Thermal design and analysis of a floating small modular reactor and wind system for a sustainable community with floating data center
Bibliographic record
Abstract
• Offshore nuclear renewable hybrid energy system powers community and data center. • Time-dependent analysis assess system performance under varying loads and sources. • Floating and integrated design maximize source utilization and minimize land use. • Excess energy is used for hydrogen and freshwater generation for better utilization. It is critical to consider the evolving conditions of the future when designing energy systems. Information and communication technology is among the most power-hungry sectors and is expected to see substantial growth in energy consumption. To achieve net-zero targets while meeting growing demand, both existing fossil fuel driven systems and new energy systems should transition to clean energy systems, including nuclear and renewable-based systems. The current paper proposes an integrated energy system driven by a floating small modular reactor and offshore wind to generate fresh water, hydrogen, district heating and cooling options, and power for a floating data center and a sustainable community. The proposed design considers to be developed on unexploited water surfaces, including ocean. A floating SMR, based on high temperature gas-cooled pebble bed reactor technology with a 500 MW of thermal energy capacity, is integrated with a 192.3 MW offshore wind farm in a case study along the shores of Nova Scotia, Canada. The system is designed to meet a combined load with peaks of 3.6 MW electricity, 124.6 MW cooling, and 235.9 MW heating in a typical year. A multi-stage flash desalination and a two-stage Rankine cycle are considered for potential application on an offshore platform to desalinate sea water and generate power using heat from the floating SMR. The proposed system is evaluated with thermodynamic aspects, considering both energy and exergy perspectives. A time-dependent analysis over 8760 hours in a typical year provides insights into system performance under variable loads and energy sources. The overall energy and exergy efficiencies are 30.78 % and 22.91 %, when operated according to demand.
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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 | 0.001 | 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 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".