Enhancing energy capacity of the car's hood door under excitation frequency via functionally graded triply periodic minimal surface material: Application of machine learning and mathematical simulation

This study investigates the nonlinear dynamic response and vibration characteristics of a car's hood door, focusing on its energy capacity under various excitation frequencies. The hood door is modeled using a functionally graded triply periodic minimal surface (FG-TPMS) material, which offers...

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Vydáno v:Mechanics of advanced materials and structures Ročník 32; číslo 16; s. 3972 - 4000
Hlavní autoři: Long, Feng, Jiang, Lijian, Yang, Xiaohua, A. El-Meligy, Mohammed, Saleem, Kashif
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Taylor & Francis 18.08.2025
Taylor & Francis Ltd
Témata:
car's hood door
Design optimization
Dynamic loads
Dynamic response
Energy absorption
Energy capacity
Equations of motion
Excitation
FG-TPMS material
Functionally gradient materials
Geometric nonlinearity
hybrid machine learning algorithm
Machine learning
Mechanical properties
Minimal surfaces
Nonlinear dynamics
Nonlinear response
Numerical methods
Numerical models
Runge-Kutta method
Shear deformation
Time integration
Vehicle safety
Vibration control
ISSN:1537-6494, 1537-6532
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Shrnutí:This study investigates the nonlinear dynamic response and vibration characteristics of a car's hood door, focusing on its energy capacity under various excitation frequencies. The hood door is modeled using a functionally graded triply periodic minimal surface (FG-TPMS) material, which offers superior mechanical properties and lightweight design. The analysis is conducted using a higher-order shear deformation theory (HSDT), which accounts for shear deformations more accurately than classical theories. The incorporation of von Karman nonlinear terms captures the geometric nonlinearity due to large deformations, providing a realistic simulation of the hood door's behavior under dynamic loads. To solve the complex equations of motion derived from the HSDT and von Karman terms, a fourth-order Runge-Kutta method is employed. This numerical method ensures accurate time integration and stability of the solution. The dynamic response is further analyzed to determine the energy absorption capacity of the hood door material, crucial for safety and durability in automotive applications. Validation of the numerical model is performed using previous published articles and a hybrid machine learning algorithm, which combines data-driven approaches with traditional physics-based models. This hybrid validation enhances the accuracy and reliability of the predicted responses, ensuring that the proposed model can be effectively used in the design and optimization of car components. The results demonstrate the potential of FG-TPMS materials in automotive applications, offering improved energy absorption and vibration control, which are essential for enhancing vehicle safety and performance.
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ISSN:1537-6494
1537-6532
DOI:10.1080/15376494.2024.2399323