Robust optimization of the rate of penetration of a drill-string using a stochastic nonlinear dynamical model

Ritto, T.G.; Soize, Christian; Sampaio, Rubens

doi:10.1007/s00466-009-0462-8

Cited by 51 publications

(27 citation statements)

References 34 publications

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“…When these works were published, design optimization with stochastic computational model was not still really possible for large scale multiscale computational models. The applications that have been published (see for instance [1,5,11,12,16,22,29] and also [35,54,57,68,70,72,80,99,96]) were devoted to optimization problems under uncertainties for which the computational models had a reasonable number of degrees of freedom, for which the optimizers were based on the use of relatively classical optimization algorithms and/or the introduction of approximations such as surface responses and surrogate models.…”

Section: Stochastic Modeling Of Biological Tissuesmentioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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This paper deals with the optimal design of a titanium mesoscale implant in a cortical bone for which the apparent elasticity tensor is modeled by a non-Gaussian random field at mesoscale, which has been experimentally identified. The external applied forces are also random. The design parameters are geometrical dimensions related to the geometry of the implant. The stochastic elastostatic boundary value problem is discretized by the finite element method. The objective function and the constraints are related to normal, shear, and von Mises stresses inside the cortical bone. The constrained nonconvex optimization problem in presence of uncertainties is solved by using a probabilistic learning algorithm that allows for considerably reducing the numerical cost with respect to the classical approaches.

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Section: Stochastic Modeling Of Biological Tissuesmentioning

confidence: 99%

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Soize

2017

Comput Mech

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“…[89][90][91] while robust updating and robust design optimiza tion with modeling uncertainties can be found in Refs. [82,[92][93][94][95]. …”

Section: Types Of Approach For Stochastic Modeling Of Uncertaintiesmentioning

confidence: 99%

Advanced Computational Dissipative Structural Acoustics and Fluid-Structure Interaction in Low-and Medium-Frequency Domains. Reduced-Order Models and Uncertainty Quantification

Ohayon¹,

Soize²

2012

International Journal of Aeronautical and Space Sciences

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This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and fluid-structure systems, in the low-and medium-frequency do mains and this includes uncertainty quantification. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic fluid. This includes wall acoustic impedances and it is surrounded by an infinite acoustic fluid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efficient reduced-order computational model is constructed by using a finite element discretization for the structure and an internal acoustic fluid. The external acoustic fluid is treated by using an appropriate boundary element method in the frequency domain. All the required modeling aspects for the analysis of the medium-frequency domain have been introduced namely, a viscoelastic behavior for the structure, an appropriate dissipative model for the internal acoustic fluid that includes wall acoustic impedance and a model of uncertainty in particular for the modeling errors. This advanced computational formulation, corresponding to new extensions and complements with respect to the state-of-the-art are well adapted for the development of a new generation of software, in particular for parallel computers. Key words:Computational mechanics, Structural acoustics, Vibroacoustic, Fluid-structure interaction, Uncertainty quantification, Reduced-order model, Medium frequency, Low frequency, Dissipative system, Viscoelasticity, Wall acoustic impedance, Finite element discretization, Boundary element method Nomenclature a ijkh = elastic coefficients of the structure b ijkh = damping coefficients of the structure c 0 = speed of sound in the internal acoustic fluid c E = speed of sound in the external acoustic fluid f = vector of the generalized forces for the internal acoustic fluid f S = vector of the generalized forces for the structure g = mechanical body force field in the structure i = imaginary complex number i k = wave number in the external acoustic fluid n = number of internal acoustic DOF n s = number of structure DOF n j = component of vector n n = outward unit normal to Abstract This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and uid-structure systems, in the low-and medium-frequency domains and this includes uncertainty quantication. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic uid. This includes wall acoustic impedances and it is surrounded by an innite acoustic uid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efcient reduced-order computational model is constructed by using a nite element discret...

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“…• modeling errors for nonlinear dynamical systems with geometrical nonlinearity in threedimensional elasticity [53,21] and with nonlinear constitutive equation [61].…”

Section: Types Of Approaches For Constructing Prior Stochastic Modelsmentioning

confidence: 99%

Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

Soize

2013

Computers & Structures

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To cite this version:Christian Soize. Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges. Computers and Structures, Elsevier, 2013, 126 (-) AbstractThe paper is devoted to the modeling and identification of uncertainties in computational structural dynamics for low-and medium-frequency ranges. A complete methodology is presented for the identification procedure. The first eigenfrequencies are used to quantify the uncertainties in the low-frequency band while the frequency response functions are used to quantify the uncertainties in the medium-frequency band. The system-parameter uncertainties are taken into account with the parametric probabilistic approach. The model uncertainties are taken in to account with the nonparametric probabilistic approach. The posterior stochastic model of systemparameter uncertainties is identified using the Bayes method.

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Robust optimization of the rate of penetration of a drill-string using a stochastic nonlinear dynamical model

Cited by 51 publications

References 34 publications

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Design optimization under uncertainties of a mesoscale implant in biological tissues using a probabilistic learning algorithm

Advanced Computational Dissipative Structural Acoustics and Fluid-Structure Interaction in Low-and Medium-Frequency Domains. Reduced-Order Models and Uncertainty Quantification

Bayesian posteriors of uncertainty quantification in computational structural dynamics for low-and medium-frequency ranges

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