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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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1. What can management do to mitigate the risk associated with infusing new technologies?
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)
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

2. TIES Step 1: Problem Definition
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Allows designer to assess feasibility of design
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

3. Why are scaling parameters important?
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
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
(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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

4. What is the goal of robust design?

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5. What are properties of a CDF?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
CDF= ?_(-8)^8
Range is always between zero and 1 monotonically increasing
Allows designer to assess feasibility of design

6. What does CLT stand for?
Central limit theorem
P(between B and A)=F(B)-F(A)
Determine the design space - baseline Method: Morphological Matrix
Has a natural zero - is a cardinal scale

7. What is the definition of ROI?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
OEC = W1X/Xbsl + W2Nbsl/N
The interest i such that 0=PE(i^)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

8. TIES Step 4: Investigate Design Space
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
RDTE - Investment/Acquisition - Operations and Support - Disposal
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.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

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

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10. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
It can be continuous or discrete
(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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Technique for Order Preference by Similarity to Ideal Solution

11. TIES Step 8: Selecting Technology

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12. How do you get the CDF from the PDF?
CDF= ?_(-8)^8
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Active UTE (additive) - Product UTE (multiplicative)
Sample size is 4 - the sample is the sum of the five dice.

13. MODM
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Technique for Order Preference by Similarity to Ideal Solution
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

14. 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?
To analytically answer 'How much design margin is really necessary?'
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Technology space limits
Sample size is 4 - the sample is the sum of the five dice.

15. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
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.
OEC = W1X/Xbsl + W2Nbsl/N
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.

16. interval scale
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
X~N(0 -1)
Does not have 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

17. 3 Probabilistic Design Methods
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Technology space limits
Central limit theorem

18. What is the difference between price and cost?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Range is always between zero and 1 monotonically increasing
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.

19. What is TRL? Range? What does a high TRL mean?
(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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

20. What is the equation for the learning curve?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
CDF= ?_(-8)^8
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
No way to tell without more information. It depends on the relation between s12+s22 and s32

21. Name the advantages of UTE.
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.
It gives the probability that a value will be met or exceeded.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
It can be continuous or discrete

22. Why is learning curve used (or what is it?)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Range is always between zero and 1 monotonically increasing
A pareto frontier represents points of a non - dominated solution based on preferences

23. If you have a two values on a CDF what is the probability of getting a value between them?
It gives the probability that a value will be met or exceeded.
The interest i such that 0=PE(i^)
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
P(between B and A)=F(B)-F(A)

24. What is the notation for a standard normal distribution?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
X~N(0 -1)
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

25. TIES Step 3: Model and Simulation
(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
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.
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
Determine the design space - baseline Method: Morphological Matrix

26. What are the different types of UTEs?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
(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
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
Active UTE (additive) - Product UTE (multiplicative)

27. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
(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
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
P(between B and A)=F(B)-F(A)

28. Write down a formula for a normal distribution
To analytically answer 'How much design margin is really necessary?'
F(x)=1/(s(2p)^(.5) )exp?(-(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
PE(i)=?Ft

29. Other than infusing technologies - how can you create design space?
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 and variance
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Has a natural zero - is a cardinal scale

30. What is another name for a normal distribution?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Gaussian Distribution

31. MADM
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.
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 space limits

32. Ratio scale
To analytically answer 'How much design margin is really necessary?'
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
RDTE - Investment/Acquisition - Operations and Support - Disposal
Has a natural zero - is a cardinal scale

33. Indirect Operating Cost
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Does not have a natural zero - is a cardinal scale
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

34. TIES Step 7: Assess Technology
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
The interest i such that 0=PE(i^)
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

35. In what regions of the graph is UTE applicable?
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.
It gives the probability that a value will be met or exceeded.
Regions 1 to 3.
The interest i such that 0=PE(i^)

36. What is TCM? What is the size and what value can it take?
To analytically answer 'How much design margin is really necessary?'
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
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

37. What are K- factors applied to?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
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.

38. Weaknesses of TOPSis...
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
P(between B and A)=F(B)-F(A)
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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

39. TIES
is bottom- up - you look at certain technologies and see what improvements they offer
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Active UTE (additive) - Product UTE (multiplicative)
It can be continuous or discrete

40. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
No way to tell without more information. It depends on the relation between s12+s22 and s32
PE(i)=?Ft

41. TIES Step 2: Design Space Conception
Determine the design space - baseline Method: Morphological Matrix
PE(i)=?Ft
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
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

42. Does TIES use MADM or MODM? Why?
Gaussian Distribution
Regions 1 to 3.
To analytically answer 'How much design margin is really necessary?'
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

43. What are the three snapshots of UTE?
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
(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
Technique for Order Preference by Similarity to Ideal Solution

44. Why is the normal distribution useful or important?

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45. What is the normal distribution that results from adding x+y and x[sub]y?
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.
X+Y and X-Y are normally distributed. - (X
Central limit theorem
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

46. Strengths of TOPSis...
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.
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.
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

47. Show and explain a pareto frontier
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
Central limit theorem
A pareto frontier represents points of a non - dominated solution based on preferences
RDTE - Investment/Acquisition - Operations and Support - Disposal

48. What is satisficing - what is optimizing?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
P(between B and A)=F(B)-F(A)
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.
X+Y and X-Y are normally distributed. - (X

49. TIES Step 5: Feasible?
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)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

50. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
OEC = W1X/Xbsl + W2Nbsl/N
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
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