Fourier Pipes: Turbulent Pipe Flow Manipulation Using Targeted Wall-Shapes
Bibliographic record
Abstract
Abstract This study introduces a novel technology to enhance geothermal energy extraction through flow manipulation inside the well casing. These novel pipe-inserts are placed at regular distances inside the casing and are characterized by smooth manufactured wall perturbations following distinct Fourier modes. The effects of insert thickness and length were studied by varying amplitude (0.05D, 0.1D, 0.15D) and length (2D, 4D, and 6D), respectively. The controlled perturbations allowed for flow deceleration near the wall and induced local mixing in the flow. The results demonstrated a linear increase in mean centerline velocity magnitude with increasing thickness and length of pipe-inserts. Turbulence intensity along the wake centerline increased with thickness, indicating greater mixing, while recovery behavior remained mostly consistent. Increasing the insert thickness enhanced pressure gradient along the pipe, which resulted in over a 35% rise in maximum pressure-drop at the insert. An increase in insert length lowered pressure-drop magnitude by at least 5.29%. This suggested that longer inserts could optimize energy transportation by minimizing pressure losses. The longest insert (6D) also lowered the maximum skin friction by 3.14%. These findings provided valuable insights for future design optimization aimed at energy efficient fluid extraction and transportation, which could enhance various geothermal energy applications and related systems.
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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 | 0.000 |
| 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) | 0.001 | 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".