Sequential optimization of spiral structures for simultaneous noise reduction and flow drag minimization

Spiral acoustic structures hold significant potential for noise suppression in fluid flow systems; however, their aerodynamic performance has often been overlooked in previous studies. To bridge this gap, the present study develops a sequential optimization strategy aimed at simultaneously maximizing acoustic transmission loss (TL) and minimizing flow drag (FD). The strategy integrates a multi-objective genetic algorithm with surrogate models for initial global exploration of the design space, followed by an adjoint optimization method for local refinement of the inner wall geometry. Under fixed outer dimensions, the spiral geometry is parameterized using inner diameter, spiral angular frequency, spiral position, and dimensionless pitch. Sensitivity analysis is employed to identify the key structural parameters. Surrogate models are constructed to efficiently map the structural parameters to FD and total transmission loss (TY), respectively. A multi-objective genetic algorithm identifies a multi-objective optimization structure achieving TY higher than 7 dB and reducing FD by 306.35 Pa at 10 m/s compared to the original structure. The adjoint optimization, building upon this multi-objective optimization structure result, further reduces the FD by an additional 89.33 Pa without compromising the TL. The sequential optimization strategy ultimately delivers a 23% increase in TY and a 53.6% reduction in FD relative to the original structure. Furthermore, the optimization results are validated through impedance tube experiments. The sequential optimization strategy provides an efficient solution for applications demanding both effective noise control and low flow drag.