Detailed Multi-Domain Modeling and Faster-Than-Real-Time Hardware Emulation of Small Modular Reactor for EMT Studies
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Bibliographic record
Abstract
Small modular reactors (SMRs) are gaining significant attention as a promising solution to address the global energy demand and simulation is pivotal in expediting the construction of SMRs. However, the point-reactor neutron-kinetics equations of SMRs are strongly stiff nonlinear ordinary differential equations (ODEs), which poses a great difficulty for numerical computation of electromagnetic transients (EMT) of power systems coupled with SMRs. In this paper, a semi-analytical solution is proposed to streamline the comprehensive SMR mathematical model and reduce the model order from 25th to 18th. Additionally, the conglomeration of selected SMR-based EMT power system benchmark, which includes synchronous machines (SMs), modular multilevel converters (MMCs), power distribution networks, and varying loads, is described in detail and implemented on the Xilinx <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circledR$</tex-math></inline-formula> VCU 118 field-programmable gate array (FPGA) based hardware-in-the-loop (HIL) real-time transient emulation platform. The results demonstrate a significant improvement in computational speed and stability achieved by the proposed solution, which achieves a computational accuracy of IEEE 32-bit single-precision floating-point numbers, with a minimum calculation interval of 800 ns, resulting in a remarkable 12.5- fold acceleration in faster-than-real-time (FTRT) performance. This advancement greatly facilitates the simulation of intricate SMR-based models for EMT studies.
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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 | 0.000 |
| 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 it