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1. 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
CDF= ?_(-8)^8
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Range is always between zero and 1 monotonically increasing

2. What are properties of a CDF?
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.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
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.

3. Other than infusing technologies - how can you create design space?
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
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
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

4. TIES Step 3: Model and Simulation
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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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
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

5. What can management do to mitigate the risk associated with infusing new technologies?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Cumulative Distribution Function
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

6. What does CLT stand for?
P(between B and A)=F(B)-F(A)
Determine the design space - baseline Method: Morphological Matrix
It can be continuous or discrete
Central limit theorem

7. MADM
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
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.
Sample size is 4 - the sample is the sum of the five dice.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

8. What is another name for a normal distribution?
Gaussian Distribution
Active UTE (additive) - Product UTE (multiplicative)
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 sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

9. How do you get the CDF from the PDF?
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
CDF= ?_(-8)^8
P(between B and A)=F(B)-F(A)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

10. What is satisficing - what is optimizing?
It can be continuous or discrete
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.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

11. In what regions of the graph is UTE applicable?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Regions 1 to 3.
Allows designer to assess feasibility of design
PE(i)=?Ft

12. What are the different types of UTEs?
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
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)
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

13. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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
It can be continuous or discrete

14. interval scale
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
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Does not have a natural zero - is a cardinal scale

15. MODM
(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
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

16. What is the definition of inflation?
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
The interest i such that 0=PE(i^)

17. Why are scaling parameters important?
Gaussian Distribution
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
No way to tell without more information. It depends on the relation between s12+s22 and s32
It can be continuous or discrete

18. What is the notation for a standard normal distribution?
Allows designer to assess feasibility of design
X~N(0 -1)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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

19. What is the equation for the learning curve?
PE(i)=?Ft
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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

20. $/RPM Equation
Mean =0 Variance =1
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

21. What is probability density contour plot
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
(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
P(between B and A)=F(B)-F(A)
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

22. What is TCM? What is the size and what value can it take?
Technique for Order Preference by Similarity to Ideal Solution
Does not have a natural zero - is a cardinal scale
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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

23. TIES
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Mean =0 Variance =1
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
is bottom- up - you look at certain technologies and see what improvements they offer

24. What can be done about uncertainty in requirement?
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

25. Why is learning curve used (or what is it?)
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
X~N(0 -1)

26. If you have a two values on a CDF what is the probability of getting a value between them?
CDF= ?_(-8)^8
P(between B and A)=F(B)-F(A)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

27. TIES Step 2: Design Space Conception
RDTE - Investment/Acquisition - Operations and Support - Disposal
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Determine the design space - baseline Method: Morphological Matrix
CDF= ?_(-8)^8

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

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29. TIES Step 4: Investigate Design Space
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
F(x)=1/(s(2p)^(.5) )exp?(-(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.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.

30. What are the three snapshots of UTE?
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
RDTE - Investment/Acquisition - Operations and Support - Disposal
P(between B and A)=F(B)-F(A)
(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

31. What does TOPSIS stand for?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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
Does not have a natural zero - is a cardinal scale

32. 3 Probabilistic Design Methods
It gives the probability that a value will be met or exceeded.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
No way to tell without more information. It depends on the relation between s12+s22 and s32
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.

33. What is the goal of robust design?

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34. What are K- factors applied to?
Regions 1 to 3.
Technology space limits
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
X~N(0 -1)

35. Why do we use a sample?
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.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
No way to tell without more information. It depends on the relation between s12+s22 and s32

36. What is the normal distribution that results from adding x+y and x[sub]y?
RDTE - Investment/Acquisition - Operations and Support - Disposal
X+Y and X-Y are normally distributed. - (X
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline

37. Weaknesses of TOPSis...
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
Range is always between zero and 1 monotonically increasing
Technique for Order Preference by Similarity to Ideal Solution

38. Ratio scale
A pareto frontier represents points of a non - dominated solution based on preferences
CDF= ?_(-8)^8
Has a natural zero - is a cardinal scale
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

39. Does TIES use MADM or MODM? Why?
The interest i such that 0=PE(i^)
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

40. Indirect Operating Cost
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
#=2^n = 2^15
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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.

41. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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

42. What is the goal of probabilistic design?

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43. What does CDF stand for?
To analytically answer 'How much design margin is really necessary?'
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Cumulative Distribution Function
RDTE - Investment/Acquisition - Operations and Support - Disposal

44. What does the CLT state - be specific!
Active UTE (additive) - Product UTE (multiplicative)
No way to tell without more information. It depends on the relation between s12+s22 and s32
To analytically answer 'How much design margin is really necessary?'
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

45. Assumptions Used in TOPSis...
RDTE - Investment/Acquisition - Operations and Support - Disposal
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
CDF= ?_(-8)^8

46. TIES Step 5: Feasible?
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
(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
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

47. Show and explain a pareto frontier
is bottom- up - you look at certain technologies and see what improvements they offer
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.
A pareto frontier represents points of a non - dominated solution based on preferences
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.

48. Why is the normal distribution useful or important?

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49. What is the difference between price and cost?
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Determine the design space - baseline Method: Morphological Matrix

50. What are the four difference life cycle costs?
A pareto frontier represents points of a non - dominated solution based on preferences
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
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