A software component model is the cornerstone of any Component-based Software Development (CBSD) methodology. Such a model defines the modelling elements for constructing software systems. In software system modelling, it is necessary to capture the three elements of a system's behaviour: (i) control (ii) computation and (iii) data. Within a system, computations are performed according to the flow of control or the flow of data, depending on whether computations are control-driven or data-driven. Computations are function evaluations, assignments, etc., which transform data when invoked by control or data flow. Therefore a component model should be able to model control flow, data flow as well as computations. Current component models all model computations, but beside computations tend to model either control flow only or data flow only, but not both. In this thesis, we present a new component model which can model both control flow and data flow. It contains modelling elements that capture control flow and data flow explicitly. Furthermore, the modelling of control flow is separate from that of data flow; this enables the modelling of both control-driven and data-driven computations. The feasibility of the model is shown by means of an implementation of the model, in the form of a prototype tool. The usefulness of the model is then demonstrated for a specific domain, the embedded systems domain, as well as a generic domain. For the embedded systems domain, unlike current models, our model can be used to construct systems that are both control-driven and data-driven. In a generic domain, our model can be used to construct domain models, by constructing control flows and data flows which together define a domain model.

The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. The tool should be able to time efficiently calculate flow parameters from the data, and automatically create overview images of the flow field while doing so, to aid the user's analysis process. The tool is coded using Python programming language, and the Python scripts are inputted to the application ParaView for processing of the simulation data.  The tool generates 3 chamber views of the heart by calculating three points from the given patient data, which represent the aortic and mitral valves, and the apex of the heart. A plane is generated that pass through these three points, and the heart is sliced along this plane to visualize 3 chambers of the heart. The camera position is also manipulated to optimize the 3 chamber view. The maximum outflow velocity over the cardiac cycle in the left atrial appendage (LAA) is determined by searching in a time range around the maximum outflow rate of the LAA in a cardiac cycle, and finding the highest velocity value that points away from the LAA in this range. The flow component analysis is calculated in the LAA and left ventricle (LV) by seeding particles in each at the start of the cardiac cycle, and tracking these particles forwards and backwards in time to determine where the particles end up and come from, respectively. By knowing these two aspects, the four different flow components of the blood can be determined in both the LAA and LV.  The tool can successfully create 3 chamber views of the heart model from three semi-automatically determined points, at a manipulated camera location. It can also calculate the maximum outflow velocity of the flow field over a cardiac cycle in the LAA, and perform a flow component analysis of the LAA and the LV by tracking particles forwards and backwards in time through a cardiac cycle. The maximum velocity calculation is relatively time efficient and produces results similar to those found manually, yet the output is dependent on the user-defined inputs and processing techniques, and varies between users. The flow component analysis is also time efficient, produces results for the LV that are comparable to pre-existing research, and produces results for the LAA that are comparable to the LVs' results. Although, the extraction process of the LAA sometimes includes part of the left atrium, which impacts the accuracy of the results. After processing each part, the tool creates a single file containing each part's main results for easier analysis of the patient data. In conclusion, the tool is capable of semi-automatically processing CFD simulation data which saves the user time, and it has thus met all the project aims