Design and control intended low-order model for transient investigation of an electric oven: Static mode

Abstract

In general, electric ovens represent a low-efficiency class among home appliances, contributing to overall environmental changes in buildings and apartments. To improve their low efficiency, which is typically between 10% to 12%, industries are increasingly focused on developing effective technologies. One suitable strategy is to design and control the oven's center temperature, as specified by the EN 60350-1 European standard, which regulates the energy efficiency index (EEI) test. The present simulation study explains the transient thermo-fluid behavior of a domestic electric oven subjected to free convection, i.e., static mode. For this purpose, appropriate dynamic models are employed, intended for design and control. A lumped-parameter based methodology is considered to develop a low-order dynamic model, resulting in reduced computational cost and run time. A finite level of discretization is introduced into the model, enabling the capture of limited insights, such as temperature, heat flux, mass flow rate, pressure drop, etc., particularly at the cavity center, internal and external cavity wall surfaces, and at suction and exit openings. The electric oven is mainly divided into four sub-systems: door, cavity, ventilation, and cabinet. Each sub-system has a suitable level of discretization signifying a lumped mass parameter to reproduce the transient behavior within the electric oven. Heaters are considered as individual lumped masses and are integrated within the cavity sub-system. The above sub-systems employ both the first principle equations and correlations based on experimental data, giving a low-order grey-box identity to the system. In addition to conduction and convection, a non-linear form of radiation is considered to increase model accuracy. The developed model is further calibrated with available physical data. Once calibrated, the model is then compared with the remaining physical data produced at different heat loads to understand the accuracy in terms of transient temperature prediction at various temperature set points.