MétaCan
Menu
Back to cohort
Record W4407558133 · doi:10.29169/1927-5129.2025.21.07

Optimal Design of a Biconvex Airfoil for a Supersonic Aircraft Using the Basin-Hopping and Exhaustive Search Methods

2025· article· en· W4407558133 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Basic & Applied Sciences · 2025
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Multi-Objective Optimization Algorithms
Canadian institutionsnot available
Fundersnot available
KeywordsAirfoilSupersonic speedAerospace engineeringComputer scienceAngle of attackEnvironmental scienceGeologyAerodynamicsAcousticsAeronauticsEngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, based on target design conditions, an airfoil is designed for a supersonic aircraft to achieve the maximum lift-to-wave drag ratio, with constraints on the lift coefficient, pitching moment, and maximum thickness. The coefficients of lift and wave drag are calculated numerically using shock/expansion wave theory. To solve the corresponding optimization problem, the Basin-Hopping algorithm—a method commonly used in computational chemical physics for determining minimum energy structures of molecules—is employed. To enhance the search for local extrema, the Sequential Least Squares Programming (SLSQP) method, known for handling constrained optimization problems, is integrated with the Basin-Hopping algorithm. For comparison and validation, the exhaustive search method, a simple technique that evaluates various combinations of design variables to find the optimal solution, is also applied. The results show that while the exhaustive search identifies the optimal design, the Basin-Hopping algorithm yields a slightly better design and requires only about 1/60 of the computation time. This work outlines the design process and demonstrates how advanced optimization algorithms can efficiently address engineering design challenges.

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)0.005
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: Methods · Consensus signal: Methods
Teacher disagreement score0.137
Threshold uncertainty score0.425

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.066
GPT teacher head0.377
Teacher spread0.312 · 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