Engineering Papers⌕ Search

DOE OSTI · 22263954

Load responsive multilayer insulation performance testing

Abstract

Cryogenic insulation designed to operate at various pressures from one atmosphere to vacuum, with high thermal performance and light weight, is needed for cryogenically fueled space launch vehicles and aircraft. Multilayer insulation (MLI) performs well in a high vacuum, but the required vacuum shell for use in the atmosphere is heavy. Spray-on foam insulation (SOFI) is often used in these systems because of its light weight, but can have a higher heat flux than desired. We report on the continued development of Load Responsive Multilayer Insulation (LRMLI), an advanced thermal insulation system that uses dynamic beam discrete spacers that provide high thermal performance both in atmosphere and vacuum. LRMLI consists of layers of thermal radiation barriers separated and supported by micromolded polymer spacers. The spacers have low thermal conductance, and self-support a thin, lightweight vacuum shell that provides internal high vacuum in the insulation. The dynamic load responsive spacers compress to support the external load of a vacuum shell in one atmosphere, and decompress under reduced atmospheric pressure for lower heat leak. Structural load testing was performed on the spacers with various configurations. LRMLI was installed on a 400 liter tank and boil off testing with liquid nitrogen performed at various chamber pressures from one atmosphere to high vacuum. Testing was also performed with an MLI blanket on the outside of the LRMLI.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dye, S., Kopelove, A., Mills, G. L.. 2014-01-29. Load responsive multilayer insulation performance testing. https://doi.org/10.1063/1.4860740

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↗