Search NASA⌕ Search

DOE OSTI · 1888748

Computational Fluid Dynamics analysis of a jet-induced attrition

Abstract

The flow dynamics, collisional behavior and attrition rates of a grid jet attrition unit are computationally investigated using a kinetic theory-based approach. Profiles of the flow statistical properties are presented and discussed to describe the jet dynamics. Attrition behavior and rates are analyzed from the local reconstruction of the collision energy spectra. The commonly used jet-induced attrition mechanisms which involve the entrainment of the particles into the jet followed by their acceleration and collision at the jet tip is reproduced by the CFD calculations in this work. Here, supported by the local attrition rates, it is shown that the shear-based collisions around the jets, due to counter-current particle axial motions, are an additional jet-induced mechanism generating more fines than at the jet tips.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Konan, N’dri A., David Huckaby, E.. 2022-07-21. Computational Fluid Dynamics analysis of a jet-induced attrition. https://doi.org/10.1016/j.apt.2022.103707

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↗