Reliability Estimate of Linear Oscillator with Uncertain Input Parameters

1991 ◽  
pp. 139-153
Author(s):  
S. T. Quek ◽  
Y. P. Teo ◽  
T. Balendra
Keyword(s):  
Linear Oscillator ◽  
Reliability Estimate ◽  
Input Parameters ◽  
Uncertain Input

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.

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.

scholarly journals Homogenization with uncertain input parameters

2010 ◽  
Vol 135 (4) ◽  
pp. 393-402
Author(s):  
Luděk Nechvátal
Keyword(s):  
Input Parameters ◽  
Uncertain Input

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.

Uncertainty analysis of galloping based piezoelectric energy harvester system using polynomial neural network

2022 ◽  
pp. 1045389X2110725
Author(s):  
Rakesha Chandra Dash ◽  
Narayan Sharma ◽  
Dipak Kumar Maiti ◽  
Bhrigu Nath Singh
Keyword(s):  
Neural Network ◽  
Power Output ◽  
Energy Harvester ◽  
Electrical Power ◽  
Piezoelectric Energy ◽  
Polynomial Neural Network ◽  
Input Parameters ◽  
Uncertain Input ◽  
System Properties ◽  
The Impact

This paper deals with the impact of uncertain input parameters on the electrical power generation of galloping-based piezoelectric energy harvester (GPEH). A distributed parameter model for the system is derived and solved by using Newmark beta numerical integration technique. Nonlinear systems tend to behave in a completely different manner in response to a slight change in input parameters. Due to the complex manufacturing process and various technical defects, randomness in system properties is inevitable. Owing to the presence of randomness within the system parameters, the actual power output differs from the expected one. Therefore, stochastic analysis is performed considering uncertainty in aerodynamic, mechanical, and electrical parameters. A polynomial neural network (PNN) based surrogate model is used to analyze the stochastic power output. A sensitivity analysis is conducted and highly influenced parameters to the electric power output are identified. The accuracy and adaptability of the PNN model are established by comparing the results with Monte Carlo simulation (MCS). Further, the stochastic analyses of power output are performed for various degrees of randomness and wind velocities. The obtained results showed that the influence of the electromechanical coefficient on power output is more compared to other parameters.

Uncertainty Quantification of a Fluidized Bed Reactor

2019 ◽  
Author(s):  
V. M. Krushnarao Kotteda ◽  
Anitha Kommu ◽  
Vinod Kumar ◽  
William Spotz
Keyword(s):  
Sensitivity Analysis ◽  
Uncertainty Quantification ◽  
National Laboratory ◽  
Sandia National Laboratory ◽  
Computational Cost ◽  
Wide Range ◽  
Input Parameters ◽  
Fluidizing Agent ◽  
Uncertain Input ◽  
Biomass Particles

Abstract Fluidized beds are used in a wide range of applications in gasification, combustion, and process engineering. Multiphase flow in such applications involves numerous uncertain parameters. Uncertainty quantification provides uncertainty in syngas yield and efficiency of coal/biomass gasification in a power plant. Techniques such as sensitivity analysis are useful in identifying parameters that have the most influence on the quantities of interest. Also, it helps to decrease the computational cost of the uncertainty quantification and optimize the reactor. We carried out a nondeterministic analysis of flow in a biomass reactor. The flow in the reactor is simulated with National Energy Technology Laboratory’s open source multiphase fluid dynamics suite MFiX. It does not possess tools for uncertainty quantification. Therefore, we developed a C++ wrapper to integrate an uncertainty quantification toolkit developed at Sandia National Laboratory with MFiX. The wrapper exchanges uncertain input parameters and critical output parameters among Dakota and MFiX. We quantify uncertainty in key output parameters via a sampling method. In addition, sensitivity analysis is carried out for all eight uncertain input parameters namely particle-particle restitution coefficient, angle of internal friction, coefficient of friction between two-phases, velocity of the fluidizing agent at the inlet, velocity of the biomass particles at the inlet, diameter of the biomass particles, viscosity of the fluidizing agent, and the percentage of nitrogen/oxygen in the fluidizing agent.

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