Computing Platforms
Computational architectures with right-sized deployments of high performance computing, public/community cloud, and edge device computing.
The Nuclear Computational Resource Center (NCRC) was established at INL to provide easy access to computational tools, high performance computing (HPC) resources and training. The target audience includes, but is not limited to, users from the private sector, national laboratories, universities and federal agencies.
Available computational tools include INL’s extensive library of software based on the MOOSE modeling and simulation framework and relevant to nuclear energy, advanced manufacturing and environmental science applications. Examples of nuclear energy-related applications include:
- Modeling the behavior of materials in harsh environments (irradiation and high temperatures)
- Analyzing the performance of existing light water reactors and advanced nuclear reactors
- Multiscale, multiphysics analysis of nuclear fuel performance
Collaborations & Projects:
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National Reactor Innovation Center (NRIC) Program
The National Reactor Innovation Center (NRIC) leads new advanced demonstration projects using a model-based systems engineering approach. SysML and LML models are developed and traced to traditional requirements artifacts to realize this vision. Activity models are integrated with Discrete Event and Monte Carlo simulation to check for correctness, integrate cost and schedule, and monitor expected performance. NRIC is working to develop integrations between MBSE, engineering, operations, and traditional CAD software to enable a full digital thread in design.
Capabilities:
Multiphysics Object Oriented Simulation Environment (MOOSE)
INL’s Multiphysics Object Oriented Simulation Environment (MOOSE) makes modeling and simulation more accessible to a broad array of scientists. MOOSE enables simulation tools to be developed in a fraction of the time previously required. This tool has revolutionized predictive modeling because scientists seeking a new simulation capability don’t need to recruit a team of computational experts versed in, for example, parallel code development. Researchers can focus their efforts on the mathematical models for their field, and MOOSE does the rest. The simplicity has bred a herd of modeling applications describing phenomena in nuclear physics, geology, chemistry, engineering and many other fields.
Applied Visualization Laboratory (AVL)
INL’s Applied Visualization Laboratory (AVL) contains a number of advanced 3D visualization platforms including: Computer Assisted Virtual Environment (CAVE), IQ-Station, Stereo Wall, Virtual Reality Headsets, Mobile Devices Using these systems, the lab enables researchers to view, analyze, and process 3D data from both file and live data sources. The lab provides a four sided 3D Computer Assisted Virtual Environment or CAVE that allows scientists and engineers to probe, analyze, and predict trends using their data. The four-panel CAVE uses rear projection to display computer graphics on three walls and the floor. By wearing stereo glasses to create depth perception and holding a “wand” to manipulate and control data, users can tour a building still under design, plot a new transmission route over terrain, open a valve, or delve into the core of a nuclear reactor.
Diamond (Data Integration Aggregated Model and Ontology for Nuclear Deployment)
DIAMOND is a nuclear ontology. An ontology is a collection of concepts and relationships within a given domain. It is an extensible document that contains the classes, properties, and relationships that make up that domain. DIAMOND is a Web Ontology Language (OWL) document. OWL is an extension of XML that provides the ability to represent the various constructs in an ontology. DIAMOND acts as a standard taxonomy for the nuclear domain by which data sources can be integrated together through a common format. While DIAMOND is only a data model, when acting as the central schema for Deep Lynx it enables a model to which all integrating data sources can be mapped. https://github.com/idaholab/DIAMOND.
Deep Lynx
A data warehouse technology that can enable the digital thread across an organization by serving as the central point of data aggregation and exchange. Deep Lynx is built on the relational database PostgreSQL, but stores data in a graph-like format. This enables data to be exported to graph databases for analysis and for data to be queried in a graph-like format. When a data source sends data to Deep Lynx, that data is mapped to a central schema (or ontology) in Deep Lynx. This central schema creates a common data format to which all data is mapped. https://github.com/idaholab/Deep-Lynx.
Publications:
| Pillar | Citations |
|---|---|
| Digital Thread | Ahmad Al Rashdan, Jeren Browning, Christopher Ritter, “Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology”, September 11, 2019. |
| Digital Thread | Christopher Ritter, Jeren Browning, Lee Nelson, Tammie Borders, John Bumgardner, Mitchell Kerman, “Digital Engineering Ecosystem for Future Nuclear Power Plants: Innovation of Ontologies, Tools, and Data Exchange”, October 29, 2019. |
| Digital Thread | Christopher Ritter, Jeren Browning, “Towards a Digital Twin to Detect Nuclear Proliferation Activities”, September 8, 2020. |
| Digital Twin | Christopher Ritter, Jeren Browning, Lee Nelson, Tammie Borders, John Bumgardner, Mitchell Kerman, “Digital Engineering Ecosystem for Future Nuclear Power Plants: Innovation of Ontologies, Tools, and Data Exchange”, October 29, 2019. |
| Operational Artificial Intelligence | Agarwal, V., N. Lybeck, B. Pham, R. Rusaw, and R. Bickford, 2015, “Asset fault signatures for prognostic and health management in the nuclear industry,” IEEE Reliability Digest, February 2015. |
| Operational Artificial Intelligence | Agarwal, V., N. Lybeck, B. Pham, R. Rusaw, and R. Bickford, 2015, “Prognostic and health management of active assets in nuclear power plants,” International Journal of Prognostics and Health Management, Special Issue on Nuclear Energy PHM, 6:1–17. |
| Operational Artificial Intelligence | Al Rashdan, A., J. Smith, S. St. Germain, C. Ritter, V. Agarwal, R. Boring, T. Ulrich, and J. Hansen, 2018, “Development of a technology roadmap for online monitoring of nuclear power plants,” INL/EXT-18-52206, Idaho Falls, ID, USA. |
| Operational Artificial Intelligence | Al Rashdan, A., and T. Mortenson, 2018, “Automation technologies impact on the work process of nuclear power plants,” INL/EXT-18-51457, Idaho Falls, ID, USA. |
| Operational Artificial Intelligence | Al Rashdan, A. and D. Roberson, 2019, “A frequency domain control perspective on xenon resistance for load following of thermal nuclear reactors,” IEEE Transactions on Nuclear Science., Vol. 66, No. 9, pp. 2034–2041. |
| Operational Artificial Intelligence | Al Rashdan, A., C. Krome, S. St. Germain, J. Corporan, K. Ruppert, and J. Rosenlof, 2019, “Method and application of data integration at a nuclear power plant,” INL/EXT-19-54294, Idaho Falls, ID, USA. |
| Operational Artificial Intelligence | Al Rashdan, A., M. Griffel, R. Boza, and D. P. Guillen, 2019, “Subtle process anomalies detection using machine learning methods,” INL/EXT-19-55629, Idaho Falls, ID, USA. |
| Operational Artificial Intelligence | Alamamiotis, M., and V. Agarwal, 2014, “Fuzzy integration of support vector regression models for anticipatory control of complex energy systems,” International Journal of Monitoring and Surveillance Technologies Research, 2(2):26–40. |
| Operational Artificial Intelligence | Farber, J., D. Cole, A. Al Rashdan, and V. Yadav, 2019. “Using kernel density estimation to detect loss-of-coolant accidents in a pressurized water reactor,” Nuclear Technology, special issue on Big Data for Nuclear Power Plants, 205(8):1043–1052. |
| Operational Artificial Intelligence | Garcia, H., S. Aumeier, and A. Al Rashdan, 2019, “Integrated state awareness through secure embedded intelligence in nuclear systems: Opportunities and implications,” Nuclear Science and Engineering, accepted for publication. DOI:10.1080/00295639.2019.1698237. |
| Operational Artificial Intelligence | Garcia, H., S. Aumeier, A. Al Rashdan, and B. Rolston, 2020, “Secure embedded intelligence in nuclear systems: Framework and methods,” Annals of Nuclear Energy, accepted for publication. DOI:10.1016/j.anucene.2019.107261. |
| Operational Artificial Intelligence | Gentillon, C., C. L. Atwood, A. L. Mack, and Z. Ma, 2020, “Evaluation of weakly informed priors for FLEX data,” INL/EXT-20-58327, Idaho Falls, ID, USA. |
| Operational Artificial Intelligence | Guillen, D., N. Anderson, C. Krome, R. Boza, M. Griffel, J. Zouabe, and A. Al-Rashdan, 2019, “The application of physics-informed machine-learning to predict drywell cooling fan failure,” In: Proceedings of the Big Data for Nuclear Power Plants Workshop 2019. |
| Operational Artificial Intelligence | Manjunatha, K, A. L. Mack, V. Agarwal, D. Adams, and D. Koester, 2020, “Diagnosis of corrosion processes in nuclear power plants secondary piping structures,” ASME Pressure Vessels and Piping Conference, July – August (held virtually). |
| Operational Artificial Intelligence | Mandelli, D., C. Wang, S. Staples, C. S. Ritter, A. L. Mack, S. W. St. Germain, A. Alfonsi, C. Rabiti, and R. Kunz, 2018, “Cost risk analysis framework (CRAFT): An integrated risk analysis tool and its application in an industry use case,” INL/EXT-18-51442, Idaho Falls, ID, USA. |
| Cyber & Data Resilience Pillar | "Data Fidelity: Security's Soft Underbelly" (RCIS 2017),"Data Fidelity in the Post-Truth Era" (ICCWS 2018) |
| Decision Sciences & Visualization | Toba, A.L., Griffel, L.M., & Hartley, D.S., (2020). Devs Based Modeling and Simulation of Agricultural Machinery Movement. In Press, Computers and Electronics in Agriculture. |
| Decision Sciences & Visualization | Hartley, D.S., Thompson, D. N.; Griffel, L. M., Nguyen, Q. A and Roni, M.S. 2020. The effect of biomass properties and system configuration on the operating effectiveness of biomass to biofuel systems. ACS Sustainable Chemistry & Engineering. In Press. DOI: 10.1021/acssuschemeng.9b06551 |
| Decision Sciences & Visualization | Wang, Y, Wang, J, Schuler, J, Hartley, D., Volk, T and Eisenbies, M. 2020. Optimization of harvest and logistics for multiple lignocellulosic biomass feedstocks in the northeastern United States. Energy (197)117260. DOI: 10.1016/j.energy.2020.117260. |
| Decision Sciences & Visualization | Wahlen, B. D., Wendt, L. M., Murphy, A., Thompson, V. S., Hartley, D. S., Dempster, T. and Gerken, H. 2020. Preservation of Microalgae, Lignocellulosic Biomass Blends by Ensiling to Enable Consistent Year-Round Feedstock Supply for Thermochemical Conversion to Biofuels. Frontiers in Bioengineering and Biotechnology.(8)316. DOI:10.3389/fbioe.2020.00316 |
| Decision Sciences & Visualization | Meyer, P. A., Snowden-Swan, L. J., Jones, S. B., Rappe, K. G. and Hartley, D. S. 2020. The effect of feedstock composition on fast pyrolysis and upgrading to transportation fuels: Techno-economic analysis and greenhouse gas life cycle analysis. Fuel (259)116218. DOI:10.1016/j.fuel.2019.116218 |
| Decision Sciences & Visualization | Griffel, L. M., Vazhnik, V., Hartley, D. S., Hansen, J. K., and Roni, M. 2020. Agricultural field shape descriptors as predictors of field efficiency for perennial grass harvesting: An empirical proof. Computers and Electronics in Agriculture (168)105088. DOI: 10.1016/j.compag.2019.105088 |
| Decision Sciences & Visualization | Abou Ali, H., Delparte, D., & Griffel, L. M. (2020). From Pixel to Yield: Forecasting Potato Productivity in Lebanon and Idaho. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 42, 1-7. DOI: 10.5194/isprs-archives-XLII-3-W11-1-2020. |
| Decision Sciences & Visualization | Hansen, J. K., Roni, M. S., Nair, S. K., Hartley, D. S., Griffel, L. M., Vazhnik, V., & Mamun, S. 2019. Setting a baseline for Integrated Landscape Design: Cost and risk assessment in herbaceous feedstock supply chains. Biomass and Bioenergy, 130. doi:10.1016/j.biombioe.2019.105388 |
| Decision Sciences & Visualization | Jin, H., Reed, D. W., Thompson, V. S., Fujita, Y., Jiao, Y., Crain-Zamora, M., Fisher, J., Scalzone, K., Griffel, L. M., Hartley, D. and Sutherland, J. W. (2019). Sustainable bioleaching of rare earth elements from industrial waste materials using agricultural wastes. ACS Sustainable Chemistry & Engineering, 7(18), pp.15311-15319. DOI: 10.1021/acssuschemeng.9b02584. |
| Decision Sciences & Visualization | Roni, M.S., Thompson, D.N. and Hartley, D.S., 2019. Distributed biomass supply chain cost optimization to evaluate multiple feedstocks for a biorefinery. Applied Energy, 254, p.113660. |
| Decision Sciences & Visualization | Langholtz, M., Davis, M., Hartley, D., Brandt, C., Hilliard, M., Eaton, L. 2019. Cost and profit impacts of modifying stover harvest operations to improve feedstock quality. Biofuels, Bioproducts & Biorefining. |
| Decision Sciences & Visualization | Narani, A., Konda, N.V.S.N.M, Chen, C.-S., Tachea, F., Coffman, P., Gardner, J., Li, C., Ray, A.E., Hartley, D.S., Simmons, B., Pray, T.R., Tanjore, D. 2019. Simultaneous application of predictive model and least cost formulation can substantially benefit biorefineries outside Corn Belt in United States: A case study in Florida. Bioresource Technology. 271:218-227. |
| Decision Sciences & Visualization | Emerson, R.M., Hernandez, S., Williams, C.L., Lacey, J.A., Hartley, D.S. 2018. Improving bioenergy feedstock quality of high moisture short rotation woody crops using air classification. Biomass and Bioenergy. 117:56-62 |
| Decision Sciences & Visualization | Wendt, L.M., Smith, W.A., Hartley, D.S., Wendt, D.S., Ross, J.A., Sexton, D.M., Lukas, J.C, Nguyen, Q.A., Murphy, A.J., Kenney, K.L. 2018. Techno-economic assessment of a chopped feedstock logistics supply chain for corn stover. Frontiers in Energy Research. 6(90). |
| Decision Sciences & Visualization | Lamers, P., Nyugen,R., Hartley, D., Hansen, J. and Searcy, E., 2018. Biomass market dynamics supporting the large-scale deployment of high-octane fuel production in the United States. GCB Bioenergy. 10(7):460-472 |
| Decision Sciences & Visualization | Roni, M.S., Thompson, D., Hartley, D., Searcy, E. and Nguyen, Q., 2018. Optimal blending management of Biomass Resources Used for Biochemical Conversion. Biofuels, Bioproducts and Biorefining. 12(4):624-648 |
| Decision Sciences & Visualization | Griffel, L. M., Delparte, D., & Edwards, J. (2018). Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y. Computers and Electronics in Agriculture, 153, 318-324. DOI: 10.1016/j.compag.2018.08.027. |
| Decision Sciences & Visualization | Olsson, O., Roos, A., Guisson, R., Bruce, L., Lamers, P., Hektor, B., Thrän, D., Hartley, D., Ponitka, J. and Hildebrandt, J., 2018. Time to tear down the pyramids? A critique of cascading hierarchies as a policy tool. Wiley Interdisciplinary Reviews: Energy and Environment.7(2),e279 |
| Decision Sciences & Visualization | Nair, S. K., Griffel, L. M., Hartley, D. S., McNunn, G. S., & Kunz, M. R. (2018). Investigating the efficacy of integrating energy crops into non-profitable subfields in Iowa. BioEnergy Research, 11, pp. 623-637. DOI: 10.1007/s12155-018-9925-0. |
| Decision Sciences & Visualization | Narani, A., Coffman, P., Gardner, J., Li, C., Ray, A.E., Hartley, D.S., Stettler, A., Konda, S.N.M., Simmons, B., Pray, T., Tanjore, D., 2017. Predictive modeling to de-risk bio-based manufacturing by adapting to variability in lignocellulosic biomass supply, Bioresource Technology.243:676-685 |
| Decision Sciences & Visualization | Liu, W, Wang, J., Richard, T., Hartley, D., Spatari, S., Volk,T., 2017. Economic and Life Cycle Analyses of Biomass Utilization for Bioenergy and Bioproducts. Biofuels, Bioproducts & Biorefining. 11(4):633-647 |
| Decision Sciences & Visualization | Wendt, L.M., Wahlen, B.D., Li, C., Ross, J.A., Sexton, D.A., Lukas, J.A., Hartley, D.S. and Murphy, J.A., 2017. Evaluation of a high-moisture stabilization strategy for harvested microalgae blended with herbaceous biomass: Part II- techno-economic assessment. Algal Research. 25:676-685 |
| Decision Sciences & Visualization | Thompson, V.S., Lacey, J.A., Hartley, D.S., Jindra, M. A., Aston, J. E., Thompson, D. N., 2016. Application of air classification and formulation to manage feedstock cost, quality and availability for bioenergy. Fuel, 180: 497-505. |
| Computing Platforms | https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_14693.pdf |
| Practice & Culture | Coming Soon! |
| Next Gen Artificial Intelligence | Yonge, Adam, et al. "TAPsolver: A Python package for the simulation and analysis of TAP reactor experiments." arXiv preprint arXiv:2008.13584 (2020) https://arxiv.org/abs/2008.13584 |
| Next Gen Artificial Intelligence | Kunz, M. Ross, et al. "Probability theory for inverse diffusion: Extracting the transport/kinetic time-dependence from transient experiments." Chemical Engineering Journal 402 (2020): 125985 https://www.sciencedirect.com/science/article/pii/S1385894720321136 |
| Next Gen Artificial Intelligence | Medford, Andrew J., et al. "Extracting knowledge from data through catalysis informatics." ACS Catalysis 8.8 (2018): 7403-7429 https://pubs.acs.org/doi/10.1021/acscatal.8b01708 |
| Next Gen Artificial Intelligence | Kunz, M. Ross, et al. "Pulse response analysis using the Y-procedure: A data science approach." Chemical Engineering Science 192 (2018): 46-60 https://www.sciencedirect.com/science/article/pii/S0009250918304561 |
| Operational Artificial Intelligence | D.P. Guillen, N. Anderson, C. Krome, R. Boza, L. M. Griffel, J. Zouabe, and A. Al Rashdan, 2020, "A RELAP5-3D/LSTM Model for the Analysis of Drywell Cooling Fan Failure," Progress in Nuclear Energy 130, December 2020. https://doi.org/10.1016/j.pnucene.2020.103540. |
| Operational Artificial Intelligence | A. Alfonsi, C. Wang, J. Cogliati, D. Mandelli, C. Rabiti “Status of Adaptive Surrogates within the RAVEN framework”, Idaho National Laboratory, Idaho Falls, Idaho, INL/EXT 17 43438 |
| Operational Artificial Intelligence | A. S. Epiney, A. Alfonsi, C. Parisi, R. Szilard, “RISMC Industry Application #1 (ECCS/LOCA): Core characterization automation: Lattice Codes interface for PHISICS/RELAP5-3D”, Nuclear Engineering and Design, 345, pp-15-27, 2019 |
| Operational Artificial Intelligence | V. Narcisi, P. Lorusso, F. Giannetti, A. Alfonsi, G. Caruso, “Uncertainty Quantification method for RELAP5-3D© using RAVEN and application on NACIE experiments”, Annals of Nuclear Energy, vol. 127, pp. 419-432, 2019 |
| Operational Artificial Intelligence | D. Mandelli, D. Maljovec, A. Alfonsi, C. Parisi, P. Talbot, J. Cogliati, C. Smith, “Mining data in a dynamic PRA framework”, Progress in Nuclear Energy, 108, 99-110, September 2018. |
| Operational Artificial Intelligence | A. Alfonsi, G. Mesina, A. Zoino, N. Anderson, C. Rabiti, “Combining RAVEN, RELAP5-3D and PHISICS for Fuel Cycle and Core Design Analysis”, ASME Journal of Nuclear Engineering and Radiation Science, vol. 3, num. 2, # NERS-16-1120 |
| Operational Artificial Intelligence | C. Rabiti, A. Alfonsi, A. S. Epiney, “New Simulation Schemes and Capabilities for the PHISICS/RELAP5-3D Coupled Suite”, Nuclear Science and Engineering, vol.182, num. 1, pp 104-118 |
| Operational Artificial Intelligence | A. Alfonsi, A. Hummel, J. Chen, G. Strydom, H. Gougar, “Decay Heat Surrogate modeling for High Temperature Reactors”, Proceedings of HTR 2018, Warsaw, Poland, October 8-10, 2018 |
| Operational Artificial Intelligence | A. Alfonsi, C. Wang, D. Mandelli, C. Rabiti, “Adaptive Surrogates within the RAVEN Framework for Dynamic Probabilistic Risk Assessment Analysis”, Proceeding of Best Estimate Plus Uncertainty International Conference, Lucca, Italy, May 13-18. |
| Operational Artificial Intelligence | A. Alfonsi, C. Rabiti, D. Mandelli, "Assembling Multiple Models within the RAVEN Framework", Proceedings of 2017 American Nuclear Society Annual Meeting, June 11-15, 2017, San Francisco |
| Operational Artificial Intelligence | C. Picoco, T. Aldemir, V. Rychkov, A. Alfonsi, D. Mandelli, C. Rabiti, "Coupling of RAVEN and MAAP5 for the Dynamic Event Tree analysis of Nuclear Power Plants", proceedings of European Safety and Reliability Conference - ESREL, June 18-22, 2017, Portoroz, Slovenia |
| Operational Artificial Intelligence | D. Mandelli, A. Alfonsi, C. Smith, C. Rabiti, “Generation and Use of Reduced Order Models for Safety Applications Using RAVEN,” Proceedings American Nuclear Society 2015 Winter Meeting, November 8-12, 2015, Washington, DC, US |
| Operational Artificial Intelligence | A. Alfonsi, G. Mesina, A. Zoino, C. Rabiti “A fuel cycle and core design analysis method for new cladding acceptance criteria using PHISICS, RAVEN and RELAP5-3D” Proceedings of the 24th International Conference on Nuclear Engineering (ICONE24), June 26-30, 2016, Charlotte, USA |
| Operational Artificial Intelligence | A. Alfonsi, D. Mandelli, C. Rabiti “RAVEN Facing the Problem of assembling Multiple Models to Speed up the Uncertainty Quantification and Probabilistic Risk Assessment Analyses “Proceedings of 13th International Conference on Probabilistic Safety Assessment and Management (PSAM 13), Oct. 2-6 2016, Seul, South Korea |
| Operational Artificial Intelligence | C. Rabiti, D. Mandelli, A. Alfonsi, J. Cogliati, R. Kinoshita “Introduction of Supervised Learning Capabilities of the RAVEN Code for Limit Surface Analysis,” Proceedings American Nuclear Society 2014 Annual Meeting, June 15-19, 2014, Reno, NV, US |
| Operational Artificial Intelligence | D. Mandelli, C. Smith, C. Rabiti, A. Alfonsi, R. Youngblood, V. Pascucci, B. Wang, D. Maljovec, P. T. Bremer “Dynamic PRA: An Overview of New Algorithms to Generate, Analyze and Visualize Data,” Proceedings American Nuclear Society 2013 Winter Meeting, November 10-14, 2013, Washington, DC |
| Operational Artificial Intelligence | A. Alfonsi, C. Rabiti, D. Mandelli, J. Cogliati, S. Sen, C. Smith, "Improving Limit Surface Search Algorithms in RAVEN Using Acceleration Schemes," INL/EXT-15-36100, July 2015 |
