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Answer 50 questions in 15 minutes.
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1. What two variables are necessary to define a normal distribution?
(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
Mean and variance
Range is always between zero and 1 monotonically increasing
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

2. interval scale
F(x)=1/(s(2p)^(.5) )exp?(-(x-
RDTE - Investment/Acquisition - Operations and Support - Disposal
Does not have a natural zero - is a cardinal scale
(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

3. Assumptions Used in TOPSis...
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Range is always between zero and 1 monotonically increasing
Technique for Order Preference by Similarity to Ideal Solution
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln

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

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5. 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?
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
OEC = W1X/Xbsl + W2Nbsl/N
Sample size is 4 - the sample is the sum of the five dice.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

6. With 15 technologies - what is the number of possible combinations?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
#=2^n = 2^15
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

7. Why is learning curve used (or what is it?)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Technique for Order Preference by Similarity to Ideal Solution
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Gaussian Distribution

8. MODM
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Technique for Order Preference by Similarity to Ideal Solution

9. TIES Step 2: Design Space Conception
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
is bottom- up - you look at certain technologies and see what improvements they offer
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline

10. What is the goal of probabilistic design?

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11. What is satisficing - what is optimizing?
Gaussian 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.
Range is always between zero and 1 monotonically increasing
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

12. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
OEC = W1X/Xbsl + W2Nbsl/N
Central limit theorem
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

13. Does TIES use MADM or MODM? Why?
(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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Allows designer to assess feasibility of design
Technology space limits

14. What are properties of a CDF?
Has a natural zero - is a cardinal scale
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Range is always between zero and 1 monotonically increasing

15. What is the difference between price and cost?
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

16. TIES Step 1: Problem Definition
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
CDF= ?_(-8)^8
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.
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

17. TIF

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

19. What is another name for a normal distribution?
Mean =0 Variance =1
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
Gaussian Distribution

20. TIES Step 6: Identify Technology
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
Gaussian Distribution
(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
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

21. TIES Step 5: Feasible?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
OEC = W1X/Xbsl + W2Nbsl/N
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

22. What does CDF stand for?
Cumulative Distribution Function
Determine the design space - baseline Method: Morphological Matrix
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
Gaussian Distribution

23. What is probability density contour plot
Central limit theorem
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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 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

24. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
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
Active UTE (additive) - Product UTE (multiplicative)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

25. 3 Probabilistic Design Methods
RDTE - Investment/Acquisition - Operations and Support - Disposal
(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
Active UTE (additive) - Product UTE (multiplicative)

26. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
Mean =0 Variance =1
OEC = W1X/Xbsl + W2Nbsl/N
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

27. What is the definition of ROI?
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
The interest i such that 0=PE(i^)

28. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
PE(i)=?Ft
It can be continuous or discrete
Cumulative Distribution Function
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

29. 4 Measures of Dispersion
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
OEC = W1X/Xbsl + W2Nbsl/N

30. What does the CLT state - be specific!
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Range is always between zero and 1 monotonically increasing
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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

31. MADM
It can be continuous or discrete
#=2^n = 2^15
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.
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

32. What is the notation for a standard normal distribution?
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

33. 3 Measures of Central Tendency (& Defs)
Technology space limits
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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

34. TIES Step 4: Investigate Design Space
(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.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
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.

35. What are the four difference life cycle costs?
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
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo

36. TIES Step 7: Assess Technology
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
A pareto frontier represents points of a non - dominated solution based on preferences

37. How do you get the CDF from the PDF?
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
CDF= ?_(-8)^8
To analytically answer 'How much design margin is really necessary?'
Does not have a natural zero - is a cardinal scale

38. If you have a two values on a CDF what is the probability of getting a value between them?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(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
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
P(between B and A)=F(B)-F(A)

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

40. Ratio scale
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
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
RDTE - Investment/Acquisition - Operations and Support - Disposal
Has a natural zero - is a cardinal scale

41. Direct Operating Costs
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

42. What are the different types of UTEs?
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
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
Has a natural zero - is a cardinal scale
Active UTE (additive) - Product UTE (multiplicative)

43. Indirect Operating Cost
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Gaussian Distribution
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
X+Y and X-Y are normally distributed. - (X

44. 8 Steps in TIES
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) 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
Determine the design space - baseline Method: Morphological Matrix

45. What is the equation for the learning curve?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Determine the design space - baseline Method: Morphological Matrix
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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

46. TIES
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
No way to tell without more information. It depends on the relation between s12+s22 and s32
(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
is bottom- up - you look at certain technologies and see what improvements they offer

47. In what regions of the graph is UTE applicable?
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Regions 1 to 3.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
(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

48. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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

49. What is TCM? What is the size and what value can it take?
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
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
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
X+Y and X-Y are normally distributed. - (X

50. What is the goal of robust design?

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