MECHANICAL ENGINEERING SYSTEMS LABORATORY

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1 MECHANICAL ENGINEERING SYSTEMS LABORATORY Group 02 Asst. Prof. Dr. E. İlhan KONUKSEVEN

2 APPROACH TO EXPERIMENTATION FUNDAMENTAL CONCEPTS IN MEASUREMENT & EXPERIMENTATION 2/85

3 THEORY & EXPERIMENTATION in ENGINEERING PROBLEM SOLVING APPROACHES There are 2 fundamental approaches for problem solving in engineering 1. Theoretical 2. Experimental 3/85

4 PROBLEM SOLVING APPROACHES 1. Theoretical : Physical / Mathematical Modeling 2. Experimental : Measurement 4/85

5 PROBLEM SOLVING APPROACHES Regardless of the Discipline ( ME, EEE, ChE, CE,...) or the Engineering Function ( Design, Development, Research, Manufacturing, Maintenance, etc.,..) While some problems can adequately be treated by using only theory, or only experimentation, most require a well balanced and complementing mix of these two techniques. 5/85

6 PROBLEM SOLVING APPROACHES Some general characteristics of these methods which will be helpful in deciding on the proper blend when choice is necessary are identified below. This also helps to organize your thinking about the whole process. 6/85

7 PROBLEM SOLVING APPROACHES Whenever some device or process is described with mathematical equations based on physical principles, the real world is left behind to a greater or lesser degree. i.e. all physical principles and their mathematical expression when applied to the real world situations are approximations of the real behavior. 7/85

8 PROBLEM SOLVING APPROACHES These approximations may be good, fair or poor, but some discrepancy between modeled and real behavior always exists. Although the quality of these approximations are improved as time goes by, perfection is an unreachable goal. 8/85

9 PROBLEM SOLVING APPROACHES We also need to remember that practical engineering, in contrast to pure science labors under constraints, sometimes overriding constraints of Time & Money 9/85

10 PROBLEM SOLVING APPROACHES i.e. an engineer may be well aware of a nearly perfect theoretical approach to a problem but will consciously choose instead a simpler and less accurate method, which is judged good enough in terms of overall project objectives. 10/85

11 PROBLEM SOLVING APPROACHES Thus our first comparison of theory and experiment centers on the fact that Theories are always approximations involving simplifying assumptions where as Experiments are run on the actual system and when properly designed and executed reveal the true behavior. 11/85

12 Features of alternative methods of problem solving Theoretical methods Experimental methods Study mathematical models of the real world which always require simplifying assumptions. Study the real world, no simplifying assumptions are required. 12/85

13 Features of alternative methods of problem solving Theoretical methods Study mathematical models of the real world which always require simplifying assumptions. Experimental methods Study the real world, no simplifying assumptions are required. Give general results to a wide class of problems. Give results specific to the apparatus studied. 13/85

14 Features of alternative methods of problem solving Theoretical methods Study mathematical models of the real world which always require simplifying assumptions. Give general results to a wide class of problems. Relaxation of assumptions leads to more complex mathematical model Experimental methods Study the real world, no simplifying assumptions are required. Give results specific to the apparatus studied. Higher accuracy measurements require more complex instrumentation 14/85

15 Features of alternative methods of problem solving Theoretical methods Study mathematical models of the real world which always require simplifying assumptions. Give general results to a wide class of problems. Relaxation of assumptions leads to more complex mathematical model. Facilities needed to commence study can be meager (trained personnel + paper & pencil) Experimental methods Study the real world, no simplifying assumptions are required. Give results specific to the apparatus studied. Higher accuracy measurements require more complex instrumentation. Extensive (and expensive) laboratory facilities may be needed. 15/85

16 Features of alternative methods of problem solving Theoretical methods Study mathematical models of the real world which always require simplifying assumptions. Give general results to a wide class of problems. Relaxation of assumptions leads to more complex mathematical model. Facilities needed to commence study can be meager (trained personnel + paper & pencil) Study can commence promptly Experimental methods Study the real world, no simplifying assumptions are required. Give results specific to the apparatus studied. Higher accuracy measurements require more complex instrumentation. Extensive (and expensive ) laboratory facilities may be needed. Time delays may occur in apparatus construction and debugging 16/85

17 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 17/85

18 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 18/85

19 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 19/85

20 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 20/85

21 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 21/85

22 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 6. Exploratory experimentation. 22/85

23 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 6. Exploratory experimentation. 7. Acceptance testing. 23/85

24 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 6. Exploratory experimentation. 7. Acceptance testing. 8. Use of physical models and analogies. 24/85

25 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 6. Exploratory experimentation. 7. Acceptance testing. 8. Use of physical models and analogues. 9. Teaching / learning through experimentation. 25/85

26 Functional types of Engineering Experiments 1. Determination of material properties and object dimensions. 2. Determination of component parameters, variable and performance indices. 3. Determination of system parameters, variables and performance indices. 4. Evaluation and improvement of theoretical models. 5. Product / process improvement by testing. 6. Exploratory experimentation. 7. Acceptance testing. 8. Use of physical models and analogues. 9. Teaching / learning through experimentation. 26/85

27 FUNDAMENTAL CONCEPTS IN MEASUREMENT & EXPERIMENTATION 27/85

28 WHAT IS MEASUREMENT? IN GENERAL IT IS THE ACQUISITION OF INFORMATION Classification of information leads to two different types of measurements: 1. STRUCTURAL INFORMATION 2. METRIC INFORMATION 28/85

29 1. STRUCTURAL INFORMATION THIS IS THE INFORMATION ON STATE OR NATURE OF A CERTAIN CHARACTERISTIC 29/85

30 1. STRUCTURAL INFORMATION FOR EXAMPLE YOU WANT TO KNOW IF AN ELECTRIC SHAVER WILL WORK WHEN YOU PLUG IT IN TO THE SOCKET SO YOU ASK YOURSELF WHAT IS THE IMPORTANT CHARACTERISTIC OF THAT SOCKET? THE ANSWER IS : VOLTAGE 30/85

31 1. STRUCTURAL INFORMATION AS A RESULT OF STRUCTURAL INFORMATION THE INDEPENDENT VARIABLES THAT SHOULD BE OBSERVED ARE DETERMINED THE ACQUISITION OF STRUCTURAL INFORMATION IS CALLED QUALITATIVE MEASUREMENT 31/85

32 2. METRIC INFORMATION THIS IS THE INFORMATION ABOUT THE QUANTITY OF AN INDEPENDENT VARIABLE IT IS OBTAINED AS A RESULT OF QUANTITATIVE MEASUREMENT 32/85

33 IN THE FIELDS OF SCIENCE AND ENGINEERING BOTH STRUCTURAL AND METRIC INFORMATION IS NECESSARY A qualitative measurement must be carried out before proceeding with a quantitative measurement. For example, if one needs to estimate the rate of loss of body liquid from a human face on a windy day, a qualitative measurement is needed first to determine which physical quantities affect the loss of body fluid (such as skin permeability, face area, air humidity, air temperature, air velocity, etc.) before attempting to determine their values. BEFORE METRIC INFORMATION IS ACQUIRED STRUCTURAL INFORMATION IS NEEDED 33/85

34 QUANTITATIVE MEASUREMENTS ARE ABOUT : MATERIAL PROPERTIES DENSITY THERMAL CONDUCTIVITY HEATING VALUE VISCOSITY MELTING POINT LATENT HEAT OF EVAPORATION ELASTIC MODULUS SPECIFIC HEAT ETC... 34/85

35 QUANTITATIVE MEASUREMENTS ARE ABOUT : COMPONENT PARAMETERS DIAMETER MASS SPRING CONSTANT ELECTRICAL INDUCTANCE FLUID CAPACITANCE THERMAL RESISTANCE ETC... 35/85

36 QUANTITATIVE MEASUREMENTS ARE ABOUT : VARIABLES OF STATE POSITION FORCE VOLTAGE PRESSURE TEMPERATURE DIFFUSION RATE ETC... 36/85

37 SOME ASPECTS OF A MEASUREMENT DESCRIPTIVE SELECTIVE OBJECTIVE 37/85

38 SOME ASPECTS OF A MEASUREMENT 1. A measurement must be descriptive with regard to that state or that phenomenon in the world around us which we are measuring. There must be a relationship between this state or phenomenon and the measurement result. 2. A measurement must be selective. It should only provide information about what we wish to measure and not about any other of many states or phenomena around us. 3. A measurement must be objective. The outcome of the measurement must be independent of an arbitrary observer. 38/85

39 REASONS FOR PERFORMING MEASUREMENTS 1. TO PROVIDE AN IMMEDIATE QUANTITATIVE ANSWER TO A SPECIFIC PROBLEM properties of a specific material parameters of a specific object or a component the state or performance of a specific system including system identification 39/85

40 REASONS FOR PERFORMING MEASUREMENTS 1. TO PROVIDE AN IMMEDIATE QUANTITATIVE ANSWER TO A SPECIFIC PROBLEM systematic experimental tests to design and to develop new products 40/85

41 REASONS FOR PERFORMING MEASUREMENTS 1. TO PROVIDE AN IMMEDIATE QUANTITATIVE ANSWER TO A SPECIFIC PROBLEM monitor and control engineering systems for their proper operation and maintenance 41/85

42 REASONS FOR PERFORMING MEASUREMENTS 1. TO PROVIDE AN IMMEDIATE QUANTITATIVE ANSWER TO A SPECIFIC PROBLEM To perform acceptance testing of components or systems by authorized agencies to demonstrate their conformity with preset standards before their use 42/85

43 REASONS FOR PERFORMING MEASUREMENTS 2. TO COLLECT EMPIRICAL DATA WITH THE HOPE OF OBTAINING AN INSIGHT INTO A PHENOMENON AND LATER GO ON TO USE THE DATA TO FORM A THEORETICAL EXPLANATION OF IT. 43/85

44 REASONS FOR PERFORMING MEASUREMENTS 3. TO VERIFY THE EXISTING THEORY To collect data for complementing, verifying, and improving these theoretical or empirical models, hypotheses, theorems, and laws by conducting controlled experiments. 44/85

45 DISCOVERY OF NEPTUNE AT 1820 FRENCH ASTRONOMER ALEXIS BOUVARD OBSERVED A DISCREPANCY IN URANUS S PATH AS PREDICTED BY NEWTON S LAW 45/85

46 THE DEVIATION WAS SMALL BUT IT WAS MORE THAN THE UNCERTAINTY OF THE MEASUREMENT 46/85

47 NEWTON S LAW OF GRAVITY WAS FORMULATED AS A HYPOTHESIS FOR EXPLAINING THE OBSERVATIONS OF TYCO BRAHE KEPLER AND GALILEO 47/85

48 UNTIL THEN THOUSANDS OF PREDICTIONS HAD BEEN MADE AND THE PREDICTIONS HAD BEEN OBSERVED NOW THERE WAS A NEW OBSERVATION WHICH DID NOT AGREE 48/85

49 WHAT SHOULD BE DONE? 1. FORMULATE A NEW HYPOTHESIS WHICH WILL ALSO EXPLAIN THE NEW OBSERVATION 2. MODIFY THE BASIS OF THE EXISTING THEORY (ASSUMPTION OF THE FIXED NUMBER OF PLANETS) 49/85

50 A HYPOTHETICAL NEW PLANET WAS CALCULATED AND LATER OBSERVED THUS NEPTUNE WAS DISCOVERED LATER PLUTO WAS DISCOVERED IN THE SAME MANNER 50/85

51 ENGINEERING EXPERIMENTATION APPROACH 51/85

52 STEP QUESTION A.1 WHAT IS THE OBJECTIVE OF THE EXPERIMENT? 52/85

53 A.1 WHAT IS THE OBJECTIVE OF THE EXPERIMENT? STEP POSSIBLE ACTION A.1 a) THE PROBLEM MAY HAVE BEEN CLEARLY DEFINED FOR YOU b) YOUR EXPERIENCE MAY HELP YOU TO DEFINE AN OBJECT c) IN AN UNFAMILIAR SITUATION TRY A FEW TENTATIVE TESTS FROM WHICH AN OBJECTIVE MAY SUGGEST ITSELF 53/85

54 A.1 WHAT IS THE OBJECTIVE OF THE EXPERIMENT? STEP CONCLUSION A.1 OBJECTIVE IS DEFINED AND NOTED 54/85

55 STEP QUESTION B.1 WHAT ARE THE IMPORTANT VARIABLES AND ARE THEY DEFINED? 55/85

56 B.1 WHAT ARE THE IMPORTANT VARIABLES AND ARE THEY DEFINED? STEP POSSIBLE ACTION B.1 a) THE ANSWER MAY BE SELF EVIDENT b) STEP A.1 MAY GIVE YOU A LEAD c) BEWARE OF INCLUDING DEPENDENT VARIABLES 56/85

57 B.1 WHAT ARE THE IMPORTANT VARIABLES AND ARE THEY DEFINED? STEP CONCLUSION B.1 THE VARIABLES ARE SELECTED AND PERHAPS A HYPOTHESIS IS MADE 57/85

58 STEP QUESTION B.2 WILL GROUPING OF VARIABLES REDUCE THE AMOUNT OF TESTING? 58/85

59 B.2 WILL GROUPING OF VARIABLES REDUCE THE AMOUNT OF TESTING? STEP POSSIBLE ACTION B.2 a) GROUPING CAN BE BASED ON PHYSICAL ARGUMENTS b) PERFORM A DIMENSIONAL ANALYSIS 59/85

60 B.2 WILL GROUPING OF VARIABLES REDUCE THE AMOUNT OF TESTING? STEP CONCLUSION B.2 ANY GROUPINGS ARE DECIDED UPON 60/85

61 STEP QUESTION C.1 WHAT APPARATUS IS REQUIRED? 61/85

62 C.1 WHAT APPARATUS IS REQUIRED? STEP C.1 POSSIBLE ACTION a) YOU WILL HAVE TO WORK WITHIN THE LIMITS OF THE APPARATUS AVAILABLE ( DO YOU NOW HAVE TO REVISE THE CONCLUSION OF B.1)? CONCLUSION OF B.1 THE VARIABLES ARE SELECTED AND PERHAPS A HYPOTHESIS IS MADE b) REQUEST ADDITIONAL FACILITIES 62/85

63 C.1 WHAT APPARATUS IS REQUIRED? STEP CONCLUSION C.1 NECESSARY EQUIPMENT IS NOW AVAILABLE 63/85

64 STEP QUESTION C.2 HOW WILL THE TESTS BE ORGANIZED? 64/85

65 C.2 HOW WILL THE TESTS BE ORGANIZED? STEP POSSIBLE ACTION C.2 a) DECIDE WHICH QUANTITIES YOU WILL VARY AND IN WHAT ORDER b) DECIDE WHO IS GOING TO DO WHAT 65/85

66 C.2 HOW WILL THE TESTS BE ORGANIZED? STEP CONCLUSION C.2 TEST PLAN IS DRAWN UP AND JOBS ARE ALLOCATED 66/85

67 STEP QUESTION C.3 HOW WILL THE DATA APPEAR ON YOUR NOTES? 67/85

68 C.3 HOW WILL THE DATA APPEAR ON YOUR NOTES? STEP POSSIBLE ACTION C.3 a) DECIDE ON THE RANGE OVER WHICH EACH VARIABLE MAY CHANGE C.1 a) MAY IMPOSE A LIMITATION C.1 a) YOU WILL HAVE TO WORK WITHIN THE LIMITS OF THE APPARATUS AVAILABLE 68/85

69 C.3 HOW WILL THE DATA APPEAR ON YOUR NOTES? STEP C.3 POSSIBLE ACTION a) DECIDE ON THE RANGE OVER WHICH EACH VARIABLE MAY CHANGE C.1 a) MAY IMPOSE A LIMITATION b) PREPARE A TABLE INTO WHICH DATA CAN BE ENTERED. IS A REFERENCE TEST NUMBER NEEDED? c) DECIDE WHETHER ACCURACY OF THE MEASUREMENTS WILL ENSURE A MEANINGFUL RESULT d) PREPARE AXIS ON WHICH CONTROL CURVES CAN BE PLOTTED 69/85

70 C.3 HOW WILL THE DATA APPEAR ON YOUR NOTES? STEP CONCLUSION C.3 a) NOTEBOOKS ARE PREPARED b) TEST IS PERFORMED c) DATA IS RECORDED d) CONTROL CURVES ARE DRAWN 70/85

71 STEP QUESTION D.1 WHAT DO THE CONTROL CURVES SHOW? 71/85

72 D.1 WHAT DO THE CONTROL CURVES SHOW? STEP POSSIBLE ACTION D.1 a) TAKE ADDITIONAL READINGS WHERE BAD POINTS OCCUR b) TAKE ADDITIONAL READINGS IN BADLY DEFINED AREAS 72/85

73 D.1 WHAT DO THE CONTROL CURVES SHOW? STEP CONCLUSION D.1 DATA IS NOW COMPLETE 73/85

74 STEP QUESTION E.1 HOW WILL THE RESULTS BE PRESENTED? 74/85

75 E.1 HOW WILL THE RESULTS BE PRESENTED? STEP POSSIBLE ACTION E.1 a) DECIDE BETWEEN GRAPHICAL, TABULAR OR FORMULA PRESENTATION b) IF (a) DEMANDS IT PERFORM A CURVE FITTING EXERCISE 75/85

76 E.1 HOW WILL THE RESULTS BE PRESENTED? STEP CONCLUSION E.1 RESULTS ARE ANALYSED 76/85

77 STEP QUESTION E.2 WHAT DO THE RESULTS MEAN? 77/85

78 E.1 WHAT DO THE RESULTS MEAN? STEP POSSIBLE ACTION E.2 a) ESTABLISH THE VALIDITY OR OTHERWISE OF THE HYPOTHESIS MADE IN B.1 b) ESTABLISH THE CONFIDENCE WHICH CAN BE PLACED IN THE NUMERICAL RESULTS c) EXPLAIN THE NATURE OF ANY TRENDS d) EXPLAIN DEVIATIONS FROM ANY THEORETICAL EXPECTATIONS 78/85

79 E.1 WHAT DO THE RESULTS MEAN? STEP CONCLUSION E.2 a) RESULTS ARE ANALYSED b) EXPERIMENTAL ERRORS ARE INVESTIGATED c) DISCUSSION SECTION IS WRITTEN 79/85

80 STEP QUESTION F.1 IS THE TEST FINISHED? 80/85

81 F.1 IS THE TEST FINISHED? STEP POSSIBLE ACTION F.1 a) STATE THE LAWS YOU HAVE DISCOVERED b) STATE ANY FURTHER INVESTIGATION YOU CONSIDER NECESSARY 81/85

82 F.1 IS THE TEST FINISHED? STEP CONCLUSION F.1 CONCLUSIONS ARE DRAWN AND RECOMMENDATIONS MADE 82/85

83 STEP QUESTION F.2 HAVE YOU FINISHED? 83/85

84 F.2 HAVE YOU FINISHED? STEP POSSIBLE ACTION F.2 a) PROCEED AS DICTATED BY F.1(b) F.1 b) STATE ANY FURTHER INVESTIGATION YOU CONSIDER NECESSARY 84/85

85 F.2 HAVE YOU FINISHED? STEP CONCLUSION F.2 TEST CONTINUES OR REPORT IS PREPARED 85/85

FUNDAMENTAL CONCEPTS IN MEASUREMENT & EXPERIMENTATION

FUNDAMENTAL CONCEPTS IN MEASUREMENT & EXPERIMENTATION What is Measurement? In general, it is the acquisition of information. Classification of information leads to two different types of measurements: