ADM

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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. What are K- factors applied to?
Gaussian Distribution
X+Y and X-Y are normally distributed. - (X
Technology space limits
is bottom- up - you look at certain technologies and see what improvements they offer


2. Direct Operating Costs
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.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Has a natural zero - is a cardinal scale
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)


3. 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.
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
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


4. TIES Step 5: Feasible?
is bottom- up - you look at certain technologies and see what improvements they offer
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.
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.


5. Assumptions Used in TOPSis...
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
Central limit theorem
X+Y and X-Y are normally distributed. - (X


6. What is probability density contour plot
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
Does not have a natural zero - is a cardinal scale
#=2^n = 2^15
Has a natural zero - is a cardinal scale


7. Other than infusing technologies - how can you create design space?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
Gaussian Distribution


8. What two variables are necessary to define a normal distribution?
Technique for Order Preference by Similarity to Ideal Solution
Mean and variance
Gaussian Distribution
Sample size is 4 - the sample is the sum of the five dice.


9. What are properties of a CDF?
PE(i)=?Ft
is bottom- up - you look at certain technologies and see what improvements they offer
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
Range is always between zero and 1 monotonically increasing


10. TIES Step 3: Model and Simulation
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
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
Allows designer to assess feasibility of 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


11. What are the different types of UTEs?
Active UTE (additive) - Product UTE (multiplicative)
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
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
(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


12. TIES
is bottom- up - you look at certain technologies and see what improvements they offer
No way to tell without more information. It depends on the relation between s12+s22 and s32
X~N(0 -1)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


13. What are the three snapshots of UTE?
Cumulative Distribution Function
Determine the design space - baseline Method: Morphological Matrix
Active UTE (additive) - Product UTE (multiplicative)
(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


14. interval scale
X~N(0 -1)
CDF= ?_(-8)^8
A pareto frontier represents points of a non - dominated solution based on preferences
Does not have a natural zero - is a cardinal scale


15. TIES Step 8: Selecting Technology

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16. What does CLT stand for?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Central limit theorem
PE(i)=?Ft


17. What is the normal distribution that results from adding x+y and x[sub]y?
Does not have a natural zero - is a cardinal scale
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
The interest i such that 0=PE(i^)
X+Y and X-Y are normally distributed. - (X


18. TIF

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19. 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.
(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 Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
No way to tell without more information. It depends on the relation between s12+s22 and s32


20. Why is learning curve used (or what is it?)
OEC = W1X/Xbsl + W2Nbsl/N
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
To analytically answer 'How much design margin is really necessary?'
#=2^n = 2^15


21. Does TIES use MADM or MODM? Why?
No way to tell without more information. It depends on the relation between s12+s22 and s32
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
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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


22. 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.
Range is always between zero and 1 monotonically increasing
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'


23. How is inflation measured?

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24. Why is the normal distribution useful or important?

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25. What is the goal of robust design?

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26. How do you get the CDF from the PDF?
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
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
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
CDF= ?_(-8)^8


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


28. TIES Step 6: Identify Technology
(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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Central limit theorem


29. What is the definition of CDF?
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
It gives the probability that a value will be met or exceeded.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.


30. Ratio scale
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
CDF= ?_(-8)^8
Has a natural zero - is a cardinal scale
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.


31. What does CDF stand for?
Mean and variance
Active UTE (additive) - Product UTE (multiplicative)
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.
Cumulative Distribution Function


32. What is the difference between price and cost?
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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Active UTE (additive) - Product UTE (multiplicative)


33. Strengths of TOPSis...
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
(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
Allows designer to assess feasibility of design


34. In what regions of the graph is UTE applicable?
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.
CDF= ?_(-8)^8
Regions 1 to 3.
(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


35. What does the CLT state - be specific!
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
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
RDTE - Investment/Acquisition - Operations and Support - Disposal
PE(i)=?Ft


36. What is the notation for a standard normal distribution?
Mean and variance
X~N(0 -1)
(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
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


37. Write down a formula for a normal distribution
Regions 1 to 3.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Central limit theorem
Does not have a natural zero - is a cardinal scale


38. 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
Regions 1 to 3.
Technique for Order Preference by Similarity to Ideal Solution
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


39. MADM
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
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


40. 3 Measures of Central Tendency (& Defs)
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
Gaussian Distribution
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


41. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
(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
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
It can be continuous or discrete
Technique for Order Preference by Similarity to Ideal Solution


42. 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
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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


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

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44. Name the advantages of UTE.
X+Y and X-Y are normally distributed. - (X
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.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
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


45. 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)
(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
Technology space limits
#=2^n = 2^15


46. What is the definition of inflation?
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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population


47. TIES Step 7: Assess Technology
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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
P(between B and A)=F(B)-F(A)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


48. What is the equation for the learning curve?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


49. MODM
Regions 1 to 3.
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
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


50. What are the four difference life cycle costs?
(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
RDTE - Investment/Acquisition - Operations and Support - Disposal
Central limit theorem
(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