Search NASA⌕ Search

DOE OSTI · 1735047

Spark-ignited direct-injection engine combustion systems

Abstract

A direct-injection stratified charge internal combustion engine includes a combustion cylinder to receive an air-fuel mixture, and an air intake port to inlet air into the combustion cylinder. The direct-injection engine also includes a fuel injector configured to deliver fuel within the cylinder in a spray pattern substantially aligned to a cylinder central axis to create the air-fuel mixture. A spark igniter is located within a path of the spray pattern to ignite combustion of the air-fuel mixture. The direct-injection engine further includes a movable piston defining a lower boundary of the combustion cylinder to contain the combustion of the air-fuel mixture. The piston is configured to include a bowl portion having local geometric features located on an intake port side of the combustion cylinder to redirect fluid flow towards a vortex in fluid communication with a combustion location near the cylinder central axis.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Solomon, Arun S., Spruit, William F., Sczomak, David P.. 2020-07-21. Spark-ignited direct-injection engine combustion systems. https://www.osti.gov/biblio/1735047

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↗