ICONE23-2109 DESIGN AND PERFORMANCE EVALUATION OF A HEAT EXCHANGER NETWORK FOR A CO-GENERATION SMR TO VARIOUS THERMAL UTILIZATION APPLICATIONS
Bibliographic record
Abstract
Small modular nuclear reactor (SMR), with 300-400MWe electricity output, is an innovative design concept for nuclear power plant. It has some merits over large nuclear power plants, such as lower initial capital investment, flexibility, enhanced safety and security, etc. In previous applications, the balance of plant (BOP) system of the SMR was designed for supplying just electricity. In this study, the co-generation SMR which supplies both electricity and heat is under investigation. The heat exchanger network, mainly consisting of the BOP heat exchanger, water pump, and the heat exchangers that deliver heat to the thermal utilization (TU) applications, must operate in an efficient way to keep the overall system costs low. In this paper, a heat exchanger network for all end users which require the same or similar quality of energy is investigated. Numerical model for heat exchanger networks is built on the TRNSYS simulation software. Sensitivity studies are performed to estimate the energy efficiency and exergy efficiency of the whole heat exchanger network under different design and operating conditions (i.e. different temperatures, flow rates and heat exchanger effectiveness). Important design and operating parameters, which significantly impact the performance of the network, are evaluated and presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".