ADM

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• Answer 50 questions in 15 minutes.
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• Match each statement with the correct term.
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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. Show and explain a pareto frontier
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Gaussian Distribution
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
A pareto frontier represents points of a non - dominated solution based on preferences


2. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
P(between B and A)=F(B)-F(A)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem


3. TIES Step 4: Investigate Design Space
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.
Cumulative Distribution Function
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


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

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5. Weaknesses of TOPSis...
X~N(0 -1)
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) 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) End result not intuitive (2) Heavily reliant on weights - which are subjective


6. Why do we use a sample?
Active UTE (additive) - Product UTE (multiplicative)
It can be continuous or discrete
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
X+Y and X-Y are normally distributed. - (X


7. TIES
RDTE - Investment/Acquisition - Operations and Support - Disposal
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
is bottom- up - you look at certain technologies and see what improvements they offer
#=2^n = 2^15


8. Why is learning curve used (or what is it?)
PE(i)=?Ft
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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.
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee


9. TIES Step 6: Identify Technology
X+Y and X-Y are normally distributed. - (X
is bottom- up - you look at certain technologies and see what improvements they offer
(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 Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline


10. What is satisficing - what is optimizing?
Mean =0 Variance =1
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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 sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population


11. What are the parameters for a standard normal distribution?
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.
Mean =0 Variance =1
Determine the design space - baseline Method: Morphological Matrix
Does not have a natural zero - is a cardinal scale


12. What is the goal of probabilistic design?

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13. Ratio scale
Allows designer to assess feasibility of design
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
Has a natural zero - is a cardinal scale
P(between B and A)=F(B)-F(A)


14. Other than infusing technologies - how can you create design space?
Technique for Order Preference by Similarity to Ideal Solution
OEC = W1X/Xbsl + W2Nbsl/N
CDF= ?_(-8)^8
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space


15. 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.
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
Range is always between zero and 1 monotonically increasing


16. What does CLT stand for?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
Mean =0 Variance =1
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Central limit theorem


17. What does TOPSIS stand for?
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
Central limit theorem
OEC = W1X/Xbsl + W2Nbsl/N
Technique for Order Preference by Similarity to Ideal Solution


18. TIF

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19. What is the notation for a standard normal distribution?
It gives the probability that a value will be met or exceeded.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
X~N(0 -1)
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


20. Indirect Operating Cost
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Allows designer to assess feasibility of design
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


21. MADM
P(between B and A)=F(B)-F(A)
(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
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


22. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
Regions 1 to 3.
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
The interest i such that 0=PE(i^)


23. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
It gives the probability that a value will be met or exceeded.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
OEC = W1X/Xbsl + W2Nbsl/N
The interest i such that 0=PE(i^)


24. 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
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
#=2^n = 2^15


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


26. 3 Measures of Central Tendency (& Defs)
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(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: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


27. What does the CLT state - be specific!
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept 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
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Mean and variance


28. Why are scaling parameters important?
Sample size is 4 - the sample is the sum of the five dice.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
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
PE(i)=?Ft


29. What two variables are necessary to define a 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
Mean and variance
OEC = W1X/Xbsl + W2Nbsl/N
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.


30. TIES Step 7: Assess Technology
F(x)=1/(s(2p)^(.5) )exp?(-(x-
P(between B and A)=F(B)-F(A)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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. Strengths of TOPSis...
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
X+Y and X-Y are normally distributed. - (X
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
RDTE - Investment/Acquisition - Operations and Support - Disposal


32. What can be done about uncertainty in requirement?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(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
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.


33. Does TIES use MADM or MODM? Why?
It can be continuous or discrete
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Cumulative Distribution Function
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)


34. TIES Step 5: Feasible?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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
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


35. TIES Step 8: Selecting Technology

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36. What is probability density contour plot
To analytically answer 'How much design margin is really necessary?'
Does not have a natural zero - is a cardinal scale
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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


37. What is TCM? What is the size and what value can it take?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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
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
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


38. 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)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
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


39. 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
CDF= ?_(-8)^8
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)


40. With 15 technologies - what is the number of possible combinations?
#=2^n = 2^15
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
A pareto frontier represents points of a non - dominated solution based on preferences
Sample size is 4 - the sample is the sum of the five dice.


41. What is the equation for present equivalent value? Define variables.
PE(i)=?Ft
Central limit theorem
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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. What is the normal distribution that results from adding x+y and x[sub]y?
RDTE - Investment/Acquisition - Operations and Support - Disposal
X+Y and X-Y are normally distributed. - (X
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.


43. TIES Step 3: Model and Simulation
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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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


44. What are the different types of UTEs?
Active UTE (additive) - Product UTE (multiplicative)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee


45. If you have a two values on a CDF what is the probability of getting a value between them?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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.
Has a natural zero - is a cardinal scale
P(between B and A)=F(B)-F(A)


46. Assumptions Used in TOPSis...
Sample size is 4 - the sample is the sum of the five dice.
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


47. What is another name for a normal distribution?
It gives the probability that a value will be met or exceeded.
Gaussian Distribution
CDF= ?_(-8)^8
(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. What is the goal of robust design?

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49. Why use uniform dist for input variables (Gap Analysis)
(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
is bottom- up - you look at certain technologies and see what improvements they offer
Allows designer to assess feasibility of design
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order


50. What does CDF stand for?
It can be continuous or discrete
Cumulative Distribution Function
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