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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. What are the parameters for a standard normal distribution?
#=2^n = 2^15
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.
Mean =0 Variance =1
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

2. MODM
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
A pareto frontier represents points of a non - dominated solution based on preferences

3. TIES Step 5: Feasible?
Gaussian Distribution
(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
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
X+Y and X-Y are normally distributed. - (X

4. What are the different types of UTEs?
Active UTE (additive) - Product UTE (multiplicative)
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Sample size is 4 - the sample is the sum of the five dice.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

5. Define fixed cost and variable cost.
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
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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

6. 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
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.
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
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

7. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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.
No way to tell without more information. It depends on the relation between s12+s22 and s32

8. interval scale
Regions 1 to 3.
Does not have a natural zero - is a cardinal scale
A pareto frontier represents points of a non - dominated solution based on preferences
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

9. TIES Step 1: Problem Definition
X~N(0 -1)
RDTE - Investment/Acquisition - Operations and Support - Disposal
Technology space limits
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

10. TIES Step 6: Identify Technology
No way to tell without more information. It depends on the relation between s12+s22 and s32
Mean =0 Variance =1
(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
It can be continuous or discrete

11. Write down a formula for a normal distribution
Regions 1 to 3.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
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

12. What is another name for a normal distribution?
To analytically answer 'How much design margin is really necessary?'
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Gaussian Distribution
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective

13. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
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.
Gaussian Distribution
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order

14. What is probability density contour plot
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 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
A pareto frontier represents points of a non - dominated solution based on preferences
Has a natural zero - is a cardinal scale

15. TIES Step 2: Design Space Conception
Cumulative Distribution Function
RDTE - Investment/Acquisition - Operations and Support - Disposal
X+Y and X-Y are normally distributed. - (X
Determine the design space - baseline Method: Morphological Matrix

16. What is the definition of CDF?
Active UTE (additive) - Product UTE (multiplicative)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
It gives the probability that a value will be met or exceeded.
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.

17. How do you get the CDF from the PDF?
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
CDF= ?_(-8)^8
It gives the probability that a value will be met or exceeded.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

18. Why do we use a sample?
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: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

19. What is the goal of robust design?

20. TIF

21. What is satisficing - what is optimizing?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Technology space limits
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)

22. What is the normal distribution that results from adding x+y and x[sub]y?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
X+Y and X-Y are normally distributed. - (X
A pareto frontier represents points of a non - dominated solution based on preferences
(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

23. What are properties of a CDF?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
OEC = W1X/Xbsl + W2Nbsl/N
Range is always between zero and 1 monotonically increasing

24. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
A pareto frontier represents points of a non - dominated solution based on preferences
#=2^n = 2^15
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

25. 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.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
PE(i)=?Ft
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem

26. Direct Operating Costs
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

27. What are K- factors applied to?
Technology space limits
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
To analytically answer 'How much design margin is really necessary?'
Sample size is 4 - the sample is the sum of the five dice.

28. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
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 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
OEC = W1X/Xbsl + W2Nbsl/N
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline

29. Ratio scale
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
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Central limit theorem

30. TIES Step 4: Investigate Design Space
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
It gives the probability that a value will be met or exceeded.
Does not have a natural zero - is a cardinal scale
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.

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

32. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Range is always between zero and 1 monotonically increasing
(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
Technique for Order Preference by Similarity to Ideal Solution
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

33. In what regions of the graph is UTE applicable?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Mean =0 Variance =1
Regions 1 to 3.

34. What does CDF stand for?
Cumulative Distribution Function
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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

35. What is the definition of inflation?
CDF= ?_(-8)^8
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
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

36. Does TIES use MADM or MODM? Why?
No way to tell without more information. It depends on the relation between s12+s22 and s32
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space

37. 3 Measures of Central Tendency (& Defs)
Technique for Order Preference by Similarity to Ideal Solution
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
RDTE - Investment/Acquisition - Operations and Support - Disposal
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

38. Indirect Operating Cost
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

39. What is the notation for a standard normal distribution?
#=2^n = 2^15
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) 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
X~N(0 -1)

40. What are the three snapshots of UTE?
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
(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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Has a natural zero - is a cardinal scale

41. With 15 technologies - what is the number of possible combinations?
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
#=2^n = 2^15
Does not have a natural zero - is a cardinal scale
is bottom- up - you look at certain technologies and see what improvements they offer

42. Why is the normal distribution useful or important?

43. Why is learning curve used (or what is it?)
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.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
P(between B and A)=F(B)-F(A)

44. Why use uniform dist for input variables (Gap Analysis)
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
Central limit theorem
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
Allows designer to assess feasibility of design

45. TIES Step 8: Selecting Technology

46. What are the four difference life cycle costs?
It can be continuous or discrete
RDTE - Investment/Acquisition - Operations and Support - Disposal
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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.

47. What can be done about uncertainty in requirement?
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
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 space limits
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.

48. TIES Step 3: Model and Simulation
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
No way to tell without more information. It depends on the relation between s12+s22 and s32
RDTE - Investment/Acquisition - Operations and Support - Disposal
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

49. TIES Step 7: Assess Technology
#=2^n = 2^15
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Determine the design space - baseline Method: Morphological Matrix
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

50. Strengths of TOPSis...
Has a natural zero - is a cardinal scale
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective