Multi-objective Computational Fluid Dynamics (CFD) design optimisation in commercial bread-baking

Changing legislation and rising energy costs are bringing the need for more efficient baking processes into much sharper focus. High-speed air impingement bread-baking ovens are complex systems used to entrain thermal air flow. In this paper, Computational Fluid Dynamics (CFD) is combined with a multi-objective optimization framework to develop a tool for the rapid generation of forced convection oven designs. A design parameterization of a three-dimensional generic oven model is carried out to enable optimization, for a wide range of oven sizes and flow conditions, to be performed subject to appropriate objective functions measuring desirable features such as temperature uniformity throughout the oven, energy efficiency and manufacturability. Optimal Latin Hypercubes for surrogate model building and model validation points are constructed using a permutation genetic algorithm and design points are evaluated using CFD. Surrogate models are built using a Moving Least Squares approach. A series of optimizations for various oven nozzle sizes, oven depths and flow conditions are performed using a genetic algorithm with responses calculated from the surrogates, from which appropriate oven designs for a wide range of specific applications can be inferred. Results from various oven design and objective functions under investigation are presented together with ensuing energy usage and savings. Analysis suggests that 10% energy savings can be achieved for the baking process.