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Record W3197274838 · doi:10.36909/jer.v9i3a.8857

Comprehensive optimization of project cost for long supply pipelines

2021· article· en· W3197274838 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Engineering Research · 2021
Typearticle
Languageen
FieldEngineering
TopicWater Systems and Optimization
Canadian institutionsHydraTek (Canada)
FundersKing Saud University
KeywordsPipeline (software)Pipeline transportNominal Pipe SizeRange (aeronautics)Genetic algorithmEngineeringComputer scienceMarine engineeringReliability engineeringMathematical optimizationOperations researchMechanical engineeringMathematics

Abstract

fetched live from OpenAlex

This paper proposes a “global” pipeline design optimization approach that considers pipe parameters, protection device parameters, and project maintenance and operational costs over the pipeline’s service life. The objective is to search for an optimal pipeline design by analyzing alternatives with different lifespans while taking inflation and interest rates into account. A specially designed genetic algorithm routine suggests possible solutions that encompass a range of available pipe diameters, pipe materials, pipe pressure ratings, surge tank sizes, and inlet/outlet resistances. The software analyzes steady and unsteady pipe flow. The solution should provide a system that can provide the required demand without violating velocity and pressure constraints. A real-world project is selected to investigate the outcome of the optimization procedure. The proposed global optimization approach is shown to be an effective method of comparing a wide range of design alternatives for pipeline projects and identifying the one that optimizes the overall cost.

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.

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: none
Teacher disagreement score0.884
Threshold uncertainty score0.322

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 head0.069
GPT teacher head0.334
Teacher spread0.265 · 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