ADM

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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. 3 Measures of Central Tendency (& Defs)
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
(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
Mean: the average - Median: The midpoint in the data - equal # of higher and lower values - Mode: Most common value
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


2. What is TCM? What is the size and what value can it take?
Cumulative Distribution Function
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
Does not have 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


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

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4. Other than infusing technologies - how can you create design space?
Technique for Order Preference by Similarity to Ideal Solution
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Allows designer to assess feasibility of design
The interest i such that 0=PE(i^)


5. What is the goal of robust design?

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6. What does the CLT state - be specific!
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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
It gives the probability that a value will be met or exceeded.
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


7. TIES Step 8: Selecting Technology

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8. What is the definition of inflation?
Sample size is 4 - the sample is the sum of the five dice.
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
A pareto frontier represents points of a non - dominated solution based on preferences


9. What is TRL? Range? What does a high TRL mean?
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
Technology space limits
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
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.


10. What two variables are necessary to define a normal distribution?
Cumulative Distribution Function
(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.
Mean and variance


11. What are K- factors applied to?
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 bottom- up - you look at certain technologies and see what improvements they offer
PE(i)=?Ft
Technology space limits


12. What does CDF stand for?
Cumulative Distribution Function
#=2^n = 2^15
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
Sample size is 4 - the sample is the sum of the five dice.


13. 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?
Identified techs are now applied to the vehicle concepts and evaluated. Evaluation provided data/info to the decision - maker. Method: RSE: Response Surface Eqn.
Mean and variance
Sample size is 4 - the sample is the sum of the five dice.
(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. TIES Step 1: Problem Definition
Technique for Order Preference by Similarity to Ideal Solution
Mean =0 Variance =1
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
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


15. In what regions of the graph is UTE applicable?
Regions 1 to 3.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
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.


16. Indirect Operating Cost
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
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
The interest i such that 0=PE(i^)


17. What does TOPSIS stand for?
Technique for Order Preference by Similarity to Ideal Solution
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.
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


18. TIES Step 5: Feasible?
Range is always between zero and 1 monotonically increasing
Cumulative Distribution Function
To analytically answer 'How much design margin is really necessary?'
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


19. What is the equation for present equivalent value? Define variables.
F(x)=1/(s(2p)^(.5) )exp?(-(x-
PE(i)=?Ft
Technology space limits
(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. TIES Step 4: Investigate Design Space
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
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.
PE(i)=?Ft
P(between B and A)=F(B)-F(A)


21. What are the three snapshots of UTE?
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
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


22. What is the difference between price and cost?
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
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee


23. What is the definition of CDF?
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
It gives the probability that a value will be met or exceeded.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
(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


24. 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.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
No way to tell without more information. It depends on the relation between s12+s22 and s32
Mean =0 Variance =1


25. 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)
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of uncertainty?'
To analytically answer 'How much design margin is really necessary?'


26. What can management do to mitigate the risk associated with infusing new technologies?
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
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
It gives the probability that a value will be met or exceeded.
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)


27. 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.
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.
OEC = W1X/Xbsl + W2Nbsl/N


28. What is probability density contour plot
PE(i)=?Ft
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.
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
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


29. Why use uniform dist for input variables (Gap Analysis)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Sample size is 4 - the sample is the sum of the five dice.
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Allows designer to assess feasibility of design


30. What is the equation for the learning curve?
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
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.
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.


31. 3 Probabilistic Design Methods
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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
Technique for Order Preference by Similarity to Ideal Solution
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration


32. TIES Step 3: Model and Simulation
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
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.
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


33. Show and explain a pareto frontier
(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
Has a natural zero - is a cardinal scale
RDTE - Investment/Acquisition - Operations and Support - Disposal


34. Ratio scale
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
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln


35. Does TIES use MADM or MODM? Why?
CDF= ?_(-8)^8
Technique for Order Preference by Similarity to Ideal Solution
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


36. How do you get the CDF from the PDF?
CDF= ?_(-8)^8
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
Regions 1 to 3.


37. $/RPM Equation
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Required yield per revenue passenger TOC/(#OfSeats)(loadFactor)(distanceInMiles) loadFactor = % of seats filled w/ paying customers
To analytically answer 'How much design margin is really necessary?'
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.


38. 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)
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
Central limit theorem


39. How is inflation measured?

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40. 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
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)
Allows designer to assess feasibility of design


41. 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
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Determine the design space - baseline Method: Morphological Matrix
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)


42. TIES Step 7: Assess Technology
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.
Technique for Order Preference by Similarity to Ideal Solution
X~N(0 -1)


43. MADM
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
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.
Sample size is 4 - the sample is the sum of the five dice.


44. Why are scaling parameters important?
#=2^n = 2^15
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
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
is bottom- up - you look at certain technologies and see what improvements they offer


45. TIF

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46. What does CLT stand for?
Allows designer to assess feasibility of design
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
#=2^n = 2^15
Central limit theorem


47. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
Does not have a natural zero - is a cardinal scale
It can be continuous or discrete
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
OEC = W1X/Xbsl + W2Nbsl/N


48. 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.
Central limit theorem
Mean =0 Variance =1
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


49. Assumptions Used in TOPSis...
(1) Expanding ranges on engineering metrics (2) Relaxing customer requirements (3) Select a different concept space
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Active UTE (additive) - Product UTE (multiplicative)
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo


50. What is the goal of probabilistic design?

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