Power generation from localized wind energy on highways using vertical axis wind turbines
Bibliographic record
Abstract
The goal of this thesis is to perform a series of numerical simulations to investigate the amount of recoverable energy in the highly congested roads with high or even no speed limit. Thanks to the development and progress of some numerical methods like the overset grid and Dynamic Fluid-Body Interaction (DFBI) solvers, replicating the physical situation on expressways inside a Computational Fluid Dynamic (CFD) simulation is possible. However, the choice of numerical configurations including turbulence model and the order of discretization, time step, grid resolution and similar parameters can be challenging. Different options including real traffic data and the Taguchi method were explored to and base on the available data, the latter was selected to build the main test matrix. In this report, the potential of the proposed idea would be presented through the motivation of study and literature review chapters. Then, the goals of the project with some explanations regarding the translation of physical problem into a numerical simulation would be discussed in the problem description. In the next chapter, some theories about the governing equations and the fundamentals of employed techniques would be elaborated. Since there is no experimental data in regard to the performance of Vertical Axis Wind Turbine (VAWT) on highways and considering the variety of existing numerical parameters, a dedicated study in the validation chapter has been performed to ascertain the accuracy and validity of the selected numerical setup. Then in the parametric study chapter, the appropriate rotor type, grid resolution for background mesh and also the initial position of car with respect to the rotor were determined. In the next chapter, real traffic data obtained along Highway 97 in Kelowna were compared to the Taguchi method to design the main test cases. The results of these cases were explored, and some efforts were made to investigate the flow-field which can be found in the result and discussion chapter. Last but not least, some remarks were pointed out in the last chapter to justify the impossibility of using conventional parameters such as Tip Speed Ratio (TSR) in the present study in combination with some conclusions.
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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".