This book reveals to practitioner engineers and technicians involved in experimentation, modeling, and simulation, an approach based on uncertainty analysis concepts for the validation of simulation processes. This new edition incorporates new material: two methods for uncertainty propagation evaluation (Taylor Series Method, TSM, and the Monte Carlo Method, MCM) and a new chapter on "Validation of Simulations", where the contribution of the authors to the new ASME "Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer" V& V20-2009 is presented. The topic of uncertainty evaluation follows the ISO Guide: "Evaluation of Measurement Data-Supplement 1 to the "Guide to the expression of the uncertainty in Measurement"-Propagation of Distributions Using a Monte Carlo Method".The first chapter traces the leading line of the book, by detailing the importance of the uncertainty evaluation from the early stages of experiment planning through the validation of the simulations. The important definitions, the basic concepts of random and systematic errors and of measurement uncertainty are introduced, and the goal of the validation of simulations by quantitative evaluations is presented. Chapter 2 introduces the statistical distributions most encountered in uncertainty analysis, the estimators for their parameters, the confidence intervals for the mean value, and the Chauvenet's criterion for rejection of outliers from a sample. Finally, the uncertainty of one measured variable is evaluated using both TSM and MCM. Additional exercises are proposed at the end of this and every chapter.Chapter 3 continues the topic of the evaluation of the measurement uncertainty for the multivariate parameters using the TSM and MCM. For the TSM, an extrapolation of the relation of composing partial uncertainties for the limits of the confidence intervals is deduced for particular cases. The authors are unclear here because this can confuse some readers, as the relation may be seen as a method of composing confidence intervals deriving from different distributions-which has no mathematical justification. The advantage of the MCM method in evaluation of the uncertainty intervals when the uncertainty values are large and the resulting distribution is skewed is discussed through examples, along with the method of determination of the confidence intervals.Chapter 4 deals with application of the uncertainty analysis in experiment planning and validation. Through comparative, detailed examples, the uncertainty analysis is presented as a decision element in the method selection and for adjustments of experiments in the planning phase. The need of considering the domain of values of the variables and their evolving uncertainty values is discussed. The authors give advice to avoid pitfalls related to interpretation of specifications. In comparing the experimental techniques, the use of the uncertainty magnification factors and the uncertainty percentage contribution is recommended for decision making process.The fift...
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 phasechange 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.
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