The Aerospace Design Process

Overview

You may have heard that a particular new airplane was designed on the computer. Just what this means and what can or cannot be computed-aided is not obvious and while design and analysis methods are being computerized to a greater degree than was possible earlier, there are great practical difficulties in turning the design task entirely over to the computers.

The design process has, historically, ranged from sketches on napkins (Fig. 1) to trial, error, and natural selection (Fig. 2), to sophisticated computer-aided design programs (Fig. 3).

Figure 1. Aircraft concepts can start with very rough sketches, as did the human powered airplane, the Gossamer Condor.



Figure 2. Aircraft Design By Trial and Error


Figure 3. Computer-Aided Design of Aircraft

Because the process is so complex, involving hundreds or thousands of computer programs, many people at many locations, it is difficult to manage and companies are continuing to try to improve on the strategy. In the early days of airplane design, people did not do much computation. The design teams tended to be small, managed by a single Chief Designer who knew about all of the design details and could make all of the important decisions. Modern design projects are often so complex that the problem has to be decomposed and each part of the problem tackled by a different team. The way in which these teams should work together is still being debated by managers and researchers.

The goal of these processes, whatever form they take, is to design what is, in some sense, the best airplane. To do this requires that we address three basic issues:
1. What do we mean by best?
2. How can we estimate the characteristics of designs so we can compare two designs in a quantitative way?
3. How do we choose the design variables which yield an optimum?

The first of these questions is perhaps the most important one, for if we don't know what we are trying to achieve, or if we select the wrong goal, it doesn't matter how good the analysis method may be, nor how efficient is our optimization procedure. Nevertheless, this question is often not given sufficient attention in many optimization studies.

Defining the Objective

If we were to examine advertisements for aircraft it might seem that the definition of the best aircraft is very simple. Madison Ave. Aircraft Company sells the fastest, most efficient, quietest, most inexpensive airplane with the shortest field length. Unfortunately such an airplane cannot exist. As Professor Bryson puts it, "You can only make one thing best at a time." The most inexpensive airplane would surely not be the fastest; the most efficient would not be the most comfortable. Similarly, the best aerodynamic design is rather different from the best structural design, so that the best overall airplane is always a compromise in some sense (see Fig 4.). The compromise can be made in a rational way if the right measure of performance is used. Structural weight and lift to drag ratio, for example, become parts of a larger equation. The left hand side of this equation is termed the figure of merit or objective and depends on the intended application for the aircraft.


Figure 4. One can only make one thing best at a time.

Various quantities have been used for this purpose, often including some combination of airplane performance and cost. For the exercise in E-1, we have made this part of the design problem easy by stating the goal of the exercise explicitly.

Analyses and Modeling

Once we have decided on the definition of "best" we must find a way of relating the "design variables" to the goal.


For aircraft design, this process is extremely complex. The number of parameters needed to completely specify a 747 is astronomical. So one uses a combination of approximation, experience, and statistical information on similar aircraft to reduce the number of design variables to a manageable number. This may range from 1 or 2 for back-of-the envelope feasibility studies to hundreds or even thousands of variables in the case of computer-assisted optimization studies. Even when the situation is simplified the model is usually very complicated and difficult. One generally must use a hierarchy of analysis tools ranging from the most simple to some rather detailed methods.

Calculating the drag of even a simple wing is not just a matter of specifying the wing span and area. Other parameters of importance include: additional details on the wing shape, the flight speed, the orientation angle of the airplane with respect to the on-coming flow, airfoil sections, distribution of bugs, etc.

This can be programmed and available as an analysis tool, but one must be very cautious. Which of these variables is included in the model? What if the wing is operating at very high altitude? Has it been compared with experiment in this regime?

As the design progresses, more information becomes available, and more refined analyses become part of the design studies. The expertise of a designer, these days, involves knowing what needs to be computed at what time and identifying the appropriate level of approximation in the analyses.

One of the most important, but least well understood parts of the design process is the conceptual design phase. This involves deciding on just what parameters will be used to describe the design. Will this be a flying wing? A twin-fuselage airplane? Often designers develop several competing concepts and try to develop each in some detail. The final concept is "down-selected" and studied in more detail.

Design Iteration and Optimization

The last question that must be addressed seems the most straightforward, but is full of subtlety and potential pitfalls. There are several methods by which one chooses the design variables leading to the "best" design. All of these require that many analyses be carried out -- often thousands of times. This requires that the model be simplified to the point that it is fast enough, but not to the point that it is worthless. (Einstein's saying comes to mind here: "Things should be as simple as possible, but no simpler.") When the design may be described by only a few parameters, the process is very simple. One investigates several cases, and usually can easily see where the optimum occurs. (Even this may be difficult if the computations are extremely time consuming and theories called 'design of experiments', 'response surfaces', and Taguchi methods are currently used to solve such problems.) When the number of variables is more than a few, more formal optimization is required, and numerical optimization programs, such as those developed by people in the operations research department, are very helpful.


So that's it. We just put it on the computer and press Return and out pops a 777, right?

Not really. Despite its obvious utility, numerical optimization seems to have been talked about a lot more than it has been used. It certainly is talked about a great deal. Prof. Holt Ashley gave the AIAA Wright Brothers Lecture in 1982. It was entitled, "On Making Things the Best -- Aeronautical Uses of Optimization". For this lecture, he surveyed the relevant literature and found a total of 8073 papers. But Ashley had a hard time finding a single case where this formal procedure was employed by industry. A great deal has changed in the past decade, however, and optimization techniques are (only now) starting to become a standard tool for engineering design. Why has it taken so long for these methods to become well-used, and why, still, are the methods not used everywhere?

There are a host of reasons:

1) First, the analysis, itself, of a complete aircraft configuration is rather complex, even without the optimization. Program size and complexity are such that only very well-documented and well-maintained computer programs can be used. These programs are often written by many people (some of whom have retired) over many years and it is very difficult for an individual to know what the program can and cannot do. Many grandiose plans for completely integrated aircraft design systems have fallen by the wayside because they quickly become unmanageable.

2) Any analysis makes certain approximations and leaves certain things out. Optimizers, however, may not understand that certain considerations have been omitted. Optimizers are notorious for breaking programs. They exploit any weakness in the analysis if that will lead to a "better" answer. Even when the result appears reasonable, several difficult-to-quantify factors are often omitted: the compatibility with future growth versions for instance, or the advantages associated with fleet commonality. Moreover, optimums are, by definition, flat, so that leaving something out of the objective can cause large discrepancies in the answer - the optimum is never optimal. Some examples are shown in figures 5 and 6. These are examples in which real-life testing, rather than reliance on simulation, is critical.

3) Ruts, creativity, and local minima: New technology changes the assumptions, constraints, experience. An optimizer is limited to consider those designs that are described by the selected parameter set. Thus, an optimizer and analysis that was written to design conventional structures may not know enough to suggest the use of composites. An optimizer did not invent the idea of folding tips for a 777, nor would it create winglets, canards, active controls, or laminar flow, unless the programmer anticipated this possibility, or at least permitted the possibility, in the selection of design variables. (Figure 7.)

A variety of new approaches are being explored to avoid these difficulties. Improved software development environments reduce some of the problems of communication, maintenance, etc.. Simply changing the computer language (even from Fortran IV to Fortran 90) helps in understanding and maintaining the program. Artificial intelligence (AI) is being used in several ways to improve the efficiency of aircraft design.

Regardless of the approach, however, the design process involves repeated cycles of simulation, testing, and re-design. The E-1 design project is intended to show this process in miniature.


Figure 5. "Optimal" Flight Path for Landing a Sailplane - An example
of what happens when the analysis does not include sufficient constraints.


Figure 6. "Optimal" Redesign of Cessna Cardinal
Optimizer has exploited simplified lateral stability constraints.


Figure 7. A Variety of Designs Not Likely Invented by an Optimizer