ADM

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1. What are the different types of UTEs?
X~N(0 -1)
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Active UTE (additive) - Product UTE (multiplicative)
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


2. What is the definition of ROI?
Active UTE (additive) - Product UTE (multiplicative)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
The interest i such that 0=PE(i^)
X+Y and X-Y are normally distributed. - (X


3. In what regions of the graph is UTE applicable?
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Regions 1 to 3.


4. What two variables are necessary to define a normal distribution?
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
Mean and variance
CDF= ?_(-8)^8
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


5. Why are scaling parameters important?
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
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 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) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


6. What are the four difference life cycle costs?
OEC = W1X/Xbsl + W2Nbsl/N
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
Gaussian Distribution


7. TIF

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8. What are K- factors applied to?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Technology space limits
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.


9. TIES Step 1: Problem Definition
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
The interest i such that 0=PE(i^)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


10. What is the equation for present equivalent value? Define variables.
(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
PE(i)=?Ft
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


11. What is TCM? What is the size and what value can it take?
Determine the design space - baseline Method: Morphological Matrix
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
is bottom- up - you look at certain technologies and see what improvements they offer
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


12. What is another name for a normal distribution?
Gaussian Distribution
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.
It gives the probability that a value will be met or exceeded.


13. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Allows designer to assess feasibility of design
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.


14. What does TOPSIS stand for?
Does not have a natural zero - is a cardinal scale
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
Sample size is 4 - the sample is the sum of the five dice.


15. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
X+Y and X-Y are normally distributed. - (X
#=2^n = 2^15
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
It can be continuous or discrete


16. Does TIES use MADM or MODM? Why?
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
Regions 1 to 3.
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


17. What does CDF stand for?
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Cumulative Distribution Function


18. Name the advantages of UTE.
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.
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
A pareto frontier represents points of a non - dominated solution based on preferences
Gaussian Distribution


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

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20. 3 Measures of Central Tendency (& Defs)
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
(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


21. What does CLT stand for?
X~N(0 -1)
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Gaussian Distribution
Central limit theorem


22. 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
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Determine the design space - baseline Method: Morphological Matrix
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


23. What is the definition of inflation?
X+Y and X-Y are normally distributed. - (X
Gaussian Distribution
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
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


24. What can management do to mitigate the risk associated with infusing new technologies?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
X+Y and X-Y are normally distributed. - (X


25. How do you get the CDF from the PDF?
Central limit theorem
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
CDF= ?_(-8)^8
F(x)=1/(s(2p)^(.5) )exp?(-(x-


26. Why is the normal distribution useful or important?

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27. What is the goal of robust design?

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28. TIES Step 8: Selecting Technology

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29. Direct Operating Costs
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
P(between B and A)=F(B)-F(A)


30. What is the normal distribution that results from adding x+y and x[sub]y?
X+Y and X-Y are normally distributed. - (X
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
To analytically answer 'How much design margin is really necessary?'
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.


31. What is the goal of probabilistic design?

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32. Why do we use a sample?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


33. Other than infusing technologies - how can you create design space?
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
It gives the probability that a value will be met or exceeded.
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space


34. TIES Step 4: Investigate Design Space
RDTE - Investment/Acquisition - Operations and Support - Disposal
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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.


35. Ratio scale
Has a natural zero - is a cardinal scale
(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
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.
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


36. 3 Probabilistic Design Methods
It gives the probability that a value will be met or exceeded.
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) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'


37. Why use uniform dist for input variables (Gap Analysis)
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
It can be continuous or discrete
Allows designer to assess feasibility of design


38. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Sample size is 4 - the sample is the sum of the five dice.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population


39. What is the definition of CDF?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
It gives the probability that a value will be met or exceeded.
P(between B and A)=F(B)-F(A)
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.


40. 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.
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
Mean and variance
(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


41. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
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 Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Technology space limits


42. 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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Gaussian Distribution


43. With 15 technologies - what is the number of possible combinations?
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Sample size is 4 - the sample is the sum of the five dice.
#=2^n = 2^15
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


44. What is TRL? Range? What does a high TRL mean?
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 pareto frontier represents points of a non - dominated solution based on preferences
P(between B and A)=F(B)-F(A)
Gaussian Distribution


45. TIES Step 5: Feasible?
P(between B and A)=F(B)-F(A)
Mean and variance
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


46. MODM
(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 technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Does not have a natural zero - is a cardinal scale
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


47. Indirect Operating Cost
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
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.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


48. 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
CDF= ?_(-8)^8
P(between B and A)=F(B)-F(A)
X~N(0 -1)


49. What can be done about uncertainty in requirement?
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.


50. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
OEC = W1X/Xbsl + W2Nbsl/N
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)