Category Archives: Research brief

Optimizing capex/opex tradeoffs in built-asset engineering

Most often applied to manufactured product development, design exploration and optimization also hold potential to improve—some would say, bring long-overdue transformation to—the engineering of constructed assets: commercial and residential buildings, discrete manufacturing facilities, process and power plants, offshore platforms. While some EPC firms serving process/power and offshore markets have made substantial progress with these tools and methods, the A/E industry still has far to go in tapping their considerable potential to improve building design and manufacturing facility engineering. With the mounting economic, environmental and public-policy pressures to deliver higher-performing built assets, we expect to see DOE, MDO, Pareto optimization and robustness/reliability optimization increasingly utilized by firms engaged in architecture, engineering, construction and asset operation. Continue reading

Latin hypercubes and all that: How DOE works

Making design exploration software speak the language of engineers and not mathematicians has been a focus of development since the industry’s inception. Even so, our recent case study was typical in referencing the Latin hypercube design-of-experiments method, the radial basis function for generating a response surface model, the non-dominated sorting evolutionary algorithm to generate a Pareto front—all prompting this look into some of the quantitative methods that drive design space exploration. 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.

Ora_DSE_timeline
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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

Innovation and new technology insertion

Ora_Innovation-sourcesFor 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