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The DOI matches its Crossref record; 59 of the 59 references it cites were found in a registry or an index.

Degradation of Fan Performance in Cooling Electronics: Experimental Investigation and Evaluating Numerical Techniques

Abstract

Abstract This study reports on the essential forced air-cooled electronics issue of fan performance deterioration caused by the presence of obstructions inside Information Technology Equipment (ITE). Fan performance was characterized based on the fan's static pressure and flowrate. Three different experimental techniques (flow test chamber, pressure probe, and pressure taps) were used to measure the fan static pressure at different locations. Moreover, Computational Fluid Dynamics (CFD) models were built considering different fan working environments. Multi Reference Frame (MRF) and Lumped Fan (LF) model CFD techniques were employed. The experimental results were used to evaluate the modeling techniques whilst implemented in different working environments and to better understand how fans react to blockages inside ITE. Experiments showed that compared to the Free Environment (FE) readings, placing the fan inside a specific ITE reduced the flowrate delivered by the fan by 57.2% and decreased static pressure by 76.3%, which affects the thermal performance of the ITE cooling system. Moreover, comparing numerical results with the experimental ones showed that the MRF approach predicted the flowrate delivered by the fan with a relative error of 3.9%, while the LF approach overestimated the flowrate by 70.3%. The results and conclusions reported in this work can be expanded to cover many other applications in which fans are operating inside enclosed environments and surrounded by many obstructions.

Overview

Integrity signals

Registry

Crossref · 10.1016/j.ijheatmasstransfer.2021.121291

Cites

59 of 59 references verified0 citation errors · 0 not checkable

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Citations

Cites

Show all 59 references

Research works printed but not linked: 4

  1. Lee, 2016, 94, 609
  2. Ye, 2017, 126, 535
  3. Zhang, 2019, 170, 305
  4. Peric, 2005, The advantage of polyhedral meshes, Dynamics, 24, 45

Cited by

Metadata

Authors
Manaserh, Y. M., Tradat, M. I., Hoang, C. H., Sammakia, B. G., Ortega, A., Nemati, K., & Seymour, M. J.
Affiliations
  • Binghamton University (US)
  • Villanova University (US)
Corresponding author
Manaserh, Yaman.M.
Venue
International Journal of Heat and Mass Transfer
Publisher
Elsevier BV
Published
2021-08
Volume
174
Pages
121291
ISSN
0017-9310 (ISSN-L)
OpenAlex
W3156408709
Cited by
31
References
59
Citations by year
2026: 2 · 2025: 8 · 2024: 5 · 2023: 4 · 2022: 6 · 2021: 6
FWCI
2.71
Type
journal article
Language
en
Open access
closed
Locations
International Journal of Heat and Mass Transfer (publishedVersion)
Funders
NSF
Topics
  • Physical Sciences › Engineering › Aerospace Engineering › Turbomachinery Performance and Optimization
  • Physical Sciences › Engineering › Mechanical Engineering › Heat Transfer and Optimization
  • Physical Sciences › Engineering › Computational Mechanics › Heat Transfer and Numerical Methods
Keywords
information technology equipment, static pressure, CFD validation, pressure measurement technique, thermal management

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CoChat. (2026). Citation check: Degradation of Fan Performance in Cooling Electronics: Experimental Investigation and Evaluating Numerical Techniques. https://app.cochat.ai/papers/10.1016/j.ijheatmasstransfer.2021.121291
The paper
Manaserh, Y. M., Tradat, M. I., Hoang, C. H., Sammakia, B. G., Ortega, A., Nemati, K., & Seymour, M. J. (2021). Degradation of Fan Performance in Cooling Electronics: Experimental Investigation and Evaluating Numerical Techniques. International Journal of Heat and Mass Transfer. https://doi.org/10.1016/j.ijheatmasstransfer.2021.121291

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.