Coherence, Transport, and Inference in Turbulent Dynamical Systems

Abstract

Analyzing and understanding features in chaotic and turbulent systems is central to developing modern systems that can thrive in fluid media. Prom ships in oceans to airplanes in the air, to chemical mixing and biological processes, and so forth, both the local scale of the vessels as well as the macro scale of weather and oceanic currents, it is clear that the Naval relevance of these issues call for ever better technology to analyze the underlying processes. This project concerns analysis of chaotic and turbulent systems, from a Lagrangian perspective, specifically toward a modern perspective of coherent structures for understanding transport as well as persistence of underlying structures whether they be blooms of plankton in the oceans, or cascade of energy in turbulence. We emphasized two major thrusts in this work, both working toward this major theme of understanding aspects of simplicity embedded in nonlinear systems: 1) Transport from a theory of Shape Coherence. 2) Inference in Spatiotemporal Dynamical Systems: Inverse Problems and System Inference.

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Document Details

Document Type
Technical Report
Publication Date
Dec 31, 2018
Accession Number
AD1068286

Entities

People

  • Matthew E. Bollt

Organizations

  • Office of Naval Research

Tags

Communities of Interest

  • Autonomy
  • C4I
  • Energy and Power Technologies
  • Space

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Compressed Sensing
  • Computational Fluid Dynamics
  • Computational Science
  • Computer Vision
  • Data Analysis
  • Data Mining
  • Fluid Dynamics
  • Fluid Flow
  • Geometry
  • Image Processing
  • Information Processing
  • Information Science
  • Machine Learning
  • Monte Carlo Method
  • Network Science
  • Turbulent Mixing

Readers

  • Computational Fluid Dynamics (CFD)
  • Systems Analysis and Design
  • Theoretical Analysis.

Technology Areas

  • AI & ML