Reducing The Risk Of Innovative Designs To Assess Affordability
Bibliographic record
Abstract
In today's economic environment, military as well as commercial helicopter buyers are seeking rotorcraft with improved performance at lower life-cycle cost. The commercial operator must meet new demands in performance and maintain a competitive posture, which dictates low procurement and operational costs. Also, the military must secure an adequate mobile force with acceptable performance while facing shrinking budgets. To meet these and other rotorcraft goals, the rotorcraft manufacturer must rely on emerging or revamped technologies to yield innovative design approaches, materials, and manufacturing processes that provide affordable, high-performance rotorcraft with reduced maintenance. However, once candidate technologies have been identified, it is a formidable challenge to make accurate assessments in order to select the optimum solution. Many structure technologies being considered today have demonstrated affordability in the marine, transportation, or other sectors, but are unproven for aerospace applications. Tools such as process simulation and cost models provide data to make the selection; but until detail design and manufacturing activities are begun, the limitations, risks, and costs of these emerging technologies cannot be fully defined. This paper describes risk-reduction activities that were undertaken to supplement trade studies in order to more accurately assess the affordability of RTM, RFI, and pultrusion processes for application to rotorcraft structural components. Activities described include design support tests and studies conducted for an RTM horizontal stabilizer, a pultruded rotor spar, and RTM tail rotor components. Panel and subelement level components were manufactured to assess laminate strength, quality, and producibility. A summary of trade study results and descriptions of the low-cost composite parts selected for manufacture are then described.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".