What followed was a long and often demanding journey. One of the most difficult parts was identifying the right kinetic façade prototypes and translating them into working parametric models. Each system whether rotating, sliding, or folding had its own logic, and implementing them accurately was far from straightforward. Many times, models did not behave as expected, simulations failed, or results contradicted assumptions. What appears as a clear comparison in the paper was, in reality, the result of repeated trial and error.
At the same time, conducting this research without access to advanced facilities particularly in Iran, added another layer of challenge. Limited resources meant that every step required more time, creativity, and persistence. Tasks that might be straightforward in well-equipped environments often became complex and time-consuming.Data collection and analysis were equally demanding. Running hundreds of simulations, checking outputs, and ensuring consistency across different performance metrics required continuous energy and focus. There were moments of exhaustion and doubt, especially when progress felt slow or uncertain. Staying motivated throughout this process was, in itself, a challenge.
Yet, over time, patterns began to emerge. Each façade system revealed its own strengths and trade-offs, and the complexity of the problem became clearer. This was perhaps the most rewarding moment when scattered results started to form a coherent narrative, and the research question began to find its answer. Looking back, this paper is not only about kinetic façades or optimization techniques. It is about persistence in the face of limitations, about learning through failure, and about continuing the process even when it becomes difficult. The challenges were not separate from the research—they were part of what shaped it.