HEDS Division has been awarded two new Laboratory Directed Research & Development (LDRD) grants for FY21
By HED
The theory grant is led by Frederico Fiuza and is entitled: ACCELERATING THE DEVELOPMENT OF 3D PREDICTIVE MODELING FOR THE MEC PW UPGRADE. The second LDRD grant is led by Mianzhen Mo and will pursue ULTRAFAST ELECTERON-DIFRRACTION STUDIES OF RADIATION-DAMAGED MATERIALS.
ACCELERATING THE DEVELOPMENT OF 3D PREDICTIVE MODELING FOR THE MEC PW UPGRADE
Petawatt (PW)-class laser-matter interactions are a very promising driver of compact secondary sources of bright and high-energy ion, neutron, electron, and photon beams. When combined with the LCLS X-ray laser, as planned for the upgrade of the LCLS MEC instrument, PW laser-matter interactions enable unique studies of plasmas and materials in conditions relevant for fusion energy sciences. This LDRD aims to develop new machine learning (ML) tools to accelerate the modeling of PW laser-matter interactions and the generation of secondary sources relevant to the MEC PW upgrade project at LCLS. We will develop new numerical frameworks that can provide 3D modeling over the full range of temporal and spatial scales of the experiments, which are presently computationally prohibitive.
According to Fiuza:
"The ability to do 3D predictive simulations of intense laser-matter interactions will be critical for the design and optimization of plasmas and materials studies at the upgraded PW MEC end station of LCLS. The HED group at SLAC together with our colleagues from Stanford Computer Science will explore innovative machine learning approaches that can help us optimize numerical methods and greatly accelerate this type of simulations with a significant impact to SLAC and DOE."
ULTRAFAST ELECTERON-DIFRRACTION STUDIES OF RADIATION-DAMAGED MATERIALS.
This funded LDRD project will perform new experiments to determine the structural behavior of radiation-damaged materials using the state-of-the-art ultrafast MeV electron diffraction technique. The outcome will help advance the predictive modeling tools for material design to sustain extreme radiation environments and help better prepare for future in-situ characterization experiments of radiation-induced defects at the future MEC upgrade facility.
According to Mo:
“This is an exciting opportunity for us to understand how fusion materials degrade under extreme radiation environments. I am looking forward to working with my collaborators to pursue this goal.”
