Extended Abstract
Introduction
Rapid urbanization necessitates sustainable urban development, particularly optimizing urban form due to land and environmental resource limitations. Modern urban growth often overlooks traditional solar access, resulting in energy imbalances and urban heat islands, as exemplified by the poor air quality in Baharestan and Isfahan in 2019. There's an urgent need for solutions to increase building density while ensuring direct sunlight, thermal comfort, and reduced energy consumption. Algorithmic processes, especially parametric design, offer a novel approach to optimizing urban morphology for multiple, often conflicting objectives, such as maximizing building footprint and courtyard area while ensuring solar access. This study addresses this gap by presenting an integrated model for urban form organization in Baharestan City using multi-criteria algorithms and a solar envelope approach. The main objective is to propose an urban form organization algorithm for Baharestan, analyzing solar light reception and resulting energy in outdoor spaces. Sub-objectives include creating vertical green space patterns, proposing optimal urban block design rules, and developing a climate-optimized multi-variable algorithm for urban development. Ultimately, this research aims to foster a more sustainable city by maximizing building volume using the solar envelope method.
Theoretical Framework
The study integrates "compact city" principles – optimizing land use, increasing density, and promoting mixed-use development – with the "low-carbon energy city" theory, which focuses on reducing greenhouse gas emissions through energy optimization and the use of renewable sources. Solar access is fundamental to both, enabling natural illumination and reducing heating and cooling demands. Multi-criteria algorithms are crucial here, as they balance conflicting sustainability objectives (e.g., maximizing area versus solar access). The solar envelope defines maximum building heights while preserving solar access. Evolutionary computations provide tools for exploring sustainable urban forms. Recent urban planning research has increasingly leveraged Multi-Criteria Decision-Making (MCDM) algorithms, combined with advanced computational techniques such as machine learning and genetic algorithms, to achieve optimal urban design.
Methodology
This research is applied-developmental, providing a practical and generalizable algorithmic modeling approach for urban design that combines documentary, analytical, and modeling methods. Multi-criteria algorithms like Wallacei are chosen because urban design problems often involve conflicting objectives (e.g., maximizing footprint vs. solar access). These algorithms provide Pareto optimal solutions for complex decision-making.
In the modeling phase, 5000 simulations using the Wallacei multi-criteria algorithm selected the optimal urban layout for residential blocks, maximizing building footprint and courtyard area. Subsequent analyses included solar radiation energy, sky dome coverage, and solar access hours in outdoor urban spaces. Baharestan City, Isfahan (51E, 32N), was the case study, with Meteonorm climate data validated using data from the Isfahan Shahid Beheshti Airport station.
The parametric solar envelope calculation defines direct solar access conditions. Steps involved: 2D site modeling in Rhinoceros; importing EPW weather data into Ladybug plugin to set solar radiation and minimum temperature (20°C, 475 W/m² on December 21st); determining shading boundaries (1.5-2m above ground); setting modeling time (8:00 AM-4:00 PM on December 21st); and generating a 30m maximum height geometric polygon for the solar envelope. The 35-hectare Baharestan study area was divided into five blocks. Rhinoceros 6 SR30, Grasshopper v1, and DecodingSpaces 2020 were used for parametric modeling. After the initial division (Figure 1), 5,000 multi-criteria parametric models were run via Wallacei (Figures 2 and 3). Eight optimal options per area were selected, maximizing building potential and courtyard area (Figure 4). Solar envelope structures were determined for each building on December 21st (the lowest sun angle), considering shadow lines (1.5-2m) and the maximum buildable height (30m), which defined the permissible volumes. Final building volumes were obtained by placing solar envelope volumes in 3-meter voxels to ensure winter sunlight access (Figure 5).
Results and Discussion
Climate data indicated Baharestan's outdoor environment requires direct sun for comfort for six months and shade for four months (Figure 6). Buildable space categorization showed high density (Table 1). Over 95% of open spaces in all five blocks received more than 1000 kWh/m² of solar energy annually, suggesting potential for energy self-sufficiency (Figure 7). Sky dome access averaged over 70% in outdoor spaces and floor plans, aiding natural light utilization (Figure 8). Solar access hours in December (the shortest day) consistently showed over 35 hours of direct sunlight across most urban areas (Figure 9).
Conclusion
This research successfully developed a comprehensive framework for optimizing sustainable urban form in Baharestan using parametric design and multi-criteria algorithms. The Wallacei genetic algorithm facilitated the evaluation of 5,000 models, yielding optimal layouts. The findings align with previous studies on the impact of urban form on environmental sustainability, particularly in terms of solar access and thermal comfort. The study's innovation lies in its simultaneous optimization of conflicting objectives: maximizing building footprint, courtyard area, and solar access. The integrated, climate-optimized algorithm effectively handles complex urban design challenges in specific climates. Its high efficiency is evidenced by over 1,000 kWh/m² of solar radiant energy in 95% of areas, over 70% sky dome access, and over 35 hours of solar access in the shortest month. These results directly address the issues of energy consumption and air pollution in cities like Isfahan. The method facilitates urban green spaces on building levels and significantly meets daylight needs. This research enriches parametric design and multi-criteria algorithms, offering an efficient tool for urban planners to create more sustainable and resilient cities.