Search NASA⌕ Search

DOE OSTI · 1871893

Predicting EBC Temperature Limits for Industrial Gas Turbines

Also available from

Abstract

Higher turbine inlet temperatures may require the use of ceramic matrix composites (CMC) such as SiC/SIC, which require environmental barrier coatings (EBCs) to protect them against the detrimental effect of water vapor. Here, the goal of this project is to determine the maximum bond coating temperature for EBCs for land-based turbines, where the minimum coating lifetime is 25,000 h. If the temperature exceeds the 1414°C melting point of the Si bond coating, then coatings without a bond coating also need to be evaluated. Thus, current Yb 2 Si 2 O 7 EBCs with a Si bond coating and next-generation EBCs without a Si bond coating are being evaluated in laboratory testing using 1-h cycles in air+90%H 2 O. For this initial work, coatings were deposited on CVD SiC coupons. Reaction kinetics at 1250°, 1300° and 1350°C have been evaluated by measuring the thickness of the thermally grown silica scale after 100–500 h exposures. For comparison, scale growth rates for uncoated SiC and Si specimens in dry and wet environments were included as minimum and maximum values, respectively. Based on a critical scale thickness failure criteria, estimated maximum temperatures were calculated for both EBC systems using this initial data.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pint, Bruce, Stack, Padraig, Kane, Kenneth. 2021-09-16. Predicting EBC Temperature Limits for Industrial Gas Turbines. https://doi.org/10.1115/gt2021-59408

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↗