Search NASA⌕ Search
Home/Archive/DOE OSTI

DOE OSTI · 1059428

Computational Modeling of Circulating Fluidized Bed Reactors

Ibrahim, Essam A.
View original source ↗Read source document ↗Export metadata ↓

Abstract

Details of numerical simulations of two-phase gas-solid turbulent flow in the riser section of Circulating Fluidized Bed Reactor (CFBR) using Computational Fluid Dynamics (CFD) technique are reported. Two CFBR riser configurations are considered and modeled. Each of these two riser models consist of inlet, exit, connecting elbows and a main pipe. Both riser configurations are cylindrical and have the same diameter but differ in their inlet lengths and main pipe height to enable investigation of riser geometrical scaling effects. In addition, two types of solid particles are exploited in the solid phase of the two-phase gas-solid riser flow simulations to study the influence of solid loading ratio on flow patterns. The gaseous phase in the two-phase flow is represented by standard atmospheric air. The CFD-based FLUENT software is employed to obtain steady state and transient solutions for flow modulations in the riser. The physical dimensions, types and numbers of computation meshes, and solution methodology utilized in the present work are stated. Flow parameters, such as static and dynamic pressure, species velocity, and volume fractions are monitored and analyzed. The differences in the computational results between the two models, under steady and transient conditions, are compared, contrasted, and discussed.

Explore related subjects

42 ENGINEERING97 MATHEMATICS AND COMPUTINGCirculating Fluidized Bed ReactorComputational Fluid DynamicsFLUENT

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Ibrahim, Essam A.. 2013-01-09. Computational Modeling of Circulating Fluidized Bed Reactors. https://doi.org/10.2172/1059428

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related reports

DOE OSTI2026

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↗