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相关概念视频

Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

112
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
112
Unsymmetric Loading of Thin-Walled Members01:23

Unsymmetric Loading of Thin-Walled Members

116
Thin-walled members with non-symmetrical cross-sections are vital to engineering structures, offering material efficiency and structural integrity. However, unsymmetrical loading on these members leads to complex stress distributions, resulting in simultaneous bending and twisting can cause deformation or structural failure. The interaction between bending and twisting requires detailed analysis to ensure structural resilience.
The concept of the shear center is crucial in countering the...
116
Thin-Walled Hollow Shafts01:15

Thin-Walled Hollow Shafts

195
In analyzing a thin-walled hollow shaft subjected to torsional loading, a segment with width dx is isolated for examination. Despite its equilibrium state, this segment faces torsional shearing forces at its ends. These forces are quantitatively described by the product of the longitudinal shearing stress on the segment's minor surface and the area of this surface, leading to the concept of shear flow. This shear flow is consistent throughout the structure, indicating a uniform distribution...
195
Internal Loadings in Structural Members: Problem Solving01:28

Internal Loadings in Structural Members: Problem Solving

1.3K
When designing or analyzing a structural member, it is important to consider the internal loadings developed within the member. These internal loadings include normal force, shear force, and bending moment. Engineers can ensure that the structural member can support the applied external forces by calculating these internal loadings.
To illustrate this, let's consider a beam OC of 5 kN, inclined at an angle of 53.13° with the horizontal and supported at both ends. Determine the internal...
1.3K
Eccentric Axial Loading in a Plane of Symmetry01:16

Eccentric Axial Loading in a Plane of Symmetry

202
Eccentric axial loading occurs when an axial load is applied away from the centroidal axis of a structural member. This scenario is common in engineering, where structural elements may not be directly aligned due to various design or functional requirements.
202
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

57
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...
57

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相关实验视频

Updated: Jul 12, 2025

Surrogate Model Development for Digital Experiments in Welding
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用神经代用模型和遗传算法对薄壁复合轴对称结构进行多目标优化.

Bartosz Miller1, Leonard Ziemiański1

  • 1Faculty of Civil and Environmental Engineering and Architecture, Rzeszow University of Technology, Al. Powstancow Warszawy 12, 35-959 Rzeszow, Poland.

Materials (Basel, Switzerland)
|October 28, 2023
PubMed
概括

本研究使用神经网络替代模型来有效优化复合外,整合模式形状识别和网络合集,以提高复杂工程设计的准确性和可靠性.

科学领域:

  • 材料科学与工程 材料科学与工程
  • 计算力学 计算力学 计算力学

背景情况:

  • 复合材料外提供优良的强度与重量比,但需要精确的参数优化.
  • 像遗传算法这样的随机优化方法是计算密集的.
  • 使用神经网络的替代模型可以有效地近似复杂的功能.

研究的目的:

  • 调查神经网络代用模型的使用,用于复合外的多目标优化.
  • 通过模式形状识别和网络合集来提高优化流程的准确性和可靠性.
  • 评估拟议方法的计算效率和有效性.

主要方法:

  • 深度神经网络被用作替代模型来近似输入参数-目标函数关系.
  • 模式形状识别被纳入,以提高多标准优化的准确性.
  • 网络组合被用来增强模型的稳定性和可靠性.
  • 效率分析将计算成本与传统方法 (如蒙特卡洛模拟) 进行比较.

主要成果:

  • 通过模式形状识别和网络合集增强的代用模型方法,证明了高准确性和可靠性.
  • 该方法在处理复杂的输入参数和复杂的设计方面被证明是有效的.
  • 在计算成本和准确性之间实现了有利的权衡.
关键词:
人工神经网络的人工神经网络这是一个复合材料.遗传算法 遗传算法优化的优化优化优化.贝的外是一个贝,贝的外是一个贝.代孕模型的代孕模型

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结论:

  • 网络集成作为替代模型和模式形状识别的集成显著提高了复合的多目标优化.
  • 这种高效准确的方法对先进的工程设计和优化方法有广泛的影响.