Effects of radiation on a chemically reacting flow withhydrolysis
Bibliographic record
Abstract
Abstract: This study focuses on modeling of heat and fluid flow with a chemical reaction in the hydrolysis step of the Copper-Chlorine (Cu-Cl) cycle. The thermochemical Cu-Cl cycle has been established as a promising method of sustainable hydrogen production because of its low heat requirement relative to the other hydrogen production cycles. There have been several studies on the heat and mass transfer of hydrolysis reactors to better understand their relative roles and optimize the overall cycle efficiency. Few or no past studies have examined the effect of radiation during the process. This study presents a semi-analytical model to study the effects of thermal radiation on the laminar boundary layer with a similarity solution in the presence of a chemical reaction. A similarity transformation is used to convert the governing partial differential equations to ordinary differential equations. The numerical method of solution is based on the shooting method with a Runge-Kutta iteration scheme. A Rosseland approximation is utilized to study thermal radiation and numerical simulations are conducted for cases with and without radiation. Past studies indicate that the presence of thermal radiation thickens the boundary layer and broaden the temperature distribution. This concept is studied and extended to the hydrolysis step of thermochemical hydrogen production in this paper. The model is first validated by a previously established system of equations and then extended to report the effects of radiation on the temperature gradient and concentration gradient in the boundary layer during the hydrolysis process. Sensitivity analysis is performed to report the influence of radiation and chemical reaction parameter in detail. A better understanding of the effects of thermal radiation in the flow with chemical reaction will be useful to improve the design of the hydrolysis reactor in the thermochemical cycle of hydrogen production and improve the overall cycle efficiency.
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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".