相关实验视频
Updated: May 21, 2025

04:58
A Rapid Method for Modeling a Variable Cycle Engine
Published on: August 13, 2019
7.5K
提升随机森林模型预测内部草稿空运环接触器中的气体滞留情况,使用基因算法调整和解释性
Hardik Prabhu1, Charan M Ravishankar2, Abhishek Ganesan2
1School of Computing and Data Sciences, FLAME University, Pune, 412115, India.
Scientific reports
|March 19, 2025
概括
本研究使用数据驱动模型来预测内部环空运接触器 (ILAC) 中的气体滞留,从而提高设计精度. 用遗传算法优化的一种随机森林模型实现了高预测准确性,超过了传统方法.
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
- 工艺系统工程 工艺系统工程
背景情况:
- 气体保持是内部环空运接触器 (ILAC) 中的关键水力动力学参数,显著影响其性能.
- 准确预测气体留对于有效的ILAC设计至关重要,但现有的现象学模型往往缺乏可靠性.
- 经验相关性不充分,需要探索先进的建模技术来预测气体阻滞.
研究的目的:
- 开发一个改进的数据驱动模型来预测ILAC中的气体滞留.
- 为了提高气体保持预测的准确性,超出传统经验相关性的能力.
- 通过模型可解释性,了解影响气体保持的关键因素.
主要方法:
- 从现有文献中整合了324个数据点,这些数据点是关于内部设计的空桥接触器.
- 应用一个随机森林 (RF) 机器学习模型,使用遗传算法 (GA) 进行优化.
- 使用SHapley添加式解释 (SHAP) 进行解释,以识别有影响力的输入特征.
主要成果:
- 优化的射频模型实现了0.9542.2的高确定系数 (R2).
- 该模型显示了0.0059的低平均绝对误差 (MAE),表明了精确的预测.
- SHAP分析提供了关于几何,流体性质和操作条件特征相对重要性的见解.
结论:
- 数据驱动的建模,特别是GA优化的射频模型,与传统方法相比,在ILAC中为气体保留提供了更高的预测准确性.
- 开发的模型为ILAC设计提供了可靠的工具,从而提高了设备性能.
- 模型的可解释性提高了对ILACs中的水力动力控制的理解.
相关概念视频
Fast Decoupled and DC Powerflow
144
The fast decoupled power flow method addresses contingencies in power system operations, such as generator outages or transmission line failures. This method provides quick power flow solutions, essential for real-time system adjustments. Fast decoupled power flow algorithms simplify the Jacobian matrix by neglecting certain elements, leading to two sets of decoupled equations:
144
Survival Tree
48
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
Building a Survival Tree
Constructing a...
Building a Survival Tree
Constructing a...
48
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
37
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
37
Typical Model Studies
211
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.
211
General External Flow Characteristics
58
The study of external flow is essential for creating structures and objects that interact efficiently and safely with moving fluids, such as air or water. When a body is immersed in a flowing fluid, it experiences two primary forces: drag, which opposes motion along the flow direction, and lift, which acts perpendicular to the flow. The shape, size, and orientation of the object influence these forces.Streamlined and Blunt Bodies in External FlowObjects in fluid flow are classified as...
58

