A Transient Response Comparison of a Conventional and a Meso Heat Exchanger under Mass Flow and Temperature Step Changes
Bibliographic record
Abstract
The increasing worldwide energy consumption problem motivates the effective management of thermal systems.A heat exchanger is an important component in many thermal systems, such as a radiator of an engine cooling system in vehicles.Heat exchangers are generally designed and characterized to work under steady-state conditions.However, a change in operating conditions exposes heat exchangers to a transient state, therefore, it is important to study their transient responses for better control of the system and efficiency improvement.In this study, the experimental responses of two types of heat exchangers: conventional, and meso heat exchangers subjected to a sudden change in inlet temperature and mass flow rate of the hot fluid are compared and presented.The transient outlet temperature responses of both hot and cold fluids as well as the transient heat exchanger effectiveness and heat transfer rate variations are found in this work.A general empirical correlation is obtained for the hot fluid transient dimensionless outlet temperature subjected to mass flow rate step change.Furthermore, results show that the meso heat exchanger exhibited higher heat transfer rate and effectiveness as well as reached steady state faster than the conventional heat exchanger.Even though the meso heat exchanger has a longer initial delay time, it possesses a higher response time than a conventional heat exchanger, regardless of the step change.
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".