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Record W4382559860 · doi:10.18280/mmep.100339

Computational Fluid Dynamics Analysis of a Parabolic Trough Solar Collector for Enhanced Efficiency and Thermal Performance

2023· article· en· W4382559860 on OpenAlexvenueno aff
Monaem Elmnifi, Ammar M. Al-Tajer, Hasan A. Al-Asadi, Mohammed Al Saker, Laith Jaafer Habeeb

Bibliographic record

VenueMathematical Modelling and Engineering Problems · 2023
Typearticle
Languageen
FieldEnergy
TopicSolar Thermal and Photovoltaic Systems
Canadian institutionsnot available
Fundersnot available
KeywordsParabolic troughThermalComputational fluid dynamicsTrough (economics)Environmental scienceMechanicsMaterials sciencePhysicsMeteorology

Abstract

fetched live from OpenAlex

Parabolic Trough Solar Collectors (PTCs) are crucial components in achieving high efficiency and thermal performance in thermoelectric power stations.This study presents a Computational Fluid Dynamics (CFD) simulation of a PTC with dimensions of 2 m × 1.5 m, incorporating a flat glass cover to safeguard the reflector's optical properties and the glass envelope from environmental factors such as dust, moisture, wind, and rain, resulting in improved thermal insulation and efficiency.A uniform temperature distribution at the Heat Collecting Element (HCE) level is assumed.A mathematical model is developed based on simplifying assumptions, and physical phenomena, including mass transfer, light, and heat, are simulated using mathematical equations derived from equilibrium equations and the optical behavior of materials (reflection, absorption, transmission).The model's validity is established through a comparison of simulation and computational results.The effects of PTC length, water mass flow, and solar tracking pattern on the water exit temperature are subsequently explored.This investigation enables the identification of optimal PTC dimensions and mass flow rates to meet the temperature requirements for various industrial and domestic applications, while maintaining high efficiency and thermal performance.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.460
Threshold uncertainty score0.489

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.019
GPT teacher head0.215
Teacher spread0.196 · 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 designSimulation or modeling
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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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