Isogeometric shape optimization of an acoustic horn using the teaching-learning-based optimization (TLBO) algorithm

Ummidivarapu, Vinay K.; Voruganti, Hari K.; Khajah, Tahsin; Bordas, Stéphane P.A.

doi:10.1016/j.cagd.2020.101881

Cited by 22 publications

(5 citation statements)

References 49 publications

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“…So far, shape optimisations based on the IGBEM have been investigated in terms of potential problems (or steady-state heat problems) [11][12][13][14][15], elastostatic problems [16][17][18][19][20][21], including 2D thermoelastic problem [22], and acoustic problems in concern [23][24][25][26][27][28][29]. In regard to 2D, Liu et al [23] performed a shape optimisation of a Γ-shaped sound barrier, where the direct differentiation method (DDM) was employed to compute the sensitivity of the objective function with respect to CPs.…”

Section: Background and Purposementioning

confidence: 99%

“…They derived the shape derivatives with the adjoint variable method (AVM). Ummidivarapou et al [25] introduced a teaching-learning-based optimisation algorithm, which is a gradient-free method, to design a acoustic horn. Similarly, Shaaban et al [26] performed a shape optimisation by exploiting the particle swarm optimisation (PSO) algorithm, which is gradient-free.…”

Section: Background and Purposementioning

confidence: 99%

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A shape optimisation with the isogeometric boundary element method and adjoint variable method for the three-dimensional Helmholtz equation

Takahashi¹,

Sato²,

Isakari³

et al. 2021

Preprint

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This paper presents a shape optimisation system to design the shape of an acousticallyhard object in the three-dimensional open space. Boundary element method (BEM) is suitable to analyse such an exterior field. However, the conventional BEM, which is based on piecewise polynomial shape and interpolation functions, can require many design variables because they are usually chosen as a part of the nodes of the underlying boundary element mesh. In addition, it is not easy for the conventional method to compute the gradient of the sound pressure on the surface, which is necessary to compute the shape derivative of our interest, of a given object. To overcome these issues, we employ the isogeometric boundary element method (IGBEM), which was developed in our previous work. With using the IGBEM, we can design the shape of surfaces through control points of the NURBS surfaces of the target object. We integrate the IGBEM with the nonlinear programming software through the adjoint variable method (AVM), where the resulting adjoint boundary value problem can be also solved by the IGBEM with a slight modification. The numerical verification and demonstration validate our shape optimisation framework.

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Section: Background and Purposementioning

confidence: 99%

Section: Background and Purposementioning

confidence: 99%

A shape optimisation with the isogeometric boundary element method and adjoint variable method for the three-dimensional Helmholtz equation

Takahashi¹,

Sato²,

Isakari³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Anomaly detection is also classified based on the input utilized, which includes sequential data, unstructured texts, and graphs. The graph-based anomaly detection is exploited nowadays to explore the hidden graph data, suspicious nodes, etc., [4].…”

Section: Introductionmentioning

confidence: 99%

Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Muppudathi¹,

Krishnasamy²

2023

Intelligent Automation &Amp; Soft Computing

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Social Networking Sites (SNSs) are nowadays utilized by the whole world to share ideas, images, and valuable contents by means of a post to reach a group of users. The use of SNS often inflicts the physical and the mental health of the people. Nowadays, researchers often focus on identifying the illegal behaviors in the SNS to reduce its negative influence. The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide annotated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy. To overcome these issues, the proposed methodology utilizes a Modified Convolutional Neural Network (MCNN) using stochastic pooling and a Leaky Rectified Linear Unit (LReLU). Here, each word in the social media text is analyzed based on its meaning. The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting. The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions. It also doesn't stop the learning process when the values are negative. The MCNN computes a specified score value using a novel integrated anomaly detection technique. Based on the score value, the anomalies are identified. A Teaching Learning based Optimization (TLBO) algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered. In this way, the performance of the model is enhanced in terms of classification accuracy. The efficiency of the proposed technique is compared with the state-of-art techniques in terms of accuracy, sensitivity, specificity, recall, and precision. The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%, 95.45%, and 97.55% for the three social media datasets namely Facebook, Twitter, and Reddit respectively.

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“…The analysis model is used directly, where the coordinates of the surface nodes are the design parameters. Isogeometric parametrization (Ummidivarapu and Voruganti 2017;Ummidivarapu et al 2020) is a good alternative to the Vertex Morphing. Both methods have similarities, and the difference is typically in the number of design variables.…”

Section: Introductionmentioning

confidence: 99%

Relaxed gradient projection algorithm for constrained node-based shape optimization

Antonau¹,

Hojjat

Bletzinger³

2021

Struct Multidisc Optim

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In node-based shape optimization, there are a vast amount of design parameters, and the objectives, as well as the physical constraints, are non-linear in state and design. Robust optimization algorithms are required. The methods of feasible directions are widely used in practical optimization problems and know to be quite robust. A subclass of these methods is the gradient projection method. It is an active-set method, it can be used with equality and non-equality constraints, and it has gained significant popularity for its intuitive implementation. One significant issue around efficiency is that the algorithm may suffer from zigzagging behavior while it follows non-linear design boundaries. In this work, we propose a modification to Rosen’s gradient projection algorithm. It includes the efficient techniques to damp the zigzagging behavior of the original algorithm while following the non-linear design boundaries, thus improving the performance of the method.

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Isogeometric shape optimization of an acoustic horn using the teaching-learning-based optimization (TLBO) algorithm

Cited by 22 publications

References 49 publications

A shape optimisation with the isogeometric boundary element method and adjoint variable method for the three-dimensional Helmholtz equation

A shape optimisation with the isogeometric boundary element method and adjoint variable method for the three-dimensional Helmholtz equation

Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Relaxed gradient projection algorithm for constrained node-based shape optimization

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