Key Points from the Case Studies Sample Clauses

Key Points from the Case Studies. ‌ There are many key points from these descriptions that we can extract and take into account during the future development of the DSE model scripts. Each of the scenarios has a mixture of both design parameters of the system itself and parameters defining the environment in which the system is to operate. While these both contribute to the total design space to be explored it is important that we differentiate between them when, for example, grouping results by design. There are sometimes relations between simulation parameters meaning that not all combinations are valid, for example in the AI case study, the surface type of the ground affects the wheel slip parameter and in the TWT case study various parameters of the vehicles are linked, such as the battery capacity of an electric vehicle and its total mass. The parameter sweep should only visit parameter sets that respect these constraints. There is a range of different complexity levels when processing the raw simulation results to derive the objective measures needed to assess each simulation. Some are instantaneous measures that may be directly provided by the simulation outputs, such as the maximum acceleration of a vehicle, which others require more complex assessment. Exam- ples include the time taken for the car cabin temperature to reach a comfortable level, the cumulative occupant comfort level in the UTRC scenario and computation of the turning radius in the AI study. There are also constraints over the variables in the simulations that must not be breached, an example of this is the detection of collision of two trains in the CLE case study. Such a constraint results in a boolean pass or fail that should be recorded amongst the objective results. It may be advantageous to terminate a simulation when a constraint is breached to reduce wasted CPU time but this is outside the current planned capabilities of the DSE module. The UTRC case study explicitly calls for a pareto optimal type analysis to compare and rank the design results the TWT case study calls for cost functions that take into account multiple design parameters and simulation results and are unique to each vehicle simulated. In terms of presentation the case studies propose a range of visuali- sations from being able to select a range of graph types such as bar graphs, 2D and 3D plots. These plots could show a range parameter and results or focus in on interesting areas such as the neighbourhood around the maximum speed of a train....