0
5	0.720532	6.277953	9.159748	15.877644
6	0.71486	6.008319	9.267623	17.695824
7	1.415505	10.222606	15.807045	30.320668
8	0.557957	4.856584	7.018289	11.981639
9	1.343262	7.541407	10.750078	18.140042

), necessitating asymmetric window configurations or additional ventilation components for optimization.

3.1.5. Summary of Summer Simulation Analysis

Summer simulation results demonstrate that Kunming’s traditional Yikeyin dwellings with baseline WWR = 0.1 achieve dynamic balance between temperature and humidity control. Under baseline conditions, indoor temperatures (20.89–24.27 °C) and relative humidity (65.35–74.22%) remained marginally within thermal comfort thresholds despite spatial heterogeneity, validating the local concept of “air permeation without drafts”—where traditional window proportions adaptively manage hygrothermal loads through limited ventilation and shading. Comparative analysis reveals that increasing WWR to 0.15–0.2 enhances wing room ventilation efficiency (2–3 × baseline ACH) and reduces peripheral zone temperatures by 1.1–2.4 °C via cross-ventilation, but exacerbates wing room humidity by 1.6–4.5%, exceeding comfort limits locally. Conversely, reducing WWR to 0.05 suppresses the rise of humidity but intensifies central hall thermal accumulation (0.3–1.2 °C temperature rise) and reduces core zone ACH by 40–90% due to airflow obstruction. The study further identifies structural ventilation suppression in central zones under symmetrical layouts, with second-floor central hall ACH persistently below 1 time/h at WWR ≤ 0.2, while nonlinear responses in wing room airflow (e.g., 154% velocity surge in first-floor right-wing room at WWR = 0.2) indicate potential wind pressure imbalance from excessive window enlargement. For synergistic optimization of thermal comfort, humidity control, and energy efficiency, WWR should be maintained between 0.1 and 0.15. Coupled with asymmetric window strategies (e.g., enlarged wing room windows with traditional central hall proportions), this approach can activate peripheral zone cooling while mitigating core zone hygrothermal accumulation and airflow stagnation, providing quantitative guidelines for modern adaptive retrofits of traditional dwellings.

3.2. Analysis of Winter Simulation Results Under Different Window-to-Wall Ratios

3.2.1. Spatial Differentiation of Temperature Distribution (Winter)

Under the baseline scenario (WWR = 0.1), the average temperature across rooms remained within 17.3 °C~21.7 °C (Figure 14). The first-floor central hall exhibited relatively stable temperatures, while the second-floor and wing room areas showed noticeable fluctuations. This disparity stems from the first-floor central hall’s direct connection to the courtyard, experiencing minimal thermal disturbances, whereas second-floor and wing rooms are more influenced by building thermal performance and airflow patterns. When WWR decreased to 0.05, overall indoor temperatures displayed a slight upward trend. Notably, the left side of the second-floor central hall (Room 4) rose from 19.27 °C to 20.20 °C (Δ+0.93 °C), likely due to reduced indoor-outdoor heat exchange from smaller window areas. Increasing WWR to 0.15 lowered Room 4’s temperature by 0.22 °C to 19.04 °C, while the first-floor left-wing room (Room 9) cooled marginally to 17.33 °C (Δ−0.01 °C), demonstrating localized cooling effects from higher WWR. Further increasing WWR to 0.2 caused Room 4 to drop another 0.35 °C to 18.70 °C and Room 9 to 17.32 °C (Δ−0.02 °C), suggesting excessive WWR compromises thermal retention. Temperature changes remained negligible in specific rooms (e.g., first-floor right-wing rooms and central hall zones), likely due to stable airflow patterns limiting ventilation impacts (

Table 12. Data of mean indoor temperatures for each room at different WWR operating conditions in winter.

Room No.	WWR = 0.05 (C°)	WWR = 0.1 (C°)	WWR = 0.15 (C°)	WWR = 0.2 (C°)
0	21.7	21.7	21.7	21.7
1	21.7	21.7	21.7	21.7
2	18.7	19.3	19.6	19.9
3	19.2	18.6	18.5	18.3
4	20.2	19.3	19.0	18.7
5	19.1	17.5	17.4	17.4
6	21.7	21.7	21.7	21.7
7	21.7	21.7	21.7	21.7
8	18.2	17.4	17.3	17.3
9	17.6	17.3	17.3	17.3

and

Table 13. Visualized simulation outcome of mean indoor temperatures for each room at different WWR operating conditions in winter.

WWR	Average Temperatures per Room	WWR	Average Temperatures per Room
WWR = 0.05	a	WWR = 0.1	b
WWR = 0.15	c	WWR = 0.2	d

).

3.2.2. Relative Humidity Response Patterns (Winter)

Baseline relative humidity ranged between 35.4% and 47.7%, indicating generally dry conditions. Room-specific variations emerged from differing ventilation and thermal properties (Figure 15). At WWR = 0.05, humidity increased broadly: the right side of the first-floor central hall (Room 2) rose from 46.85% to 47.46% (Δ+0.61%), while Room 9 increased 0.81% to 47.13%, attributable to restricted air exchange promoting moisture accumulation. Conversely, WWR = 0.15 and 0.2 reduced humidity levels. The central zone of the second-floor central hall (Room 5) decreased from 47.48% (WWR = 0.15) to 47.20% (WWR = 0.2), and the second-floor left-wing room (Room 8) declined from 46.90% to 46.86%, demonstrating enhanced moisture dissipation through improved ventilation (

Table 14. Data of indoor relative humidity for each room at different WWR operating conditions in winter.

Room No.	WWR = 0.05 (%)	WWR = 0.1 (%)	WWR = 0.15 (%)	WWR = 0.2 (%)
0	45.1	44.1	43.7	43.3
1	41.9	41.8	41.8	41.8
2	47.5	46.8	46.4	45.8
3	47.3	46.8	46.5	46.2
4	47.5	46.0	45.7	45.4
5	47.1	47.7	47.5	47.2
6	40.8	36.0	35.7	35.5
7	37.1	35.4	35.3	35.3
8	46.2	46.9	46.9	46.9
9	47.1	46.9	46.9	46.8

and

Table 15. Visualized simulation outcome of indoor relative humidity for each room at different WWR operating conditions in winter.

WWR	Relative Humidity per Room	WWR	Relative Humidity per Room
WWR = 0.05	a	WWR = 0.1	b
WWR = 0.15	c	WWR = 0.2	d

). These findings suggest moderate WWR increases benefit winter humidity control, though excessive window areas risk heat loss.

3.2.3. Spatial Heterogeneity of Airflow Velocity Fields (Winter)

Baseline airflow velocities ranged 0.04~0.58 m/s, indicating generally stable conditions (Figure 16). WWR reduction to 0.05 decreased velocities significantly: the right side of the second-floor central hall (Room 3) dropped 60% to 0.04 m/s, while Room 9 decreased 61% to 0.17 m/s. Increasing WWR to 0.15 enhanced ventilation dramatically: the second-floor right-wing room (Room 6) reached 1.13 m/s (129% above baseline), and Room 9 increased 123% to 0.53 m/s. At WWR = 0.2, the first-floor right-wing room (Room 7) achieved 1.41 m/s (Δ+134%), though the central zone of the second-floor central hall (Room 5) surged to 1.26 m/s (

Table 16. Data of mean indoor air velocity for each room at different WWR operating conditions in winter.

Room No.	WWR = 0.05 (m/s)	WWR = 0.1 (m/s)	WWR = 0.15 (m/s)	WWR = 0.2 (m/s)
0	0.089788	0.090664	0.089789	0.095556
1	0.069376	0.070012	0.073111	0.07422
2	0.077266	0.088095	0.082772	0.073243
3	0.041767	0.104268	0.104882	0.096333
4	0.043054	0.16104	0.170317	0.173634
5	0.094752	0.487515	0.727061	1.257984
6	0.283481	0.491125	1.132275	1.016197
7	0.277864	0.572049	0.763008	1.406079
8	0.176498	0.558902	0.454821	1.239038
9	0.167438	0.439049	0.526095	0.701783

and

Table 17. Visualized simulation outcome of mean indoor air velocity for each room at different WWR operating conditions in winter.

WWR	Average Airflow Velocity per Room (m/s)	WWR	Average Airflow Velocity per Room (m/s)
WWR = 0.05	First Floor Second Floor	WWR = 0.1	First Floor Second Floor
WWR = 0.15	First Floor Second Floor	WWR = 0.2	First Floor Second Floor

), potentially causing discomfort from localized drafts.

3.2.4. Threshold Effect of Air Change Efficiency (Winter)

Air change rates (ACH) under baseline conditions varied 0~38 times/h, with wing rooms showing lower values due to ventilation constraints (Figure 17). WWR = 0.05 further reduced ACH: the first-floor right-wing room (Room 7) dropped to 3.05 times/h and Room 9 to 2.43 times/h, indicating compromised air quality management. WWR = 0.15 improved ACH substantially: Room 6 reached 20.90 times/h (Δ+167%) and Room 9 reached 29.66 times/h (Δ+157%). At WWR = 0.2, Room 7 peaked at 73.75 times/h and Room 9 at 57.58 times/h (

Table 18. Data on the number of indoor air changes for each room at different wwr operating conditions in winter.

Room No.	WWR = 0.05 (Times)	WWR = 0.1 (Times)	WWR = 0.15 (Times)	WWR = 0.2 (Times)
0	2.1037 × 10−7	2.1640 × 10−7	2.1901 × 10−7	2.1997 × 10−7
1	3.7213 × 10−7	3.8448 × 10−7	3.9009 × 10−7	3.9226 × 10−7
2	2.0228 × 10−7	2.0557 × 10−7	2.0829 × 10−7	2.0978 × 10−7
3	0	0	0	0
4	0	0	0	0
5	0.861815	11.291068	18.024976	35.715344
6	0.968853	13.072387	20.897545	41.433635
7	3.053873	24.290349	37.965653	73.75074
8	0.768884	10.22764	16.333487	32.349046
9	2.43338	18.991049	29.659304	57.575047

), demonstrating excessive ventilation potentially exacerbating heat loss in cold climates.

3.2.5. Summary of Winter Simulation Analysis

Winter simulation results demonstrate that the thermal environment regulation of Kunming’s traditional “Yikeyin” dwellings exhibits significant seasonal specificity influenced by Window-to-Wall Ratio (WWR) adjustments. Under the baseline scenario (WWR = 0.1), the design demonstrates adaptability in maintaining temperature stability (17.3–21.7 °C) and humidity balance (35.4–47.7%), though wing rooms display notable temperature and humidity gradients due to insufficient ventilation. Increasing WWR to 0.15–0.2 enhances ventilation efficiency (e.g., air change rate in the first-floor right-wing room rises to 73.75 times/h) but intensifies heat loss, with the first-floor left-wing room temperature dropping to 17.32 °C (WWR = 0.2), approaching local cold thresholds. Concurrently, elevated WWR triggers abrupt wind speed surges in wing rooms (e.g., 1.41 m/s in the first-floor right-wing room), potentially causing winter airflow discomfort, while the central hall’s symmetrical layout persistently suppresses ventilation efficiency (air change rates remain below 1 time/h). Reducing WWR to 0.05 mitigates cold air infiltration (wing room temperatures increase by 0.3–1.9 °C) but induces humidity accumulation (wing room humidity rises by 0.5–2.1%) and reduces ventilation efficiency (air change rates decline by 50–80%). The study reveals that winter WWR optimization requires balancing insulation and ventilation. It recommends maintaining WWR within 0.1–0.15 while integrating localized insulation designs in wing rooms to alleviate overcooling risks and sustain moderate ventilation, thereby establishing a technical pathway for enhancing thermal comfort during cold seasons.

3.3. Comparative Analysis of Winter and Summer Simulation Results Under Different Window-to-Wall Ratios

3.3.1. Temperature-Humidity Variations and Thermal Comfort

In winter, as the Window-to-Wall Ratio (WWR) increases, the average indoor temperature exhibits a declining trend, while relative humidity decreases overall. When WWR rises from 0.05 to 0.2, the temperature in the first-floor left-wing room (Room 9) drops from 17.63 °C to 17.32 °C, and relative humidity decreases from 47.13% to 46.85%. This indicates that higher WWR in winter leads to increased heat loss, resulting in lower indoor temperatures, while simultaneously promoting moisture dissipation and reducing relative humidity. However, the temperature decline has a more pronounced impact on thermal comfort, as colder conditions exacerbate discomfort despite partial alleviation of humidity-related dampness.

Contrarily, in summer, increasing WWR lowers indoor temperatures but elevates relative humidity. For Room 9, at WWR = 0.2, the temperature decreases to 20.53 °C (compared to 22.07 °C at WWR = 0.05), while relative humidity rises from 71.26% to 75.55%. This suggests that larger WWR enhances heat dissipation in summer but intensifies muggy conditions due to Kunming’s inherent humidity, negatively affecting thermal comfort. The coupled effects of temperature and humidity highlight seasonal divergences in WWR optimization strategies.

3.3.2. Airflow Velocity and Ventilation Efficiency

Winter indoor airflow velocity increases with WWR. In the first-floor right-wing room (Room 7), velocity rises from 0.28 m/s (WWR = 0.05) to 1.41 m/s (WWR = 0.2). Enhanced ventilation reduces humidity but risks localized drafts, particularly in occupied areas, where excessive airflow amplifies cold discomfort.

Similarly, summer airflow accelerates with WWR. Room 7’s velocity increases from 0.05 m/s (WWR = 0.05) to 0.43 m/s (WWR = 0.2). Moderate airflow improves evaporative cooling, yet excessive speeds may induce psychological unease. This reflects seasonal ventilation priorities: balancing insulation in winter and optimizing cooling without turbulence in summer.

3.3.3. Air Change Efficiency and Energy Consumption Balance

Higher WWR drastically elevates winter air change rates (ACH). For Room 7, ACH surges from 3.05 times/h (WWR = 0.05) to 73.75 times/h (WWR = 0.2). While effective for humidity control, this exacerbates heat loss, demanding higher heating energy consumption to maintain thermal stability.

Increased summer ACH also lowers temperature and humidity, improving comfort. However, excessive ventilation raises cooling energy loads, particularly with air conditioning. Seasonal optimization must thus balance thermal comfort and energy efficiency through WWR adjustments.

Table 19. Comparative effects of Window-to-Wall Ratio (WWR) increase on indoor environmental parameters in summer and winter.

Aspect	Summer (When WWR Increases)	Winter (When WWR Increases)	Thermal Comfort Implication
Indoor Temperature	Decrease	Decrease	Summer: improved cooling; Winter: heat loss
Relative Humidity	Increase	Decrease	Summer: more humid; Winter: reduced dampness
Airflow Velocity	Increase	Increase	Summer: cooling aid; Winter: potential drafts
Air Change Rate	Increase	Increase	Both: better ventilation; Winter: heat penalty

provides a direct side-by-side comparison of the seasonal effects of increasing Window-to-Wall Ratio (WWR) on indoor environmental performance. The table clearly illustrates the divergent impacts of WWR across summer and winter conditions, emphasizing the need for season-specific design strategies to balance ventilation, temperature regulation, and occupant comfort.

3.3.4. Synergistic Influence Mechanism of Climate Characteristics and WWR on Indoor Thermal and Humidity Environment

The climate characteristics of Kunming, with humid summers and dry winters, directly regulate the mechanism by which WWR influences indoor environmental conditions. During summer, the high absolute humidity of outdoor air means that increasing WWR enhances air exchange between indoor and outdoor spaces, thereby introducing a significant amount of moisture into the indoor environment. Even though ventilation provides a slight cooling effect, the intrusion of external moisture remains the fundamental cause of the significant increase in indoor relative humidity, ultimately resulting in the phenomenon of “cooling and humidification”. Conversely, during the dry winter, increasing the WWR helps introduce dry outdoor air, promoting the expulsion of indoor moisture and thereby reducing humidity. In summary, the WWR plays a dominant role as a climate condition regulator, and its optimization strategy essentially involves balancing these seasonal opposing effects.

3.3.5. Regulatory Role of Spatial Geometric Characteristics on Ventilation Performance and WWR Effects

Wing rooms (Room 6–9), due to their high exposure to the courtyard façade and close coupling with the external environment, are highly sensitive to changes in the WWR, exhibiting significant fluctuations in temperature, humidity, and airflow. In contrast, the central hall (Room 0–5), as an inward-facing space surrounded by other rooms, exhibits stronger thermal buffering effects. Its geometric form results in relative decoupling from external environmental influences. This layout causes minimal impact from adjustments to the WWR size, leading to a stable yet stagnant environmental state in this area across all simulation scenarios. The extremely low annual Air Changes per Hour (ACH < 1) further confirms this.

The significant difference in ventilation efficiency between the wing rooms and the central hall can be attributed to the building’s symmetrical design. This symmetrical layout results in a balanced pressure distribution of airflow along the inner façade of the courtyard. Effective cross-ventilation requires a significant pressure difference between the inlet and outlet. However, in the symmetrical layout of the “Yi Ke Yin” design, such a pressure difference cannot be formed in the building’s core area (such as the central hall), leading to airflow short-circuiting or stagnation and preventing effective air replacement within the space. In contrast, an asymmetrical design (such as adjusting the size or position of windows on different sides) can disrupt this pressure balance, creating stable high-pressure and low-pressure zones on either side of the building’s core area. This drives airflow through the building, significantly enhancing overall ventilation performance.

Through integrated evaluation of all simulated parameters, this study reveals a fundamental performance trade-off in WWR selection driven by Kunming’s distinct seasonal climate. The analysis demonstrates that an optimal WWR range of 0.1 to 0.15 provides the best year-round balance by managing the complex interplay between thermal and humidity control. This optimal range effectively moderates the seasonal conflicts: while larger WWRs improve summer ventilation but exacerbate humidity, they become detrimental in winter by accelerating heat loss. Furthermore, the building’s symmetrical layout creates persistent spatial imbalances in air distribution that WWR adjustments alone cannot overcome. These comprehensive findings, supported by the detailed parameter analysis in preceding sections, provide valuable insights for optimizing traditional dwelling designs in similar climatic regions.

4. Conclusions

This study systematically investigates the influence of Window-to-Wall Ratio (WWR) on the thermal comfort and ventilation performance of the traditional “One-Seal” dwelling in Kunming under natural wind conditions. By integrating dynamic wind-thermal coupling simulations with parametric modeling, the research provides quantitative insights into the seasonal adaptability of window design in transitional climate zones.

4.1. Key Findings

4.1.1. Seasonal Trade-Offs in WWR Optimization

In summer, a baseline WWR of 0.1 achieves a thermal-humidity balance, with temperature ranging from 20.89 to 24.27 °C and humidity between 65.35 and 74.22%, adopting a “breathable but not drafty” ventilation pattern. Increasing the WWR to 0.15–0.2 enhances air exchange rates by two to three times, yet it concurrently exacerbates humidity, particularly in the wing rooms, where levels can rise by as much as 4.5%.

In winter, a larger WWR (≥0.15) improves humidity dissipation, but at the cost of thermal retention, with wing room temperatures falling to 17.32 °C—near cold discomfort thresholds. A reduced WWR of 0.05 helps retain heat but leads to moisture accumulation, highlighting the trade-off and the necessity for seasonally balanced designs.

These findings reveal a causal linkage: in transitional climates like Kunming’s, the interplay of high humidity and moderate wind speeds shapes the optimal WWR differently across seasons. Low WWR reduces humid air inflow in summer while preserving indoor heat in winter, thus climate characteristics directly mediate how WWR influences temperature and humidity patterns.

4.1.2. Spatial Heterogeneity in Thermal-Ventilation Performance

Due to the symmetrical interior geometry, the central hall shows persistently low air change rates (<1/h), indicating stagnant airflow regardless of WWR variation. In contrast, wing rooms, with greater façade exposure, experience pronounced ventilation gains under increasing WWR. For example, at WWR = 0.2, wind speed in the first-floor right-wing room rises by 154%, and ACH increases nearly threefold.

Humidity accumulates in wing rooms during summer (up to 75.55% at WWR = 0.2), while winter ventilation reduces humidity but risks overcooling. However, this enhanced ventilation also leads to humidity buildup in summer (up to 75.55% at WWR = 0.2) and overcooling in winter.

These spatial variations reflect how interior geometry—such as the enclosure of central halls and openness of wing rooms—modulates the effectiveness of WWR adjustments. Inward-facing window placements in compact rooms intensify the impact of WWR on both airflow and moisture, underscoring a geometric dependency in thermal-humidity outcomes.

4.1.3. Threshold Effects of WWR on Comfort

Based on the simulation outputs, the optimal WWR range for both summer and winter is between 0.1 and 0.15. In summer, this range ensures sufficient ventilation to reduce indoor temperature while minimizing humidity rise. In winter, it provides a compromise between reducing humidity and limiting heat loss, achieving a tolerable thermal comfort band without triggering overcooling or moisture buildup. This confirms the presence of threshold effects: beyond WWR = 0.15, both comfort and energy efficiency deteriorate due to either excess ventilation or thermal leakage.

4.2. Theoretical and Practical Implications

The study validates the “One-Seal” dwelling’s passive strategies (e.g., small, inward-facing windows) for transitional climates, demonstrating their scientific rationale in managing dynamic thermal-humidity trade-offs. More broadly, it offers a methodological blueprint for quantitatively assessing other forms of vernacular architecture, bridging traditional wisdom with modern building performance simulation to inform sustainable design globally.

The use of a coupled EnergyPlus–OpenFOAM simulation framework enables more dynamic, site-specific insights compared to static thermal models. This approach allows for data-driven refinement of vernacular features based on actual environmental response.

Based on the results, modern retrofit strategies should prioritize asymmetric window distribution. It is specifically recommended that the WWR in the wing rooms be increased while maintaining conservative values in the central zones. This recommendation is because the wing rooms were identified as zones prone to insufficient ventilation, leading to summer heat and humidity accumulation. The simulations demonstrate that selectively enlarging window openings in these peripheral rooms significantly enhances local airflow, increasing air change rates by nearly threefold and wind speeds by over 150%. This targeted approach effectively resolves the spatial imbalance by improving ventilation where it is most needed, offering a replicable strategy to resolve airflow imbalances across rooms with different geometries, while still adhering to the aesthetic integrity of heritage structures.

4.3. Limitations and Future Work

The current study models vacant, unoccupied conditions and thus does not account for metabolic heat gain, behavioral adaptation, or diurnal occupancy cycles. Future work should incorporate these variables to refine thermal comfort thresholds under realistic use. Meanwhile, the model incorporates a typical seasonal window opening schedule, which adds a layer of realism compared to a static assumption. However, this schedule is fixed and does not yet account for the stochastic, real-time decision-making of occupants, who may alter their behavior based on immediate weather changes or individual comfort preferences. Future work could develop probabilistic occupant behavior models for even greater accuracy.

Material-wise, traditional walls and windows were modeled as-is; the potential for integrating modern materials—such as insulated glass or low-emissivity coatings—remains unexplored. Such elements could shift optimal WWR boundaries significantly and merit further study.

These limitations point toward several clear directions for future research:

• Incorporate dynamic occupancy schedules and metabolic heat gains to create more realistic thermal comfort models and refine comfort thresholds.
• Investigate the performance benefits of integrating modern materials and technologies, such as high-performance glazing or phase-change materials, to identify optimal WWR boundaries for retrofitted dwellings.
• Develop and test dynamic control strategies for window openings, potentially linked to real-time indoor and outdoor environmental data, to maximize passive comfort.
• Expand the analysis to include the impact of microclimatic variations and future climate change scenarios on the dwelling’s long-term resilience and adaptability.
• Extrapolate the validated methodology to other traditional building typologies in similar high-altitude, subtropical climates (e.g., in Latin America or East Africa) to build a broader knowledge base on passive design strategies.

Given the parametric framework’s adaptability, the findings may be extrapolated to regions sharing similar seasonal humidity-wind characteristics (e.g., high-altitude subtropical climates in Latin America or East Africa). In more humid climates, WWR optimization should prioritize humidity control, implying lower thresholds (~0.05–0.1), while in arid climates, larger WWRs (~0.15–0.25) may be preferable to enhance ventilation without exceeding comfort humidity ranges.

The data confirms that in transitional climates like Kunming, a WWR of 0.1–0.15 consistently balances ventilation and thermal retention across seasons. Geometric asymmetries in room layout further condition the local response to WWR adjustments, suggesting that thermal and humidity control must be space-specific. Therefore, practical optimization should integrate both climate-specific and geometry-responsive WWR tuning to ensure passive comfort without sacrificing energy efficiency. In conclusion, this research bridges traditional architectural wisdom with modern performance analysis, providing a data-driven foundation for sustainable design in Kunming and analogous regions. By harmonizing historical adaptability with contemporary energy goals, the study contributes to the global discourse on low-carbon built environments.