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Record W4391302720 · doi:10.2514/6.2024-1187

Performance of Paraffin Wax Embedded with Lattice and Protrusion in Hybrid Rocket

2024· article· en· W4391302720 on OpenAlexaff
Annapurna Basavaraju, J. Wong, Craig T. Johansen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsWaxParaffin waxMaterials scienceComputer scienceComposite material

Abstract

fetched live from OpenAlex

A hybrid rocket has many benefits compared to solid and liquid rockets, especially in terms of safety and ease of use. However, a major challenge in hybrid rockets that use paraffin wax as fuel is that wax exhibits poor mechanical properties resulting in sloughing events that can lead to motor failure. Therefore, the current work aims to improve the mechanical strength of paraffin wax without compromising on combustion performance. To achieve this, an optically accessible hybrid rocket slab burner was used to experimentally investigate the combined effect on fuel regression due to the addition of an additively manufactured, tailored PLA lattice embedded into the fuel grain in the presence of a protrusion. Generally, the slower burn rate of the lattice compared to the wax, reduces the fuel regression rate. In addition to the lattice, an incombustible protrusion is inserted in the paraffin wax, which disturbs the flow, enhances the heat transfer, and thus increases the regression rate. In the current work, mullite and silicon carbide (SiC) were chosen as protrusion materials, and their influence on regression behavior was examined. The results reveal that lattice-embedded fuel grains with mullite protrusions resulted in a 13% increase in regression rate whereas SiC resulted in a 30% decrease in regression rate compared to the neat paraffin case. This difference in regression rates is mainly due to the higher thermal conductivity of SiC compared to Mullite.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.196

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 head0.011
GPT teacher head0.237
Teacher spread0.226 · 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 designOther design
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

Citations3
Published2024
Admission routes1
Has abstractyes

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