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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 does CLT stand for?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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

2. Define fixed cost and variable cost.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
RDTE - Investment/Acquisition - Operations and Support - Disposal
#=2^n = 2^15
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

3. How do you get the CDF from the PDF?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
CDF= ?_(-8)^8
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

4. What are the parameters for a standard normal distribution?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Mean =0 Variance =1
To analytically answer 'How much design margin is really necessary?'
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

5. TIES Step 6: Identify Technology
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Technique for Order Preference by Similarity to Ideal Solution
(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
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

6. $/RPM Equation
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
It gives the probability that a value will be met or exceeded.
Determine the design space - baseline Method: Morphological Matrix
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

7. TIES Step 2: Design Space Conception
Determine the design space - baseline Method: Morphological Matrix
Cumulative Distribution Function
(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 sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

8. What is the definition of inflation?
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
Cumulative Distribution Function
(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
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

9. What can be done about uncertainty in requirement?
Sample size is 4 - the sample is the sum of the five dice.
CDF= ?_(-8)^8
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Technology space limits

10. TIES Step 5: Feasible?
Technique for Order Preference by Similarity to Ideal Solution
X+Y and X-Y are normally distributed. - (X
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

11. What is the notation for a standard normal distribution?
Range is always between zero and 1 monotonically increasing
X~N(0 -1)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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.

12. 3 Measures of Central Tendency (& Defs)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
It gives the probability that a value will be met or exceeded.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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

13. What can management do to mitigate the risk associated with infusing new technologies?
Cumulative Distribution Function
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
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~N(0 -1)

14. TIES Step 8: Selecting Technology

15. What are the three snapshots of UTE?
(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
(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 first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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

16. What does CDF stand for?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Mean =0 Variance =1
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Cumulative Distribution Function

17. In what regions of the graph is UTE applicable?
Regions 1 to 3.
Has a natural zero - is a cardinal scale
Allows designer to assess feasibility of design
X+Y and X-Y are normally distributed. - (X

18. How is inflation measured?

19. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Range is always between zero and 1 monotonically increasing
OEC = W1X/Xbsl + W2Nbsl/N
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Technology space limits

20. What is the definition of ROI?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
The interest i such that 0=PE(i^)
Active UTE (additive) - Product UTE (multiplicative)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

21. Direct Operating Costs
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
A pareto frontier represents points of a non - dominated solution based on preferences
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

22. Weaknesses of TOPSis...
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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.
#=2^n = 2^15

23. Write down a formula for a normal distribution
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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.
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
F(x)=1/(s(2p)^(.5) )exp?(-(x-

24. What are the four difference life cycle costs?
Mean =0 Variance =1
RDTE - Investment/Acquisition - Operations and Support - Disposal
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)

25. What is TCM? What is the size and what value can it take?
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
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.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

26. Other than infusing technologies - how can you create design space?
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
OEC = W1X/Xbsl + W2Nbsl/N

27. TIES
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(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
is bottom- up - you look at certain technologies and see what improvements they offer
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

28. Indirect Operating Cost
Range is always between zero and 1 monotonically increasing
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Mean =0 Variance =1

29. What is the definition of CDF?
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
It gives the probability that a value will be met or exceeded.
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
Central limit theorem

30. MADM
No way to tell without more information. It depends on the relation between s12+s22 and s32
Technology space limits
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.

31. interval scale
Does not have a natural zero - is a cardinal scale
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.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

32. What are K- factors applied to?
Active UTE (additive) - Product UTE (multiplicative)
Mean =0 Variance =1
Technology space limits
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.

33. What is satisficing - what is optimizing?
Cumulative Distribution Function
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.
Central limit theorem
Sample size is 4 - the sample is the sum of the five dice.

34. 3 Probabilistic Design Methods
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

35. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
It can be continuous or discrete
Central limit theorem
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

36. What is the normal distribution that results from adding x+y and x[sub]y?
X+Y and X-Y are normally distributed. - (X
Active UTE (additive) - Product UTE (multiplicative)
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
P(between B and A)=F(B)-F(A)

37. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
(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
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

38. With 15 technologies - what is the number of possible combinations?
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
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
#=2^n = 2^15

39. TIF

40. What two variables are necessary to define a normal distribution?
Mean and variance
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Determine the design space - baseline Method: Morphological Matrix
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

41. TIES Step 3: Model and Simulation
CDF= ?_(-8)^8
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to 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
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

42. What is the goal of probabilistic design?

43. TIES Step 4: Investigate Design Space
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
X+Y and X-Y are normally distributed. - (X
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.
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

44. 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?
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Sample size is 4 - the sample is the sum of the five dice.

45. Ratio scale
Allows designer to assess feasibility of design
Has a natural zero - is a cardinal scale
To analytically answer 'How much design margin is really necessary?'
(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

46. What does the CLT state - be specific!
CDF= ?_(-8)^8
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
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.
Does not have a natural zero - is a cardinal scale

47. TIES Step 1: Problem Definition
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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
Cumulative Distribution Function
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

48. What is probability density contour plot
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Cumulative Distribution Function
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
Allows designer to assess feasibility of design

49. Does TIES use MADM or MODM? Why?
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
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

50. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Allows designer to assess feasibility of design
No way to tell without more information. It depends on the relation between s12+s22 and s32
P(between B and A)=F(B)-F(A)