Design modification of cyclically periodic structure using Gaussian process

2013 ◽  
Author(s):  
K. Zhou ◽  
J. Tang
Keyword(s):  
Gaussian Process ◽  
Periodic Structure ◽  
Design Modification

Design Optimization Toward Alleviating Forced Response Variation in Cyclically Periodic Structure Using Gaussian Process

2016 ◽  
Vol 139 (1) ◽  
Author(s):  
K. Zhou ◽  
A. Hegde ◽  
P. Cao ◽  
J. Tang
Keyword(s):  
Optimal Design ◽  
Gaussian Process ◽  
Design Optimization ◽  
Periodic Structure ◽  
Periodic Structures ◽  
Forced Response ◽  
Design Modification ◽  
Vibration Localization ◽  
Bladed Disk ◽  
Response Variation

Cyclically periodic structures, such as bladed disk assemblies in turbomachinery, are widely used in engineering systems. It is well known that small uncertainties exist among their substructures, which in certain situations may cause drastic change in the dynamic responses, a phenomenon known as vibration localization. Previous studies have suggested that the introduction of small, prespecified design modification, i.e., intentional mistuning, may alleviate vibration localization and reduce response variation. However, there has been no systematic methodology to facilitate the optimal design of intentional mistuning. The most significant challenge is the computational cost involved. The finite-element model of a bladed disk usually requires a very large number of degrees-of-freedom (DOFs). When uncertainties occur in a cyclically periodic structure, the response may no longer be considered as simple perturbation to that of the nominal structure. In this research, a suite of interrelated algorithms is proposed to enable the efficient design optimization of cyclically periodic structures toward alleviating their forced response variation. We first integrate model order reduction with a perturbation scheme to reduce the scale of analysis of a single run. Then, as the core of the new methodology, we incorporate Gaussian process (GP) emulation to conduct the rapid sampling-based evaluation of the design objective, which is a metric of response variation under uncertainties, in the parametric space. The optimal design modification can thus be directly identified to minimize the response variation. The efficiency and effectiveness of the proposed methodology are demonstrated by systematic case studies.

Integrated Fractional white Noise as an Alternative to Multifractional Brownian Motion

2007 ◽  
Vol 44 (02) ◽  
pp. 393-408 ◽  
Author(s):  
Allan Sly
Keyword(s):  
Stochastic Process ◽  
Brownian Motion ◽  
White Noise ◽  
Gaussian Process ◽  
Discrete Data ◽  
Hölder Exponent ◽  
Scaling Properties ◽  
Multifractional Brownian Motion ◽  
Local Properties

Multifractional Brownian motion is a Gaussian process which has changing scaling properties generated by varying the local Hölder exponent. We show that multifractional Brownian motion is very sensitive to changes in the selected Hölder exponent and has extreme changes in magnitude. We suggest an alternative stochastic process, called integrated fractional white noise, which retains the important local properties but avoids the undesirable oscillations in magnitude. We also show how the Hölder exponent can be estimated locally from discrete data in this model.

Periodic Structure in the Matrix Protein of an Insect Virus

1982 ◽  
Vol 40 ◽  
pp. 302-303
Author(s):  
H.M. Mazzone ◽  
W.F. Engler ◽  
R. Zerillo ◽  
G.F. Bahr
Keyword(s):  
Inclusion Body ◽  
Periodic Structure ◽  
Matrix Protein ◽  
Malacosoma Disstria ◽  
Insect Virus ◽  
Virus Particles ◽  
The Matrix ◽  
Nucleopolyhedrosis Virus

The nucleopolyhedrosis virus (NPV) of the forest tent cater - pillar (Malacosoma disstria Hubner) has been analyzed in our laboratories. As a representative of the Baculovirus class, the NPV has virus particles enclosed with in a proteinaceous structure, the inclusion body.

A Stochastic Model for the Inheritance of the Cancer Proneness Phenotype

1987 ◽  
Vol 26 (03) ◽  
pp. 117-123
Author(s):  
P. Tautu ◽  
G. Wagner
Keyword(s):  
Stochastic Model ◽  
Gaussian Process ◽  
Covariance Function ◽  
Neoplastic Disease ◽  
Disease Phenotype ◽  
Probabilistic Representation ◽  
Temporal Behaviour ◽  
Continuous Trait ◽  
Continuous Parameter ◽  
Level Zero

SummaryA continuous parameter, stationary Gaussian process is introduced as a first approach to the probabilistic representation of the phenotype inheritance process. With some specific assumptions about the components of the covariance function, it may describe the temporal behaviour of the “cancer-proneness phenotype” (CPF) as a quantitative continuous trait. Upcrossing a fixed level (“threshold”) u and reaching level zero are the extremes of the Gaussian process considered; it is assumed that they might be interpreted as the transformation of CPF into a “neoplastic disease phenotype” or as the non-proneness to cancer, respectively.

Identification of Continuous-time Nonlinear Systems via Local Gaussian Process Models

2014 ◽  
Vol 134 (11) ◽  
pp. 1708-1715
Author(s):  
Tomohiro Hachino ◽  
Kazuhiro Matsushita ◽  
Hitoshi Takata ◽  
Seiji Fukushima ◽  
Yasutaka Igarashi
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
Continuous Time ◽  
Process Models ◽  
Gaussian Process Models

INVERSE UNCERTAINTY QUANTIFICATION OF A CELL MODEL USING A GAUSSIAN PROCESS METAMODEL

2020 ◽  
Vol 10 (4) ◽  
pp. 333-349
Author(s):  
Kevin de Vries ◽  
Anna Nikishova ◽  
Benjamin Czaja ◽  
Gábor Závodszky ◽  
Alfons G. Hoekstra
Keyword(s):  
Gaussian Process ◽  
Uncertainty Quantification ◽  
Cell Model ◽  
A Cell

OPERATION OF THE DIFFRACTION RADIATION OSCILLATOR ON HIGHER SPACE HARMONICS OF PERIODIC STRUCTURE

2010 ◽  
Vol 69 (4) ◽  
pp. 367-378
Author(s):  
V. S. Miroshnichenko ◽  
Victor Konstantinovich Korneyenkov ◽  
Ye. B. Senkevich ◽  
D. V. Yudintsev
Keyword(s):  
Periodic Structure ◽  
Diffraction Radiation ◽  
Radiation Oscillator ◽  
Diffraction Radiation Oscillator ◽  
Space Harmonics
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