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Study Of Nanoparticle Dispersed Phase Change Materials And The Impact Of Temperature Gradient On The Potential For Particle Migration, Udit Sharma
Dissertations, Master's Theses and Master's Reports
Supercooling in phase change materials (PCMs) and the associated challenges in enhancing thermal conductivity through nanoparticle dispersion prompted this investigation. Existing literature exhibits inconsistencies in thermal conductivity improvements, suggesting a potential correlation with nanoparticle migration induced by thermophoresis. To address this, a novel temperature-dependent scaling parameter, \(\xi\), was introduced to predict particle migration propensity. A strong association was observed between higher \(\xi\) values and diminished thermal conductivity enhancements, indicating a significant influence of nanoparticle movement on heat transfer.
To further elucidate this relationship, a Nanoparticle Interaction Parameter \(N_\text{{pl}}\) was developed, incorporating critical fluid properties and interfacial effects. The derived critical …
Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa
Data Driven And Machine Learning Based Modeling And Predictive Control Of Combustion At Reactivity Controlled Compression Ignition Engines, Behrouz Khoshbakht Irdmousa
Dissertations, Master's Theses and Master's Reports
Reactivity Controlled Compression Ignition (RCCI) engines operates has capacity to provide higher thermal efficiency, lower particular matter (PM), and lower oxides of nitrogen (NOx) emissions compared to conventional diesel combustion (CDC) operation. Achieving these benefits is difficult since real-time optimal control of RCCI engines is challenging during transient operation. To overcome these challenges, data-driven machine learning based control-oriented models are developed in this study. These models are developed based on Linear Parameter-Varying (LPV) modeling approach and input-output based Kernelized Canonical Correlation Analysis (KCCA) approach. The developed dynamic models are used to predict combustion timing (CA50), indicated mean effective pressure (IMEP), …