The effort of numerical heat transfer calculations increases with the complexity and size of the domains and surfaces under consideration. When calculating heat transfers on finned arrays, one way to reduce this effort is to substitute the fins. Therefore, this work defines the fin substitution factor by considering that a smooth surface behaves thermally sufficiently similar to a specific finned array. A process for determining the case-specific most accurate analytical computation path for fin substitution factors is also defined. The performance of the process and the resulting solution is demonstrated using the example of vertical rectangular finned arrays under natural convective heat transfer with a constant fin base temperature and air as the surrounding fluid. The heat flows determined in solid-state simulations of flat plates considering fin substitution factors deviated by an average of 6.2% from the heat flows resulting from detailed CFD simulations of the corresponding finned arrays.

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This work aims to evaluate the current state of research on the use of artificial intelligence, deep learning, digitalization, and Data Mining in product development, mainly in the mechanical and mechatronic domain. These methods, collectively referred to as “digital engineering”, have the potential to disrupt the way products are developed and improve the efficiency of the product development process. However, their integration into current product development processes is not yet widespread. This work presents a novel consolidated view of the current state of research on digital engineering in product development by a literature review. This includes discussing the methods of digital engineering, introducing a product development process, and presenting results classified by their individual area of application. The work concludes with an evaluation of the literature analysis results and a discussion of future research potentials.

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