Abstract

Using ‘flame-synthesized’ nanoparticles (nps) as one prototypical application, we illustrate our recent progress in two broad areas of current CRE-interest, viz., the development of: 1. Improved rate laws/transport coefficients for next-generation Eulerian, multi-(state) variable population-balance formulations, and 2. Quadrature-based multi-variate moment methods (hereafter QMOM) suitable for articulation with evolving Eulerian CFD simulation methods Admittedly, in previous work much insight was obtained by introducing deliberately (over-) simplified rate laws (for nucleation, Brownian coagulation, vapor growth/evaporation, sintering, thermophoresis, ) into the generally nonlinear integro-partial differential equation called the ‘population balance’ equation (PBE). However, despite the complexity of this equation, and the need to satisfy it along with many other local PDE-balance principles in multi-dimensional CRE environments, in our view current requirements for reactor design, as well as the frequent need to infer meaningful physico-chemical parameters based on laboratory measurements on populations rather than individual ‘particles’, make the introduction of more accurate rate/transport laws essential for next-generation particle synthesis reactor models. Our present examples are motivated both by measurements/calculations of the structure of laminar counterflow flames synthesizing Al2O3 nps and/or the predicted performance of well-mixed steady-flow devices in which sintering or sublimation occurs. Corresponding illustrative results, which focus on the rate laws for sphere dissolution or aggregate Brownian coagulation support our contentions that: i) systematic introduction of more accurate rate laws (including nucleation, sintering, growth, )/transport coefficients will be essential to meet the quantitative demands of next-generation PBE-based CRE-simulation models for high-value particulate synthesis equipment, and, ii) QMOM is able to incorporate realistic rate laws and faithfully generate their effects on important ‘moments’ characterizing the product joint distribution functions.

Full Text
Paper version not known

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.