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ADM

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Answer 50 questions in 15 minutes.
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Match each statement with the correct term.
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This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.

1. What is the goal of robust design?

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2. TIES Step 1: Problem Definition
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
OEC = W1X/Xbsl + W2Nbsl/N
Central limit theorem
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli

3. What does CLT stand for?
CDF= ?_(-8)^8
The interest i such that 0=PE(i^)
X+Y and X-Y are normally distributed. - (X
Central limit theorem

4. What are the three snapshots of UTE?
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
X+Y and X-Y are normally distributed. - (X
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P

5. If you have a two values on a CDF what is the probability of getting a value between them?
P(between B and A)=F(B)-F(A)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
It gives the probability that a value will be met or exceeded.
PE(i)=?Ft

6. 4 Measures of Dispersion
Does not have a natural zero - is a cardinal scale
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness

7. TIES Step 3: Model and Simulation
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
is bottom- up - you look at certain technologies and see what improvements they offer
Central limit theorem
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE

8. What is the definition of ROI?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
The interest i such that 0=PE(i^)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

9. What is probability density contour plot
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

10. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
P(between B and A)=F(B)-F(A)
OEC = W1X/Xbsl + W2Nbsl/N
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

11. What is the goal of probabilistic design?

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12. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Regions 1 to 3.
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE

13. What is the definition of inflation?
It can be continuous or discrete
Range is always between zero and 1 monotonically increasing
Inflation is the decrease in the buying power/value of money. It is caused by the when amount of available money changes wrt amount of product/services available
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

14. What are the different types of UTEs?
Range is always between zero and 1 monotonically increasing
Active UTE (additive) - Product UTE (multiplicative)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

15. What is the definition of CDF?
(1) Identify potential technologies that may improve technical & economical feasibility (2) Establish physical compatibility rules for diff techs (3) Determine expected impact (improvements and degradations) to systems of interest Method: TRL - Techn
CDF= ?_(-8)^8
It gives the probability that a value will be met or exceeded.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

16. What is TRL? Range? What does a high TRL mean?
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for
Cumulative Distribution Function
Mean and variance
The interest i such that 0=PE(i^)

17. What is satisficing - what is optimizing?
Regions 1 to 3.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
CDF= ?_(-8)^8
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.

18. Weaknesses of TOPSis...
Mean and variance
Mean =0 Variance =1
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

19. TIES Step 7: Assess Technology
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Technology space limits
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

20. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

21. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.
No way to tell without more information. It depends on the relation between s12+s22 and s32
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(1) Problem def - (2) Design space conception (3) Model and Simulation (4) Investigate Design Space (5) Feasible? (6) Identify Technologies (7) Evaluate Technologies (8) Select Technology

22. You have a group of 5 dice. You roll the groups and sum the results of the 5 dice 4 times. What is the sample size? What are you sampling?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
Sample size is 4 - the sample is the sum of the five dice.
Technology space limits

23. Why is learning curve used (or what is it?)
Technology space limits
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Allows designer to assess feasibility of design

24. MODM
Mean and variance
Has a natural zero - is a cardinal scale
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

25. Show and explain a pareto frontier
A pareto frontier represents points of a non - dominated solution based on preferences
(1) Mission Requirements - Input: Mission metrics and requirements Output: Delta response for requirements (2) Design Variables - Input: Geometric and economic design variables Output: Delta response for design variable - (3) Technologies Input: P
Central limit theorem
#=2^n = 2^15

26. TIF

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27. What does CDF stand for?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
#=2^n = 2^15
Cumulative Distribution Function
Provide for rapid trade- off capability between the three elements and search for feasible solutions - Allow graphical visualization of the combined space - Address mission requirements ambiguity and technology uncertainty.

28. Assumptions Used in TOPSis...
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
PE(i)=?Ft
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
F(x)=1/(s(2p)^(.5) )exp?(-(x-

29. How is inflation measured?

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30. What is the difference between price and cost?
Inflation is the decrease in the buying power/value of money. It is caused by the when amount of available money changes wrt amount of product/services available
X+Y and X-Y are normally distributed. - (X
Range is always between zero and 1 monotonically increasing
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee

31. With 15 technologies - what is the number of possible combinations?
#=2^n = 2^15
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Gaussian Distribution
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

32. Other than infusing technologies - how can you create design space?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
A probability density contour plot is a visualization method for Joint probability density function (a 2D representation). Their shapes (contour shapes) tell if the metric analyzed in each axis are correlated or not (Circular -> no correlation) (elli
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the

33. How do you get the CDF from the PDF?
CDF= ?_(-8)^8
Active UTE (additive) - Product UTE (multiplicative)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

34. Name the advantages of UTE.
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the
Provide for rapid trade- off capability between the three elements and search for feasible solutions - Allow graphical visualization of the combined space - Address mission requirements ambiguity and technology uncertainty.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

35. Write down a formula for a normal distribution
P(between B and A)=F(B)-F(A)
Technology Compatability Matrix - For n techs - is nxn matrix - Tells whether the intersecting technologies are compatible - It only has 0s and 1s - 0 means the technologies are not compatible with each other - 1 means techs are compatible with each
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for
F(x)=1/(s(2p)^(.5) )exp?(-(x-

36. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Has a natural zero - is a cardinal scale
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

37. 3 Probabilistic Design Methods
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Allows designer to assess feasibility of design

38. What two variables are necessary to define a normal distribution?
Mean and variance
Range is always between zero and 1 monotonically increasing
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
OEC = W1X/Xbsl + W2Nbsl/N

39. TIES
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
is bottom- up - you look at certain technologies and see what improvements they offer
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Gaussian Distribution

40. Why are scaling parameters important?
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Scaling parameters photographically scale the size of the vehicle to take full advantage of technology -(e.g. increase CL -> Can decrease S -> Decreases D -> Decreases Fuel Consumed -> etc...) This assumes that the physics of the problem remains the

41. TIES Step 2: Design Space Conception
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Mean =0 Variance =1
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Determine the design space - baseline Method: Morphological Matrix

42. Indirect Operating Cost
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
Does not have a natural zero - is a cardinal scale
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

43. TIES Step 4: Investigate Design Space
X+Y and X-Y are normally distributed. - (X
Technology space limits
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

44. What can be done about uncertainty in requirement?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
No way to tell without more information. It depends on the relation between s12+s22 and s32
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.

45. Strengths of TOPSis...
A technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

46. What are K- factors applied to?
Range is always between zero and 1 monotonically increasing
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Sample size is 4 - the sample is the sum of the five dice.
Technology space limits

47. What is the normal distribution that results from adding x+y and x[sub]y?
Range is always between zero and 1 monotonically increasing
X+Y and X-Y are normally distributed. - (X
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

48. In what regions of the graph is UTE applicable?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Regions 1 to 3.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee

49. interval scale
As you add n number of identical & independent distributions (IIDs) together - as n --> inf - the resulting distribution will be normal - regardless of the shape of the IIDs
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Does not have a natural zero - is a cardinal scale

50. Ratio scale
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Has a natural zero - is a cardinal scale
#=2^n = 2^15