Developing 1-D heat transfer correlations for supercritical water and carbon dioxide in vertical tubes
Bibliographic record
Abstract
Taking into account the expected increase in global energy demands and increasing climate change issues, there is a pressing need to develop new environmentally sustainable energy systems. Nuclear energy will play a major role in being part of the energy mix since it offers a relatively clean, safe and reliable source of electrical energy. However, opportunities for building new generation nuclear systems will depend on their economic and safety attractiveness as well as their flexibility in design to adapt in different countries and situations. Keeping these objectives in mind, a framework for international cooperation was set forth in a charter of Generation IV International Forum (GIF) (GIF Charter, 2002) and six design concepts were selected for further development.\nTo achieve high thermal efficiencies of up to 45 ??? 50%, the use of SuperCritical Fluids (SCFs) as working fluids in heat transfer cycles is proposed Generation IV designs. An important aspect towards development of SCF applications in novel Gen IV Nuclear Power Plant (NPP) designs is to understand the thermodynamic behavior and prediction of Heat Transfer Coefficients (HTCs) at supercritical (SC) conditions. In addition to the nuclear power industry applications; SCFs are also expected to play a vital role in a number of other important technologies such as refrigeration systems, and geothermal systems, to name a few.\nGiven the potential for vast number of applications of SCFs in industry, the objective of this work was to gain an understanding on the behavior of SCFs and to develop a fundamental knowledge of the heat-transfer processes and correlations for SC Water and SC CO2 flowing in bare circular tubes.\nExperimental datasets for SC Water and SC CO2 were compiled and used to obtain a basic 1-D empirical correlation that can predict HTC in bare circular tubes during the transient phases. The accuracy of these correlations was also analyzed using statistical techniques. Limitations and applications for 1-D correlations are discussed as well. The new correlations showed promising results for HTC and Tw calculations for the reference dataset with uncertainty of about ??25% for HTC values and about ??10-15% for the calculated wall temperature.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".