Design of Greenhouse Ventilation Systems with Computational Fluid Dynamics: Balancing Performance and Energy Sustainability

Greenhouses are energy-intensive agricultural systems, where the sustainable design of natural ventilation could markedly reduce energy demand while maintaining optimal conditions for plant growth.The performance of natural ventilation arises from a multifaceted interaction among several determinants, including the geometric configuration of the greenhouse, prevailing environmental conditions, and the structural characteristics of ventilation openings and ducts.This study employed computational fluid dynamics (CFD) to assess the influence of roof inlet design on airflow distribution, regulation of canopy temperature, and energy performance in a single-span greenhouse measuring 20 10 6 meters.Six ventilation configurations were evaluated by varying the quantity and shape of roof inlets: three large inlets and ten smaller inlets, each with rectangular, oval, or circular geometries.The plant canopy was modeled as a porous medium to realistically capture aerodynamic resistance.Mesh independence was validated using outlet mass flux, and simulations were conducted under steady-state natural ventilation conditions.Key performance indicators included airflow velocity, temperature distribution, ventilation rate, wall shear stress (WSS), air changes per hour (ACH), and estimated annual energy saving.Results of the analysis revealed that circular and oval inlets enhanced air mixing and reduced thermal gradients within the canopy, whereas rectangular inlets generated localized recirculation zones and elevated WSS, resulting in lower energy efficiency.The inlet geometry and quantity played a critical role in the sustainable design of greenhouse ventilation.By integrating CFD-based airflow analysis with energy-saving assessments, this study offered a practical framework to guide greenhouse operators in optimizing ventilation strategies that balance productivity, thermal comfort, and long-term energy sustainability.