Efficient Computation of Radiative Heat Recovery from Porous Ceramic Monoliths for Efficient Solar Thermochemical Fuel Production
Bibliographic record
Abstract
Solar thermochemical hydrogen (STCH) produced by heat-driven water-splitting is a promising route for producing green hydrogen and other zero-emission synfuels. However, the efficiency of STCH must be dramatically increased for it to make an impact on decarbonization efforts. We have previously presented a novel Reactor Train System (RTS) for significantly increasing the efficiency of STCH by employing heat recovery from the redox material and efficient gas exchange processes. In this paper we present a higher-fidelity model for the RTS that accommodates the slow heat diffusion through the STCH redox material. For this purpose, a novel method is introduced for transient modelling of radiative heat in participating media. This method, called GREENER: Generalized Radiation Exchange Factors and Net Radiation, combines the accuracy of Monte Carlo Ray Tracing with the low computational cost of the P1 or Rosseland diffusion approximations. Along with STCH, GREENER has application for modelling volumetric solar receivers, high temperature heat recovery systems like heat exchangers and regenerators, and packed bed reactors. Using the GREENER method, the RTS counterflow radiative heat exchanger is shown to achieve heat recovery effectiveness greater than 70%. The performance of non-uniform porous redox morphologies is evaluated, and high-performing configurations are identified.
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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".