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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.