The analysis definition process has been upgraded to streamline the definition. The new design facilitates data reuse, simplifies the definition steps and results in a reduced setup time. All the information related to communication with different compute resources has been removed from the analysis setup and moved into its own section.
The process definition now provides support for the use of logic to drive the flow in the analysis process. One or more conditions can be defined at the analysis level to control whether an analysis will be attempted. Several scenarios that would have required specialized external scripts to capture the desired process behavior are now easily handled with this feature. Some of these scenarios are listed below:
- Improve efficiency in the evaluation process by skipping compute intensive analyses when it is clear that the design would not be acceptable
- Continue execution of the process even when some of the analyses result in errors
- Use of an appropriate physics model based on the results of an upstream analysis Continue reading
The calculator functionality in HEEDS MDO has been redeveloped using Python. The new calculator implementation provides significant enhancements over the previous releases. These new enhancements allow you to setup complex formulas with ease without requiring use of an external script or tool. Continue reading
Recent advances in process automation and optimization search technology have made it easier than ever to perform automated design studies and discover innovative solutions. But we still have to define the problem we want to solve and then decide how to represent that problem in our models. These two tasks are sometimes challenging, and they rely heavily on experience and intuition. In this article, as well as some future ones, we will share some of our experience in defining and representing various types of optimization problems. Hopefully you will find some of these techniques useful in your applications.
A common design scenario is to optimize the number and location of certain design features to satisfy performance goals and requirements. For the sake of discussion, let’s consider a specific example of this type of problem. Continue reading
Welcome to the HEEDS Design Space Exploration Blog, your trusted source for education and conversation about Design Space Exploration and HEEDS. From classical algorithms to modern techniques, structural methods to multidisciplinary strategies, and simple tutorials to advanced commercial applications, we provide the information you need to successfully apply design space exploration to virtually any problem. Discover Better Designs, Faster!
Hybrid refers to something that is made up of two or more diverse ingredients. The goal in combining them is to capture and merge the advantages of each ingredient, while overcoming any disadvantages. But ingredients can be combined in many ways, resulting in considerable variation in performance depending on how they are combined.
In optimization search algorithms, as with electric vehicles, there are two main categories of hybrids: series and parallel. To better understand the basic differences between the series and parallel hybrid approaches, let’s consider a simple illustration.
Many engineers still resist the use of optimization algorithms to help improve their designs. Perhaps they feel that their hard-earned intuition is just too important to the solution process. In many cases, they are right.
At the same time, most optimization algorithms still refuse to accept input from engineers to help guide their mathematical search. The assumption is that the human brain cannot possibly decipher complex relationships among multiple system responses that depend upon large numbers of connected variables. Unfortunately, this is true. Continue reading
I am currently training to compete in my first sprint triathlon race. Well, compete may be an exaggeration, and there won’t be much sprinting. But I do hope to cross the finish line before the sun sets.
If you are unfamiliar with the sport, a sprint triathlon is a race with three components. Participants swim about one-half mile in a lake, then ride a bike about 12 miles along a marked road course, and finally run 3.1 miles to reach the finish line.
To an athlete, this race sounds like a fun challenge. To an engineer, it is a fascinating multi-objective optimization problem. Continue reading
There is little debate that Steve Jobs was one of the greatest innovators of our time. His curiosity and creativity are benchmarks for both individuals and companies. One of my favorite Jobs quotes appeared in Wired, February 1996:
“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it, they just saw something. It seemed obvious to them after a while. That’s because they were able to connect experiences they’ve had and synthesize new things. And the reason they were able to do that was that they’ve had more experiences or they have thought more about their experiences than other people.”“Unfortunately, that’s too rare a commodity. A lot of people in our industry haven’t had very diverse experiences. So they don’t have enough dots to connect, and they end up with very linear solutions without a broad perspective on the problem. The broader one’s understanding of the human experience, the better design we will have.”
Steve Jobs was not referring to mathematical optimization when he made these statements, but it would be difficult to find better words than these to motivate people to use a global optimization search process. Let me explain why. Continue reading
When it comes to money, people have different perspectives about how much it takes to be satisfied or feel rich. According to oil tycoon John D. Rockefeller, the answer is “just a little bit more.”
In multi-disciplinary design optimization (MDO), a similar question comes up: “how many design evaluations are needed to converge on the optimal solution?” Unfortunately, as with money, there is no universal answer. The number of evaluations required depends on the problem you are trying to solve, your approach to solving it, and the starting point for the solution. Let’s unpack this a bit. Continue reading