Probability Aspects in Foundation Plate Design

2016 ◽  
Vol 837 ◽  
pp. 64-67
Author(s):  
Katarina Tvrda
Keyword(s):  
Probabilistic Methods ◽  
Random Input ◽  
Mean Values ◽  
Software Model ◽  
Input Parameters ◽  
Distribution Parameters ◽  
Input Variables ◽  
Uncertain Input ◽  
Foundation Plate ◽  
Distribution Type

The probabilistic design analyses a plate involving uncertain input parameters. These input parameters (geometry, material properties, boundary conditions, etc.) are defined in the software model. The variations of input parameters are defined as random input variables and are characterized by their distribution type (Gaussian, lognormal, etc.) and by their distribution parameters (mean values, standard deviation, etc.). During a probabilistic analysis, software executes multiple analysis loops to compute the random output parameters as a function of the set of random input variables. The values for the input variables are generated either randomly (using Monte Carlo simulation) or as prescribed samples (using Response Surface Methods). In the conclusion, some results of these probabilistic methods are presented.

Uncertainty Quantification for Direct Aeroacoustic Simulations of Cavity Flows

2019 ◽  
Vol 27 (01) ◽  
pp. 1850044 ◽  
Author(s):  
Thomas Kuhn ◽  
Jakob Dürrwächter ◽  
Fabian Meyer ◽  
Andrea Beck ◽  
Christian Rohde ◽  
...  
Keyword(s):  
Uncertainty Quantification ◽  
Acoustic Noise ◽  
High Order ◽  
Open Cavity ◽  
Small Scale ◽  
Cavity Flows ◽  
Generation Process ◽  
Random Input ◽  
Input Parameters ◽  
Uncertain Input

We investigate the influence of uncertain input parameters on the aeroacoustic feedback of cavity flows. The so-called Rossiter feedback requires a direct numerical computation of the acoustic noise, which solves hydrodynamics and acoustics simultaneously, in order to capture the interaction of acoustic waves and the hydrodynamics of the flow. Due to the large bandwidth of spatial and temporal scales, a high-order numerical scheme with low dissipation and dispersion error is necessary to preserve important small scale information. Therefore, the open-source CFD solver FLEXI, which is based on a high-order discontinuous Galerkin spectral element method, is used to perform the aforementioned direct simulations of an open cavity configuration with a laminar upstream boundary layer. To analyze the precision of the deterministic cavity simulation with respect to random input parameters, we establish a framework for uncertainty quantification (UQ). In particular, a nonintrusive spectral projection method with Legendre and Hermite polynomial basis functions is employed in order to treat uniform and normal probability distributions of the random input. The results indicate a strong, nonlinear dependency of the acoustic feedback mechanism on the investigated uncertain input parameters. An analysis of the stochastic results offers new insights into the noise generation process of open cavity flows and reveals the strength of the implemented UQ framework.

SAMS: Stochastic Analysis With Minimal Sampling—A Fast Algorithm for Analysis and Design Under Uncertainty

2004 ◽  
Vol 127 (4) ◽  
pp. 558-571 ◽  
Author(s):  
A. Mawardi ◽  
R. Pitchumani
Keyword(s):  
System Performance ◽  
Stochastic Analysis ◽  
Sampling Method ◽  
Latin Hypercube Sampling ◽  
Analysis And Design ◽  
Process Outcomes ◽  
Input Parameters ◽  
Computationally Intensive ◽  
Uncertain Input ◽  
Reliability And Robustness

Design of processes and devices under uncertainty calls for stochastic analysis of the effects of uncertain input parameters on the system performance and process outcomes. The stochastic analysis is often carried out based on sampling from the uncertain input parameters space, and using a physical model of the system to generate distributions of the outcomes. In many engineering applications, a large number of samples—on the order of thousands or more—is needed for an accurate convergence of the output distributions, which renders a stochastic analysis computationally intensive. Toward addressing the computational challenge, this article presents a methodology of S̱tochastic A̱nalysis with M̱inimal S̱ampling (SAMS). The SAMS approach is based on approximating an output distribution by an analytical function, whose parameters are estimated using a few samples, constituting an orthogonal Taguchi array, from the input distributions. The analytical output distributions are, in turn, used to extract the reliability and robustness measures of the system. The methodology is applied to stochastic analysis of a composite materials manufacturing process under uncertainty, and the results are shown to compare closely to those from a Latin hypercube sampling method. The SAMS technique is also demonstrated to yield computational savings of up to 90% relative to the sampling-based method.

The Effect of the Variation of Input Parameters on the Onset of Corrosion of the Bridge Deck Exposed to Chlorides Considering Selected Reinforcement Protection

2020 ◽  
Vol 832 ◽  
pp. 147-157
Author(s):  
Petr Konečný ◽  
Petr Lehner
Keyword(s):  
Reinforced Concrete ◽  
Bridge Deck ◽  
Probabilistic Method ◽  
Concrete Bridge ◽  
Probabilistic Estimation ◽  
Reinforced Concrete Bridge ◽  
Corrosion Initiation ◽  
Input Parameters ◽  
Concrete Bridge Deck ◽  
Input Variables

The contribution focuses on the effect of selected input parameters on probabilistic estimation of chloride induced reinforced concrete bridge deck corrosion initiation. The reinforced concrete bridge deck with steel protected by epoxy-coating is considered. A finite element diffusion model in conjunction with a probabilistic method using Monte Carlo technique is used to address the inherited randomness of input variables. Presented parametric study shows the sensitivity of estimation of the corrosion initiation likelihood on variation of input parameters.

Optimisation Techniques Applied to the Design of Gas Turbine Blades Cooling Systems

2006 ◽  
Author(s):  
Gianlorenzo Bucchieri ◽  
Massimo Galbiati ◽  
Daniele Coutandin ◽  
Stefano Zecchi
Keyword(s):  
Best Practice ◽  
Optimization Algorithms ◽  
Turbine Blades ◽  
Geometrical Parameters ◽  
Cfd Model ◽  
Cfd Validation ◽  
Mean Values ◽  
Input Parameters ◽  
And Performance ◽  
Practice Methods

This paper addresses the methodology used to design the layout of the tip cooling nozzles of a high pressure rotor blade turbine. The methodology used is through a complete CAE approach, by means of a parametric CFD model which is run several times for the exploration of several designs by an optimizer. Hence the design is carried out automatically by parallel computations, with the optimization algorithms taking the decisions rather than the design engineer. The engineer instead takes decision regarding the physical settings of the CFD model to employ, the number and the extension of the geometrical parameters of the blade tip holes and the optimization algorithms to be employed. From CFD validation the final design of the tip cooling geometry found by the optimizer has proved to be better than the base design, which used mean values of all input parameters, and than the design proposed by an experienced heat transfer AVIO engineer, who used standard best practice methods. Furthermore the large number of experiences gained by the simulations run by the optimizer allowed the designer to find laws, functions and correlation between input parameters and performance output, with a further and deeper insight on this specific design problem.

Modelling of tribological responses of composites using integrated ANN-GA technique

2020 ◽  
pp. 002199832096052
Author(s):  
Santanu Sardar ◽  
Swati Dey ◽  
Debdulal Das
Keyword(s):  
Optimization Technique ◽  
Matrix Composites ◽  
Large Numbers ◽  
Wide Range ◽  
Input Parameters ◽  
Input Variables ◽  
Velocity Input ◽  
Abrasive Size

In the present article, artificial neural networks (ANNs) and genetic algorithm (GA) methodology were integrated to model tribological characteristics of stir-cast Al-Zn-Mg-Cu matrix composites under two-body abrasion considering large numbers of experimentally generated results. Tribo-responses of wear rate (Wrt), coefficient of friction (COF) and roughness of abraded surface (RAS) were evaluated under wide range of intrinsic ( i.e., particle quantity) and extrinsic ( i.e., abrasive size, load, distance and velocity) input parameters. Characteristics of Wrt, COF and RAS are often mutually contradictory in nature and so, multi-objective optimization technique becomes imperative for selection and design of machine components. Accordingly, those were optimized through Pareto solutions. Sensitivity of different factors was analyzed on each of the tribo-performances and validated via experimental evidences. Amongst the input variables, particle quantity and abrasive size dominated significantly over other variables except load which imparted modest influences. The role of various input parameters was explained through determination of different micromechanisms via exhaustive post wear characterizations, microstructural and surface topography attributes. Lowest values of Wrt and COF with a modest value of RAS were identified at 15 ± 2 wt.% particle quantity.

scholarly journals Use of the correlation between input variables in estimating the risk of feedlot finishing of steers and young steers

2014 ◽  
Vol 86 (2) ◽  
pp. 945-954 ◽  
Author(s):  
PAULO S. PACHECO ◽  
JOÃO RESTLE ◽  
LEONIR L. PASCOAL ◽  
FABIANO N. VAZ ◽  
RICARDO Z. VAZ ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Stochastic Dominance ◽  
Net Present Value ◽  
Latin Hypercube Sampling ◽  
Risk Estimate ◽  
Present Value ◽  
Random Input ◽  
Financial Indicator ◽  
Input Variables ◽  
Diet Intake

The objective of this study was to evaluate the risk of feedlot finishing of steers (22.8 months) and young steers (15.2 months), using or not a correlation between the random input variables (data collected from 2004 to 2010) in the simulation of the Net Present Value (NPV) financial indicator. The animals were fed a diet containing roughage:concentrate ratio of 60:40 for 34 and 143 days, respectively, until they had reached a predetermined slaughter weight of 430 kg. For the NPV simulation, Latin Hypercube sampling was used, with 2000 interactions. The stochastic dominance analysis, test of differences between pairs of curves of cumulative distributions and sensitivity analysis were carried out. The NPV simulation using the correlation resulted in the best option for risk estimate. The confinement of young steers was the alternative of investment most viable than confinement of steers (NPV ≥ 0 of 80.4 vs. 62.3% in the simulation with correlation, respectively). Sensitivity analysis determined the following items had the greatest impact on the estimate of NPV: prices of fat and thin cattle, initial and final weights, diet costs, minimum rate of attractiveness and diet intake.

Using DOProC Method in Structural Reliability Assessment

2013 ◽  
Vol 300-301 ◽  
pp. 860-869 ◽  
Author(s):  
Martin Krejsa ◽  
Petr Janas ◽  
Radim Čajka
Keyword(s):  
Probability Theory ◽  
Structural Reliability ◽  
Reliability Assessment ◽  
Numerical Approach ◽  
Probabilistic Methods ◽  
Simulation Techniques ◽  
Load Carrying ◽  
Input Parameters ◽  
Calculation Procedures

Reliability of load-carrying structures has been assessed by various calculation procedures based on probability theory and mathematic statistics, which have been becoming more and more popular. The calculation procedures are well-suited for the design of elements in load-carrying structures with the required level of reliability if at least some input parameters are random and contribute to a qualitatively higher level of the reliability assessment and, in turn, higher safety of those who use the buildings and facilities. This paper discusses application of the original and new probabilistic methods – the Direct Optimized Probabilistic Calculation (“DOProC”), which uses a purely numerical approach without any simulation techniques. This provides more accurate solutions to probabilistic tasks, and, in some cases, to considerably faster completion of computations.

scholarly journals Global Sensitivity Analysis of Fuzzy Distribution Parameter on Failure Probability and Its Single-Loop Estimation

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Lei Cheng ◽  
Zhenzhou Lu ◽  
Luyi Li
Keyword(s):  
Sensitivity Analysis ◽  
Failure Probability ◽  
Global Sensitivity Analysis ◽  
Computational Cost ◽  
Single Loop ◽  
Global Sensitivity ◽  
Sensitivity Indices ◽  
Distribution Parameters ◽  
Input Variables ◽  
Sampling Probability

An extending Borgonovo’s global sensitivity analysis is proposed to measure the influence of fuzzy distribution parameters on fuzzy failure probability by averaging the shift between the membership functions (MFs) of unconditional and conditional failure probability. The presented global sensitivity indices can reasonably reflect the influence of fuzzy-valued distribution parameters on the character of the failure probability, whereas solving the MFs of unconditional and conditional failure probability is time-consuming due to the involved multiple-loop sampling and optimization operators. To overcome the large computational cost, a single-loop simulation (SLS) is introduced to estimate the global sensitivity indices. By establishing a sampling probability density, only a set of samples of input variables are essential to evaluate the MFs of unconditional and conditional failure probability in the presented SLS method. Significance of the global sensitivity indices can be verified and demonstrated through several numerical and engineering examples.

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