Parametric Study on the Performance of Gas Springs in FPSE With Pneumatic Supporting System
Bibliographic record
Abstract
ABSTRACT The introduction of a pneumatic supporting system composed of gas springs and hydrostatic bearings in the free‐piston Stirling generator (FPSG) can effectively increase the power density of the nuclear space power plant. In this paper, a 1 kW FPSG with pneumatic supporting system was developed, and the working characteristics of dual gas springs were obtained under an external driving condition. A quasi‐one‐dimensional numerical model is established, the relative error between piston strokes is within 5%, and the maximum phase uncertainty is 5.36° between experimental and numerical results. The generator outputs 1011.3 W with a total thermo‐electric efficiency of 14.85% under the rated condition, and the hysteresis loss of gas springs is 535.6 W, which accounts for 7.85% of the total power input, and the global stiffness reaches 96.43 N/mm. A multitude of influencing factors (including operational, constitutive, and sealing parameters) on the performance of gas springs are evaluated. An expression for the correction coefficient of stiffness with respect to five influencing parameters is established through data regression, as well as an empirical formula for the hysteresis loss as a percentage of the total input power. The correlations indicate that the order of weights affecting the performance of gas springs is as follows: compression ratio, configuration factor, gap thickness, gap length, and wall temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".