Important Input Parameters

Before any simulation or basic calculation of a heat storage can be performed, the thermophysical properties of the PCM have to be determined. These properties are the most important input parameters for any analytical or numerical model. 

Figures 10.9S(a,b) show isopleths calculated between (a) corium and siliceous concrete and (b) corium and limestone concrete. Comparison between experimental (Roche et al. 1993) and calculated values for the solidus are in reasonable agreement, but two of the calculated liquidus values are substantially different. However, as the solidus temperature is more critical in the process, the calculations can clearly provide quite good-quality data for use in subsequent process simulations. Solidus values are critical factors in controlling the extent of crust formation between the melt-concrete and melt-atmosphere interface, which can lead to thermal insulation and so produce higher melt temperatures. Also the solidus, and proportions of liquid and solid as a function of temperature, are important input parameters into other software codes which model thermal hydraulic progression and viscosity of the melt (Cole et al. 1984).

The IRT method was applied initially to the kinetics of isolated spurs. Such calculations were used to test the model and the validity of the independent pairs approximation upon which the technique is based. When applied to real radiation chemical systems, isolated spur calculations were found to predict physically unrealistic radii for the spurs, demonstrating that the concept of a distribution of isolated spurs is physically inappropriate [59]. Application of the IRT methodology to realistic electron radiation track structures has now been reported by several research groups [60-64], and the excellent agreement found between experimental data for scavenger and time-dependent yields and the predictions of IRT simulation shows that the important input parameter in determining the chemical kinetics is the initial configuration of the reactants, i.e., the use of a realistic radiation track structure. 

For some cities and areas no detailed source apportionment was available. As with the datasets described above, which are important input parameters for source apportionment, a guidance document or further support might be helpful. 

A few other examples are examined here to illustrate the range of geometries that may exist in optimized heat exchangers for cryocoolers. These examples are for Joule-Thomson cryocoolers, which are easily miniaturized. Table 1 lists the operating conditions and important input parameters for all of... 

The methods described in Chapter 6 can be used to estimate Tg, which is the most important input parameter. For polymers of low Mn (Mn Mcr), it is important to apply the correction described in Section 6.C for the effects of the molecular weight on Tg. 

The Pesticide Root Zone Model (PRZM) is being utilized to calculate a daily concentration of aldicarb leaching into the groundwater. Daily meteorological records and irrigation schedules are input into the model. Other important input parameters include... 

These are often important input parameters in combustion testing. Furnace temperature has a direct effect on a number of output parameters such as material processing throughput rate, NOx emissions [50], and heat flux. Realistic tests then normally need to match the furnace temperature(s) that will be seen in the field. This means there needs to be some method for adjusting the temperature in the test furnace. This is typically done by varying the heat load. One method that is commonly used is water-cooled tubes or bayonets. Figure 1.46 shows an... 

MacQuarrie, 1996 Steefel and van Cappellen, 1998). Though these models are quite advanced in simulating processes, important input parameters such as the spatial distribution of hydraulic conductivity and of chemical parameters are unknown and the models are therefore poorly parameterised on the scale relevant for remediation measures. 

Until now, much research work has been done on the prediction of composite material coefficient of thermal expansion and elastic modulus by forefathers, and many prediction methods have been developed such as the sparse method (Guanhn Shen, et al. 2006), the Self-Consistent Method (Hill R.A. 1965), the Mori-Tanaka method (Mori T, Tanaka K. 1973) and so on. However, none of these formulas take into account the parameters variation with concrete age, and there is little research on the autogenous shrinkage and creep. In the mesoscopic simulation of concrete, thermal and mechanical parameters of mortar and aggregate (coefficient of thermal expansion, autogenous shrinkage, elasticity modulus, creep, strength) are important input parameters. In fact, there is abundant of test data on concrete, but much less data on mortar while it is one of the important components. Also parameter inversion is an essential method to obtain the data, but there are few studies on this so far. 

Figure 1. Example of correlation sign switch over time for a very important input parameter.

A generalization of this example to multi-well and multi-channel systems is straightforward and easily implemented in kinetic software (discussed later). All integrals are replaced by finite sums and energy graining will be one important input parameter to such codes. 

In the estimation of the dose conversion factor, DCF, by dose model calculations, the activity size distribution in terms of potential alpha energy concentration (PAEC) is an important input parameter. For practical reasons related to measurements, the activity size distribution should be divided into three parts as follows ... 

The model should produce statistical statements on the lifetime in terms of the overall applied stress field, the overall volume of material, and boundary effects. Important input parameters are fiber packing geometry, fiber strength, matrix and interface creep exponents, rate factors in the stress-corrosion chemistry, and the applied stress level. 

Not surprisingly, such an ideal method that is applicable to all model types does not exist, since the requirements of the various features described above have trade-offs against each other. In reality, the comprehensive analysis of a model may require the combined application of several methods (Tebes-Stevens and Valocchi 2000 Ratto and Paladino 2000). In particular, common practice is to use computationally cheaper methods to screen out unimportant parameters and then subsequently to perform a global analysis on a subset of important input parameters. The features of various available methods are summarised in Table 5.2. 

Finally, the last group is concerning all the data required to quantify the expected net benefits due to production operations. On the outcome side, raw materials and operation costs are included. The revenues are generated by selling the final products to the target marketplace, thus two important input parameters to define are the market... 

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