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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.
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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.
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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...
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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.
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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.
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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.
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Multi-Objective Optimization of Thin-Walled Composite Axisymmetric Structures Using Neural Surrogate Models and

Bartosz Miller1, Leonard Ziemiański1

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

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Summary

This study uses neural network surrogate models to efficiently optimize composite shells, integrating mode shape identification and network ensembles for enhanced accuracy and reliability in complex engineering designs.

Keywords:
artificial neural networkscompositegenetic algorithmsoptimizationshellsurrogate model

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Area of Science:

  • Materials Science and Engineering
  • Computational Mechanics

Background:

  • Composite shells offer excellent strength-to-weight ratios but require precise parameter optimization.
  • Stochastic optimization methods like genetic algorithms are computationally intensive.
  • Surrogate models using neural networks can approximate complex functions efficiently.

Purpose of the Study:

  • To investigate the use of neural network surrogate models for multi-objective optimization of composite shells.
  • To enhance the accuracy and reliability of optimization processes through mode shape identification and network ensembles.
  • To evaluate the computational efficiency and efficacy of the proposed methodology.

Main Methods:

  • Deep neural networks were employed as surrogate models to approximate input parameter-objective function relationships.
  • Mode shape identification was incorporated to improve accuracy in multi-criteria optimization.
  • Network ensembles were utilized to enhance model robustness and reliability.
  • Efficiency analysis compared computational costs against traditional methods like Monte Carlo simulations.

Main Results:

  • The surrogate model approach, enhanced by mode shape identification and network ensembles, demonstrated high accuracy and reliability.
  • The methodology proved efficient in handling complex input parameters and intricate designs.
  • A favorable trade-off between computational cost and accuracy was achieved.

Conclusions:

  • The integration of network ensembles as surrogate models and mode shape identification significantly enhances multi-objective optimization of composite shells.
  • This efficient and accurate approach has broad implications for advanced engineering design and optimization methodologies.