![]() The paper presents the results of the study of possibility using a body with an optimized shape with a minimum aerodynamic drag force as a heat sink in a convective gas flow for cooling machine parts. The relevance of cooling or heat sink systems for mechanical engineering is not in doubt. Maintaining the optimum thermal state of machine parts within the specified limits is necessary to prevent heating, which can cause violations of normal operating conditions, and, as a result, increased wear, jamming and breakage of parts. Conventional fixed-wing systems generally use aerofoils with a high thickness-to-chord ratio to achieve high efficiency and wing loading. To ensure the normal operation of the system, it is necessary to cool the parts contacting the hot gases, removing heat from them to the atmosphere directly, or using an intermediate body (water, low-freezing liquid). High aerodynamic efficiency is a key design driver for airborne wind energy systems as it strongly affects the achievable energy output. Heat sink is an aerodynamic process, a process of heat mass transfer of a substance, and aerodynamic characteristics and, in particular, the nature of streamlining of bodies of the simplest forms are of great scientific and practical interest. In the airfoil optimization based on deep learning, due to the uncertainty in the neural network, the optimization results deviate from the true value. The airfoil geometry causes the flow to deflect, creating suitable air circulation conditions for lift. Bodies of complex shape can always be represented as a combination of simpler ones, for which it is easy to explore and calculate the flow paths, on the basis of which analytical methods for calculating aerodynamic characteristics are compiled. As compared with the computational fluid dynamics(CFD), the airfoil optimization based on deep learning significantly reduces the computational cost. ![]() A computational experiment was conducted in the software product Ansys Fluent.
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