scholarly journals Uncertainty and sensitivity analysis applied to a voltage series operational amplifier

2021 ◽  
Vol 21 (3) ◽  
pp. 1347
Author(s):  
Mohammad Nizam Ibrahim ◽  
Zainal Hisham Che Soh ◽  
Nor Shahanim Mohamad Hadis ◽  
Ali Othman
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Uncertainty Analysis ◽  
Operational Amplifier ◽  
Voltage Gain ◽  
Amplifier Circuit ◽  
Matlab Simulink ◽  
Uncertainty And Sensitivity Analysis ◽  
Sample Points

This paper investigates the applied uncertainty and sensitivity analysis to resistors used in voltage series operational amplifier circuit. Two resistors bands are considered which are the gold band (5% uncertainty) and the silver band (10% uncertainty). To generate resistors uncertainty sample points, the SIMLAB uncertainty and sensitivity tool is used. A total of  sample points based on Sobol’ technique has been created for each resistor band. The voltage series amplifier is modelled in MATLAB/Simulink. A MATLAB script has been written to execute Monte-Carlo simulations for reading the resistor sample points, updating and executing the voltage series model and finally calculating the voltage gain. The result of uncertainty analysis shows that the produced voltage gain is uncertain within the range of  for the gold band and  for the silver band with respect to a reference voltage gain. The result of sensitivity analysis shows that each resistor, although their values are different, contributes equally contribution to the uncertainty of voltage gain.

scholarly journals Diffusion of oxygen interstitials in UO2+ using kinetic Monte Carlo simulations: Role of O/M ratio and sensitivity analysis

2016 ◽  
Vol 472 ◽  
pp. 89-98 ◽  
Author(s):  
Rakesh K. Behera ◽  
Taku Watanabe ◽  
David A. Andersson ◽  
Blas P. Uberuaga ◽  
Chaitanya S. Deo
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Kinetic Monte Carlo ◽  
Kinetic Monte Carlo Simulations

Design of facultative ponds based on uncertainty analysis

1996 ◽  
Vol 33 (7) ◽  
pp. 41-47 ◽  
Author(s):  
Marcos von Sperling
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Wastewater Treatment ◽  
Uncertainty Analysis ◽  
Accurate Determination ◽  
Effluent Quality ◽  
Uniform Distributions ◽  
Wastewater Treatment Systems ◽  
Selection Of

The paper presents a methodology for the utilisation of the Uncertainty analysis based on Monte Carlo simulations for the design of wastewater treatment systems. The special and important case of facultative-ponds design is exemplified. The design is carried out a large number of times, each run with different values of the inputs, randomly selected from uniform distributions within ranges which define the designer's uncertainty with the data. The results are interpreted statistically, giving elements for the selection of more or less conservative designs, according to the resulting effluent quality. The procedure for undertaking a Sensitivity Analysis is also described and exemplified, allowing the designer to concentrate more efforts on a more accurate determination of those inputs found to be significantly important.

scholarly journals Deterministic Sensitivity Analysis for First-Order Monte Carlo Simulations: A Technical Note

2009 ◽  
Vol 12 (1) ◽  
pp. 96-97 ◽  
Author(s):  
Benjamin P. Geisler ◽  
Uwe Siebert ◽  
G. Scott Gazelle ◽  
David J. Cohen ◽  
Alexander Göhler
Keyword(s):  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Monte Carlo Simulations ◽  
Technical Note ◽  
First Order ◽  
Deterministic Sensitivity Analysis

scholarly journals Uncertainty and sensitivity analysis of control strategies using the benchmark simulation model No1 (BSM1)

2009 ◽  
Vol 59 (3) ◽  
pp. 491-499 ◽  
Author(s):  
Xavier Flores-Alsina ◽  
Ignasi Rodriguez-Roda ◽  
Gürkan Sin ◽  
Krist V. Gernaey
Keyword(s):  
Growth Rate ◽  
Sensitivity Analysis ◽  
Monte Carlo ◽  
Carbon Source ◽  
Simulation Model ◽  
Correction Factor ◽  
Control Strategies ◽  
Hydrolysis Rate ◽  
External Carbon Source ◽  
Uncertainty And Sensitivity Analysis

The objective of this paper is to perform an uncertainty and sensitivity analysis of the predictions of the Benchmark Simulation Model (BSM) No. 1, when comparing four activated sludge control strategies. The Monte Carlo simulation technique is used to evaluate the uncertainty in the BSM1 predictions, considering the ASM1 bio-kinetic parameters and influent fractions as input uncertainties while the Effluent Quality Index (EQI) and the Operating Cost Index (OCI) are focused on as model outputs. The resulting Monte Carlo simulations are presented using descriptive statistics indicating the degree of uncertainty in the predicted EQI and OCI. Next, the Standard Regression Coefficients (SRC) method is used for sensitivity analysis to identify which input parameters influence the uncertainty in the EQI predictions the most. The results show that control strategies including an ammonium (SNH) controller reduce uncertainty in both overall pollution removal and effluent total Kjeldahl nitrogen. Also, control strategies with an external carbon source reduce the effluent nitrate (SNO) uncertainty increasing both their economical cost and variability as a trade-off. Finally, the maximum specific autotrophic growth rate (μA) causes most of the variance in the effluent for all the evaluated control strategies. The influence of denitrification related parameters, e.g. ηg (anoxic growth rate correction factor) and ηh (anoxic hydrolysis rate correction factor), becomes less important when a SNO controller manipulating an external carbon source addition is implemented.

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