Calculation of Sound Transmission Loss in Ocean Using Different Wave Equation Models


Underwater acoustics is the study of the propagation of sound in water and the interaction of the acoustic (sound) waves with the water, its contents, and its boundaries. While sound moves at a much faster speed in the water than in air, the distance that sound waves travel is primarily dependent upon the sound speed profile of the ocean. The need to model and study acoustic propagation in the sea has always been in demand. Practical issues with forecasting sonar performance in support of anti-submarine warfare (ASW) operations during World War II led to the earliest attempts at modeling sound propagation in the sea. The theoretical basis underlying all mathematical models of acoustic propagation is the wave equation. The wave equation itself is derived from the more fundamental equations of state, continuity, and motion. As a mathematical expression of acoustic physical properties, a numerical acoustic field can describe the physical laws of ocean acoustic propagation with simple and clear numerical solutions. Commonly used computational ocean acoustic theories include the parabolic equation (PE) model, normal modes, the wavenumber integration method and the ray model. This paper aims at studying all these models briefly and focusing on the PE models as they are considered to be fast and flexible for range-dependent acoustic propagation problems like those of the Indian Ocean Region (IOR). Additionally, it compares the three models of PE equations and states that the accuracy, speed, and efficiency of any model depend on the sound speed profiles. The findings include that there has not been a single model that guarantees to work efficiently in all places and situations. Rather, different models work perfectly with different scenarios, making them compatible with different profiles. The PE-RAM model, although the most widely used model, is shown to work poorly when it comes to smooth sound profiles. On the other hand, it proved to be the fastest for rough profiles

Arnab Das, Shridhar Prabhuraman. Akshita Mangal