Comparative Study of the Flow and Thermal Characteristics of Non-Stochastic Lattice and Bio-Inspired Multi-Scale Structures for Gas Turbine Engine Applications
Bibliographic record
Abstract
Abstract Metal Additive Manufacturing has presented gas turbine designers with additional design tools such as cellular solids. However, there is limited research on the early selection of these structures and how these structures interact with flows. Lattice structures are known for their capability to be tailored for achieving specific properties such as high porosity and strength, impact energy absorption, and light-weighting. A literature survey has shown that the mechanical performance of the strut-based and surface-based lattice structures has already been investigated in the past. However, very little research has been conducted to investigate their flow and heat transfer performance, especially for strut-based lattice structures. This research systematically investigates the friction factor and convective heat transfer (CHT) coefficient across strut and surface-based lattice structures. Results show that the complex shape of the Triply Periodic Minimal Surface (TPMS) lattice structure topologies give the flow a better capability to mix and recirculate for convection at the expense of a significant pressure drop. However, topologies with less pressure drop/friction factor and high convective heat transfer coefficient are more suitable for gas turbine engines. Furthermore, this paper investigates the use of newly developed multifunctional bio-inspired design method called as Domain Integrated Design (DID) for an innovative concept to achieve low pressure drop and high effective heat transfer. Comparative study shows that bio-inspired designs have achieved low friction factor as compared to the lattice structures whereas TPMS structures shows better convective heat transfer performance.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".