Designing and optimization of different types of graded lattice structures of turbine blade
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Additive manufacturing by direct metal fabrication represents one of the fastest-growing areas in material science and manufacturing. Modern manufacturing demands that parts be engineered to have high strength, be lightweight with complex geometrical details, and be suitable for operation upon completion. A very good example of such engineering-manufacturing involves the design and manufacturing of turbine blades for energy efficiency. On the other hand, topology-optimized lattice structures have huge potential and flexibility available to designers operating in the area of designing lightweight structures and high-strength ones at the same time, in contrast to solid form structures. The key issues involved in the research include designing graded density structures made from different lattice architectures for dense materials by characterization of the thermo-mechanical properties for a number of lattice settings in Gyroid, Diamond, Schwarz, Lidinoid, Split P, and Neovius lattices for varied parameters. This paper questions how appropriately the design structure functions in high-speed-rotating elements, such as turbine blades. The current research work will be aimed at the design, finite element analysis for simulation, and manufacturing through additive manufacturing of the turbine blades, considering several designs and lattice structures that satisfy the requirements of lightweight construction and high strength. A detailed preliminary design study has already been performed with the aim of justifying the idea presented in this paper and to create an initially validated basis. It therefore presents findings from the design of different lattice structures, supported by simulations that explain the potential, extent, and limitations of the proposed paper with regard to its general scope.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it