Search NASA⌕ Search

DOE OSTI · 1958874

Making a Deal with the Devilfish: Biometric-Informed Screening Technology CRADA - 514 (Final Report)

Abstract

This report documents an investigation into the performance of a novel fish-protection screen intended to prevent small aquatic and marine organisms from entering water intakes in free-flowing water bodies. The proposed prototype screen resembles conventional, slotted fish-protection screens with parallel wires or bars; however, the prototype bars are shaped to mimic the filter elements in the mouths of filter-feeding fishes such as the devil ray (Mobula tarapacana). Millions of years of evolution have perfected these structures to provide energetically efficient and clog-resistant filtering of zooplankton from seawater. The intent of the prototype is to exploit these mechanisms for the protection of similarly sized, or larger, organisms in settings where the freely flowing current is approximately parallel with the screen face (i.e., sweeping flow) and perpendicular to the screen bars.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mater, Benjamin D., Coutant, Charles C., Ham, Kenneth D., Perkins, William A., Christ, Josef F., Anderson, David M., Singh, Rajesh K., Rahrooh, Andre A., Tarufelli, Brittany L., Mueller, Robert P.. 2022-03-10. Making a Deal with the Devilfish: Biometric-Informed Screening Technology CRADA - 514 (Final Report). https://doi.org/10.2172/1958874

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

KEEP EXPLORING

Related reports

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↗