Optimal design of four stage launch vehicle considering multi objective NSGA II algorithm and mass-energetic concepts
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.
Bibliographic record
Abstract
A solid fuel launch vehicle is a rocket with an engine that has been widely used in aerospace missions. Utilizing such launch vehicles depends on the simplicity of the manufacturing, maintenance, operation and development of the control systems. The purpose of optimization in solid fuel launch vehicles design is to find the best possible design for the mission with regard to the available equipment, constraints and infrastructures. Therefore, the main purpose of this research is to optimally design a launch vehicle for customized missions based on successful experiences, as well as technology, manufacturing capabilities and facilities. In this context, NSGA-II Intelligent Optimization Algorithm is considered based on multi-objective optimization principles and Mass-Energetic concepts. The optimal design of the launch vehicle is performed by applying intelligent algorithms and technological opportunities and limitations. The result showed that the present optimization method can design the launch vehicle based on technological limitations.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it