Thankfully, the first planes to ever take off look nothing like those we use today. Certainly, such progress requires scientific discoveries and engineering development. But less obvious, I believe, is how research and engineering interact together to make a technology progress. That’s what I want to discuss with this article.
Ideas, knowledge, and products
The process to go from ideas to an actual product is called engineering product development. And the process to discover new knowledge is called research.
A map of technology progress
With this article I wish to correct the misconception that technological progress happens in a linear fashion, where scientists make new fundamental discoveries, which engineers then turn into useful products. The reality is a little more complex. If I challenge anyone to make a lightbulb, I doubt they would benefit from knowing Planck’s law for black body radiation. Edison—and the many people that made light bulbs before him—didn’t use it. In fact, they didn’t have it. Edison made its first successful attempt around 1879, and Max Planck published the law in 1900. The same goes for boundary layer theory. Very useful in aerodynamics, the theory can help predict important phenomena such as boundary layer separation. It was published by Prandtl in 1904; the Wright brother had already flown at Kitty Hawk in December 1903. It seems as if inventors don’t really need to fully understand their prototypes to make them work. It really helps however—and we’ll comeback to this later—but historically, it looks like doing comes before understanding. Now, before making the connection between research and engineering, let me discuss them separately.
What is research?
The engineering design process
What is engineering product development?
Turning knowledge into products
Better knowledge accelerates engineering
Given that doing often comes before understanding, I would argue that trial and error is almost inevitable for cutting-edge technologies. Take the F-1 rocket engine for example, the engine that powered the first stage of the Saturn V, the rocket that carried humans to the moon in 1969. The F-1 engine was enormous—to this day, it is still the most powerful single chamber rocket engine to have ever flown. The design team for the F-1 was surely knowledgeable, but they still designed the injector plate for the combustion chamber by trial and error. The challenge is that as rocket engines scale up, they become more susceptible to combustion-induced instabilities. Modelling the combustion dynamics was definitely out of reach, so engineers simply iterated though several injector designs. The experiments consisted of detonating an explosive charge in the combustion chamber to generate oscillations, and observe whether they would dampen down or grow exponentially. Trial and error remained the best—if not the only—solution they had, even if an “error” meant a massive explosion.
In several industries, companies are required to conduct experiments to get certified. Crash tests are a good example. Modelling the physics of car crash is not trivial. Of course, engineers are getting pretty good at it, but historically it was not the case. Thus, crash tests are still mandatory for now.
Despite the rich information that can be extracted from a physical experiment, it remains that whenever possible, mathematical models are preferable to iterate through design concepts.
Products motivate research
Technology progress is a positive feedback loop
As an answer to the original question, I would argue that technologies progress with the feedback interaction of research and engineering. Oftentimes, this interaction takes place on long cycles, with the research and engineering teams being totally disconnected. A research team can publish a paper that will benefit a non-related engineering team with their product development, maybe without ever knowing it. But sometimes the R&D cycles can also be very small. An example is the invention of the transistor in 1947 by Bardeen, Brattain, and Shockley at Bell Labs. These well-trained physicists both advanced the understanding of solid-state physics and developed the first working transistor prototype concurrently. I believe this is a remarkable example however, not necessarily the norm.
Why so slow?
The next question is what limits the rate of technological progress. Take for example the blended wing-body aircraft architecture. It has been over 60 years that commercial aircrafts are mostly based on the classical tube and wing architecture. Below are pictures of the Boeing 707—a model that flew for the first time in 1957—and the 787, with a first flight in 2009. Of course, things have changed: the 787 is about twice as efficient as the 707, which is a tremendous improvement. This is partly due to the engines’ larger bypass ratio, the lighter weight of composite materials, the extensive use of electrical components to remove some hydraulics, and even a boundary layer injection system on the tail of the aircraft. There is no doubt that plenty of money and research efforts went into the technological developments. But we still don’t have commercial planes based on the blended wing-body architecture, despite the well-documented (theoretical) superiority of the concept. Why is that?
I believe design inertia have two root causes: the need for reliability, and the desire to maximize short term profit. First, let’s explore reliability. Reliability is expensive, and can only be obtained through extensive engineering. A crucial output of the engineering process is predictability—a well-engineered product will do exactly what it is intended to do, all the time. Engineers need not only to make things work, but also understand and characterize the limits of their products. For products where the tolerance for failure is extremely low—e.g., an aircraft—reaching an acceptable level of reliability becomes a tedious process. It involves more than simply testing the product, it is also about validating all the design assumptions and mathematical models used during the design process. The goal is to build confidence in the predictions obtained during design analysis. When a design is completely new, as opposed to an iteration on a previous design, predictability is very expensive. In effect, it becomes harder to recycle the design-validation efforts of previous projects. Everything has to be restarted from scratch.
One aerodynamic advantage of the blended wing-body concept comes from the tailless design. It makes the aircraft harder to control, however. It’s not impossible; for instance, Northrop Grumman did it in the 80’s with the B-2 Spirit Bomber. Nevertheless, to develop a control law for a blended wing-body design, Boeing could not simply recycle a control law from the 787 design. To foster advanced concept exploration, NASA provides assistance for this kind of early technology development. For example, Boeing and NASA collaborated on the X-48 program to better understand the aerodynamic characteristics of blended wing-body aircrafts. For new aircraft designs, the road is long between the proof-of-concept stage and commercial use.
Finally, let’s discuss how the desire to maximize short term profit creates design inertia. The electric vehicle is a prime example. In June 2021, the Tesla Model 3 became the first electric-car model to reach 1 million sales. It could have been done by any other long-standing automaker, but it wasn’t. It was achieved by a company that was not even 20 years old. The Innovator’s dilemma suggests why successful companies tend to neglect new technologies in the short term. New technologies typically underperform at first, but can (sometimes) outclass established ones in the long run. It can be attractive for established companies to capitalized on their technological-development efforts of the past—and profit off their current market-leading position—by simply incrementally improving their products, and avoiding drastic technology changes. To break off the pattern, established companies could aim for smaller markets at first, and gradually transfer the new technologies to the larger ones, much like a start-up would. After all, this is exactly what Tesla did by starting with the Roadster, then expanding with the Model S, and eventually the Model 3. Again, there is no reason to believe that it could not have been done by a long-standing automaker. This suggests that a choice was made: let the electric-vehicle market grow first, then tackle it when it is too large to ignore. While the Innovator’s dilemma mainly concerns which companies get the larger part of the pie, I would argue that this tendency also affects the rate of technology progress. Could we have had electric cars a few decades earlier?