Tag Archives: Systems modeling

Strategies for accelerating the move to simulation-led, systems-driven engineering

Systems modeling, “systems thinking” and systems-driven engineering are topics often discussed by professionals engaged in the engineering of discretely manufactured products. But how many engineering organizations have succeeded in implementing a consistent, sustained culture of simulation-led engineering practices grounded in system-level physical modeling and simulation software technology? And how were they able to accomplish it? Continue reading

System-level physical modeling and simulation: Research background, objectives, methodology

Discussions of how to bring simulation to bear starting in the early stages of product development have become commonplace today. Driving these discussions, we believe, is growing recognition that engineering design in general, and conceptual and preliminary engineering in particular, face unprecedented pressures to move beyond the intuition-based, guess-and-correct methods that have long dominated product development practices in discrete manufacturing. To continue meeting their enterprises’ strategic business imperatives, engineering organizations must move more deeply into applying all the capabilities for systematic, rational, rapid design development, exploration and optimization available from today’s simulation software technologies. Continue reading

System-level physical modeling and simulation: Potential adoption accelerators

Our last two briefs described our research project to investigate the contemporary state of adoption and application of systems modeling technologies and work processes in the engineering design of off-highway equipment and mining machinery. After identifying present-day adoption drivers as well as current constraints on adoption, finally we sought to learn practitioners’ visions, strategies and best practices for accelerating and institutionalizing the implementation and usage of systems modeling tools and practices in their organizations. Continue reading

System-level physical modeling and simulation: Adoption constraints

Systems modeling, “systems thinking” and systems-driven engineering are topics of frequent discussion among professionals engaged in the engineering of many discretely manufactured products today. Yet comparatively few engineering organizations have succeeded in implementing a consistent, sustained culture of simulation-led engineering practices grounded in system-level physical modeling and simulation software technology. Continue reading

System-level physical modeling and simulation: Accelerating the move to simulation-led, systems-driven engineering

Systems modeling, “systems thinking” and systems-driven engineering are topics of frequent discussion among professionals engaged in the engineering of many discretely manufactured products today. But how many engineering organizations have actually succeeded in implementing a consistent, sustained culture of simulation-led engineering practices grounded in system-level physical modeling and simulation software technology? And how did they do it? Continue reading

Model-based design optimization of a hybrid electric vehicle

Last week’s post surveyed the trend of integration between design exploration and optimization software and systems modeling and 0D/1D simulation tools. This week’s case study shows how the two technologies were used together in development of a new hybrid electric vehicle (HEV) to achieve the competing goals of improving fuel efficiency and meeting emissions targets.

Executive summary—A leading automotive OEM used Noesis SolutionsOptimus design optimization and process integration software in conjunction with Maplesoft’s MapleSim multi-domain systems modeling and simulation tool in developing the HEV’s combined electric and combustion propulsion system. Optimus was used to automate the traditional guess-and-correct simulation-based design process, its optimization algorithms efficiently directing the system simulation campaign to identify the best HEV configurations. Using Optimus’ capabilities for design of experiments (DOE), response surface modeling (RSM) and multi-objective optimization (MOO), engineers improved fuel efficiency by 21% and traveling performance (legal emissions compliance) by 15%. With MapleSim providing HEV modeling and Optimus controlling simulation workflow execution, the design optimization was accomplished in just two weeks. Continue reading

Model-based design exploration and optimization

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

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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