Hammerstein–Wiener based reduced-order model for vortex-induced non-linear fluid–structure interaction

Gallardo, Daniele; Sahni, Onkar; Bevilacqua, Riccardo

doi:10.1007/s00366-016-0467-9

Cited by 5 publications

(4 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Understanding characteristics of the flow regime around a bluff body and resultant forces are of importance in various engineering applications where fluid-structure interaction has to be considered such as wind engineering and offshore engineering. Extensive studies have been performed on analysing the unsteady wake around circular and square cylinders, through numerical modelling (Braza et al 1986;Gallardo et al 2017;Meliga et al 2016;Norberg 1993;Palei and Seifert 2007;Park et al 1998;Sohankar et al 1998b;Swaddiwudhipong et al 2007;Travin et al 2000;Baldock at al. 2014) and laboratory experiments (Bearman and Zdravkovich 1978;Norberg 1994;Sumner and Akosile 2003;Williamson 1989;Wood et al 2016;Zdravkovich 2003).…”

Section: Introductionmentioning

confidence: 99%

Modelling of flow around hexagonal and textured cylinders

Karampour

Lefebure

et al. 2018

Proceedings of the Institution of Civil Engineers - Engineering

View full text Add to dashboard Cite

The flow regime around a hexagonal polygon with low Reynolds numbers Re<200 is numerically investigated in two different orientations namely face-oriented and corneroriented. The basic flow characteristics, including drag coefficient, lift coefficient, Strouhal number and critical Reynolds number of the hexagonal cylinders, are calculated using 2D transient numerical analysis. Within the studied range of Re, the predicted lift coefficient and Strouhal number of the face-oriented hexagon were higher than those of the corneroriented hexagon. In contrast, the predicted drag coefficient and critical Reynolds number of the corner-oriented hexagon were greater than those of the face-oriented. Flow characteristics of a novel textured geometry are also studied using 3D transient analysis. The Strouhal number St of the textured geometry was found to be in between the St of both the hexagonal cylinders, and its lift coefficient is lower than that of the hexagonal cylinders.

show abstract

Section: Introductionmentioning

confidence: 99%

Modelling of flow around hexagonal and textured cylinders

Karampour

Lefebure

et al. 2018

Proceedings of the Institution of Civil Engineers - Engineering

View full text Add to dashboard Cite

show abstract

“…SPLm is the obtained SPL at each fundamental frequency center in dB; re 1 lPa, SPL1 reduces SPL to 1 Hz bandwidth in dB; re one lPa, and Df represents the bandwidth for each 1/3 Octave band filter. Wiener filter has been used to minimize the square mean error (MSE) between ambient noise and marine mammal noise [8].…”

Section: The Ambient Noise Reduction and Reverberation Suppressionmentioning

confidence: 99%

Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network

Saffari

Khishe

Zahiri

2022

Analog Integr Circ Sig Process

View full text Add to dashboard Cite

Chimp optimization algorithm (ChOA) is a robust nature-inspired technique, which was recently proposed for addressing real-world challenging engineering problems. Due to the novelty of the ChOA, there is room for its improvement. Recognition and classification of marine mammals using artificial neural networks (ANNs) are high-dimensional challenging problems. In order to address this problem, this paper proposed the using of ChOA as ANN's trainer. However, evolving ANNs using metaheuristic algorithms suffers from high complexity and processing time. In order to address this shortcoming, this paper proposes the fuzzy logic to adjust the ChOA's control parameters (Fuzzy-ChOA) for tuning the relationship between exploration and exploitation phases. In this regard, we collect underwater marine mammals sounds and then produce an experimental dataset. After pre-processing and feature extraction, the ANN is used as a classifier. Besides, for having a fair comparison, we used a benchmark audio database of marine mammals. The comparison algorithms include ChOA, coronavirus optimization algorithm, harris hawks optimization, black widow optimization algorithm, Kalman filter benchmark algorithms, and also comparative benchmarks include convergence speed, local optimal avoidance ability, classification rate, and receiver operating characteristics (ROC). The simulation results show that the proposed fuzzy model can tune the boundary between the exploration and extraction phases. The convergence curve and ROC confirm that the convergence rate and performance of the designed recognizer are better than benchmark algorithms.

show abstract

“…SPLm is the obtained SPL at each fundamental frequency center in dB; re 1 μ Pa, SPL1 is the SPL reduced to 1 Hz bandwidth in dB; re 1 μ Pa, and Δ f represents the bandwidth for each 1/3 Octave band filter. To reduce the square mean error (MSE) between ambient noise and marine mammal noise, a Wiener filter was utilized [ 54 ]. Following that, the results were computed using ( 3 ) to identify sounds with a low SNR, less than 3 dB, that should be eliminated from the database.…”

Section: The Experiments Design and Data Acquisitionmentioning

confidence: 99%

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Takhti

Saffari²,

Martín

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The existence of various sounds from different natural and unnatural sources in the deep sea has caused the classification and identification of marine mammals intending to identify different endangered species to become one of the topics of interest for researchers and activist fields. In this paper, first, an experimental data set was created using a designed scenario. The whale optimization algorithm (WOA) is then used to train the multilayer perceptron neural network (MLP-NN). However, due to the large size of the data, the algorithm has not determined a clear boundary between the exploration and extraction phases. Next, to support this shortcoming, the fuzzy inference is used as a new approach to developing and upgrading WOA called FWOA. Fuzzy inference by setting FWOA control parameters can well define the boundary between the two phases of exploration and extraction. To measure the performance of the designed categorizer, in addition to using it to categorize benchmark datasets, five benchmarking algorithms CVOA, WOA, ChOA, BWO, and PGO were also used for MLPNN training. The measured criteria are concurrency speed, ability to avoid local optimization, and the classification rate. The simulation results on the obtained data set showed that, respectively, the classification rate in MLPFWOA, MLP-CVOA, MLP-WOA, MLP-ChOA, MLP-BWO, and MLP-PGO classifiers is equal to 94.98, 92.80, 91.34, 90.24, 89.04, and 88.10. As a result, MLP-FWOA performed better than other algorithms.

show abstract

Hammerstein–Wiener based reduced-order model for vortex-induced non-linear fluid–structure interaction

Cited by 5 publications

References 33 publications

Modelling of flow around hexagonal and textured cylinders

Modelling of flow around hexagonal and textured cylinders

Fuzzy-ChOA: an improved chimp optimization algorithm for marine mammal classification using artificial neural network

Classification of Marine Mammals Using the Trained Multilayer Perceptron Neural Network with the Whale Algorithm Developed with the Fuzzy System

Contact Info

Product

Resources

About