Development of a reduced model of formation reactions in Zr-Al nanolaminates

Abstract

A computational model of anaerobic reactions in metallic multilayered systems with an equimolar composition of zirconium and aluminum is developed. The reduced reaction formalism of M. Salloum and O. M. Knio, Combust. Flame 157(2): 288–295 (2010) is adopted. Attention is focused on quantifying intermixing rates based on experimental measurements of uniform ignition as well as measurements of self-propagating front velocities. Estimates of atomic diffusivity are first obtained based on a regression analysis. A more elaborate Bayesian inference formalism is then applied in order to assess the impact of uncertainties in the measurements, potential discrepancies between predictions and observations, as well as the sensitivity of predictions to inferred parameters. Intermixing rates are correlated in terms of a composite Arrhenius law, which exhibits a discontinuity around the Al melting temperature. Analysis of the predictions indicates that Arrhenius parameters inferred for the low-temperature branch lie within a tight range, whereas the parameters of the high-temperature branch are characterized by higher uncertainty. The latter is affected by scatter in the experimental measurements, and the limited range of bilayers where observations are available. For both branches, the predictions exhibit higher sensitivity to the activation energy than the pre-exponent, whose posteriors are highly correlated.

Document Details

Document Type
Pub Defense Publication
Publication Date
Dec 15, 2014
Source ID
10.1063/1.4903816

Entities

People

  • Justin Winokur
  • Kyle R Overdeep
  • Manav Vohra
  • Omar M. Knio
  • Paul Marcello
  • Timothy P. Weihs

Organizations

  • Defense Threat Reduction Agency
  • Duke University
  • Johns Hopkins University
  • King Abdullah University of Science and Technology
  • United States Department of Energy

Tags

Readers

  • Combustion science or combustion engineering.
  • Electromagnetic Wave Scattering and Antenna Radiation Engineering
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference