Cross-Spectral-Density Sink Frequency Behavior for Coupled-Core Argonaut Reactors

1970 ◽  
Vol 42 (3) ◽  
pp. 419-421 ◽  
Author(s):  
Mohamed El-Sayed Nagy ◽  
Richard A. Danofsky
Keyword(s):  
Spectral Density ◽  
Cross Spectral Density ◽  
Frequency Behavior

A New Random Data Description and Its Use in Transferring Road Roughness to Vehicle Response

1974 ◽  
Vol 96 (2) ◽  
pp. 676-679 ◽  
Author(s):  
J. C. Wambold ◽  
W. H. Park ◽  
R. G. Vashlishan
Keyword(s):  
Spectral Density ◽  
Frequency Distribution ◽  
Descriptive Analysis ◽  
Joint Probability ◽  
Amplitude Distribution ◽  
Matrix Functions ◽  
Initial Portion ◽  
Power Spectral ◽  
Road Roughness ◽  
Cross Spectral Density

The initial portion of the paper discusses the more conventional method of obtaining a vehicle transfer function where phase and magnitude are determined by dividing the cross spectral density of the input/output by the power spectral density (PSD) of the input. The authors needed a more descriptive analysis (over PSD) and developed a new signal description called Amplitude Frequency Distribution (AFD); a discrete joint probability of amplitude and frequency with the advantage of retaining amplitude distribution as well as frequency distribution. A better understanding was obtained, and transfer matrix functions were developed using AFD.

Turbulent Wall-Pressure Fluctuations: A New Model for Off-Axis Cross-Spectral Density

1997 ◽  
Vol 119 (2) ◽  
pp. 277-280 ◽  
Author(s):  
B. A. Singer
Keyword(s):  
Boundary Layer ◽  
Turbulent Boundary Layer ◽  
Spectral Density ◽  
Flow Direction ◽  
Wall Pressure ◽  
Mean Flow ◽  
New Approach ◽  
Cross Spectral Density ◽  
The Mean ◽  
The Cross

Models for the distribution of the wall-pressure under a turbulent boundary layer often estimate the coherence of the cross-spectral density in terms of a product of two coherence functions. One such function describes the coherence as a function of separation distance in the mean-flow direction, the other function describes the coherence in the cross-stream direction. Analysis of data from a large-eddy simulation of a turbulent boundary layer reveals that this approximation dramatically underpredicts the coherence for separation directions that are neither aligned with nor perpendicular to the mean-flow direction. These models fail even when the coherence functions in the directions parallel and perpendicular to the mean flow are known exactly. A new approach for combining the parallel and perpendicular coherence functions is presented. The new approach results in vastly improved approximations for the coherence.

Specular cross-spectral density functions

1988 ◽  
Vol 68 (4) ◽  
pp. 239-243 ◽  
Author(s):  
F. Gori ◽  
G. Guattari ◽  
C. Palma ◽  
C. Padovani
Keyword(s):  
Spectral Density ◽  
Density Functions ◽  
Cross Spectral Density ◽  
Spectral Density Functions

A New Algorithm for Long-Term Estimation Based on AR Model

2014 ◽  
Vol 614 ◽  
pp. 440-443 ◽  
Author(s):  
Wen Jun Su ◽  
Hai Tao Chen
Keyword(s):  
Spectral Density ◽  
Poor Performance ◽  
Ar Model ◽  
Estimation Methods ◽  
Wiener Model ◽  
Training Set ◽  
Power Spectral ◽  
Cross Spectral Density ◽  
Term Data

Traditional estimation methods have poor performance for long-term data forecast. Using Wiener model to estimate, power spectral density of the input signal, and cross-spectral density of the input and output signals are needed, that are difficult to obtain. And the large amount of calculation is needed using Wiener model. Using AR model and Kalman model, estimated results tend to mean of the training set while the estimated distance increases. For these cases, a new algorithm for long-term estimation based on AR model, named sampling AR model, is presented. Grouping the training set and using a different group of the training set to estimate each value. Sampling AR model improves the accuracy of long-term estimation.

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