scholarly journals Sensitivity analysis on the pollutant trapping efficiencies of a novel sewerage overflow screening device

2016 ◽  
Vol 18 (6) ◽  
pp. 1007-1018
Author(s):  
M. A. Aziz ◽  
M. A. Imteaz ◽  
H. M. Rasel ◽  
M. Samsuzzoha
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Capture Efficiency ◽  
Industry Standard ◽  
Informed Decisions ◽  
Input Parameters ◽  
Perform Sensitivity Analysis ◽  
Hydro Jet ◽  
Screening Device

A novel ‘Comb Separator’ was developed and tested with the aim of improving sewer solids capture efficiency and reducing blockages on the screen. Experimental results were compared against the industry standard ‘Hydro-Jet™’ screen. Analysing the parameter sensitivity of a hydraulic screen is a standard practice to get better understanding of the device performance. In order to understand the uncertainties of the Comb Separator's input parameters, it is necessary to undertake sensitivity analysis; this will assist in making informed decisions regarding the use of this device. Such analysis will validate the device's performance in urban sewerage overflow scenarios. The methodology includes multiple linear regression and sampling using the standard Latin hypercube sampling technique to perform sensitivity analysis on different experimental parameters, such as flowrate, effective comb spacing, device runtime, weir opening and comb layers. The input parameters ‘weir opening’ and ‘comb layers’ have an insignificant influence on capture efficiency; hence, they were omitted from further analysis. Among the input parameters, ‘effective spacing’ was the most influential, followed by ‘inflow’ and ‘runtime’. These analyses provide better insights about the sensitivities of the parameters for practical application. This will assist device managers and operators to make informed decisions.

The Effect of Mineral Variability on the Solubility of Some Actinides: an Uncertainty Analysis

1996 ◽  
Vol 465 ◽  
Author(s):  
Christian Ekberg ◽  
Allan T. Emrén ◽  
Anders Samuelsson
Keyword(s):  
Sensitivity Analysis ◽  
Spent Nuclear Fuel ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Rock Composition ◽  
Water Composition ◽  
Uncertainty And Sensitivity Analysis ◽  
Rock Water Interaction ◽  
Simulated Groundwater ◽  
Log Normal

ABSTRACTThe use of computer simulations in the performance assessment for a repository for spent nuclear fuel, are in many cases the only method to get information on how the rock-repository system will work. One important factor is the solubility of the elements released if the repository is breached. This solubility may be determined experimentally or simulated. Ifit is simulated, several factors such as thermodynamical uncertainties will affect the reliability of the results. If these uncertainties are assumed to be small, the composition of the water used in the calculations may play a major part in the uncertainties in solubility. The water composition, in tum, is either determined experimentally or calculated through water-rock interactions. Thus, if the mineral composition of the rock is known, it is possible to foresee the water composition. However, in most cases a determination of the rock composition is made from drilling cores and is thus quite uncertain. Therefore, if solubility calculations are to be based on water properties calculated from rock-water interactions another uncertainty is introduced. This paper is focused on uncertainty and sensitivity analysis of rock-water interaction simulations and the uncertainties thus obtained are propagated through a program making uncertainty and sensitivity analysis of the solubility calculations. In both cases the latin hypercube sampling technique have been used. The results show that the solubilities are in most cases log normal distributed while the different elements in the simulated groundwater in some cases diverge significantly from such a distribution. The numerical results are comforting in that the uncertainty intervals of the solubilities are rather small, i.e. up to 30%.

scholarly journals An Integrated Sensitivity Analysis Method for Energy and Comfort Performance of an Office Building along the Chinese Coastline

Buildings ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 371
Author(s):  
Ruijun Chen ◽  
Yaw-Shyan Tsay
Keyword(s):  
Sensitivity Analysis ◽  
Random Sampling ◽  
Energy Demand ◽  
Sampling Methods ◽  
Latin Hypercube Sampling ◽  
Building Energy ◽  
Assessment Method ◽  
Solar Absorptance ◽  
Sensitivity Assessment ◽  
Input Parameters

This study aimed to evaluate the comprehensive percentage influence of input parameters on building energy and comfort performance by a new approach of sensitivity analysis (SA) and explore the most reliable and neutral sampling and sensitivity assessment method. The research combined 7 sampling methods with 13 SA methods to comprehensively integrate the percentage influence of 25 input parameters on building energy and comfort performance in 24 coastal cities of China. The results have found that the percentage influence of many important input parameters is affected by geographical position. Considering both energy and comfort performance of the building, the key parameters are heating setpoint, infiltration rate, cooling setpoint, roof U value, roof solar absorptance, window solar heat gain coefficient, equipment, and occupant density, all of which could comprehensively impact 70% of energy demand and comfort performance along the Chinese coastline. This is of great significance for policymakers to formulate relative building regulations. After comparing the F-test and the exceed percentage test, we recommended the Pearson with Quasi-random sampling method as the most reliable SA assessment method in building simulation, followed by the standardized regression coefficient in random sampling and Latin hypercube sampling methods, which can achieve data closest to the average value.

scholarly journals Subsurface Stormflow Modeling with Sensitivity Analysis Using a Latin-Hypercube Sampling Technique

Ground Water ◽  
1996 ◽  
Vol 34 (5) ◽  
pp. 811-818 ◽  
Author(s):  
J. P. Gwo ◽  
L. E. Toran ◽  
M. D. Morris ◽  
G. V. Wilson
Keyword(s):  
Sensitivity Analysis ◽  
Sampling Technique ◽  
Latin Hypercube Sampling ◽  
Latin Hypercube

scholarly journals Sensitivity analysis and parameter selection for detecting aggregations in acoustic data

2006 ◽  
Vol 64 (1) ◽  
pp. 160-168 ◽  
Author(s):  
Julian M. Burgos ◽  
John K. Horne
Keyword(s):  
Sensitivity Analysis ◽  
Latin Hypercube Sampling ◽  
Detection Algorithm ◽  
Design Sensitivity ◽  
Walleye Pollock ◽  
Parameter Selection ◽  
Minimum Size ◽  
Acoustic Data ◽  
Input Parameters ◽  
Selection For

Abstract Burgos, J. M., and Horne, J. K. 2007. Sensitivity analysis and parameter selection for detecting aggregations in acoustic data. ICES Journal of Marine Science, 64: 160–168. A global sensitivity analysis was conducted on the algorithm implemented in the Echoview ® software to detect and describe aggregations in acoustic backscatter. Multiple aggregation detections were performed using walleye pollock (Theragra chalcogramma) data from the eastern Bering Sea. Walleye pollock form distinct aggregations and dense and diffuse layers. In each aggregation detection, input parameters defining minimum size, density, and distance to other aggregations were selected at random using a Latin hypercube sampling design. Sensitivity was quantified by testing for correlation among input parameters and a series of aggregation descriptors. In all, 336 correlation tests were performed, corresponding to a combination of seven detection input parameters, eight aggregation descriptors, and six transects. Among these, 181 tests were significant, indicating sensitivity between input parameters and aggregation descriptors. The aggregation-detection algorithm is sensitive to changes in threshold and minimum size, but less sensitive to changes in the connectivity criterion among aggregations.

Sobol′ Sensitivity Analysis Using a Neural Network Model of a LB-LOCA in the ZION Nuclear Power Plant With CATHARE-2 V2.5 Code

2013 ◽  
Author(s):  
Fabrice Fouet ◽  
Pierre Probst
Keyword(s):  
Neural Network ◽  
Sensitivity Analysis ◽  
Nuclear Power ◽  
Uncertainty Propagation ◽  
Correlation Coefficients ◽  
Computer Code ◽  
Numerical Application ◽  
Loss Of Coolant Accident ◽  
Input Parameters ◽  
Artificial Neural Network Ann

In nuclear safety, the Best-Estimate (BE) codes may be used in safety demonstration and licensing, provided that uncertainties are added to the relevant output parameters before comparing them with the acceptance criteria. The uncertainty of output parameters, which comes mainly from the lack of knowledge of the input parameters, is evaluated by estimating the 95% percentile with a high degree of confidence. IRSN, technical support of the French Safety Authority, developed a method of uncertainty propagation. This method has been tested with the BE code used is CATHARE-2 V2.5 in order to evaluate the Peak Cladding Temperature (PCT) of the fuel during a Large Break Loss Of Coolant Accident (LB-LOCA) event, starting from a large number of input parameters. A sensitivity analysis is needed in order to limit the number of input parameters and to quantify the influence of each one on the response variability of the numerical model. Generally, the Global Sensitivity Analysis (GSA) is done with linear correlation coefficients. This paper presents a new approach to perform a more accurate GSA to determine and to classify the main uncertain parameters: the Sobol′ methodology. The GSA requires simulating many sets of parameters to propagate uncertainties correctly, which makes of it a time-consuming approach. Therefore, it is natural to replace the complex computer code by an approximate mathematical model, called response surface or surrogate model. We have tested Artificial Neural Network (ANN) methodology for its construction and the Sobol′ methodology for the GSA. The paper presents a numerical application of the previously described methodology on the ZION reactor, a Westinghouse 4-loop PWR, which has been retained for the BEMUSE international problem [8]. The output is the first maximum PCT of the fuel which depends on 54 input parameters. This application outlined that the methodology could be applied to high-dimensional complex problems.

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.

Seismic hazard sensitivity analysis: A multi-parameter approach

1991 ◽  
Vol 81 (3) ◽  
pp. 796-817
Author(s):  
Nitzan Rabinowitz ◽  
David M. Steinberg
Keyword(s):  
Sensitivity Analysis ◽  
Seismic Hazard ◽  
Subjective Probability ◽  
Probability Distributions ◽  
Input Parameter ◽  
The Other ◽  
Probabilistic Assessment ◽  
Input Parameters ◽  
Parameter Approach

Abstract We propose a novel multi-parameter approach for conducting seismic hazard sensitivity analysis. This approach allows one to assess the importance of each input parameter at a variety of settings of the other input parameters and thus provides a much richer picture than standard analyses, which assess each input parameter only at the default settings of the other parameters. We illustrate our method with a sensitivity analysis of seismic hazard for Jerusalem. In this example, we find several input parameters whose importance depends critically on the settings of other input parameters. This phenomenon, which cannot be detected by a standard sensitivity analysis, is easily diagnosed by our method. The multi-parameter approach can also be used in the context of a probabilistic assessment of seismic hazard that incorporates subjective probability distributions for the input parameters.

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