Evidence map›Paper›PMID 37416665›Full record

ArticleHeliyon2023

Experimental study of thermal conductivity coefficient of GNSs-WO3/LP107160 hybrid nanofluid and development of a practical ANN modeling for estimating thermal conductivity.

Mohammad Hossein Razavi Dehkordi, As'ad Alizadeh, Hussein Zekri, Ehsan Rasti, Mohammad Javad Kholoud, Ali Abdollahi, Hamidreza Azimy

Abstract read
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In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Mohammad Hossein Razavi DehkordiDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
As'ad AlizadehDepartment of Civil Engineering, College of Engineering, Cihan University-Erbil, Erbil, Iraq.
Hussein ZekriCollege of Engineering, The American University of Kurdistan, Duhok, Kurdistan Region, Iraq.
Ehsan RastiDepartment of Mechanical Engineering, Sarvestan Branch, Islamic Azad University, Sarvestan, Iran.
Mohammad Javad KholoudDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Ali AbdollahiDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.
Hamidreza AzimyDepartment of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the present study, the effects of nanoparticles, mass fraction percentage and temperature on the conductive heat transfer coefficient of Graphene nanosheets- Tungsten oxide/Liquid paraffin 107160 hybrid nanofluid was investigated. For this purpose, four different mass fractions were used in the range of 0.005%-5% in a number of examinations. The results illustrated that the thermal conductivity coefficient was increased with the increment of the mass fraction percentage and the temperature of Graphene nanosheets- Tungsten oxide nanomaterials in the base fluid. Then, a feed-forward artificial neural network was used to model the thermal conductivity coefficient. In general, with the increase in temperature and concentration of nanofluid, the value of thermal conductivity increases. The optimum value of thermal conductivity for this experiment was observed in the volume fraction of 5% and at the temperature of 70 °C. The results of this modeling indicated that the fault of the data estimated for the coefficient of thermal conductivity in the Graphene nanosheets- Tungsten oxide/Liquid paraffin 107160 nanofluid, as a function of mass fraction percentage and temperature, was less than 3%, as compared to the experimental data.

Indexed as

Feed-forward neural networkGraphene nanosheetsHybrid nanofluidLiquid paraffinThermal conductivity coefficientTungsten nanoparticles

Identifiers

PMID37416665
PMCPMC10320273

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.