Date of Award

Fall 12-12-2025

Degree Type

Thesis

Degree Name

Master of Science in General Engineering

Department

Mechanical Engineering

Committee Chair

Nathan Huft

First Advisor

Janice Lucon

Second Advisor

Grant Wallace

Abstract

This thesis validates equations that predict power input with machine data for the LabRAM II resonant acoustic mixer (RAM), an industrial tool for mixing powders used in advanced manufacturing and other applications. While RAM offers unique advantages such as gentle mixing and adaptability for sensitive or heterogeneous blends, the manufacturer’s power equation lacked published experimental validation. To address this gap, a custom constantvolume calorimeter was designed to directly measure the energy imparted during RAM operation under controlled conditions. Finite element analysis and analytical heat transfer models ensured calorimeter assumptions were valid. Calorimetric experiments revealed that the vendor’s power input equation was statistically different from measured energy input. A fundamentally derived equation provided a calibrated match to measured data. This calibrated equation was then applied to calculate specific power input (SPI) on a powder system. SPI results helped with understanding energy input variance with changes in particle sizes, fill amounts, and vessel internal pressure. In conjunction with the powder system testing, high-speed videography was used to identify and characterize flow regimes that were present and develop flow regime maps. This work offers the first independent, experimentally validated model for RAM power input, delivering a robust calorimetric testing framework. The findings enable better benchmarking, scale-up, and optimization for industrial powder mixing using resonant acoustic methods.

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