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Uncertainty Analysis
Published in Patrick F. Dunn, Fundamentals of Sensors for Engineering and Science, 2019
Experimental uncertainty analysis involves the identification of errors that arise during all stages of an experiment and the propagation of these errors into the overall uncertainty of a desired result. The specific goal of uncertainty analysis is to obtain a value of the overall uncertainty (expanded uncertainty), Ux, of the variable, x, such that either the next-measured value of a sample or the true value of the sample’s population can be estimated. In general, this is expressed as xestimate=x±Ux(%C), in which the estimate is made with %C confidence.
Uncertainty Analysis
Published in Patrick F. Dunn, Michael P. Davis, Measurement and Data Analysis for Engineering and Science, 2017
Patrick F. Dunn, Michael P. Davis
Experimental uncertainty analysis involves the identification of errors that arise during all stages of an experiment and the propagation of these errors into the overall uncertainty of a desired result. The specific goal of uncertainty analysis is to obtain a value of the overall uncertainty (expanded uncertainty), Ux, of the variable, x, such that either the next-measured value of a sample or the true value of the sample’s population can be estimated. In general, this is expressed as () xestimat=x±Ux(%C),
Study on performance-emission trade-off and multi-objective optimization of diesel-ethanol-palm biodiesel in a single cylinder CI engine: a Taguchi-fuzzy approach
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2020
Suman Dey, Narath Moni Reang, Madhujit Deb, Pankaj Kumar Das
Precious measurement with accurate calibration and atmospheric condition is the important necessity for any reliable and precision measurements. By counting proper errors, uncertainty analysis gives a proper explanation about the repeatability of the investigations. The experimental uncertainty analysis is a technique to quantify error associated with emission measurement and the derived engine performance parameters on account of the employed instrumentation, calibration, observation accuracy, and the methodology of the experiment in a given ambient condition. The uncertainty analysis has two main components, namely, bias related to accuracy and the random variation that occurs during repeated measurements. The uncertainty values of performance parameters and gas analyzer (AVL Digas 444) are depicted in Tables 2 and 4. At last, example of TSU calculation of emission in a particular operation is calculated in Table 5.
Analyzing the impact of adding aluminum oxide and cerium oxide nanoparticles to waste cooking biodiesel on engine performance, combustion and emissions characteristics
Published in Petroleum Science and Technology, 2022
Suraj Bhan, Raghvendra Gautam, Pushpendra Singh
Experimental uncertainty analysis is the review and assessment of uncertainty in an experimental study. The uncertainties in the measured values may develop because of errors in the measuring instruments, human errors in measurement of the experimental data and computations errors, and slightly affected by the environmental factors (Hussain, F. et al. 2020). The experimental process carried the few possibility of uncertainties which are concern to all estimations. Uncertainty are due to environment condition, inaccurate selection of the instrument, encompassing state of the lab, engine instrument calibration, modification and change of the engine set-up, operating condition, and manual calibration of reading (Yusuf et al. 2022; Ranganatha Swamy et al. 2021)
Statistical and experimental investigation of the influence of fuel injection strategies on gasoline/diesel RCCI combustion and emission characteristics in a diesel engine
Published in International Journal of Green Energy, 2021
Ramachander Jatoth, Santhosh Kumar Gugulothu, G.Ravi Kiran Sastry, M.Siva Surya
Precise calibration with optimal atmospheric conditions is equally important while using precision measurement devices for reliability. Uncertainty analysis gives a broad view of experimental repeatability by counting critical errors during measurement. In given atmospheric conditions, quantifying performance and emission parameter measurement errors due to the methodology of the experiment, observation accuracy, calibration, and instrumentation is called the experimental uncertainty analysis. Two major components are identified with uncertainty analysis: repeated measurement random variation, and second is accuracy bias.