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
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
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
When Thomas Edison developed the first long-lasting, high-quality light bulb in 1879, his successful design was the result of a lengthy and laborious trial-and-error search for the best filament material, a process we now call the Edisonianapproach.
While Edison had no fundamental knowledge of how various materials resist electrical current, today’s engineers are often armed with greater technical knowledge and experience about their domain. This allows them to create initial designs based on intuition before testing the designs to failure. Design flaws observed during a test can then be incrementally improved through what we call the make-it-and-break-it method.
Advances in computing power and in computer-aided engineering (CAE) software now make it possible to create virtual prototypes of potential designs prior to building and testing expensive physical prototypes. This reduces the cost and time required to perform each design iteration and provides greater understanding of how a design performs. Continue reading