BM Shaughnessy’s research while affiliated with Cranfield University and other places

A low-emissivity oven (LEO) provides an energy-efficient alternative to a conventional domestic electric oven. A prototype domestic LEO is described and its energy performance is assessed and compared with that of a current generation domestic electric oven and previous test-rig ovens. In standard tests, the prototype achieved energy-savings of 36–57% when compared with the commercially-available oven, providing thermal performances similar to those of ‘idealised’ test-rig ovens. It is, therefore, recommended that manufacturers implement low-emissivity oven designs for domestic users as a means of speeding thermal performance, reducing energy consumption, and thereby reducing the emissions and peak electrical demands associated with domestic cooking.

Low-emissivity ovens have linings of εe<0.1 that enhance the thermal radiation exchange between sheathed electrical heating elements (that are exposed within the cavity) and the thermal load(s). These ovens are a quick and energy-efficient alternative to conventional ovens; however, due to the high levels of thermal radiation, they must be carefully designed and controlled so that uneven radiation distributions over loads are minimised. In this paper, a Monte Carlo ray-tracing model was employed to undertake a parametric analysis of low-emissivity ovens. The factors that influence the magnitudes and uniformities of radiation distributions occurring over loads are identified, and recommendations for optimal heating configurations and control algorithms are presented.

The Monte Carlo (MC) approach is a powerful means of solving radiative heat transfer problems involving realistic geometries and properties. As described elsewhere (eg, Siegel and Howell, 1972; Modfest, 1993), radiation exchange factors, Fif, between surfaces in an enclosure are calculated by ray-tracing (or tracking) the paths of a large number of 'energy bundles' according to probability functions that describe emission, reflection, and absorption. Unfortunately, the time penalty associated with computing ray-surface intersections is high, and this inhibits high-accuracy solutions. This article presents a new method for tracking the reflected paths of energy bundles, which is much faster than traditional ray-tracing approaches (Shaughnessy, 1996). It is referred to here as 'Discrete Fucntion Monte Carlo' (DFMC).