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Ecological data have been collected for all the products in this project as well as for all process steps required (see Table 13.1). First a life-cycle inventory was established for all relevant ecological inputs. In a second step a data assessment was carried out aggregating individual data into 10 environmental categories such as anthropogenic green house effect, eutrophication potential, and the like (Fig. 13.21). Only ve categories that were found relevant will be dealt with in this chapter. The other categories such as ozone depletion, mineral resources, total water, or total mass of radioactivity did not show any or only very little differences between different routes of lling the basket of goods. The report, discussed here, was criticized by the expert panel for not having included an evaluation of the in uence on human toxicity. This evaluation was considered in the beginning of the study, but it soon was realized that very little data existed or were available. A second reason for excluding it was the nonexistence of a scienti c approved and accepted way of assessing these category at the time of the study. Meanwhile, some proposals exist [11].

The cyclic boundary, the air inlet mass flow, and the exit pipe are specified in the normal way. However, the inlet liquid flowrate condition is a particularly difficult boundary condition to impose accurately. The connecting pipes are horizontal connections that do not run full during injection conditions therefore the water approaches in a stratified flow condition. Furthermore, the flow conditions that arise from liquid entry into the annulus and redistribution around the downcomer is a complex multi-dimensional flow problem in itself. The experimental tests suggested that the extent of liquid distribution around the annulus was connected with the flow conditions at the pipe annulus connection. Unfortunately the model in its present form could not address these issues. Therefore, a boundary condition was required at the inlet to account for the inlet mass flow and the redistribution effect. This was

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where m is the number of ribs supporting the faceplate. It should be noticed that n = m are both even or both odd. The maximum relative sensitivity is 2 and occurs for f 180 . Figure 5.28 shows the interferogram for a mirror with four ribs. 5.3.1. Source Size Uncompensated Rotational Shear Interferometers The degree of coherence g12 of a uniform circular source with angular diameter 2a is given in Eq. (5.10). Thus to maintain good fringe contrast, the pinhole of a rotational shear interferometer must be of the proper size. If a point of the wavefront is sheared through an angle f, the distance in the wavefront between two interfering points is given by d 2r sin f : 2 5:28

response were the effect on a bridge and associated abutments, and the cost was the repair and recovery cost.) Where the set of outcome and response scenarios is common across risk events, the probability tree structure may be simplified (Figure 23.4). For an individual risk, several steps are involved. 1. Identify a representative, mutually exclusive set of risk events. 2. Identify a set of non-overlapping outcome and response scenarios that might arise as a consequence of the risk occurring, and describe each in detail. The description should include a statement of the effect of the risk event, the main assumptions, possible responses to the risk event and the best response, any secondary risks that might arise as a result of the response mode chosen, and the consequences expressed in terms of the benefit and cost measures of interest. 3. Assess a matrix of conditional probabilities of each scenario arising, given that a specific risk event occurs. 4. Calculate the unconditional probability of each scenario arising. 5. Calculate the overall effect of the risk in terms of distributions of the benefit and cost measures. The distributions derived from this process become the inputs to the appraisal structure of Figure 23.2. The process illustrated in Figure 23.2 produces a distribution of the output measure of interest. For example, Figure 23.5 shows a cumulative distribution of net benefit for a project option, indicating the probability that the net benefit will be less than the value on the horizontal axis. Figure 23.5 shows the probability the net benefit will be negative, i.e. the likelihood that the option will generate a loss.

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