Design Optimization of Compound Cylinders Subjected to Autofrettage and Shrink-Fitting Processes
Bibliographic record
Abstract
The autofrettage and shrink-fit processes are used to increase the load bearing capacity and fatigue life of the pressure vessels under thermomechanical loads. In this paper, a design optimization methodology has been proposed to identify optimal configurations of a two-layer cylinder subjected to different combinations of shrink-fit and autofrettage processes. The objective is to find the optimal thickness of each layer, autofrettage pressure and radial interference for each shrink-fit, and autofrettage combination in order to increase the fatigue life of the compound cylinder by maximizing the beneficial and minimizing the detrimental residual stresses induced by these processes. A finite element model has been developed in ansys environment to accurately evaluate the tangential stress profile through the thickness of the cylinder. The finite element model is then utilized in combination with design of experiment (DOE) and the response surface method (RSM) to develop a smooth response function which can be effectively used in the design optimization formulation. Finally, genetic algorithm (GA) combined with sequential quadratic programming (SQP) has been used to find global optimum configuration for each combination of autofrettage and shrink-fit processes. The residual stress distributions and the mechanical fatigue life based on the ASME code for high pressure vessels have been calculated for the optimal configurations and then compared. It is found that the combination of shrink-fitting of two base layers then performing double autofrettage (exterior autofrettage prior to interior autofrettage) on the whole assembly can provide higher fatigue life time for both inner and outer layers of the cylinder.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".