Mean compressive stress constitutive equation and crashworthiness optimization design of three novel honeycombs under axial compression

He, Qing; Ma, Dawei; Zhang, Zhendong; Yao, Lin

doi:10.1016/j.ijmecsci.2015.05.021

Cited by 39 publications

(10 citation statements)

References 43 publications

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“…Because of its ease of implementation and fast searching speed, the PSO algorithm has been widely used in various engineering applications [54,[56][57][58] including several studies that utilized PSO in honeycomb sandwich composite investigations [41,[59][60][61][62][63]. However, it is surprising that these studies did not consider all the honeycomb core's physical parameters [core thickness (D), cell wall angle (φ), cell wall thickness (t) and the cell wall length (a)] in their optimization problems, considering the impact that each of these parameters has on the performance of honeycomb cores.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

2021

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The necessity to diminish the heliostats’ cost so that central tower concentrating solar power (CSP) systems can stride to the forefront to become the technology of choice for generating renewable electricity is obliging the industry to consider innovative designs, leading to new materials being implemented into the development of heliostats. Honeycomb sandwich composites offer a lightweight but stiff structure that appear to be an ideal substitute for existing heliostat mirrors and their steel supporting trusses, avoiding large drive units and reducing energy consumption. However, realizing a honeycomb sandwich composite as a heliostat, among a multitude of possible combinations can be tailored from, that delivers the best trade-off between the panel’s weight reduction (broadly equates to cost) and structural integrity is cumbersome and challenging due to the complex nonlinear material behaviour, along with the large number of design variables and performance constraints. We herein offer a simulation–optimization model for behaviour prediction and structural optimization of lightweight honeycomb sandwich composite heliostats utilizing artificial neural network (ANN) technique and particle swarm optimization (PSO) algorithm. Considering various honeycomb core configurations and several loading conditions, a thorough investigation was carried out to optimally choose the training algorithm, number of neurons in the hidden layer, activation function in a network and the suitable swarm size that delivers the best performance for convergence and processing time. Carried out for three case scenarios, each with different design requirements, the results showed that the proposed integrated ANN-PSO approach provides a useful, flexible and time-efficient tool for heliostat designers to predict and optimize the structural performance of honeycomb sandwich composite-based heliostats as per desired requirements. Knowing that heliostats in the field are not all subjected to the same wind conditions, this method offers flexibility to tailor heliostats independently, allowing them to be made lighter depending on the local wind speed in the field. This could lead to reductions in the size of drive units used to track the heliostat, and the foundations required to support these structures. Such reductions would deliver real cost savings, which are currently an impediment to the wider spread use of CSP systems.

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Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

2021

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“…A reinforced honeycomb [Tiwari et al 2018, He et al 2015, Thomas et al 2019, Thomas et al 2018] is a core with reinforced layer in between each individual layer of conventional hexagonal honeycomb, see Fig 1 . Reinforced honeycomb core was modeled with varying cell size 7 mm, 10 mm, and 15 mm respectively. While cell wall thickness was taken as 0.1, 0.2, 0.3 and 0.4 mm respectively.…”

Section: Numerical Modelingmentioning

confidence: 99%

Performance evaluation of Reinforced honeycomb structure under blast load

Thomas¹,

Tiwari²

2021

JER is an international, peer-reviewed journal that publishes f

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In this study, numerical simulations were carried out to explore the response of the reinforced honeycomb sandwich structure with varying cell size (7, 10 and 15 mm), node length (25, 37.5 and 50 mm) and cell wall thickness (0.1, 0.2, 0.3 and 0.4 mm). The honeycomb structure made of aluminum alloy 8011 were subjected to low intensity blast loads by varying mass of TNT (10 g, 15 g, 20 g and 25 g) and standoff distance (200 mm, 250 mm and 300 mm). Commercial finite element code LS-DYNA was employed to carry out numerical simulations for both conventional and reinforced honeycomb sandwich structure keeping identical geometrical and blast load parameters. The deformation of the back face sheet was a major parameter to establish blast resistance of the core. Failure mechanisms of reinforced honeycomb was characterized as fully folded region, partially folded region, and clamped region. Reinforced honeycomb sandwich outperformed the conventional honeycomb sandwich structure of identical geometry parameters under similar blast loads. Increase in cell-wall thickness and node length enhanced the blast resistance whereas, increment in cell size reduced the blast resistance of the reinforced honeycomb sandwich structure.

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“…Approximate Model. This study uses the Latin hypercube test method [36] to obtain 70 sets of training data combined with the design variables and crash performance indicators of the vehicle finite element model (Table 3). On the basis of these data, this study establishes the Kriging approximation model [37]; in order to verify the prediction accuracy of the approximate model, 20 sample points were randomly selected in Table 3, and the accuracy of the approximate model of the established 100% frontal collision model was evaluated based on the results obtained from these sample points.…”

Section: Multiobjective Optimization Of Minibus Collisionmentioning

confidence: 99%

Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

Gao

Chen

2019

Mathematical Problems in Engineering

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In the optimization design process, particle swarm optimization (PSO) is limited by its slow convergence, low precision, and tendency to easily fall into the local extremum. These limitations make degradation inevitable in the evolution process and cause failure of finding the global optimum results. In this paper, based on chaos idea, the PSO algorithm is improved by adaptively adjusting parameters r1 and r2. The improved PSO is verified by four standard mathematical test functions. The results prove that the improved algorithm exhibits excellent convergence speed, global search ability, and stability in the optimization process, which jumps out of the local optimum and achieves global optimality due to the randomness, regularity, and ergodicity of chaotic thought. At last, the improved PSO algorithm is applied to vehicle crash research and is used to carry out the multiobjective optimization based on an approximate model. Compared with the results before the improvement, the improved PSO algorithm is remarkable in the collision index, which includes vehicle acceleration, critical position intrusion, and vehicle mass. In summary, the improved PSO algorithm has excellent optimization effects on vehicle collision.

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Mean compressive stress constitutive equation and crashworthiness optimization design of three novel honeycombs under axial compression

Cited by 39 publications

References 43 publications

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) Approach for Behaviour Prediction and Structural Optimization of Lightweight Sandwich Composite Heliostats

Performance evaluation of Reinforced honeycomb structure under blast load

Application of Improved Particle Swarm Optimization in Vehicle Crashworthiness

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