Next Gen Artificial Intelligence

Driving revolutionary advancements in scientific applications using next-gen AI emerging architectures, methodologies and tools.

Commercial-off-the-Shelf (COTS) AI technologies are inadequate for highly-specialized scientific, high-consequence mission space. INL PIs focus on a number of relevant AI/ML science and technology gaps to drive research and demonstration, including 1) coupling domain-aware methods with machine learning, particularly for safeguards and nuclear nonproliferation; developing novel machine learning informed modeling and simulation approaches coupled directly to experimental results to develop mechanistic ML models for battery and catalysis mechanisms, and 3) extending innovative frameworks to incorporate explainability in bioenergy systems as well as critical infrastructure analysis.

Collaborations & Projects:

Explainable AI

The objective of machine learning at its core is the use of available data to predict unobserved instances that may be difficult to measure. Application of machine learning in the physical science domain is particularly difficult as the features must be carefully constructed to provide an interpretable connection to previously developed theory. As such, explainable AI leverages inference and statistical based machine learning to develop not only better prediction performance but also an understanding on how the model is interpreting the data.

Battery Lifetime and Failure Mode Estimation

The prediction of the failure mode of a battery is a difficult and constantly evolving problem as new materials, electrolyte chemistries and environmental conditions affect the overall performance. Traditionally, this is solved by measuring specific conditions and validating root cause performance through physics-based methods. With an increased capability of battery cell testing at Idaho National Laboratory, detailed battery performance data may be collected for the use of machine learning. The goal of this project is to not just predict the lifetime of a battery but integrate the experimental and physics representation to understand the underlying degradation mechanisms providing early and robust measure of a battery’s health.

Example Paper: Bor-Rong Chen, M. Ross Kunz, Tanvir Tanim, Eric Dufek. A Machine Learning Framework for Early Detection of Lithium Plating using Physics-Based Signatures. In Preparation

Figure 1: Classification results over a variety of rates and protocols. Results from Logistic Elastic Net Regression are provided as a probability (P) of being Li plated and compared to the user-based decision tree classification results (N.A. means ambiguous cases) for the (a) training, (b) test, and (c) validation datasets. The various rates and protocols are represented as data points with different shapes. The color inside the data points in (a) and (b) presents the percentage of Li plated area when applicable. Data points overlapping with each other (marked by “+”) are shifted for clear visualization. A misclassified case in the test dataset is marked by the red arrow and a borderline case in the validation dataset is marked by the green arrow.

Insights of Reaction Mechanisms via Transient Kinetics

Understanding of the set of elementary steps and kinetics in each reaction is extremely valuable to make informed decisions about creating the next generation of catalytic materials. Computational catalysis has emerged as the most prominent strategy for obtaining this information through extensive atomistic simulations that are typically validated using steady-state experimental data. However, due to the rough granularity of commonly used steady-state kinetic analysis techniques, there typically exist multiple reaction mechanisms and rate constants that fit the experimental results. This project proposes to help bridge the gap between simulation and experiment through the combination of highly dense transient kinetic measurement and integrated physics-based machine learning. This methodology is proposed as a new approach to characterize how materials control complex reaction mechanisms relying exclusively on experimental data driven approaches.

Example Paper: M. Ross Kunz, Adam Yonge, Rakesh Batchu, Yixiao Wang, Zongtang Fang, Andrew J. Medford, Denis Constales, Gregory Yablonsky and Rebecca Fushimi. Data Driven Reaction Mechanism Estimation via Transient Kinetics and Machine Learning. In Preparation

Figure 2: The comparison of the covariance structure for the reactants and products over the Eley-Rideal (a) and Langmuir-Hinshelwood (b) mechanisms for carbon monoxide oxidation. This covariance structure can be realized with the combination of reactor theory and high-throughput transient data. This process is used in initially classifying the reaction mechanism and then determining a fingerprint of the kinetic coefficients applied to industrial catalysts.

Capabilities: 

High Performance Computing (HPC)

Nuclear Science User Facilities (NSUF) High Performance Computing (HPC) resources offered through Idaho National Laboratory provide scientific computing capabilities to support efforts in advanced modeling and simulation. These resources support a wide range of research activities, including performance of materials in harsh environments (such as the effects of irradiation and high temperatures), performance of existing light water and advanced nuclear reactors, and multiscale multiphysics analysis of nuclear fuel performance.

INL HPC computing resources are available to industry, universities, national laboratories, and federal agencies to support research and development. Access is generally granted for research related to the DOE Office of Nuclear Energy and INL’s mission focusing on nuclear energy development, workforce development, and education.

Resources

Sawtooth: an HPE SGI 8600-based system with 99,792 cores, 403 TB of memory and a LINPACK rating of 5.6 Petaflop/s. Sawtooth’s network is a nine-dimensional enhanced hypercube utilizing EDR and HDR InfiniBand. Individual compute nodes contain dual Xeon Platinum 8268 processors with 24 cores each. The majority of compute nodes contain 196 GB of memory while 27 contain 384 GB of memory coupled with four NVIDIA V100 GPUs with 32 GB of on-GPU memory each. Sawtooth came online in late 2019 and ranked #37 on the November 2019 TOP500 list.

Lemhi: a Dell 6420-based system with 20,160 cores, 94 TB of memory and a LINPACK rating of 1.0 Petaflop/s. Lemhi’s network is an OmniPath fat tree. Individual compute nodes contain dual Xeon Gold 6148 processors with 20 cores each and 192 GB of memory. Lemhi came online in fall 2018 and ranked #427 on the November 2018 TOP500 list.

Falcon: a SGI ICE-X distributed memory system with 34,992 cores, 121 TB of memory and a LINPACK rating of 1.1 Petaflop/s. Falcon’s network is a seven-dimensional enhanced hypercube utilizing FDR InfiniBand. Individual compute nodes contain dual Xeon E5-2695 v4 processors with 18 cores each and 128 GB of memory. Falcon came online in fall 2014 and ranked #97 on the November 2014 TOP500 list.

Applied Visualization Laboratory (AVL)

The Applied Visualization Laboratory contains several 3D immersive environments for scientists and engineers to walk into their data, examine it, and provide deep analysis in pursuit of their research. As mixed, virtual, and augmented reality technology evolves, the opportunities for portable, in-depth analysis of complex data sets increases. Augmented reality solutions are envisioned to allow researchers to have CAVE-like experiences anywhere. Web-based 3D geographic information systems, mobile applications (for both phone and tablet) and serious games (games built for training or educational purposes) allow users to conduct research at their desks or in the field, enabling discovery outside the lab. Virtual reality exploration systems offer the ability to create visualizations of large datasets that can be projected and run in real-time simulations. Using six-degrees-of-freedom input devices – which allow a body to move forward and backward, up and down, left to right – and stereoscopic output, they offer the benefits of more realistic interaction.

The Center for Advanced Energy Studies (CAES) opened its first Cave Automatic Virtual Environment (CAVE) in 2010. With the new CAVE installed in 2017, CAES’ Applied Visualization Laboratory is even better equipped to provide researchers from universities, industry and government agencies with a user facility where they can visualize and address scientific and technical challenges.

Publications:

Pillar Citations
Digital Thread Christopher 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 Thread 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.
Digital Thread 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.
Digital Thread Ross Hays, Peter Suyderhoud, Jeren Browning, Christopher Ritter. “Requirements Management and Data Models for the Versatile Test Reactor”, June 21, 2021.
Digital Thread 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.
Digital Thread Ross 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 Thread Ahmad 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.
Computing Platforms 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
Computing Platforms 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
Computing Platforms Manzoor, A., Zhang, Y., & Aidhy, D. (2021). Factors affecting the vacancy formation energy in Fe70Ni10Cr20 random concentrated alloy. Computational Materials Science 110669-110679.
Computing Platforms 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.
Computing Platforms 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
Computing Platforms 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
Computing Platforms Zhang, 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 Platforms 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
Computing Platforms 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.
Decision Sciences & Visualization 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).
Decision Sciences & Visualization 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.
Decision Sciences & Visualization 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.
Decision Sciences & Visualization 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.
Computing Platforms 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
Computing Platforms 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
Computing Platforms 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
Practice and Culture 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.
Instrumentation and Controls 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
Instrumentation and Controls Troy Unruh, Benjamin Chase, Joy Rempe, David Nigg, George Imel, Jason Harris, Todd Sherman & Jean-François 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 Controls 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.
Instrumentation and Controls 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.
Digital Twin NL/JOU-21-63018, Versatile Test Reactor Open Digital Engineering Ecosystem
Digital Twin INL/JOU-21-63077, Requirements Management and Data Models for the Versatile Test Reactor
Artificial Intelligence Kunz, M. Ross, et al. “Early battery performance prediction for mixed use charging profiles using hierarchal machine learning” Batteries & Supercaps 2021, 4, 1186.
Artificial Intelligence 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
Artificial Intelligence Kunz, M. Ross, et al. "Data driven reaction mechanism estimation via transient kinetics and machine learning." Chemical Engineering Journal 420 (2021): 129610.
Cyber & Data Resilience 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"
Cyber & Data Resilience 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"
Cyber & Data Resilience CS Wickramasinghe, K Amarasinghe, DL Marino, C Rieger, M Manic IEEE Access, 2021/9/14, "Explainable Unsupervised Machine Learning for Cyber-Physical Systems"
Digital Thread Christopher Ritter, Jeren Browning, “Towards a Digital Twin to Detect Nuclear Proliferation Activities”, September 8, 2020.
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
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
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
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
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
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
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
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
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.
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
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
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
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.
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
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
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
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.
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
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
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
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
Practice & Culture Coming Soon!
Computing Platforms https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_14693.pdf
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.
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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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.
Cyber & Data Resilience "Data Fidelity: Security's Soft Underbelly" (RCIS 2017),"Data Fidelity in the Post-Truth Era" (ICCWS 2018)
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.
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).
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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 Ahmad Al Rashdan, Jeren Browning, Christopher Ritter, “Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology”, September 11, 2019.

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