Numerical optimisation of a micro-wave rotor turbine using a quasi-two-dimensional CFD model and a hybrid algorithm
Why this work is in the frame
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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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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