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


2. What is satisficing - what is optimizing?
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
Technology space limits
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.


3. Direct Operating Costs
(1) Easy to compute order of large # of alternatives (2) Gives specific ranking order
Cost required to perform a function - without which the function cannot be performed. (e.g. fuel costs - pilot wages)
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
Technique for Order Preference by Similarity to Ideal Solution


4. TIES Step 6: Identify Technology
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
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
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.


5. What is the difference between price and cost?
Determine the design space - baseline Method: Morphological Matrix
Technique for Order Preference by Similarity to Ideal Solution
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee


6. How do you get the CDF from the PDF?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)
CDF= ?_(-8)^8
No way to tell without more information. It depends on the relation between s12+s22 and s32


7. TIES Step 7: Assess Technology
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
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.


8. Does TIES use MADM or MODM? Why?
No way to tell without more information. It depends on the relation between s12+s22 and s32
(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
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline


9. What are K- factors applied to?
Technology space limits
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.
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


10. What is the equation for present equivalent value? Define variables.
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Technique for Order Preference by Similarity to Ideal Solution
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
PE(i)=?Ft


11. 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
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
is bottom- up - you look at certain technologies and see what improvements they offer
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future


12. 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)
is bottom- up - you look at certain technologies and see what improvements they offer
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
Consumer Price Index (CPI) measures the cost of an average 'basket of goods' a typical consumer would purchase.


13. What does CLT stand for?
Gaussian Distribution
To analytically answer 'What can be done to reduce the impact of sensitivities of objective to sources of 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
Central limit theorem


14. Weaknesses of TOPSis...
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.
(1) End result not intuitive (2) Heavily reliant on weights - which are subjective
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


15. TIES Step 1: Problem Definition
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
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


16. 4 Measures of Dispersion
No way to tell without more information. It depends on the relation between s12+s22 and s32
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
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
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


17. TIES Step 2: Design Space Conception
Has a natural zero - is a cardinal scale
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.
Determine the design space - baseline Method: Morphological Matrix
MADM - since we are selecting from existing alternatives for technology infusion. Also - TOPSIS is a MADM technique.


18. Write down a formula for a normal distribution
Cumulative Distribution Function
It gives the probability that a value will be met or exceeded.
No way to tell without more information. It depends on the relation between s12+s22 and s32
F(x)=1/(s(2p)^(.5) )exp?(-(x-


19. In what regions of the graph is UTE applicable?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
PE(i)=?Ft
Does not have a natural zero - is a cardinal scale
Regions 1 to 3.


20. 8 Steps in TIES
Carry a diverse portfolio of technologies during conceptual design - Limit the number of technologies in the final design - Utilize only mature technologies (high TRL)
(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 used to determine the best alternative with all interactions between the constraints. Used for product design.
is bottom- up - you look at certain technologies and see what improvements they offer


21. What two variables are necessary to define a normal distribution?
A pareto frontier represents points of a non - dominated solution based on preferences
Mean and variance
Chosen alternative should be closest to positive ideal soln - and farthest from neg ideal soln
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.


22. TIES Step 8: Selecting Technology

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23. Define fixed cost and variable cost.
Fixed cost does not depend on production rate and/or size - Variable cost changes with production rate and/or size.
X+Y and X-Y are normally distributed. - (X
Central limit theorem
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem


24. Why is the normal distribution useful or important?

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25. With 15 technologies - what is the number of possible combinations?
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
#=2^n = 2^15
Cost: investment required to produce and item - Price: amount required to purchase said item - Price = cost + profit/fee
Mean =0 Variance =1


26. 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?
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
Sample size is 4 - the sample is the sum of the five dice.
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.


27. Strengths of TOPSis...
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) Easy to compute order of large # of alternatives (2) Gives specific ranking order
No way to tell without more information. It depends on the relation between s12+s22 and s32
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


28. What is the equation for the learning curve?
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor
Is top- down - you aren't looking at specific technologies - you're just looking at what you need in the future
To analytically answer 'How much design margin is really necessary?'
X+Y and X-Y are normally distributed. - (X


29. 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 pareto frontier represents points of a non - dominated solution based on preferences
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.
Does not have a natural zero - is a cardinal scale


30. How is inflation measured?

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31. What is the equation for OEC if X is a benefit (maximize) and N is a cost (minimize)?
OEC = W1X/Xbsl + W2Nbsl/N
Cumulative Distribution Function
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
No way to tell without more information. It depends on the relation between s12+s22 and s32


32. What is the normal distribution that results from adding x+y and x[sub]y?
Sample size is 4 - the sample is the sum of the five dice.
Cost related to function - but not explicitly necessary. (e.g. attendant wages - advertising)
Convenient properties - Various physical - astronomic - and real life examples have roughly 'normal' behaviors - good approximation for measurements due to central limit theorem
X+Y and X-Y are normally distributed. - (X


33. What is the definition of inflation?
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 =0 Variance =1
A pareto frontier represents points of a non - dominated solution based on preferences
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


34. What are the four difference life cycle costs?
RDTE - Investment/Acquisition - Operations and Support - Disposal
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
Technology Impact Matrix - for n tech & m metrics of interest - nxm matrix - has 'k' factor with degradation/improvement from baseline
Mean =0 Variance =1


35. 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
To analytically answer 'How much design margin is really necessary?'
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
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


36. interval scale
Does not have a natural zero - is a cardinal scale
It gives the probability that a value will be met or exceeded.
#=2^n = 2^15
F(x)=1/(s(2p)^(.5) )exp?(-(x-


37. Why is learning curve used (or what is it?)
CDF= ?_(-8)^8
Efficiency improves as better techniques are learned. As more efficient techniques are found - the learning curve begins to level off as incremental improvements decrease.
A pareto frontier represents points of a non - dominated solution based on preferences
F(x)=1/(s(2p)^(.5) )exp?(-(x-


38. What are properties of a 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
The interest i such that 0=PE(i^)
Range is always between zero and 1 monotonically increasing
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.


39. What are the parameters for a standard normal distribution?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
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 sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
Mean =0 Variance =1


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

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41. 3 Probabilistic Design Methods
Technique for Order Preference by Similarity to Ideal Solution
P(between B and A)=F(B)-F(A)
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Technology Impacts Requirements uncertainty (creep/change) - Quantified by probability of success/satisfaction: P(success)


42. TIES Step 4: Investigate Design Space
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.
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.
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.
y = kx^n - y: production effort k: effort for first unit x: # of units n: learning factor


43. What does CDF stand for?
Determining how feasible your design is / if your current baseline (or a variation in geometry) can meet your customer requirements. Method: Monte Carlo
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population
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


44. Name two uncertainties accounted for by UTE. What metric does UTE use to quantify this risk?
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
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
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)


45. MODM
OEC = W1X/Xbsl + W2Nbsl/N
Technology space limits
A technique used to determine the best alternative with all interactions between the constraints. Used for product design.
Technique for Order Preference by Similarity to Ideal Solution


46. What is the goal of robust design?

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47. What can be done about uncertainty in requirement?
Has a natural zero - is a cardinal scale
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
Look at multiple weight scenarios and find techs that are robust regardless of where the emphasis is put.
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD


48. TIES Step 3: Model and Simulation
(1) Sophisticated Analysis Code + Monte Carlo (2) Metamodel/Response Surface + Monte Carlo (3) Sophisticated Analysis Code + Fast Probability Integration
Allows designer to assess feasibility of design
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 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


49. Show and explain a pareto frontier
Central limit theorem
Determine the design space - baseline Method: Morphological Matrix
Regions 1 to 3.
A pareto frontier represents points of a non - dominated solution based on preferences


50. What is the definition of CDF?
The first step is defining the problem - mapping customer requirements to engineering metrics. Method: QFD
(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.
A sample is a subset of a population. We use samples because we very rarely have the resources to test/examine an entire population