Artificial Intelligence
Driving revolutionary advancements in scientific applications with machine learning by predicting behaviors and automating controls.
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.
The use of artificial intelligence (AI) in energy applications has a game-changing potential in automating expensive and manual human activities in various types of industries. In the energy industry, power plants (especially nuclear) rely on staff performing several types of manual activities on a regular basis. Future energy plants, including nuclear advanced reactors, are designed to reduce the dependence on people for the operations, maintenance, and support activities of a plant. A light water nuclear power plant is typically full of analog gauges and manual actuators. By comparing the nuclear power plant control room to a modern airplane cockpit where the plane can fly itself and the pilot’s role can be reduced to simply monitoring the airplane, it is obvious that a significant technology gap exists that needs to be closed. Human intelligence needs to be replaced by machine intelligence in various forms of AI if this vision is to be realized.
Idaho National Laboratory (INL) has several efforts currently in progress to apply various forms of AI methods into energy applications. These methods are often available in a ready-to-use form (i.e., the method algorithms are available). However, the applicability of these methods on the various applications in the plants often requires the selection of compatible methods, customizing the methods, combining multiple methods, and integrating the methods with other science processes (e.g., modeling tools) for improved applicability and performance. Through the matching of methods and applications, gaps could be identified that are used to drive the national operational AI research focus.
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.
Light Water Reactor Sustainability Program
The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has been a pioneer in introducing AI methods to automate human activities for the nuclear energy sector, as the program has recognized the potential of AI for enabling great cost-savings for nuclear plants. The program works directly with the nuclear power industry. Several reports discussing the application of AI can be found at the LWRS website (https://lwrs.inl.gov). Here are two examples of the efforts currently ongoing within the LWRS program:
Anomalies detection using sensors data inference: Anomalies in a plant represent changes in a process that could be considered as early indicators of equipment failure. Escalation of an anomaly to a functional failure can result in severe economic or safety consequences to a plant, which would demand a responsive effort with costs that often far exceed what would have been invested to mitigate the issue. Several machine learning methods were used to monitor a dry well cooling fan in a nuclear power plant, which provided an applied perspective into the method’s performance. Details of this study can be found in: https://lwrs.inl.gov/Advanced%20IIC%20System%20Technologies/Subtle_Process-Anomalies_Detection_Using_Machine-Learning_Methods.pdf.
Automating fire watch using object recognition methods to identity fire in a video stream: The U.S. Nuclear Regulatory Commission (NRC) Regulatory Guide 1.189 defines the fire watch as “Individuals responsible for providing additional (e.g., during hot work) or compensatory (e.g., for system impairments) coverage of plant activities or areas to detect fires or to identify activities and conditions that present a potential fire hazard.” A person conducting a fire watch could spend hours watching certain equipment, a room, or an environment. Multiple methods were evaluated for automated visual recognition of fire; neural networks coupled to a color-based feature-extraction method to extract fire regions resulted in accurate fire recognition results. Details can be found in: https://lwrs.inl.gov/Advanced%20IIC%20System%20Technologies/Automating_Fire_Watch_in_Industrial_Environments_through_Machine.pdf.
MOOSE Simulation Environment
Modeling and simulation has now become standard practice in nearly every branch of science. Building a useful simulation capability has traditionally been a daunting task because it required a team of software developers working for years with scientists to describe a given phenomenon.
Idaho National Laboratory’s MOOSE (Multiphysics Object Oriented Simulation Environment) now makes modeling and simulation more accessible to a broad array of scientists. MOOSE enables simulation tools to be developed in a fraction of the time previously required. The tool has revolutionized predictive modeling, especially in the field of nuclear engineering — allowing nuclear fuels and materials scientists to develop numerous applications that predict the behavior of fuels and materials under operating and accident conditions.
Scientists who don’t have in-depth knowledge of computer science can now develop an application that they can “plug and play” into the MOOSE simulation platform. In essence, MOOSE solves the mathematical equations embodied by the model.
Such a tool means scientists seeking a new simulation capability don’t need to recruit a team of computational experts versed in, for example, parallel code development. Researchers can focus their efforts on the mathematical models for their field, and MOOSE does the rest. The simplicity has bred a herd of modeling applications describing phenomena in multi-scale nuclear fuels (BISON, Marmot), reactor physics (MAMMOTH, Rattlesnake), geology (FALCON), geochemistry (RAT), nuclear power plant systems/safety analysis (RELAP-7), and reactor engineering (Pronghorn).
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Light Water Reactor Sustainability Program
The U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program has been a pioneer in introducing AI methods to automate human activities for the nuclear energy sector, as the program has recognized the potential of AI for enabling great cost savings for nuclear plants. The program works directly with the nuclear power industry.
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.
MOOSE Simulation Environment
Modeling and simulation has now become standard practice in nearly every branch of science. Building a useful simulation capability has traditionally been a daunting task because it required a team of software developers working for years with scientists to describe a given phenomenon.
Idaho National Laboratory’s MOOSE (Multiphysics Object Oriented Simulation Environment) now makes modeling and simulation more accessible to a broad array of scientists. MOOSE enables simulation tools to be developed in a fraction of the time previously required. The tool has revolutionized predictive modeling, especially in the field of nuclear engineering — allowing nuclear fuels and materials scientists to develop numerous applications that predict the behavior of fuels and materials under operating and accident conditions.
Scientists who don’t have in–depth knowledge of computer science can now develop an application that they can “plug and play” into the MOOSE simulation platform. In essence, MOOSE solves the mathematical equations embodied by the model.
Such a tool means scientists seeking a new simulation capability don’t need to recruit a team of computational experts versed in, for example, parallel code development. Researchers can focus their efforts on the mathematical models for their field, and MOOSE does the rest. The simplicity has bred a herd of modeling applications describing phenomena in multi-scale nuclear fuels (BISON, Marmot), reactor physics (MAMMOTH, Rattlesnake), geology (FALCON), geo-chemistry (RAT), nuclear power plant systems/safety analysis (RELAP-7), and reactor engineering (Pronghorn).
Deep Lynx
A data warehouse technology that can enable the digital thread across an organization by serving as the central point of data aggregation and exchange. Deep Lynx is built on the relational database PostgreSQL, but stores data in a graph-like format. This enables data to be exported to graph databases for analysis and for data to be queried in a graph-like format. When a data source sends data to Deep Lynx, that data is mapped to a central schema (or ontology) in Deep Lynx. This central schema creates a common data format to which all data is mapped. https://github.com/idaholab/Deep-Lynx.
RAVEN
RAVEN (https://raven.inl.gov) is a flexible and multi-purpose probabilistic risk assessment, uncertainty quantification, artificial intelligence, data analysis and model optimization framework. Depending on the tasks to be accomplished and, in case, on the probabilistic (or not) characterization of the problem, RAVEN perturbs (e.g., Monte Carlo, Latin hypercube, reliability surface search, AI-guided, etc.) the response of the system to be analyzed altering the input parameters. The system is modeled by third party software (e.g. RELAP5-3D, MAAP5, BISON, etc.) and accessible to RAVEN either directly (software coupling) or indirectly (via input/output files). The outcomes of the sampling process are analyzed using statistical and data mining approaches. RAVEN also manages the parallel dispatching (i.e. both on desktop/workstation and large HPC systems) of the software representing the physical model. RAVEN relies on artificial intelligence algorithms to construct surrogate models of complex physical systems to accelerate the analysis. RAVEN can be employed for several types of applications, such as UQ, Sensitivity Analysis, PRA, Regression Analysis, Data Mining, Model Optimization, and design of experiments. An overview of the software is resented at raven.inl.gov. The software itself is open-source and can be downloaded at https://github.com/idaholab/raven
Publications:
| Pillar | Date | Citations |
|---|---|---|
| Artificial Intelligence | 2021 | Shaya Wolf, Rita Foster, Jed Haile, Mike Borowczak “Data-Driven Suitability Analysis to Enable Machine Learning Explainability and Security” IEEE Explore |
| Artificial Intelligence | 2021 | 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 | 2021 | 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. |
| Artificial Intelligence | 2021 | 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 | 2021 | Kunz, M. Ross, et al. "Data driven reaction mechanism estimation via transient kinetics and machine learning." Chemical Engineering Journal 420 (2021): 129610. |
| Artificial Intelligence | 2020 | 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 | 2020 | 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 | 2020 | 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 | 2020 | 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 | 2020 | 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. |
| Artificial Intelligence | 2019 | 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 | 2019 | 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 | 2019 | 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 | 2019 | 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", Proceedings of the Big Data for Nuclear Power Plants Workshop 2019. |
| Artificial Intelligence | 2019 | 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 | 2019 | 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 | 2019 | 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 | 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. |
| Artificial Intelligence | 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 |
| Artificial Intelligence | 2018 | 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 | 2018 | 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 | 2018 | 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 | 2018 | 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 | 2018 | 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 | 2018 | 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 | 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. |
| Artificial Intelligence | 2018 | 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 | 2017 | 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 | 2017 | 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 | 2017 | 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 | 2016 | 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 | 2016 | 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 | 2015 | 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 | 2015 | 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 | 2015 | 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 | 2015 | 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 | 2015 | 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. |
| Artificial Intelligence | 2014 | 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 | 2014 | 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 | 2013 | 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 | 2008 | 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 |
| Computing Platforms | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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 | 2021 | 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. |
| Computing Platforms | 2021 | 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 | 2021 | 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 |
| Computing Platforms | 2019 | 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 | 2018 | https://inldigitallibrary.inl.gov/sites/sti/sti/Sort_14693.pdf |
| Resilient Controls and Instrumentation Systems | 2021 | 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" |
| Resilient Controls and Instrumentation Systems | 2021 | 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" |
| Resilient Controls and Instrumentation Systems | 2021 | CS Wickramasinghe, K Amarasinghe, DL Marino, C Rieger, M Manic IEEE Access, 2021/9/14, "Explainable Unsupervised Machine Learning for Cyber-Physical Systems" |
| Resilient Controls and Instrumentation Systems | 2017 | "Data Fidelity: Security's Soft Underbelly" (RCIS 2017),"Data Fidelity in the Post-Truth Era" (ICCWS 2018) |
| Decision Science, Visualization and Human Computer Interaction | 2021 | 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 Science, Visualization and Human Computer Interaction | 2021 | 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 Science, Visualization and Human Computer Interaction | 2021 | 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 Science, Visualization and Human Computer Interaction | 2021 | 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. |
| Decision Sciences & Visualization | 2020 | 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 | 2020 | 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 | 2020 | 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 | 2020 | 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 | 2020 | 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 | 2020 | 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 Science, Visualization and Human Computer Interaction | 2020 | Toba, A.L., Griffel, L.M., & Hartley, D.S., (2020). Devs Based Modeling and Simulation of Agricultural Machinery Movement. In Press, Computers and Electronics in Agriculture. |
| Decision Science, Visualization and Human Computer Interaction | 2019 | 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 Science, Visualization and Human Computer Interaction | 2019 | 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 Science, Visualization and Human Computer Interaction | 2019 | 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 Science, Visualization and Human Computer Interaction | 2019 | 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 Science, Visualization and Human Computer Interaction | 2019 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2018 | 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 Science, Visualization and Human Computer Interaction | 2017 | 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 Science, Visualization and Human Computer Interaction | 2017 | 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 Science, Visualization and Human Computer Interaction | 2017 | 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 Science, Visualization and Human Computer Interaction | 2016 | 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. |
| Digital Thread | 2021 | 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 | 2021 | 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 | 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. |
| Digital Thread | 2021 | Ross Hays, Peter Suyderhoud, Jeren Browning, Christopher Ritter. "Requirements Management and Data Models for the Versatile Test Reactor", June 21, 2021. |
| Digital Thread | 2021 | 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 | 2021 | 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 | 2020 | 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. |
| Digital Thread | 2020 | Christopher Ritter, Jeren Browning, "Towards a Digital Twin to Detect Nuclear Proliferation Activities", September 8, 2020. |
| Digital Thread | 2019 | 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 | 2019 | Ahmad Al Rashdan, Jeren Browning, Christopher Ritter, "Data Integration Aggregated Model and Ontology for Nuclear Deployment (DIAMOND): Preliminary Model and Ontology", September 11, 2019. |
| Digital Twin | 2020 | NL/JOU-21-63018, Versatile Test Reactor Open Digital Engineering Ecosystem |
| Digital Twin | 2020 | INL/JOU-21-63077, Requirements Management and Data Models for the Versatile Test Reactor |
| Digital Twin | 2019 | 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 | 2019 | 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 | 2019 | 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. |
| Instrumentation and Controls | 2014 | 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 |
| Instrumentation and Controls | 2011 | 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 |
| Practice and Culture | 2019 | 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. |
| Decision Science, Visualization and Human Computer Interaction | 2022 | 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) |
