ADM

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1. What are K- factors applied to?
(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
Technology space limits
Sample size is 4 - the sample is the sum of the five dice.
Technology Readiness Level Ranges 1-9 - where 1 means that the basic principle have been observed and reported and 9 means the technology has had successful missions A high tech means the technology is pretty developed and should be (or is) ready for


2. If you have a two values on a CDF what is the probability of getting a value between them?
X+Y and X-Y are normally distributed. - (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.
P(between B and A)=F(B)-F(A)
A pareto frontier represents points of a non - dominated solution based on preferences


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

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4. What is TRL? Range? What does a high TRL mean?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Cumulative Distribution Function
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 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


5. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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


6. What is satisficing - what is optimizing?
Regions 1 to 3.
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
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.
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.


7. What is the goal of probabilistic design?

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8. TIES Step 6: Identify Technology
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
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
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.


9. What are the three snapshots of UTE?
(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
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.
X~N(0 -1)


10. Other than infusing technologies - how can you create design space?
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
(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
F(x)=1/(s(2p)^(.5) )exp?(-(x-


11. TIES Step 1: Problem Definition
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
To analytically answer 'How much design margin is really necessary?'


12. With 15 technologies - what is the number of possible combinations?
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
It gives the probability that a value will be met or exceeded.
(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
#=2^n = 2^15


13. What does CLT stand for?
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
Central limit theorem
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
F(x)=1/(s(2p)^(.5) )exp?(-(x-


14. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
#=2^n = 2^15
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
OEC = W1X/Xbsl + W2Nbsl/N


15. What does TOPSIS stand for?
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.
Technique for Order Preference by Similarity to Ideal Solution
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order


16. 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.
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem


17. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
Has a natural zero - is a cardinal scale


18. What is the difference between price and cost?
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Central limit theorem
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


19. TIES Step 2: Design Space Conception
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Determine the design space - baseline Method: Morphological Matrix
P(between B and A)=F(B)-F(A)
A pareto frontier represents points of a non - dominated solution based on preferences


20. What is the equation for the learning curve?
Active UTE (additive) - Product UTE (multiplicative)
Mean and variance
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


21. What are properties of a CDF?
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Range is always between zero and 1 monotonically increasing
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


22. 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
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
No way to tell without more information. It depends on the relation between s12+s22 and s32
Central limit theorem


23. What is the definition of CDF?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
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
CDF= ?_(-8)^8
It gives the probability that a value will be met or exceeded.


24. What are the different types of UTEs?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
It gives the probability that a value will be met or exceeded.
Active UTE (additive) - Product UTE (multiplicative)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


25. What is the normal distribution that results from adding x+y and x[sub]y?
OEC = W1X/Xbsl + W2Nbsl/N
X+Y and X-Y are normally distributed. - (X
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.


26. TIES Step 5: Feasible?
The interest i such that 0=PE(i^)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
It can be continuous or discrete


27. TIES Step 7: Assess Technology
CDF= ?_(-8)^8
Technique for Order Preference by Similarity to Ideal Solution
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.


28. What is the definition of inflation?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Mean =0 Variance =1
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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


29. MADM
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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
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.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


30. What is the goal of robust design?

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31. What can be done about uncertainty in requirement?
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.
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
OEC = W1X/Xbsl + W2Nbsl/N


32. Ratio scale
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Has a natural zero - is a cardinal scale
is bottom- up - you look at certain technologies and see what improvements they offer


33. 3 Measures of Central Tendency (& Defs)
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


34. Show and explain a pareto frontier
RDTE - Investment/Acquisition - Operations and Support - Disposal
A pareto frontier represents points of a non - dominated solution based on preferences
Cumulative Distribution Function
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


35. What are the parameters for a standard normal distribution?
Mean =0 Variance =1
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Technique for Order Preference by Similarity to Ideal Solution


36. 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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Central limit theorem
Mean and variance


37. What are the four difference life cycle costs?
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.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
RDTE - Investment/Acquisition - Operations and Support - Disposal
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


38. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
A technique used to determine the best alternative with all interactions between the constraints. Used for product 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.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


39. Indirect Operating Cost
Active UTE (additive) - Product UTE (multiplicative)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Sample size is 4 - the sample is the sum of the five dice.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


40. What is another name for a normal distribution?
Optimizing - finds the set of criteria that maximizes or minimizes a design criteria or several design criteria - Satisficing - finds the conditions where the constraints or requires are met but no optimization occurs.
Gaussian Distribution
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem


41. Why are scaling parameters important?
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 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
RDTE - Investment/Acquisition - Operations and Support - Disposal
CDF= ?_(-8)^8


42. Why use uniform dist for input variables (Gap Analysis)
Active UTE (additive) - Product UTE (multiplicative)
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.
Allows designer to assess feasibility of design
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


43. What can management do to mitigate the risk associated with infusing new technologies?
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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


44. In what regions of the graph is UTE applicable?
Regions 1 to 3.
It gives the probability that a value will be met or exceeded.
Allows designer to assess feasibility of design
OEC = W1X/Xbsl + W2Nbsl/N


45. How do you get the CDF from the PDF?
Technology space limits
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
CDF= ?_(-8)^8
(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


46. TIES Step 4: Investigate Design Space
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Technique for Order Preference by Similarity to Ideal Solution
X~N(0 -1)
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.


47. How is inflation measured?

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48. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
Has a natural zero - is a cardinal scale
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


49. What is the notation for a standard normal 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
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
X~N(0 -1)
Mean and variance


50. TIES Step 8: Selecting Technology

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