相关实验视频

Updated: May 28, 2025

A Microfluidic Model of Biomimetically Breathing Pulmonary Acinar Airways

A Microfluidic Model of Biomimetically Breathing Pulmonary Acinar Airways

Published on: May 9, 2016

ANFIS算法用于绘制水库同质化与气泡流动的计算数据

Lioua Kolsi¹, Iman Behroyan², Moustafa S Darweesh³

¹Department of Mechanical Engineering, College of Engineering, University of Ha'il, Ha'il City, 81451, Saudi Arabia.

Scientific reports

|February 12, 2025

概括

这项研究开发了人工智能 (AI) 相关性,以预测泡柱反应堆中的空气旋转,简化了复杂的流体动力学模拟. 人工智能模型显示出高精度,可与计算流体动力学 (CFD) 相比.

科学领域:

化学工程是化学工程的重要组成部分.
流体动力学流体动力学
人工智能的人工智能

背景情况:

气泡列反应器 (BCR) 对于液体同质化和混合至关重要.
传统的BCR计算流体动力学 (CFD) 模拟是复杂且耗时的.
精确预测空气旋对于优化BCR性能至关重要.

研究的目的:

开发一种简化方法,用于预测3D气泡柱反应堆中的空气旋流.
利用自适应网络和模糊推理系统 (ANFIS) 调查人工智能 (AI) 方法的准确性和效率.
为了建立空气旋预测的相关性,可以取代CFD模拟.

主要方法:

使用CFD模拟了一个装满水的3D气泡柱反应堆.
使用了一种人工智能算法,特别是具有高斯成员函数的ANFIS.
人工智能模型被训练使用空气速度,压力和方向数据 (x,z) 作为输入,空气旋作为输出. 在培训期间,会员职能和输入参数的数量发生了变化.

主要成果:

人工智能模型的准确性随着更多的会员功能和输入参数的增加而增加.
开发的AI模型实现了高精度,结果与CFD模拟非常一致 (回归系数接近1).
该研究确定了最佳的AI参数:五个成员函数和四个输入变量 (空气速度,压力,x和z方向).

结论:

基于AI开发的相关性可以准确预测气泡柱反应堆中的空气旋.
这种由人工智能驱动的方法为传统的CFD模拟提供了更简单,更快的替代方案.
这项研究为BCRs中的空气旋预测提供了新的相关性,解决了现有文献中的差距.

关键词:

安菲斯 (ANFIS) 是一个字母.人工智能的人工智能是人工智能.气泡柱反应堆中的气泡柱反应堆.在 CFD 交易中,我们可以看到 CFD.

更多相关视频

Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics

Fabrication, Operation and Flow Visualization in Surface-acoustic-wave-driven Acoustic-counterflow Microfluidics

Published on: August 27, 2013

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Determining 3D Flow Fields via Multi-camera Light Field Imaging

Published on: March 6, 2013

相关概念视频

Plane Potential Flows

Plane Potential Flows

Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...

Rapidly Varying Flow

Rapidly Varying Flow

Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...

Typical Model Studies

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.

Uniform Depth Channel Flow: Problem Solving

Uniform Depth Channel Flow: Problem Solving

To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...