data generation for an artificial neural network for mox

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Data Generation for an Artificial Neural Network for MOX Criticality Prediction Using SCALE/TRITON

Jin Whan Bae,

Benjamin R. Betzler

Andrew Worrall

22 Open slide master to edit

Background

• Automation of data generation with SCALE/TRITON

• Automation of SCALE/TRITON data extraction in Python


Background

• Data to train Artificial Neural Network (ANN)

– Large, system-scale fuel cycle simulators

• 2 purposes of regression models:

– Surrogate model

– Predictive model


What is done:

A Neural network model to predict:

• Input:

– Fuel Composition (U, Pu, Am)

– Burnup

• Output:

– BOC / EOC Kinf


Methods(https://code.ornl.gov/4ib/mox_neural_net)

1. Randomly sample:

plutonium vector from a range

Plutonium decay time (Pu241 -> Am241)

Plutonium content in MOX (Pu/( Pu+depleted U))

2. Generate 200,000 SCALE/TRITON cases for MOX assembly

3. Parse criticality calculation results into one database (hdf5)

4. Train / Test/ Validate ANN

5. Implement ANN


Methods

Data Generation

ANN Training and Deployment


Random sampling

1. Randomly sample:

Plutonium vector from a range (Tab. 1)

Plutonium decay time (Pu241 -> Am241) [0,9 years]

Avg. plutonium content in MOX (Pu/( Pu+depleted U)) [4, 10 wt%]

TABLE 1. Plutonium vector ranges for random sampling


2. Generate 200,000 SCALE/TRITON cases for MOX assembly

Fig. 1: Histogram of plutonium vector distribution.

Fig. 2: Quarter assembly geometry(17x17 Westinghouse).


3. Parse criticality calculation results into one database (hdf5)

Fig. 3: Histogram of BOC and EOC kinf

200,000 unique MOX composition

* 25 burnup steps (~ 72 GWd/MTU)

= 5,000,000 data points (~ 10GB)


4. Train / Test/ Validate ANN

60 – 20 –20 (train – validate – test)

Outer loop to find `best' set of hyperparameters (fig. 4)

Fig. 4: Outer hyperparameter search loop

Fig 5. Final ANN model after hyperparameter search


Results

Test using the test set:

EOC keff prediction:97% less than 0.5% relative error

BOC keff prediction:99% less than 0.5% relative error


5. Implement ANN

Fuel Cycle Simulator

Cyclus

Fig. 6: Modular architecture of Cyclus allows easy implementation of a new model

Fig. 7: Fuel fabrication and MOX reactor module in Cyclus. Communication between two facilities allows dynamic fuel cycle modeling.


Results – Implementation into Cyclus

Reactor module demands MOX to maintain average k of 1.1

Higher power reactors require more Pu content


Conclusion

• SCALE/TRITON automation to generate large amounts of data for ANN training.

• ANN can predict MOX assembly BOC and EOC kinf within 0.5% error.

• ANN can be deployed to system-scale simulators as a surrogate model for complex physics calculations, like criticality of MOX fuel.


Drawbacks

• Black box

– When deployed, it's unknown if its completely wrong

• Implement checker (non-physical checker) in usage

– e.g. If EOC kinf > BOC kinf, raise Error

• Large dataset generation

– Hard to take into account `all' potential scenarios

• e.g. Weapon-grade plutonium vectors

• Plutonium vector / content range for dataset


Discussion

• Export Control

– This model can be on Github (pickled file size < 100kB)

– How do we (should we?) control / regulate surrogate models of export control software?

– Should data from export control software be controlled?

• Value of Data

– How should this community curate / collect data?

• Applications of Neural Network / Validation

– Emerging field, but how do we know if it is right / can be deployed with confidence?

– Danger of blind trust in algorithms

– No physics behind ANN models


Acknowledgements

• Benjamin R. Betzler (ORNL)

• Andrew Worrall (ORNL)

• Germina Ilas (ORNL)

• Baptiste Mouginot (U Wisc – Madison)


Questions

Thank you

data generation for an artificial neural network for mox

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