Dynamic Characterization of STC Stirling Convertor and Improved Mechanical Integration in SRG110 Power System
Bibliographic record
Abstract
The U.S. Department of Energy, Lockheed Martin, Stirling Technology Company (STC), and NASA Glenn Research Center (GRC) are developing the 110-Watt Stirling Radioisotope Generator (SRG110) for use as an advanced power system for potential future NASA missions. The launch environment enveloping potential missions is higher than historical radioisotope power system launch levels and presents a challenge for designers. A finite element model (FEM) of the SRG110 system containing detailed representations of the Stirling convertors was used to determine the response of critical components to the launch environment. A prototype STC Stirling convertor was characterized by modal testing at NASA GRC’s Structural Dynamics Laboratory. The understanding of convertor dynamics gained from analysis and testing led to improved mechanical packaging of the convertors in the SRG110. Concepts were evaluated utilizing the FEM; vibration input was applied at the SRG110 mounting interface, and component vibration responses were calculated. An interface isolation mount for the SRG110 was investigated as a means to further reduce system level responses. This paper presents the convertor modal test setup and methodology, the measured convertor modes and frequencies, and component random launch vibration load responses for the baseline SRG110 and for the SRG110 with improvements to the convertor mounting approach and with a vibration isolation interface. The results show a reduction in response due to improved convertor mounting and further reductions through the use of the isolation interface. This enables the SRG110 power system to be considered for a broad range of potential missions, including those with demanding launch environments.
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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 | 0.000 | 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".