Parallel Coordinates in Computational Engineering Design

Abstract

Modern Engineering Design involves the deployment of many computational tools. Research on challenging real-world design problems is focused on developing improvements for the engineering design process through the integration and application of advanced computational search/optimization and analysis tools. Successful application of these methods generates vast quantities of data on potential optimum designs. To gain maximum value from the optimization process, designers need to visualise and interpret this information leading to better understanding of the complex and multimodal relations between parameters, objectives and decision-making of multiple and strongly conflicting criteria. Initial work by the authors has identified that the Parallel Coordinates interactive visualisation method has considerable potential in this regard. This methodology involves significant levels of user-interaction, making the engineering designer central to the process, rather than the passive recipient of a deluge of pre-formatted information.In the present work we have applied and demonstrated this methodology in two different aerodynamic turbomachinery design cases; a detailed 3D shape design for compressor blades, and a preliminary mean-line design for the whole compressor core. The first case comprises 26 design parameters for the parameterisation of the blade geometry, and we analysed the data produced from a three-objective optimization study, thus describing a design space with 29 dimensions. The latter case comprises 45 design parameters and two objective functions, hence developing a design space with 47 dimensions. In both cases the dimensionality can be managed quite easily in Parallel Coordinates space, and most importantly, we are able to identify interesting and crucial aspects of the relationships between the design parameters and optimum level of the objective functions under consideration. These findings guide the human designer to find answers to questions that could not even be addressed before. In this way, understanding the design leads to more intelligent decision-making and design space exploration. Downloaded by ROKETSAN MISSLES INC. on February 8, 2015 | http://arc.aiaa.org |

WorkWays: Interacting with Scientific Workflows

“…Parallel Coordinates has been used effectively in aerodynamic optimization design to examine correlations between a very large dimensional parameter space and performance metrics [6]. Although this technique has been used mainly to post-process the optimization results, it can also be used to steer an optimization at runtime [6]. While implementing these workflows, we have expanded the existing IO model to include complex, domain-specific data types and new interaction modes.…”

“…As an example, brain-imaging workflows may deal with processing image files, and it is useful to allow users to visualize and interact with these images as they are processed. In another example, the Parallel Coordinates technique [6] is a common way to visualize high dimensional data. Parallel Coordinates has been used effectively in aerodynamic optimization design to examine correlations between a very large dimensional parameter space and performance metrics [6].…”

“…In another example, the Parallel Coordinates technique [6] is a common way to visualize high dimensional data. Parallel Coordinates has been used effectively in aerodynamic optimization design to examine correlations between a very large dimensional parameter space and performance metrics [6]. Although this technique has been used mainly to post-process the optimization results, it can also be used to steer an optimization at runtime [6].…”

Interacting with scientific workflows

“…They may also generate a large amount of data, and scientific visualisation can be used to present the information for analysis and interpretation (Kipouros et al 2013). Although in most cases the optimisation processes can be automated, human input can also be used to guide the search Nguyen et al 2015).…”

“…(Kipouros et al 2008) propose a method to conduct such analyses, in which the Parallel Coordinates representation is used to postanalyse a multi-objective optimisation process of turbo-machinery compressor blades. (Kipouros et al 2013) then further expand this method to support a higher order of data dimensionality and to support human interaction. Since human interaction is supported, it allows the domain expert to interact with such design systems to identify the region of interest in the design parameters that will reveal more interesting and realistic optimum design configurations in a shorter period of time.…”