The DOI matches its Crossref record.
Thermal Conductivity of Nanoparticle - Fluid Mixture
Abstract
Effective thermal conductivity of mixtures of uids and nanometer-size particles is measured by a steady-state parallel-plate method. The tested uids contain two types of nanoparticles, Al2O3 and CuO, dispersed in water, vacuum pump uid, engine oil, and ethylene glycol. Experimental results show that the thermal conductivities of nanoparticle– uid mixtures are higher than those of the base uids. Using theoretical models of effective thermal conductivity of a mixture, we have demonstrated that the predicted thermal conductivities of nanoparticle– uid mixtures are much lower than our measured data, indicating the de ciency in the existing models when used for nanoparticle– uid mixtures. Possible mechanisms contributing to enhancement of the thermal conductivity of the mixtures are discussed. A more comprehensive theory is needed to fully explain the behavior of nanoparticle– uid mixtures. Nomenclature cp = speci c heat k = thermal conductivity L = thickness Pe = Peclet number Pq = input power to heater 1 r = radius of particle S = cross-sectionalarea T = temperature U = velocity of particles relative to that of base uids ® = ratio of thermal conductivityof particle to that of base liquid ¯ =. ® ¡ 1/=. ® ¡ 2/ ° = shear rate of ow = density Á = volume fraction of particles in uids Subscripts e = effective property f = base uid property g = glass spacer p = particles r = rotationalmovement of particles t = translationalmovement of particles I.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Convective Transport in Nanofluids
Buongiorno, J. (2006) · Journal of Heat Transfer · doi:10.1115/1.2150834 · Cited by 7,013
Buoyancy-driven heat transfer enhancement in a two-dimensional enclosure utilizing nanofluids
Khanafer, K., Vafai, K., & Lightstone, M. (2003) · International Journal of Heat and Mass Transfer · doi:10.1016/s0017-9310(03)00156-x · Cited by 2,975
Ho, C. J., Liu, W. K., Chang, Y. S., & Lin, C. C. (2010) · International Journal of Thermal Sciences · doi:10.1016/j.ijthermalsci.2010.02.013 · Cited by 816
Palacios, A., Cong, L., Navarro, M. E., Ding, Y., & Barreneche, C. (2019) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2019.03.020 · Cited by 237
Nitrate salts doped with CuO nanoparticles for thermal energy storage with improved heat transfer
Myers, P. D., Alam, T. E., Kamal, R., Goswami, D. Y., & Stefanakos, E. (2016) · Applied Energy · doi:10.1016/j.apenergy.2015.11.045 · Cited by 192
Metadata
- Authors
- Wang, X., Xu, X., & Choi, S. U. S.
- Venue
- Journal of Thermophysics and Heat Transfer
- Published
- 1999-10
- Volume
- 13
- Issue
- 4
- Pages
- 474-480
- ISSN
- 0887-8722 (ISSN-L)
- 1533-6808
- OpenAlex
- W2122795128
- Cited by
- 2,386
- References
- 0
- FWCI
- 2.45
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
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.