TAILIEUCHUNG - Heat Transfer Handbook part 94

Heat Transfer Handbook part 94. The Heat Transfer Handbook provides succinct hard data, formulas, and specifications for the critical aspects of heat transfer, offering a reliable, hands-on resource for solving day-to-day issues across a variety of applications. | 926 EXPERIMENTAL METHODS T Figure Graphical representation of the observed probability distribution r shown as black triangles and the Gaussian distribution fc straight line from Table . thumb when rx2 it cannot be said with confidence whether or not the expected distribution is followed. However if rx2 the chi-square test shows that the distribution expected is not being followed. The other extreme range when rx2 is very unlikely to happen naturally therefore this range is regarded as suspicious Dally et al. 1993 . Note It is important that the number of events of each measurement be at least five for the result to be statistically meaningful. To bring into a better perspective the meaning of the chi-square test example considered in the preceding paragraph the probability distribution r and Gaussian distribution rG observed presented in Table are shown graphically in Fig. . It is clear from Fig. that the distribution r diverges from the Gaussian distribution fG at the extreme temperature values of T 22 C and T 24 C. CALCULATION ERROR Once the uncertainty of a certain measured quantity is determined the next complication arises when a quantity is calculated from this measurement. The question is how the uncertainty of a measured quantity will affect the uncertainty of the quantity calculated. This question becomes even more difficult to answer when more than one measuring quantity is used for the calculation. The most common procedure used to evaluate the uncertainty of the quantity calculated in this case is based on the Kline and McClintock 1953 method. CURVE FITTING 927 When a certain quantity Q is calculated from a number n of measured quantities T and each measured quantity has uncertainty U the corresponding uncertainty of the calculated quantity Uq can be estimated from Uq dQ 2 u 012 The conceptual basis for eq. is isuussdi by Coleman and Steel 1989 . Equation 2. 55 provides a good estimate of the .

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