Strain-Rate-Dependent Parameters of Lithium as a Plasma Facing Material for Magnetized Target Fusion Application
Bibliographic record
Abstract
Abstract Lithium is an ideal plasma facing material for fusion plasmas due to its low atomic mass, ability to breed tritium fuel and its gettering characteristics. Liners made of lithium are used as magnetic flux conservers, which can be compressed for Magnetized Target Fusion (MTF) applications. Lithium has been extensively examined in the literature aiming at its mechanical properties for its various applications, and yet the knowledge about its mechanical properties is insufficient for the MTF application in two aspects. First, most of the available studies focus on the quasi-static loading cases. Second, the tests in those studies were commonly performed via uniaxial tensile test where the stress state was not representative for the compression loading scenario. In this paper, we present an approach to estimate parameters that govern lithium’s behavior at high strain rates up to 500 s−1. In our approach, a series of drop-piston experiments were combined with ANSYS LS-DYNA simulation to determine the material parameters. The bi-linear strain hardening model and Johnson-Cook material model were considered in our studies where the former one accounts for the plastic stress-strain relationship and the latter one describes the plastic stress-strain relationship with the strain-rate effects. In our drop-piston experiments, we use high-speed camera imaging and a feature tracking algorithm to record the velocity and extract the piston impact time. A bi-linear strain hardening model was initially used to estimate plastic behavior of lithium. The strain-rate dependent material parameters were then determined using a reverse engineering approach with the help of numerical simulations that reproduce the tests. A series of numerical simulations with different combinations of material parameters were computed to establish the combination of material parameters that best fit the test results. The proposed material model will be used in the lithium compression experiments for MTF applications, which accounts for the hardening behavior at different strain rates.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".