Stochastic Acoustic Ray Tracing with Dynamically Orthogonal Equations

Abstract

Developing accurate and computationally efficient models for ocean acoustics is inherently challenging due to several factors including the complex physical processes and the need to provide results on a large range of scales. Furthermore, the ocean itself is an inherently dynamic environment within the multiple scales. Even if we could measure the exact properties at a specific instant, the ocean will continue to change in the smallest temporal scales, ever increasing the uncertainty in the ocean prediction. In this work, we explore ocean acoustic prediction from the basics of the wave equation and its derivation. We then explain the deterministic implementations of the Parabolic Equation, Ray Theory, and Level Sets methods for ocean acoustic computation. We investigate methods for evolving stochastic fields using direct Monte Carlo, Empirical Orthogonal Functions, and adaptive Dynamically Orthogonal (DO) differential equations.

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Document Details

Document Type
Technical Report
Publication Date
May 15, 2020
Accession Number
AD1115157

Entities

People

  • Michael J. Humara

Organizations

  • Massachusetts Institute of Technology
  • Woods Hole Oceanographic Institution

Tags

Communities of Interest

  • Autonomy
  • Energy and Power Technologies
  • Sensors

DTIC Thesaurus Topics

  • Acoustic Measurement
  • Acoustic Propagation
  • Acoustic Signals
  • Acoustic Tomography
  • Acoustic Waves
  • Acoustics
  • Computational Fluid Dynamics
  • Computational Science
  • Differential Equations
  • Floating Point Operations
  • Helmholtz Equations
  • Ocean Environments
  • Oceanography
  • Partial Differential Equations
  • Physics Laboratories
  • Reflection
  • Seabed
  • Signal Processing
  • Three Dimensional
  • Two Dimensional
  • Underwater Acoustics
  • Wave Propagation

Readers

  • Regression Analysis.
  • Systems Analysis and Design
  • Wave Propagation and Nonlinear Chaotic Dynamics.