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1.1.6 Scientific Workflows
The engineering teams typically divide-up the overall design process into smaller
subtasks, each of which can be considered to be an individual step. Within such
problem-solving multidisciplinary environments, the scientific workflows (SW)
[
34
] are the necessary software tools, which facilitate the integration of data
management, analysis, simulation, and visualization task, as they enable the
engineers to create and execute them, and later on easily modify such complex
engineering tasks in an automated manner.
It is important to mention that the SW application separates two modeling
concerns: component communication and overall workflow coordination. It has
been proposed [
35
] that the separation of the conventional data modeling (struc-
tural data type) and the conceptual modeling (semantic type) gives rise to an
advance SW design, where the validation of SW, as well as the discovery of type-
conforming workflow implementations via replacement rules and by inserting
appropriate semantic and structural adapters for workflow integration, prove that
the related semantics presence through ontology modeling has become an indis-
pensable part in the software development of such multidisciplinary tools.
For ontology creation, the Protégé [
21
], an open-source platform, is applied. It
comes with a suite of tools for the construction of domain models and knowledge-
based applications using ontologies (Fig.
1.12
). Protégé can be customized, to
provide domain-friendly support for creating knowledge models and their popu-
lation. Protégé has extendable plug-in architecture and a Java-based Application
Programming
Interface
(API)
for
building
knowledge
bases
and
related
applications.
The multiple layers present in the described FSI workflow, usually are not
needed to be modeled all. If the goal is the CAD part assembly model, which takes
into account the stress deformations coming from FEM, the workflow will consist
of the CAD layer and the FEM layer. If the goal is the product production, which
needs only the CAD assembly parts, the workflow will be reduced to the CAD
layer. If the goal is to solve the FSI problem, the FEM and CFD layers need to be
involved. In order to describe such SW, the necessary ontology models need to be
combined, requiring that each layer comes with its defined ontology [
36
]
(Fig.
1.12
). In our example, the four ontologies are mapped to the X3D ontology,
which is the common ontology and thus the integration of these layers can be
semantically validated, resulting that such SW can be enabled. In addition, each
layer present as a separate X3D model together with the overall scene bringing all
them together may represent the final end-user result, which also results in the
validated software integration process, when creating such SW (Fig.
1.13
).
SW [
37
,
38
] has emerged as a useful paradigm to describe, manage, and share
complex scientific analysis data (Fig.
1.14
). SW represents declaratively the
components or applications that need to be executed in the complex M&S software
environment, as well as model dependencies among those components.
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