Tag Archives: SIMULIA

Aerodynamic optimization: Automotive engineering’s next strategic frontier

Source: Exa

With the unprecedented demands on today’s vehicle engineering organizations, auto makers face a daunting challenge to reach their next targets for aerodynamics drag using traditional tools and methods. Trial-and-error development using wind tunnel testing achieved a coefficient of drag of 0.3. Introducing digital simulation to sequentially improve designs brought CD down to 0.24 for today’s best performing cars. But most companies have the next target set to 0.2. Without either a radical increase in time and resources—not a realistic solution for most—or else a radically more efficient and effective approach to aerodynamics engineering, this target will remain all but out of reach. Continue reading

Weld and adhesive optimization in vehicle body structure development

Executive summary—Passenger-vehicle structural performance is extremely sensitive to welds and adhesive bonds. Traditionally, multidisciplinary optimization (MDO) has been performed largely using thickness, shape and material grade as variables. This project’s objective was to optimize the spot weld count and linear length of adhesives in the body while balancing vehicle structural performance and weight. Various optimization scenarios were carried out: maintain current structural performance but minimize weld count, adhesive length and body weight; maintain current weld count and adhesive length but maximize structural performance and minimize weight; and others. Including welds and adhesives as variables in the MDO process provided additional design space to improve structural performance and reduce cost through spot weld and adhesive minimization. Continue reading

Design space exploration: Institutionalizing the practice

EVENT NOTICE—If you’re in the Detroit area, join Noesis Solutions’ free seminar April 9 on Innovations in Process Integration & Design Optimization. You’ll hear three perspectives on how this technology can help you streamline your simulation processes and identify benchmark product designs in less time, including a presentation by me on Innovative Uses of Optimization for Engineering Design Problems. Event and registration information here.

  ⦁  ⦁

Achieving institutional adoption of design space exploration, design optimization and process integration is a crucial goal for engineering organizations working to establish these as strategic competencies, not just tactical. Too often implemented at only the department or workgroup level, the technologies and their attendant work processes need to be recognized and given backing as enterprise capabilities to have their greatest impact on engineering’s ability to advance corporate strategic objectives. [See also Design space exploration: Justifying the investment.] Continue reading

Design space exploration industry timeline

A timeline of company formations, product launches and M&A activity among design exploration and optimization software vendors maps the pace and direction of the industry’s development.

Click to view 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

Take our software user satisfaction survey

If you use design space exploration software, we invite you to take our five-minute survey of satisfaction with your software and vendor, and the benefits you’re realizing, via one of the links below. In return you’ll receive a report of the findings that will let you benchmark your experiences against those of your peers and competitors. 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 brand where the survey asks for it. We value your input and look forward to sharing the findings with you.

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

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

Design exploration vs. design optimization

Automating the search for solutions to engineering problems can take either of two broad approaches: design exploration or design optimization. Practitioners making technology choices need to understand which tools do one, which do the other, and which approach best fits their needs. A global roster of commercially available design exploration and optimization software appears at the end of this post. Continue reading