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• Answer 50 questions in 15 minutes.
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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. TIES Step 1: Problem Definition
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
No way to tell without more information. It depends on the relation between s12+s22 and s32
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


2. 4 Measures of Dispersion
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 space limits
Range: Gives the magnitude of the spread - min and max - Variance: Indicates how spread out the data is - Skewness: Indicates if the distribution is biased - Kurtosis: Peakness
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective


3. What is the notation for a standard normal distribution?
X~N(0 -1)
No way to tell without more information. It depends on the relation between s12+s22 and s32
Allows designer to assess feasibility of design
The interest i such that 0=PE(i^)


4. TIES
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
is bottom- up - you look at certain technologies and see what improvements they offer
Sample size is 4 - the sample is the sum of the five dice.


5. Show and explain a pareto frontier
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 pareto frontier represents points of a non - dominated solution based on preferences
Does not have a natural zero - is a cardinal scale
OEC = W1X/Xbsl + W2Nbsl/N


6. In what regions of the graph is UTE applicable?
P(between B and A)=F(B)-F(A)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Regions 1 to 3.
(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


7. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
Has a natural zero - is a cardinal scale
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
OEC = W1X/Xbsl + W2Nbsl/N


8. What is the normal distribution that results from adding x+y and x[sub]y?
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
X+Y and X-Y are normally distributed. - (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
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


9. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
OEC = W1X/Xbsl + W2Nbsl/N
To analytically answer 'How much design margin is really necessary?'
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)


10. Why use uniform dist for input variables (Gap Analysis)
Allows designer to assess feasibility of design
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.
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
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


11. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most 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
Range is always between zero and 1 monotonically increasing
No way to tell without more information. It depends on the relation between s12+s22 and s32
OEC = W1X/Xbsl + W2Nbsl/N


12. What is probability density contour plot
Active UTE (additive) - Product UTE (multiplicative)
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) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Mean =0 Variance =1


13. What is satisficing - what is optimizing?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(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
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
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.


14. What is another name for a normal distribution?
Determine the design space - baseline Method: Morphological Matrix
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
Gaussian Distribution
Has a natural zero - is a cardinal scale


15. What is TCM? What is the size and what value can it take?
Active UTE (additive) - Product UTE (multiplicative)
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) 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
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order


16. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
To analytically answer 'How much design margin is really necessary?'
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective


17. 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.
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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.


18. TIES Step 6: Identify Technology
It can be continuous or discrete
(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
Regions 1 to 3.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


19. What can be done about uncertainty in requirement?
Gaussian Distribution
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
(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
Cumulative Distribution Function


20. MODM
#=2^n = 2^15
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
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


21. Why is the normal distribution useful or important?

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22. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
Gaussian Distribution
It can be continuous or discrete
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.
Regions 1 to 3.


23. 3 Measures of Central Tendency (& Defs)
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
M&S environment is needed to facilitate rapid assessments with minimal time and monetary expenditures of the alternative concepts identified in the Morphological Matrix Method: DoE
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order


24. TIES Step 8: Selecting Technology

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25. What is the goal of robust design?

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26. What does TOPSIS stand for?
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) 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
Technique for Order Preference by Similarity to Ideal Solution
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space


27. TIF

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28. What are K- factors applied to?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
To analytically answer 'How much design margin is really necessary?'
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Technology space limits


29. MADM
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.
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
Technique for Order Preference by Similarity to Ideal Solution


30. Assumptions Used in TOPSis...
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) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(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. 8 Steps in TIES
CDF= ?_(-8)^8
P(between B and A)=F(B)-F(A)
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.
(1) 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


32. If you have a two values on a CDF what is the probability of getting a value between them?
P(between B and A)=F(B)-F(A)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Central limit theorem


33. Does TIES use MADM or MODM? Why?
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Allows designer to assess feasibility of design


34. With 15 technologies - what is the number of possible combinations?
#=2^n = 2^15
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
(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


35. What does the CLT state - be specific!
(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
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.
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 related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


36. Name the advantages of UTE.
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
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.


37. Indirect Operating Cost
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Technology space limits
Allows designer to assess feasibility of design


38. Weaknesses of TOPSis...
PE(i)=?Ft
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Technology space limits
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.


39. What is the equation for the learning curve?
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.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
#=2^n = 2^15
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


40. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
OEC = W1X/Xbsl + W2Nbsl/N
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'


41. What can management do to mitigate the risk associated with infusing new technologies?
RDTE - Investment/Acquisition - Operations and Support - Disposal
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
Active UTE (additive) - Product UTE (multiplicative)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)


42. TIES Step 3: Model and Simulation
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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) 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
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


43. What is the goal of probabilistic design?

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44. What are the different types of UTEs?
Mean and variance
X+Y and X-Y are normally distributed. - (X
Active UTE (additive) - Product UTE (multiplicative)
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective


45. 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.
X+Y and X-Y are normally distributed. - (X
Technique for Order Preference by Similarity to Ideal Solution
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


46. Ratio scale
Has a natural zero - is a cardinal scale
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Mean =0 Variance =1
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)


47. How do you get the CDF from the PDF?
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: 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
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
CDF= ?_(-8)^8


48. What are the parameters for a standard normal distribution?
Regions 1 to 3.
Trying to determine the metric values for any combination of design variables/ what the metrics are as a function of design variables Method: RSE: Response Surface Eqn.
Mean =0 Variance =1
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.


49. Why are scaling parameters important?
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to 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
To analytically answer 'How much design margin is really necessary?'
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


50. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
The interest i such that 0=PE(i^)
Does not have a natural zero - is a cardinal scale