Thermoacoustic modeling and uncertainty analysis of two-dimensional conductive membranes

Abstract

A model for two-dimensional graphene-based thermoacoustic membranes is investigated analytically and numerically validated using Bayesian statistics in this study. The temperature and the pressure variables are first analytically determined in one-dimension by noticing that the magnitude of the pressure time derivative is small in the heat transfer equations and by taking advantage of the large disparity between the length scales. The one-dimensional findings are then extended to three-dimensions, where pressure fluctuation produced by the surface temperature variation is determined using an acoustic piston model. Through the one and three-dimensional model analysis, the dependence of acoustic pressure as a function of frequency is studied. The acoustic response with respect to the frequency shows different characteristics when assuming Dirichlet (temperature) or Neumann (heat flux) boundary conditions. The thermoacoustic model is validated with a graphene-on-paper loudspeaker using Bayesian statistical methods and a Delayed Rejection Adaptive Metropolis algorithm to identify model parameters and their uncertainty. The findings provide insights into the heat transport mechanisms associated with sound generation from thermally cycling thin conductive membranes at high frequencies.

Document Details

Document Type
Pub Defense Publication
Publication Date
Feb 13, 2015
Source ID
10.1063/1.4908067

Entities

People

  • Jonghoon Bin
  • Kunihiko Taira
  • William S Oates

Organizations

  • United States Army Research Laboratory

Tags

Fields of Study

  • Physics

Readers

  • Combustion and Flow Dynamics.
  • Finite Element Method (FEM) for solving Partial Differential Equations (PDEs)
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms
  • Microelectronics