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in primary and secondary systems are idealized, with a possibility to
impose a capacity limitation upon them. An example application is to
use the predicted room cooling/heating peak loads to determine the
required HVAC system size. Most state-of-the-art BPS tools can be
used to model systems using this approach. Some, like ESP-r,
introduce certain complexity by modeling conceptual systems —
thermal zone interactions through control algorithms. Thus, even
though the pure conceptual system model is used, system processes
are not completely idealized. Their interaction with the building is
more realistically modeled since their characteristics can be included
in terms of aspects such as heat injection/extraction point, flux limit
values, response time, and convective/radiant split. In [32] the authors
state that this method of system simulation is often misunderstood
and under-rated.
System-based modeling approach represents the case with pre-
configured common system types, such as variable air volume system
and constant-volume variable-temperature system. This modeling
approach is implemented in DOE-2, eQUEST, Building Energy
Analyzer, BLAST, DesignBuilder, HAP, etc. The user has flexibility in
specifying capacities, system flow rates, efficiencies and off-design
system component characteristics, but is restricted to the system
configurations and control strategies that are pre-defined in the tool.
Component-based system modeling approach represents the case
where a system is specified by (a) network(s) of interconnected
componentmodels. This approach ismore flexible in terms of possible
system configurations and control strategies compared to the
previous approach.
Component-based multi-domain system modeling approach repre-
sents the case where component representation is further partitioned
into multiple interrelated balance concepts, e.g. fluid flow, heat and
electrical power balance concepts. Each balance concept is then solved
simultaneously for the whole system. Thus, the overall system of
equations is broken into smaller systems of equations. Different
solvers, well adapted for the equation types in question, can be used
for different problem partitions. It is also possible to easily remove
partitions as a function of the problem at hand.
As an addition to the above four categories defined by Hensen [31],
this paper lists a fifth category: the equation-based system modeling
approach. This modeling approach represents the case where a
system is represented by a basic modeling unit that is physically
“smaller” than a component and that is in the formof an equation or a
low-level physical process model. It has evolved from the need to
improve the BPS tools that had been based on technology available in
the early seventies [33]. Equation-based simulation tools are [34,35]:
• input–output free (all models are declarative in nature) as opposed
to the traditional procedural,
• modular (supported by object-oriented programming languages),
• hierarchical (enable incrementalmodeling, i.e.models can consist of
sub-models in multiple levels), which helps in managing the
complexity of large systems,
• universal (model definition in a generic form, e.g. using NMF and
Modelica).
• They provide separation of modeling the physics from numerical
solution algorithms.
• They provide faster developments of simulation models, etc.
Examples of equation-based tools are:
SPARK (Simulation Problem Analysis and Research Kernel),
formerly EKS/US and SPANK, is developed by the Lawrence Berkeley
laboratory [36]. The primary goal of the EKS/US was improvement of
the modeling and solution processes which resulted in SPARK. It is an
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