Accelerating the rate of discovery: Toward high-repetition-rate HED science

ORAL

Abstract

As high-intensity short-pulse lasers that can operate at high-repetition-rate (HRR) (>10 Hz) come online around the world, the high-energy-density (HED) science they enable will experience a radical paradigm shift. The >1000x increase in shot rate over today’s shot-per-hour drivers translates into dramatically faster data acquisition and more experiments, and thus the potential to significantly accelerate the advancement of HED science

 

Current energetic driver facilities depend on the ability to manually tune the lasers, the targets, the diagnostics settings, and more, between single shots or sets of shots through a manual feedback loop of data collection, data analysis, and optimization largely driven by experience and intuition. At 10 Hz, this paradigm is no longer sustainable as more complex data is collected more quickly than is possible to analyze manually.

 

Fully realizing the potential benefits of HRR facilities requires a fundamental shift in the design and execution of experiments done on them, the development of supporting technologies such as high-throughput targetry and diagnostics, and the evolution of machine learning techniques to couple traditional scientific computing with advanced data analytics. On-the-fly optimization of experiments will become ever more crucial as higher repetition rates will lead to more deliberate inter-shot variations and the improved operational range to allow exploration over larger regions of phase space. 

 

We will present the vision and ongoing work to realize a HRR framework for rapidly delivered optimized experiments coupled to cognitive simulation to provide new insights in HED science.

*This work was performed under the auspices of the U.S. DOE by LLNL under contract DE-AC52-07NA27344, and supported by LDRDs 20-ERD-048 and 21-ERD-015, DOE Early Career SCW1651, and DOE-SC SCW1720 and SCW1722.

Presenters

  • Tammy Ma

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory

Authors

  • Tammy Ma

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Derek Mariscal

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • MariAnn Albrecht

    • Lawrence Livermore National Laboratory
  • Rushil Anirudh

    • LLNL
    • Lawrence Livermore National Laboratory
  • Peer-Timo Bremer

    • Lawrence Livermore National Laboratory
  • Blagoje Djordjevic

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Scott Feister

    • California State University, Channel Isl
    • Department of Computer Science, California State University Channel Islands, Camarillo, California 93120, USA
    • California State University Channel Islands
  • Thomas Galvin

    • Lawrence Livermore National Laboratory
  • Elizabeth S Grace

    • Georgia Institute of Technology
  • Sandrine Herriot

    • Lawrence Livermore National Laboratory
  • Sam A Jacobs

    • Lawrence Livermore National Laboratory
  • Bhavya Kailkhura

    • Lawrence Livermore National Laboratory
  • Andreas J Kemp

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Reed C Hollinger

    • Colorado State University
    • Electrical and Computer Engineering Department, Colorado State University, Fort Collins, CO 80521 USA
  • Joohwan Kim

    • University of California, San Diego
  • Shusen Liu

    • Lawrence Livermore National Laboratory
  • Joshua Ludwig

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Jorge J Rocca

    • Colorado State University
    • Electrical and Computer Engineering Department, Colorado State University, Fort Collins, CO 80521 USA
  • Graeme G Scott

    • Lawrence Livermore National Laboratory
    • Lawrence Livermore Natl Lab
  • Raspberry A Simpson

    • Massachusetts Institute of Technology MI
    • Massachusetts Institute of Technology
  • Brian K Spears

    • Lawrence Livermore Natl Lab
  • Thomas Spinka

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Kelly Swanson

    • Lawrence Livermore National Laboratory
  • Vincent Tang

    • Lawrence Livermore National Laboratory
  • Jayaraman J Thiagarajan

    • LLNL
    • Lawrence Livermore National Laboratory
  • Brian Van Essen

    • Lawrence Livermore National Laboratory
  • Shoujun Wang

    • Colorado State University
    • Electrical and Computer Engineering Department, Colorado State University, Fort Collins, CO 80521 USA
  • Scott Wilks

    • LLNL
    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Laboratory
  • Jackson J Williams

    • Lawrence Livermore Natl Lab
    • Lawrence Livermore National Lab
    • Lawrence Livermore National Laboratory
  • Ghassan Zeraouli

    • Colorado State University
  • Jize Zhang

    • Lawrence Livermore National Laboratory
  • Mark C Herrmann

    • Lawrence Livermore National Lab
    • Lawrence Livermore National Laboratory
    • Lawrence Livermore Natl Lab
  • Constantin Haefner

    • Fraunhofer Institute for Laser Technology