Cooling Strategies for Urban Courtyard Blocks: A Comprehensive Analysis and Research Gap Assessment

4.1 History of courtyards

Courtyards are known functional living spaces that existed in human constructions for many years; their use can be traced back to Mesopotamia and Rome to the Islamic period. They were especially relevant in the hot climate zones as they provided an opportunity to manage heat and actions, and maintain a collective serenity. Originally, the inception of a courtyard involved the practice of functionalism while at the same time being an architectural design element that has been used over the years; today’s concept of a courtyard optimization aims at refining features of a courtyard that were signifying benefits in the past through methods that are modern though based on history principles where comfort, technology, sustainability and the use of the right material in construction are given paramount importance and hence qualitative analysis is also essential. Many studies have been focused on the history of courtyards [12, 13]. Optimizing courtyards for various purposes, including cooling and enhancing outdoor comfort, has a rich history rooted in architectural and urban design principles. The evolution of courtyard optimization can be understood through key milestones and developments over time [14]. This is stated briefly in Figure 6.

Courtyard optimization includes several key concepts and innovations. First, microclimate control is central to courtyard design, leveraging elements such as shading, vegetation, and water features to influence local environmental conditions [15]. Second, courtyards act as natural ventilation systems, effectively channeling airflow through buildings to enhance indoor air quality. Privacy is another important aspect, providing enclosed, private spaces within urban settings for outdoor living and recreation. Following Figure 7, depicts the traditional courtyards from various countries and their variations in design. Shape and form affect cooling in courtyards by the promotion of cross ventilation where spaces are opened to allow the air to move and cool the surfaces. Water sprinkling, or plants in the courtyard can also help cool the air by evaporation and shading based on the area. As to the size, form, and orientation of the courtyard, they also play a role in the chilling effect or the amount of sunlight able to penetrate the building. 

figure6.png

Figure 6. Evolution of courtyard optimization

image009.png

Figure 7. Variation of courtyard designs

Modern field development emphasizes sustainability by combining passive design with energy-efficient design elements, reducing environmental impact. In addition, energy-efficient arenas focus on reducing energy consumption by choosing smart designs. Finally, achieving thermal comfort is a modern goal, and there are aspects of the field such as temperature and humidity that are carefully monitored to ensure the health of the occupant [16]. The thermal comfort and microclimatic conditions of a modern society depends on the design of urban courtyard blocks. If the courtyard design is neglected, this may result in higher building temperatures, increased energy consumption and reduced human comfort. Considering this, researchers have made important contributions over past years to overcome a variety of challenges faced due to irregular courtyard design [17]. Our review of literature offers a detailed analysis on the studies by various researchers. We here focus mainly on the type of algorithm used by researchers, numerical modeling techniques and more, to make right decisions related to urban courtyards. The urban heat island effect is the foremost challenge that has been faced by the modern blocks, where the urban societies experience higher temperatures compared to their rural counterparts. Urban courtyards if optimized effectively can help in eliminating this issue of "heat island effect". With the help of this research, we can say that the researchers have discovered a few ways to manage the rising temperatures and increase energy efficiency.

4.2 Traditional approach for optimization

Generally, genetic algorithms, a type of optimization algorithm, is used extensively in this field. This is because these algorithms are inspired by the principles of natural selection. These algorithms, through iterative processes, help in identifying optimal configurations that can maximize cooling and minimize energy consumption within these urban blocks to eliminate the heat island effect [18]. Additionally, research is being carried out in the field of HVAC systems as well for optimization and efficiency energy savings [11, 12]. In order to achieve this, a very basic workflow is displayed in Figure 8, which involves dataset collection (height, orientation, directions etc.) and goes up to real life implications for sustainability.

image010.png

Figure 8. Basic workflow

Additionally, Numerical modeling is also used as a tool for optimizing urban courtyards. The researchers also used computational simulations to simulate heat flow, wind movement and temperature distribution in these open spaces [19]. By creating virtual mathematical modelling, one can experiment with different design parameters and evaluate their impact on various aspects of heat transfer in various applications [20]. These models allow for accurate forecasts and quantitative evaluations, enabling designers and urban planners to make informed decisions about courtyard layout, shading elements and material choices.

Computationally, one begins their processes by identifying the problem and collecting relevant datasets. The next step is choosing appropriate design parameters and variables. The mathematical model is then created for a systematic algorithm selection and implementation. Further in this procedure simulations are carried out for effective constraint handling. If further optimization converges, we visualize our results and validate them and the model is ready for real-life implementation [21]. A streamlined flow to the basic approach to courtyard optimization is represented in Figure 9.

image011.png

Figure 9. General methodology for optimization

Furthermore, the records-pushed technique has been utilized in a new era of city courtyard optimization. By collecting and reading actual-time environmental facts, inclusive of temperature, humidity, and solar radiation, researchers have gained insights into the overall performance of present courtyards. Machine studying algorithms were employed to pick out patterns and correlations in this information, supporting to uncover the powerful techniques for boosting microclimate conditions and thermal comfort. This records-driven method allows for adaptive design solutions that respond dynamically to converting city situations.

4.3 Key concepts and the need for Machine Learning (ML) and Deep Learning (DL) algorithms

Before exploring the literature on city courtyard cooling, it is essential to understand the basic key concepts as shown in Figure 10 and highlight the role of advanced methods, such as ML and DL algorithms, in optimizing urban courtyard designs.

• Thermal Comfort:

Thermal comfort is an important aspect of urban courtyard design, as it reflects an individual's state of satisfaction or dissatisfaction with their surrounding thermal environment. Key contributing factors include temperature, humidity, and wind speed, as well as their interrelations. Ensuring thermal comfort is very important for the well-being and productivity of the urban residents. Most of the time, the traditional approach fails to model the spatial and temporal variability of these influencing factors. ML and DL algorithms process huge amounts of past and present environmental circumstances which form more accurate forecasts and optimizations. These models help in the design by enhancing the ability of the system to make adjustments in response to changes in the prevailing conditions so that thermal comfort is easily sustained, and by extension, so is the quality of urban courtyards [22].

• Sustainable Design:

Urban courtyard sustainable design is attempting to balance human needs with natural resource conservation, reducing resource consumption rates, minimizing environmental footprints, and improving quality of life. When it comes to sustainable design, it requires the least amount of energy, emissions, and resource use costs. However, the standard simulation tools are not designed for the kind of complicated, multi-objective optimization (MOO) in the initial use cases required for these objectives. To achieve this, ML and DL models are capable of handling such multi-variable scenarios that together constitute the aforementioned data pillars (e.g., building geometry, material properties, energy usage, etc.) to not only aggregate the data from different sources, but also provide feedback near real-time. Such an ability serves to inform sustainable design choices, resulting in environmentally achievable, economically feasible, and socially enhancing urban courtyards [22].

• Energy Efficiency:

Energy-efficient design is a fundamental principle for the urban courtyard approach, and it seeks to minimize energy consumption without damage to its purpose or comfort. Traditional approaches are often inaccurate predictors of how design modifications impact energy performance. The procedure must encompass efficient layout design, construction material choice, and spatial organization if a decrease in energy requirements is to be achieved in a dense urban context. When driving energy efficiency through modeling, ML and DL can mimic and predict various courtyard configurations utilizing a diverse dataset of attributes-i.e., building orientations, insulation characteristics, and external states. This data-driven strategy supports energy-efficient urban courtyards that mitigate the urban heat island effect, the building-related energy use, and sustainability objectives [22]. Energy saving using AIML results not only in sustainability but also may result in heat management [23, 24].

image012.png

Figure 10. Interconnectivity of key concepts

4.4 Review of selected studies

Out of the 34 studies, 10 most relevant papers were shortlisted. The 10 screened papers were about courtyard optimization in urban blocks and hot arid zones only. The research papers under the scope of this review focus on the implementation of different optimization methods to enhance the thermal comfort levels for outdoor spaces, mainly urban courtyards, across diverse climatic regions. The optimization approaches employed across these enquiries include a selection of genetic algorithm, multi-objective evolutionary algorithm (MOE) and artificial neural network (ANNs) techniques [19, 25]. In addition, these searches are based on a range of data sources including actual measurements, simulation-based observations as well as historical records. Results from these studies demonstrate the applicability of optimization approaches for increasing thermal comfort within urban courtyards. However, these questions expose a series of challenges and constraints in the actual/processual enactment of such strategies. These challenges include:

• The need for accurate and detailed datasets.
• The need to consider a wide range of factors, such as climate, building geometry, and vegetation.
• The constant need to develop good optimization algorithms that can solve complex problems.

Meng-Ren et al. [26] in the study use Grasshopper for the purpose of optimizing the space structure of aerial courtyards in super high-rise buildings. Super high rise buildings are detrimental to the overall coordination of the functional subsystems of the building. Incorporation of aerial courtyards will improve the surroundings of each floor and make the surroundings more dynamic. If several courtyard-centered clusters are constructed on each higher floor, the general organization of the space can be enhanced. In their paper, space syntax theory is applied to systematically study the strategies on connecting aerial courtyard of super high-rises. It implements a space syntax model developed using Grasshopper to study and enhance courtyard patterns within various kinds of spatial configurations while providing enhanced design solutions based on its results. Ibrahim et al. [27] implemented a study that builds on Ladybug simulation tools to investigate the impact of shape, arrangement of three different blocks of cities scattered, linear, and courtyard on environment and out-door comfort and energy consumption established in Cairo Egypt, their study involves 3430 different designs with different parameters. The analyses reveal a high correlation between design parameters and thermal comfort and energy consumption (R² = 0.84). The most dominant variable is urban density which affects comfort and energy use significantly (R² = 0.7 and 0.95). Their study shows that, for the purpose of natural ventilation, compact, medium density urban layouts work best when their orientation is about 45°. Wortmann and Natanian [28] tried to determine the ratio with which one has to construct more densely on an urban site and for how much area one has to provide sufficient sunlight in the hot and dry climate of Tel Aviv. They use multi-objective optimization (MOO) to study this trade-off, testing seven MOO algorithms on two design types: courtyard and high-rise. The main objectives on which the maximum points are to be awarded include Floor Area Ratio and a fresh emerging index known as Context Exposure Index that has been derived from the Israeli green buildings standards. The high-rise design is found to be superior to the conventional design and three powerful and reliable MOO algorithms examined in this study include HypE, SPEA2, and RBFMOpt. Ibrahim et al. [29] focus on optimizing the design of courtyard blocks for the hot, dry climate of Cairo, Egypt. Using simulations in Rhino 3D with Grasshopper and Ladybug tools, the study looks at factors like building height, orientation, and spacing. Three types of courtyard blocks were tested: square and two elongated shapes (1.5 and 2.5 times the width of the inner courtyard). The best designs had the smallest spacing between buildings. For the square type, a 45° orientation worked best, while for the elongated types, 135° was preferred. The optimized designs reduced cooling loads by up to 31.7% and lowered the Universal Thermal Climate Index (UTCI) by up to 1.6℃, compared to a reference case. Additionally, the designs increased the percentage of exposed surfaces by 4%, which could help reduce heating needs in winter.

Tabadkani et al. [30] their research, seek to understand comfort levels of indoor environment and energy consumption and cost implications of residential building courtyards. In the course of the research design variations and simulations with Grasshopper, Ladybug, Honeybee tools, the options such as courtyard shape, window size, building material, and location are investigated. A Machine Learning approach is used to forecast the effects of such factors on thermal comfort and energy budgets. The findings of the study indicate that weather, and design settings (such as temperature ranges) significantly affect perceived comfort and costs. In warm climates such as Singapore, the concept of having a courtyard may not work so well. The study indicates that the thermal comfort and utility cost predictions offered by deep learning models are accurate. Toris-Guitron et al. [31] examine the impacts of the courtyards on housing internally established temperature of conventional houses in hot and humid regions. They discover that rather than having a cooling effect from the sun, residences with courtyards actually introduce more heat when these courtyards are extensive and broad. Location is also very crucial especially the orientation of the courtyard since east-west orientation causes minimal heat build up. Similarly, the study indicates that regulation of window apertures can lead to a decrease of indoor temperature by 1 degree C at night.

Li et al. [32] indicated that multi-storied buildings have to manage the interrelated factors of capacity and solar rights during urban redevelopment. In order to design better buildings with more area, less shading and more solar radiation their study has employed automatic workflow and optimization. There were three building expansion methods whereby: Horizontal, Vertical, and a mixed test. Using the mixed method of construction yielded the best results because floor space was raised by 70%. The vertical method reduced the solar shading to the least extent while the horizontal was appropriate where height limitations existed. This approach may benefit reasoning for capacity and solar needs in urban renewal. Yu et al. [33] focused on the influences of building design in energy and daylight in old Beijing’s courtyard houses. It suggests an efficient approach towards quick building performance evaluation through ML. Their research employed genetic algorithm to collect the building data after which they fine-tuned it with LightGBM model with high accuracy. The purpose is to develop a rapid assessment tool useful for building performance determination and equally time-saving during the design process in urban regeneration to minimize reversals. Dhanraj et al. [19] use multi-objective DL algorithms to increase cooling rates in urban courtyard blocks in hot desert regions. They apply the Radial Basis Function Network (RBFN), Multi-Layer Perceptron (MLP) Regressor, and Recurrent Neural Network - Long Short-Term Memory (RNN-LSTM) algorithms in the study. The research shows that the RBFN model had the highest accuracy of all models achieving the lowest Mean Squared Error (MSE) of 0.0016 and an RMSE of 0.0395 for predicting cooling loads; they reduced cooling loads and increased thermal comfort and energy economy by optimizing courtyard designs for better harsh climate resilience. Optimization variables include building height, courtyard orientation, and surrounding geometric elements that improve the cooling ability of an urban courtyard block. Xu et al. [34] employ Multi-Objective Optimization, and Machine Learning in modifying village courtyard design. Calculations prove that flexing spatial and architectural variables can enhance energy recovery by 32.3%, winter thermal comfort by 8.3% and summer thermal comfort by 3.8%. During the data-driven phase, courtyard performance is forecasted with 83% accuracy and an F1 of 0.81. The validation also demonstrates that the performance score increases from the initial design value of 59.12 to 85.62. It helps in improving the design time and also aids in better planning of the villages.

Sun et al. [35] applied a neural network model together with a MOO method to optimize energy consumption, cost, and thermal comfort for rural homes located in the cold regions of China. Using parametric modeling and ML allowed the team to better predict a number of design parameters on building performance. Their study was able to achieve an MSE of 0.0148 and an R² of 0.988. Their research would be practical in providing insights into building energy-efficient and sustainable housing solutions in rural areas, improving living conditions while bringing down the cost to an environmentally-friendly minimum. The research highlights the potential of these methods for informed decision-making in building design, although further refinements are needed for broader applicability across different climatic zones. The error metric in real life indicates that the model's predictions closely match actual data, leaving very little unexplained error.

Table 4 gives a generalized summary of the review made on 10 most relevant papers on courtyard optimization and also gives a brief insight into how the authors from relevant studies may be focusing on three important factors of Thermal Comfort, Energy Efficiency and Sustainable Design additionally the next paragraph represents the full forms of prominent algorithms used in the research.

The most prominent research in many fields frequently uses a variety of algorithms to accomplish its objectives. Some of these key algorithms include ANN, which simulate human neural processes for ML tasks, Environmental Meteorology and Microclimate (ENVI-met), a tool for studying environmental conditions and Hypervolume Estimation (HypE), Multi-objective Ant Colony Optimization (MACO), MOEA, Multi-Objective Evolutionary Algorithm based on Decomposition (MOEAD). They have also used Multi-Layer Perceptron (MLP), Non-dominated Sorting Genetic Algorithm II (NSGA-II), Non-dominated Sorting Particle Swarm Optimization (NSPSO), Radial Basis Function-based Multi-objective Optimization (RBFMOpt) and Strength Pareto Evolutionary Algorithm 2 (SPEA-2).

Table 4. Summary of the reviewed paper and authors' research focus area