Thermal Issues in Materials Processing

Abstract

This paper considers the thermal aspects that frequently arise in practical materials processing systems. Important issues such as feasibility, product quality, and production rate have a thermal basis in many cases and are discussed. Complexities such as property variations, complex regions, combined transport mechanisms, chemical reactions, combined heat and mass transfer, and intricate boundary conditions are often encountered in the transport phenomena underlying important practical processes. The basic approaches that may be adopted in order to study such processes are discussed. The link between the basic thermal process and the resulting product is particularly critical in materials processing. The computational difficulties that result from the non-Newtonian behavior of the fluid, free surface flow, moving boundaries, and imposition of appropriate boundary conditions are important in several processes and are discussed. Some of the important techniques that have been developed to treat these problems are presented, along with typical results for a few important processes. Validation of the model is a particularly important aspect and is discussed in terms of existing results, as well as development of experimental arrangements to provide inputs for satisfactory validation. The importance of experimentation and linking the micro/nanoscale transport processes with conditions and systems at the macroscale are discussed. Future trends and research needs, particularly with respect to new materials and new processes, are also outlined.

Computation of non-similar solution for magnetic pseudoplastic nanofluid flow over a circular cylinder with variable thermophysical properties and radiative flux

“…The parameters varied and their values are: Weissenberg number ( We = 0.0,0.5,1.0,1.5), magnetic field ( M a = 0.0,0.5,1.0,1.5), variable viscosity parameter ( γ * = 0.0,0.2,0.4,0.6), variable thermal conductivity parameter ( δ * = 0.0,0.5,1.0,1.5), Brownian motion ( N B = 0.2,0.4,0.6,0.8), thermophoresis ( N T = 0.01,0.1,0.3,1.5), radiation ( R a = 0.1,0.3,0.5,0.8), Soret number ( S r = 0.4,0.2,0.1,0.075) and Dufour number ( D u = 0.15,0.3,0.6,0.8). All used data in the simulations is based on practically viable nano-materials processing systems which is extracted from Das et al (2007) and Jaluria (2013). Figures 3–22 depict the variation in momentum, heat transfer and nanoparticle concentration characteristics and consistently smooth profiles are achieved in the free stream, testifying to the prescription of an adequately larger infinity boundary condition.…”

“…Evidently, the inclusion of thermal conductivity variation produces results which more accurately predict the velocity and temperature magnitudes. Absence of this parameter ( δ * =0) leads to an under-prediction in both quantities which results in lower momentum and lower thermal boundary layer thickness estimates, which are undesirable in manufacturing operations and can incur expenses, as noted by Jaluria (2013).…”

“…All used data in the simulations is based on practically viable nanomaterials processing systems which is extracted fromDas et al (2007) andJaluria (2013). Figures 3-22 depict the variation in momentum, heat transfer and nanoparticle concentration characteristics and consistently smooth profiles are achieved in the free stream, testifying to the prescription of an adequately larger infinity boundary condition.…”

Finite difference computation of free magneto-convective Powell-Eyring nanofluid flow over a permeable cylinder with variable thermal conductivity

“…Evidently, inclusion of thermal conductivity variation produces results which more accurately predict the velocity and temperature magnitudes. Absence of this parameter γ * =0 leads to an under-prediction in both quantities and lower momentum and lower thermal boundary layer thickness estimates, which are undesirable in manufacturing operations and can incur expenses, as noted by Jaluria [51].…”