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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. Other than infusing technologies - how can you create design space?
F(x)=1/(s(2p)^(.5) )exp?(-(x-
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Mean and variance
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


2. What is the definition of inflation?
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
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.
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


3. If you have two compatible mature technologies to infuse - or one not mature technology - which will have the most variance?
Technology space limits
No way to tell without more information. It depends on the relation between s12+s22 and s32
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
Has a natural zero - is a cardinal scale


4. MADM
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
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 technique that determines the best alternative based on a multi- attribute utlity function which is closest to hypothetical best solution. Used for product selection.


5. What can be done about uncertainty in requirement?
Does not have a natural zero - is a cardinal scale
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Sample size is 4 - the sample is the sum of the five dice.


6. Write down a formula for a normal distribution
F(x)=1/(s(2p)^(.5) )exp?(-(x-
X+Y and X-Y are normally distributed. - (X
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Range is always between zero and 1 monotonically increasing


7. What are properties of a CDF?
is bottom- up - you look at certain technologies and see what improvements they offer
Range is always between zero and 1 monotonically increasing
Gaussian Distribution
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


8. Assumptions Used in TOPSis...
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
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value


9. What does CLT stand for?
Central limit theorem
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.
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.
Regions 1 to 3.


10. What is TRL? Range? What does a high TRL mean?
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.
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


11. 8 Steps in TIES
Range is always between zero and 1 monotonically increasing
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
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


12. What is satisficing - what is optimizing?
RDTE - Investment/Acquisition - Operations and Support - Disposal
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
X+Y and X-Y are normally distributed. - (X
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.


13. What are the parameters for a standard normal distribution?
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Mean and variance
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Mean =0 Variance =1


14. What are the three snapshots of UTE?
(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
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


15. TIES Step 4: Investigate Design Space
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
No way to tell without more information. It depends on the relation between s12+s22 and s32
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.
F(x)=1/(s(2p)^(.5) )exp?(-(x-


16. Why do we use a sample?
To analytically answer 'How much design margin is really necessary?'
F(x)=1/(s(2p)^(.5) )exp?(-(x-
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) 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


17. TIES Step 5: Feasible?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
X+Y and X-Y are normally distributed. - (X
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


18. TIES Step 8: Selecting Technology

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19. What is the equation for the learning curve?
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
(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


20. Show and explain a pareto frontier
Regions 1 to 3.
(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 pareto frontier represents points of a non - dominated solution based on preferences
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


21. What does TOPSIS stand for?
X~N(0 -1)
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Range is always between zero and 1 monotonically increasing
Technique for Order Preference by Similarity to Ideal Solution


22. 3 Measures of Central Tendency (& Defs)
X+Y and X-Y are normally distributed. - (X
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
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


23. What is the definition of ROI?
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
The interest i such that 0=PE(i^)
#=2^n = 2^15
RDTE - Investment/Acquisition - Operations and Support - Disposal


24. What is TCM? What is the size and what value can it take?
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
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
PE(i)=?Ft


25. Weaknesses of TOPSis...
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
X+Y and X-Y are normally distributed. - (X
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.


26. Define fixed cost and variable cost.
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
X~N(0 -1)
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.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


27. interval scale
RDTE - Investment/Acquisition - Operations and Support - Disposal
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 and variance
Does not have a natural zero - is a cardinal scale


28. How is inflation measured?

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29. What are K- factors applied to?
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
Does not have a natural zero - is a cardinal scale
F(x)=1/(s(2p)^(.5) )exp?(-(x-
Technology space limits


30. 4 Measures of Dispersion
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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
CDF= ?_(-8)^8


31. What is the notation for a standard normal distribution?
X~N(0 -1)
Allows designer to assess feasibility of design
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
OEC = W1X/Xbsl + W2Nbsl/N


32. TIES Step 1: Problem Definition
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(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
Sample size is 4 - the sample is the sum of the five dice.
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


33. What is the goal of robust design?

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34. TIES Step 6: Identify Technology
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
(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
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


35. Name the advantages of UTE.
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) Easy to compute order of large # of alternatives (2) Gives specific ranking order
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
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.


36. What is the goal of probabilistic design?

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37. What does the CLT state - be specific!
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
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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
X~N(0 -1)


38. What is probability density contour plot
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
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
Has a natural zero - is a cardinal scale
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


39. Is CDF discrete or continuous - if it is discrete give the continuous equivalent - if it continuous give the discrete equivalent.
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
Range is always between zero and 1 monotonically increasing
Regions 1 to 3.
It can be continuous or discrete


40. Why is learning curve used (or what is it?)
(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
X~N(0 -1)
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
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.


41. What is another name for a normal distribution?
Allows designer to assess feasibility of design
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.
Gaussian Distribution
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


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

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43. Ratio scale
Has a natural zero - is a cardinal scale
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.
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln


44. TIF

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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)
Does not have a natural zero - is a cardinal scale
OEC = W1X/Xbsl + W2Nbsl/N
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.


46. TIES
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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem


47. In what regions of the graph is UTE applicable?
Regions 1 to 3.
Central limit theorem
(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.


48. Indirect Operating Cost
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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.


49. How do you get the CDF from the PDF?
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
CDF= ?_(-8)^8
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.


50. Direct Operating Costs
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.
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
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)