cv.bib

@inproceedings{pohlmannSensorAnomaliesCharacterization2024,
  title      = {Sensor {{Anomalies Characterization}} and {{Detection}} via {{Machine Learning Methods}} for {{Nuclear Power Plants}}},
  author     = {Pohlmann, Liam M. and Bhowmik, Palash K. and Wang, Congjian and Sabharwall, Piyush},
  date       = {2024-09-24},
  publisher  = {American Society of Mechanical Engineers Digital Collection},
  doi        = {10.1115/ES2024-131031},
  url        = {https://dx.doi.org/10.1115/ES2024-131031},
  urldate    = {2026-01-02},
  abstract   = {Abstract. Next-generation nuclear reactors pose challenges for effective system monitoring and data management, necessitating the differentiation of anomalous sensor data from noise. While digital twin models hold promise, fundamental analyses using simplified models are needed. This study proposes a foundational approach utilizing simulated data from PCTRAN-generated datasets to emulate reference pressurized-water reactor (PWR) conditions and introduces typical sensor performance and anomalies. The method employs data partitioning, linear regression for preprocessing, and a K-means clustering algorithm for anomaly detection, achieving over 95\% precision in identifying anomalies. A parametric study using Monte Carlo Sampling on the anomaly detection algorithm’s input values reveals critical factors such as window size’s impact on accuracy and computational time. Utilizing the Risk Analysis Virtual Environment (RAVEN) tools, a Pareto optimal frontier is determined to balance accuracy and execution time. Sensitivity and uncertainty analysis highlight window size as a critical factor. While further refinement is necessary for practical application, these techniques show promise for enhancing nuclear system monitoring and data management.},
  eventtitle = {{{ASME}} 2024 18th {{International Conference}} on {{Energy Sustainability}} Collocated with the {{ASME}} 2024 {{Heat Transfer Summer Conference}} and the {{ASME}} 2024 {{Fluids Engineering Division Summer Meeting}}},
  langid     = {english},
  keywords   = {ConfPaper},
  author+an  = {1=highlight;}
}

@report{pohlmannSensorAnomalyDetection2023,
  title     = {Sensor {{Anomaly Detection}} for  {{Nuclear Reactor Systems}}  {{Utilizing Linear Regression}} and  {{K-Means Unsupervised}}  {{Machine Learning}}},
  author    = {Pohlmann, Liam M and Bhowmik, Palash Kumar and Sabharwall, Piyush},
  date      = {2023-08-10},
  langid    = {english},
  keywords  = {TechReport},
  author+an = {1=highlight;}
}

@report{pohlmannSolutionBurgersEquation2024,
  title     = {Solution of {{Burgers}}' {{Equation}} Using the {{Finite Volume Method}} and {{Runge-Kutta}} 4 {{Time-Stepping}} under {{Various Boundary Conditions}}},
  author    = {Pohlmann, Liam},
  date      = {2024-12-18},
  number    = {ORNL/TM--2024/3511, 2483416},
  pages     = {ORNL/TM--2024/3511, 2483416},
  doi       = {10.2172/2483416},
  url       = {https://www.osti.gov/servlets/purl/2483416/},
  urldate   = {2026-01-02},
  langid    = {english},
\  keywords  = {TechReport},
  author+an = {1=highlight;}
}

@report{shemonANLNEAMS2532025,
  title       = {{{ANL}}/{{NEAMS-25}}/3 {{FY25 MOOSE Usability Improvements}}: {{3D Meshing Capabilities}}, {{Initiation}} of {{Geometry Support}} for {{Monte Carlo Tools}}, and {{Enhancement}} of {{MOOSE}}/{{Workbench User Input Interactions}}},
  author      = {Shemon, Emily and Miao, Yinbin and Kumar, Shikhar and Kiesling, Kalin and Oaks, Aaron and Soon Kyu, Lee and Stogner, Roy H. and Pohlmann, Liam and Harbour, Logan and Giudicelli, Guillaume and Lefebvre, Robert A. and Langley, Brandon},
  date        = {2025-09-30},
  institution = {Argonne National Laboratory},
  url         = {https://www.osti.gov/servlets/purl/2588956},
  urldate     = {2026-01-02},
  langid      = {english},
  keywords    = {TechReport},
  author+an   = {}
}