Abstract
The use of computational methods in engineering design is a long-standing issue. Several mathematical approaches have been investigated in the literature to support the design optimization of engineering products. Most of them are focused on the optimization of a single structure, without considering a system of structures. The design of supports for electric lines requires tools for the management and sizing of a system of structures that interacts with each other under specific load conditions. This paper deals with a framework to support the design optimization of an overhead line using methods related to the theory of the Constraint Satisfaction Problem. The object-oriented model of a transmission line has been described and then implemented into a prototypical software platform. The parameters to be considered as variables are defined by the designer at the beginning of the optimization process. These variables are geometrical dimensions, poles locations, cable pre-tension, etc. The set of constraints is related to normative, climate conditions, datasheets, material limits, and expert knowledge. To demonstrate the effectiveness of this approach, a case study has been developed considering a variable number of constraints and parameters. In particular, the case study is focused on the design of a low-voltage sub-network between two distribution substations.
Highlights
The use of tools for design optimization is related to the recent improvements in computational methods such as evolutionary algorithms [1]
Their application is mostly related to the multi-objective optimization (MOO) analysis instead of a problem with linear complexity where maximin fitness function (MFF) can be used [8]
This section describes the method for optimizing a system of structures such as an overhead line using a Constraint Satisfaction Problem approach
Summary
The use of tools for design optimization is related to the recent improvements in computational methods such as evolutionary algorithms [1]. Evolutionary algorithms are widely applied in multidisciplinary fields for optimizing structures such as steel trusses [2, 3] and towers [4, 5], and processes such as additive manufacturing [6] and machining [7] In literature, their application is mostly related to the multi-objective optimization (MOO) analysis instead of a problem with linear complexity where maximin fitness function (MFF) can be used [8]. These tools can be numerical software, such as Finite Element Method (FEM), or analytical solvers They can be Computer-Aided Engineering (CAE) applications or Design for X (DfX) solutions for the engineering design. In the context of design exploration, CAE tools are generally applied to select a feasible system architecture that satisfies all requirements
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