An adaptable virtual engineering platform for distributed design based on open source game technology

This paper presents the RMIT Adaptable Platform for Interactive Distributed Design, Customisation and Optimisation (rapidDCO), a CAD/CAE research platform which is an extension of distributed component based gaming technology. rapidDCO is designed for use in multidisciplinary, collaborative and distributed engineering environments where conflicting design parameters have to be resolved by remote input from a range of disciplines and techniques. rapidDCO leverages the open source game engine Delta3D, which itself leverages existing open source technologies to provide features such as: cross-platform 3D rendering, physical simulation and networked interaction. Our approach enables the CAD/E platform to seamlessly adopt advances in the technology but leveraging work done through the broader open source community. Through these technologies and the access to the underlying source code rapidDCO is highly adaptable and can be applied to novel problems not readily supported by conventional CAD/CAE environments. Rather than building CAD/CAE capability from a “top down” tool perspective we take a model-centric approach, building capability from the model “up”. In rapidDCO individual models are defined as software components which are then shared as libraries that are used by the “component aware” environment. This component based approach means that development can be focused on defining specialised models to address a specific need and not on the infrastructure to realise and interact with those models. We present rapidDCO with a formal design customisation and optimisation example demonstrating the use of rapidDCO’s custom interface capability, distributed design and integration with the modeFrontier® design optimisation tool and Microsoft® Excel.

► We created a novel CAD/CAE platform for distributed virtual engineering design.
► We extended the distributed component based gaming technology to create the design platform.
► We resolved conflicting parameters by enabling remote input from a range of disciplines and techniques.
► We distributed processing of tasks to CPU and GPU of connected systems, to replicate HPC clusters.