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Polyethylene glycol-nano composite thermal conductivity: Introduction of data-driven models

Abstract The thermal conductivity of polyethylene glycol (PEG)-nanocomposites is crucial for their use in thermal management systems, underscoring the need for accurate predictive models. This study develops a Gradient Boosting Machine (GBM) model optimized with four algorithms: Batch Bayesian Optim...

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Asıl Yazarlar: ZAHRAA SABAH GHNIM, AYAT HUSSEIN ADHAB, ASHA RAJIV, ANUPAM YADAV, HAIDER RADHI SAUD, AMAN SHANKHYAN, SACHIN JAIDKA, KAMAL KANT JOSHI, MORUG SALIH MAHDI, ASEEL SALAH MANSOOR, USAMA KADEM RADI, NASR SAADOUN ABD, MEHRDAD MOTTAGHI
Materyal Türü: Artigo
Dil:Inglês
Baskı/Yayın Bilgisi: Academia Brasileira de Ciências 2026-01-01
Seri Bilgileri:Anais da Academia Brasileira de Ciências
Konular:
Machine learning-based modeling
Thermal conductivity of polyethylene glycol (PEG)-nano composites
Advanced predictive system
Gradient Boosting Machine
Optimization techniques
Online Erişim:http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0001-37652025000400508&lng=en&tlng=en
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