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Systems engineering process
Published in Lawrence A. Klein, ITS Sensors and Architectures for Traffic Management and Connected Vehicles, 2017
High-level design defines the project-level architecture for the system. System-level requirements are further refined and allocated to the subsystems of hardware, software, databases, and people. Requirements for each subsystem element are documented in the same manner as the system-level requirements. This process is repeated until the system is fully defined and decomposed. Each subsystem has its own set of interfaces defined (these may include hardware, software, and data flow specifications, standards, and protocols) and each requires an integration plan for incorporation into the eventual system. The control gate used for the final review of this stage is referred to as the preliminary design review (PDR).
Comparison between two genetic algorithms minimizing carbon footprint of energy and materials in a residential building
Published in Journal of Building Performance Simulation, 2019
Richard Gagnon, Louis Gosselin, Sumee Park, Sebastian Stratbücker, Stéphanie Decker
In an early stage of design optimization, several choices of sub-systems should be considered including their inherited variables. However, standard GAs do not usually understand the link existing between a high-level design variable (e.g. sub-system selection such as the type of heating system) and the low-level design variables (e.g. the inherited variables belonging to a specific sub-system such as the design parameters related to the specific type of heating system). Dasgupta and McGregor (1993) have developed a more robust algorithm for such cases: a structured Genetic Algorithm (sGA). Based on the theoretical approach of sGA, the present study proposes a modified NSGA-II implemented in DEAP1 named sNSGA. The main goal of this study is to compare the performance of sNSGA and NSGA-II for a building optimization problem with hierarchical variables. The algorithms are applied to a relevant and timely multi-objective optimization problem related to the energy and environmental performance of a research facility building (Strachan et al. 2016). Since the optimal solutions returned by both algorithms are also valuable to the industry and research community, their analysis is briefly presented at the end of the paper.