Research on Temperature Field Control in a Thermostatic Chamber with Static Baffle-Mediated Natural Convection

Temperature uniformity in thermostatic chambers is critical for material testing, biological incubation, and precision measurements, as even minor thermal gradients can compromise reliability. However, in chambers designed to avoid airflow disturbances, such as those used in semiconductor fabrication and optical experiments, forced convection and mechanical stirring are often impractical. Consequently, natural convection becomes the dominant heat transfer mechanism, introducing significant nonlinearity, large thermal inertia, and multivariable coupling among multiple heat sources. To address these issues, this study develops a multi-input–multi-output (MIMO) temperature control strategy for a rectangular chamber equipped with eight heating elements (grouped into four channels) and adjustable-angle baffles. The proposed method combines a multi-PID controller array with genetic algorithm (GA)-based parameter tuning using a transfer-function matrix model. Experiments demonstrate that baffle angles below 90° improve spatial uniformity, and the relative grouping of heaters outperforms adjacent grouping in both thermal inertia and correlation. Using GA-optimized PID parameters, the controller maintains steady-state error within ±0.5 °C and reduces settling time by approximately 140 s compared to conventional Ziegler–Nichols tuning. Validated through simulations and experiments, the proposed approach provides a reliable and cost-effective alternative to forced convection for airflow-sensitive applications, achieving superior uniformity and steady-state accuracy.