In this paper, a new combination method for sound field prediction is proposed. An optimization approach based on the genetic algorithm is employed for optimizing the transition frequency of the combined sound field prediction method in classrooms. The selected optimization approach can identify the optimal transition frequency so that the combined sound field prediction can obtain more efficient and accurate prediction results. The proposed combined sound field prediction method consists of a wave-based method and geometric acoustic methods that are separated by the transition frequency. In low frequency domain (below the transition frequency), the sound field is calculated by the finite element method (FEM), while a hybrid geometric acoustic method is employed in the high frequency domain (above the transition frequency). The proposed combined prediction models are validated by comparing them with previous results and experimental measurements. The optimization approach is illustrated by several examples and compared with traditional combination results. Compared to existed sound field prediction simulations in classrooms, the proposed combination methods take the sound field in low frequencies into account. The results demonstrate the effectiveness of the proposed model.

Practical applications: This study proposes a combined sound field prediction method separated by transition frequency. A genetic algorithm optimization method is employed for searching the optimal transition frequency. The outcomes of this paper are essential for acoustical designs and acoustical environmental assessments.

本文提出了一种新的声场预测组合方法。采用基于遗传算法的优化方法对教室组合声场预测方法的转换频率进行优化。选择的优化方法可以确定最佳的过渡频率，以便组合声场预测可以获得更有效和准确的预测结果。所提出的组合声场预测方法由基于波的方法和由过渡频率分开的几何声学方法组成。在低频域（低于过渡频率）中，声场是通过有限元法（FEM）计算的，而在高频域（高于过渡频率）中采用了混合几何声学方法。通过将它们与先前的结果和实验测量值进行比较，可以验证所提出的组合预测模型。通过几个示例说明了优化方法，并将其与传统组合结果进行了比较。与教室中现有的声场预测模拟相比，所提出的组合方法考虑了低频声场。结果证明了该模型的有效性。

实际应用：本研究提出了一种由过渡频率分开的组合声场预测方法。采用遗传算法优化方法搜索最优过渡频率。本文的结果对于声学设计和声学环境评估至关重要。