Transient and Steady-State Inverse Problems in Sierra/Aria

Technical Report · Sun Dec 01 04:00:00 EST 2019

DOI:https://doi.org/10.2172/1592851· OSTI ID:1592851

Wagman, Ellen Brooke ^[1]; Kurzawski, Andrew ^[1]; Bunting, Gregory ^[1]; Walsh, Timothy F. ^[1]; Aquino, Wilkins ^[1]; Brunini, Victor ^[1]

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1. Sandia National Lab. (SNL), Albuquerque, NM, and Livermore, CA (United States)

Inverse problems arise in a wide range of applications, whenever unknown model parameters cannot be measured directly. Instead, the unknown parameters are estimated using experimental data and forward simulations. Thermal inverse problems, such as material characterization problems, are often large-scale and transient. Therefore, they require intrusive adjoint-based gradient implementations in order to be solved efficiently. The capability to solve large-scale transient thermal inverse problems using an adjoint-based approach was recently implemented in SNL Sierra Mechanics, a massively parallel capable multiphysics code suite. This report outlines the theory, optimization formulation, and path taken to implement thermal inverse capabilities in Sierra within a unit test framework. The capability utilizes Sierra/Aria and Sierra/Fuego data structures, the Rapid Optimization Library, and an interface to the Sierra/InverseOpt library. The existing Sierra/Aria time integrator is leveraged to implement a time-dependent adjoint solver.

• Research Organization: Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• DOE Contract Number: AC04-94AL85000; NA0003525
• OSTI ID: 1592851
• Report Number(s): SAND--2019-15379; 681971
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING