Crawly 1

In certain crystals, e.g. in quartz, there is chirality in the crystal structure. Molecular chirality is possible in compounds which have no chiral carbon atoms and yet possess non-superimposable mirror image structures. Restricted rotation about the C=C = C bonds in an allene abC = C = Cba causes chirality and the existence of two optically active forms (i)  [c.91]

Fischer projection A method of representing three-dimensional structures in two-dimensional drawings in which the chiral atom(s) lies in the plane of the paper. The two enantiomeric forms of glyceraldehyde are represented as  [c.175]

The R, S convention is a scheme which has largely superseded the D, i. system to denote configuration about a chiral centre in a molecule. The convention allows unequivocal designation of the absolute configuration in a description of the positions in space of ligands attached to a chiral centre, in relation to an agreed standard of chirality like a right-hand helix.  [c.288]

Groups attached to the chiral centre are given an order of priority according to the sequence rules. For an enantiomeric carbon compound the group of lowest priority is  [c.288]

The Monte Carlo Method  [c.166]

Figure 6.10 Probability distributions for two variables input for Monte Carlo Figure 6.10 Probability distributions for two variables input for Monte Carlo
The Monte Carlo simulation is generating a limited number of possible combinations of the variables which approximates a distribution of all possible combinations. The more sets of combinations are made, the closer the Monte Carlo result will be to the theoretical result of using every possible combination. Using Crystal Ball , one can watch the distribution being constructed as the simulation progresses. When the shape ceases to change significantly, the simulation can be halted. Of course, one must remember that the result is only a combination of the ranges of input variables defined by the user the actual outcome could still lie outside the simulation result if the input variable ranges are constrained.  [c.167]

Figure 6.11 Schematic of Monte Carlo simulation 6.2.5 The parametric method Figure 6.11 Schematic of Monte Carlo simulation 6.2.5 The parametric method
The parametric method is an established statistical technique used for combining variables containing uncertainties, and has been advocated for use within the oil and gas industry as an alternative to Monte Carlo simulation. The main advantages of the method are its simplicity and its ability to identify the sensitivity of the result to the input variables. This allows a ranking of the variables in terms of their impact on the uncertainty of the result, and hence indicates where effort should be directed to better understand or manage the key variables in order to intervene to mitigate downside and/or take advantage of upside in the outcome.  [c.168]

From the probability distributions for each of the variables on the right hand side, the values of K, p, o can be calculated. Assuming that the variables are independent, they can now be combined using the above rules to calculate K, p, o for ultimate recovery. Assuming the distribution for UR is Log-Normal, the value of UR for any confidence level can be calculated. This whole process can be performed on paper, or quickly written on a spreadsheet. The results are often within 10% of those generated by Monte Carlo simulation.  [c.169]

The theoretical model of the CT-data collection process is shown schematically in figure 5. The X-ray spectrum generated in the X-ray source is shown to the left. Spectra for the actual X-ray source were measured with high accuracy with a Compton spectrometer [4, 5]. The spectrum chosen is pre-shaped with filters and then divided into three paths. Sj penetrates the object beside the defect, S2 penetrates both object and defect, and So passes beside the object. The X-ray spectrum will be filtered differently along each path, that is, both by number of photons and change of energy distribution. The expectation value for the energy imparted to the detector screen at each path, Ei(e) where i=0-2, is a product of the expectation value for the number of photons in each spectra, Ei(N), and the expectation value of the single event distribution, Ei(e ). The single event distributions for paths S0.2 in the middle in the figure represent the detector screen simulated with Monte Carlo technique [6].  [c.210]

V. Carl, E. Becker, A. Sperling - Siemens Power Generation Group, Germany.  [c.400]

Carl V, Quantitative Wallthickness Measurement with Impulse-Video Thermography, 7 ECNDT Copenhagen, May 1998  [c.407]

The results regarding resolution as measured by CERL double wire IQI s show results for a class G2 film very close to those obtained by X-rays. Some results from the large range of published data are summarized in fig. 5 and 6.  [c.426]

Table 6 Comparison of CERL double wire sensitivity vs. steel thickness for Selenium, Iridium and X-rays [4] Table 6 Comparison of CERL double wire sensitivity vs. steel thickness for Selenium, Iridium and X-rays [4]
The quality of visible image is mainly determined at the stage of a latent image formation and depends on speetral, angular and spatial distribution of eleetrons and quanta, emitted from electrodes and produced in a gas. The electron density distribution in GDC structure members was determined through solution of transport equations using Monte-Carlo method. These transport equations have been solved for sources of X- and y-radiation with photon energy in the range from several tens of keV to 70 MeV.  [c.539]

Both the Monte Carlo and the molecular dynamics methods (see Section III-2B) have been used to obtain theoretical density-versus-depth profiles for a hypothetical liquid-vapor interface. Rice and co-workers (see Refs. 72 and 121) have found that density along the normal to the surface tends to be a  [c.79]

Several groups have studied the structure of chiral phases illustrated in Fig. IV-15 [167,168]. These shapes can be understood in terms of an anisotropic line tension arising from the molecular symmetry. The addition of small amounts of cholesterol reduces X and produces thinner domains. Several studies have sought an understanding of the influence of cholesterol on lipid domain shapes [168,196].  [c.139]

C4H10O2. There are five glycols of this formula, three chiral. They are colourless, rather viscous liquids. The important isomers are  [c.72]

A molecule is chiral if it cannot be superimposed on its mirror image (or if it does not possess an alternating axis of symmetry) and would exhibit optical activity, i.e. lead to the rotation of the plane of polarization of polarized light. Lactic acid, which has the structure (2 mirror images) shown exhibits molecular chirality. In this the central carbon atom is said to be chiral but strictly it is the environment which is chiral.  [c.91]

Reservoir rocks are either of clastic or carbonate composition. The former are composed of silicates, usually sandstone, the latter of biogenetically derived detritus, such as coral or shell fragments. There are some important differences between the two rock types which affect the quality of the reservoir and its interaction with fluids which flow through them.  [c.13]

Keywords deterministic methods, STOllP, GllP, reserves, ultimate recovery, net oil sands, area-depth and area-thickness methods, gross rock volume, expectation curves, probability of excedence curves, uncertainty, probability of success, annual reporting requirements, Monte-Carlo simulation, parametric method  [c.153]

A Monte Carlo simulation is fast to perform on a computer, and the presentation of the results is attractive. However, one cannot guarantee that the outcome of a Monte Carlo simulation run twice with the same input variables will yield exactly the same output, making the result less auditable. The more simulation runs performed, the less of a problem this becomes. The simulation as described does not indicate which of the input variables the result is most sensitive to, but one of the routines in Crystal Ball and Risk does allow a sensitivity analysis to be performed as the simulation is run.This is done by calculating the correlation coefficient of each input variable with the outcome (for example between area and UR). The higher the coefficient, the stronger the dependence between the input variable and the outcome.  [c.167]

The unsharpness is generally measured with the duplex wire IQI (CERL B), strip pattern IQI-s (here we used a Siemens star) or adduced via the modulation transfer function (MTF). References [1,2,3] give the MTF s determined for the BAS2000, BAS2500 and BAS5000.  [c.471]

BE-3743 On-line quality control, production process assessment and tracking system for mechanical Darts ProT. Cerloa Fernandea UnN. Pol. Madrid  [c.935]

Two simulation methods—Monte Carlo and molecular dynamics—allow calculation of the density profile and pressure difference of Eq. III-44 across the vapor-liquid interface [64, 65]. In the former method, the initial system consists of N molecules in assumed positions. An intermolecule potential function is chosen, such as the Lennard-Jones potential, and the positions are randomly varied until the energy of the system is at a minimum. The resulting configuration is taken to be the equilibrium one. In the molecular dynamics approach, the N molecules are given initial positions and velocities and the equations of motion are solved to follow the ensuing collisions until the set shows constant time-average thermodynamic properties. Both methods are computer intensive yet widely used.  [c.63]

In Fig. III-7 we show a molecular dynamics computation for the density profile and pressure difference P - p across the interface of an argonlike system [66] (see also Refs. 67, 68 and citations therein). Similar calculations have been made of 5 in Eq. III-20 [69, 70]. Monte Carlo calculations of the density profile of the vapor-liquid interface of magnesium how stratification penetrating about three atomic diameters into the liquid [71]. Experimental measurement of the transverse structure of the vapor-liquid interface of mercury and gallium showed structures that were indistinguishable from that of the bulk fluids [72, 73].  [c.63]

The use of fluorescence and Brewster angle microscopy to study Langmuir monolayers has revealed a rich morphology of coexisting phases in both singlecomponent and binary layers (see Section IV-3 and Refs. [167,168,184]. Circular domains sometimes form ordered arrays [196,197], while under different conditions the circular shapes are unstable to higher harmonic shapes such as those illustrated in Fig. IV-19 [226-230]. Another supercrystalline structure in coexisting domains is the stripe phase or alternating parallel stripes [168,198,231]. Finally, the presence of chiral amphiphiles produces curved, spiral domains as shown in Fig. IV-16 [168,170,232,233]. We briefly summarize the physical basis for these shape transitions and refer interested readers to the references cited above.  [c.136]

Y. Tamai, T. Matsunaga, and K. Horiuchi, J. Colloid Interface ScL, 60,112 (1977). See also Y. Tamai, J. Phys. CherrL, 79, 965 (1975).  [c.389]

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