Prediction of acoustic field in 1-D uniform duct with varying mean flow and temperature using neural networks
This work addresses acoustic modeling in ducts for engineering applications, but it is incremental as it applies existing neural network methods to a specific domain.
The authors tackled the problem of predicting acoustic fields in a 1-D duct with varying flow and temperature by using neural networks constrained by physical laws, achieving predictions validated against a traditional Runge-Kutta solver.
Neural networks constrained by the physical laws emerged as an alternate numerical tool. In this paper, the governing equation that represents the propagation of sound inside a one-dimensional duct carrying a heterogeneous medium is derived. The problem is converted into an unconstrained optimization problem and solved using neural networks. Both the acoustic state variables: acoustic pressure and particle velocity are predicted and validated with the traditional Runge-Kutta solver. The effect of the temperature gradient on the acoustic field is studied. Utilization of machine learning techniques such as transfer learning and automatic differentiation for acoustic applications is demonstrated.