Discrete Svelteness: Evaluating flow structures in generative constructal design

Abstract

Constructal design theory posits that natural and engineered systems evolve toward improved flow efficiency, ensuring maximal access and adaptability. Generative design, in this context, functions as an evolutionary computational framework, leveraging algorithmic methods to simulate processes analogous to natural selection and self-organization. This approach enables the exploration and refinement of complex design spaces, fostering higher modeling resolution and morphological diversity beyond traditional parametric methods. In this study, we introduce Discrete Svelteness (DS), a spatially resolved metric that quantifies geometric efficiency at every point in a domain, addressing the limitations of traditional, global Svelteness measures.

We apply this framework to generative designs, specifically area-to-point (ATP), circle-to-point (CTP), and vascular flow (VF) configurations, demonstrating how DS reveals performance differences shaped by local adaptations to the environment and emergent patterns that conventional metrics overlook. Our analysis reveals that DS provides critical design insights aligned with the Constructal Law, which predicts that systems evolve to enhance flow efficiency through increased branching and spatial access. Furthermore, we examine the probability density functions (PDFs) of DS values, identifying distinct power-law and lighter-tailed right-skewed distributions (such as gamma or log-normal) that reflect the statistical signatures of self-organizing, evolutionary systems found in nature.

Additionally, we explore the trade-offs and optimization challenges in generative design, showing that increased degrees of freedom lead to more robust, diverse, and high-performing solutions. These dynamics parallel evolutionary processes in biological systems, where adaptability and efficiency emerge from complex interactions between structural constraints and environmental demands. Our findings position DS as a powerful tool for evaluating and guiding the evolution of flow architectures in both natural and engineered systems. By bridging global efficiency metrics with localized refinement, this work advances multi-scale evolutionary constructal design methodologies and offers new insights into the computational modeling of biological self-organization and evolutionary optimization.