Shape optimization of pipeline components
Bibliographic record
Abstract
Abstract When it comes to shape optimization of processes and equipment in an industrial environment, adjoint methods together with computational fluid dynamics have been a great solution. The success of these methods is due to the total cost of obtaining the sensitivity derivatives once it is independent of the number of shape parameters. However, few developments for multiphase flows have been proposed, as there are still fundamental limitations in adjoint models that might prevent their common use. The adjoint theory is not applicable, for example, to the Lagrangian formulation, which has been the workhorse in particle‐induced erosion simulations. Despite these circumstances, it is intuitively possible to think that the optimization of the carrier flow is also expected to ‘optimize’ the particle flow. For instance, reducing total losses in a pipe junction will lead to a more streamlined design. This, in turn, will prevent sudden changes in fluid motion and, consequently, in particle path. As a direct outcome, erosion is expected to be mitigated, as it is mostly influenced by the particle velocity. Given the lack of rigorous mathematical proof of that, the present work investigates how the optimization of single‐phase flow can also mitigate erosion. The erosive wear problem was tackled in three different bend pipes, and the correlation with Stokes number was further explored. As general results, substantial reductions in peak erosion have been found as a consequence of minimizing total losses for all addressed cases. Accordingly, these pipeline components may have an increase in their service life.
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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 | 0.000 | 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) | 0.002 | 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".