Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1833812

MTW-OPAL: a technology development platform for ultra-intense optical parametric chirped-pulse amplification systems

Bromage, J.·Bahk, S. -W.·Bedzyk, M.·Begishev, I. A.·Bucht, S.·Dorrer, C.·Feng, C.·Jeon, C.·Mileham, C.·Roides, R. G.·Shaughnessy, K.·Shoup, III, M. J.·Spilatro, M.·Webb, B.·Weiner, D.·Zuegel, J. D.
View original source ↗Read source document ↗Export metadata ↓

Abstract

Optical parametric chirped-pulse amplification implemented using multikilojoule Nd:glass pump lasers is a promising approach for producing ultra-intense pulses (>10 23 W/cm 2 ). We report on the MTW-OPAL Laser System, an optical parametric amplifier line (OPAL) pumped by the Nd:doped portion of the multi-terawatt (MTW) laser. This midscale prototype was designed to produce 0.5-PW pulses with technologies scalable to tens of petawatts. Technology choices made for MTW-OPAL were guided by the longer-term goal of two full-scale OPALs pumped by the OMEGA EP to produce 2 × 25-PW beams that would be co-located with kilojoule–nanosecond ultraviolet beams. Several MTW-OPAL campaigns that have been completed since “first light” in March 2020 show that the laser design is fundamentally sound, and optimization continues as we prepare for “first-focus” campaigns later this year.

Explore related subjects

42 ENGINEERINGnonlinear opticsoptical parametric chirped-pulse amplificationultra-intense lasersultrafast lasers

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Bromage, J., Bahk, S. -W., Bedzyk, M., Begishev, I. A., Bucht, S., Dorrer, C., Feng, C., Jeon, C., Mileham, C., Roides, R. G., Shaughnessy, K., Shoup, III, M. J., Spilatro, M., Webb, B., Weiner, D., Zuegel, J. D.. 2021-10-06. MTW-OPAL: a technology development platform for ultra-intense optical parametric chirped-pulse amplification systems. https://doi.org/10.1017/hpl.2021.45

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

Large language models for transportation research: Methodologies, state of the art, and future opportunities

The rapid rise of large language models (LLMs) is transforming transportation research, with significant advancements emerging between 2023 and 2025, a period marked by the inception and swift growth of adopting and adapting LLMs for various transportation applications. Despite these significant advancements, however, a systematic review and synthesis of the existing literature remains lacking. This paper aims to fill this gap by providing a comprehensive review of the methodologies and applications of LLMs in transportation. We explore key applications, including autonomous driving, travel behavior prediction, and general transportation-related queries, alongside LLM methodologies such as zero- or few-shot learning, prompt engineering, and fine-tuning. From the review, critical research gaps are identified. From the methodological perspective, many of the research limitations can be addressed by integrating LLMs with existing tools and refining LLM architectures. From the application perspective, research opportunities for LLMs to address various transportation challenges are also explored. By synthesizing these findings, this review not only presents the state-of-the-art LLM adoption and adaptation in transportation, but also proposes future research directions as well as insights and recommendations for policymakers and practitioners, paving the way for greater LLM-driven research innovations in transportation in the future.

42 ENGINEERING↗