Workshops

OpenMC Model of ATR

OpenMC is a general purpose Monte Carlo neutron and photon transport simulation code. It is capable of simulating 3D models based on constructive solid geometry with second-order surfaces as well as CAD-based geometries using the DAGMC library. It also has built-in capabilities for activation/depletion to track material evolution with time. OpenMC was originally developed by members of the Computational Reactor Physics Group at the Massachusetts Institute of Technology starting in 2011 with a specific focus on high performance computing and has now evolved into a community developed code with contributions from many institutions.

OpenMC Model of the BEAVRS PWR Benchmark

This workshop will present a brief overview of the code and its growing list of features, and a walk-through on how to setup input files using the Python API using nuclear reactor examples. The workshop will also demonstrate how to leverage powerful Python packages for post-processing of results.

Participants should bring their laptop to follow along and run OpenMC. A link will be provided to access the software on a cloud computing platform.

Organizer: Xu Wu, North Carolina State University

Machine Learning (ML) is a subset of Artificial Intelligence (AI) which studies computer algorithms that can improve automatically through experience (data). Deep Learning (DL) is a subset of ML that uses multi-layered neural networks to deliver state-of-the-art accuracy in tasks such as object detection, speech recognition, language translation and others. Scientific Machine Learning (SciML), more specifically, consists of computational technologies that can be trained with scientific data to augment or automate human skills. ML has been very successful in areas such as computer vision, natural language processing, etc. But its application in scientific computing is relatively new, especially in Nuclear Engineering (NE). This workshop aims at augmenting the applications of AI/ML in scientific computing in nuclear computational science, and promoting ML-based transformative solutions across various DOE missions.

Recently, ML/DL have been applied in areas such as data-driven closure model development for nuclear thermal-hydraulics, data-driven material discovery and qualification, Digital Twins for integrated energy systems, small modular reactors (SMRs) and micro-reactors, AI-based autonomous operation and control for advanced nuclear reactors, AI-based diagnosis, prognosis and predictive maintenance, etc. In this workshop, we will have five presentations that cover a wide range of topics, including:

1. Latent Neural Controlled Differential Equations for Time Series Forecasting (William Gurecky, ORNL)2
2. Sparse Sensing and Sparse Learning for Nuclear Digital Twins (Mohammad Abdo, INL)
3. Neutron Flux Measurement and Thermal Limits modeling: Impact on operational performance (Thomas Gruenwald, Blue Wave AI Labs)
4. Physics-informed, data-driven reduced-order models for nuclear applications (Jean Ragusa, TAMU)
5. Causal inference as a bridge between simulation models and observed phenomena (Diego Mandelli, INL)

The High-Performance Computing (HPC) resources and the constant improvement of both numerical simulation accuracy and the experimental measurements with which they are confronted, bring a new compulsory step to strengthen the credence given to the simulation results: uncertainty quantification. This can have different meanings, according to the requested goals (rank uncertainty sources, reduce them, estimate precisely a critical threshold or an optimal working point) and it could request mathematical methods with greater or lesser complexity. This workshop introduces the URANIE platform, an open source framework currently developed at the Alternative Energies and Atomic Energy Commission (CEA), in the Energy Division (DES), in order to deal with uncertainty propagation, surrogate models, optimization issues, code calibration. These methods can then be applied to many kinds of code (considered as black boxes by URANIE) so to many fields of physics as well. In this workshop, a neutronic use-case will be introduced to show how URANIE can be used to perform a large range of analysis: generate a design-of-experiments to propagate uncertainty, construct a surrogate model, perform an optimization or a sensitivity analysis based on this surrogate model and finally calibrate code parameters from a database of experimental measurements.

The material used throughout this session will be Jupyter NoteBooks ...

sent to the participants prior to the beginning of the workshop, and you will need to install the URANIE platform and the Jupyter tool on your laptop.

The proposed agenda is :

• Overview of the Uncertainty/Sensitivity Analysis/Optimization/Calibration methods
• Presentation of the URANIE platform
• Presentation of the neutronic use-case code
• Getting started with URANIE on Jupyter Notebooks
• Generation of a design of experiments
• Uncertainty propagation
• Build a surrogate model from a database
• Sensitivity Analysis
• Calibration of code parameters from a database of experimental measurements

Laptop requirements: laptop is required to participate in this session.

Organizer: Prof. Alireza Haghighat, Virginia Tech

RAPID (Real-time Analysis for Particle-transport In-situ Detection) is developed based on the MRT (Multi-stage Response-function particle Transport) methodology that enables its real-time simulation capability. The current version of RAPID is capable of simulating nuclear systems such as spent fuel pools, spent fuel casks, and reactor cores. RAPID solves for pin-wise, axially-dependent fission density, critical/subcritical multiplication, and detector response. Recently, new algorithm for 3-D fuel burnup (bRAPID) calculation and reactor kinetics (tRAPID) have been developed and benchmarked for test problems. These algorithms are experimentally validated using the Jozef Stefan Institute’s TRIGA research reactor.

Further, a multi-user virtual reality system (VRS) has been developed that provides a web application for input preparation, real-time simulation, and output processing and visualization in a virtual environment. For an introduction, please view the following demo https://www.youtube.com/watch?v=1Q2ytjBrmXc

Topics to be covered

1. RAPID’s MRT methodology and formulation
2. RAPID code system, its algorithms and their benchmarking and validation
3. VRS-RAPID demonstration
4. Hands-on use by participants

Requirements: There will be access to wireless internet so that the participants can have remote access to VRS-RAPID. The current version of VRS-RAPID is optimized for a Personal Computer using the Google Chrome browser, but it can be accessed through iPad, Tablet, etc. using any other browser.

To facilitate establishing individual accounts, participants are encouraged to contact Prof. Haghighat prior to the workshop.