Failure probability evaluation of passive system using fuzzy Monte Carlo simulation

Prasad, M. A.; Gaikwad, Avinash J.; Srividya, A.; Verma, Ajit Kumar

doi:10.1016/j.nucengdes.2011.02.025

Cited by 12 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Now, premultiply by Van T on both sides of Equation (21). Then, ðV an T * V anÞ * A mls ¼ ðV anÞ T * Y Therefore, the co-efficient of MLS is computed as…”

Section: Methods Of Multivariate Least Squarementioning

confidence: 99%

“…Also, the regression analysis and curve fitting models are used to approximate the analytic expression of given discrete data. However, the linear and non‐linear regression models are widely used for passive system reliability estimation [ 21 ]. Multivariate polynomial fit based on the RSM produces reasonably realistic estimation.…”

Section: Related Workmentioning

confidence: 99%

“…Therefore, we necessarily need to quantify the uncertainty. Some uncertainties may happen due to insufficient information, such a case the fuzzy set theory is more appropriate instead of probability concepts [ 21 ]. For instance, to handle large amount of data with finite number of thermal‐hydraulic code runs, the regression analysis was performed.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity

Muthusamy¹,

Ramalingam²,

Chandran

et al. 2021

IET Nanobiotechnology

View full text Add to dashboard Cite

Herein, multivariate Lagrange's interpolation polynomial (MLIP) and multivariate least square (MLS) methods are used to derive linear and higher‐order polynomials for two varied applications. (1) For an effective fabrication of Pectin degrading Fe3O4‐SiO2 Nanobiocatalyst activity (IU/mg). Here, the three parameters namely: pH value, pectinase loading and temperature as independent variables are optimized for the maximal of anobiocatalyst activity as a dependent variable. (2) For a passive system reliability estimation of decay heat removal (DHR) of a nuclear power plant. The success criteria of the system depend on three types temperature that do not exceed their respective design safety limits and are considered as dependent variables and 14 significant parameters were used as independent variables. Statistically, the validation of these multivariate polynomials are done by testing of hypothesis. Comparative study of the proposed approach gives significance results in the first application have the optimum conditions for maximum activity using linear MLIP method is: 58.64 with pH = 4, pL = 250 and Temp = 4°C. The maximum activity using second order MLIP method is 59.825 and method of MLS is 59.8249 with the optimized values of an independent variables pH = 4, pL = 300 and Temp = 8°C depicted in Table 1. In DHR system, the significance results are obtained and depicted in Table 2.

show abstract

“…Now, premultiply by Van T on both sides of Equation (21). Then, ðV an T * V anÞ * A mls ¼ ðV anÞ T * Y Therefore, the co-efficient of MLS is computed as…”

Section: Methods Of Multivariate Least Squarementioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity

Muthusamy¹,

Ramalingam²,

Chandran

et al. 2021

IET Nanobiotechnology

View full text Add to dashboard Cite

show abstract

“…Many authors from different fields have reported the usefulness of FMCS in making engineering decisions under uncertainty/imprecision. For example, see the works of Kentel and Aral, 11 Zonouz and Miremadi, 12 Baraldi and Zio, 13 Sadeghi et al, 14 and Prasad et al 15 In FMCS, one can model a set of input parameters by using both pdf ( p 1 ,…, p n ) and possibility distributions (i.e., fuzzy numbers) ( f 1 ,…, f m ) to see the variability in the output y , as schematically illustrated in Figure 1. 14 As seen from Figure 1, the input parameters modeled by the pdf ( p 1 ,…, p n ) directly participate in the simulation of output y , whereas the input parameters modeled by the fuzzy numbers ( f 1 ,…, f m ) do not directly participate in the simulation of output y .…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Monte Carlo Simulation using point-cloud-based probability–possibility transformation

Ullah

Shamsuzzaman

2013

SIMULATION

View full text Add to dashboard Cite

Fuzzy Monte Carlo Simulation (FMCS) uses both the probability density function (pdf) and possibility distributions (e.g., fuzzy numbers) to model the uncertainty/imprecision associated with the input parameters and, then, to simulate the uncertainty/imprecision associated with the output parameters. A probability–possibility transformation is needed to transfer the information of a fuzzy number into its equivalent pdf, while performing the simulation. This study deals with an approach of FMCS that uses a point-cloud-based probability–possibility transformation. Let x(t), t = 0,1,…, n, be a set of points that represents some random states of an uncertain/imprecise quantity. The collection of points (x(t), x(t+i)), t = 0,…, n–i, i∈ {1,2,…} is called point-cloud, providing a visual/computational representation of variability, modality, and ranges associated with the quantity. This study identifies the pdf and possibility distribution (fuzzy number) underlying a given point-cloud. Using these distributions, the relationships between the triangular fuzzy number and unimodal pdf (normal/uniform distributions) are identified. Two numerical examples are described elucidating the effectiveness of the proposed transformation. The first example deals with the issue of monitoring a FMCS process. The other example deals with the issue of making a decision by using FMCS.

show abstract

Time Variant Reliability Analysis of Passive Systems

Muruva

2019

Springer Series in Reliability Engineering

View full text Add to dashboard Cite

Failure probability evaluation of passive system using fuzzy Monte Carlo simulation

Cited by 12 publications

References 5 publications

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity

Fuzzy Monte Carlo Simulation using point-cloud-based probability–possibility transformation

Time Variant Reliability Analysis of Passive Systems

Contact Info

Product

Resources

About

Failure probability evaluation of passive system using fuzzy Monte Carlo simulation

Cited by 12 publications

References 5 publications

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe 3 O 4 ‐SiO 2 nanobiocatalyst activity

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe 3 O 4 ‐SiO 2 nanobiocatalyst activity

Fuzzy Monte Carlo Simulation using point-cloud-based probability–possibility transformation

Time Variant Reliability Analysis of Passive Systems

Contact Info

Product

Resources

About

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity

Multivariate polynomial fit: Decay heat removal system and pectin degrading Fe ₃ O ₄ ‐SiO ₂ nanobiocatalyst activity