Search NASA⌕ Search

DOE OSTI · 1848447

Flow-induced oscillation patterns for two tandem cylinders with turbulence stimulation and variable stiffness and damping

Abstract

Herein, the oscillation patterns of two 1-DOF cylinder oscillators, undergoing VIV and galloping, are investigated in a free surface water channel for 3.2 × 104 ≤ Re ≤ 1.2 × 10 5 . The cylinders are arranged in tandem and supported by springs for a range of different spring stiffness and damping parameters. The efficiency of a current energy converter (CEC), based on flow-induced oscillation (FIO) of multiple cylinders in tandem, is critically related to the cylinder oscillation patterns. Due to the limited number of studies on the FIO for multiple cylindrical or prismatic bodies, their oscillation patterns have neither been identified nor classified. The surfaces of the cylinders are modified by turbulence stimulation to enhance FIO. Three different center-to-center spacing, five stiffness, and six damping ratios, for a total of 90 sets of experiments were conducted. The current velocity range is from 0.34 m/s to 1.32 m/s. From more than 2000 tests, five major patterns, nine sub-patterns are identified and classified. The patterns are defined based on the amplitudes, frequencies and the phase angle differences between the two cylinders. The characteristics and mechanics of each oscillation pattern are explored and explained from the perspective of fluid-structure interaction (FSI). By systematically varying the parameters, the underlying hydrodynamic mechanisms, including the coupling level between vortices and cylinders, the shielding effect, the wake effect, and the stability states are revealed. Few preliminary observations on the connection between oscillation pattern and harnessed power by the tandem cylinders are reported.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Yuan, Wenyong, Sun, Hai, Li, Hui, Bernitsas, Michael M.. 2020-10-30. Flow-induced oscillation patterns for two tandem cylinders with turbulence stimulation and variable stiffness and damping. https://doi.org/10.1016/j.oceaneng.2020.108237

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↗