Collaborative Design Exploration

Intuition plays a critical role in all stages of a design exploration study, from defining the problem statement to building the simulation model to interpreting the results. But what about the search process itself? Should we make design improvements based on intuition, or should we allow a mathematical search engine to explore the design space for better designs? The answer is both. We call this shared process collaborative design exploration.

The SHERPA search strategy allows you to inject your design ideas before and during an exploration study. Before you start a study, you can seed it with multiple ideas (in the form of actual designs) that might help SHERPA to locate productive regions of the design space more quickly, thus speeding up the overall search. For example, in addition to the baseline design, you might consider seeding the study with other potentially good designs that:

  • you have investigated or produced in the past
  • your competitors have used
  • are feasible, but perhaps not optimal
  • are high performing relative to one or more criteria, but not all of them
  • have some desirable features, but don’t necessarily perform well
  • you have a hunch may work well
  • are from a previous HEEDS MDO study

One or more of these injected ideas might contribute to a more efficient search, while the cost of doing this is only the time to enter the variable values that define each of the designs. SHERPA will evaluate the injected designs when the search process is launched, so there is no need to simulate them before injection.  Continue reading

Using Design Sensitivities

Design sensitivities are a measure of how much an objective or constraint response varies due to a small change in a design variable. Based on this definition, they are sometimes referred to as sensitivity derivatives. Let’s discuss how to use them properly, as well as how not to use them.

First, note that the design sensitivities we refer to here are calculated for a particular design, not for a design space. Statistical methods of sensitivity analysis can provide useful information about a design space, but not the type of information we seek here.

Since a design represents a point in the design space, it is clear that sensitivities are defined at a point, as are mathematical derivatives. Two distinct designs within a design space will probably have different sensitivities unless the design space is linear, which is seldom the case for engineering problems. Continue reading

How to Accelerate Your Design Exploration Studies

We all feel the “need for speed” when trying to find better design solutions through simulation. What can we do to speed up our design exploration studies? Let’s discuss all the options here, including one that may not be obvious.

In a typical study, the total CPU time needed to perform a design exploration study is determined using this simple formula:


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HEEDS Analysis Templates

The first step in any design exploration study is to define the way in which the virtual prototype simulation model is to be constructed and modified.  This typically involves identifying the various modeling and simulation tools that are involved, specifying where they are executed, choosing the simulation models that are being modified, selecting the parameters being driven and monitored, and documenting what outputs are being stored for each design point.

While the actual simulation models may change from project to project, the workflow and the way the models are tested often remains the same.  For example, the workflow for finding the best lower control arm configuration for a vehicle front suspension is identical (or very similar) across vehicle platforms. The input for geometry ranges, loads and required performance change.

Figure 1. Example HEEDS workflow

Figure 1. Example HEEDS workflow

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Exploring Design Performance Relationships

Highlighting a Few New Features that Help You Discover Better Designs, Faster

Often, improvements to the simplest things can have a big impact on your daily tasks. There are many tasks we perform repeatedly when working with HEEDS, and streamlining those saves time and reduces effort. HEEDS 2015.11 contains many enhancements focused on simplifying workflows and I want to highlight a few that help in exploring design performance relationships.

To explore relationships between variables and responses in detail, you typically require multiple plots of the same type, but with different variables to gain a clearer understanding of dependency or influence. However, there are many plot features that are tailored to suit the particular way you want to view the results such as axis scales, data symbols, curves styles, title fonts, and so on.

To avoid having to create a new plot from scratch and redefine all these settings, you can now right click and select the Copy Plot option. This makes an exact copy of the existing plot, with all the customization. You then just need to alter the variables or responses being displayed saving a lot of setup time.


Figure 1. Make a copy of an existing plot with a single right click option

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Thinking in Parallel

There are a lot of great tools in HEEDS to help you gain insight into finding the best design. One area of enhancements in HEEDS 2015.11 focused on parallel plots. In this article, we’ll highlight some ways to use new features of parallel plots in HEEDS to discover better designs, faster.

Parallel plot background

To help show the new capabilities in the context of an engineering problem, let’s look at exploring shape options for a human powered vehicle. There are obviously many dimensions that can be adjusted to improve the design.


Figure 1. Possible parameters to change for a Human Powered Vehicle

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We often need for a design or a model to perform in a specified way. For example, the parameters in a nonlinear material model should be selected to best match the experimental stress-strain response. The geometrical parameters of a rubber bushing should be designed so that its force-deflection response matches the desired nonlinear stiffness behavior.

Optimization problems like these arise frequently. We refer to them as curve-fitting problems, because the goal is to minimize the difference between the specified target curve and the actual response curve
of our design or model.

Figure 1.

Figure 1. The difference between a target curve and a design curve is minimized in a curve fitting optimization problem.

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HEEDS MDO 2015: Compute Resources Set

This new feature provides the ability to treat multiple computer resources as a single resource, without a job queuing system, to parallelize your HEEDS study. It allows for tremendous flexibility in the use and management of disparate computer resources.

As the name suggests, a compute resource set is a set of previously defined compute resources in HEEDS. The set allows you to define a pool of hardware resources that can be used to parallelize one or more analyses in the HEEDS study. The definition is not limited to homogeneous resources but can include any type of compute resource available. For example, a resource set could be a set of Windows workstations (as shown in the example image below), a set of Windows and Linux workstations, some workstations along with a cluster, several different clusters, etc. During the run, HEEDS will manage the job submissions to the resources defined in the set to make sure they are used effectively.  Continue reading