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Technical Approach to Human Health Endangerment Assessment
Published in Kofi Asante-Duah, Management of Contaminated Site Problems, 2019
Sensitivity analysis is generally defined as the assessment of the impact of changes in input values on model outputs. Often a useful adjunct to the traditional uncertainty analysis, sensitivity analysis is comprised of a process that examines the relative change or response of output variables caused by variation of the input variables and parameters (Calabrese and Kostecki, 1991; Iman and Helton, 1988; USEPA, 1992a, 1997a). It is indeed a technique that tests the sensitivity of an output variable to the possible variation in the input variables of a given model. Accordingly, the process serves to identify the sensitivity of the calculated result vis-à-vis the various input assumptions—and thus identify key uncertainties, as well as help bracket potential risks so that policy-makers can make more informed decisions or choices.
Advanced economic analysis of alternatives
Published in John Vail Farr, Isaac Faber, Engineering Economics of Life Cycle Cost Analysis, 2018
Sensitivity analysis is the study of how inputs, variations, and assumptions affect the output of a mathematical model. Excel has made sensitivity analysis not only easy but also an important component of all economic analysis. Sensitivity analysis allows us toIdentify the key input elements, which can allow for more effort quantifying the value of the most important inputs,Develop a visual presentation of the effects of various inputs on the output, andAsk “what if” to determine the amount of change in a data point that might change the output of the analysis.
Risk Analysis
Published in Mohammad Modarres, Mark P. Kaminskiy, Vasiliy Krivtsov, Reliability Engineering and Risk Analysis, 2016
Mohammad Modarres, Mark P. Kaminskiy, Vasiliy Krivtsov
Sensitivity analysis is the method for determining the significance of choice of a model or its parameters, the assumptions for including or not including a barrier, phenomenon, or hazard, the performance of specific barriers, the intensity of hazards, and the significance of any highly uncertain input parameters or variables to the final risk value calculated. The process of sensitivity analysis is straightforward. The effects of the input variables and assumptions in the PRA are measured by modifying them one at a time by several folds, factors, or even one or more orders of magnitude, and measuring relative changes observed in the PRA’s risk results. Those models, variables, and assumptions whose change leads to the highest change in the final risk values are determined as sensitive. In such a case, revised assumptions, models, additional failure data, and more mechanisms of failure may be needed to reduce the uncertainties associated with sensitive elements of the PRA.
Fatigue condition assessment of subsea pipelines under vortex induced vibration and cyclical lateral displacement
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2023
Xinhong Li, Yaping Hu, Ziyue Han
Sensitivity analysis assumes that input parameters to the model are not accurate, which shows the variation of system reliability, given some variation of input parameters values. It refers to how sensitive the performance of a model is to minor changes in input parameters (Cai et al. 2013). In this paper, the uncertainty of key factors affecting CLD of pipelines is assumed to be ± 10%, and the variations of pipeline fatigue damage are observed. The results are shown in Figure 12. We can know that the changes of fatigue damage of pipelines due to the change of span length are largest. The fatigue damage corresponding to 10% decrease is 6.19E–05, and the fatigue damage corresponding to 10% increase is 2.52E–05. It shows that spanning length is an important factor leading to fatigue failure of pipeline under cyclic displacement. These factors can be ordered by their sensitivity, that is, spanning length > pipe diameter > tidal period > pipe length. The sensitivity analysis indicates the effectiveness of fatigue damage model.
Development of flood susceptibility map using a GIS-based AHP approach: a novel case study on Idukki district, India
Published in Journal of Spatial Science, 2023
Zohaib Ahmed Khan, Bharat Jhamnani
Sensitivity analysis is performed to determine the key factors that have the greatest impact on the development of the flood susceptibility map. Sensitivity analysis is a method employed to ascertain the impact of changes or shifts in specific inputs or parameters of a given system on the resultant outputs or outcomes of that system. The evaluation of the strength and dependability of models is a prevalent practice in decision-making procedures. The goal of performing sensitivity analysis is to comprehend the interrelationships and interdependencies that exist between the input parameters and the resultant output of a given model or system. The outcomes of sensitivity analysis facilitate the identification of crucial variables or parameters that exert a significant impact. These data can be utilised to allocate resources effectively in data-gathering, concentrate on the most significant variables and enhance the decision-making procedures. Additionally, it has the potential to offer valuable perspectives on the hazards and unpredictability linked to the system under examination. The present investigation conducts a sensitivity analysis by utilising the final set of weights obtained from the AHP model. According to the sensitivity analysis as shown in Figure 7, parameters such as rainfall, elevation, distance from river, slope and TWI are the key contributing variables, while aspect, geology, LULC and soil are the least important variables among all the flood conditioning parameters.
Design and Double-Stage Optimization of Synchronous Reluctance Motor for Electric Vehicles
Published in Electric Power Components and Systems, 2023
Erdal Bekiroglu, Sadullah Esmer
Sensitivity analysis is an analysis method in which the output expression is observed by changing the input parameters within certain limits. This method has many options for input parameters. Many parameter optimization studies of electric motors focused on the skew technique in the literature. Some of these studies made a skewed rotor design [14–16] and some made a skewed stator design [17, 18]. In this study, unlike other studies, genetic algorithm is applied in the first stage and stator skewing technique is used in the second stage to minimize torque ripple using sensitivity analysis. Thus, it is aimed to obtain better results by using a double optimization technique. The stator skew angle parameter has been changed from 0 to 7° in one-degree increments.