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Record W4407415641 · doi:10.2514/6.2025-0128

Experimental Design, Fabrication and Validation of a Small-Scale Liquid Bi-Propellant Rocket Engine

2025· article· en· W4407415641 on OpenAlexaboutno aff
Aryan Chaudhary, Bismanjyot Kaur Bindra, Prabhjeet Singh, Rakesh Kumar

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsPropellantLiquid-propellant rocketFabricationAerospace engineeringScale (ratio)Rocket engineRocket (weapon)Rocket propellantComputer scienceMaterials scienceAutomotive engineeringEngineeringPhysics

Abstract

fetched live from OpenAlex

This study presents the design, fabrication, and testing of a small-scale liquid bi-propellant rocket engine with a target thrust of 1000 N, using gaseous oxygen (GOx) as the oxidizer and a 70% ethanol-30% water mixture as the fuel. The engine’s components, including the thrust chamber, de Laval nozzle, and propellant feed system, were engineered with a focus on affordability and accessibility, utilizing locally sourced materials and in-house fabrication processes. The combustion chamber, constructed from SAE 316L stainless steel, was actively cooled using a soft-copper tubing-based conductive cooling system, ensuring safe operation under high thermal loads. Key design parameters were derived through extensive theoretical calculations and validated using MATLAB simulations. The injector system, featuring a conical spray pattern with 18 oxidizer ports, was calibrated to achieve an injection pressure of 25.34 bar, maintaining efficient propellant mixing. Testing included four hot-fire runs of three to five seconds each, with combustion chamber pressures reaching 15 bar, demonstrating subsonic combustion stability. Data acquisition was conducted using pressure transducers and load cells integrated into a custom graphic user interface for real-time monitoring. Despite challenges such as suboptimal atomization and material constraints, the engine operation was successful, validating the theoretical models. This research highlights the feasibility of developing cost-effective liquid rocket engines in resource-constrained environments, emphasizing the importance of interdisciplinary collaboration and innovation. The results pave the way for future enhancements, including advanced injector designs and thrust vectoring systems, to optimize performance and broaden the engine’s application scope.

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 score0.119
Threshold uncertainty score0.268

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 head0.274
Teacher spread0.244 · 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
Published2025
Admission routes1
Has abstractyes

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