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Record W2748736256 · doi:10.11159/ffhmt17.142

A Comparative Numerical Study on Heat Transfer Characteristics of a Shell and Tube Heat Exchanger with Segmental and Helical Baffles

2017· article· en· W2748736256 on OpenAlexaffvenue
Ramtin Barzegarian, Tooraj Yousefi, Alireza Aloueyan

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2017
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer and Optimization
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBaffleShell and tube heat exchangerHeat exchangerHeat transferMaterials scienceTube (container)MechanicsShell (structure)Concentric tube heat exchangerMechanical engineeringEngineeringComposite materialPhysics

Abstract

fetched live from OpenAlex

As their name implies, Heat exchangers are used where is needed to heat transfer between a fluid and a solid object or two or more fluids in order to control the system temperature. In such devices, Heat transfer could be feasible by two ways: direct and indirect contact of fluids. Heat exchangers are very common in many industrial applications such as chemical and petrochemical plants, power stations, refrigeration and air conditioning systems, petroleum refineries, sewage treatment, etc. a good commonplace example of using heat exchangers in our daily life is the car radiator. Shell and tube heat exchangers are the most usual types of heat exchangers in industries which are robust and heavy duty because of their shape (they are typically suitable for higher pressure use). There are various types of baffles for shell and tube heat exchangers such as segmental (single and double segmented baffles), helical, disc and doughnut, etc. which are used to direct the fluid across the tube bundle, reinforce the endurance of long length tubes against sagging and decrease the system's vibration in total. In the recent years, some experimental and computational studies on heat transfer performance of various heat exchangers by changes in their physical specification or in thermo-physical properties of their working fluid were conducted by investigators [1-5]. In the present paper, a computational study of the comparison between using segmental (5 baffles with 1 mm thickness, 30 mm spacing and 50% baffle cut) and helical (with tilt angle of 45 o , baffle length of 115 mm and thickness of 1 mm) baffles on heat transfer rate of the same shell and tube counter-flow heat exchanger is carried out. The analysis have been accomplished for different Reynolds numbers (turbulent flow condition) of hot water in tubes side ranging from 4516 to 9032. In order to simulate, mesh and analyze the heat transfer apparatus, a commercial computational fluid dynamic (CFD) software (ANSYS Fluent) was adopted. The test section (shell & tube heat exchanger) with 174 mm length, consist of 9 stainless steel tubes which were installed with a triangular arrangement. The tubes' inner and outer diameters are 5 and 7 mm respectively. For shell side, the Inner and the outer diameters are around 33 and 39 mm respectively. The results demonstrate that the mean overall heat transfer coefficient of the shell and tube heat exchanger with segmental baffles is about 27% more than that of the same heat exchanger with helical baffle at defined velocity of hot fluid. Also it can be seen that the difference in the overall heat transfer coefficient of mentioned heat exchanger by using two types of baffles, rises with increment of Reynolds number and their values are specified around 13 and 39% at the minimum and the maximum Reynolds numbers, respectively. Finally, in order to evaluate the accuracy of the simulation, the numerical results were validated with Gnielinski [6] correlation (used for turbulent flow inside a tube) which was illustrated a good agreement between computational and predicted data.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.488
Threshold uncertainty score0.772

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.254
Teacher spread0.224 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations1
Published2017
Admission routes2
Has abstractyes

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