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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. In what regions of the graph is UTE applicable?
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
X+Y and X-Y are normally distributed. - (X
Does not have a natural zero - is a cardinal scale
Regions 1 to 3.

2. Other than infusing technologies - how can you create design 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
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
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

3. MADM
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.
Does not have a natural zero - is a cardinal scale
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.

4. What are the different types of UTEs?
P(between B and A)=F(B)-F(A)
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) 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
Active UTE (additive) - Product UTE (multiplicative)

5. What does CDF stand for?
Cumulative Distribution Function
Mean and variance
It gives the probability that a value will be met or exceeded.
(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

6. With 15 technologies - what is the number of possible combinations?
PE(i)=?Ft
#=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.
Range is always between zero and 1 monotonically increasing

7. What does the CLT state - be specific!
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.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to 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
(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

8. What two variables are necessary to define a normal distribution?
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Mean and variance
Does not have a natural zero - is a cardinal scale
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

9. 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.
Cumulative Distribution Function
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.
PE(i)=?Ft

10. TIES Step 1: Problem Definition
To analytically answer 'How much design margin is really necessary?'
(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
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. Direct Operating Costs
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
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)

12. Weaknesses of TOPSis...
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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.

13. 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
Regions 1 to 3.
Allows designer to assess feasibility of design
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.

14. TIES Step 3: Model and Simulation
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
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
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
is bottom- up - you look at certain technologies and see what improvements they offer

15. What does TOPSIS stand for?
Determine the design space - baseline Method: Morphological Matrix
Technique for Order Preference by Similarity to Ideal Solution
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

16. Indirect Operating Cost
RDTE - Investment/Acquisition - Operations and Support - Disposal
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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 related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

17. What is the difference between price and cost?
Sample size is 4 - the sample is the sum of the five dice.
Technology space limits
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
(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

18. What is satisficing - what is optimizing?
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.
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Technique for Order Preference by Similarity to Ideal Solution
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'

19. What is the normal distribution that results from adding x+y and x[sub]y?
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.
X+Y and X-Y are normally distributed. - (X
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Mean and variance

20. interval scale
Has a natural zero - is a cardinal scale
Does not have a natural zero - is a cardinal scale
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)

21. Define fixed cost and variable cost.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
PE(i)=?Ft
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

22. What are the three snapshots of UTE?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(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
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
A pareto frontier represents points of a non - dominated solution based on preferences

23. 3 Probabilistic Design Methods
Central limit theorem
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
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) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

24. What is the goal of robust design?

25. What are properties of a CDF?
Range is always between zero and 1 monotonically increasing
Regions 1 to 3.
To analytically answer 'How much design margin is really necessary?'
(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

26. What is TRL? Range? What does a high TRL mean?
(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 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
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

27. 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
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) End result not intuitive (2) Heavily reliant on weights - which are subjective
F(x)=1/(s(2p)^(.5) )exp?(-(x-

28. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
No way to tell without more information. It depends on the relation between s12+s22 and s32
PE(i)=?Ft
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
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

29. TIES Step 2: Design Space Conception
P(between B and A)=F(B)-F(A)
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
Determine the design space - baseline Method: Morphological Matrix

30. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Has a natural zero - is a cardinal scale
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

31. Does TIES use MADM or MODM? Why?
PE(i)=?Ft
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
(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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

32. What is the goal of probabilistic design?

33. Ratio 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
Has a natural zero - is a cardinal 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.
(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

34. What are the parameters for a standard normal distribution?
Mean =0 Variance =1
Has a natural zero - is a cardinal scale
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
RDTE - Investment/Acquisition - Operations and Support - Disposal

35. Assumptions Used in TOPSis...
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Technology space limits
To analytically answer 'How much design margin is really necessary?'
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

36. 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
#=2^n = 2^15
Sample size is 4 - the sample is the sum of the five dice.
Central limit theorem

37. How is inflation measured?

38. Strengths of TOPSis...
Mean and variance
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)

39. 4 Measures of Dispersion
Cumulative Distribution Function
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
X+Y and X-Y are normally distributed. - (X
Allows designer to assess feasibility of design

40. Why use uniform dist for input variables (Gap Analysis)
Does not have a natural zero - is a cardinal scale
Allows designer to assess feasibility of design
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD

41. Why is the normal distribution useful or important?

42. What are the four difference life cycle costs?
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
#=2^n = 2^15
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.
RDTE - Investment/Acquisition - Operations and Support - Disposal

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

44. What is the definition of CDF?
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
Regions 1 to 3.
OEC = W1X/Xbsl + W2Nbsl/N
It gives the probability that a value will be met or exceeded.

45. TIES Step 8: Selecting Technology

46. Show and explain a pareto frontier
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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
A pareto frontier represents points of a non - dominated solution based on preferences
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)

47. What does CLT stand for?
Mean and variance
Central limit theorem
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
It gives the probability that a value will be met or exceeded.

48. What is the definition of ROI?
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Has a natural zero - is a cardinal scale
The interest i such that 0=PE(i^)
X~N(0 -1)

49. Name the advantages of UTE.
It gives the probability that a value will be met or exceeded.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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.

50. What are K- factors applied to?
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
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Technology space limits