A Novel Three-Phase Tubular Permanent Magnet Linear Generator for Free-Piston Stirling Engines
Bibliographic record
Abstract
This article proposes an air-cored three-phase tubular permanent magnet linear generator (3P-TPMLG) with a novel free-piston Stirling engines (FPSEs) structure. First, the concrete construction and initial dimensions of the 3P-TPMLG are given through design principles and experience. Second, the mover’s basic working principle and operational process are discussed using relevant equations and the isolines distribution of magnetic vector potential at several typical mover positions. Third, the 2-D and 3-D machine models established by finite element analysis (FEA) software FLUX and electromagnetic field analysis are presented. Furthermore, two pivotal parameters are optimized to improve operating conditions and output performance of 3P-TPMLG. Finally, the output parameters of 3P-TPMLG are solved separately by FLUX under the constant and sinusoidal velocities. Following finite element dynamic simulation verification, 3P-TPMLG has good power output at both unchanged and sinusoidal velocities. Meanwhile, when the reciprocating frequency is 80 Hz, and the proportion of permanent magnet (PM) length in half pole pitch is 7/10, the comprehensive evaluation of the generator is optimal. The novel structural 3P-TPMLG for FPSEs can realize symmetrical three-phase power generation when the mover reciprocates linear motion at a sinusoidal velocity. Besides, the comparisons prove that the 3P-TPMLG has the advantages of high efficiency, high power generation density, and low construction price.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".