MétaCan
Menu
Back to cohort
Record W2044249852 · doi:10.5589/q10-005

Conceptual design of wrap-around-fin rockets using the MDO methodology

2010· article· en· W2044249852 on OpenAlexvenueno aff
Liangyu Zhao, Gyung-Jin Park, Kwan-Soo Lee, Shuxing Yang

Bibliographic record

VenueCanadian aeronautics and space journal · 2010
Typearticle
Languageen
FieldEngineering
TopicRocket and propulsion systems research
Canadian institutionsnot available
Fundersnot available
KeywordsMultidisciplinary design optimizationAerodynamicsConceptual designAerospace engineeringPayload (computing)FinEngineeringRocket (weapon)Systems engineeringMultidisciplinary approachComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

The conceptual design of rockets is a system engineering that involves configuration, aerodynamics, engine, dynamic stability, and coupling effects. To obtain benefits from the synergistic effects, the latest research achievements, and the most powerful analysis tools efficiently, this paper focuses on how to apply the multidisciplinary design optimization (MDO) technology to the conceptual design of the long-range, large length to diameter ratio wrap-around-fin (WAF) rockets. The software iSIGHT was employed as the MDO framework, the multidisciplinary feasible (MDF) method as the MDO architecture, and the multi-island genetic algorithm combined with sequential quadratic programming as the search algorithm. By maximizing the payload ratio, the structural analysis, aerodynamics, engine, trajectory, and dynamic stability were considered comprehensively. A case study demonstrated that MDO could improve the rocket performance effectively.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.119
GPT teacher head0.307
Teacher spread0.189 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueCanadian aeronautics and space journalSame topicRocket and propulsion systems researchFrench-language works237,207