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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 are properties of a CDF?
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
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.
X+Y and X-Y are normally distributed. - (X

2. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
Mean =0 Variance =1
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
No way to tell without more information. It depends on the relation between s12+s22 and s32
It can be continuous or discrete

3. Direct Operating Costs
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
P(between B and A)=F(B)-F(A)

4. TIES Step 6: Identify Technology
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
Active UTE (additive) - Product UTE (multiplicative)
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) 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

5. In what regions of the graph is UTE applicable?
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.
Mean and variance
Regions 1 to 3.
(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

6. Does TIES use MADM or MODM? Why?
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Allows designer to assess feasibility of design
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

7. Why use uniform dist for input variables (Gap Analysis)
Does not have a natural zero - is a cardinal scale
Active UTE (additive) - Product UTE (multiplicative)
Allows designer to assess feasibility of 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

8. What is the normal distribution that results from adding x+y and x[sub]y?
X+Y and X-Y are normally distributed. - (X
RDTE - Investment/Acquisition - Operations and Support - Disposal
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Has a natural zero - is a cardinal scale

9. Why are scaling parameters important?
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
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.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

10. What does CDF stand for?
PE(i)=?Ft
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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.
Cumulative Distribution Function

11. What are the three snapshots of UTE?
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
Mean and variance
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
(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

12. What can management do to mitigate the risk associated with infusing new technologies?
X+Y and X-Y are normally distributed. - (X
Active UTE (additive) - Product UTE (multiplicative)
Technology space limits
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

13. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Active UTE (additive) - Product UTE (multiplicative)
Allows designer to assess feasibility of design
F(x)=1/(s(2p)^(.5) )exp?(-(x-

14. interval scale
Does not have a natural zero - is a cardinal scale
P(between B and A)=F(B)-F(A)
Sample size is 4 - the sample is the sum of the five dice.
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

15. 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
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
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.
Gaussian Distribution

16. What two variables are necessary to define a normal distribution?
Mean and variance
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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

17. TIES Step 8: Selecting Technology

18. What is the definition of inflation?
CDF= ?_(-8)^8
Technology space limits
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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

19. 8 Steps in TIES
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Mean =0 Variance =1
(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
It gives the probability that a value will be met or exceeded.

20. 3 Probabilistic Design Methods
Technique for Order Preference by Similarity to Ideal Solution
Sample size is 4 - the sample is the sum of the five dice.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
To analytically answer 'How much design margin is really necessary?'

21. TIES Step 1: Problem Definition
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
PE(i)=?Ft
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

22. What is another name for a normal distribution?
Gaussian Distribution
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
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

23. What is the definition of CDF?
PE(i)=?Ft
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
It gives the probability that a value will be met or exceeded.
RDTE - Investment/Acquisition - Operations and Support - Disposal

24. What can be done about uncertainty in requirement?
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.
The interest i such that 0=PE(i^)
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.

25. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Mean and variance
Cumulative Distribution Function
OEC = W1X/Xbsl + W2Nbsl/N
PE(i)=?Ft

26. What is the equation for the learning curve?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Regions 1 to 3.
Has a natural zero - is a cardinal scale
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

27. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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.
Technique for Order Preference by Similarity to Ideal Solution

28. What is TRL? Range? What does a high TRL mean?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Cumulative Distribution Function
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
X~N(0 -1)

29. With 15 technologies - what is the number of possible combinations?
Determine the design space - baseline Method: Morphological Matrix
P(between B and A)=F(B)-F(A)
#=2^n = 2^15
It gives the probability that a value will be met or exceeded.

30. How do you get the CDF from the PDF?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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)
CDF= ?_(-8)^8

31. What is the goal of robust design?

32. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
No way to tell without more information. It depends on the relation between s12+s22 and s32
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

33. Ratio scale
Has a natural zero - is a cardinal scale
(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
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
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

34. Why do we use a sample?
No way to tell without more information. It depends on the relation between s12+s22 and s32
Sample size is 4 - the sample is the sum of the five dice.
Active UTE (additive) - Product UTE (multiplicative)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

35. What are the different types of UTEs?
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)
RDTE - Investment/Acquisition - Operations and Support - Disposal
(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

36. $/RPM Equation
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
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

37. Name the advantages of UTE.
Mean and variance
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.
No way to tell without more information. It depends on the relation between s12+s22 and s32
is bottom- up - you look at certain technologies and see what improvements they offer

38. 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
X~N(0 -1)

39. TIES Step 5: Feasible?
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.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
A pareto frontier represents points of a non - dominated solution based on preferences
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

40. TIES
is bottom- up - you look at certain technologies and see what improvements they offer
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

41. Why is learning curve used (or what is it?)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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~N(0 -1)

42. What is satisficing - what is optimizing?
Central limit theorem
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

43. 3 Measures of Central Tendency (& Defs)
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
(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
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.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

44. TIF

45. What does the CLT state - be specific!
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Cumulative Distribution Function
is bottom- up - you look at certain technologies and see what improvements they offer

46. What are the parameters for a standard normal distribution?
Cumulative Distribution Function
Mean =0 Variance =1
X~N(0 -1)
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

47. What is probability density contour plot
It can be continuous or discrete
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
OEC = W1X/Xbsl + W2Nbsl/N

48. Show and explain a pareto frontier
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
A pareto frontier represents points of a non - dominated solution based on preferences
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

49. Assumptions Used in TOPSis...
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Gaussian Distribution
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

50. If you have a two values on a CDF what is the probability of getting a value between them?
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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