An Unconditionally Stable Conformal LOD-FDTD Method for Curved PEC Objects and its Application to EMC Problems
Bibliographic record
Abstract
The traditional finite-difference time-domain (FDTD) method is constrained by the Courant–Friedrich–Levy condition and suffers from the notorious staircase error in electromagnetic simulations. This article proposes a 3-D conformal locally one-dimensional FDTD (CLOD-FDTD) method to address the two issues for modeling perfectly electrical conducting (PEC) objects. By considering the partially filled cells, the proposed CLOD-FDTD method can significantly improve the accuracy compared with the traditional locally one-dimensional FDTD (LOD-FDTD) method and the FDTD method. At the same time, the proposed method preserves unconditional stability, which is analyzed and numerically validated using the von Neumann method. Significant gains in central processing unit time are achieved by using large time steps without sacrificing accuracy. Two numerical examples, including a PEC cylinder and a missile, are used to verify its accuracy and efficiency with different meshes and time steps. It can be found from these examples that the CLOD-FDTD method shows better accuracy and can improve the efficiency compared with those of the traditional FDTD method and the traditional LOD-FDTD method.
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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) | 0.000 | 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 | 0.001 |
| 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".