A Novel Finite-Element Optimization Algorithm with Applications to Power Cable Thermal Circuit Design
Bibliographic record
Abstract
This paper presents the results of a recent study to develop an optimization model for underground power cable thermal circuit based on generated gradient approach. A new concept of perturbed finite-element analysis is utilized, which involves the use of derived sensitivity coefficients associated with various cable parameters of the interest. A subsequent utilization of such sensitivities as gradients of objective functions is realized in a general framework of power cable performance optimization. Therefore, based on the work of this paper, it is now possible to optimize the thermal circuit parameters including the thermal conductivities, boundary conditions and heat generation with respect to cable temperatures defined in a desired objective function and/or constraints. This enables more effective dealing with the nonlinearity of such temperatures, as implicit functions, using the more accurate perturbed finite element method. The proposed method minimizes the objective function value, without sacrificing the modeling accuracy in order to suit other exiting traditional methods. The developed algorithm was applied to various practical utility cable systems of 132-kV XLPE and 380-kV oil filled cables with their actual in-service configurations and for different practical cable performance optimization objectives demanded by the power utility operators.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".