Engineering Papers⌕ Search

DOE OSTI · 1632517

Multi-mode advanced combustion engine with supervisory control

Abstract

An engine system uses data associated with at least one operating condition of an engine to set the engine system to an AI mode when the engine is in an SI mode 1) within first operating condition limits, and 2) when a rate of change of a first operating condition is within rate of change limits, maintain the engine system in the SI mode when the engine is outside of first operating condition limits or when the rate of change of the first operating condition is not within rate of change limits, set the engine system to the SI mode when the engine is in the AI mode outside second operating condition limits, and maintain the engine system in the AI mode when the engine is within second operating condition limits, wherein the second operating condition limits are different from the first operating condition limits.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ravi, Nikhil, Oudart, Joel, Jade, Shyam, Schwanke, Jason, Jiang, Li. 2020-02-04. Multi-mode advanced combustion engine with supervisory control. https://www.osti.gov/biblio/1632517

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↗