Numerical optimisation of a micro-wave rotor turbine using a quasi-two-dimensional CFD model and a hybrid algorithm
Bibliographic record
Abstract
Abstract Wave rotors are unsteady flow machines that exchange energy through pressure waves. This has the potential for enhancing efficiency over a wide spectrum of applications, ranging from gas turbine topping cycles to pressure-gain combustors. This paper introduces an aerodynamic shape optimisation of a power generating non-axial micro-wave rotor turbine and seeks to enhance the shaft power output while preserving the wave rotor’s capacity to function as a pressure-exchanging device. The optimisation considers six parameters including rotor shape profile, wall thickness, and number of channels and is done using a hybrid genetic algorithm that couples an evolutionary algorithm with a surrogate model. The underlying numerical model is based on a transient, reduced-order, quasi-two dimensional computational fluid dynamics model at a fixed operating condition. The numerical results from the quasi-two-dimensional optimisation indicate that the best candidate design increases shaft power by a factor of 1.78 and imply a trade-off relationship between torque generation and pressure exchange capabilities. Further evaluation of the optimised design using three-dimensional computational fluid dynamics simulations confirms the increase in power output at the cost of increased entropy production. It is further disclosed that increased incidence losses during the initial opening of the channel to the high-pressure inlet duct compromise the shock strength of the primary shock wave and account for the decrease in pressure ratio. Finally, the numerical trends are validated using experimental data.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".