Tag Archives: Simulation process and data management

Ford adopts ESTECO’s SOMO to institutionalize enterprise MDO

SOMO_optimization_composition
Source: ESTECO

Ford Motor Company adopted ESTECO’s SOMO as a tool to enable what it terms an enterprise multidisciplinary design optimization (EMDO) system, announced Dr. Yan Fu, technical leader of business strategy and engineering optimization at Ford. SOMO is an enterprise collaboration and distributed execution framework developed by ESTECO and customized to meet Ford’s engineering processes and IT requirements.

Ford’s move affirms two key points we have long observed:

  • MDO is a strategic capability for engineering organizations, not just tactical.
  • To have strategic impact, MDO needs to be institutionalized at the enterprise level, not implemented as just a departmental or workgroup capability.

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From PIDO to design space exploration

ora_dse_functionstack_141208Design 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.

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