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Urban water supply and life cycle assessment
Published in Thomas Bolognesi, Francisco Silva Pinto, Megan Farrelly, Routledge Handbook of Urban Water Governance, 2023
Uncertainty refers to random or systematic errors (e.g., modelling choices, measurement inaccuracies, or lack of scientific knowledge) relative to the system under study. Thus, uncertainty analysis refers to procedures that aim to quantify how these uncertainties are transferred to the LCA results. The most applied method for this in LCAs is the Monte Carlo technique. Sensitivity analysis is performed to obtain additional information to aid interpretation, as it quantifies the contribution of model parameters in the LCA result. It can be done locally, by varying a specific input at a time and checking its effect on the output result, or globally, by checking the contribution of each input during the propagation of several of them on the output result variance (Igos et al. 2019).
Introduction to Solar Radiation Measurements
Published in Daryl R. Myers, Solar Radiation, 2017
A measurement equation describing the process to be analyzed (such as an equation describing the derivation of a calibration factor from measured parameters) is required. Estimates or derived standard uncertainties are assigned to each variable in the measurement equation. Standard uncertainty Us is representative of the statistical standard deviation of Gaussian or normally distributed data for the response, especially from empirical data that can be analyzed with statistical procedures. The standard uncertainty is dependent on the distribution of the response data being analyzed. The standard uncertainty may need to be derived or estimated from specifications, independent test data, or engineering experience. If functions or equations are fit or used in the measurement or calibration process, the standard errors of estimates or mean residuals from fitted functions serve as additional sources of standard uncertainty in the process.
Engineering: Making Hard Decisions Under Uncertainty
Published in John X. Wang, Decision Making Under Uncertainty, 2002
Engineering decision making theory is a body of knowledge and related analytical techniques of different degrees of formality designed to help an engineer choose among a set of alternatives in light of their possible consequences. Engineering decision-making theory can apply to conditions of certainty, risk, or uncertainty. Decision making under certainty means that each alternative leads to one and only one consequence, and a choice among alternatives is equivalent to a choice among consequences. In decision under risk each alternative will have one of several possible consequences, and the probability of occurrence for each consequence is known. Therefore, each alternative is associated with a probability distribution, and a choice among probability distributions. When the probability distributions are unknown, one speaks about decision making uncertainty.
Extraction of algae biodiesel for power generation and comparison of sustainable fuels using MCDM
Published in International Journal of Ambient Energy, 2022
Alpesh Virendrabhai Mehta, Nirvesh Sumanbhai Mehta
The measurements of all physical quantities have few degree of uncertainty that may come from a various sources. An uncertainty or error analysis is necessary to establish the bounds on the accuracy of the estimated parameters. Uncertainty defined as an estimation of the error, is a numerical value, where as error is defined as the difference between the measured value and the true value of the thing being measured. Uncertainties in the experiments may result from calibration, selection of instruments, test planning, environment, observation, condition, and reading. Uncertainty analysis is required to prove the accuracy of the tests. The uncertainties for the basic measurements like temperature, time, length, weight, pressure etc., are equal to the least count of respective instruments (Kavathia 2018; Arunkumar et al. 2018; Santhosh and Padmanaban 2016; Vasudeva et al. 2016; Wamankar 2015; Vrijdag 2014; Bettuzzi 2009; Lau 2005).
Marine accident learning with Fuzzy Cognitive Maps: a method to model and weight human-related contributing factors into maritime accidents
Published in Ships and Offshore Structures, 2022
Beatriz Navas de Maya, R. E. Kurt
In addition, to combine the results obtained from both data sources (i.e. historical data and expert judgement), a sensitivity analysis is conducted in the fourth stage of the MALFCMS method. The purpose of a sensitivity analysis is to understand how the uncertainty in the output of a mathematical model or system can be divided and allocated to different sources of uncertainty in its inputs. Hence, as the aim of this stage is to combine the outputs from the historical data and expert opinion analyses, a sensitivity analysis seems adequate to perform this task. Thus, sensitivity analyses have been already applied in the literature to merge the outputs from expert analysis and questionnaires (A. Azadeh et al. 2014). Hence, in order to combine the finding from the historical data analysis stage obtained by de Maya et al. (2019) and the expert opinion stage developed on this study, a sensitivity analysis is performed in this section. Table 13 includes the weights of each HF normalised from both, the historical data analysis stage and the expert opinion stage, and the final weights proposed, in which the same importance has been assigned to both sources of data. In addition, Figure 5 represents the sensitivity analysis to provide a better understanding of the process.
Experimental Investigation on Thermal Protection of High Temperature and High Velocity Jet Impinging a Cross-Shaped plate
Published in Heat Transfer Engineering, 2020
Jian Cai, Wei Ye, Shuyang Tu, Shaochen Tian, Xu Zhang
Uncertainty refers to the degree to which the measured value cannot be affirmed because of the existence of the measurement error. And here we only consider the uncertainty caused by the instrument itself, regardless of the deviation caused by probe fouling, probe performance changes or measurement conditions. In the experiment, we used 28 K-type thermocouples to measure the temperature of the inner surface of the specimen, and 44 T-type thermocouples to measure the outer surface temperature. The measurement of temperature accounted for a large proportion of the whole testing process. So it is necessary to analyze the uncertainty of all cases. The uncertainty of K-type thermocouple is 1.1 °C () and the uncertainty of FLUKE 2638 A with K-type thermocouple using internal cold-junction compensation is 0.6 °C (). It is 0.5 °C () and 0.56 °C () for T-type thermocouple, respectively. According to the law of propagation of uncertainty [23], see Eq. 14, the combined uncertainty for K-type thermocouple is And the uncertainty of T-type is 0.75 °C.