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Advanced Design of Heat Exchangers using multiscale models and machine learning

Project description

A closer look at aircraft engine cooling

Heat exchangers (HXs) are vital components in energy systems, including aviation engine cooling. As aircraft systems become more compact and efficiency demands rise, the need for advanced HX design is more pressing than ever. The MSCA-funded ADeHEx project will design custom compact HXs for the engines of tomorrow. Specifically, it combines topology optimisation (TO) and additive manufacturing (AM) techniques to create efficient HXs. While TO for thermo-fluidic problems has advanced, its industrial applications have been hindered by computational costs. As such, ADeHEx’s goal is to develop a comprehensive design methodology for multi-scale 3D fluid-to-fluid HXs using machine learning-based de-homogenisation. This includes constructing homogenised thermo-hydraulic computational models and employing convolutional neural networks to reduce computational time.

Objective

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 computational model; b) use convolutional neural networks to recover detailed micro-channel design from homogenization-based TO, reducing computational time by at least two orders of magnitude. Implemented at University of Southern Denmark and at the secondment, Brown University, a two-way transfer of knowledge is guaranteed through my expertise in level-set-based TO and the expertise of the supervisors in complex multiphysics modelling and physics-based machine learning. ADeHEx will a) consolidate my academic excellence and professional maturity through new skills and competences in machine learning, homogenized thermohydraulic models, teaching, supervision, project management, dissemination, industrial engagement, networking; b) harness the complementary expertise of two strong multidisciplinary teams, pushing research to the forefront of design engineering and computer science and informatics; c) revolutionize the design process of HXs and multiphysics system optimisation, while benefiting EU companies in manufacturing, aviation, etc., and raising European economic competitiveness.

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Programme(s)

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Topic(s)

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Funding Scheme

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HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships

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Call for proposal

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(opens in new window) HORIZON-MSCA-2022-PF-01

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Coordinator

SYDDANSK UNIVERSITET
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 214 934,40
Address
CAMPUSVEJ 55
5230 Odense M
Denmark

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Region
Danmark Syddanmark Fyn
Activity type
Higher or Secondary Education Establishments
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Total cost

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