W. G. Steele

Engineering application of experimental uncertainty analysis

Publication in late 1993 by the International Organization for Standardization (ISO) of the Guide to the Expression of Uncertainty in Measurement in the name of ISO and six other international organizations has, in everything but name only, established a new international experimental uncertainty standard. In this article, an analysis of the assumptions and approximations used in the development of the methods in the ISO guide is presented, and a comparison of the resulting equation with previously published uncertainty analysis approaches is made. Also discussed are the additional assumptions necessary to achieve the less complex large sample methodology that is recommended in AIAA Standard S-071-1995, Assessment of Wind Tunnel Data Uncertainty, issued in 1995. It is shown that these assumptions are actually less restrictive than those associated with some previously accepted methodologies. The article concludes with a discussion of some practical aspects of implementing experimental uncertainty analysis in engineering testing.

Use of previous experience to estimate precision uncertainty of small sample experiments

The determination of precision uncertainty is described for experiments where the test measurements are obtained from single readings or from a limited number of independent readings. For these cases, the limits of precision uncertainty are best determined from previous experience based on larger sample sizes over the full range of variations that occur for a specific test. We describe the appropriate procedures to determine the limits of precision uncertainty based on previous experience for small sample experiments. A Monte Carlo simulation technique is used to model the experiments to demonstrate the effectiveness of using the precision uncertainty to determine the range that covers the true test result

Computer Simulation of a High-Temperature Thermal Energy Storage System Employing Multiple Families of Phase-Change Storage Materials

Previous work by one of the authors entailed modeling of a packed bed thermal energy storage system utilizing phase-change materials (PCM). A principal conclusion reached is that the use of a single family of phase-change storage material may not in fact produce a thermodynamically superior system relative to one utilizing sensible heat storage material. This paper describes the model constructed for the high-temperature thermal energy storage system utilizing multiple families of phase-change materials and presents results obtained in the exercise of the model. Other factors investigated include the effect on system performance due to the thermal mass of the containment vessel wall and variable temperature of the flue gas entering the packed bed during the storage process. The results obtained indicate efficiencies for the system utilizing the five PCM families exceeding those for the single PCM family by as much as 13 to 26 percent. It was also found that the heat transfer to the containment vessel wall could have a significant detrimental effect on system performance.

Heat Transfer in a High-Temperature Packed Bed Thermal Energy Storage System—Roles of Radiation and Intraparticle Conduction

A model is developed and experimentally verified to study the heat transfer in a high-temperature packed bed thermal energy storage system utilizing zirconium oxide pellets. The packed bed receives flue gas at elevated temperatures varying with time during the storage process and utilizes air for the recovery process. Both convection and radiation are included in the model of the total heat transfer between the gas and the pellets. It is found that thermal radiation and intraparticle conduction do not play a major role in the overall heat transfer in the packed bed under the specified operating conditions. An uncertainty analysis is performed to investigate the propagation of the uncertainties in the variables to the overall uncertainty in the model predictions and the experimental results.

Comparison of ANSI/ASME and ISO models for calculation of uncertainty

Abstract The procedures for determining experimental uncertainty contained in the current version of the ANSI/ASME Standard (PTC19.1-1985) Measurement Uncertainty are compared with the procedure presented in the 1993 ISO Guide to the Expression of Uncertainty in Measurement . A Monte Carlo technique is used to stimulate several example experiments to demonstrate the effectiveness of the models for predicting an appropriate uncertainty interval at a given confidence level, either 95% or 99%. The simulation results show that the ISO model is more consistent in providing an uncertainty interval at the desired level of confidence. The simulations also show that if a determined results has degrees of freedom of nine or more, simplified models using values of t 95 = 2.0 for a 95% confidence level and t 99 = 2.6 for a 99% confidence level can be used to determine the uncertainty to a good approximation.

Electrostatic separation of ultrafinely ground low-rank coals

ABSTRACT Low-rank coals have an as mined ash content in the range of 10 to 30 per cent. It has been well documented that ash in these coals occurs as organically bound inorganics and as finely divided minerals which are dispersed throughout the coal structure. Consequently, burning some of these coals in conventional power plants may result in slagging of furnace walls and added costs for pollution abatement equipment. Coal cleaning by dry methods has special appeal where coal is utilized as dry solids since moisture removal after treatment and water pollution control are not required. Utilizing the differences in the electrostatic properties of finely ground coal and minerals is not commercially available but is one dry method with potential for further development. This method depends on the deflection of small particles by an electrical field with the positively charged particles moving in the direction of the field while the negatively charged particles move in the opposite direction. A key factor in ...

Computer-assisted uncertainty analysis

A computer program has been developed to perform uncertainty analysis calculations based on user input of the data reduction equation, nominal values of the variables in the equation, and uncertainty estimates for each variable. The code is based on techniques consistent with those recommended in the 1993 ISO Guide to the Expression of Uncertainty in Measurement. Various options and features of the program allow it to be used in preliminary and detailed test design as well as in data analysis.

Estimating uncertainty intervals for linear regression

The best straieht line throueh a set of exoerimental data is ofteu-obtained through the appiication of linear regression analysis, which provides the values of the slope and intercept for this line. The uncertainty intervals that should be associated with the values of the slope and intercept are also impnant and needed information. Standard statistical techniques to estimate the uncertainties in the slope and intercept are of limited use, primarily due to their assumptions which preclude their use with bias uncertainties. The approach to determining the uncertainty in the values of slope and intercept presented in this paper is based upon applying the uncertainty propagation equations to the regression analysis equations for the slope and intercept. This approach provides for the inclusion of precision uncertainties, bias uncertainties, and orrelated bias uncertainties. Using a Monte Carlo type simulation technique, it is shown that this approach provides appropriate estimates of the uncertainty intervals in cases with bias uncertainties and precision uncertainties in both the X and Y measurements. Nomenclature B,-bias limit B*=covariance estimator c=Y-intercept m=slope of lime M=uumber of dependent variable measuremen& per independent variable N=number of data points &precision limit r=experimental resnlt Si=standard deviation X=independent variable X,=measured variables Y=dependent variable U,=uncertainty interval p=actual bias error

Bias error reduction using ratios to baseline experiments - Heat transfer case study

Concluding Remarks In this study, numerical simulations by means of a finite difference method have been performed to render the effects of the relevant physical parameters of the problem considered (Re, Gr, and (/>) on the interaction between the shear-driven flow and the buoyant recirculating flow during the heating process of the moving plate. Results indicate that the sheardriven flowfields in the divided subchannels can be strongly affected by the buoyancy force due to an increase of Grashof number; a bicellular recirculation arises respectively within the subchannels in the region around the heaters. Moreover, an increase in Grashof number tends to promote the upstream diffusion effect due to the buoyant recirculation, particularly for the upper channel. Accordingly, the local Nusselt number on the top surface of the moving plate becomes less localized shifting in the upstream direction. Moreover, the flowfield and temperature distribution in the upper subchannel are found to be more sensitive to the inclination of the duct. For the range of inclination considered, the temperature as well as the heat transfer on the moving plate are rather unaffected by the variation of the duct orientation.

Asymmetric systematic uncertainties in the determination of experimental uncertainty

A new method is presented for determining a 95% confidence uncertainty interval for an experimental result when some of the measured variables have asymmetric systematic uncertainties. The technique is compared with the approximate method given in the American National Standards Institute/American Society of Mechanical Engineers (ANSUASME) standard on measurement uncertainty.