Sawtooth Simulation

Dan R. Funk

Dan R. Funk is the President of Sawtooth Simulation. He received a B.S. in Mathematics from Georgia Tech and was commissioned an Ensign in the U.S. Navy in 1975. He completed Nuclear Power School and Nuclear Prototype Training and Qualification. He then served as a Naval Nuclear Propulsion Officer until 1990. Dan retired in 2013 as a USNR Commander. Dan was a program manager with DOE-NE from 1990 – 2018. In this role, Dan was director of the NEAMS program from 2014-2018. The NEAMS simulation tools are the basis of Sawtooth Simulation’s computational capability.

James W. (“Jim”) Dolan

Jim Dolan is the CEO and CFO of Sawtooth Simulation. He graduated with honors from Cal State University at Fullerton with a Bachelor's degree in accounting. Jim worked in the audit department at the accounting firm of KPMG LLP for 12 years, primarily working with publicly traded multinational organizations. After leaving KPMG, Jim has served as the Chief Financial and/or Operating Officer of several publicly traded and privately owned companies. Jim has owned and operated several technology and real estate development businesses.

Dr. Richard Martineau

Dr. Richard Martineau is the Founder and Chief Scientist of Sawtooth Simulation. He has over thirty years of experience managing numerical methods R&D and computational engineering investigations at INL. He led the development of the MOOSE framework and many of the MOOSE-based physics tools. Rich's technical knowledge includes multi-physics, CFD, and compressible material dynamics. Rich received multiple INL Laboratory Director's Awards for leading MOOSE and MOOSE-based physics efforts and two R&D 100 Awards for MOOSE and Cardinal.

Dr. David Andrs

Dr. David Andrs obtained both an M.S. and Ph.D. in computer science at the University of West Bohemia, Czech Republic. He is the Sawtooth Simulation’s Chief Software Designer. In April 2010, he joined the Fuel Modeling and Simulation department at INL, where he worked as one of the primary developers of the MOOSE framework. David received two INL Laboratory Director's Awards for his work on the MOOSE framework and an R&D 100 Award for MOOSE.

Dr. Elia Merzari

Dr. Elia Merzari received his degree from the Tokyo Institute of Technology with a thesis on using advanced CFD techniques to simulate flows in rod bundles. In 2009, he joined ANL, where he served in the NEAMS program and is currently the thermal-fluids lead. In 2019, he joined the faculty at Penn State as an associate professor and is now a full professor. Elia has received several awards, including the ANS Landis Young Member Engineering Achievement Award, ASME's George Westinghouse Silver Medal, ANS Fellow, NURETH Fellow, and two R&D100 awards for Nek5000 and Cardinal.

Dr. April Novak

Dr. April Novak graduated from the University of California, Berkeley in 2020. April received many awards for her academic achievements, including the 2018 DOE-NE Innovations in Nuclear Technology R&D Award, 1st Place in Advanced Reactor Systems. April is the lead developer of the MOOSE-based Cardinal multi-physics application, for which she received a 2023 R&D 100 Award. She is currently the Maria Goeppert-Mayer Fellow in ANL's Computational Science Division. She is also an Associate Professor in the University of Illinois (UIUC) Nuclear Engineering Department.

Dr. William R Martin

Dr. William R Martin is a Professor of Nuclear Engineering and Radiological Sciences at the University of Michigan. Professor Martin’s research is world-renowned in developing deterministic and Monte Carlo numerical methods to solve the Boltzmann transport equation. His recent work in the deterministic methods includes modeling resonance absorption phenomena and applying the Method of Manufactured Solutions to the MOC method, including multiphysics applications. Professor Martin’s work in the Monte Carlo area includes methods for analyzing stochastic media, improved methods for nonlinear thermal radiation transport, acceleration methods for fission source convergence, functional tallies, and time-dependent Monte Carlo methods. Professor Martin won the Pomraning Award in August 2023 at M&C23 in Niagara Falls.