Case-wise validation errors.

Amid climate change and resource constraints, sustainable building design increasingly requires wind-environment optimization to improve energy efficiency and thermal comfort. However, most simulation tools target late design stages, overlooking early phases where small geometric choices have an outsized performance impact. Through comparative software analysis and questionnaire survey, this study addresses the disconnect between designers’ workflows and existing tools, rooted in divergent thinking paradigms: designers’ design thinking and engineers’ scientific thinking. Accordingly, we propose “Energy Diagram,” a grey-box-based tool that integrates 2D Zonal models simplified by the Lattice–Boltzmann method with deep neural networks (DNNs) to predict wind fields by seamlessly coupling architectural diagrams with numerical simulations. Validation against wind-tunnel experiments, field measurements, and CFD simulations shows that, the mean MAPE of Energy Diagram is 16.85% (vs. experiments) and 10.45% (vs. simulations) for a cube case, and 19.21% (vs. measurements) and 13.79% (vs. simulations) for a reading-room case. Through application in an architectural studio, the characteristics of the tool, i.e., the visual integration, geometric transition, and human-machine collaboration, are verified and discussed. This research underscores the potential of human-centric tools to democratize performance simulation, empowering designers as proactive agents in sustainable architecture development.