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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 is TRL? Range? What does a high TRL mean?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
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

2. Why use uniform dist for input variables (Gap Analysis)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Allows designer to assess feasibility of design
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Regions 1 to 3.

3. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
Regions 1 to 3.
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
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

4. TIES Step 2: Design Space Conception
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Determine the design space - baseline Method: Morphological Matrix
P(between B and A)=F(B)-F(A)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

5. What is satisficing - what is optimizing?
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.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

6. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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
No way to tell without more information. It depends on the relation between s12+s22 and s32

7. What can be done about uncertainty in requirement?
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.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

8. 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
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
F(x)=1/(s(2p)^(.5) )exp?(-(x-
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

9. What is the definition of ROI?
The interest i such that 0=PE(i^)
Technique for Order Preference by Similarity to Ideal Solution
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

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

11. Show and explain a pareto frontier
A pareto frontier represents points of a non - dominated solution based on preferences
It gives the probability that a value will be met or exceeded.
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

12. In what regions of the graph is UTE applicable?
Cumulative Distribution Function
Regions 1 to 3.
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
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

13. How is inflation measured?

14. What is the definition of CDF?
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Technique for Order Preference by Similarity to Ideal Solution
It gives the probability that a value will be met or exceeded.
Sample size is 4 - the sample is the sum of the five dice.

15. Ratio scale
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Has a natural zero - is a cardinal scale
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.
Range is always between zero and 1 monotonically increasing

16. Weaknesses of TOPSis...
The interest i such that 0=PE(i^)
(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.
X~N(0 -1)

17. With 15 technologies - what is the number of possible combinations?
#=2^n = 2^15
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Allows designer to assess feasibility of design
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

18. 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?
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
Regions 1 to 3.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Sample size is 4 - the sample is the sum of the five dice.

19. TIES Step 7: Assess Technology
Mean =0 Variance =1
To analytically answer 'How much design margin is really necessary?'
(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
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. TIF

21. TIES Step 4: Investigate Design Space
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
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.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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

22. What is the notation for a standard normal distribution?
#=2^n = 2^15
X~N(0 -1)
is bottom- up - you look at certain technologies and see what improvements they offer
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

23. What is another name for a normal 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)
Gaussian Distribution
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
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

24. Other than infusing technologies - how can you create design space?
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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Mean and variance

25. Does TIES use MADM or MODM? Why?
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.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
X+Y and X-Y are normally distributed. - (X

26. What are the parameters for a standard normal distribution?
The interest i such that 0=PE(i^)
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
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 =0 Variance =1

27. What are K- factors applied to?
Technology space limits
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
(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)

28. What are the three snapshots of UTE?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
(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
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.

29. What is the difference between price and cost?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
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
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline

30. What does TOPSIS stand for?
Active UTE (additive) - Product UTE (multiplicative)
Technique for Order Preference by Similarity to Ideal Solution
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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

31. Why are scaling parameters important?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

32. 4 Measures of Dispersion
Determine the design space - baseline Method: Morphological Matrix
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
RDTE - Investment/Acquisition - Operations and Support - Disposal
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

33. TIES
(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
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
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline

34. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Range is always between zero and 1 monotonically increasing
(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

35. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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
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

36. Write down a formula for a normal distribution
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Central limit theorem
OEC = W1X/Xbsl + W2Nbsl/N
F(x)=1/(s(2p)^(.5) )exp?(-(x-

37. Define fixed cost and variable cost.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
#=2^n = 2^15
(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

38. Name the advantages of UTE.
Regions 1 to 3.
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.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Has a natural zero - is a cardinal scale

39. 3 Probabilistic Design Methods
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
To analytically answer 'How much design margin is really necessary?'
Determine the design space - baseline Method: Morphological Matrix

40. If you have a two values on a CDF what is the probability of getting a value between them?
Has a natural zero - is a cardinal scale
P(between B and A)=F(B)-F(A)
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

41. 8 Steps in TIES
(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 can be continuous or discrete
PE(i)=?Ft
Technique for Order Preference by Similarity to Ideal Solution

42. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
To analytically answer 'How much design margin is really necessary?'
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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.

43. Why is the normal distribution useful or important?

44. 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
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
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

45. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Active UTE (additive) - Product UTE (multiplicative)
P(between B and A)=F(B)-F(A)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
X+Y and X-Y are normally distributed. - (X

46. What can management do to mitigate the risk associated with infusing new technologies?
(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)
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
Mean =0 Variance =1

47. TIES Step 8: Selecting Technology

48. What two variables are necessary to define a normal distribution?
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 and variance
(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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

49. $/RPM Equation
Central limit theorem
Has a natural zero - is a cardinal scale
OEC = W1X/Xbsl + W2Nbsl/N
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

50. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
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
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
F(x)=1/(s(2p)^(.5) )exp?(-(x-
It can be continuous or discrete