Unshrouded Plate Fin Heat Sinks for Electronics Cooling: Validation of a Comprehensive Thermal Model and Cost Optimization in Semi-Active Configuration

Luigi Ventola, Gabriele Curcuruto, Matteo Fasano, Saverio Fotia, Vincenzo Pugliese, Eliodoro Chiavazzo and Pietro Asinari
Additional contact information
Luigi Ventola: Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (TO), Italy
Gabriele Curcuruto: Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (TO), Italy
Matteo Fasano: Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (TO), Italy
Saverio Fotia: DENSO Thermal Systems, 10046 Poirino (TO), Italy
Vincenzo Pugliese: DENSO Thermal Systems, 10046 Poirino (TO), Italy
Eliodoro Chiavazzo: Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (TO), Italy
Pietro Asinari: Energy Department, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino (TO), Italy

Energies, 2016, vol. 9, issue 8, 1-16

Abstract: Plate Fin Heat Sinks (PFHS) are among the simplest and most widespread devices for electronics cooling. Because of the many design parameters to be considered, developing both cost and thermal effective PFHS is a critical issue. Here, a novel thermal model of PFHS is presented. The model has a broad field of applicability, being comprehensive of the effects of flow bypass, developing boundary layers, fin efficiency and spreading resistance. Experiments are then carried out to validate the proposed thermal model, and its good accuracy is demonstrated. Finally, an optimization methodology based on genetic algorithms is proposed for a cost-effective selection of the design parameters of PFHS, which is particularly effective with semi-active configurations. Such an optimization methodology is then tested on a commercial heat sink, resulting in a possible 53% volume reduction at fixed thermal performances.

Keywords: heat transfer enhancement; electronics cooling; plate fin heat sinks; cost optimization; genetic algorithms (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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