Search NASA⌕ Search

DOE OSTI · 1643228

Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Phase 1 Final Report)

Abstract

The Phase 1 Final Report describes the results of the work completed during the “Advanced Modular Sub-Atmospheric Hybrid Heat Engine” project. The objectives of Phase 1 work were to develop a conceptual design of the turbine-based modular hybrid heat engine (MHHE), including thermodynamic cycle analysis, define the nominal engine component boundary conditions, identify technology gaps, and develop a detailed test plan to address these gaps through bench scale testing in a potential subsequent Phase 2 project. To achieve the project objective, the following activities were performed in Phase 1 work: (1) Market analysis and primary fuel selection, (2) Conceptual design and integrated layout development, (3) Design support and key components characterization, (4) Planning to move technology to pre-commercial demonstration.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Chudnovsky, Yaroslav, Kozlov, Aleksandr. 2020-07-11. Advanced Modular Sub-Atmospheric Hybrid Heat Engine (Phase 1 Final Report). https://doi.org/10.2172/1643228

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↗