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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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This is a study tool. The 3 wrong answers for each question are randomly chosen from answers to other questions. So, you might find at times the answers obvious, but you will see it re-enforces your understanding as you take the test each time.

1. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
Active UTE (additive) - Product UTE (multiplicative)
(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
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

2. In what regions of the graph is UTE applicable?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Regions 1 to 3.
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
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

3. 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.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
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

4. What is the definition of ROI?
CDF= ?_(-8)^8
(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
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

5. 3 Probabilistic Design Methods
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.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Range is always between zero and 1 monotonically increasing
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee

6. With 15 technologies - what is the number of possible combinations?
P(between B and A)=F(B)-F(A)
No way to tell without more information. It depends on the relation between s12+s22 and s32
#=2^n = 2^15
CDF= ?_(-8)^8

7. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
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)
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

8. TIF

9. What are the four difference life cycle costs?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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
RDTE - Investment/Acquisition - Operations and Support - Disposal

10. 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
OEC = W1X/Xbsl + W2Nbsl/N
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

11. $/RPM Equation
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Technique for Order Preference by Similarity to Ideal Solution
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers

12. What is the definition of CDF?
Allows designer to assess feasibility of design
It gives the probability that a value will be met or exceeded.
PE(i)=?Ft
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

13. Name the advantages of UTE.
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.
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.
Mean =0 Variance =1
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

14. How is inflation measured?

15. What is the goal of robust design?

16. What is the equation for the learning curve?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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.
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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

17. TIES Step 2: Design Space Conception
Determine the design space - baseline Method: Morphological Matrix
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Range is always between zero and 1 monotonically increasing
Active UTE (additive) - Product UTE (multiplicative)

18. Indirect Operating Cost
The interest i such that 0=PE(i^)
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
It can be continuous or discrete
Active UTE (additive) - Product UTE (multiplicative)

19. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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
Technology space limits
#=2^n = 2^15

20. What are the different types of UTEs?
Active UTE (additive) - Product UTE (multiplicative)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

21. Does TIES use MADM or MODM? Why?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
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
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

22. TIES Step 6: Identify Technology
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
X+Y and X-Y are normally distributed. - (X
Range is always between zero and 1 monotonically increasing
(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. 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?
Sample size is 4 - the sample is the sum of the five dice.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
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
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.

24. TIES Step 7: Assess Technology
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.
Central limit theorem
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
A pareto frontier represents points of a non - dominated solution based on preferences

25. What is TRL? Range? What does a high TRL mean?
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.
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 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.

26. Other than infusing technologies - how can you create design space?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Allows designer to assess feasibility of design
Central limit theorem
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee

27. What does the CLT state - be specific!
Allows designer to assess feasibility of design
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
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

28. What are the three snapshots of UTE?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(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
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

29. What does CLT stand for?
Technique for Order Preference by Similarity to Ideal Solution
Central limit theorem
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

30. TIES
Mean =0 Variance =1
is bottom- up - you look at certain technologies and see what improvements they offer
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
#=2^n = 2^15

31. 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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

32. Strengths of TOPSis...
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A pareto frontier represents points of a non - dominated solution based on preferences
(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
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

33. 3 Measures of Central Tendency (& Defs)
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
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
PE(i)=?Ft

34. If you have a two values on a CDF what is the probability of getting a value between them?
Technology space limits
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
P(between B and A)=F(B)-F(A)
Has a natural zero - is a cardinal scale

35. 4 Measures of Dispersion
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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
Does not have a natural zero - is a cardinal scale

36. What can management do to mitigate the risk associated with infusing new technologies?
Central limit theorem
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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

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

38. What is the normal distribution that results from adding x+y and x[sub]y?
CDF= ?_(-8)^8
X+Y and X-Y are normally distributed. - (X
Central limit theorem
PE(i)=?Ft

39. TIES Step 1: Problem Definition
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
P(between B and A)=F(B)-F(A)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

40. TIES Step 3: Model and Simulation
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
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.
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
Central limit theorem

41. How do you get the CDF from the PDF?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
CDF= ?_(-8)^8
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Technology space limits

42. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
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
is bottom- up - you look at certain technologies and see what improvements they offer
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

43. Write down a formula for a normal distribution
No way to tell without more information. It depends on the relation between s12+s22 and s32
F(x)=1/(s(2p)^(.5) )exp?(-(x-
It can be continuous or discrete
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

44. interval scale
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
(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
Allows designer to assess feasibility of design

45. 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
A pareto frontier represents points of a non - dominated solution based on preferences
X~N(0 -1)
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

46. 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
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
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Has a natural zero - is a cardinal scale

47. Why do we use a sample?
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) 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 sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

48. What can be done about uncertainty in requirement?
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.
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.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee

49. Why are scaling parameters important?
Central limit theorem
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
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?'

50. What is the goal of probabilistic design?