A Multidisciplinary Possibilistic Approach to Size the Empennage of Multi-Engine Propeller-Driven Light Aircraft
Bibliographic record
Abstract
In considering aircraft design, it is very important to effectively size the tail configuration for stability and control. Multidisciplinary design optimization (MDO) focuses on the use of numerical optimization in the design of systems with multiple subsystems or disciplines of consideration. However, MDO uses deterministic calculations, and does not consider the uncertainties that arise from the employed analyses, including errors due to linearization and simplification. For problems with inadequate input data, the possibility-based design optimization (PBDO) scheme can be implemented in its stead to achieve reliable designs using membership functions for epistemic uncertainties. A multidisciplinary, possibilistic approach is presented to define the sizing of the empennage configuration of a twin-engine propeller-driven aircraft by changing shape parameters while satisfying the design requirements given the tailless aircraft configuration, the flight conditions, and various uncertainties. The corresponding disciplines are aerodynamics, stability and control, propulsion and weight and balance. Herein, different design requirements are considered including longitudinal/lateral/directional trim and stability characteristics, manufacturing and controllability criteria, handling qualities, operational requirements, airworthiness and survivability. The resulting aerodynamic characteristics and flight dynamic stability outcomes show that the optimized tail configuration for the proposed aircraft fully complied with airworthiness requirements and predefined constraints while considering several uncertainties due to the use of early-stage statistical estimations. The proposed approach can be used to enhance the preliminary design of multi-engine propeller-driven light aircraft where only low-fidelity, statistical estimations are available. The resulting output is not only an optimized aircraft configuration, but one where the stability of the design has been ensured. In this work, the aerodynamic characteristics have been determined using a validated semi-empirical program called MAPLA, developed for light aircraft designs and development in the preliminary design phase. Furthermore, the optimization framework consists of a deterministic optimizer that runs sequentially with a possibility assessment algorithm.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".