Freeform generative design of complex functional structures.

Autor: Pereira GG; CSIRO Data61, Private Bag 10, Clayton South, VIC, 3169, Australia. gerald.pereira@csiro.au., Howard D; CSIRO Data61, Private Bag 10, Clayton South, VIC, 3169, Australia., Lahur P; CSIRO IMT, Private Bag 10, Clayton South, VIC, 3169, Australia., Breedon M; CSIRO Manufacturing, Private Bag 10, Clayton South, VIC, 3169, Australia., Kilby P; CSIRO Data61, Private Bag 10, Clayton South, VIC, 3169, Australia., Hornung CH; CSIRO Manufacturing, Private Bag 10, Clayton South, VIC, 3169, Australia.
Jazyk: angličtina
Zdroj: Scientific reports [Sci Rep] 2024 May 24; Vol. 14 (1), pp. 11918. Date of Electronic Publication: 2024 May 24.
DOI: 10.1038/s41598-024-62830-5
Abstrakt: Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications.
(© 2024. The Author(s).)
Databáze: MEDLINE
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