A Multi-Scale Thermal-Mechanical Numerical Method for Mini-Channel Heat Exchanger Subjected to Fluid Pressure Loads
Bibliographic record
Abstract
Abstract This study proposes a novel multi-scale numerical method for thermal-mechanical analysis of mini-channel heat exchangers (MCHEs) under internal fluid pressure and temperature loads. The method comprises a macro-scale model for global analysis and a meso-scale model for detailed submodel analysis, specifically focusing on the internal fluid pressure effects within the MCHEs. The macroscopic model divides the MCHE into cover plate and homogenized regions subjected to pressure and temperature loads. To incorporate internal pressures into the homogenized MCHE model, mathematical equations are formulated to convert internal fluid pressures into equivalent strain loads. Additionally, a novel equivalent thermal expansion method is introduced, integrating internal fluid pressure loads by prescribing equivalent thermal expansion coefficients alongside spatially-varying nodal temperature fields within the MCHE. The meso-scale models with detailed channel patterns are assigned to specific portions of the homogenized region. The integration of the mesoscale model into the macroscopic framework is achieved through the application of the submodel method. Comparisons between the equivalent and actual MCHE models show that the proposed equivalent method can provide accurate predictions for thermal-mechanical deformations and stresses, and significantly reduce the computational expenses.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".