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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. TIES Step 5: Feasible?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
is bottom- up - you look at certain technologies and see what improvements they offer
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. What is another name for a normal distribution?
CDF= ?_(-8)^8
Gaussian Distribution
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

3. Why is learning curve used (or what is it?)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
is bottom- up - you look at certain technologies and see what improvements they offer
Mean =0 Variance =1

4. Indirect Operating Cost
is bottom- up - you look at certain technologies and see what improvements they offer
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
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
X~N(0 -1)

5. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
Does not have a natural zero - is a cardinal scale
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

6. What is the definition of CDF?
It gives the probability that a value will be met or exceeded.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Technique for Order Preference by Similarity to Ideal Solution
X+Y and X-Y are normally distributed. - (X

7. What are properties of a CDF?
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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
Range is always between zero and 1 monotonically increasing

8. What is satisficing - what is optimizing?
X~N(0 -1)
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.
#=2^n = 2^15
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

9. Direct Operating Costs
No way to tell without more information. It depends on the relation between s12+s22 and s32
Mean =0 Variance =1
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
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

10. What is the difference between price and cost?
It can be continuous or discrete
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Mean =0 Variance =1
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

11. TIF

12. MODM
Technology space limits
No way to tell without more information. It depends on the relation between s12+s22 and s32
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
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

13. interval scale
PE(i)=?Ft
Does not have a natural zero - is a cardinal scale
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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.

14. What does CLT stand for?
To analytically answer 'How much design margin is really necessary?'
Central limit theorem
Active UTE (additive) - Product UTE (multiplicative)
#=2^n = 2^15

15. 4 Measures of Dispersion
P(between B and A)=F(B)-F(A)
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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.
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

16. What is probability density contour plot
The interest i such that 0=PE(i^)
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
OEC = W1X/Xbsl + W2Nbsl/N

17. What is the definition of ROI?
The interest i such that 0=PE(i^)
Technique for Order Preference by Similarity to Ideal Solution
Cumulative Distribution Function
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

18. What are the three snapshots of UTE?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
(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
The interest i such that 0=PE(i^)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

19. Write down a formula for a normal distribution
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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
Does not have a natural zero - is a cardinal scale

20. TIES Step 1: Problem Definition
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Has a natural zero - is a cardinal scale
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
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

21. How do you get the CDF from the PDF?
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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
CDF= ?_(-8)^8
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

22. What is TIM? What is the size and what value can it take?

23. TIES Step 8: Selecting Technology

24. 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?
It can be continuous or discrete
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Sample size is 4 - the sample is the sum of the five dice.

25. TIES Step 6: Identify Technology
(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
Select final tech comb. For any multi attribute - constraint - or criteria problem - the selection of the 'best' family of alternatives is inherently subjective. Various selection techniques are used to provide decision maker with extensive info. Met
The interest i such that 0=PE(i^)
(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

26. Why do we use a sample?
Has a natural zero - is a cardinal scale
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Sample size is 4 - the sample is the sum of the five dice.

27. What is the notation for a standard normal distribution?
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
X~N(0 -1)
Central limit theorem
PE(i)=?Ft

28. What is the equation for the learning curve?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Mean =0 Variance =1
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
X~N(0 -1)

29. What are K- factors applied to?
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
Select final tech comb. For any multi attribute - constraint - or criteria problem - the selection of the 'best' family of alternatives is inherently subjective. Various selection techniques are used to provide decision maker with extensive info. Met
Mean =0 Variance =1
Technology space limits

30. TIES Step 3: Model and Simulation
Technique for Order Preference by Similarity to Ideal Solution
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

31. What is the goal of robust design?

32. 8 Steps in TIES
Does not have a natural zero - is a cardinal scale
Sample size is 4 - the sample is the sum of the five dice.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
(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

33. In what regions of the graph is UTE applicable?
X~N(0 -1)
Regions 1 to 3.
Range is always between zero and 1 monotonically increasing
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

34. TIES
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.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
is bottom- up - you look at certain technologies and see what improvements they offer

35. What can management do to mitigate the risk associated with infusing new technologies?
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) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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.

36. What is the goal of probabilistic design?

37. What does the CLT state - be specific!
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
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.
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

38. What is TCM? What is the size and what value can it take?
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Active UTE (additive) - Product UTE (multiplicative)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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

39. Why is the normal distribution useful or important?

40. MADM
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
(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
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.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

41. With 15 technologies - what is the number of possible combinations?
Mean =0 Variance =1
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
#=2^n = 2^15

42. What does TOPSIS stand for?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
X+Y and X-Y are normally distributed. - (X
Technique for Order Preference by Similarity to Ideal Solution

43. What can be done about uncertainty in requirement?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
OEC = W1X/Xbsl + W2Nbsl/N
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
X+Y and X-Y are normally distributed. - (X

44. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Mean =0 Variance =1
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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
No way to tell without more information. It depends on the relation between s12+s22 and s32

45. Why use uniform dist for input variables (Gap Analysis)
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
X~N(0 -1)
Range is always between zero and 1 monotonically increasing
Allows designer to assess feasibility of design

46. Ratio scale
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
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Has a natural zero - is a cardinal 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

47. Does TIES use MADM or MODM? Why?
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
RDTE - Investment/Acquisition - Operations and Support - Disposal
Technology space limits

48. What is TRL? Range? What does a high TRL mean?
A pareto frontier represents points of a non - dominated solution based on preferences
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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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

49. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
Gaussian Distribution
X+Y and X-Y are normally distributed. - (X
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

50. If you have a two values on a CDF what is the probability of getting a value between them?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
It can be continuous or discrete
P(between B and A)=F(B)-F(A)