Efficient Sensitivity Analysis of Structures with Local Modifications. II: Transfer Functions and Spectral Densities

2014 ◽  
Vol 140 (9) ◽  
pp. 04014068 ◽  
Author(s):  
Erik A. Johnson ◽  
Steven F. Wojtkiewicz
Keyword(s):  
Sensitivity Analysis ◽  
Transfer Functions ◽  
Spectral Densities

Numerical Prediction of Noise Radiated From a Panel Subjected to Boundary Layer Excitation

1999 ◽  
Author(s):  
Michael Allen ◽  
Nickolas Vlahopoulos
Keyword(s):  
Boundary Layer ◽  
Finite Element ◽  
Boundary Element ◽  
Transfer Functions ◽  
Element Model ◽  
Acoustic Response ◽  
Spectral Densities ◽  
Wind Noise ◽  
Element Analysis ◽  
Power Spectral

Abstract In this paper an algorithm is developed for combining finite element analysis and boundary element techniques in order to compute the noise radiated from a panel subjected to boundary layer excitation. The excitation is presented in terms of the auto and cross power spectral densities of the fluctuating wall pressure. The structural finite element model for the panel is divided into a number of sub-panels. A uniform fluctuating pressure is applied as excitation on each sub-panel separately. The corresponding vibration is computed, and is utilized as excitation for an acoustic boundary element analysis. The acoustic response is computed at any data recovery point of interest. The relationships between the acoustic response and the pressure excitation applied at each particular sub-panel constitute a set of transfer functions. They are combined with the spectral densities of the excitation for computing the noise generated from the vibration of the panel subjected to the boundary layer excitation. The development presented in this paper has the potential of computing wind noise in automotive applications, or boundary layer noise in aircraft applications.

A Study on the Influence of Ship Roll Characteristics on the Risk of Cargo Shifting

2004 ◽  
Vol 41 (02) ◽  
pp. 51-59
Author(s):  
Anna Ryrfeldt
Keyword(s):  
Sensitivity Analysis ◽  
Transfer Function ◽  
Computational Efficiency ◽  
Transfer Functions ◽  
Resonance Peak ◽  
Roll Motion ◽  
Ship Motions ◽  
Load Case ◽  
Contributing Factors ◽  
Ship Roll

In a previous work a methodology for assessing the risk of cargo shifting has been developed and used to study the influence of different parameters on the risk of cargo shifting. It has been found that ship rolling is one of the major contributing factors of cargo shifting. Linear theory of ship motions is presently used in the methodology because of computational efficiency and simplicity. Because the roll motion is complex and difficult to predict because of nonlinearities, the present study has been performed in order to study the influence of the roll motion on the risk of cargo shifting. This study may be seen as a sensitivity analysis of roll motion with respect to cargo shifting. The risk has been studied by the number of potentially dangerous conditions and how they depend on such parameters as wave height and period, and ship heading toward waves. The influence of roll amplitude and phase, as well as the influence of roll stabilizing devices, on the number of dangerous conditions is studied for two vessels and two load cases each. Roll amplitude influence is analyzed by changing the amplitude of the transfer function, and the results show that the influence of roll amplitude is very large. This influence is especially marked when the roll amplitude is large and the vertical and horizontal accelerations are small to moderate. The influence of roll stabilizing devices is studied by cutting of the resonance peak in the transfer functions. The results show that roll stabilizing is often efficient but that it can be more important to choose load case in order to attain good seakeeping characteristics, especially with respect to roll motion.

scholarly journals Sensitivity analysis of transfer functions of laminar flames

2011 ◽  
Vol 158 (12) ◽  
pp. 2384-2394 ◽  
Author(s):  
F. Duchaine ◽  
F. Boudy ◽  
D. Durox ◽  
T. Poinsot
Keyword(s):  
Sensitivity Analysis ◽  
Transfer Functions ◽  
Laminar Flames

scholarly journals Pyrolysis of Mixed Plastic Waste: II. Artificial Neural Networks Prediction and Sensitivity Analysis

2021 ◽  
Vol 11 (18) ◽  
pp. 8456
Author(s):  
Ibrahim Dubdub ◽  
Mohammed Al-Yaari
Keyword(s):  
Sensitivity Analysis ◽  
Transfer Functions ◽  
Pearson Correlation ◽  
Input Parameter ◽  
Propagation Model ◽  
Ann Model ◽  
Polymer Mixtures ◽  
Feed Forward Back Propagation ◽  
Proposed Model ◽  
Artificial Neural

In this work, an artificial neural network (ANN) model was efficiently developed to predict the pyrolysis of mixed plastics, including pure polystyrene (PS), polypropylene (PP), low-density polyethylene (LDPE), and high-density polyethylene (HDPE), at a heating rate of 60 K/min using thermogravimetric analysis (TGA) data. The data of seventeen experimental tests of polymer mixtures with different compositions were used. A feed-forward back-propagation model, with 15 and 10 neurons in two hidden layers and TANSIG-TANSIG transfer functions, was constructed to predict the weight left percent during the pyrolysis of the mixed polymer samples. The model input variables include the composition of each polymer (PS, PP, LDPE, and HDPE), and temperature. The results showed an excellent agreement between the experimental and the predicted weight left percent values, where the correlation coefficient (R) is greater than 0.9999. In addition, to validate the proposed model, a highly efficient performance was found when the proposed model was simulated using new input data. Furthermore, a sensitivity analysis was performed using Pearson correlation to find the uncertainties associated with the relationship between the output and the input parameters. Temperature was found to be the most sensitive input parameter.

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