Building the Simulation

In this problem we will simulate a batch adsorption process that takes place with two adsorbate components. The simulation will allow us to do computational experiments with the aim of learning how the adsorption and desorption parameters affect the behavior of this process. Building the simulation will provide new experience in developing the model equations, utilizing more complex constitutive relationships, finding numerical solutions to these equations, and displaying the results graphically. 

Click the Enter Simulation Environment button when you are ready to start building the simulation. 

The first task you perform when building the simulation case is choosing a unit set. HYSYS does not allow you to change any of the three default unit sets listed, however, you can create a new unit set by cloning an existing one. For this chapter, you will create a new unit set based on the HYSYS Field set, then customize it. 

The number and shape of the grid blocks in the model depend upon the objectives of the simulation. A 100 grid block model may be sufficient to confirm rate dependent processes described in the previous section, but a full field simulation to be used to optimise well locations and perforation intervals for a large field may contain up to 100,000 grid blocks. The larger the model, the more time consuming to build, and slower to run on the computer. 

Four methods for industrial air technology design are presented in this chapter computational fluid dynamics (CFD), thermal building-dynamics simulation, multizone airflow models, and integrated airflow and thermal modeling. In addition to the basic physics of the problem, the methods, purpose, recommended applications, limitations, cost and effort, and examples are pro vided. 

The main purposes of thermal building-dynamics simulation are... 

T he total or global solar radiation has a direct part (beam radiation) and a diffuse part (Fig. 11.31). In the simulation, solar radiation input values must be converted to radiation values for each surface of the building. For nonhorizontal surfaces, the diffuse radiation is composed of (a) the contribution from the diffuse sky and (b) reflections from the ground. The diffuse sky radiation is not uniform. It is composed of three parts, referred to as isotropic, circumsolar, and horizontal brightening. Several diffuse sky models are available. Depending on the model used, discrepancies for the boundary conditions may occur with the same basic set of solar radiation data, thus leading to differences in the simulation results. 

This section does not contain any fundamentals or mathematics bur tries to describe the basic energy flows and the methods used in thermal building-dynamics simulation codes to model these. Also, the methods are described without stating the underlying algorithms and equations, for which the reader is referred to the literature and references. A short outline of how these models affect the application possibilities and limits is given at the end of this section and also in Section 11.3.7. 

In thermal building-dynamics simulation codes, outdoor conditions are mostly input by the so-called weather data file, containing (usually hourly) data for air temperature, wind speed and direction, air humidity, and global and diffuse solar radiation on horizontal surfaces. 

For the calculation of such glazing in building-dynamics simulation codes, each pane is considered as a layer, exchanging energy with the other panes, with the room, and with the exterior. 

Moisture-transport simulation includes transport as well as storage phenomena, quite similar to the thermal dynamic analysis, where heat transfer and heat storage in the building elements are modeled. The moisture content in the building construction can influence the thermal behavior, because material properties like conductance or specific heat depend on moisture content. In thermal building-dynamics simulation codes, however, these... 

The information required to run a thermal building-dynamics simulation can be classified on the base of the following items ... 

The identification of zones can be made in different degrees of detail, depending on the specific purpose of thermal building-dynamics simulation. In most cases a zone can include several rooms having the same thermal conditions. Sometimes it is necessary to split a very large room into two or more thermal zones in order to have more accurate results. 

The results of thermal building-dynamics simulation are generally referred to by zone level. They can be divided into instantaneous results and summary results. 

The primary results of thermal building-dynamics simulations are hourly values of the following quantities ... 

IDA Indoor Climate and Energy (ICE) is a new generation of building performance simulation tools. The mathematical models are described in terms of equations in a formal language, NMF. Whenever appropriate, models recommended by ASHRAE have been used. Advanced database features support model reuse. 

The results shown above build on a thorough investigation of water interaction and effects related to the electrical field in the double layer near the surface. While these details are important when doing the simulations, they are less important for the understanding of the results presented above. We have therefore chosen to present them here at the end of the section. 

Application to heterogeneous polymer solids, and elastic composites, is presented in the Section 7 (Gusev, Suter), which is followed by a summary and the outlook for the various methods reviewed here. It will be apparent to the reader that this review thus assembles several building blocks for the difficult task to bridge the gaps from the atomistic to the macroscopic scales in space and times for the simulation of polymeric materials. Integrating these building blocks into one coherent framework still is not fully solved and a matter of current research. 

There are recent building projects in Canada that generally confirm the simulation results presented. Cost-effectiveness of some of these buildings is even greater than the simulated results due to savings in areas not modeled. 

The modification factor plays a central role in a WL simulation and has several effects. First, its presence violates microscopic detailed balance because it continuously alters the state probabilities, and hence acceptance criterion. Only for g = 0 do we obtain a true Markov sampling of our system. Furthermore, we obviously cannot resolve entropy differences which are smaller than g, yet we need the modification factor to be large enough to build up the entropy estimate in a reasonable amount of simulation time. Wang and Landau s resolution of these problems was to impose a schedule on g, in which it starts at a modest value on the order of one and decreases in stages until a value very near to zero (typically in the range 10 5-10 8). In this manner, detailed balance is satisfied asymptotically toward the end of the simulation. 

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