Simulations are powerful tools for process design, for study of new processes, and for understanding how existing systems Function and might be improved. However, there are limits to what can be done with them.

First, there is implicit in this discussion of simulations the assumption that they arc properly done, ft is easy to make program errors, assume erroneous constants, neglect factors which may be important, and err in a variety of other ways. As in other engi­neering calculations, a high level of skill and judgment Is requited in order to produce useful results.

As noted above, it is possible, in principle, to model a system to whatever degree is required to extract the desired information. In practice, it may be difficult to represent in detail some of the phenomena occurring in a system. Physical world realities include leaks, plugged or restricted pipes, scale on heat exchangers, failure of controllers, poor installation of equipment, and so on. The simulations discussed here are of the thermal processes but mechanical and other considerations can affect the thermal performance of systems.

There is no substitute for carefully conceived and carefully executed experiments. Such experiments wifi reveal whether or not the theory is adequate and where difficulties lie iu design and operation of the systems. At its best, a combination of numerical experiment (simulation) and physical experiment will lead to better systems, better un­derstanding of how processes work, better knowledge of what difficulties can be expected and what can be done about them, and what next logical Steps should be taken in the evolution of new systems.

Simulations and development laboratory experiments are complementary. Compari­sons of the results of measurements in the field of performance of purchased and installed systems Vith simulations hove in some instances shown greater differences than those with experiments. The reasons arc two. first, field measurements are often very difficult to make, and differences may be ascribed to poor measurements. Second, commercially installed systems are not always built and operated with the same care and knowledge

as laboratory systems, and they may not work as well as laboratory systems.


In brief, simulations are powerful tools for research and development, for under­standing how systems function, and for design. They must, however, be done with care and skill.

The liquid amt air system configurations described in Section 13.2 arq Sommon config-

unitionx, and there is considerable information and experience on which to base desigirs..;|f|T

For resident! я I-scale applications, where the cost of the project does not warrant іЬє-‘|Ше

expense of a simulation, performance predictions can be done with “short-cut" methods. – вЩІ

Design procedures are available for many of these systems that are easy to use апфсЩр

provide adequate estimates of long-term thermal performance. In this chapter we briefly.’§pjf-

note some of these methods. The /-chart method, applicable to heating of buildings where f^||

die minimum temperature for energy delivery is approximately 20°C, is outlined in detail, /pgr

Methods for designing systems delivering energy at other minimum temperatures.

encountered in solar absorption air conditioning or industrial process heat applications,

arc presented in Chapter 21. ;

Подпись:. ‘Mjg-

Design methods for solar thermal processes can be put m three general categories, ac – gpg

cording to the assumptions on which they arc based and the ways in which the calcu – і nitons are done. They produce estimates of annual useful outputs of solar processes, but they do not provide information on process dynamics.

The Hret category applies to systems in which the collector operating temperature is known or can be estimated and for which critical radiation levels can be established. The ||p_ first of these, the ulitizabifity methods, are based on analysis of hourly weather data to |Щ obtain the fraction of tiie total month’s radiation that is above a critical level.[39] Another example in this category is the heat table method of Morse as described by Proctor ;^

(1975) . This is a straightforward tabulation of integrated collector performance as a time – lion of collector characteristics, location, and orientation, assuming fixed fluid inlet tern – s||: peratures. •flip’s’

The second category of design methods includes those that are correlations of the fijf results of a large number oF detailed simulations. The /-chart method of Klein et al. "Йр (1976, 1977) and Beckman et al. (1977) is an example. The results of many numerical Щ experiments (simulations) are correlated in terms of easily calculated dimensionless var – |ff iablcs.»Thc results of the /-chart method have served as the basts for further correlations, fjj

for example, by Ward (1976), who has used only January results to characterize a year’s system operation; by Bariey and Winn (1978), who used a two-point curve fit to obtain location-dependent annual results; and by Lametro and Bendt (1978), who also obtained location-dependent annual results with three-point curve fits. The SEU (Solar Energy Unit of University College Cardiff) methods of Kenna (1984a, b) am correlation methods which are applicable to designing open-loop and closed-loop heating systems. Another example in the second category is the method of Los Alamos Scientific Laboratory (Bal – comb and Hedstrom, 1976), which is a correlation of the outputs of simulations for Specific systems and two collector types.

The third category of design methods is based on short-cut simulations. In these methods, simulations are done using representative days of meteorological data and the results are related to longer tenn performance. The SOLCOST method (Connelly et ah, 1976) simulates a clear day and a cloudy day and then weights the results according to average cloudiness to obtain a monthly estimate of system performance.

In recent years annual simulations are replacing design methods as a result of the ever-increasing computational speed of computers. However, design methods are still much faster and so are useful for early design studies, general-snrvey-type-studies, and system design where simulations are too expensive.

Updated: August 18, 2015 — 5:11 pm