Yield uncertainty

The partial factor for steel reinforcement, covering resistance uncertainties, yield strength variability and deterioration effects, varies within the range from 1.15 to 1.35. 

Introduction and Commercial Application JUe objective of performing appraisal activities on discovered accumulations is to reduce the uncertainty in the description of the hydrocarbon reservoir, and to provide information with which to make a decision on the next action. The next action may be, for example, to undertake more appraisal, to commence development, to stop activities, or to sell the prospect. In any case, the appraisal activity should lead to a decision which yields a greater value than the outcome of a decision made in the absence of the information from the appraisal. The improvement in the value of the action, given the appraisal information, should be greater than the cost of the appraisal activities, otherwise the appraisal effort is not worthwhile. 

The number of injectors required may be estimated in a similar manner, but it is unlikely that the exploration and appraisal activities will have included injectivity tests, of say water injection into the water column of the reservoir. In this case, an estimate must be made of the injection potential, based on an assessment of reservoir quality in the water column, which may be reduced by the effects of compaction and diagenesis. Development plans based on water injection or natural aquifer drive often suffer from lack of data from the water bearing part of the reservoir, since appraisal activity to establish the reservoir properties in the water column is frequently overlooked. In the absence of any data, a range of assumptions of injectivity should be generated, to yield a range of number of wells required. If this range introduces large uncertainties into the development plan, then appraisal effort to reduce this uncertainty may be justified. 

The uncertainties in choice of potential function and in how to approximate the surface distortion contribution combine to make the calculated surface energies of ionic crystals rather uncertain. Some results are given in Table VII-2, but comparison between the various references cited will yield major discrepancies. Experimental verification is difficult (see Section VII-5). Qualitatively, one expects the surface energy of a solid to be distinctly higher than the surface tension of the liquid and, for example, the value of 212 ergs/cm for (100)... 

For some experiments, the solar neutrino flux and the rate of decay of the proton being extreme examples, tire count rate is so small that observation times of months or even years are required to yield rates of sufficiently small relative uncertainty to be significant. For high count rate experiments, the limitation is the speed with which the electronics can process and record the incoming infomiation. 

The computation of mesopore size distribution is valid only if the isotherm is of Type IV. In view of the uncertainties inherent in the application of the Kelvin equation and the complexity of most pore systems, little is to be gained by recourse to an elaborate method of computation, and for most practical purposes the Roberts method (or an analogous procedure) is adequate—particularly in comparative studies. The decision as to which branch of the hysteresis loop to use in the calculation remains largely arbitrary. If the desorption branch is adopted (as appears to be favoured by most workers), it needs to be recognized that neither a Type B nor a Type E hysteresis loop is likely to yield a reliable estimate of pore size distribution, even for comparative purposes. 

Although a torsion test is simple to carry out, it is not commonly accepted as an integral part of a material specification furthermore, few torsion data exist in handbooks. If, as is usually the case, the design needs to be based on tensile data, then a criterion of elastic failure has to be invoked, and this introduces some uncertainty in the calculated yield pressure (8). 

A tabulation of the ECPSSR cross sections for proton and helium-ion ionization of Kand L levels in atoms can be used for calculations related to PIXE measurements. Some representative X-ray production cross sections, which are the product of the ionization cross sections and the fluorescence yields, are displayed in Figure 1. Although these A shell cross sections have been found to agree with available experimental values within 10%, which is adequate for standardless PKE, the accuracy of the i-shell cross sections is limited mainly by the uncertainties in the various Zrshell fluorescence yields. Knowledge of these yields is necessary to conven X-ray ionization cross sections to production cross sections. Of course, these same uncertainties apply to the EMPA, EDS, and XRF techniques. The Af-shell situation is even more complicated. 

The major artifacts contributing to uncertainties in PDCE results stem from effects caused by bombardment of nonideal specimens, particularly thick specimens. The ideal thick specimen would be a homogeneous, smooth electrical conductor that does not change during bombardment. Except for rather simple, well-defined layered structures (e.g., surface oxide layers), specimens having compositional variations with depth yield spectra whose analyses can have large inaccuracies. 

Obviously for this method to work the ratio T1IT2 must be appreciably smaller than unity. Provided this condition is met, this method is a simple and reliable way to test for an isokinetic relationship or to detect deviations from such a relationship. Exner shows examples of systems plotted both as log 2 vs. log and as AH vs. A5, demonstrating the inadequacy of the latter plot. Exner has also developed a statistical analysis of the Petersen method this analysis yields p and an uncertainty estimate of p. Exner has applied his statistical methods to 100 reaction series, finding that 78 of them follow approximately valid isokinetic relationships. 

Thus, tlie focus of tliis subsection is on qualitative/semiquantitative approaches tliat can yield useful information to decision-makers for a limited resource investment. There are several categories of uncertainties associated with site risk assessments. One is tlie initial selection of substances used to characterize exposures and risk on tlie basis of the sampling data and available toxicity information. Oilier sources of uncertainty are inlierent in tlie toxicity values for each substance used to characterize risk. Additional micertainties are inlierent in tlie exposure assessment for individual substances and individual exposures. These uncertainties are usually driven by uncertainty in tlie chemical monitoring data and tlie models used to estimate exposure concentrations in tlie absence of monitoring data, but can also be driven by population intake parameters. As described earlier, additional micertainties are incorporated in tlie risk assessment when exposures to several substances across multiple patliways are suimned. 

Abundances of lUPAC (the International Union of Pure and Applied Chemistry). Their most recent recommendations are tabulated on the inside front fly sheet. From this it is clear that there is still a wide variation in the reliability of the data. The most accurately quoted value is that for fluorine which is known to better than I part in 38 million the least accurate is for boron (1 part in 1500, i.e. 7 parts in [O ). Apart from boron all values are reliable to better than 5 parts in [O and the majority arc reliable to better than I part in 10. For some elements (such as boron) the rather large uncertainty arises not because of experimental error, since the use of mass-spcctrometric measurements has yielded results of very high precision, but because the natural variation in the relative abundance of the 2 isotopes °B and "B results in a range of values of at least 0.003 about the quoted value of 10.811. By contrast, there is no known variation in isotopic abundances for elements such as selenium and osmium, but calibrated mass-spcctrometric data are not available, and the existence of 6 and 7 stable isotopes respectively for these elements makes high precision difficult to obtain they are thus prime candidates for improvement. 

Ip = hG/c y is the Planck length. Since c6t < 6x, A and B are outside of each other s light cone and local field theory assures us that these two experiments can be performed completely independently of one another. Heisenberg s uncertainty principle, however, asserts that these two measurements will also yield an energy fluctuation on the order of AE > Ip. We know that the gravitational... 

A brown additive compound of iodine with the dibromo compound may separate during the titration this compound usually dissolves during the subsequent titration with thiosulphate, yielding a yellow solution so that the end point with starch may be found in the usual manner. Occasionally, the dark-coloured compound, which contains adsorbed iodine, may not dissolve readily and thus introduces an uncertainty in the end-point this difficulty may be avoided by adding 10 mL of carbon disulphide before introducing the potassium iodide solution. 

How does yield stress depend on a filler concentration It is shown in Fig. 9 that appreciable values of Y appear beginning from a certain critical concentration cp and then increase rather sharply. Though the existence of cp seems to be quite obvious from the view point of the possibility of contacts of the filler, i.e. the beginning of a netformation in the system, practically the problem turns on the accuracy of measuring small stresses in high-viscosity media. It is quite possible to represent the Y(cp) dependence by exponential law, as follows from Fig. 10, for example, leaving aside the problem of the behavior of this function at very low concentrations of the filler, all the more the small values of are measured with a significant part of uncertainty. 

Use the Third Law to calculate the standard entropy, S°nV of quinoline (g) p — 0.101325 MPa) at T= 298,15 K. (You may assume that the effects of pressure on all of the condensed phases are negligible, and that the vapor may be treated as an ideal gas at a pressure of 0.0112 kPa, the vapor pressure of quinoline at 298.15 K.) (c) Statistical mechanical calculations have been performed on this molecule and yield a value for 5 of quinoline gas at 298.15 K of 344 J K l mol 1. Assuming an uncertainty of about 1 j K 1-mol 1 for both your calculation in part (b) and the statistical calculation, discuss the agreement of the calorimetric value with the statistical... 

With the currently available information, the largest uncertainty is in the oxygen-potential model and the parameter values within the model. A recent assessment of the Pu/0 system (42) has indicated that the values of the parameters used in the Blackburn model yield slightly smaller oxygen potentials than those of Alexander (22), those of Tetenbaum (22-42) and those extrapolated from the data of Woodley (43). A reevaluation of the model parameters would allow a better fit to these experimental data ... 

The uncertainties given are calculated standard deviations. Analysis of the interatomic distances yields a selfconsistent interpretation in which Zni is assumed to be quinquevalent and Znn quadrivalent, while Na may have a valence of unity or one as high as lj, the excess over unity being suggested by the interatomic distances and being, if real, presumably a consequence of electron transfer. A valence electron number of approximately 432 per unit cell is obtained, which is in good agreement with the value 428-48 predicted on the basis of a filled Brillouin polyhedron defined by the forms 444, 640, and 800. ... 

Dicarbonyls. A third area of uncertainty is the treatment of dicarbonyls formed from aromatic or terpene hydrocarbon oxidation. (The simplest is glyoxal, CHOCHO, but a large number have been identified, 47. The yields and subsequent reactions of these compounds represent a major area of uncertainty in urban air photochemistry (186) and since they may be a significant source of HOjj through photolysis, inaccuracies in their portrayal may result in errors in calculated values of HO. and HO2.. 

We take a Bayesian approach to research process modeling, which encourages explicit statements about the prior degree of uncertainty, expressed as a probability distribution over possible outcomes. Simulation that builds in such uncertainty will be of a what-if nature, helping managers to explore different scenarios, to understand problem structure, and to see where the future is likely to be most sensitive to current choices, or indeed where outcomes are relatively indifferent to such choices. This determines where better information could best help improve decisions and how much to invest in internal research (research about process performance, and in particular, prediction reliability) that yields such information. 

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