Discussions of how to simulate early in product development fixate too often on FEA, overlooking the power of systems modeling and 0D/1D simulation for studying, exploring and optimizing designs at the beginning of projects, when product geometry is seldom available for 3D CAE but engineering decision-making can have its greatest impact and leverage on project success. Continue reading
In conceptual and preliminary design, many aspects of mechanical products are most efficiently modeled for simulation using 0D/1D/rigid entities. In vehicle drivelines, for example, these include beams, bushings, bearings, point masses and the like. Combining these models with other product components best represented by 2D/3D CAE models can yield systems models that are highly revealing in design exploration activities such as parameter studies, design of experiments and optimization runs. But bringing multiple levels of fidelity together in a single model has conventionally been a labor-intensive manual process, severely limiting the number of design variants able to be studied this way when not precluding the practice altogether.
While mainstream CAE vendors are beginning to progress on this front, breakthrough technologies addressing the problem are available today that work with the industry’s leading solvers. Continue reading
Design space exploration as an engineering formalism originated in the embedded-systems industry as a set of methodologies for hardware/software co-design, configuration of software product lines, and real-time software synthesis. “The set of all possible design alternatives for a system is referred to as a design-space, and design-space exploration (DSE) is the systematic exploration of the elements in a design-space” (Saxena and Karsai, “Towards a Generic Design Space Exploration Framework,” Proceedings of 2010 IEEE 10th International Conference on Computer and Information Technology).
In mechanical engineering, design space exploration is rooted in the technological domain often referred to as process integration and design optimization, or PIDO, first identified and defined (Jenkins, Daratech, 2001) as software and methods to help:
- Automate and manage the setup and execution of digital simulation and analysis;
- Integrate/coordinate analysis results from multiple disciplines and domains to produce a more holistic model of product performance; and
- Optimize one or more aspects of a design by iterating analyses across a range of parameter values toward specified target conditions.