A PINN recovers jet-impingement convective heat transfer coefficients from sparse, noisy in-solid temperatures, matching CHT-based benchmarks with relative errors below 8% for noise up to 10% and sampling rates at or above 0.5 s^{-1}.
Rough surfaces with enhanced heat transfer for electronics cool- ing by direct metal laser sintering
1 Pith paper cite this work, alongside 198 external citations. Polarity classification is still indexing.
1
Pith paper citing it
198
external citations · OpenAlex
citation-role summary
background 1
citation-polarity summary
fields
physics.flu-dyn 1years
2025 1verdicts
CONDITIONAL 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
Physics-Informed Neural Networks for Estimating Convective Heat Transfer in Jet Impingement Cooling: A Comparison with Conjugate Heat Transfer Simulations
A PINN recovers jet-impingement convective heat transfer coefficients from sparse, noisy in-solid temperatures, matching CHT-based benchmarks with relative errors below 8% for noise up to 10% and sampling rates at or above 0.5 s^{-1}.