Test your basic knowledge |

ADM

Instructions:

Answer 50 questions in 15 minutes.
If you are not ready to take this test, you can study here.
Match each statement with the correct term.
Don't refresh. All questions and answers are randomly picked and ordered every time you load a test.

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 is TRL? Range? What does a high TRL mean?
#=2^n = 2^15
Determine the design space - baseline Method: Morphological Matrix
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
Range is always between zero and 1 monotonically increasing

2. TIES Step 8: Selecting Technology
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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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

3. What does CDF stand for?
Cumulative Distribution Function
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
PE(i)=?Ft

4. What is satisficing - what is optimizing?
Gaussian 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.
Determine the design space - baseline Method: Morphological Matrix
The interest i such that 0=PE(i^)

5. What does the CLT state - be specific!
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.
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.

6. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
OEC = W1X/Xbsl + W2Nbsl/N
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Range is always between zero and 1 monotonically increasing
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

7. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
The interest i such that 0=PE(i^)

8. 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)
CDF= ?_(-8)^8
(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
RDTE - Investment/Acquisition - Operations and Support - Disposal

9. 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)
Mean and variance
OEC = W1X/Xbsl + W2Nbsl/N
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

10. Direct Operating Costs
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Technique for Order Preference by Similarity to Ideal Solution
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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. 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?
CDF= ?_(-8)^8
Sample size is 4 - the sample is the sum of the five dice.
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 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

12. What is the difference between price and cost?
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
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
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. Indirect Operating Cost
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Has a natural zero - is a cardinal scale
(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
Allows designer to assess feasibility of design

14. What is the definition of ROI?
The interest i such that 0=PE(i^)
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
It can be continuous or discrete
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

15. What is another name for a normal distribution?
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
Cumulative Distribution Function
Gaussian Distribution
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

16. Weaknesses of 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
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.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration

17. What is the equation for present equivalent value? Define variables.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
(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
PE(i)=?Ft
F(x)=1/(s(2p)^(.5) )exp?(-(x-

18. Show and explain a pareto frontier
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
A pareto frontier represents points of a non - dominated solution based on preferences
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

19. Define fixed cost and variable cost.
PE(i)=?Ft
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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

20. With 15 technologies - what is the number of possible combinations?
#=2^n = 2^15
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.

21. 3 Measures of Central Tendency (& Defs)
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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
#=2^n = 2^15

22. What are properties of a CDF?
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
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
Range is always between zero and 1 monotonically increasing

23. TIES
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.
OEC = W1X/Xbsl + W2Nbsl/N
is bottom- up - you look at certain technologies and see what improvements they offer
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)

24. $/RPM Equation
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
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.

25. 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)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
is bottom- up - you look at certain technologies and see what improvements they offer
Cumulative Distribution Function

26. TIES Step 2: Design Space Conception
is bottom- up - you look at certain technologies and see what improvements they offer
OEC = W1X/Xbsl + W2Nbsl/N
Gaussian Distribution
Determine the design space - baseline Method: Morphological Matrix

27. Assumptions Used in TOPSis...
(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)
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future

28. Does TIES use MADM or MODM? Why?
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
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
OEC = W1X/Xbsl + W2Nbsl/N

29. What are the parameters for a standard normal distribution?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
Mean =0 Variance =1
P(between B and A)=F(B)-F(A)

30. Why do we use a sample?
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
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
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population

31. Other than infusing technologies - how can you create design space?
Regions 1 to 3.
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Mean and variance
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

32. Ratio scale
P(between B and A)=F(B)-F(A)
No way to tell without more information. It depends on the relation between s12+s22 and s32
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Has a natural zero - is a cardinal scale

33. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.

34. What is the goal of robust design?
CDF= ?_(-8)^8
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
Cumulative Distribution Function

35. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
The interest i such that 0=PE(i^)
No way to tell without more information. It depends on the relation between s12+s22 and s32
It can be continuous or discrete
(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

36. What are the different types of UTEs?
Determine the design space - baseline Method: Morphological Matrix
Active UTE (additive) - Product UTE (multiplicative)
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
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.

37. Why use uniform dist for input variables (Gap Analysis)
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.
It gives the probability that a value will be met or exceeded.
Allows designer to assess feasibility of design

38. What can be done about uncertainty in requirement?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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
(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

39. TIES Step 7: Assess Technology
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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.
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.

40. TIF
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
The interest i such that 0=PE(i^)
X+Y and X-Y are normally distributed. - (X
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor

41. MADM
X~N(0 -1)
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
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.
The interest i such that 0=PE(i^)

42. What is TIM? What is the size and what value can it take?
Sample size is 4 - the sample is the sum of the five dice.
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value

43. What is the notation for a standard normal distribution?
Regions 1 to 3.
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) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
X~N(0 -1)

44. What does CLT stand for?
The interest i such that 0=PE(i^)
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
X~N(0 -1)
Central limit theorem

45. Why are scaling parameters important?
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
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.
CDF= ?_(-8)^8
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

46. Strengths of TOPSis...
Cumulative Distribution Function
The interest i such that 0=PE(i^)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
To analytically answer 'How much design margin is really necessary?'

47. TIES Step 5: Feasible?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
It gives the probability that a value will be met or exceeded.
(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 are the three snapshots of UTE?
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.
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) 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
P(between B and A)=F(B)-F(A)

49. Why is learning curve used (or what is it?)
A pareto frontier represents points of a non - dominated solution based on preferences
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
X~N(0 -1)
Gaussian Distribution

50. How do you get the CDF from the PDF?
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
CDF= ?_(-8)^8
(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
Cumulative Distribution Function

Test your basic knowledge |

ADM

Link to This Test

Related Subjects