Global sensitivity analysis of nuclear district heating reactor primary heat exchanger and pressure vessel optimization
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Recently, small modular reactors (SMRs) have received greater interest as a source for clean and affordable district heating (DH). Compared to power plants, the low-pressure, low-temperature design and nearly 100% efficiency reduce the cost of produced energy considerably. However, few practical implementations exist yet, and cost estimates and design principles are subject to uncertainties whose interactions remain largely unknown. In this work, we present a techno-economic optimization and sensitivity analysis of a natural circulation DH SMR primary heat exchanger. A Cuckoo Search variant augmented with a modified Hooke-Jeeves search was used as the optimizer, with SimDec (simulation decomposition) subsequently employed for global sensitivity analysis. The reactor pressure vessel and containment vessel specific costs exhibited the greatest impact on the cost of heat and the optimized configurations. While low-pressure, low-temperature design is central to heating reactor cost-effectiveness, optimized primary circuit temperatures clearly exceeded previous assumptions. In a 5260 full-load hours mid-load application, a 34-41 €/MWh cost range was found for produced heat at 8% interest and 20-year lifetime. For heat exchanger optimization, the results indicate the potential for considerable performance improvement from using deterministic local search for terminal convergence and sensitivity analysis for dimensionality reduction. • Small heating reactor appears an affordable source of clean district heating • Pressure vessel specific costs important for cost of heat and optimized design • Novel approach combines global sensitivity analysis and engineering optimization • Dimensionality reduction and local terminal search for performance improvement • SimDec tool reveals interaction of decision variables and uncertainties
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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 | 0.001 |
| 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 it