CPE 401 /6002/ 6003
Research Methods
Lecture: Hypothesis formation and
Experimental Design II
Prof Will Zimmerman
Topics
Paradigms for programmes: How do
we do research?
– Basic science
– Fishing expedition
– Engineering problem solving
– Future problem solving
Hypothesis formation:
– Brainstorming
– TRIZ: Inventive problem solving
– Dimensional analysis
Programme type: Basic Science
•Established discipline based (physics, chemistry,
biology and derivatives)
•Motivation is unresolved questions in the field
•Strong scholarship needed to place the central
question in context.
•Creative step (hypothesis formation) is usually
motivated by deep understanding.
•Must learn array of experimental procedures and
protocols traditional to the field.
•Contributions are frequently incremental – new way of
representing old ideas gives new insight, improvement
on technique gives better quality data. “Building on
the shoulders of giants.”
Programme type: Fishing expedition
•Typically newer area of enquiry.
•Motivation is new technology. A new piece of equipment
or measurement device is acquired to be applied to the
“open” field.
•New fields have only weak scholarship needed to place the
central question in context, since not much is known.
•Creative step (hypothesis formation) is usually motivated
by the production of the fishing expedition. Observations
leading to pattern matching. Clustering. Perhaps there is no
“conceptual model” that can be usefully applied.
•Must learn all “lore” of the new technology – how does it
work and on what principles is it based and designed.
Programme type: engineering
problem solving.
•Typically industrially directed – new problem arises for
which there is no well understood solution. Could be
multidisciplinary.
•Industrial applications may have a large “practical
literature” given long-standing interest, but typically very
little “scholarly” literature – deep theoretical understanding.
•Creative step (hypothesis formation) is usually conducted
by applying deep theoretical understanding and state-of-theart modelling tools
•Must learn all “lore” of the deep theoretical understanding
as well as the industrial practice.
•Problem providers looking for solution providers – nail
needing hammer!
Programme type: Future problem
solving
•Typically industrially directed – future problems are
envisaged for which the tools can be developed to solve.
•Industrial applications may have a large “practical
literature” given long-standing interest, but typically very
little “scholarly” literature – deep theoretical understanding.
•Creative step (hypothesis formation) is usually conducted
by applying deep theoretical understanding and state-of-theart modelling and experimental tools. Frequently proposes
developing new technology.
•Must learn all “lore” of the deep theoretical understanding
as well as the industrial practice.
•Solution providers looking for appropriate applications –
hammer needing nails!
Hypothesis formation:
Brainstorming
Multidisciplinary brainstorming
(ideation). Receptive to new ideas
and “thinking outside the box” but
directed by “problem owner”.
Should have people known to be
creative in their own disciplines
and receptive to multidisciplinary
working.
The whole is greater than the sum
of the parts.
Near-sighted disciplined based
problem solving
If disciplines were going to solve their open problems with
their own methodologies – given generations of bright
people and long time scales – wouldn’t they have already?
Research is driven by new methodologies. Most are
“borrowed” from other fields. But are they truly inventive?
What are the problem-solving inventive steps? Are they all
within any discipline?
E.g. how many engineers look for biomimetic solutions?
Engineers usually manipulate energy to achieve
technological solutions.
Natural biology usually implements information to achieve
solutions.
So how do people invent?
Genrich S. Altshuller, the Father of TRIZ
An approach, relying on technology was developed by Genrich S. Altshuller, born in the
former Soviet Union in 1926. His first invention, for scuba diving, was when he was only 14
years old. His hobby led him to pursue a career as a mechanical engineer. Serving in the
Soviet Navy as a patent expert in the 1940s, his job was to help inventors apply for patents.
He found, however, that often he was asked to assist in solving problems as well. His
curiosity about problem solving led him to search for standard methods. What he found were
the psychological tools that did not meet the rigors of inventing in the 20th century.
At a minimum, Altshuller felt a theory of invention should satisfy the following conditions:
•be a systematic, step-by-step procedure
•be a guide through a broad solution space to direct to the
ideal solution
•be repeatable and reliable and not dependent on
psychological tools
•be able to access the body of inventive knowledge
•be able to add to the body of inventive knowledge
•be familiar enough to inventors by following the general
approach to problem solving.
Classification of invention
•Altshuller screened over 200,000 patents looking for inventive problems and how
they were solved. Of these (over 1,500,000 patents have now been screened), only
40,000 had somewhat inventive solutions; the rest were straight forward
improvements.
•Altshuller more clearly defined an inventive problem as one in which the solution
causes another problem to appear, such as increasing the strength of a metal plate
causing its weight to get heavier. Usually, inventors must resort to a trade-off and
compromise between the features and thus do not achieve an ideal solution. In his
study of patents, Altshuller found that many described a solution that eliminated or
resolved the contradiction and required no trade-off.
•Altshuller categorized these patents in a novel way. Instead of classifying them by
industry, such as automotive, aerospace, etc., he removed the subject matter to
uncover the problem solving process. He found that often the same problems had
been solved over and over again using one of only forty fundamental inventive
principles. If only later inventors had knowledge of the work of earlier ones,
solutions could have been discovered more quickly and efficiently.
Categories of invention
In the 1960s and 1970s, he categorized the solutions into five levels.
•Level one. Routine design problems solved by methods well known within the specialty. No
invention needed. About 32% of the solutions fell into this level.
•Level two. Minor improvements to an existing system, by methods known within the
industry. Usually with some compromise. About 45% of the solutions fell into this level.
•Level three. Fundamental improvement to an existing system, by methods known outside the
industry. Contradictions resolved. About 18% of the solutions fell into this category.
•Level four. A new generation that uses a new principle to perform the primary functions of
the system. Solution found more in science than in technology. About 4% of the solutions fell
into this category.
•Level five. A rare scientific discovery or pioneering invention of essentially a new system.
About 1% of the solutions fell into this category.
He also noted that with each succeeding level, the source of the solution required broader
knowledge and more solutions to consider before an ideal one could be found. His findings
are summarized in the next Table.
Inventiveness pyramid
Table 1. Levels of Inventiveness.
Level
Degree of
inventiveness
% of
solutions
Source of
knowledge
Approximate
# of solutions
to consider
1
Apparent
solution
32%
Personal
knowledge
10
2
Minor
improvement
45%
Knowledge within
company
100
3
Major
improvement
18%
Knowledge within
the industry
1000
4
New concept
4%
Knowledge
outside the
industry
5
Discovery
1%
All that is
knowable
100,000
1,000,000
What Altshuller tabulated was that over 90% of the problems engineers faced had been
solved somewhere before. If engineers could follow a path to an ideal solution, starting with
the lowest level, their personal knowledge and experience, and working their way to higher
levels, most of the solutions could be derived from knowledge already present in the
company, industry, or in another industry.
Maximizing the ideality function.
Ideality = benefits : harmful effects
TRIZ: Step-by-step
•IDENTIFY THE PROBLEM
Boris Zlotin and Alla Zusman, principles TRIZ scientists at the American company
Ideation and students of Altshuller have developed an "Innovative Situation
Questionnaire" to identify the engineering system being studied, its operating
environment, resource requirements, primary useful function, harmful effects, and ideal
result.
•FORMULATE THE PROBLEM: THE PRISM OF TRIZ
Restate the problem in terms of physical contradictions. Identify problems that could
occur. Could improving one technical characteristic to solve a problem cause other
technical characteristics to worsen, resulting in secondary problems arising? Are there
technical conflicts that might force a trade-off?
•SEARCH FOR PREVIOUSLY WELL-SOLVED PROBLEM
Altshuller extracted from over 1,500,000 world-wide patents these 39 standard
technical characteristics that cause conflict. These are called the 39 Engineering
Parameters shown in the next table. Find the contradicting engineering principles. First
find the principle that needs to be changed. Then find the principle that is an
undesirable secondary effect. State the standard technical conflict.
39 Conflicting Principles
Look for Analogous Solutions and
Adapt to My Solution
Altshuller also extracted from the world wide patents 40
inventive principles. These are hints that will help an
engineer find a highly inventive (and patentable) solution to
the problem. Examples from patents are also suggested with
these 40 inventive principles. (Handout). To find which
inventive principles to use, Altshuller created the Table of
Contradictions, (see Mazur, 1995).. The Table of
Contradictions lists the 39 Engineering Parameters on the Xaxis (undesired secondary effect) and Y-axis (feature to
improve). In the intersecting cells, are listed the appropriate
Inventive Principles to use for a solution.
Dimensional analysis
 Continuum mechanics approach to
analysis that exploits supramolecular
scales and subrelativistic scales are
“self-similar” – no intrinsic units
enforced by nature.
 Quantum mechanics imposes molecular
scalings: Planck’s constant h and
electron charge (and mass) and atomic
mass. TOO SMALL at continuum scales.
 Relativistic dynamics impose a speed
limit. TOO LARGE at human scales.
Relevance to continuum mechanics
at human scale?
We can arbitrarily decide which units to measure quantities
in any dimension:
 length: meters, feet, rods, furlong, yards, miles
 time: seconds, minutes, hours, days, years
 temperature: kelvin, rankine, fahrenheit, celcius
 mass: grams, kilograms, slugs, poundals (pound-mass)
 force: lbs, ounces, newtons, dynes
 energy: joules, cal, BTU, ergs, electron volts
 pressure: atm, mmHg, pascals, psi
Quantities
There are two types of quantities
• Pure numbers: 2.56, p, e.
• Measurements: 6ft, 20yr, 10°C. All have units and are
meaningless without the units.
In all physical problems, variables have units. Nevertheless, Nature has no
preferred scales (in the continuum hypothesis). This has powerful repercussions.
It allows to specify the minimum number of parameters to solve a problem.
The idea is that physical processes can be dynamically similar, analogous to
geometric similarity. For instance all triangles, regardless of their size, that
have the same internal angles are geometrically similar, regardless of their size.
In a dynamical problem, the forces exerted all depend on size – if we scale out
the extensive properties, we are left with a smaller set of intensive properties
that are dynamically similar.
Repercussions: Buckingham PI
theorem and simple modelling
If the RANK of the of the dimensional matrix of variables is q,
and the number of fundamental units is u, then the number of
dimensionless quantities is q-u. Further, an independent set of
q-u dimensionless quantities must be functionally dependent.
Typically, q=the total number of quantities in the problem.
This theorem is the basis of dynamic similarity and physical
modelling.
It is also essential to modelling, since the BLACK ART of
dimensional analysis is selection of the important quantities
that are representative of the system. Expressing the functional
dependence and seeking a functional form for it are modelling.
Example: pipe flow
Quantities:
 pressure drop Dp
 mean velocity U
 diameter D
 length L
 fluid viscosity m
 fluid density r
Dimensionless groups q-u=3
rUD
Re

1. Aspect ratio L/D
m
2. Reynolds number
1 D Dp
f 
3. Friction factor
4 L 1 rU 2
Functional dependence
2
L

f  f  , Re 
Laminar regime:
D

64
f 
Re
Example: Flow over a sphere
Drag on sphere II
Drag on sphere III: An independent set of Pi’s
Drag on sphere: remarks.
Example: Ballistics
Example: Droplet
A rain droplet with internal viscosity m and density r with
diameter d moves with velocity U in air. What is the drag
force F exerted on the droplet as a function of the other
dimensionless groups if inertia is unimportant?
Droplet: Matching orders of units
Bibliography
G. Mazur, Theory of Inventive Problem
Solving (TRIZ), 1995,
http://www.mazur.net/triz/
http://www.mazur.net/triz/contradi.htm
http://www.triz-journal.com/
 Altshuller, Henry. 1994. The Art of
Inventing (And Suddenly the Inventor
Appeared). Translated by Lev Shulyak.
Worcester, MA: Technical Innovation
Center. ISBN 0-9640740-1-X
 Braham, James. "Inventive ideas grow
with 'Triz.'" Machine Design. Vol. 67 No.
18. October 12, 1995.