ADM

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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 CDF stand for?
(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
Determine the design space - baseline Method: Morphological Matrix
Cumulative Distribution Function
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


2. Does TIES use MADM or MODM? Why?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


3. interval scale
Does not have a natural zero - is a cardinal scale
Determine the design space - baseline Method: Morphological Matrix
P(between B and A)=F(B)-F(A)
Active UTE (additive) - Product UTE (multiplicative)


4. How do you get the CDF from the PDF?
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
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
Gaussian Distribution


5. What is the goal of robust design?

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6. Name the advantages of UTE.
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
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.
Technique for Order Preference by Similarity to Ideal Solution


7. $/RPM Equation
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers


8. Ratio scale
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Has a natural zero - is a cardinal scale
Technology space limits
A pareto frontier represents points of a non - dominated solution based on preferences


9. 3 Measures of Central Tendency (& Defs)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
is bottom- up - you look at certain technologies and see what improvements they offer
X~N(0 -1)
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


10. Show and explain a pareto frontier
A pareto frontier represents points of a non - dominated solution based on preferences
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
(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
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


11. What are the parameters for a standard 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.
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.
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Mean =0 Variance =1


12. TIES Step 6: Identify Technology
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
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
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) 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


13. TIES
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
is bottom- up - you look at certain technologies and see what improvements they offer
Determine the design space - baseline Method: Morphological Matrix
F(x)=1/(s(2p)^(.5) )exp?(-(x-


14. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
A pareto frontier represents points of a non - dominated solution based on preferences
Cumulative Distribution Function


15. Define fixed cost and variable cost.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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.


16. TIES Step 5: Feasible?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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
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


17. What is another name for a normal distribution?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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.
Gaussian Distribution


18. Assumptions Used in TOPSis...
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
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
X+Y and X-Y are normally distributed. - (X


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

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20. MADM
Range is always between zero and 1 monotonically increasing
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.
Active UTE (additive) - Product UTE (multiplicative)


21. TIES Step 3: Model and Simulation
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
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
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Sample size is 4 - the sample is the sum of the five dice.


22. What is the goal of probabilistic design?

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23. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
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
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
No way to tell without more information. It depends on the relation between s12+s22 and s32


24. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Cumulative Distribution Function
No way to tell without more information. It depends on the relation between s12+s22 and s32


25. What are the four difference life cycle costs?
The interest i such that 0=PE(i^)
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
RDTE - Investment/Acquisition - Operations and Support - Disposal


26. Other than infusing technologies - how can you create design space?
To analytically answer 'How much design margin is really necessary?'
(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
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


27. TIES Step 8: Selecting Technology

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28. Why are scaling parameters important?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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.
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
P(between B and A)=F(B)-F(A)


29. 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
Does not have 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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


30. What can be done about uncertainty in requirement?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
No way to tell without more information. It depends on the relation between s12+s22 and s32
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


31. If you have a two values on a CDF what is the probability of getting a value between them?
(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
P(between B and A)=F(B)-F(A)
The interest i such that 0=PE(i^)
Active UTE (additive) - Product UTE (multiplicative)


32. What two variables are necessary to define a normal distribution?
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 and variance
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 Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline


33. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
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 space limits
X~N(0 -1)


34. How is inflation measured?

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35. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
PE(i)=?Ft
It can be continuous or discrete
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)


36. What are properties of a CDF?
(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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Range is always between zero and 1 monotonically increasing


37. What is the definition of ROI?
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
The interest i such that 0=PE(i^)
Central limit theorem


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


39. What is TRL? Range? What does a high TRL mean?
(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
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Determine the design space - baseline Method: Morphological Matrix


40. 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
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
To analytically answer 'How much design margin is really necessary?'


41. What does the CLT state - be specific!
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
X+Y and X-Y are normally distributed. - (X
Mean and variance
Does not have a natural zero - is a cardinal scale


42. What is the equation for the learning curve?
Gaussian Distribution
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Regions 1 to 3.


43. In what regions of the graph is UTE applicable?
Technology space limits
Regions 1 to 3.
Sample size is 4 - the sample is the sum of the five dice.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline


44. What is the normal distribution that results from adding x+y and x[sub]y?
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.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
X+Y and X-Y are normally distributed. - (X
P(between B and A)=F(B)-F(A)


45. TIF

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46. What is probability density contour plot
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
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) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
#=2^n = 2^15


47. What is satisficing - what is optimizing?
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
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 space limits
CDF= ?_(-8)^8


48. Why is learning curve used (or what is it?)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Technology space limits
(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


49. Strengths of TOPSis...
F(x)=1/(s(2p)^(.5) )exp?(-(x-
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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
Central limit theorem


50. TIES Step 4: Investigate Design Space
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
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.
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) 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