Visual data analytics for design space exploration

Note to readers—If you use design space exploration software, we invite you to take our software satisfaction survey and receive a report of the findings when we compile and analyze them.


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. Many design exploration software products include these capabilities to a greater or lesser degree, as do most mainstream CAE product lines. At the same time, some of the best regarded technology comes from developers focused exclusively on this area.

Integrated results of lift and drag plus detailed flow field data on 191 configurations of a concept space shuttle vehicle in Tecplot Chorus
Source: Tecplot

A highly capable example is Tecplot Chorus from Tecplot, Inc., which lets engineers and analysts explore design spaces in a unified environment that includes integrated CFD post-processing, field and metadata management, and an analytics tool. The software is designed to help users manage and analyze collections of CFD simulations, evaluating overall system performance by visually comparing tens or thousands of simulation cases in a single view. Customer use cases include design studies to modify the wake behind Formula racing cars to make passing easier, airframe design and optimization for a supersonic UAV, and others.

The company terms Chorus a “simulation analytics” tool that provides a framework for managing CFD projects requiring multiple simulation cases with tools to evaluate the resulting metadata. Tecplot usefully describes simulation analytics as “the application of visualization, data management, statistics, and data mining to related collections of datasets generated by computer-aided engineering (CAE) codes. In particular, simulation analytics involves the coupled analysis of the detailed field data and the associated meta-data for a related collection of datasets.”

Another respected product with a long history in this area is EnSight from CEI (Computational Engineering International) Inc., for visualizing, analyzing and communicating data from computer simulations and/or experiments. EnSight is used for CSM (computational structural mechanics such as FEA and crash), CFD and other CAE processes in automotive, aerospace, defense, combustion, energy production, high-tech manufacturing and other markets that require very high precision in computer-based physics modeling.

Design of experiments with full factorial design (left), response surface with second-degree polynomial (right) obtained with LIONsolver
Source: Robiminer

A recent, innovative entry is LIONsolver from LIONlab and Reactive Search srl. An integrated software package for data mining, business intelligence, analytics and modeling, LIONsolver originated from the founders’ research into reactive search optimization using self-tuning search schemes. Designed to let users build and visualize models to improve business and engineering processes, the software aids and enables decision-making based on data and quantitative models. Its architecture supports interactive multi-objective optimization, with a user interface for visualizing results and facilitating the solution analysis and decision-making process.

LION (for “machine Learning and Intelligent OptimizatioN”) Laboratory says its mission is to “foster research and development in intelligent optimization and reactive search optimization (RSO) techniques for solving relevant problems arising in different application areas, including marketing automation and e-commerce, telecommunication networks, ICT, mobile services, big data, cost management, social networks, clustering and pattern recognition in bio-informatics.” LIONoso is a version of the software available for nonprofit research and academic use.

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Take our software user satisfaction survey

If you use design space exploration software, we invite you to take our five-minute user satisfaction survey via one of the links below. In thanks we’ll send you a report of the survey findings when we compile and analyze them. Your participation and responses are strictly confidential. Findings will be discussed in aggregate only; no information about individual responses will be released. This survey is our own undertaking and is not commissioned by, nor executed in cooperation with, any software vendor or other party.

To take the survey, click the link for the software you use. If you use more than one brand, take the survey for each brand you use. If you don’t see your software here, click the Other brand link and write in your software brand where the survey asks for it. We value your input and look forward to sharing the findings with you.

Innovation and new technology insertion


For engineering organizations, where does innovation come from?

When we put that question to EPC firms serving the process and power industries, the most frequent answer was “our projects” and the people working directly in project execution. Forty percent of respondents said their firms’ most important source of innovation is the discovery and application of new technologies and approaches by discipline leads, engineers and managers seeking solutions to pressures and exigencies in a specific project or program.

In second place was “anywhere and everywhere”—27% said innovation at base is a function of their organizations’ culture, and thus can arise from any area in the firm.

In third place was the IT department, named as the top source of innovation by 17% of respondents. While not quite the picture painted in some CIO-oriented publications, these findings align with what our research and others’ suggests is an evolving role for the CIO’s office: to provide enabling infrastructure in support of digital technology initiatives that, more and more, originate from the project execution centers of engineering, manufacturing and construction enterprises. Continue reading

Design space exploration: Justifying the investment

The foundational business value of design space exploration is the ability it confers on engineering teams and organizations to gain more complete, higher-fidelity visibility into product performance earlier in project schedules than was possible or practicable with older technologies and approaches. In essence, it does this by enabling more efficient, effective and revealing application of simulation, analysis and digital prototyping assets—tools, expertise, methods, work processes—to the perennial business drivers for any organization’s investments in those assets:

  • To become more competitive by gaining increased capability to explore, create and innovate.
  • To apply that capability to create better performing products.
  • To improve product quality and reliability—yielding expanded opportunity and customer appeal at the same time as lowered warranty expenses, liability exposure and lifecycle costs.
  • To control or, better yet, reduce product development schedules and budgets by supplanting costly, time-intensive physical testing with digital prototyping.

Continue reading

Design exploration software industry M&A outlook 2015

After 2013 saw Red Cedar Technology acquired by CD-adapco and FE-DESIGN by Dassault Systèmes, mergers and acquisitions in the design exploration and optimization software industry took a breather last year. What could drive M&A activity in 2015? Continue reading

Parametric vs. non-parametric optimization

Parametric shape optimization “searches the space spanned by the design variables to minimize or maximize some externally defined objective function” (Jiaqin Chen, Vadim Shapiro, Krishnan Suresh and Igor Tsukanov, Spatial Automation Laboratory, University of Wisconsin–Madison, “Parametric and Topological Control in Shape Optimization,” Proceedings of ASME 2006 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference). “In other words, parametric shape optimization is essentially a sizing problem that is a natural extension of parametric computer-aided design.

“The downside of parametric shapes is that they do not provide any explicit information about the geometry or topology of the shape’s boundaries. This, in turn, leads to at least two widely acknowledged difficulties: boundary evaluation may fail, and topological changes in the boundaries may invalidate boundary conditions or the solution procedure.”

Non-parametric optimization, by contrast, operates at the node/element level to derive an optimal structure. It can offer greater design freedom, and can make use of existing CAE models without the need for parameterization. “The main advantage of non-parametric shape optimization is the ease of setup, avoiding tedious parameterization that may be too restrictive with respect to design freedom” (Michael Böhm and Peter Clausen, FE-DESIGN GmbH, “Non-Parametric Shape Optimization in Industrial Context,” PICOF (Problèmes Inverses, Contrôle et Optimisation de Formes) ’12). “One of the major disadvantages on the other hand is that the CAD interpretation of the shape optimization result is not trivial.” Continue reading

Challenge of multifidelity, multiphysics modeling

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

From PIDO to design space exploration


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.

Continue reading

Anatomy of design space exploration

Design space exploration is both a class of quantitative methods and a category of software tools for systematically and automatically exploring very large numbers of design alternatives and identifying those with the most optimal performance parameters. The mathematical techniques that underpin design space exploration have been long known—and sometimes applied, in cases where the attendant costs in expertise, time and labor could be justified. What’s changing now is the way fresh software technologies are at last converting these powerful but formerly difficult-to-use methods into practical everyday engineering aids. Continue reading

Optimization fundamentals

Design optimization is the search for a structural design that is optimal in one or more respects. In all the various methods available for optimization, the design is guided to satisfy operating limits imposed on the response of the structure, and by further limits on the values that the structural parameters can assume. The power of numerical optimization is its ability to rationally and rapidly search through alternatives for the best possible design(s). Continue reading