A Numerical Study on Shock Wave Characteristics for the Underwater Explosion

2012 ◽  
Vol 594-597 ◽  
pp. 2680-2683
Author(s):  
Wei Ju ◽  
Yi Liu ◽  
Jue Ding
Keyword(s):  
Shock Wave ◽  
Numerical Study ◽  
Bubble Formation ◽  
Underwater Explosion ◽  
Scientific Basis ◽  
Energy Output ◽  
Damage Effect ◽  
Variable Step Size ◽  
Step Size ◽  
Output Characteristics

Underwater explosion is very important for underwater weapons-design technology and research on the damage effect of target structure. In this paper, the flow-out boundary and variable step-size multi-material Euler algorithm were utilized to analyze numerically the whole process of shock wave generation and propagation, as well as bubble formation and impulse of underwater explosion. The computed results reveal the energy output characteristics of underwater explosion by TNT charge, which provide an important scientific basis for formulation design of charge and improvement of damage effects for underwater target.

scholarly journals An Improved Sparsity Adaptive Matching Pursuit Algorithm and Its Application in Shock Wave Testing

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jiahui Zhang ◽  
Xiao Wang ◽  
Mingchi Ju ◽  
Tailin Han ◽  
Yingzhi Wang
Keyword(s):  
Shock Wave ◽  
Matching Pursuit ◽  
Greedy Algorithms ◽  
Estimation Accuracy ◽  
Variable Step Size ◽  
Reconstruction Algorithms ◽  
Reconstruction Accuracy ◽  
Step Size ◽  
Candidate Set ◽  
Double Threshold

In the compressed sensing (CS) reconstruction algorithms, the problems of overestimation and large redundancy of candidate atoms will affect the reconstruction accuracy and probability of the algorithm when using Sparsity Adaptive Matching Pursuit (SAMP) algorithm. In this paper, we propose an improved SAMP algorithm based on a double threshold, candidate set reduction, and adaptive backtracking methods. The algorithm uses the double threshold variable step-size method to improve the accuracy of sparsity judgment and reduces the undetermined atomic candidate set in the small step stage to enhance the stability. At the same time, the sparsity estimation accuracy can be improved by combining with the backtracking method. We use a Gaussian sparse signal and a measured shock wave signal of the 15psi range sensor to verify the algorithm performance. The experimental results show that, compared with other iterative greedy algorithms, the overall stability of the DBCSAMP algorithm is the strongest. Compared with the SAMP algorithm, the estimated sparsity of the DBCSAMP algorithm is more accurate, and the reconstruction accuracy and operational efficiency of the DBCSAMP algorithm are greatly improved.

A Variable Step-Size for Sparse Nonlinear Adaptive Filters

2021 ◽  
Author(s):  
Alberto Carini ◽  
Markus V. S. Lima ◽  
Hamed Yazdanpanah ◽  
Simone Orcioni ◽  
Stefania Cecchi
Keyword(s):  
Adaptive Filters ◽  
Variable Step Size ◽  
Step Size ◽  
Variable Step

Active control for vehicle interior noise using the improved iterative variable step-size and variable tap-length LMS algorithms

2019 ◽  
Vol 67 (6) ◽  
pp. 405-414 ◽  
Author(s):  
Ningning Liu ◽  
Yuedong Sun ◽  
Yansong Wang ◽  
Hui Guo ◽  
Bin Gao ◽  
...  
Keyword(s):  
Steady State ◽  
Noise Control ◽  
Convergence Speed ◽  
Interior Noise ◽  
Lms Algorithm ◽  
Variable Step Size ◽  
Step Size ◽  
Vehicle Interior ◽  
Vehicle Interior Noise ◽  
Variable Step

Active noise control (ANC) is used to reduce undesirable noise, particularly at low frequencies. There are many algorithms based on the least mean square (LMS) algorithm, such as the filtered-x LMS (FxLMS) algorithm, which have been widely used for ANC systems. However, the LMS algorithm cannot balance convergence speed and steady-state error due to the fixed step size and tap length. Accordingly, in this article, two improved LMS algorithms, namely, the iterative variable step-size LMS (IVS-LMS) and the variable tap-length LMS (VT-LMS), are proposed for active vehicle interior noise control. The interior noises of a sample vehicle are measured and thereby their frequency characteristics. Results show that the sound energy of noise is concentrated within a low-frequency range below 1000 Hz. The classical LMS, IVS-LMS and VT-LMS algorithms are applied to the measured noise signals. Results further suggest that the IVS-LMS and VT-LMS algorithms can better improve algorithmic performance for convergence speed and steady-state error compared with the classical LMS. The proposed algorithms could potentially be incorporated into other LMS-based algorithms (like the FxLMS) used in ANC systems for improving the ride comfort of a vehicle.

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