Speaker
Description
Oak Ridge National Laboratory’s (ORNL) Second Target Station (STS) is designed to become the world’s highest peak-brightness spallation source of cold neutrons. Exceptionally bright cold neutron beams will provide transformative capabilities to examine novel materials for advanced technologies in the decades to come. Bright beams will enable new neutron scattering experiments using innovative instruments under more extreme conditions, using smaller samples and shorter irradiation time. A comprehensive optimization study of neutron production is necessary to generate such bright beams. This work presents an automated optimization workflow that combines high-fidelity neutronics modeling with structural stress analyses and modern optimization algorithms. The coupled multi-physics, multi-parameter optimization workflow is essential to completing the STS project successfully. The workflow can be applied in the design process at other neutron, experimental, and accelerator facilities.
The current design of the STS consists of a 700-kW water-cooled rotating tungsten target and two compact pure para-hydrogen neutron moderators at 20 K. The target will be driven by a short-pulsed (<1 us) 1.3 GeV proton beam at 15 Hz from the Spallation Neutron Source’s (SNS) linear accelerator. Neutrons with a broad energy spectrum will be generated in the target via spallation reactions. Some neutrons will enter hydrogen moderators surrounded by a light water premoderator and a beryllium reflector. After their moderation, cold neutrons will exit through small 3x3 cm emission windows and travel towards one of the eventually 18 modern instruments.
A compact arrangement of the target and moderators is key to generating bright neutron beams.
However, arrangements that improve neutronics output typically reduce the structural integrity and thus increase the probability of failure. The goal of the coupled neutronics and structural optimization is to maximize neutron production while maintaining high factor of safety. In the past, one iteration through neutronics and structural analysis took several weeks to months. With the new automated workflow, the duration has been reduced to hours, which allows us to find the optimal solution much faster.
The optimization workflow uses parametrized solid CAD engineering models of the key STS components, such as the target and moderators. The detailed models are converted with Attila4MC [1] into Unstructured Mesh (UM) models for neutronics calculations with MCNP6.2 [2,3]. The automated CAD to MCNP conversion improves the fidelity of the models and minimizes the time necessary for their generation. The high-fidelity energy deposition data from neutronics calculations are extracted together with neutron brightness. Energy deposition serves as input for the calculation of the factor of safety, which automatically evaluates both mean and peak amplitude stress with Sierra [4]. Neutron brightness and factor of safety are passed to the Dakota [5] optimization toolkit, which analyzes the results and proposes a new set of design parameters using one of the state-of-the-art optimization algorithms. This cycle repeats until the optimization workflow converges and the optimal design is found.
This talk reviews the current STS design and the optimization workflow. We will describe individual steps of the workflow, share some practical information about its implementation, and discuss recent results.
References
[1] Attila4MC 10.2 Overview of Core Functions, Silver Fir Software, Inc., Gig Harbor, WA, USA, 2020, SFSW-UR-2020-OCF102.
[2] C. Werner, et al., MCNP® User’s Manual, Code Version 6.2, Los Alamos National Laboratory, 2017, LA-UR-17-29981.
[3] R. Martz, The MCNP6 Book on Unstructured Mesh Geometry: User’s Guide for MCNP 6.2, Los Alamos National Laboratory, 2017, LA-UR-17-22442.
[4] Beckwith, F. N., et al., “Sierra/SolidMechanics 4.56 User’s Guide”, United States: N. p., 2020. Web. doi:10.2172/1608404.
[5] Adams, B.M., et al, “Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization, Parameter Estimation, Uncertainty Quantification, and Sensitivity Analysis: Version 6.14 User’s Manual,” Sandia Technical Report SAND2021-5822, May 2021.
