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Uncertainty Analysis for Hydraulic Measurements
Published in Marian (Editor-in-Chief) Muste, Dennis A. Lyn, David M. Admiraal, Robert Ettema, Vladimir Nikora, Marcelo H. Garcia, Experimental Hydraulics: Methods, Instrumentation, Data Processing and Management, 2017
Marian (Editor-in-Chief) Muste, Dennis A. Lyn, David M. Admiraal, Robert Ettema, Vladimir Nikora, Marcelo H. Garcia
Uncertainty analysis is a rigorous methodology for determining uncertainties of measurement results using statistical and engineering concepts. The measurement process for a specific measurand does not include just the measurement system used to produce the final result, but also the effects induced by the experimental facility, operator actions, and other measurement environment influences (e.g., change in the room temperature). Collectively, these components form the experimental process, as described in Section 4.1, Volume I. The implementation of GUM protocols requires a measurement model that is associated with the measurement process. The model is referred to as the functional relationship. The items required by a model to define a measurand are labeled as input quantities (independent variables). The output quantity in a measurement model is the measurand (dependent variable). The input and output quantities are treated mathematically as random variables characterized by their probability distributions, mean values, and standard deviations (see also Section 6.2.1, Volume I). The probability distributions are determined from measurements or by using the best available knowledge. Correction terms should be included in the model when the conditions of the measurements are not exactly as stipulated. There will be an uncertainty associated with the estimate of a correction term, even if the estimate is zero, as is often the case. Data about the quantities representing physical constants involved in the functional relationship should also be considered in the model.
Introduction to Solar Radiation Measurements
Published in Daryl R. Myers, Solar Radiation, 2017
A detailed discussion of the propagation of uncertainty in various measurement devices is not the object of this book. But, a description of the concepts involved and basic results is in order. Uncertainty analysis is the derivation of the possible error in a measurement instrument or data point derived from an instrument. Uncertainty consists of precision (repeatability, scatter of data) and accuracy (how different in magnitude and direction) with respect to the exact, correct, or “true” value of the measurement. The accuracy of models cannot be greater than the uncertainty in the data used to validate the models. Models developed using correlations between input parameters and irradiance measurements inherently carry the uncertainty in the measurements into the models.
Commercial Prospects and Manufacturing Costs
Published in Kunwu Fu, Anita Wing Yi Ho-Baillie, Hemant Kumar Mulmudi, Pham Thi Thu Trang, Perovskite Solar Cells, 2019
Kunwu Fu, Anita Wing Yi Ho-Baillie, Hemant Kumar Mulmudi, Pham Thi Thu Trang
Chang et al.8 recognize the uncertainties in the cost input data for perovskite cell technology and therefore, developed a costing method that builds on the commonly used CoO approach but factors the uncertainties into the calculations through a Monte Carlo analysis. In their analysis, three values are selected for each cost parameter which are the “nominal,” “low”, and “high” values representing the uncertainty range. The impact of the uncertainty in each parameter is assessed using a Monte Carlo analysis by generating a few thousand scenarios. For each scenario, the value of each cost parameter is generated randomly according to its two half normal distribution, and then the cost calculations are completed using these generated values. The distribution of the cost outputs from the scenarios can then be analyzed to understand the uncertainty of these cost estimates. The advantage of this method is that it provides a very quick assessment of the process sequence allowing for uncertainties without the long and involved process of collecting “sufficiently accurate” data, which at times are unavailable. Using this method, the manufacturing cost estimate is delivered very rapidly with a list of cost drivers that highlight the opportunities for the greatest cost improvement. The uncertainty analysis identifies key sources of cost uncertainty highlighting areas that require better data sourcing. Chang et al. applied this method to analysis three perovskite cell structures (Figure 18.1) based on demonstrated process sequences (Table 18.1) to produce functional laboratory-scale modules of series-connected cells.
The effect of hydrogen addition to Cynara biodiesel on engine performance and emissions in diesel engine
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Hüseyin Köse, Mustafa Acaroğlu
Any experimental result involves some level of uncertainty that may occur due to several reasons such as reading, observation and environmental conditions, and the precision of the measuring equipment. Uncertainty analysis is a procedure that determines the measurement errors which result from measurement tools or individuals and provides a methodological approach regarding the effect of such errors on the results. According to this method, let R be the quantity to be measured in the system, and x1, x2, x3, … …, xn be the n number of independent variables that affect this quantity. In this case;
Energy, exergy, economic and environmental (4E) assessments of a tea withering trough coupled with a solar air heater having an absorber plate with Al-can protrusions
Published in International Journal of Ambient Energy, 2022
Anindita Sharma, Partha P. Dutta
The uncertainty analysis helps to detect the errors in the estimated quantities from the measured ones. The uncertainty in the dependent variable is determined by Eq. (33)- where, ΔY is the uncertainty in the estimated value and ΔX1, ΔX2, .… , ΔXn are the errors in the independent variables (Holman 2007).
Sustainable living with risks: meeting the challenges
Published in Human and Ecological Risk Assessment: An International Journal, 2019
Environmental risks are associated with various uncertainties, and these uncertainties originate from the uncertain properties of the natural world and the uncertain development of human society. In general, these uncertainties do not affect directly on the sustainable living of humans in short term. However, they will definitely have effect in the end. For example, the subjective uncertainty in determining the quality standards for water, air and soil will result in the overestimate or underestimation of the environmental risks (Smith 2018), affecting the sound decision-making. The uncertainties in natural media such as climate and soil properties and anthropogenic factors such as agricultural practices and pesticide properties will affect the accuracy of risk prediction, and lack of quantifying the probability and reliability of the prediction will weaken the management of the environmental risks (Lammoglia et al.2018). As such, obtaining reliable estimation of the risks and reducing the uncertainties are critical to live a sustainable lifestyle. At least, it is quite necessary to perform uncertainty analysis to understand the sources and properties of these uncertainties to reduce the uncertainties (Baustert et al.2018). However, the uncertainties from human factors are more difficult to locate and quantify, because human activities are too complex to quantify. Large volume of data can be helpful to quantify and reduce the uncertainty. We are now in a big data era, and big data have brought us lots of benefits. However, data in natural science are still limited and the data sharing mechanisms are still not satisfactory, which has been criticized by many scholars (Li 2016, 2018; Chen et al.2018; Li et al.2018b; Li and Qian 2018b). Poor data sharing is an obstacle restricting the sustainable living in a world full of risks and uncertainties. Therefore, the biggest challenge restricting sustainable living is how to quantify and reduce uncertainties by an improved data sharing mechanism.