Instrumentation and Controls

An essential component of digital innovation that enables a high degree of automation and charts a path towards autonomous control.

The development of advanced instrumentation is an essential component of digital innovation. Sensing technologies with real time or faster than real time capability provide a dramatic increase in the volume and quality of process variables data in digital I&C systems, enabling a higher degree of automation and charting the path towards autonomous control. Optical fiber and acoustic based interrogation systems for example enable collection of data with unprecedented time and space resolution. Multi-point and multi-mode analog sensors contribute to higher data availability, while embedded digitizers allow efficient integration in modern I&C systems.

Controls can be defined as the science and engineering of methods and tools that initiate actions to actuators through measurements captured by sensors. Digital controls, and particularly those utilizing Artificial Intelligence/Machine Learning and advanced methods to control a process, capitalize on advanced instrumentation capabilities. They require large volume of data and/or are coupled to a system model, often of reasonable fidelity and deployable scale (using reduced order modeling methods), to react to current system conditions (control) or predict and prepare for future actions (condition monitoring).

The integration of advanced instrumentation and control (I&C) technologies in the development of Digital Twins for modern industrial applications is a key element of digital innovation.

Collaborations & Projects:

In 2012, the Department of Energy’s Office of Nuclear Energy (DOE-NE) initiated the Nuclear Energy Enabling Technologies (NEET) program to conduct research, development, and demonstration (RD&D) to support existing, next generation of advanced reactor designs, and fuel cycle technologies. One of the NEET elements is Crosscutting Technology Development (CTD), which supports crosscutting RD&D activities to advance the state of nuclear technology, improve its competitiveness, and promote continued contribution to meet the Nation’s energy and environmental challenges. Within CTD, the Advanced Sensors and Instrumentation (ASI) program addresses critical technology gaps in the nuclear industry’s monitoring and control capabilities. This program is driving innovation in the measurement science field by supporting research on sensors, I&C, communication, big data analytics, machine learning (ML), and artificial intelligence (AI).

More: https://www.energy.gov/ne/nuclear-energy-enabling-technologies/advanced-sensors-and-instrumentation

The selection of appropriate instruments is a key factor in enabling digital innovation. The Department of Energy’s Office of Nuclear Energy (DOE-NE) maintains a searchable sensors technology database for nuclear applications. It provides information on current state of sensors development, availability, use cases, and also helps identify needs and gaps for sensor development to inform DOE-NE program priorities.

More: https://nes.energy.gov/

Capabilities: 

INL Measurement Science Laboratories (MSL)

Measurement Science Laboratories (MSL) are a collection of laboratory spaces, equipment and capabilities that provide broad support to many programs within the U.S. Department of Energy’s Office of Nuclear Energy (DOE-NE) and allow access to researchers and engineers from organizations inside and outside INL. Instrumentation research, development and deployment activities at MSL leverage the portfolio of DOE-NE Nuclear Energy Enabling Technologies Advanced Sensors and Instrumentation program as needed to meet customer needs. MSL contain an array of specialized equipment for nuclear instruments development, fabrication and testing. The MSL provides research and development, testing and characterization, and engineering services including: developing and fabricating nuclear instrumentation, engineering services for instrumented irradiation rigs, and development of innovative sensing technologies.

More information can be found at:

https://factsheets.inl.gov/FactSheets/Measurement%20Science%20Laboratories.pdf

Center for Reactor Instrumentation and Sensor Physics (CRISP)

CRISP connects experts from diverse organizations to devise solutions for sensing and instrumentation, and to test these systems under irradiation. CRISP’s mission is to advance the current state of automation in nuclear systems by developing foundational technologies to significantly reduce staffing levels needed in current and future nuclear power plants.

More information can be found on the following website hosted by MIT:

http://crisp.mit.edu/

Publications:

PillarDateCitations
Artificial Intelligence2021Kunz, M. Ross, et al. "Early battery performance prediction for mixed use charging profiles using hierarchal machine learning" Batteries & Supercaps 2021, 4, 1186.
Artificial Intelligence2021Rafer 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.
Artificial Intelligence2021Chen, 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
Artificial Intelligence2021Kunz, M. Ross, et al. "Data driven reaction mechanism estimation via transient kinetics and machine learning." Chemical Engineering Journal 420 (2021): 129610.
Artificial Intelligence2020D.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.
Artificial Intelligence2020Kunz, 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
Artificial Intelligence2020Manjunatha, 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).
Artificial Intelligence2020Gentillon, 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.
Artificial Intelligence2020Garcia, 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.
Artificial Intelligence2019V. 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
Artificial Intelligence2019A. 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
Artificial Intelligence2019A. 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
Artificial Intelligence2019Guillen, 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", Proceedings of the Big Data for Nuclear Power Plants Workshop 2019.
Artificial Intelligence2019Garcia, 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.
Artificial Intelligence2019Al 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.
Artificial Intelligence2019Al 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.
Artificial Intelligence2019Al 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.
Artificial Intelligence2018A. 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
Artificial Intelligence2018C. 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
Artificial Intelligence2018A. 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
Artificial Intelligence2018D. 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.
Artificial Intelligence2018Kunz, 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
Artificial Intelligence2018Medford, 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
Artificial Intelligence2018Mandelli, 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.
Artificial Intelligence2018Al 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.
Artificial Intelligence2018Al 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.
Artificial Intelligence2017C. 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
Artificial Intelligence2017A. 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
Artificial Intelligence2017A. 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.
Artificial Intelligence2016A. 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
Artificial Intelligence2016A. 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
Artificial Intelligence2015A. 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
Artificial Intelligence2015D. 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
Artificial Intelligence2015Farber, 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.
Artificial Intelligence2015Agarwal, 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.
Artificial Intelligence2015Agarwal, 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.
Artificial Intelligence2014C. 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
Artificial Intelligence2014Alamamiotis, 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.
Artificial Intelligence2013D. 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
Artificial Intelligence2008Yonge, 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
Computing Platforms2021Biaggne, 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
Computing Platforms2021Li, 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
Computing Platforms2021Manzoor, A., Zhang, Y., & Aidhy, D. (2021). Factors affecting the vacancy formation energy in Fe70Ni10Cr20 random concentrated alloy. Computational Materials Science 110669-110679.
Computing Platforms2021Greenquist, 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.
Computing Platforms2021Bajpai, 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
Computing Platforms2021Merzari, 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
Computing Platforms2021Zhang, Y., Manzoor, A., Jiang, C., Aidhy, A., & Schwen, D. (2021). A statistical approach for atomistic calculations of vacancy formation energy and chemical potentials in concentrated solid-solution alloys. Computational Materials Science 190 110308-110312.
Computing Platforms2021Biaggne, 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
Computing Platforms2021Greenquist, 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.
Computing Platforms2021Merzari, 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
Computing Platforms2021Zongtang 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
Computing Platforms2019Jin, 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
Computing Platforms2018https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_14693.pdf
Cyber & Data Resilience2021TB 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"
Cyber & Data Resilience2021G 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"
Cyber & Data Resilience2021CS Wickramasinghe, K Amarasinghe, DL Marino, C Rieger, M Manic IEEE Access, 2021/9/14, "Explainable Unsupervised Machine Learning for Cyber-Physical Systems"
Cyber & Data Resilience2017"Data Fidelity: Security's Soft Underbelly" (RCIS 2017),"Data Fidelity in the Post-Truth Era" (ICCWS 2018)
Decision Science, Visualization and Human Computer Interaction2021Nguyen 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).
Decision Science, Visualization and Human Computer Interaction2021Toba 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.
Decision Science, Visualization and Human Computer Interaction2021Burli 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.
Decision Science, Visualization and Human Computer Interaction2021Hossain 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.
Decision Sciences & Visualization2020Abou 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 & Visualization2020Griffel, 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 & Visualization2020Meyer, 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 & Visualization2020Wahlen, 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 & Visualization2020Wang, 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 & Visualization2020Hartley, 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 Science, Visualization and Human Computer Interaction2020Toba, 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 Science, Visualization and Human Computer Interaction2019Narani, 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 Science, Visualization and Human Computer Interaction2019Langholtz, 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 Science, Visualization and Human Computer Interaction2019Roni, 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 Science, Visualization and Human Computer Interaction2019Jin, 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 Science, Visualization and Human Computer Interaction2019Hansen, 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 Science, Visualization and Human Computer Interaction2018Nair, 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 Science, Visualization and Human Computer Interaction2018Olsson, 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 Science, Visualization and Human Computer Interaction2018Griffel, 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 Science, Visualization and Human Computer Interaction2018Roni, 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 Science, Visualization and Human Computer Interaction2018Lamers, 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 Science, Visualization and Human Computer Interaction2018Wendt, 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 Science, Visualization and Human Computer Interaction2018Emerson, 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 Science, Visualization and Human Computer Interaction2017Wendt, 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 Science, Visualization and Human Computer Interaction2017Liu, 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 Science, Visualization and Human Computer Interaction2017Narani, 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 Science, Visualization and Human Computer Interaction2016Thompson, 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.
Digital Thread2021Christopher Ritter, Ashley Shields, Ross Hays, Jeren Browning, Ryan Stewart, Samuel Bays, Gustavo Reyes, Mark Schanfein, Adam Pluth, Piyush Sabharwall, Ross Kunz, John Koudelka, Porter Zohner, "NNSA Digital Twin: Explainable AI Report", August 20, 2021.
Digital Thread2021Jeren 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.
Digital Thread2021Christopher 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.
Digital Thread2021Ross Hays, Peter Suyderhoud, Jeren Browning, Christopher Ritter. "Requirements Management and Data Models for the Versatile Test Reactor", June 21, 2021.
Digital Thread2021Christopher 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.
Digital Thread2021Ross Hays, Christopher Ritter, Jeren Browning, Samuel Bays, Gustavo Reyes, Mark Schanfein, Adam Pluth, Piyush Sabharwall, Ross Kunz, Ashley Shields, John Koudelka, "Data Model for Analysis of Proliferation Resistance", January 14, 2021.
Digital Thread2020Ahmad Al Rashdan, Cameron Krome, Jeren Browning, Kellen Giraud, Jared Wadsworth, Shawn St Germain, "Use Cases of DIAMOND for Data-Enabled Automation in Nuclear Power Plants", September 23, 2020.
Digital Thread2020Christopher Ritter, Jeren Browning, "Towards a Digital Twin to Detect Nuclear Proliferation Activities", September 8, 2020.
Digital Thread2019Christopher 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 Thread2019Ahmad Al Rashdan, Jeren Browning, Christopher Ritter, "Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology", September 11, 2019.
Digital Twin2020NL/JOU-21-63018, Versatile Test Reactor Open Digital Engineering Ecosystem
Digital Twin2020INL/JOU-21-63077, Requirements Management and Data Models for the Versatile Test Reactor
Digital Twin2019Christopher 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.
Instrumentation and Controls2019M. 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.
Instrumentation and Controls2019P. 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.
Instrumentation and Controls2014Troy 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
Instrumentation and Controls2011Bong 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
Practice and Culture2019Christopher 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.
Decision Science, Visualization and Human Computer Interaction2022Khadka, 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)

Our Team:

Pattrick Calderoni

Instrumentation and Controls Lead

Vivek Agarwal

Team Member

Ahmad Al Rashdan

Team Member

Sacrit Cetiner

Team Member

Craig Primer

Team Member

Troy Unruh

Team Member

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