## Optimizing the Number and Location of Design Features (without using a CAD model)

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

## Series Hybrid vs. Parallel Hybrid

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.

## Collaborative Optimization

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

## Triathlon Strategy

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

## Collecting the Dots

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

## How Much Is Enough?

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

## Location! Location! Location!

What are the three most important things to consider when buying a house? Location. Location. Location. The repetition intentionally over-emphasizes this point:  if the location of your new real estate purchase is not good, then the details of the home don’t really matter.

A spacious kitchen and updated bathrooms are nice, but even a perfect house located near the end of a busy airport runway will not bring a high value in the real estate market. For this reason, many savvy investors buy the worst house in a great neighborhood and then upgrade it to suit their tastes, rather than buying the best house in a mediocre neighborhood.

So should you spend money to improve your current residence, or use those funds to move to a different neighborhood altogether? Clearly, the answer depends on the quality of your current location.  Continue reading

## Forgotten Reasons

You may have heard the story about the woman who always sliced about one inch off the end of a large roast before placing it in the pan to be cooked. When asked why she did this, she did not know the reason. But she was sure that it was important, because her mother always did exactly the same thing.

Now curious, the woman called her mother to ask why it is important to cut off the end of a roast before cooking it. Her mother did not know the reason, but she was confident that it was important, because her mother always did exactly the same thing.

A phone call to the woman’s grandmother finally revealed the true reason why two subsequent generations of cooks always cut off the end of a roast before cooking it. The grandmother explained, “A long time ago, the only size of roast at the local store was too large to fit in my pan. So I had to cut a bit off the end in order to cook it. I haven’t had to do that in years!”

I’ll bet the entire family had a good laugh about this situation. Many years ago, there was a really good reason to cut off the end of the roast, but that reason didn’t exist anymore. Yet that step in the process was passed down to future generations, as though it were crucial to the success of the meal.

There are probably many situations in which current limitations give rise to a process that continues to be used long after those limitations are gone. Often the facts become blurred and the philosophical reasoning becomes stronger, so no one questions whether the process is valid.  A paradigm is created that is not easily broken.  Continue reading

## A Brief History of Optimization

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

## Mind vs. Machine

In 1997, IBM’s Deep Blue computer won a six-game chess match against world champion Gary Kasparov. More recently, IBM’s Watson supercomputer defeated two of the greatest Jeopardychampions of all time during a three-day competition.

Despite these impressive exhibitions, machines cannot think like humans – yet.  For example, given different sets of information, humans have a natural ability to detect patterns and perceive order. Computers, on the other hand, must be told what types of pattern structures to look for.

But Deep Blue and Watson have demonstrated that when the rules of the game are well defined and the desired patterns are known, humans can program computers to solve these problems better or faster than our own minds can solve them.

Another example is engineering design optimization, where mathematical computer algorithms are being used to optimize engineered systems and components. In this context, the roles of mind and machine are still being debated. Unlike in chess or Jeopardy, no one has proposed a suitable competition to determine whether machines can outperform humans on design optimization tasks.

In my opinion, it is not a question of mind versus machine. The more important issue is how we can best leverage mind plus machine. While there is some overlap in their skill sets, the most powerful capabilities of the human mind complement those of computers.   Continue reading