1. Introduction
With accelerating urbanization, the demand for housing has continued to surge, resulting in a continuous increase in residential land and the development of high-density and high-level residential spaces [
1,
2]. However, as these areas are no longer able to meet the needs of high-quality development in the new era, some urban construction plans have proposed the development of multistory housing in China. Public spaces are considered a vital component in residential areas and are closely linked to the daily lives of the residents [
3]. They provide spaces for rest, socializing and recreational activities. The “Interaction and Space” concept promotes the use of extensive lawns in modern urban areas as they encourage interactions among residents and help to enrich their social lives. Perspective views on public spaces showcase a vibrant variety of activities in these areas [
4].
The design of modern public spaces in settlements often prioritizes form, aesthetics or function over the quality of the outdoor thermal environment [
5]. Unfortunately, this approach often overlooks the importance of considering the outdoor microclimate and results in the deterioration of the public space environment. To address this issue, researchers have focused on microclimate optimization design as a way to optimize public spaces in settlements. By doing so, we can mitigate the conflict between people and the environment, resulting in a more pleasant and energy-efficient living environment [
6,
7].
When simulating microclimatic environments, the relationships between buildings and their surroundings, as well as between the buildings themselves, are also taken into consideration. This involves optimizing the overall layout and form of the buildings, including the cluster layout, orientation, streets and open spaces between the buildings. Golony G.S proposed that the influence of urban forms on the urban microclimate should be paid attention to when designing public spaces in 1996 [
8]. As early as the 1870s, some scholars had already conducted research on the influence of mesoscale urban forms on the thermal environment, having focused more on neighborhood-scale morphological indicators early on and later gradually carrying out research on specific spaces such as green spaces and square layouts. C.S.B Grimmond conducted a study on the urban isthmus [
9] and found that building height, street height-to-width ratio and wall material all had an impact on the OHM (Objective Hysteresis Model) parameters in the OHM. Akira et al. investigated the impact of the building layout and morphological indicators on the thermal environment of the largest residential project in Japan, Domus City [
10], and summarized six layout patterns, including the row, enclosure, point, rotating row and diagonal. F Bourbia and H.B Awbi studied the effect of different street aspect ratios on solar radiation [
11] and came up with the most suitable aspect ratio for different orientations. A. Yezioro et al. [
12] studied the thermal environment of urban plazas with the SHADING model to investigate the relationship between the aspect ratio, surrounding building heights and thermal environment of urban squares, and the results showed that the thermal environment of rectangular squares elongated along the N-S direction was optimal, that of the rectangular squares elongated along the E-W direction was the worst, that of the rectangular squares elongated in the NW-SE and NE-SW directions performed better and the surrounding building heights should not exceed half of the width of the square. Chirag and A. Ramachandraiah [
13] studied the effect of the street aspect ratio and greenery coverage on the thermal environment through actual measurements and an ANOVA and found that the correlation between the street aspect ratio or greenery coverage and PET (physiological equivalent temperature) was small, so they proposed a new index called the HXG (read as H cross G) scale that had a satisfactory correlation (R
2 = 0.648), showed the product of the aspect ratio and greenery coverage and verified that the HXG had a stronger correlation with thermal comfort. iMohammad et al. [
14], by simulating the thermal environment of three different urban forms: the point, row and courtyard, found that the average radiation temperature and direct solar radiation had a greater impact on the thermal environment in different layouts and that the courtyard was the optimal layout for a Dutch thermal environment in June. iGamero et al. [
15] studied the thermal environment of high-density slab outdoor spaces by measuring a total of 63 semioutdoor spaces and classifying them into five types: the perimeter buffer (PB), sky terrace (ST), horizontal breezeway (HB), breezeway (BAT) and vertical breezeway (VB). These five types of semioutdoor spaces were then compared in terms of thermal comfort (based on the PMV *), environmental parameters and building form indicators, and it was found that thermal comfort was most desirable factor; the type VB and HB spaces had high comfort, and the lowest comfort was found in the type PB, BAT and ST spaces. In 2023, Dayi [
16] simulated 17 tree layout abstraction models by using numerical simulations and found that the correlation between the tree layout and mean radiation temperature and physiological equivalent temperature was very high, with a maximum reduction of 20 °C and 11 °C, respectively. Moreover, the tree layout mainly affects the wind environment, so when considering summer thermal comfort, it is recommended to place trees downstream of the wind. The study of the microclimate in settlements has primarily focused on the morphological indicators of buildings and urban design strategies. However, there is still a gap in the research on the direct impact that outdoor public spaces have on people’s comfort during outdoor activities. In this regard, this research takes a unique approach by considering public space in settlements as the main focus of investigation. By incorporating public spaces into the analysis, this research aims to fill the gap in the research on outdoor comfort in settlements. This approach has the potential to provide valuable insights into the design of public spaces that enhance the overall comfort and livability of settlements.
As technology advances, research on the physics of buildings is increasingly relying on computer simulations rather than traditional field measurements. Artificial intelligence is also becoming more integrated into the field, promising greater efficiency and more comprehensive simulations under various conditions. This shift from field measurements to simulations has opened up new possibilities that were previously not attainable. Specifically, computer simulations allow researchers to examine the complex relationship between urban forms and the microclimate. By modeling different building materials, layouts and site orientations, researchers can better understand how the microclimate is impacted by urban forms. As a result, this approach allows for more in-depth analyses and more opportunities to improve the comfort and livability of urban areas. In 1983, British meteorologist Luke Howard conducted the earliest measurements of the urban heat island effect and proposed the concept in his book “The Climate of London” [
17]. Following this, many researchers have studied the urban microclimate through field measurements. Hoyano [
18] studied the impact of green space types on the thermal environment through field measurements, and Koichi Nagara [
19] studied the greater susceptibility to discomfort near intersections by using both field measurements and subjective evaluation questionnaires. It was not until the development of computer science made it possible to simulate the physical urban environment with software that researchers began to use simulation software to carry out studies. Michael [
20] and others used ENVI-met software to simulate the effects of greenery, buildings and street gaps on the thermal environment. Fazia [
21] studied the impact of street canyon geometry and sky view factors on the thermal environment by conducting numerical simulations. The application of genetic optimization algorithms allowed the researcher to continually innovate the research methodology. The application of genetic optimization algorithms has led researchers to innovate their methods. Xing Shi [
22] integrated Energy Plus into an optimization tool by writing a DOS file and applied a multiobjective genetic algorithm to find the optimal set of Pareto solutions to obtain the best design strategy for office buildings, with the goal of ensuring thermal insulation and energy saving; today, Ecotect [
23], Energy Plus [
24] and other performance simulation platforms have been widely popularized. However, the performance is only used as a reference condition in the design and cannot be used directly to guide the creation of buildings, and the mutual limitations of the different technology platforms result in a lack of integrated involvement of the environmental influences in the design. As technology evolves and architects master interdisciplinary software, the performance-driven optimization of design is becoming the focus of the moment. Ibrahim [
25] used the ladybug tool to study the relationship between three urban form parameters—the courtyard, row and point—and outdoor thermal comfort and energy efficiency, and the results showed a strong correlation between the form parameters and combined the thermal comfort and energy performance, with the building density having the greatest impact on thermal comfort and energy consumption. Yan Hainan [
26] constructed a workflow for the rapid prediction and evaluation of the comprehensive performance of office buildings, which was used to predict and evaluate solar radiation, indoor and outdoor thermal comfort and indoor lighting, and it achieved a classification prediction accuracy of 0.77 with a recall of 0.59 and an F-1 of 0.75 through XGBoost, which helps to optimize the comprehensive performance of office buildings in the early design stage. This paper aims to focus on the automatic optimization search of public spaces by using genetic algorithms and optimizing the physical performance of buildings. Previous research has already investigated the commonly used layout methods and drawn valuable conclusions; hence, this study will not delve into creating public spaces with varying layout methods. The objective of this optimization search is to improve microclimate comfort during the summer and winter. This research was conducted by using Rhino(RH) and Grasshopper(GH) software and platforms.
The main research components include:
Conducting a comprehensive review of the existing literature on microclimate research in settlements as well as the research methods that combine building planning and design with environmental physical performance optimization.
Analyzing meteorological data to summarize the basic characteristics of Changsha’s climate and creating an abstract model of multistory settlements in Changsha by conducting field research and utilizing web data.
Setting the location and number of public spaces as the independent variables and measuring the dependent variables, which include the average outdoor thermal comfort and sunshine hours in the summer and winter, by conducting a multiobjective search for optimization and utilizing the ideal model as the research object.
Conducting qualitative and quantitative analyses to rigorously analyze the results of the search. The qualitative analysis primarily focuses on the overall layout of the public space, while the quantitative analysis scrutinizes the morphological elements of the public space.
3. Results
3.1. Overall Optimization Results
In this experiment, based on previous climate parameters, a genetic algorithm [
50] was carried out to find the optimum public space layout, whereby five node-type public space locations were used as the independent variables and optimizing the mean value of the UTCI in the summer and winter was the objective, which took a total of 52 h. The mean values of the summer UTCI and winter UTCI converged, but the percentage of days where the sunshine hours were less than two fluctuated during the two seasons without obvious signs of convergence, indicating that there were checks and balances among the three optimization objectives and the correlation between sunshine hours and the summer and winter UTCI was weak when changing the layout of the public spaces, so the subsequent discussion focuses more on the thermal environment in the summer and winter (
Figure 13,
Figure 14 and
Figure 15). The three objective values fluctuated up and down during the genetic optimization search due to the genetic algorithms causing continuous fluctuations in the objective values, which occurred so that the algorithm could explore all solutions, the premature convergence of the values could be prevented and a local optimum solution could be obtained [
26].
During the whole optimization process, two of the three dependent variables reached convergence after the optimization iterations occurred, in which the mean value of the UTCI in the summer decreased from 33.97 °C to 33.26 °C with a difference of 0.71 °C and the mean value of the UTCI in the winter increased from 9.95 °C to 10.58 °C with a total increase of 0.63 °C; because the objective was to optimize the thermal comfort, the optimization objectives were achieved (
Figure 16).
Only the relationship between the mean UTCI values in the summer and winter was considered when analyzing the Pareto evolution [
51]. While optimizing the summer and winter UTCI, it was observed that the mean summer UTCI and the mean winter UTCI reached convergence; however, the winter sunshine hours were shown to continuously fluctuate. This implies that the objective of optimizing the winter sunshine hours cannot outweigh the advantages and disadvantages of the other two optimization objectives. To address this issue and make the analysis more accurate, the three-dimensional coordinates were converted to two-dimensional coordinates, and the optimization of the summer UTCI and winter UTCI were used as the primary objectives for the analysis. Additionally, the Pareto solutions for the 5th, 11th, 18th, 25th, 32nd and 39th generations were plotted with the mean thermal comfort in the summer as the horizontal axis (red axis) and the mean thermal comfort in the winter as the vertical axis (green axis). As shown in
Figure 17, the overall trend of the solution distribution increased and then decreased and gradually approached the origin of the coordinates. As the number of generations increased, the solution gradually resulted in the balance of winter comfort and summer comfort.
From the Pareto front solutions generated during the iterative optimization process, we selected 20 cases at random. Throughout the optimization process, random deletions were performed on the blocks.
Figure 18 shows that the process of block deletion went from being disorderly to more regular over time. The number of blocks deleted on the west and north sides decreased while those on the east and south sides increased. We also observed a higher frequency of block deletions in the central location during the later stages of the process.
3.2. Location
In the process of multiobjective [
52,
53] optimization seeking, the positions of the node space appearances gradually converged after being disorderly. After conducting a statistical analysis on the node records that were collecting during the optimization process as shown in
Figure 19, we found that the number and probability of each node being selected varied, and the more times the censored number was selected during this experiment, the better the optimization [
54] effect was for the overall UTCI value. From the frequency graph of the statistical results, it can be seen that the points with a higher frequency of occurrence were distributed in the southeast corner and middle of the plot, whereby the lowest frequency of occurrence occurred in the west and north. The body number 19 was selected for censoring the most times at 13.11%, followed by the body numbers 20, 15 and 14; additionally, the body in the center of the plot was also selected for censoring more frequently, and the body point that was selected for censoring the least was point number 6. The difference between the highest and lowest was large, indicating that there was a clear tendency for the genetic algorithm to find the best body numbers.
These results suggest that there is a correlation between the geometric properties of public spaces and thermal comfort in the summer and winter. The openness of public spaces is conducive to the flow of prevailing winds, which increases the wind speed and removes the heat in the summer but increases the wind speed and reduces the thermal comfort in the winter. In the summer, when there is a certain number of public spaces, the process of finding the optimum convergence involves a tradeoff between the two. When designing the layout of public spaces in multistory settlements, priority can be given to the south center and the most southerly position on the east side of public spaces when considering heat dissipation in the summer and warmth in the winter, which means that the west and north sides do not need to be treated as public space nodes.
3.3. Dispersion
The graphs indicated that as the dispersion of the public spaces increased, the average UTCI tended to rise during the summer and decline during the winter (
Figure 20 and
Figure 21). This indicates that public spaces that are too scattered in a residential area have a less favorable UTCI in both the summer and winter, while public spaces that are more concentrated or have a larger number of buildings and a more concentrated distribution are more favorable in terms of comfort, so the process of finding the optimal solution involves constantly weighing the needs of public spaces during both the summer and winter to find a better solution set. To provide a further explanation, the dispersion of public spaces refers to how spread out or concentrated they are within a residential area. When public spaces are too scattered, it can lead to less favorable thermal comfort levels for people during both the summer and winter. This is because dispersed public spaces may not provide sufficient shade, ventilation [
55,
56] or greenery to help cool or warm the surrounding areas, which results in discomfort for those who use them. In contrast, public spaces that are more concentrated or have a larger number of buildings and a more concentrated distribution can create microclimates [
57] that are more favorable for thermal comfort during both the summer and winter.
3.4. Relationship with the Windward Entrance
The results of the statistical analysis on the upwind opening rate are presented in
Table 2. The analysis demonstrated that there was no clear linear connection between the upwind opening rate and the UTCI during both the summer and winter. Additionally, the upwind opening rate was consistently greater than 0.33 in the Pareto front solution over every generation of the optimization process. These findings suggest that when designing a residential area, introducing openings in the first row of buildings can enhance the thermal comfort of the entire zone. By incorporating upwind openings and considering the prevailing wind direction during different seasons, other public space layout configurations can be aligned to facilitate the flow of air within the residential area. This linked approach to public space design can further contribute to enhancing thermal comfort in residential areas.
3.5. Larger Squares
During the typical generation of the genetic optimization process, the layout scheme of the larger square [
58,
59] was screened out, and the screening principle was that the adjacent censored body blocks were greater than or equal to three. Since the total number of censors was five, three censors or more were greater than the average and were considered to reflect a larger common space, and the final screening results are shown in
Table 3. As can be seen from the table, 90% of the larger squares were distributed at the edges of the plots, one scheme distributed the public spaces inside the plots [
60] and placed the spaces in the center of the plots and 82.4% of the larger squares that were located at the edges were in the southeast end while the rest were distributed in the south and east (
Table 4). These results were obtained by using the 3.6. Pareto Decentralization Typical Solution.
The last generation of solutions needed to be traded off before analyzing the typical solutions, and since the total sample of results was large due to the 55 Pareto solutions of the last generation, the ranking method [
36] was adopted in this paper to reduce the complexity of the analysis by ranking the samples. In order to rank the used results, the following formula was adopted:
Note: SUi is the mean value of the summer UTCI, SUmax is the maximum value of the summer UTCI, SUmin is the minimum value of the summer UTCI, WUi is the mean value of the winter UTCI, WUmax is the maximum value of the winter UTCI, WUmin is the minimum value of the winter UTCI, HPi is the number of public spaces with sunshine hours less than 2 h, HPmax is the maximum number of hours of sunshine in public spaces with sunshine hours less than 2 h and HPmin is the minimum number of hours of sunshine in public spaces with sunshine hours less than 2 h.
This formula was used to find an equally weighted rating of the three objectives, and it could give different weights to each objective so that they could be ranked again according to the final score. After calculating the rating of solution by using the formula, the top ten solutions were selected as shown in
Table 5.
From the table, it can be seen that the top ten solutions prioritized the three optimization objectives differently. The difference between the maximum and minimum scores of the winter UTCI was 0.28, which is not a big difference; the difference between the maximum and minimum scores of the summer UTCI was 0.48, which was somewhat large; and the difference between the maximum and minimum scores of the sunshine duration was 0.35, which is a small difference, so it can be deduced that the influence of the layout of the public spaces of the settlement on the three optimization objectives was ranked as follows: summer UTCI > sunshine duration > winter UTCI. Each solution in the list had different scores, among which the highest score for sunshine duration was 0.87 for solution 10, which had low UTCI scores for both the summer and winter, and solution 8 had the highest score for the winter UTCI, but this solution resulted in the lowest UTCI score in the summer and its total score was pulled down. The highest UTCI scores were found with solution 6 and solution 7, and only the winter UTCI scores were different between these two solutions, while the total scores and insolation were the same. In this paper, the top five solutions in the table were selected as typical cases for analysis, and these five cases were also balanced and met the three optimization objectives.
Firstly, the location of the public spaces in all five cases was concentrated in the center and southeast of the plot (
Table 6), which had some reference value for planning the layout of the multistory residential areas considering the thermal environment in the summer and winter. Secondly, if we hope to have a more balanced optimization effect in the summer and winter, the decentralized public space layout is better than the centralized one. Finally, if we hope to reduce the mean summer UTCI value, then the layout of a larger area of public spaces in the southeast location of a multistory settlement is favorable in terms of the summer wind penetration.
4. Discussion and Conclusions
4.1. Main Findings of This Study
After 39 generations of optimization, 55 valid solutions were obtained, in which the mean value of the summer UTCI decreased from 33.97 °C to 33.26 °C with a difference of 0.71 °C, and the mean value of the winter UTCI increased from 9.95 °C to 10.58 °C with a total increase of 0.63 °C. The ranking method was used to rank all the solutions in the last generation, and we selected the top five solutions as typical cases. An analysis was carried out, and it provided a programmatic reference for the layout design of multistory settlements in cold winter and hot summer areas.
Studies have demonstrated that strategic modifications to public space design hold immense potential in ameliorating the surrounding outdoor microclimate environment. In order to promote ventilation throughout various localities within the residential area, open nodes should be dispersed evenly while taking into consideration the need for optimal heat retention practices in the winter and efficient cooling mechanisms in the summer. The wind velocity can be augmented by expanding the width and surface area of summer-facing openings, which in turn facilitates steady inlets of fresh air and promotes the air current flow throughout the residential area. Public spaces such as plazas should ideally be located in south-facing regions, while a layout pattern that gradually reduces the area of public spaces from the southeast to the northwest is conducive to mitigating the prevalence of chilly winter winds, which thus ensures a suitably warm and cozy living environment for residents.
4.2. Public Space Design Strategy
Based on the above experiments and analysis, the following design strategies are proposed for the layout [
61] of public spaces in multistory settlements in hot-summer and cold-winter areas:
- (1)
Consider the layout of the windward location in summer and winter
The main wind direction in Changsha in the winter is northwest, so reducing the amount of public spaces in the west and north side can effectively reduce the influence of the prevailing winds in the winter. Public spaces should be prevalent in the east and south side, which is conducive to the prevailing wind blowing in the summer and promotes the airflow in the internal passage. However, we should avoid creating vertical public spaces in areas with prevailing wind as this may produce a static wind effect, so the overall layout should include vertical public spaces in the north and west side as this will prevent the static wind effect. Therefore, the overall layout should include buildings on the north and west side, and the amount of public spaces in the east and south side should increase [
62].
- (2)
Uniform dispersion is better than dispersion
Uniform public spaces result in more shading being provided to the public spaces by buildings, which reduces the impact of solar radiation on the overall microclimate, while uniform and continuous public spaces are conducive to introducing the inflowing summer breeze into each public space, which has a certain effect on improving the local environmental comfort.
- (3)
Larger squares are arranged in the south or southeast
Larger squares arranged in the south can help reduce the shading of sunlight [
63,
64] generated by buildings, while large south-facing squares can better introduce the summer southeast wind and improve the overall environmental comfort.
This paper analyzed the influence of public space layout elements on the thermal environment of multistory settlements in hot-summer and cold-winter regions. Due to the complexity of settlement forms and the differences between different climatic zones, the research results are only relevant to the design of settlements under specific conditions. In future research, the optimization of more types of settlements in different climate zones can be considered.
4.3. Limitations and Future Work
This study has some limitations that need to be addressed in future studies.
First, with regard to the scope of this study, this article exclusively scrutinized multilayer residential areas, and although the typology of residential neighborhoods is diverse, research on high-rise residential areas and mixed-use residential areas is scarce. In upcoming research dedicated to the layout of public spaces within residential areas, high-rise residential areas or mixed-use residential areas ought to be the subject of scrutiny and further optimization through simulation.
Second, in terms of technical software, this article employed the LB toolset and octopus plugin embedded within GH for the simulation. When executing simulations of the thermal environment, Open studio was leveraged. However, if a Python program is developed, Envi-met could be considered to enhance the accuracy of the calculations.
Third, in relation to the design variables, the present study only focused on a few independent variables, namely the number, location and area of public spaces. Nonetheless, open-air public spaces are influenced by intricate environmental factors such as underlayment materials and spatial heights, which warrant further investigation.
Fourth, in the context of the optimization objectives, the geographical locale of interest was a region with a hot summer and cold winter, and summer and winter thermal comfort served as the optimization objective. If the region is a colder zone or a distinct climatic region, different optimization objectives must be selected in accordance with its respective climatic characteristics.