Optimization of Landscape Spatial Configuration and Form for Thermal Comfort: A Case Study of Urban Square, Shanghai

Abstract

Outdoor public spaces that provide a comfortable microclimate significantly contribute to urban livability. However, more elaborate investigations are needed to enhance the research-based design of landscape spatial form for optimal thermal comfort performance. This study aimed to optimize the morphological design of urban squares considering summer and winter microclimates by integrating multiple landscape elements of vegetation, waterbodies, buildings, and ground. The built environment microclimate simulation and validation of multi-physics coupling were conducted for thermal comfort, planar heterogeneity diagramming, and spatial typology identification. Further, research through design (RtD) was applied to reconstruct various spatial configurations and orientation forms to compare the relative thermal comfort areas of these geometrical prototypes in the target time period and square core zone. Among the landscape types, two identified spatial types for achieving better thermal comfort are the opening of a windward enclosure structure (ECS) that draws wind into the square in summer and an ECS without a windward opening that creates a more extended wind protection area in winter. Moreover, results of RtD show that the prototype with the smallest orientation angle to the prevailing wind direction has the most optimized thermal comfort during summer, while the form with a smaller angle to the prevailing wind direction is more favorable in winter. These findings provide methodological guidance for climate-adapted landscape square form optimization.

1. Introduction

With the significant increase in the number of thermally uncomfortable days and the urgent need to improve the quality of urban public spaces in China [1], outdoor thermal comfort design is an effective measure to improve the situation. Municipal squares are the result of the spatial production of urban power in China and are nodes of socio-spatial organization, accommodating large-scale assemblages of political and domestic activities [2,3]. Therefore, they are generally characterized by hardened paving and huge, unshaded areas. There is still a lack of practical schematic guidelines for open squares [4], and methodological research for the design of microclimate-adaptive squares is necessary. This study is based on a ternary framework [5,6] to investigate the design optimization of the spatial configuration and form of Century Square in Shanghai (Figure 1).

High-resolution modeling of the current built environment and simulation of the microclimate’s spatial performance by multiple landscape design elements such as vegetation, water bodies, and buildings are essential. Most architecture and urban design studies ignore the integral role of vegetation and water bodies [4,7,8,9,10]. Landscape researchers are insensitive to modeling spatial details at small scales [11], such as microtopography and structures, and tend to omit the influence of the surrounding complex urban environment [12].

Landscape studies that verify validity through field investigations are hypothetical prerequisites. Most of the microclimate simulation studies conducted by landscape architects use ENVI-met, which lacks grid sensitivity verification and temporal wind correction [13,14,15,16]. The case studies and spatial program analyses were not sufficient to clearly define the design target zones and time periods; the ones for 12:00 or 14:00 obviously did not directly satisfy the demand for thermal comfort in winter and summer [7,17,18,19,20].

Thermal comfort mapping of coupled multi-physics and spatial differentiation of microclimate perception are the mediators for research interventions in the landscape design process. Outdoor thermal comfort is revealed to be the most important factor affecting the use and activities of public spaces. Human perception of microclimate used as a design purpose and clue is influenced by a combination of meteorological parameters such as air velocity, temperature, humidity, radiation, etc., rather than a single one [21,22,23]. Optimized design enables comprehensive environmental regulation [24] and provides more attractive places. Furthermore, in the engineering tradition of landscape architecture, both design and construction work with “diagrams” [25,26,27]. Corbusier said that “the plane is the generator” [28], and Stan Allen argued that interdisciplinary knowledge must be transformed into graphics if it is to intervene in design [29].

Design is fundamentally about visible operations of form, not just pattern types for narration. Most typology-based studies propose only vague strategic texts of better spatial types [30,31,32], lacking material form [33,34,35]. However, research in landscape rarely produces geometric knowledge that is directly applied by designers [36,37]. In contrast, there has been much research through design (RtD) in architecture and industrial design for performance-based form generation, optimization, and morphogenesis [22,27,38,39,40,41].

This study reorganizes the square configuration after screening the better spatial types of the current built environment, explores a geometric optimization method under the constraints of one type, and generates a square form by the comparative test of different spatial orientation prototypes [42,43,44,45] that promotes thermal comfort for the target time period and the core zone (Figure 1).

2. Materials and Methods

2.1. Case Study Area and Formal Analysis

Shanghai, the largest city in China and a Global City of Excellence, belongs to the hot–humid summer and cold winter climate zone [46], with prevailing Southeast (SE) winds in summer and Northwest (NW) winds in winter [47]. Century Square (121°5′ E, 31°2′ N), located in the municipal district of Pudong, Shanghai, was constructed in 1996 and is the starting point of the famous Lujiazui Urban Axis (Figure 2). Reasons as a case study:

• It is a typical urban square in a new low-density and high-FAR city, complete with a large central ground, and new city construction is emulated throughout China;
• Unlike Tiananmen Square (44 hm²) in Beijing, People’s Square (14 hm²) in Shanghai, and Quancheng Square (17 hm²) in Jinan, which have extremely high-level political functions, it is universal and classic as a local municipal square;
• A total of 800 m to the east of it is the national basic station of the Pudong Meteorological Bureau, and the synchronized observation data are suitable as the boundary condition for simulation [48];
• Its built environment is dominated by landscape design elements.

Pudong Century Square was constructed to serve as a central place to set off the spatial power and order of the surrounding government and public buildings. Furthermore, unlike traditional large Chinese municipal squares that sit entirely in the north–south direction [49], its spatial orientation (123.3° from the N-direction) is close to parallel to the direction of the prevailing winds (SE, 135°) [47], which will be tested in this study to see if this is the geometrical form that is optimized for thermal comfort [50,51].

The area of Century Square is 3.6 hm², with a side length of 180 m × 200 m (Figure 3). The 180 m × 180 m topographical building (5 m height, with a green slope as the outer façade) and the forest belt (9 m height of Camphora officinarum and 6 m height of Osmanthus fragrans) together form a square enclosure structure (with an opening [52] in the midpoint of each side) that defines the inner visual space of the square (Figure 4) [50]. The space is on a medium sense-of-place scale. The center of the square is a 60 m diameter circular granite ground surrounded by a sunken water surface (water depth 10 cm).

The circular ground is the core program of Century Square, a container and node for collective public activities [40,53], the so-called “urban meeting room”. The enclosure structure (ECS), composed of a forest belt and topographical buildings, is the main design element that vertically forms the square space and intervenes in the microclimatic environment.

2.2. Current Built Environment Simulation

2.2.1. Microclimate Measurement and Questionnaire

Nine watchdog 2000 weather stations (Table S1) [54,55], shown in Figure 3, were placed to record microclimate parameters, including relative humidity (RH, %), solar radiation (SR, W/m²), air temperature (T_a, °C), and air velocity (V_a, m/s), at 1.5 m height above the ground with a 1 min data log interval from 8:30 to 17:30. And the criteria for their location were based on different microclimatic types (opening of enclosed structures, edge of the waterbody, hard ground surface of the unshaded square, shaded area under canopy, roof, and bottom of the topographic building). Measurements were taken on March 10 (winter) and August 6 (summer), both of which were sunny and less cloudy. The measured data were used to calibrate the validity and reliability of the simulation.

The questionnaires in Century Square lasted from 8:30 to 17:30 on March 10 in the winter and July 27 and August 6 in the summer. The cumulative total of participants was 41, with 28 females and 13 males, mainly normal college students with an average height of 167 cm, a weight of 55 kg, and an age of 21.5 years. Participants spent 5 min at hourly intervals to freely find a location in the square, then remained standing at ease (1.2 Met, metabolic equivalent), followed by 5 min to complete the questionnaire (Table S2).

The questionnaire asked a participant to vote subjectively on the wind sensation (WSV), humidity sensation (HSV), air temperature sensation (ASV), solar sensation (RSV), thermal comfort (TCV), and thermal sensation (TSV) using ASHRAE (American Society of Heating, Refrigerating, and Air-Conditioning Engineers) survey questions ranging from five to seven point scales [56]. A total of 364 valid questionnaires were received. To avoid outdoor heat acclimatization [57], participants were arranged back into the air-conditioned room during each questionnaire interval and were asked to find a different location every hour.

This experiment was approved by the university’s ethics committee and conformed to the guidelines contained within the Declaration of Helsinki.

2.2.2. Numerical Simulation of Multi-Physics

In the local climate, the CFD (Computational Fluid Dynamics) solver scStream V14 (Software Cradle Co., Ltd., Osaka, Japan) based on the finite volume method and structured grid meshing was used. We employed it to simulate the microclimate of wind, temperature, humidity, and radiation field in the square composed of the underlayment, water body, and greenery, as well as in the surrounding urban environment.

• Governing equations and turbulence model

We conducted an unsteady-state Reynolds-averaged Navier–Stokes (URANS) simulation to obtain the multi-physics fields [7]. The resolution includes mass continuum, momentum, energy conservation, standard k-ε, and diffusion equations as follows [58]:

$$\frac{\partial u_i}{\partial x_i}=0$$

(1)

$$\frac{\partial \rho u_i}{\partial t}+\frac{\partial u_j\rho u_i}{\partial x_j}=-\frac{\partial p}{\partial x_i}+\frac{\partial }{\partial x_j}\mu \frac{\partial u_i}{\partial x_j}-\rho g_i\beta \left(T-T_0\right)+F_i$$

(2)

$$\frac{\partial \rho C_{p}T}{\partial t}+\frac{\partial u_j\rho C_{p}T}{\partial x_j}=\frac{\partial }{\partial x_j}K\frac{\partial T}{\partial x_j}+h$$

(3)

$$\frac{\partial \rho k}{\partial t}+\frac{\partial u_i\rho k}{\partial x_i}=\frac{\partial }{\partial x_i}\left(\frac{{\mu }_t}{{\sigma }_k}\frac{\partial k}{\partial x_i}\right)+G_s+G_T-\rho \epsilon +F_k$$

(4)

$$\frac{\partial \rho \epsilon }{\partial t}+\frac{\partial u_i\rho \epsilon }{\partial x_i}=\frac{\partial }{\partial x_i}\left(\frac{{\mu }_t}{{\sigma }_{\epsilon }}\frac{\partial \epsilon }{\partial x_i}\right)+C_1\frac{\epsilon }{k}\left(G_s+G_T\right)-C_2\frac{\rho {\epsilon }^2}{k}+F_{\epsilon }$$

(5)

$$G_s={\mu }_t\left(\frac{\partial u_i}{\partial x_j}+\frac{\partial u_j}{\partial x_i}\right)\frac{\partial u_i}{\partial x_j}$$

(6)

$$G_T=g_i\beta \frac{{\mu }_t}{{\sigma }_t}\frac{\partial T}{\partial x_i}$$

(7)

$${\mu }_t=C_{\mu }\rho \frac{k^2}{\epsilon }$$

(8)

$$\frac{\partial C}{\partial t}+\frac{\partial u_{j}C}{\partial x_j}=\frac{\partial }{\partial x_j}{D}_m\frac{\partial C}{\partial x_j}+s$$

(9)

where x_i is the coordinates (m), u_i is the air velocity of flow in the i-direction (m/s), ρ is the density (kg/m³), and t is time (s). p is the pressure of a fluid (N/m²), g_i is the gravity (m/s²), β is the thermal expansion coefficient (1/K), T is the temperature (K), and C_p is the specific heat at constant pressure (J kg⁻¹K⁻¹). K is the thermal conductivity (J m⁻¹s⁻¹K⁻¹), h is the heat source (J m⁻³s⁻¹), and k is the kinetic energy (m²/s²). ε is the dissipation rate (m²/s³), σ is the stress tensor (kg m⁻¹s⁻¹), μ is the viscosity (kg m⁻¹s⁻¹), and R is the gas constant (J kg⁻¹K⁻¹). D_m is the diffusion coefficient (m²/s), s is the source term of diffusive species (1/s), and turbulence model empirical constant coefficients (C_μ = 0.09, C₁ = 1.44, C₂ = 1.92, σ_k = 1, σ_ε = 1.3, σ_t = 0.9) [12]. The drag effect of tree leaves was calculated from the LAD [59,60,61]:

$$F_i=-\rho \eta \mathrm{LAD}{C}_d{u}_i\left|u_i\right|$$

(10)

$$F_k=2\rho \eta \mathrm{LAD}{C}_d{u}_i{}^3$$

(11)

$$F_{\epsilon }=\frac{\epsilon }{k}{F}_k$$

(12)

where F_i, F_k, and F_ε are the source or sink terms that express the effects of the tree [62]. F_i is the additional term in an equation of flow in the i-direction, F_k is the source term in the k equation, and F_ε is the source term in the ε equation. C_d is the coefficient of friction, a non-dimensional parameter to indicate the pressure loss in the flow over the tree establishment. LAD is the area density of the leaf, the total area of the leaves in the unit volume (m²/m³), η is the ratio of open green space coverage, a non-dimensional parameter to indicate the ratio of plant coverage on the ground.

2.

Transfer of heat and moisture

Water bodies and vegetation are the major sources of heat and moisture transfer in the built environment. Plants as a porous medium for convective heat transfer and transpiration [63] and water as a static interface for convective heat transfer and evaporation [64] have radiative absorption, transmission, and reflection effects. The heat balance equation is as follows [65]:

$$R_n=H_c+H_e+R_d+H_g$$

(13)

$$H_c=\mathrm{CHTC}\left(T_s-T_a\right)$$

(14)

$$H_e=EL$$

(15)

where R_n is the absorbed radiant energy (W/m²), H_c is the sensible heat flux (W/m²), and H_e is the latent heat flux (W/m²). R_d is the surface long wave radiation (W/m²), H_g is the heat storage (W/m²), and T_s is the surface temperature (°C). T_a is the air temperature (°C), and L is the latent heat of evaporation (2500 kJ/kg).

Convection transfers part of the heat to the air as sensible heat, and evapotranspiration exchanges the other part of the heat for latent heat. The sensible heat flux is related to the temperature difference and V_a. The latent heat flux is related to the water vapor partial pressure difference and V_a, which are modeled in this study as a function of the convective heat transfer coefficient, CHTC (W m⁻²K⁻¹) [66], and the evapotranspiration moisture flux rate, E (kg m⁻²s⁻¹) [67], respectively, with the mean values from the related studies [8,61,68,69,70,71,72]. Surface temperatures were not measured by the equipment but by observing the near-surface air temperature as the initial surface temperature. These enable the simulation of heat and moisture transfer from the water body and vegetation canopy to the air.

3.

High-resolution 3D model of the site and multi-nested computational domain

Through surveying and mapping, the case study area was modeled in three dimensions at a wall scale of 0.3 m resolution (Figure 4), with detailed references to reality in terms of the scale and materiality of landscape elements. The prerequisite for microclimate simulation is a detailed 3D model of the current built environment. In order to retain the effect of the surrounding city in the simulation, the Century Square of the research object is taken as the shape center. The surrounding 2.25 km² range is taken as the modeling scope, and the east is to the Pudong Meteorological Bureau (Figure 5).

Since the outermost boundary of the computational domain of CFD adopts the wall function method, the distance from the study area to the wall boundary should be more than five heights or three lengths of the study object [73,74]. Based on the size of the computational domain and Century Square, a three-layer multi-block is used for grid delineation (Figure 5):

• The outermost domain 1 is 1650 m (x) × 1500 m (y) × 200 m (z), the standard length of the grid is 20 m, and the threshold length is 5 m;
• The middle domain 2 is 300 m (x) × 400 m (y) × 100 m (z), the standard length of the grid is 5 m, and the threshold length is 1 m;
• The innermost domain 3 is 200 m (x) × 200 m (y) × 60 m (z), the standard length of the grid is 3 m, and the threshold length is 0.3 m.

The mesh is generated by representative local encryption with a geometric transition ratio of 1 to 1.2 and a total grid count of 11,793,413.

4.

Boundary condition and solver setting

The top and side thermal boundaries are solar radiation boundaries. The intensity of solar radiation and the solar direction are specified by the observational data library in the ASHRAE handbook (2013). This is determined by the geographic location of Century Square and the simulation time period, modified with measured data. Set the emissivity, absorption, reflectivity, and transmittance of buildings, ground, water, and vegetation to calculate the radiation configuration factor (View Factor) of blowing particles as radiation energy [75].

The flow boundary and initial condition are driven by the reference height wind velocity and direction, air temperature, ground surface temperature, relative humidity, and cloudiness coefficient data synchronized with the Pudong National Meteorological Basic Station. Century Square is within the logarithmic development area and the urban boundary layer, so the air temperature and humidity of inflow are set to the same value at height. The velocity is from the power-law boundary. The lateral wall boundaries of the domain were no-slip conditions; the top wall boundary is free-slip, whereas the power-law velocity was applied to the ground and building surfaces:

$$V=V_B{(\frac{Z-Z_0}{Z_B-Z_0})}^{\frac{1}{n}}$$

(16)

where Z₀ is the ground elevation (m), Z_B is the reference height (m), V_B is the air velocity of flow at the reference height (m/s), V is the air velocity of flow at the Z-height (m/s), and the surface roughness parameter (n = 3.7).

In the multi-physics simulation of the diurnal microclimate on March 10 in winter and August 6 in summer 2018, the pressure–velocity coupling was implicitly solved using the SIMPLE (Semi-Implicit-Method for Pressure-Linked Equations) scheme [76]. The advection terms were discretized using quadratic upwind interpolation of the convective kinematics in a staggered grid system [77]. The convergence criteria of the inner iterations were the stabilized residual curve and the scaled residual values of 10⁻⁶ for k and ε and 10⁻⁴ for T [73], employed with a courant number of 0.9 and automatic calculation of the variable time step.

2.3. Spatial Differentiation of Thermal Comfort

Based on the microclimate simulation, we generated planar diagrams of the thermal comfort indices, specifically the wet-bulb globe temperature (WBGT) and new standard effective temperature (SET*). These diagrams of microclimate perception illustrate how different landscape types impact the spatial distribution of thermal comfort within the square from the standpoint of thermal comfort zone collection.

2.3.1. Thermal Comfort Index

WBGT is an important implication for human health [78], an empirical index selected by ACGIH (American Conference of Governmental Industrial Hygienists) for assessing thermal stress, and has been certified by the ISO 7243 standard system [79,80,81,82]. This is widely used in the estimation of thermal environments for outdoor physical activities. WBGT is calculated by the four environmental elements [80,83]: V_a, RH, T_a, and SR. Evaluation of outdoor thermal perception by WBGT and SET* was found to be appropriate [84].

SET* is a thermal comfort index enrolled in the ASHRAE standard [56,85], which is widely used for the metabolic level of human activity around 1 Met [19,86]. SET* is calculated from the previously simulated V_a, RH, T_a, and sMRT (spatial Mean Radiation Temperature. In addition, it requires the atmospheric pressure measured at the meteorological station and the human status data (clothing thermal resistance of 0.5 clo in summer and 1.0 clo in winter, metabolic rate of 1.2 Met, height of 167 cm, and weight of 55 kg) [87].

2.3.2. Planar Diagram

Knowledge of thermal comfort needs to be spatialized, discretized, and geometrized if it is to truly intervene as a driver for material design, form optimization, and morphogenesis [4]. For instance, subjective and cognitive maps intervene in thermal perception to create planar patterns [88]. Networked, densely arranged weather stations provide Kriging-interpolated point data. However, the arithmetic capacity of thermal comfort simulation exceeds the number of realistic measurement points, allowing for a diagrammatic interpretation of the data. Some studies have visualized the collection of fields that reach the wind comfort range as the “wind comfort area” by simulation [10,45].

This study utilizes the geometric scale of the relative comfort area as the thermal perception assessment criteria to identify the better spatial type in the current design of Century Square [86].

2.4. Reconstruction of Square Landscape Spatial Design

2.4.1. Abstraction of Configuration Type and Geometry of Orientation Form

Based on the abstraction of the identified Century Square spatial types, prototypes are designed through the paradigm of RtD. Geometric variables are controlled for quantitative comparison of the thermal comfort of different forms.

The idealized present design is used as a base for element simplification and reconfiguration of landscape spatial types, leading to five types of square modes: (1) OP4, the existing Century Square design, with four openings in the enclosure structure (ECS), one at the midpoint of each side. (2) OP3, an ECS with three openings, eliminates the opening in the direction of the prevailing winter wind. (3) OP1, with one opening in the ECS, retaining only the opening facing the windward direction of the dominant summer wind. (4) HEC, a semi-enclosure structure facing the winter and summer wind directions. (5) HEP, retaining only the plantation oriented to the prevailing winter and summer wind.

For each type of spatial configuration, it is emphasized to establish three boundary inflow wind directions (SE/SSE/S in summer and NW/NNW/N in winter) to generate different geometry angles between spatial orientation and wind direction (11.7°, 34.2°, and 56.7°). This means that it corresponds to a univariate simplified performance test of three urban square design prototypes with different forms in summer (S1/S2/S3) and winter (W1/W2/W3) (Figure 6).

The computational domain of the simulation for the abstracted reconstruction is 3400 m (x) × 2200 m (y) × 200 m (z). The boundary conditions and other microclimate settings are the same as those in the previous simulation of the built environment simulation, except that the target time period is fixed at 16:30.

2.4.2. Grid-Independence Verification

CFD numerical simulations suffer from mesh discretization errors that characterize the error between the converged solution of the adopted grid scheme and the precise solution of the equations. The grid convergence verification is undertaken with the scenarios of progressive grid encryption. The schematic settings of the verification cases, including the total number of computational meshes and the threshold grid size, are given in

Table 1. Case scenarios for grid sensitivity verification.

Grid Scenario	Total Number of Grids	Threshold Length	Ratio of Error Area
W1−1.0	236 × 233 × 19 = 1,044,772	1.0 m (coarse)	6.544%
W1−0.7	287 × 290 × 19 = 1,581,370	0.7 m (moderate)	5.248%
W1	356 × 357 × 22 = 2,796,024	0.5 m (fine)	1.231%
W1−0.3	486 × 484 × 26 = 6,115,824	0.3 m (referential)	0%

.

Figure 7 shows that the air velocity error area of each scenario, particularly in the case of the extremely dense grids denoted as W1−0.3, decreases from 6.544% and 5.248% to 1.231% with the increase in the grid number. This trend indicates that the effect of continuing to encrypt the grid on the error has entered the stabilization interval of the convergence curve. Therefore, the mesh scheme W1 belongs to the convergence-independent solution, which can take into account both computational economy and grid discretization error control.

3. Results

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

3.1. Spatial and Temporal Validation of an Environmental Model

The error analysis between the measured and simulated data of V_a, RH, and T_a at the nine observation points showed an overall mean absolute percentage error (MAPE) of 3.9%, a correlation coefficient of 0.89, and a determination coefficient (R²) of 0.79 (Figure 8). The root-mean-squared error (RMSE) for V_a was 0.25 m/s. For RH, it was 2.6%, and for T_a, it was 0.65 °C. The errors were within a normal range, validating the effectiveness of the microclimate environmental simulation.

Table 2. Correlation analysis for thermal response votes.

	ASV	HSV	WSV	RSV
TCV (Summer)	−0.726 **	0.323 **	0.309 **	−0.752 **
TCV (Winter)	0.292 **	0.038	−0.101	0.178 *
TSV (Summer)	0.884 **	−0.332 **	−0.251 **	0.872 **
TSV (Winter)	0.855 **	−0.228 **	−0.266 **	0.717 **

** p < 0.01 (Wilcoxon signed-rank test). * p < 0.05 (Wilcoxon signed-rank test).

verifies that, in Shanghai, microclimate perception is affected by a combination of several meteorological parameters and cannot be studied for a single parameter. Moreover, the demand for outdoor thermal comfort and meteorological parameters in summer is opposite to that in winter [20]. In summer, Ta and SR need to be lowered and V_a raised, while T_a and SR need to be raised and V_a lowered in winter.

Averaged TCV (Figure 9) reveals that the diurnal variation in winter is a convex curve, with people preferring the sunny, warmer midday hours. In contrast, in summer, it is the opposite concave curve, with people feeling more comfortable with lower morning and evening temperatures and solar radiation. Notably, the most comfortable hours of the winter are the least comfortable in the summer. However, 16:30 in both winter and summer is the time of the thermal comfort transition. In winter, 17:30 is the worst time for daytime microclimate, and 16:30 is the deterioration phase. In summer, 17:30 is the best time for daytime thermal comfort, and 16:30 is the improvement phase.

Optimization of thermal comfort in the 16:30 h elongates the outdoor thermal comfort time in both winter and summer. Considering the time when people are out and about, the landscape design should prioritize 16:30 as the target time to improve the thermal comfort of the site. This program aims to efficiently achieve a faster improvement of the microclimate perception in summer and a slower deterioration in winter.

The simulation plots for 1.5 m pedestrian height reproduce the regular trend of T_a, SR, and RH over time (Figure 10 and Figure 11). In contrast, the wind direction varies between E and SE on the experimental day in summer, and V_a is influenced by the local area and does not correlate with the time. The temporal aspect of the thermal comfort simulation is consistent with the trend of the TCV.

3.2. Identification of Spatial Types That Improve Thermal Comfort

3.2.1. Summer

In summer, the opening of enclosure structure (ECS) is the type identified, better superior to the windward area of ECS than the leeward area. Thermal comfort is better when the opening of the ECS is oriented at an angle that coincides with the direction of the wind. It is very important to divert the wind into the square. The water landscape has a significant effect on the improvement of thermal comfort in the surrounding space, especially downwind. However, a constant increase in relative humidity may also have a negative effect. The space under the trees inside the enclosure improves the microclimate by substantially reducing solar radiation. Still, it does not affect at all the core area of the square’s central ground.

Specifically indicated by the qualitative interpretation of Figure 10:

• The wind direction is SSE at 8:30, but it is a quiet wind environment. The square is comfortable in the morning, with better thermal comfort in the space around the water body;
• At 9:30, the wind direction is SSW; better thermal comfort for SW–NE openings than NW–SE openings; poorer thermal comfort for the zone at the southwest edge of the square as a leeward area of the enclosing structure;
• The wind direction at 10:30 is SSE, the NW–SE openings are better than the SW–NE openings, and the spatial thermal comfort on the north side of the square is better than that on the SE side;
• At 11:30 and 12:30, the wind direction is SE; the overall microclimate reaches the extreme daytime heat state, in which the thermal comfort of the southeast opening is relatively better, the southeast side and northwest side of the plaza are both worse, and the northwest side is high in humidity due to the moisture accumulation, although it serves as the windward area;
• At 13:30, the wind direction is ESE, the thermal comfort of the space on the northwest side as the windward area is better than that on the southeast side, the NW–SE openings are oriented at an angle more parallel to the wind direction, which is the better spatial type, while the NE opening is the very uncomfortable type, and the overall thermal comfort of the square starts to improve;
• The wind direction is E at 14:30 and 15:30; the west side space has better thermal comfort than the east side, which is the leeward area; and the four sides of the openings are oriented at a similar angle to the wind direction and produce similar thermal comfort;
• At 16:30 and 17:00, the wind direction is ESE, and the thermal comfort of the square on the northwest side is better than the space on the southeast side as a leeward area, and the NE–SE opening is the best type now;
• With wind direction ESE at 17:30, the square as a whole has reached the most comfortable state during the summer daytime, and the northwest opening and the space northwest of the water body are the best types.

In summer, the opening of ECS is the type identified, better superior to the windward area of ECS than the leeward area. Thermal comfort is better when the opening of the ECS is oriented at an angle that coincides with the direction of the wind. It is also very important to divert wind into the square. The water landscape has a significant effect on the improvement of thermal comfort in the surrounding space, especially downwind, but a constant increase in relative humidity may also have a negative effect. The space under the trees inside the enclosure improves the microclimate by substantially reducing solar radiation. Still, it does not affect at all the core area of the square’s central ground.

3.2.2. Winter

In winter, the shaded space with evergreen trees is the least comfortable type, and the effect of water bodies on thermal comfort is not significant. Enclosure structure (ECS) acts as protection as a vertically confining element, and their leeward area is identified as relatively optimized, with better thermal comfort than windward areas and openings. The ECS provides better thermal comfort when oriented in the same direction as the wind. The Century Square design avoids shading of SR on the center ground, but wind-oriented openings in the enclosure should also be eliminated to provide better wind protection.

It can be identified from Figure 11:

• At 9:30, the wind direction is SSW, the thermal comfort is better in the leeward area of the ECS and the subway entrance building in the center, and the SW–NE openings are the worst;
• At 10:30, the wind direction is S. The south side of the square has better thermal comfort as the leeward area of the ECS, and the northeast opening is the least comfortable space, followed by the northwest and southwest openings;
• At 11:30, the wind direction is SSW, the relative thermal comfort area is also distributed in the leeward zone on the southwest side of the square, and the SW–NE openings are the worse spatial types for thermal comfort;
• The wind direction at 13:30 is SSE, the southeast side serves as the leeward area of the ECS, the thermal comfort is high in the southeast space and low in the northwest, and the northwest opening is the worst type;
• At 14:30, the wind direction is WNW, the NW–SE openings are the worst, and the overall thermal comfort of the square is high in the northwest and low in the southeast. The space on the northeast side of the square serves as a leeward area for ECS, which enhances thermal comfort;
• At 15:30, the wind direction is S. Thermal comfort is high in the south and low in the north. All four openings are worse, especially the northeast opening;
• Wind direction is S at 16:30; the space on the south side of the square towards nightfall becomes a leeward protection zone and is a relatively better type; thermal comfort is higher in the south and lower in the north but has become worse overall, with SW–NE openings and NW opening being worse;
• At 17:00, the wind direction is S. The square as a whole reaches the most uncomfortable state of the winter daytime, and the day ends. With the SSE/S wind direction, the square is affected by the huge shape of the Shanghai Science and Technology Museum, and the thermal comfort reduction effect of the SE opening is blurred.

However, influenced by the local complexity of the built environment in the reproducibility simulation, the different angles between the spatial orientation form and the wind direction have not been clearly quantified and compared with respect to the thermal comfort performance of the square, especially in the core zone (the circular ground in the center), in winter and summer.

3.3. Optimization of Geometric Forms for Thermal Comfort Performance

As mentioned earlier, the circular ground in the center is the core program of Century Square, which is the target zone for thermal comfort optimization in this study. Numerical simulations of the form prototypes with different orientations at the target time period of 16:30 in summer (S1—11.7°, S2—34.2°, S3—56.7°) and winter (W1—11.7°, W2—34.2°, W3—56.7°) were converted to WBGT and SET* plane distributions at 1.5 m pedestrian height. The relative thermal comfort area (RTCA) within the target zone was used as a performance criterion.

3.3.1. Summer

In summer, the cooler regions below 31 °C for WBGT and SET* were counted as relative thermal comfort areas [79,86,87,89]. For all five configuration types, it was the S1 prototype with the smallest angle between form orientation and wind direction that had the largest RTCA and the best thermal comfort in the target zone (Figure 12 and Figure 13).

The spatial configuration type of HEC, with the best thermal comfort in summer, was superior to OP4, OP3, and OP1. However, the differences between OP4, OP3, and OP1 were not considerable, suggesting that the landscape form should be designed with more permeable verticals in the windward direction in summer. The comparison between HEC and HEP suggests that the more compact ECS with forest belt and topographic building together produces better thermal comfort improvement from its opening compared to vegetation alone.

3.3.2. Winter

In winter, the warmer regions above 9 °C in WBGT and SET* were regarded as the relative thermal comfort area. With five spatial configuration types, the RTCA of the center ground decreases gradually as the angle between the form orientation and the wind direction changes from minor to major. The prototype with the smallest angle, W1, has the best thermal comfort (Figure 12 and Figure 13).

OP4 performs better than HEC and HEP, demonstrating that more spatial enclosure of the target zone in directions except the windward direction enhances thermal comfort in winter. OP3 and OP1 have the best thermal comfort in the winter, and the difference between them is quite slight. Both of them are much better than OP4, which verifies that in winter, the vertical ECS of the landscape should not be designed with an opening in the windward direction in order to facilitate the ECS and give the target zone a protection enhancement.

Meanwhile, OP3–S1W1 is a more appropriate design prototype for Century Plaza optimization if the thermal comfort improvement in both winter and summer is taken into account.

4. Discussion

Current research on urban squares has demonstrated that landscape design and construction are essential measures for achieving a comfortable and healthy outdoor microclimate and has concluded that patterns of certain configuration types or choices of particular materials are preferable strategies [27,32,33,90]. However, there is a lack of design guidelines for the operation of geometry [20,88].

4.1. Screening Types from Spatial Heterogeneity

In this study, a verified high-resolution simulation of the current built environment was conducted to reproduce the microclimate spatial efficiency of multiple landscape design elements such as plant canopy, water body, and building in winter and summer. Additionally, the spatial type was identified based on daytime multi-physics fields and thermal comfort planar diagrams:

Century Square is composed of a variety of spatial types, of which the opening of the square ECS is the recommended type in summer. The ECS serves as the vertical definition of the square space, and its windward opening produces an accelerating effect that draws wind into the square [48,91]. On the other hand, in winter, the square ECS acts as a vertical enclosure for wind protection, and its leeward area is the proposed spatial type and should adhere to an ECS with no openings on the windward face [34].

Consistent with related studies, waterscapes have an improving effect on downwind thermal comfort in summer, but increasing humidity can also lead to a negative effect [9]. In winter, its effect on thermal comfort is not apparent.

Shaded space inside the ECS in summer is indeed the best type of thermal comfort [17,30]. However, it is completely unable to affect the core and target zones of the square (the center ground). Space under evergreen trees in winter, on the other hand, is less thermally comfortable due to the absence of solar radiation [55].

4.2. Design Matters: Geometric Form Operation beyond Categorical Typology

Identifying the screened space types, five configurations of enclosure structures (ECS) and three prototypes of forms with different orientation angles were reconfigured, with 16:30 as the target time period. The test revealed that:

In summer, for any of the types, it is the form with the smallest angle between the spatial orientation and the prevailing wind direction that has the largest RTCA of the target zone, which is in agreement with the studies related to the design of building façade opening [22,23,76]. In winter, it is also the form that the smaller the prevailing wind direction, the larger the RTCA of the target zone [91] and the better the thermal comfort. In addition, it is suggested that the ECS on the northwest side of the square, which is facing the NW of the winter prevailing winds, eliminate the opening as an optimized design [34].

The current spatial orientation of Century Square is designed in a form that is almost parallel to the prevailing wind direction (SE), which improves thermal comfort compared to traditional south–north-oriented urban squares [11]. This provides an alternative explanation for Century Square and Century Avenue in Pudong, an extremely important urban axis in China.

4.3. Limitations of the Present Study

Microclimate CFD simulation is not a completely black-box automated process, which requires the assimilation of many environmentally measured indicators in addition to boundary conditions. The C_d, CHTC, and evapotranspiration rate E of water and vegetation are variables influenced by T_a, Rh, SR, V_a, and material properties [63,92], which are separate basic science topics. In this study, only a diurnal mean value could be taken based on related studies [93,94]. The study area subsurface was a totally hard material and did not address moisture and heat features of the lawn; the canopy 3D modeling used cylinders that could be modified for finer detail in association with LiDAR [62,69,95]. These will be further investigated in the future.

RtD is a suitable paradigm for practical disciplines [37,40]. Thermal comfort and multi-physics microclimate simulations and diagrams as design tools still need to be explained and refined. This includes elaborating on the spatial function of diagrams from the perspective of design disciplines, adding dimensions and parameters to form design, and reshaping the workflow of planners and designers. However, this study designed a handful of varying orientation angles for testing. It provides an optimization approach for climate-sensitive squares beyond shading [17,30]. This will be used for much more morphogenesis and the form optimization of geometry prototypes with more morphological parameters, such as heights, radians, and distances.

5. Conclusions

The multi-physics microclimate simulation of multiple landscape design elements in winter and summer is utilized for the thermal comfort performance of different configuration types and form designs. The main conclusions are emphasized as follows:

(1) The field investigation of Century Square verified the validity of the numerical simulations. The thermal comfort index simulation is compatible with the trend of the on-site questionnaire. The most comfortable time period in winter is opposite that in summer, but 16:30 in both winter and summer is the time of daytime thermal comfort transition. Landscape design should prioritize 16:30 as the target time period.

(2) In the summer, it was identified that the opening space of the enclosure structure (ECS) has better thermal comfort and is the type screened for Century Square, which makes inducing wind entry effective. Additionally, it was revealed that the shadow inside the ECS can significantly improve the microclimate of the space under trees but that it does not work at all in the center ground, which is the target area of the square. In winter, the leeward area of square ECS is the better type, and windward openings should be eliminated.

(3) A comparison of the relative thermal comfort area within the target zone confirms that the form with the smallest angle between the spatial orientation and the prevailing wind direction is the most thermally comfortable in the summer in any landscape configuration. The geometry with the smaller angle is also better in the winter. That is, the spatial form should be designed to be more parallel to the prevailing wind direction. Ultimately, an optimized Century Square prototype is proposed, taking into account both winter and summer thermal comfort.