Or: Luck; it’s a thing!
In this article I am going to go over 3 case studies from different engineering fields to show:
Are you familiar with the development of the vulcanisation of rubber? If you are, then you know how remarkable and strange that story is. If not, then be prepared to be amazed or maybe even disappointed at how much randomness can be a major influence over engineering success. Charles Goodyear had become obsessed with doing something with rubber. He believed that he was directed by God to take rubber and turn it into something beneficial to humanity. This obsession drove him to bankruptcy numerous times (meaning his imprisonment for a period), and his family into poverty. To top it off, he almost killed himself a number of times experimenting. Despite the consequences – his obsession drove him on. Some call him an eccentric, but mentally unbalanced could be a better description. While he had shown technical insights as a child, he had not been given any advanced education. He was more a tinkerer. And it was a tinkerer’s approach he used to improving rubber. A number of experiments had shown some promise, but they ultimately failed. Then, either by accident or elimination of other options, sulphur was mixed with the rubber. This cross linked the molecules in the rubber – making it tough and durable while keeping its compliance. Some say he called in vulcanisation and some say it was a contemporary British inventor of the same process. Personally, given his religiosity – I think he would refrain from giving praise to any god but his own. See below for the short video showing a perspective on Goodyear and how he made this discovery/invention.
Something to note. Charles Goodyear did not found the company Goodyear. The company was named after him by others who wanted to recognise his achievement.
Case Study 2 – Electronics Engineering Are you familiar with the development of the blue LED? Watch the video below if not.
The key points are:
Case Study 3 – Marine engineering Everyone today assumes boat propellers are short. They are screw propellers, but they are not long like other screws. We know this because that’s how propellers have been for over a century. However, as you might infer from drawings by Leonardo Da Vinci, people first assumed screw propellers would be long – like the screw drill bits used to drill holes in wood or screw presses or water screws used to lift water. It just seemed obvious that screw propellers would also be long. Around the 1830s there were many people exploring improved screw propellers. One of them was Francis Pettit Smith. He had been fascinated with boats as a boy and later in life reached the conclusion that screw propellers were superior to paddles. As did many others. There was thus a competition on to find and patent the best screw propeller. Most were doing the sensible thing of trying different configurations: pitch, speed diameter, length. They would build a propeller of one combination, note its performance, build another, note the change in performance, and then work out what to try next. Our man Francis though ended up doing things a bit differently. He was not known as being the most precise and methodical of his contemporaries. That probably would have been John Ericsson, and if not for what I am about to mention, John Ericsson, while being noted as a contributor to the screw propeller, would have likely been noted as the sole contributor. Thus, Francis was a bit more random than would normally be considered ideal for an engineer. Despite this lack of method, Francis had the good fortune to have struck a log while testing a propeller on a boat. This collision knocked the majority of the propeller off – leaving only a fraction connected to the shaft. Wisdom of the time would have expected the boat to founder. However, instead, the boat surged forward. Francis Pettit Smith had discovered/invented a far superior screw propeller. The one that was then adopted by later ship builders. What do we take from this? The above examples show how sometimes we need to use experimental approaches. Our understanding of the natural laws is insufficient. When we do, there is always the chance that someone else will try that near perfect permutation before we do. Or maybe we will be the ones who try it first. Regardless, the lesson to take from this is the same. This was not a matter of genius. Effort and commitments certainly played a role – but luck was the decider once those involved committed. So don’t beat yourself up if you did not win in those circumstances, do not think you are amazing if you did win, do not lionise those who do win, and certainly do not think less of those who did not. Because, in such cases, all you can do is commit and hope.
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You’ve probably already thought about the answer to that question. I hope you at least believe that one of these three is essential—rather than relying solely on instinct or heuristics for all your engineering decisions. Most, however, haven’t thought about each of the three in enough detail to fully grasp the implications and limitations of each approach. Ideally, at this point, you’re thinking about first principles. Indeed, each of these three represents a different way of applying first principles. So let’s consider how you can best use them for your first principles.
ExperimentationIt’s hard to argue with reality. And that’s what experimentation offers. If the experiment fails, it doesn’t matter if your calculations or simulations say it should work. Experimental outcomes are the ultimate judge. The issue with experimentation from an engineering perspective is that it always "works"—even if you aren’t aware of what’s important. You can’t choose to ignore or suppress key variables. They’re always present and always have a value, whether you’ve thought about them or not. You might set all the key variables you think are important, but there are still others you’ve set inadvertently—because reality has already given them values. That means you might believe you’ve experimentally found a solution to your problem, but when you implement it, an issue arises. Why? Because you were unaware of a key variable—and its value during implementation differs enough from what it was during your experimentation to cause a failure. Experimentation won’t alert you to your ignorance of key variables until it’s too late. An example I mentioned in my book involves an engineer designing a device to control water flow for watering plants. Their experimentally developed design worked, but once the system was implemented, variations in temperature—and thus viscosity—rendered it useless. The engineer had conducted all the experiments at roughly the same temperature. Since they didn’t realize how temperature-sensitive viscosity is, they didn’t factor it into their tests—and reality had silently set that variable for them. CalculationCalculations have the advantage of forcing you to account for all key variables. The formulae you use have been developed after considerable attention by experts who have identified the important variables at play. If the engineer in the earlier example had taken the time to read up on the theory and find the appropriate formula, they would have learned how critical viscosity is. Then, while looking up viscosity values to put in the formula, they would have seen how much viscosity changes with temperature. Formulae also reveal opportunities for optimization. You can see which variables are raised to a higher power and thus offer more "bang for your buck." You can also work out whether variables should be increased or decreased to maximize your output—which isn’t always obvious. Sometimes, you can even deduce if an optimum point exists. However, there aren’t always formulae available for your exact situation. Consider again the water control device: what if it had an outlet orifice that was non-standard, and the engineer couldn’t find a discharge coefficient for it to plug into the formula? Experimentation could be an answer—but it might be time-consuming if multiple variants had to be fabricated and tested. SimulationObviously the newest of the three, simulation is almost like a mix of the other two. Simulation can come very close to reality—assuming it’s a well-developed system—and it can force you to specify all variable values, forcing you to note all those at play. However, some simulation systems “help” you by asking you to specify a material instead of individual material properties. Thus, you might find yourself back in the same situation: unaware of all the important variables, and unaware of which ones are best to adjust for optimization. Also, simulations still rely on limiting assumptions. Often, we must simplify systems for the sake of usability. So your simulation might not be a perfect representation of what you’re actually working on. While simulation can offer tremendous benefits when it comes to testing ideas and improving systems, it’s still not a silver bullet. What to Do Then?It’s likely clear to you by now that you need to use all three—experimentation, calculation, and simulation—if you want the insights, speed, and confirmation needed to find the optimum solution. Beware of any engineer who suggests you should focus on one and ignore the others. |
AuthorClint Steele is an expert in how engineering skills are influenced by your background and how you can enhance them once you understand yourself. He has written a book on the - The Global Engineer - and this blog delves further into the topic. Archives
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