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The major motive for the adoption of object-oriented software
engineering approaches has been its support for modularity in
modeling. However, a model for the simulation of a complex system,
such as a building, in object-oriented languages is not trivial [47]. One
of the main questions is to what the objects should correspond.
Should they correspond to real-world entities, or to the equations
associated with those entities. The lack of the agreement upon the
above issue has resulted in a limited presence of object-oriented
programming in the domain of BPS.
3.4. Solution techniques for HVAC system simulation models
The differences in solution techniques employed by different
simulation tools are based on the distinction in the way the integrator
is employed [48].
Simultaneous modular solution, where the various components are
integrated simultaneously by a common integrator. In general, the
tools that employ this solution technique use model equations that
are based on first principles [48]. Each component is described with
time-averaged discretized heat and/or mass conservation statements,
which are combined to form a system matrix, and which are solved
simultaneously for each simulation time step using either an implicit,
explicit or mixed numerical scheme.
Independent modular solution, where eachmodule is providedwith
inpidual integrator routines. In general, the tools that employ this
solution technique use model equations that can be based on first
principles but can also be empirical input/output correlations [48].
The component's modules encapsulate all information relevant to the
component's simulation model setting and execution. Each compo-
nent is executed sequentially and the system solver iterates until a
convergent solution has been found.
Equation-based solution using formula manipulation, which has
emerged in recent years with the developments of equation-based
tools. Models composed with these tools cannot be executed directly.
To be executed, a model needs to be transferred into a programming
language that can be compiled. Tools employ different techniques to
reduce the dimensionality of the linear and non-linear systems
defined in the model in order to increase the execution efficiency of
the compiled program. For example, in SPARK [49], mathematical
graph algorithms are used for problem decomposition and reduction,
greatly reducing solution time for wide classes of problems [50].
4. Integration of building and HVAC system models
The integration of building and HVAC system models is accom-
plished at different levels. The models can be (i) sequentially coupled
(many duct/pipe sizing tools, BLAST, DOE-2, etc.) – without system
model feedback to the building model or (ii) fully integrated (ESP-r,
EnergyPlus, IDA ICE, TRNSYS, etc.) – allowing the system deficiencies
to be taken into account when calculating the building thermal
conditions. Levels of detail of both building and system models can
vary from simple (e.g. the bin method and pure conceptual
representation for system model) to complex (numerical model of
physical processes).
5. Issues in selecting HVAC modeling approach
Different HVAC system modeling approaches demand different
levels of user skills, different modeling resolutions and details, and
different levels of user customization capability. Higher explicitness in
system representation requires more knowledge about the system
because of the increasing number of model parameters for system
specification, often difficult to obtain as they are not supplied by
manufacturers. In addition, for higher explicitness in system repre-
sentation the computational requirements become more intensive