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.
Since the beginnings of PIDO in the 1990s, accelerating software development together with practitioner achievements have solidified and expanded the reality of design space exploration for manufactured product development. To help decision-makers assess the state of today’s technology and discern and select among contemporary software offerings, we developed the functional stack diagram above. This delineates our view of essential technological elements of a holistic environment for design space exploration:
Systems modeling and 0D/1D simulation—Systems modeling software consists of tools and languages for systems engineering: the specification, analysis, design, verification and validation of systems and systems-of-systems. In discrete manufacturing, systems engineering is the coordinated specification-through-validation of complex physical systems across multiple domains—mechanical, electrical, electronic, hydraulic, thermal, control, electric power, others.
0D/1D simulation uses the time dimension only (0D), or time plus a single spatial dimension (1D), to model and evaluate critical aspects of a system’s behavior. By contrast, 3D CAE takes account of a model’s behavior in all three spatial dimensions (or 2D for plate structures and other cases where the third dimension can safely be neglected).
Together, systems modeling and 0D/1D simulation are used early in engineering programs to make a product’s most crucial functional and architectural decisions. An encouraging trend of technology integration agreements between design space exploration vendors and developers of systems modeling and 0D/1D simulation tools promises significant benefits for systems engineering professionals in their high-value, high-leverage role of innovating and optimizing designs at the product architecture level.
CAD, pre/post-processors, 2D/3D CAE, other calculation tools—These include the conventional software applications serving up product geometry; finite element, grid and other mesh generators; solvers for the various analysis disciplines; and other applications as called and directed by the process automation and multi-tool integration of design space exploration frameworks. An important member of the “other calculation tools” category is of course Excel, in reality engineers’ most commonly used design exploration aid today.
Multifidelity, multiphysics modeling—A challenge for efficient design space exploration is finding ways to readily create simulation models that incorporate multiple levels of fidelity—0D, 1D, 2D, 3D—as well as multiple physical domains. In conceptual and preliminary design, many aspects of mechanical products are most efficiently modeled for simulation using 0D/1D/rigid entities; combining these models with other product components best represented by 2D/3D CAE models can yield systems models that are highly revealing in 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. A future post will look at promising new developments in this area.
PIDO (process integration and design optimization)—Applications for design of experiments, multidisciplinary optimization, Pareto optimization and stochastic studies, supported by process automation and multi-tool integration. Last week’s post examined this layer of the stack in detail.
Engineering work-in-progress configuration management—Capture of design history and current design state of engineering work-in-progress. Our functional stack calls this out separately from simulation process and data management (SPDM) because practitioners report that current SPDM software, while far advanced from early efforts to apply CAD-centric PDM to CAE, still could benefit from greater fluidity of use and better automated history journaling to support the rapid pace and often ad hoc nature of day-to-day engineering project work, especially during conceptual and preliminary design.
Visual data analytics—Design exploration studies frequently produce large data sets for which it is useful to have tools for post-processing the data and presenting it in ways that facilitate visual discernment of patterns and information in the data. Visual data mining and analytics tools allow plots and tables to be viewed, queried and operated on to better understand the design space, explore design sensitivities, visualize correlations and investigate tradeoffs.
Integrated results of lift and drag plus detailed flow field data on 191 configurations of a concept space shuttle vehicle
Live decision dashboards—Essentially an application of the business-dashboard concept to the needs of engineering organizations, this is an easy-to-read, usually single-page, real-time user interface graphically presenting current status and historical trends of a product development/refinement project, with focus on key parameters of current design, to enable informed decision-making at a glance.
Futures—Some developments we expect to see in the near to intermediate term:
- Deeper integration of systems modeling and 0D/1D simulation tools into design space exploration environments and work processes.
- More application of design space exploration technology and methods to product costing, LCA (life cycle assessment) and other PLM-adjacent domains.
- Increasingly systemic adoption and institutionalization of design space exploration across extended engineering organizations working to capture, synthesize and broadly deploy expert knowledge and corporate IP.
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