Analyzing thermal buckling in curvilinearly stiffened composite plates with arbitrary shaped cutouts using isogeometric level set method

Devarajan, Balakrishnan

doi:10.1016/j.ast.2022.107350

Cited by 30 publications

(6 citation statements)

References 43 publications

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“…As part of future developments we will try to look for even more state-of-the-art constraint handling procedures with higher efficacy across a discrete feasible space. Consequently, we are also researching in the use of this constrained optimization algorithm for solving mechanical systems problems like thermal buckling load [99] utilising isogeometric finite element analysis of stiffened laminated composite plates [100]. Also, we would like to investigate the performance of the algorithm across a wide range of transformation operations like bias, f 0.0000e+00 4.2089e+03 -1.7989e+04 -9.9986e-01 -3.6466e+03 9.9790e-01 6.9962e-01 -1.0000e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Median f 0.0000e+00 6.2822e+03 -8.7264e+03 -9.9262e-01 -3.2793e+03 1.6035e+00 1.1227e+00 -2.2630e-01 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 4.4616e-01 0.0000e+00 Mean f 5.1768e+00 6.1573e+03 -9.5337e+03 -9.9042e-01 -3.2717e+03 1.5354e+00 1.3067e+00 -4.1083e-01 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.2211e-02 6.1918e-01 0.0000e+00 Worst f 4.1790e+01 6.6728e+03 -3.8688e+03 -9.6503e-01 -2.7061e+03 2.2681e+00 2.3718e+00 6.1899e-02 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 7.2799e-02 1.8102e+00 0.0000e+00 Std f 1.1940e+01 5.6421e+02 3.8138e+03 8.8730e-03 2.1472e+02 4.1420e-01 4.7718e-01 4.5155e-01 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 1.8026e-02 6.5119e-01 0.0000e+00 FR c v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Median f 7.1534e-01 1.0000e-01 5.5000e+00 2.9248e+00 2.2311e+04 4.3727e+04 2.8338e+03 1.1756e-02 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Mean f 7.4605e-01 1.0001e-01 5.1650e+00 2.5176e+00 2.2333e+04 4.4398e+04 2.7687e+03 1.5230e-02 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Worst f 1.0194e+00 1.0001e-01 2.5359e+01 2.9248e+00 2.2474e+04 6.3315e+04 2.9117e+03 9.3064e-02 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Std f 1.5561e-01 1.3136e-05 1.0870e+01 5.3610e-01 4.5170e+01 9.7984e+03 1.2283e+02 1.6230e-02 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 FR c f 2.5000e-03 1.5902e+01 4.4079e-02 0.0000e+00 1.2498e-01 5.2325e-01 0.0000e+00 0.0000e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Median f 2.5000e-03 6.9090e+02 2.7365e-01 0.0000e+00 1.2555e-01 5.2883e-01 1.9380e-01 3.7666e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Mean f 2.5000e-03 7.9146e+02 3.1193e-01 0.0000e+00 1.2951e-01 5.3127e-01 1.2245e+00 3.4398e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Worst f 2.5000e-03 1.8843e+03 6.0141e-01 0.0000e+00 1.8938e-01 5.4981e-01 7.4791e+00 4.3945e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Std f 4.4262e-19 5.5281e+02 1.6515e-01 0.0000e+00 1.2938e-02 6.5552e-03 2.1986e+00 1.1048e+00 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 FR c f -3.5453e+18 2.4811e+01 1.2123e+02 4.9896e+03 1.1493e+06 3.5975e-04 0.0000e+00 -3.2217e+04 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Median f -4.5838e+13 5.9894e+01 3.4911e+02 5.4819e+03 1.6227e+06 1.1425e-03 4.3450e-19 -3.2217e+04 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Mean f -1.5007e+17 5.8173e+01 3.3100e+02 5.9970e+03 1.6213e+06 1.3582e-03 7.8479e-11 -3.2217e+04 v 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 0.0000e+00 Worst f -1.6765e+11 9.6575e+01 4.5297e+02 1.4182e+04 2.1506e+06 6.0318e-03 1.5603e-09 -3.2217e+04 v 0.0000e+00 0.0000e+00 0....…”

Section: Discussionmentioning

confidence: 99%

“…) 3) Selection: Once the offspring is generated, the final step is to perform a greedy based selection as to whether the parent or the offspring solution should survive till the next generation. Most of the times, this decision is taken based on the objective function value of the parent and offspring also known as fitness value given by Eq (5).…”

Section: A Differential Evolution (De) Algorithmmentioning

confidence: 99%

“…In particular, we have observed widespread use of AGSK in transportation problems [88], [89], path planning [90], knapsack problems [91], [92], electrochemical systems such as photo-voltaic cells [93], [94], engineering problems like fault diagnostics in power systems [95], as well as adoption in machine learning [96] and RL techniques [97]. As a result, it is reasonable to claim that constrained evolutionary algorithms offer enormous potential in the application of many engineering design challenges [1], [98]- [100].…”

Section: Applicationsmentioning

confidence: 99%

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Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

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Real-world optimization problems are often governed by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Optimization Problems (COPs) fueled by advances in computational intelligence. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose an ensemble of multi- method hybrid EA framework with four mutation operators, two crossover operators, multi-search [Differential Evolution (DE) & Gaining Sharing Knowledge (GSK)] optimization algorithm, and ensemble of constraint handling techniques to solve global real- world constrained optimization problem. The proposed frame- work FEPEA has an ascendancy of multiple adaptation strategies concerning the control parameters, search mechanisms, two sub-populations as well as uses knowledge sharing mechanism between junior and senior phases. The algorithm also combines the power of four popular constraint handling techniques (CHT) and uses a voting mechanism to select any particular CHT. On top of that, this algorithm also uses both linear and non- linear population size reduction in every step of the evolutionary process. We test our method on 57 real-world problems provided as part of the CEC 2020 special session & competition on real- world constrained optimization benchmark suite. Experimental results indicate that FEPEA is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: A Differential Evolution (De) Algorithmmentioning

confidence: 99%

Section: Applicationsmentioning

confidence: 99%

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Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Saha¹,

De²

2022

Preprint

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show abstract

“…2) DE Control Parameters: The control parameter adaption is enlivened from the success history-based hyperparameter adaptation [25], [46], [48]. It keeps a memory archive of the effective DE hyperparameters, to be specific, scaling factor F in Eq (5) and crossover rate Cr in Eq (6). The memory archives M F and M Cr are initialized to values F 0 and Cr 0 , respectively, i.e., M F,i = F 0 , i = 1, .…”

Section: A Mode Algorithmmentioning

confidence: 99%

CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

Saha¹,

Sallam²,

De³

et al. 2022

Preprint

View full text Add to dashboard Cite

Real-world optimization problems are often gov- erned by one or more constraints. Over the last few decades, extensive research has been performed in Constrained Opti- mization Problems (COPs) fueled by advances in computational power. In particular, Evolutionary Algorithms (EAs) are a preferred tool for practitioners for solving these COPs within practicable time limits. We propose a novel hybrid Evolutionary Algorithm based on the Differential Evolution algorithm and Adaptive Parameter Gaining Sharing Knowledge-based algo- rithm to solve global real-world constrained parameter space. The proposed CHAGSKODE algorithm leverages the power of multiple adaptation strategies concerning the control parameters, search mechanisms, as well as uses knowledge sharing between junior and senior phases. We test our method on the benchmark functions taken from the CEC2020 special session & competition on real-world constrained optimization. Experimental results indicate that CHAGSKODE is able to achieve state-of-the- art performance on real-world constrained global optimization when compared against other well-known real-world constrained optimizers.

show abstract

“…Khorasani et al 8 carried out the vibration analysis of the GPLs reinforced sandwich plate by employing Hamilton's principle and Navier's scheme. Devarajan and Kapania [9][10][11] carried out an isogeometric finite element analysis of stiffened laminated composite plates, employing the non-uniform rational B-splines (NURBS) method. Moreover, the application of ultrasonic guided waves generated by piezoelectric smart transducers has been observed in the field of structural health monitoring, particularly in the context of the aerospace sector.…”

Section: Introductionmentioning

confidence: 99%

Non‐linear thermoelastic vibration and optimum frequency prediction of sandwich composite corrugated panels with honeycomb auxetic core

Kumar,

Rathore,

Kar

et al. 2023

Polymer Composites

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In this article, the non‐linear free vibration behavior of sandwich composite panels with sinusoidal corrugations is examined. These panels consist of a top and bottom skin made of a graphene‐reinforced composite, with an auxetic honeycomb core having negative Poisson's ratio. The non‐linear kinematic field is based on Kant's higher‐order shear‐deformation theory and the Green‐strain, which incorporates all the higher‐order terms. By implementing the Hamilton principle, the non‐linear governing equations are affirmed and solved further through 2D‐isoparametric finite element approximations via Lagrangian elements in conjunction with Picard's successive iterative scheme. The comparison and validation tests demonstrate the correctness of the present model. The established sandwich model is further employed for various parametric conditions by varying the core‐to‐face sheet thickness, auxetic honeycomb properties, graphene volume fraction, shell configurations, temperature, and support conditions. These numerous results exemplify the non‐linear frequencies at small‐to‐large amplitudes for the sandwich shell structures and are discussed in detail. In addition, a multi‐objective parametric optimization is conducted using the response surface method to investigate the optimal values of the corrugated auxetic honeycomb shell panel's parameters, resulting maximum frequency.Highlights Non‐linear vibration of corrugated sandwich shell panel. Graphene reinforced skins and honeycomb auxetic core. Kant's higher‐order shear‐deformation theory and Green‐Lagrange non‐linearity. Hamilton principle and Picard's successive iterative scheme. Multi‐objective parametric optimization and prediction using response surface methodology.

show abstract

Analyzing thermal buckling in curvilinearly stiffened composite plates with arbitrary shaped cutouts using isogeometric level set method

Cited by 30 publications

References 43 publications

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

Framework of Ensemble Parmeter Adapted Evolutionary Algorithm for Solving Constrained Optimization Problems

CHAGSKODE Algorithm for Solving Real World Constrained Optimization Problems

Non‐linear thermoelastic vibration and optimum frequency prediction of sandwich composite corrugated panels with honeycomb auxetic core

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