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Record W2894680407 · doi:10.1115/es2018-7161

Application of Low Temperature Phase Change Materials to Enable the Cold Weather Operability of B100 Biodiesel in Diesel Trucks

2018· article· en· W2894680407 on OpenAlexaff
Obiajulu J. Nnaemeka, Eric Bibeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsOperabilityBiodieselDiesel fuelIgnition systemEnvironmental scienceFuel tankFlash pointTruckNuclear engineeringWaste managementAutomotive engineeringEngineeringMechanical engineeringAerospace engineeringChemistry

Abstract

fetched live from OpenAlex

The use of pure biodiesel for compression ignition engines during the winter poses a challenge due to gelling and plugging of engine filters and fuel lines. The most common method to prevent this issue is blending with petroleum diesel and many engine manufacturers limit the biodiesel in blends to 20% or less for warrantee purposes; as low as 5% may be set for winter months. In a previous work, the authors proposed a novel fuel tank design that could potentially solve this problem and presented a numerical validation of the concept of using phase change materials (PCM) to enable cold weather operability of 100% biodiesel by maintaining its temperature above a cloud point of 5 degrees Celsius for over 3 days at an ambient temperature of −25 degrees Celsius and initial temperature of 20 degrees Celsius. In this research, an experimental analysis is performed using a scaled model of the fuel tank with canola oil as a test fluid in the tank. The tank is subjected to an ambient temperature of −20 degrees Celsius in an icing tunnel facility with air velocity at 10 m/s. The results show that the time above cloud point was increased from 18.6 hours to 22.5 and 33 hours respectively when 4 and 12 PCM tubes were inserted in the tank containing 33 litres of canola oil. A simple numerical model was formulated to predict the transient temperature of the oil and comparison with experimental results showed excellent agreement.

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 score0.019
Threshold uncertainty score0.306

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 head0.018
GPT teacher head0.263
Teacher spread0.245 · 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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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