Problem Dimensions

The problem space defines dimensions of design improvement. These dimensions are:


Description: Durability measures the persistence in time of the ability to achieve the intended function. By this definition, durability covers such diverse criteria as reliability (MTBF) and the production of extremely short-duration events such as femtosecond laser pulses.



Description: Efficiency measures the amount of resources consumed to achieve desired output Efficiency is usually measured as a percentage of some theoretical optimum. Perfect efficiency is ruled out by the second law of thermodynamics.

Efficiency is (at least) a second order problem in that it describes the ratio of desired output to resource consumption.

We divide resources into consumable and fixed (not consumed) resources to get a more complete picture. So a rocket consumes fuel but also has mass (weight). The efficiency of a rocket isn't simply how completely it turns fuel into kinetic energy. It must also take into account the mass of the rocket.

If we take into account the *cost* of the structure and fuel, we get a measure of the cost per pound of payload, which is a more practical measure of efficiency. Next, we account for the cost of labor, maintenance, etc over the entire life-cycle of the project. So thermodynamic efficiency is only a piece of the picture.

Technologies don't become practical until they reach some threshold of efficiency. The possibilities of a technology aren't even explored until that threshold is reached. E.g., electric power generation didn't take off until the Gramme Dynamo.

Corollary: Incremental improvements in efficiency almost always win out over radical redesigns for larger gains. (cf., silicon semiconductors vs. III-IV semiconductors, gasoline automobiles)

Conjecture: Conservation of efficiency - integration and automation often increase operating efficiency, but reduce maintainability and therefore ultimate durability. Total efficiency does not increase linearly with operating efficiency.



Description: Flexibility measures the ability to adapt a design to achieve a different function. E.g., a truck is more flexible than a train.

Why design for flexibility?



Description: Generality measures the narrowness or specificity of the function to be achieved. (applicability) E.g., a programmable computer is more general than a fixed function calculator. A material is more general than a device.

Why design for generality?



Description: Power measures the adequacy with which the design achieves the function. It characterizes the scaling of the output in terms of work per unit time, peak speed, accuracy, etc.



Description: One of the great success stories of 20 century technology was the tremendous increase in computing power for a given cost. This has been due largely to miniaturization, the ability to implement increasingly more circuitry in ever smaller packages. Scaling down a technology often makes it economical. On the other hand, economies of scale often arise from scaling up, in order to amortize the fixed costs of manufacture and generation. In so far as natural laws and mathematical properties don't all scale uniformly, barriers to scalability form one of the main obstacles to design and technology improvement.

The other scaling truism is in science, the smaller the object you want to observe, the more energy (and larger machine) you need to look at it. Particle accelerators are big.