HAMNA CHAUDHRY

National Bureau of Standards Reactor 

(NBSR) Plant State Predictor (PSP)
INTRODUCTION 

• National Institute of Standards and Technology 

(NIST) hosts a 20 MW nuclear reactor 

• Operators control the reaction of nuclear reactor 

from the reactor console in the control room

• NBSR reactor is currently undergoing console 

upgrades 

• The NBSR Reactor Plant State Predictor is part of a 

larger project 

This Photo by Unknown Author is 

licensed under CC BY-SA
GOALS

• Create The NBSR Reactor Plant State Predictor (PSP) 

• Implement the components and functionality of the physical 

console that would behave comparable to the NIST NBSR 

• Operators can wear a HoloLens to view and interact with 

virtual console

• Also develop a mixed reality holographic experience in 

HoloLens environment 
WHAT IS A CONSOLE?
WHAT IS A HOLOLENS? 

• known as a mixed reality headset

• Holographic computer

• Runs on windows 10

• The holograms help in training the operators 

• Advantages:

• Responsive to the presence of people 

• Provides a modular and effective environment 

• Virtual console can be operated anywhere 
HOLOGRAMS

• Objects made of light and 

sound that appear in the world 

around you, just as if they were 

real objects

• The hologram is a realistic 

reactor operation console

This Photo by Unknown Author is licensed under CC BY-NC-ND
This Photo by 

Unknown Author is 

licensed under CC 

BY-NC-ND

This Photo by Unknown Author is 

licensed under CC BY-NC-ND This Photo by Unknown 

Author is licensed under 

CC BY-SA-NC

This Photo by Unknown Author is 

licensed under CC BY-SA-NC

DEVELOPMENT PROCESS
VIRTUAL CONSOLE

Annunciators
ANNUNCIATORS 

Annunciators are alarms based on plant 

condition 

The trending changes are increased in 

reactor systems, such as setting alarm points

Worked on alarms to be turned on and off at 

a given alarm condition 

Worked on scheduled alarms that actuate at 

a given time
BEFORE AFTER
VIRTUAL CONSOLE

Reactor Power
PLANT STATE PREDICTOR

• Purpose is to use real data from the NIST NBSR reactor to teach 

a machine which would predict reactor states based on the 

user input

• Use the data to predict the values through neural networks 

neural network is 

represented by 

a series of layers 

that work much 

like a living brain

The neural 

engine was 

trained using 

historical 

operation data

neural network 

class in python 

to make 

accurate 

predictions
NEURAL NETWORK

• Supervised learning technique utilizing labeled data:

• Feature vector Xi = [x1, x2,... xn]

• Label yi

• Thermal Power

• Function approximation 

• Training learns f*(X)  y

• Regression is the task of predicting a continuous output 

Reactor Inlet 

Temperature

Reactor Outlet 

Temperature

Reactor Flow 

Neural Network Thermal Power
NIST NBSR 

REACTOR DATA

• Data of NBSR reactor 

when the rundown 

occurred on May 

23rd

• Normalized Values • Weight is typically 0

-1

• To compare the 

relative changes 

Rundown Full Power 

Operation
WHY ARE WE PREDICTING THE DATA?

• So we can have a more realistic 

simulator 

• Neural networks were implemented 

in Python with Keras library 

• To deploy the data through neural 

networks to HoloLens for the operators 

to be trained 

Neural 

Network

Python

Unity

Windows 

OS 

HoloLens
RESULTS

• Demonstrated neural 

networks for PSP

• Generated a 

prediction for each 

element on the test set 

• Actual and Predicted 

values of Thermal 

power 
CONCLUSION

Goal: Have an intelligent, 

responsive data trend analysis 

and tracking system

 Demonstrated neural 

networks for Plant State 

Predictor 

Goal: The operator’s 

interactions should result in 

feedback that are alike the 

real reaction operation 

 Annunciators were 

implemented 

FUTURE WORK

• Train and test data for all the other factors

• In order to deploy from Python to HoloLens, the Visual Code will be used

• Add extensions to create a Python development environment 
ACKNOWLEDGEMENTS

• Dagistan Sahin 

• Danyal Turkoglu

• Joseph Dura

• Julie Borchers

• Sam MacDavid

• Don Lopez

• Scott Arneson

• Daniel Keyser
DISCLAIMER

Certain commercial entities, equipment, or materials may be 

identified in this document in order to describe an experimental 

procedure or concept adequately. Such identification is not 

intended to imply recommendation or endorsement by the National 

Institute of Standards and Technology, nor is it intended to imply that 

the entities, materials, or equipment are necessarily the best 

available for the purpose.