Chapter 02 - Engineering design and scientific research

Saturday, July 1, 2023
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Human beings function in two domains: the domain of material reality and the domain of the mind. Despite the intense interaction between the two domains, two main directions can be distinguished, namely a process from the outside to the inside (from the material reality to the domain of the mind) – a process that we call “knowledge acquisition” – and a process from the inside to the outside (from the domain of the mind to the domain of the material reality), which we call “technical action”.

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The process from the outside to the inside is directed towards knowledge of the world. The process from the inside to the outside is directed towards change of the world. In the former process, science plays the main part and technology has possible (but often) a supporting role. In the latter, technology plays the main part and science has (also often) a supporting role.

Scientific research occupies itself with the existing real world and with our representation thereof in factual statements. Engineering design, on the other hand, occupies itself with a not yet existing, but (hopefully) feasible world, or worlds. Designing is the construction of possible worlds in which the design product or process could appear and function. There is but one actually existing world, but there are many possible worlds. Possible worlds exist only in the domain of the mind. A flawless design process can thus take place entirely in the domain of the mind. But a flawless empirical empirical research process cannot. In this process, there must be an interaction between the domain of the material reality and the domain of the mind.

Scientific research

Scientific research is a systematized form of knowledge acquisition. Science (and technology) are called “systematic” (or “methodological”), because these activities are not haphazard. Scientific research starts with a problem. This problem points to an unsatisfactory situation which one wants to change into a more satisfactory one. In the research cycle the problem is that the available knowledge (a collection of factual statements about the world) is not aligned, or is insufficiently aligned, to the empirical facts. The facts are unassailable; hence the aim of scientific research is to change, respectively expand, the collection of factual statements (which appeared to be insufficiently “true”), in such a manner that they align better with the facts.

The model of the basic research cycle is shown in the next figure.

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The problem at the onset of the research cycle is a discrepancy between the facts and our knowledge – a package of truth statements concerning these facts. The aim of the process is adjustment of our knowledge to the facts.


One might think that the elements “problem” and “observation” are in the wrong order. For, did the problem not originate from the observation of facts which did not agree with theory? Indeed, but in order to improve the theory, we usually need more than the establishment of one or a few “anomalous” instances. From the moment the problem has been recognized and we have decide to resolve it by means of research, we want to map out the deviations in as large an area as possible. This requires purposeful observation, in many cases supported by experiments.

Note that the observation is directed towards gathering factual statements and should be carried out as objectively as possible: unwelcome observations should not be concealed, dishonest samples are not allowed, etc. Values and interests should be kept out as much as possible.


Induction is the process by which a number of individual observations of facts are “summarized” in a general law. Induction always concerns a certain aspect of the reality concerned, for example the color, the temperature, the pressure or the chemical behavior, but never the reality in its totality. It gives us, so to speak, a generally valid photograph of that certain aspect of that certain reality. It images that aspect of that reality. But first there was the reality, and then came the photograph.

Induction in the research cycle is “a posteriori” (found afterwards) the material which was studied.


Scientific research aims at explaining present and past phenomena and predicting future ones. Explaining is indicating the general grounds or causes from which the observed phenomena can be deduced. Predicting is to deduce future phenomena from the present ones. Empirical generalizations and laws and theories, gained from induction, provide for these “general grounds”. If they have explanatory power, generally they also have predictive power, but the reverse is not true.

It should be possible to derive the phenomena to be explained or predicted by means of deduction, from the theoretical relationships acquired from induction. This is what one tries to do in the “deduction phase”. In doing so, does one try in the research cycle to include the totality of phenomena in all their aspects? No, for as we already noted: sciences are essentially “aspect sciences”. Deduction thus aims at only one or a few aspects(s) of the phenomena, and leaves out all the other aspects.

Although scientific laws are often formulated in the form of hypothetical propositions (material implication), deduction takes place via a proposition syllogism, often according to the modus ponendo ponens. The following reasoning is an example: if Venus is a planet, then Venus rotates around the sun (p → q is true); Venus is a planet (p is true); therefore Venus rotates around the sun (q is true). The material implication is the major in the syllogism, the minor categorically confirms the antecedent, and the conclusion categorically confirms the consequence.

Recapitulating, we can state that deduction in the research cycle leads to a categorical explanation and/or prediction of one or more aspects of reality.

Deduction in the research cycle takes place entirely in the domain of the mind.


Testing can direct itself to the explanatory power or to the predictive power of the postulated laws or theories. For simplicity’s sake, we shall restrict ourselves here to the testing of predictive power. In view of the inductively acquired insight, deductively a prediction has been made (with or without the help of an experiment) on factos to be observed in the future. In the testing procedure these facts are observed and compared with the prediction. Dos it fit the facts? If not, to what extent do the facts “support” the hypothesis, that is how “true” is the hypothesis? (In Figure 2.1 we used the qualitative notion of “degree of truth” of the hypothesis for this). In testing, the research cycle again enters the domain of the material reality. The conclusions are factual statements in the domain of the mind.


“Evaluation” in the research cycle does not judge only on the findings of the entire process. A decision is also taken of whether the goal laid down (more, better knowledge) has been sufficiently attained, or whether the results should be sharpened up in none or more iterations. (Also in science, such a decision implies a value judgment.) Hence, the feedback arrows which run in the previous figure from the element of “evaluation” back upwards. But if the evaluation has been satisfactory, it is decided to add the knowledge which the process has yielded to the acreage of knowledge in the domain of the mind. Usually this takes place more explicitly in the form of a scientific publication.

Engineering design

Engineering design also begins with a problem that points to an unsatisfactory situation which one wants to change into a more satisfactory one.

The model of the basic engineering design cycle is shown in the next figure.

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In the engineering design cycle the problem at the onset is that the facts are not aligned with our values and preference concerning these facts. And since (in the first instance) our values are unassailable, this discrepancy leads to us making it our aim to change the facts. We want to create a material condition which does agree with our values and preferences. This requires technical action. Technical action requires technical means, and these must be designed.


Since design is aimed at possible, but not actually existing worlds, there is, initially, nothing to observe. But one can ask oneself in reasoning under what conditions a world that has been thought up will be both feasible and desirable. This process of reasoning takes place in the analysis phase. It often occurs that in analyzing a problem we come to the conclusion that we know too little to be able to carry out the analysis properly and that the lacking knowledge cannot be found anywhere. Then we have to make recourse to research, in order to fill the gaps in our knowledge. In doing so, the design process is left, strictly speaking. With th knowledge gained from scientific research, the design cycle is again entered, in order to further work on one or more possible worlds.


The synthesis phase in the design cycle is aimed at the totality of the entity to be designed, not only at a certain aspect, but at the entirety. A design is a kind of panoramic photograph, encompassing all aspects. Moreover, in designing (if we may talk of a photograph) we first have the photograph, and at a later stage (when the design has been realized) the object that was depicted. Synthesis in the design cycle is a priori (given before) the material reality which can e possible realized. The pattern of reasoning in the synthesis phase follows the logical pattern of innoduction.


The synthesis phase has yielded a provisional design for a product. Before we manufacture the product, we want to get an impression of how this product would behave if we manufactured it. We cannot be satisfied with merely one aspect, we want a comprehensive image of the behavior with respect to many aspects: functioning, durability, safety, usability, energy use, environmental consequences, cost price, etc. We would like to explore the possible worlds in which our product might occur before we make it into the real world.

Recapitulating, we can state that simulation in the design cycle leads to a conditional prediction of the behavior of the designed product, with respect to this product in its totality, that is to say with respect to as many aspects as possible.

At the beginning of the simulation phase we stand with empty hands. Thus we start the simulation phase, not immediately with deduction, like in the research cycle, but we first have to build up a logical system for that deduction. Since in designing, a number of aspects is involved, it often means that we have to construct a number of these deductive systems. Such systems are called models. Thus, at the beginning of the simulation phase, models should be constructed on the basis of which the desired deductive operations can be carried out. In Chapter 5 we will go more extensively into the different models for simulation. Here we shall restrict ourselves to the following remarks.

Deduction always takes place in a logical system. Logical systems belong to the domain of the mind (although they can be interpreted materially in books and the like). Some simulation models, for example mathematical models, directly form such logical systems within which deductions can be performed. Yet, many simulations call upon the help of experiments with physical models, which are considered, with respect to a certain aspect, as homomorphisms of the original.

Thus we see that the simulation phase in the design cycle contains one element more than the deduction phase in the research cycle, namely the construction of a simulation model (or simulation models). The simulation phase in the design cycle often enters the field of material reality, via experiments with physical models.


During evaluation in the design cycle, comparisons are made between simulated design behavior and the desired behavior of the product to be designed. So factual statements are compared to value statements, resulting in value judgments as to the quality or utility of the design proposal, in the light of the design specification. Evaluation takes place entirely in the domain of the mind, largely in the domain of value judgments.


In the design cycle we encounter the element “decision” at this level. The decision aspect is expressed more explicitly in the design process. Here it concerns various possibilities to continue. First of all, the pro- visional design concerned can be improved in one or more iterations. The feedback arrows reflect this. But a decision can also be made to generate more design alternatives. The feedback arrows also clearly show this option. And finally, the decision can refer to choosing an attractive alternative from the collection of generated designs. The process ends with the yield of a number of acceptable designs, or – one decision step further – with the manufacturing of the most attractive design.

All of these designs are (irrespective of their often beautiful representations in drawings, mock-ups, scale models and the like) in the domain of the mind. Yet, that is not the intended final station, like knowledge in the research cycle. The design result requires to be realized in the domain of material reality, in order to leave eht possible world in which it was conceived and to enter the factual world as a product. The design as a result of the design cycle is an intermediate station. It points to the realized product as a final station of the entire technological process, and that lies in the domain of the material reality.


Conclusion text.

[1^]: Eekels and Roozenburg.