Painting with light: An interactive evolutionary system for daylighting design

Abstract

Painting with Light is a user-guided interactive evolutionary system for daylighting design. It allows architects to use color to specify desired light levels in spaces, and searches for solutions that bring building geometry and materials close to performance targets desired by the architect. The proposed interface directly addresses a main limitation of generative design systems based on building performance metrics, by allowing the user to specify daylight spatial patterns with a high degree of granularity. This development poses new challenges for both objective function and penalty function definitions, addressed in this paper. Painting with Light automatically computes and displays statistical indexes that inform the user on the deviation error between the performance of the solution found by the system and the desired targets. The system was implemented in Python on top of a popular Computer-Aided Design software, Rhinoceros, as an add-on to its Visual Programming Language, Grasshopper. This choice of implementation allows the use of Grasshopper's built-in functions and methods for 3D parametric modeling, of tools that provide direct access to Radiance, a lighting simulation software, and of different types of genetic algorithms. Five experiments were conducted on a freeform parametric model for progressive system calibration, which encompassed four steps: 1) adjustment of painted targets to fit the problem feasible solution space; 2) devise appropriate weights and penalty factors for the fitness function; 3) test two different evolutionary solvers; 4) test the system's capability to find a predefined solution where the optimal values were known. After calibration, the system was able to produce solutions that closely approximate the painted goals.

Optimization of Window Positions for Wind-Driven Natural Ventilation Performance

“…Grasshopper [14] is one of the widely-adopted parametric design platforms of Rhino thanks to its ability to connect to environmental simulation engines such as EnergyPlus. Numerous architectural design and simulation studies utilized the powerful functions made available by these two tools, from lighting and thermal environment to energy simulations [15][16][17][18][19]; however, there still exists a lack of connectivity between the parametric design platform and building performance simulation particularly for natural ventilation prediction, due to the susceptibility airflow has to the surrounding environment and the complexity in interpretations. Although an airflow simulation tool, such as computational fluid dynamics (CFD), would provide useful information about natural ventilation, the communication between the software and the optimization of inputs is one of the challenges; therefore, customized add-ons in Grasshopper were created to enable data exchange from airflow simulation to energy simulation and to optimize the data, and a new way to interpret the airflow simulation results was developed.…”

Supporting the Problem-Solving Loop: Designing Highly Interactive Optimisation Systems

“…A number of user studies have evaluated the effectiveness of interactive optimisation systems [4,8,10,12,13,15,17,18,23,25,27,30,32,37,45,50,52,56]. In particular, Caldas and Santos [13] developed a user-guided interactive system for daylighting design and found the proposed system could produce good-quality solutions. Similarly, Matejka et al [37] presented Dream Lens, an interactive visual analysis tool to explore and visualise generative design.…”

“…In order to examine the usefulness of the nine recommendations proposed above, we will now examine (retrospectively) whether the recommendations are met by 15 representative systems from the literature [4,8,10,12,13,15,17,25,27,30,32,45,50,52,56]. The examination is not exhaustive as there many interactive optimisation systems.…”

Building Retrofitting through Coupling of Building Energy Simulation-Optimization Tool with CFD and Daylight Programs

“…Regarding optimization tools, several algorithms, software systems, and platforms have been commonly integrated with building performance tools. For example, GenOpt [5][6][7]29], MOO [31,36], GAMS [37], jEPlus [25][26][27], Rhinoceros [38], MATLAB [15,39,40], non-dominated sorting genetic algorithm II (NSGA II) [13,28,35], and CPLEX algorithm [14,41] are among the widespread optimization tools and platforms. A recent study has shown that the integration of artificial neural networks such as Multilayer Feedforward Neural Networks (MFNN) with metaheuristic algorithms such as NSGA II and Multi-Objective Particle Swarm Optimization (MOPSO) can minimize the computation time [42].…”