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Advanced Design of Heat Exchangers using multiscale models and machine learning

CORDIS · observation · Publication date unknown

Advanced Design of Heat Exchangers using multiscale models and machine learning Heat exchangers (HXs) are at the heart of many energy systems, one example being engine cooling in the aviation industry. Advanced design of HXs is urgent since aircraft systems are becoming smaller and need to become more efficient. Through topology optimisation (TO) and additive manufacturing (AM), custom compact HXs will be designed to cool the engines of tomorrow. Although TO of thermofluidic problems has recently undergone tremendous development, the technology is mostly limited to academic problems, since existing approaches are restricted to macroscopic design with extreme computational cost being prohibitive for industrial applications. The objective of ADeHEx is therefore to propose an integrated design methodology for multi-scale 3D fluid-to-fluid HXs using machine learning-based de-homogenization. Specifically, I will a) construct a homogenized thermohydraulic

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recordType

award

status

SIGNED

region

EU

value

230774.4

unit

EUR