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Nuclear Energy

Projects

These high-fidelity projects in the nuclear space improve safety, optimize performance and reduce costs. This innovative approach is key to developing smarter, more resilient nuclear energy solutions. 

AGN-201 Reactor Digital Twin

Athena Workforce and Development

Beartooth Fuel Processing Test Bed

Demonstration and Operation of Microreactor Experiments (DOME) Test Bed

Laboratory for Operation and Testing in the United States (LOTUS)

Microreactor AGile Non-Nuclear Experimental Testbed (MAGNET)

Microreactor Applications Research Validation and EvaLuation (MARVEL)

Project PELE

Versatile Test Reactor

Featured Publications

Title
Microelectronics Simulating Energy and Security Interactions in Semiconductor Manufacturing: Insights from the Intel Minifab Model
MicroelectronicsMachine Learning for Additive Manufacturing of Functionally Graded Materials
MicroelectronicsConstrained Optimization of Sensor Placement for Nuclear Digital Twins
MicroelectronicsDigital Engineering Sensor Architectures for Future Microreactor Builds
MicroelectronicsDigital-twin-based improvements to diagnosis, prognosis, strategy assessment, and discrepancy checking in a nearly autonomous management and control system
NuclearEnergyDigital Engineering Implementation in Nuclear Demonstration and Nonproliferation Projects at Idaho National Laboratory
NuclearEnergyMultiscale and Machine Learning Modeling for Process-informed Microstructure Prediction in Additively Manufactured Materials Using MALAMUTE
NuclearEnergyModeling for a Digital Twin-Based Remote Operation System Framework
NuclearEnergyCreation Synthetic Data to Train a Digital Twin to Predict Reactor Operations
NuclearEnergy, NuclearSecurityRegulatory Considerations for Nuclear Energy Applications of Digital Twin Technologies
NuclearSecurityProtecting and Defending against Autonomous Control Systems and Digital Twin Cyber Attacks: Response Strategy for Hyperparameter attacks of Digital Twin Machine Learning Models in Nuclear Power Plants (Final)
NuclearSecurityAdvanced Data Science Model for Detecting Intelligent Malware
NuclearSecurityAutonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)
NuclearSecurityCyber threat assessment of machine learning driven autonomous control systems of nuclear power plants
GridSecurityExploring Advanced Computational Tools and Techniques with Artificial Intelligence and Machine Learning in Operating Nuclear Plants
GridSecurityPrioritizing ICS Beachhead Systems for Cyber Vulnerability Testing
GridSecurityCyber Threat Assessment Methodology for Autonomous and Remote Operations for Advanced Reactors (Conference Presentation)
GridSecurity, ScientificComputingValidation Studies using ELK and the Open-Source MOOSE Framework Application Zapdos for Electromagnetic Coupled Plasma Simulation
ScientificComputingElectromagnetics and Advanced Manufacturing Simulation Development within the MOOSE Ecosystem
ScientificComputingAn IIPG-Based Finite Element Framework in MOOSE for Modeling Fiber Reinforced Composite Failure Governed by Extrinsic Cohesive Laws
ScientificComputingCoupling Finite Element and Finite Volume Simulation Within MOOSE
ScientificComputingContinued Validation Studies using the MOOSE Framework for Plasma Simulation with Electromagnetics
"Data Fidelity: Security's Soft Underbelly" (RCIS 2017),"Data Fidelity in the Post-Truth Era" (ICCWS 2018)
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
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
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
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.
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
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
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
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
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
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.
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.
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.
Ahmad Al Rashdan, Jeren Browning, Christopher Ritter, "Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology", September 11, 2019.
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.
Al Rashdan, A. Y., J. A. Smith, S. W. St Germain, C. S. Ritter, V. Agarwal, R. L. Boring, T. A. Ulrich, “Development of a Technology Roadmap for Online Monitoring of Nuclear Power Plants,” December 5, 2018.
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.
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.
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.
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.
Al Rashdan, A., V. Agarwal, S. W. St Germain, R. L. Boring, T. A. Ulrich, C. S. Ritter, J. A. Smith, “Technology Roadmap to Migrate Nuclear Power Plants to Data Driven Monitoring,” January 28, 2019.
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.
Argonne National Laboratory, Idaho National Laboratory, National Energy Technology Laboratory, National Renewable Energy Laboratory. Advanced Research Directions on AI for Energy: Report on Winter 2023 Workshops. Report No. ANL-23/69. Washington, DC: U.S. Department of Energy, 2024.
Bajpai, P., Poschmann, M., & Piro, M. (2021). Derivations of Partial Molar Excess Gibbs Energy of Mixing Expressions for Common Thermodynamic Models. Journal of Phase Equilibria and Diffusion 1-15. https://doi.org/10.1007/s11669-021-00886-w
Biaggne, A., Knowlton, W., Yurke, B., Lee, J., & Li, L. (2021). Substituent Effects on the Solubility and Electronic Properties of the Cyanine Dye Cy5: Density Functional and Time-Dependent Density Functional Theory Calculations. Molecules 26 524-524. https://doi.org/10.3390/molecules26030524
Biaggne, A., Noble, G., & Li, L. (2021). Adsorption and Surface Diffusion of Metals on a-Al2O3 for Advanced Manufacturing Applications. JOM 73 1062-1070. https://doi.org/10.007/s11837-021-04589-y
Bong Goo Kim, Joy L. Rempe, Jean-François Villard & Steinar Solstad (2011) Review Paper: Review of Instrumentation for Irradiation Testing of Nuclear Fuels and Materials, Nuclear Technology, 176:2, 155-187, DOI: 10.13182/NT11-A13294
Burli PH, Nguyen RT, Hartley DS, Griffel LM, Vazhnik V, Lin Y. Farmer characteristics and decision-making: A model for bioenergy crop adoption. Energy. 2021 Jun 15:121235.
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
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
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
Chen, Bor-Rong, et al. "A machine learning framework for early detection of lithium plating combining multiple physics-based electrochemical signatures." Cell Reports Physical Science 2.3 (2021): 100352
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.
Christopher Ritter, Lee Nelson, Jeren Browning, AnnMarie Marshall, Ross Hays, Taylor Ashbocker, Peter Suyhderhoud, John Darrington, "Versatile Test Reactor Open Digital Engineering Ecosystem", June 7, 2021.
Christopher Ritter, Ross Hays, Jeren Browning, Ryan Stewart, Samuel Bays, Gustavo Reyes, Mark Schanfein, Adam Pluth, Piyush Sabharwall, Ross Kunz, Ashley Shields, John Koudelka, Porter Zohner, "Digital Twin to Detect Nuclear Proliferation: A Case Study" August 10, 2021.
Crowder, N., J. Lee, A. Gupta, K. Han, S. Bodda, C. Ritter. “Digital Engineering for Integrated Modeling and Simulation for Building-Piping Systems Through Interoperability Solutions,” Nuclear Science and Engineering 196, no. Issue Sup1 (May 2022): 260–277.
CS Wickramasinghe, K Amarasinghe, DL Marino, C Rieger, M Manic IEEE Access, 2021/9/14, "Explainable Unsupervised Machine Learning for Cyber-Physical Systems"
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
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
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.
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.
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
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.
Finan, A., C. Ritter, A. Marshall, et al., “Digital Engineering in Nuclear Demonstration and Nonproliferation Projects at INL,” Frontiers in Energy Research, 2024.
G Michail Makrakis, C Kolias, G Kambourakis, C Rieger, J Benjamin, arXiv e-prints, arXiv: 2109.03945, 2021/9, "Vulnerabilities and Attacks Against Industrial Control Systems and Critical Infrastructures"
Garcia, H., S. Aumeier, A. Al Rashdan, and B. Rolston, 2020, "Secure embedded intelligence in nuclear systems: Framework and method", Annals of Nuclear Energy, accepted for publication. DOI:10.1016/j.anucene.2019.107261.
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.
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.
Greenquist, I., & Powers, J. (2021). 25-Pin metallic fuel performance benchmark case based on the EBR-II X430 experiment series. Journal of Nuclear Materials 556 153211-153211.
Greenquist, I., Cunningham, K., Hu, J., Powers, J., & Crawford, D. (2021). Development of a U-19Pu-10Zr fuel performance benchmark case based on the IFR-1 experiment. Journal of Nuclear Materials 553 152997-152997.
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.
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
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
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
Hossain T, Jones D, Hartley D, Griffel LM, Lin Y, Burli P, Thompson DN, Langholtz M, Davis M, Brandt C. The nth-plant scenario for blended feedstock conversion and preprocessing nationwide: Biorefineries and depots. Applied Energy. 2021 Jul 15;294:116946.
https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_14693.pdf
Jeren Browning, Andrew Slaughter, Ross Kunz, Joshua Hansel, Bri Rolston, Katherine Wilsdon, Adam Pluth, Dillon McCardell. "Foundations for a Fission Battery Digital Twin", August 16, 2021.
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.
Jin, M., Cao, P., & Short, M. (2020). Achieving exceptional radiation tolerance with crystalline-amorphous nanocrystalline structures. Acta Materialia 186 587-596. https://doi.org/10.1016/j.actamat.2019.12.058
Khadka, R., Koudelka, J., Kenney, K., Egan, E. Casanova, K., Hillman, B., Reed, T., Newman, G., & Issac, B. (2022, March). Mobile Hot Cell Digital Twin: End-of-life Management of Disused High Activity Radioactive Sources – 22057. In Waste Management Symposia (WMS)
Kunz, M. Ross, et al. "Data driven reaction mechanism estimation via transient kinetics and machine learning." Chemical Engineering Journal 420 (2021): 129610.
Kunz, M. Ross, et al. "Early battery performance prediction for mixed use charging profiles using hierarchal machine learning" Batteries & Supercaps 2021, 4, 1186.
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
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
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
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.
Li, Z., Zhan, X., Bai, X., Lee, S., Zhong, W., Sutton, B., Heuser, B., (2021). Modified Microstructures in Proton Irradiated Dual Phase 308L Weldment Filler Material. Journal of Nuclear Materials 548 152825-152825. https://doi.org/10.1016/j.jnucmat.2021.152825
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
M. A. S. Zaghloul, A. M. Hassan, D. Carpenter, P. Calderoni, J. Daw and K. P. Chen, "Optical Sensor Behavior Prediction using LSTM Neural Network," 2019 IEEE Photonics Conference (IPC), 2019, pp. 1-2, doi: 10.1109/IPCon.2019.8908337.
Mandelli, D., C. Wang, S. Staples, C. S. Ritter, A. L. Mack, S. W. St Germain, A. Alfonsi, “Cost Risk Analysis Framework (CRAFT): An Integrated Risk Analysis Tool and its Application in an Industry Use Case,” September 26, 2018.
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).
Manzoor, A., Zhang, Y., & Aidhy, D. (2021). Factors affecting the vacancy formation energy in Fe70Ni10Cr20 random concentrated alloy. Computational Materials Science 110669-110679.
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
Merzari, E., Gaston, D., Martineau, R., Fischer, P., Hassan, Y., Haomin, Y., Min, M., Shaver, D., Rahaman, R., Shriwise, P., Romano, P., Talamo, A., Lan, Y., (2021). Cardinal: A Lower-Length-Scale Multiphysics Simulator for Pebble Bed Reactors. Nuclear Technology 7 1118-1141. https://doi.org/10.1080/00295450.2020.1824471
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
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.
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
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.
Nguyen RT, Lionel Toba DA, Severson MH, Woodbury E, Carey A, Imholte DD. A market-oriented database design for critical material research. The Journal of The Minerals, Metals & Materials Society (JOM). 2021 Jun 30;1(INL/JOU-21-61669-Rev000).
NL/JOU-21-63018, Versatile Test Reactor Open Digital Engineering Ecosystem 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
P. Calderoni, D. Hurley, J. Daw, A. Fleming and K. McCary, "Innovative sensing technologies for nuclear instrumentation," 2019 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2019, pp. 1-6, doi: 10.1109/I2MTC.2019.8827129.
Rafer Cooley, Michael Cutshaw, Shaya Wolf, Rita Foster, Jed Haile, Mike Borowczak, 2021, “Comparing Ransomware using TLSH and @DisCo Analysis Frameworks,” Idaho National Lab, National and Homeland Security, Critical Infrastructure Protection, Idaho Falls, ID.
Ritter, C. and M. Rhoades, “Incorporating Digital Twins In Early Research and Development of Megaprojects To Reduce Cost and Schedule Risk,” INSIGHT 26, no. 3 (Sept. 2023): 57–65. https://doi.org/10.1002/inst.12457.
Ritter, C., “Digital Engineering Framework for Complex Systems,” CQSDI20 2020 Collaboration on Quality in the Space & Defense Industries, March 9, 2020. https://asq.org/conferences/quality-space-defense.
Ritter, C., J. Browning, L. Nelson, T. Borders, J. Bumgardner, M. Kerman. “Digital Engineering Ecosystem for Future Nuclear Power Plants: Innovation of Ontologies, Tools, and Data Exchange,” In Recent Trends and Advances in Model Based Systems Engineering, edited by Zad M. Madni, Barry Boehm, Daniel Erwin, Mahta Moghaddam, Michael Sievers, Marilee Wheaton, 15–74. Cham, Switzerland: Springer, 2022.
Ritter, C., J. Browning, P. Suyderhoud, R. Hays, A. Marshall, K. Han, T. Ashbocker, J. Darrington, L. Nelson. “Versatile Test Reactor Open Digital Engineering Ecosystem.” INSIGHT 25, no. 1 (April 2022): 56–60. https://doi.org/10.1002/inst.12374.
Ritter, C., R. Hays, J. Browning, R. Stewart, S. Bays, G. Reyes, M. Schanfein, A. Pluth, P. Sabharwall, R. Kunz, A. Shields, J. Koudelka, P. Zohner, "Digital twin to detect nuclear proliferation: a case study," ASME. Journal of Energy Resources Technology, October 2022, 144(10): 102108, 2022.
Ritter, C., T. Borders, M. Kerman, “Digital Engineering Ecosystem for Innovative Nuclear Technologies,” Presented at 22nd Systems and Mission Engineering Conference, October 24, 2019. https://www.ndia.org/-/media/sites/ndia/meetings-and events/2019/october/systems-engineering/agendas/0870_sme_program-10_17.ashx.
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
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.
Sabharwall, P., J. Gibb, C. Ritter, K. Araújo, A. Gupta, I. Ferguson, B. Rolston, R. Fisher, J. Gehin, Y. Ballout, "Cyber Security for Microreactors in Advanced Energy Systems," Cyber Security: A Peer-Reviewed Journal, 4(4), (2021): 345–367. https://scholarworks.boisestate.edu/epi_facpubs/15.
Shaya Wolf, Rita Foster, Jed Haile, Mike Borowczak “Data-Driven Suitability Analysis to Enable Machine Learning Explainability and Security” IEEE Explore
Stewart, R., A. Shields, C. Pope, J. Darrington, K. Wilsdon, S. Bays, K. Heaps, N. Woodruff, J. Scott, G. Reyes, M. Schanfein, E. Trevino, J. Palmer, C Ritter, "A digital twin of the AGN-201 reactor to simulate nuclear proliferation," INMM/ESARDA 63rd Joint Annual Meeting, Vienna, 2023.
Stewart, R., A. Shields, C. Ritter, et al., "The AGN-201: A digital twin test bed for monitoring nuclear reactors during operations," Annals of Nuclear Engineering, 2024.
Stewart, R., A. Shields, S. Bays, C. Ritter, G. Reyes, M. Schanfein, "Utilizing digital twins fornuclear safeguards and security," INMM/ESARDA 63rd Joint Annual Meeting, Vienna, 2023.
Stewart, R., C. Ritter, S. Bays, J. Browning, M. Kunz, A. Pluth, J. Koudelka, P. Zohner, S. Wen, M. Schanfein, G. Reyes, P. Sabharwall, F. Gleicher, K. Wilsdon, R. Hays, "Using a Digital Twin to Investigate Nuclear Proliferation Across Various Reactor Types," Proceedings of the INMM 62nd Annual Meeting, 2022.
Stewart, R., Shields, A., Wen, S., Gleicher, F., Bays, S., Schanfein, M., Browning, J., Jesse, K. & Ritter, C., "Utilizing a Virtual Sodium-Cooled Fast Reactor Digital Twin to Aid in Diversion Pathway Analysis for International Safeguards Applications," In: Science & Global Security. 31, 3, p. 137-161 25 p. 2024.
TB Phillips, TR McJunkin, CG Rieger, JF Gallego-Calderon, JP Lehmer, Idaho National Lab (INL), Idaho Falls, ID (United States), 2021/8/6 "Power Distribution Designing For Resilience Application"
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.
Toba AL, Nguyen RT, Cole C, Neupane G, Paranthaman MP. US lithium resources from geothermal and extraction feasibility. Resources, Conservation and Recycling. 2021 Jun 1;169:105514.
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.
Troy Unruh, Benjamin Chase, Joy Rempe, David Nigg, George Imel, Jason Harris, Todd Sherman & Jean-Francois Villard (2014) In-Core Flux Sensor Evaluations at the ATR Critical Facility, Nuclear Technology, 187:3, 308-315, DOI: 10.13182/NT13-122
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
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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).
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Yadav, V., V. Agarwal, A. V. Gribok, R. D. Hays, A. J. Pluth, C. S. Ritter, H. Zhang, “Technical Challenges and Gaps in Digital Twin Enabling Technologies for Nuclear Reactor Applications,” December 2021.
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Zongtang Fang, Matthew P. Confer, Yixiao Wang, Qiang Wang, M. Ross Kunz, Eric J. Dufek, Boryann Liaw, Tonya M. Klein, David A. Dixon*, and Rebecca Fushimi*, "Formation of Surface Impurities on Lithium Nickel Manganese Cobalt Oxides in the Presence of CO2 and H2O", July 2, 2021 https://doi.org/10.1021/jacs.1c03812
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