---
license: cc-by-nc-4.0
language:
- en
tags:
- nuclear
- nuclear energy
- reactor
- pwr
- smr
- smr anomaly
- monte-carlo
- nuclear physics
---

# ⚛️ KEFF Data | PWR-SMR-2026-01 Dataset (Community Edition)

*Bridging academia and industry with high-fidelity synthetic simulation data for next-generation nuclear AI models.*

## 1. Overview
The **PWR-SMR-2026-01** dataset is a high-fidelity Monte Carlo neutron transport dataset generated for static reactor states of a generic Small Modular Reactor (SMR). It is designed strictly for research, model development, design-space exploration, benchmarking, and offline anomaly detection workflows.

This "Community Edition" contains **1,518 unique simulation cases** representing varying operational states, core perturbations, and anomaly regimes.

### Quick Specifications
| Parameter | Specification |
| :--- | :--- |
| **Simulation Engine** | OpenMC (Continuous-energy Monte Carlo) |
| **Cross-Section Library** | ENDF/B-VIII.0 |
| **Total Simulation Cases** | 1,518 unique static reactor states |
| **Histories per Case** | 12,500,000 neutron histories |
| **Target Architecture** | 17x17 PWR Fuel Assembly SMR Lattice |
| **Dataset Size** | ~90+ GB (Uncompressed HDF5 files) |

---

## 2. Anomaly Classes & Data Distribution
To ensure robust training for multi-variable deep learning architectures, the 1,518 unique statepoints are distributed across three independent safety-critical operational anomalies and a nominal baseline[cite: 291, 297]:

| Anomaly Class | Cases (N) | Perturbation Range | Primary Physics Mechanism |
| :--- | :--- | :--- | :--- |
| **Control Rod (CR) Misalignment** | 555 | 0–200 cm insertion depth  | Control rod worth: negative reactivity insertion  |
| **Fuel Temperature (Doppler)** | 481 | 950–1200 K  | Doppler broadening of U-238 resonance capture  |
| **Coolant Void Fraction** | 481 | 0–35% void fraction  | Under-moderated regime density feedback  |
| **Nominal Baseline** | 1 | Reference state  | Unperturbed core benchmark (keff = 1.26660$)  |

### Verification & Validation (V&V) Baselines
Every simulation case in this community release has been rigorously audited against industrial safety standards to guarantee physical integrity before deployment[cite: 228, 241]:
**Source Convergence:** Fundamental-mode spatial distribution verified with a Shannon entropy drift of 0.0419% (Passing industry threshold of < 0.1%)[cite: 66, 300].
**Statistical Reliability:** Adjusted for a Lag-1 autocorrelation coefficient of 0.62, raising the conservative industrial noise floor to ±56 pcm[cite: 85, 92, 300].
**Code-to-Benchmark Accuracy:** Verified against standard reference benchmarks for fresh PWR fuel pins with a stable code bias of -339.7 pcm, safely within the standard ±1000 pcm acceptance limit[cite: 51, 184, 300].
  

## 3. Dataset Structure
The dataset provides raw output directly from the OpenMC engine to preserve maximum physical fidelity. To protect proprietary methodologies, source XML generation files (`metadata/`) are excluded from this open-access tier.

For each simulation case, you will find:
* **`statepoint.250.h5`**: The complete state of the simulation at the final batch, containing k-effective eigenvalues, source site distributions, and runtime metadata.
* **`tallies.h5`**: Spatial and energy-dependent scoring matrices (flux, fission rates, heating) across the reactor geometry.
* **`manifest.json`**: Master cryptographic signature file (SHA-256) for verifying data integrity.

---

## 4. Anomaly Detection Regimes (Machine Learning Guidance)
This dataset operates in two distinct detectability regimes based on the separability index (Z), where the corrected statistical uncertainty floor is ±56 pcm.

$$Z = \frac{|\Delta\rho|}{\sigma_{corrected}}$$

* **Regime A: Macro-Anomalies (Z > 3.0)**
  * *Examples:* Major control rod drops (≥50 cm), severe voiding (>20%).
  * *Approach:* Single-frame models (CNNs, MLPs) can detect these anomalies in individual snapshots due to a strong signal-to-noise ratio.
* **Regime B: Micro-Perturbations (Z < 3.0)**
  * *Examples:* Temperature drifts (+200 K), minor voiding (<20%), slight rod misalignments (<50 cm).
  * *Approach:* The signal is partially masked by the statistical noise floor. Requires temporal models (LSTMs, Transformers), trend-based analysis, or denoising autoencoders.
---

## 5. How to Use (Python Snippet)
Due to the large file sizes (90+ GB), we recommend using the `huggingface_hub` library to download specific files or the entire dataset locally.

```python
from huggingface_hub import snapshot_download

# Download the entire dataset to your local machine
local_dir = snapshot_download(
    repo_id="keffdata/pwr-smr-2026-01-community",
    repo_type="dataset",
    local_dir="./keff_dataset"
)
print(f"Dataset successfully downloaded to {local_dir}")
```

## 6. Licensing & Commercial Use

**Phase 1 Open-Access Framework**
To accelerate global nuclear AI research, KEFF Data provides this Community Edition dataset under the **Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)** license. It is entirely free for academic, educational, and non-commercial R&D workflows.

**Enterprise & Commercial Licensing**
Any commercial entity, corporate R&D division, or national laboratory wishing to utilize this dataset, its underlying methodology, or its outputs for proprietary development, commercial product training, or operational benchmarking must obtain explicit written authorization and a commercial license. 

For enterprise licensing structures and access to our production-tier datasets, contact: **contact@keffdata.com**
## 7. Citation
If you use this dataset in your academic research or non-commercial projects, please cite it using the official DOI:

**DOI:** [10.57967/hf/8914](https://doi.org/10.57967/hf/8914)

```bibtex
@dataset{aslan2026keff,
  author       = {Caglayan Aslan},
  title        = {PWR-SMR-2026-01: High-Fidelity Monte Carlo Dataset for Nuclear Anomaly Detection},
  publisher    = {Hugging Face},
  year         = {2026},
  month        = {March},
  doi          = {10.57967/hf/8914},
  url          = {[https://doi.org/10.57967/hf/8914](https://doi.org/10.57967/hf/8914)},
  note         = {KEFF Data - Document ID: RED-PAPER-PH1-SMR}
}