Post on 05-Apr-2018
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Daniel GuggenheimSchool ofAerospace Engineering
Overview of Uncertainty inAerospace Design
Dr. Douglas StanleyGeorgia Institute of TechnologyNational Institute of Aerospace
757.325.6811stanley@nianet.org
Dr. Alan WilhiteGeorgia Institute of TechnologyNational Institute of Aerospace
757.864.6810wilhite@nianet.org
With Special Thanks to:Dr. Dimitri MavrisandDr. Michelle Kirbyof Georgia Tech
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Daniel GuggenheimSchool ofAerospace Engineering Outline
Definitions
Brief Review of Probability Random Events
Probability Distributions
Sampling
Functions of Random Variables
Overview of Risk and Continuous Risk Management Risk Identification
Risk Analysis
Risk Planning
Risk Tracking and Control
Expert Elicitation in Risk Management Uncertainty and Margin in Design Weight/Performance Margins and Uncertainty
Cost Margins and Uncertainty
Schedule Margins and Uncertainty
Technology Risk Mitigation
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Daniel GuggenheimSchool ofAerospace Engineering Outline
Risk Leveling
Probabilistic Risk Assessment
Response Surface Methods
Probabilistic Design
Example of Probabilistic Design Under Uncertainty
Decision Making Process Characteristics
Common Biases
Figures of Merit
Multi-Attribute Utility Theory
Making Design Decisions Under Uncertainty Summary
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Daniel GuggenheimSchool ofAerospace Engineering Definitions
Uncertainty
The state of being uncertain; Doubt
The estimated amount by which a calculated value may differ from thetrue value
Uncertain
Not known or established; Not determined, Not having sure knowledge
Risk
The possibility of suffering harm or loss; Danger; Hazard
The chance of loss; The degree of probability of loss
Probability of a non-desirable event
Probability
The relative possibility that an event will occur; Likelihood
The relative frequency with which an event is likely to occur
Ratio of number of occurrences to number of possible occurrences
We wish to use probabilityto measure uncertainty, in order to reduce
uncertaintyto mitigate risk or to make designs robust to uncertaintyDictionary.reference.com
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Summary
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Risk comes from uncertainty in the design, development, production and
operations processes If no uncertainty there is no riskreducing uncertainty reduces risk
Very high level of uncertainty in exploration systems due to lack of
development and operations experience base
Sources of uncertainty include:
Uncertainty in performance, safety, cost, and schedule models
Uncertainty/changes in customer requirements
Uncertainty in integration effects on performance
Uncertainty in manufacturing variation/tolerances
Uncertainty in test results
Uncertain operating environments (temperature, pressure, acoustics, etc.)
Uncertain responses to operating environments/failure modes
Uncertain component/subsystem/system life
Potential human errors in design, development, production or operation
Summary of Risk and Uncertainty
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Judicious use of MARGIN is the most important risk mitigation strategy!
Includes cost, schedule, and performance/weight margins
Use of margin is necessary but not sufficient for risk management
Still need to identify and mitigate root causes of risk
Increasing margin decreases risk, but at the expense of other FOMs
May decrease performance/payload or increase cost at some point Eventually reaches diminishing returns in customer value proposition
Finding the right balance between margin/risk and other FOMs is, once
again, a multi-attribute decision problem
How do I know how much increasing margin decreases risk?
Through probabilistic analysis using historical data regression or expert
elicitation coupled with Monte Carlo simulation
How do I know how much increasing margin affects other FOMs?
Through integrated systems analysis capability or expert elicitation
Summary of Margin and Risk
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Daniel GuggenheimSchool ofAerospace Engineering Summary of Design Under Uncertainty
The level of uncertainty modeled in the design process depends on
the nature of problem, available resources, and criticality of decision Risk identification, analysis, planning, tracking and control require
many decisions that require formal methods such as expert elicitation
Judicious use of performance, cost and schedule margin is the mostimportant risk mitigation strategy in dealing with uncertainty
Risk leveling prevents resources from being focused on risks that donot have a significant relative effect on the system
A set of complete, independent, and well-defined Figures of Merit areessential for good design decisions
Multi-Attribute Utility Theory provides the most analytically sound and
comprehensive process for design decision making An integrated systems analysis capability is essential to good design
Sensitivity analysis enables better decisions by testing assumptions
Probabilistic analysis enables better designs by quantifying risk
Monte Carlo Analysis and Response Surface Methods are key tools
that enable efficient methods for probabilistic design
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Probability
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Daniel GuggenheimSchool ofAerospace Engineering Probability and Random Events
A basic underlying assumption of probability theory is that
it deals with random events
A randomevent is one in which the conditions are suchthat each member of the population, N, has an equalchance of being chosen
A special and precise system of language and notation isused in probability theory
Two events, A and B, are said to be independentif theoccurrence of either one has no effect on the occurrence
of the other Two events that have no elements in common are said to
be mutually exclusiveevents
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Daniel GuggenheimSchool ofAerospace Engineering Errors and Samples
The act of making any type of experimental observation
involves two types of errors:
Systematic errors (which exert a nonrandom basis)
Experimental, or random, errors
When a large number of observations are made from arandom sample, a method is needed to characterize thedata
Histograms
Frequency Distribution
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Daniel GuggenheimSchool ofAerospace Engineering Probability Distribution
A probability frequency distributionis a characterization of the possible
values that a random variable may assume along with the probability ofassuming these values.
The probability functionhas the following characteristics
0 f(xi) 1; f(xi) = 1
A probability density function, f(x), is characterized by the probabilities ofvarious outcomes of continuous random variables. Probabilities are definedover intervals computed as the area under the density function between x1and x2.
A cumulative distribution functionspecifies the probability that a randomvariable X will assume a value less than or equal to a specified value, x
denoted as P(X 1).
f(x)
x6 7 8
P(X > 7.3) = .33
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Measures of Central Tendency andDispersion
A probability frequency distributioncan be described with
numbers that indicate the central location of thedistribution and how the observations are spread out fromthe central location (dispersion)
Arithmetic mean, or average
Median
Mode
Variance
Standard Deviation
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Types of Distributions Normal Distribution
Normal Distributions
Very important in sampling because of the Central Limit Theorem
Many physical measurements follow the symmetrical, bell-shaped curveof the normal, or Gaussian, frequency distribution
f(x) = (1/( (2 )
0.5
))exp(-0.5((x- )/ )
2
)
Normal Distribution Standard Normal ( = 0, = 1)
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Types of Distributions Weibull Distribution
Weibull Distribution
Widely used for many engineering problems because of its versatility,since many random variables follow a bounded, nonsymmetricaldistribution, such as fatigue life of components
Used to include infant mortality in component life modeling (bathtub)
f(x) = ((m/ )/(x/ )
m-1
)exp(-(x/ )
m
), x > 0
Weibull Distribution for = 1and various values of m
m = Shape Parameter
= Scale Parameter
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Types of Distributions Gamma Distribution
Gamma Distribution
Used to describe random variables that are bounded at one end
Measures time required for total of h independent events to take place ifevents occur at a constant rate of l
Used to model failures and in queuing theory
Chi-square and exponential distributions are special cases
f(x) = ( x -1e- x)/(0
x -1e-xdx), x > 0, > 0, > 0
Gamma Distribution for = 3and various values of
= Shape Parameter
= Scale Parameter
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Types of Distributions Exponential Distribution
Exponential Distribution
Measures time required for first event to take place if events occur at aconstant rate of l (widely used to measure time to failure)
Special case of the gamma distribution for = 1
Special case of the Weibull distribution for m =1 and x0 = 0
Exponential Distribution for= 1/ where = Failure
Rate
f(x) = (1/ )e-x/ , x > 0
1/
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Engineering Statistics Sampling Distributions
The central problem in statistics is relating the population
and the samples that are drawn from it
This problem is viewed from two perspectives:
What does the population tell us about the behavior ofthe samples?
What does a sample or series of samples tell us aboutthe population form which the sample came?
Central Limit Theoremtells us that:
If a sample size is sufficiently large, the mean of arandom sample from a population has a samplingdistribution that is approximately normal, regardless ofthe shape of the relative frequency distribution of thetarget population
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Probability Sampling AnalysisExample
12.8 15.6 13.5 15.7 15.3 15.2 20.1 14.2 12.9 14.016.9 14.3 15.5 14.6 13.0 14.7 19.0 13.0 11.3 14.214.5 14.8 14.2 13.0 13.1 12.5 16.1 19.1 16.7 13.215.0 12.7 13.6 13.3 13.2 14.7 12.9 13.1 17.3 15.417.9 13.0 14.3 14.2 15.7 15.6 13.0 13.9 14.2 16.012.9 13.1 13.3 12.3 13.1 13.6 13.2 18.5 13.2 13.712.6 14.4 14.5 13.9 17.0 13.7 12.7 16.8 13.3 14.714.2 13.0 14.6 14.0 12.9 14.7 12.8 12.0 14.2 12.813.7 15.2 14.8 13.0 11.7 12.2 13.3 13.8 14.2 14.314.7 12.6 18.9 14.3 14.4 15.5 16.8 17.0 13.2 12.9
Sample Times (in Seconds) to Inspect Test Devices for Calibration
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Probability Sampling AnalysisExample
Histogram for the frequency distribution
of inspection times.
Suggested shape of smoothed frequencycurve for the entire population of
inspection times.
Frequency polygon for thefrequency distribution of
inspection times.
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Daniel GuggenheimSchool ofAerospace Engineering Probability Sampling Analysis Example
50%
90%
CumulativeDistribution
Function
ProbabilityDistribution
Function
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Sampling Distributions andStatistical Intervals
Distribution of Sample Means (t Distribution)
Distribution of Sample Variances (2 and F Distributions)
Determination of Confidence Intervals Confidence Interval containing with probability 1-:
Where 1- is given by:
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Daniel GuggenheimSchool ofAerospace Engineering Statistical Tests of Hypotheses
The statistical decision-making process can be put on a rational,
systematic basis by considering various statistically basedhypotheses
Null hypothesis Ho: = o
Alternative hypothesis H1: < o
Example: Use to test if mean of sample meets minimum
acceptable value Type I error, it was acceptable but we concluded it was not
Type II error, it was not acceptable but we concluded it was (oops!)
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Daniel GuggenheimSchool ofAerospace Engineering Functions of Random Variables
Functions can be as simple or as complicated as desired
Random variables can be independent or correlated.Some closed form solutions exist for addition andsubtraction of random variables.
For closed form solutions, information on the input
random variables distribution is used to describe thedistribution of the output random variable given certainconditions.
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Daniel GuggenheimSchool ofAerospace Engineering Addition of 2 RVs
Lets start with a simple case.
Uniform Distribution
Y1 = X1 + X2 Assumptions:
X1 ~ U(0,1) X2 ~ U(0,1)
Can you speculate on the distribution of Y1?
Shape
Mean Upper and lower bounds?
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10,000 Monte Carlo runs
Theory
Bounds: [0.0, 2.0]
Mean: 1.0
Empirical data Bounds: [0.01 , 1.9]
Mean: 1.0
Looks like a triangular distribution. Do you understand
mathematically why it makes sense that it is? Monte Carlorandomly selects points from the distribution
and operates on them
Named after casino by Los Alamos scientists in 1947
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Daniel GuggenheimSchool ofAerospace Engineering Addition of 3 RVs
Y2 = X1 + X2 + X3 Assumptions:
X1 ~ U(0,1)
X2 ~ U(0,1)
X3 ~ U(0,1) Can you speculate on the distribution of Y2?
Shape
Mean
Upper and lower bounds?
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Daniel GuggenheimSchool ofAerospace Engineering Addition of 3 RVs
10,000 Monte Carlo runs
Theory
Bounds: [0.0, 3.0]
Mean: 1.5
Empirical data Bounds: [0.12 , 2.88]
Mean: 1.49
No longer looks like a triangular distribution, but its not
quite a normal distribution either. Its simply bell-shaped.
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Daniel GuggenheimSchool ofAerospace Engineering Addition of 5 RVs
Y3 = X1 + X2 + X3 + X4 + X5 Assumptions:
X1 , X2 , X3 , X4 , X5 ~ U(0,1)
Can you speculate on the distribution of Y2?
Shape Mean
Upper and lower bounds?
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10,000 Monte Carlo runs
Theory
Bounds: [0.0, 5.0]
Mean: 2.5
Empirical data Bounds: [0.38 , 4.62]
Mean: 2.49
Looks much more like a normal.
What is happening to the bounds of the empirical datawith respect to the theoretical data?
What would happen with infinity Xi ~ U( 0,1)
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Daniel GuggenheimSchool ofAerospace Engineering Observations
This was a simple case:
Uniform distribution is the simplest one.
Distributions are symmetric
All distributions were equal
Distribution limits (0, 1) make theoretical estimationeasier.
Addition of RVs is very intuitive
Things to vary:
Number of Monte Carlo runs Number AND type of RVs
Ranges, means and other parameters of the RVs
The actual function of the RVs
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Daniel GuggenheimSchool ofAerospace Engineering The Normal Distribution
De Moivre developed the normal distribution as an
approximation to the binomial distribution Used by Laplace in 1783 to study measurement errors
Used by Gauss in 1809 in the analysis of astronomicaldata
Normal distributions have many convenient properties, sorandom variables with unknown distributions are oftenassumed to be normal
Normal distribution is often a good approximation due to
a result known as the Central Limit Theorem Many common attributes such as test scores, height, etc.,
follow roughly normal distributions, with few members atthe high and low ends and many in the middle
D i l G h i
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Daniel GuggenheimSchool ofAerospace Engineering Adding 2 Normal RVs
X1~N(0,1) X2~N(0,1)
Y1 = X1 + X2
= -0.01
= 1.43
2 = 2.04
D i l G h i
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Daniel GuggenheimSchool ofAerospace Engineering Adding 2 Normal RVs
X1~N(0,1) X3~N(3,2)
Y2 = X1 +X3
= 3.01
= 2.26
2 = 5.12
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Daniel GuggenheimSchool ofAerospace Engineering Subtracting 2 Normal RVs
X1~N(0,1) X3~N(3,2)
Y2 = X1 - X3
= -2.96
= 2.23
2 = 4.96
Daniel Guggenheim
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Daniel GuggenheimSchool ofAerospace Engineering More Complex Functions
Daniel Guggenheim
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Daniel GuggenheimSchool ofAerospace Engineering More Complex Functions
Y1 = X1 + X2 + X3 + X4 + X5 Entire range is from -1.22 to 15.21
Mean = 6.26 Std. Dev.= 2.12 Kurt. = 3.13
Daniel Guggenheim
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Continuous Risk Management
Daniel Guggenheim
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Daniel GuggenheimSchool ofAerospace Engineering Continuous Risk Management Process
Make Decisions Under Uncertainty at Every Step in Process!
Daniel Guggenheim
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Daniel GuggenheimSchool ofAerospace Engineering Continuous Risk Management Process
Make Decisions Under Uncertainty at Every Step in Process!
Daniel Guggenheim
Risk Identification Decisions
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ggSchool ofAerospace Engineering
Risk Identification Decisions:Knowing the Unknown
Ref: EAI-632
As we know, there are known knowns; there are things we know we
know. We also know there are known unknowns; that is to say weknow there are some things we do not know. But there are also
unknown unknowns -- the ones we don't know we don't know."
-- Don Rumsfeld
Daniel Guggenheim
Risk Identification Decisions:
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Risk Identification Decisions:Identify Risk Early
Early Risk Identification Enables Good Design Decisions.
Daniel Guggenheim
Risk Identification Decisions:
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Study historic sources of performance, safety, cost, and schedule risks
NASA/DoD/Industry lessons learned databases (e.g., LLIS, REDSTAR)
CAIB Report and other event investigations
Systematically parse project/system WBS looking for risk drivers:
New or adapted technology/designs
Significant design challenges due to complexity or high level of integration Harsh or new operational environments
Optimistic design assumptions and inadequate design margins
Inadequate testing
Historic root sources of unreliability for your mission/system (RoSA)
Systematically examine Risk Breakdown Structure from ESMD Risk
Management Plan, Section 4.2.5, for issues, or other check list
Systematically examine mission timeline and major events (e.g., EDL,
rendezvous, deployments) for potential failures (perform PRA).
Risk Identification Decisions:How Do I Identify Risks?
Daniel Guggenheim
Risk Identification Decisions:
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Systematically examine project schedule, comparing allocations to
historic norms (especially software and integrated test and evaluation.
Systematically examine project cost estimates, comparing allocations
to historic norms.
Systematically examine project staffing plan and labor estimates,
comparing allocations to historic norms Systematically examine all margins (e.g., mass, power, Isp), factors of
safety, flight performance reserves, manufacturing tolerances, etc. and
compare to historic norms.
Have manufacturing and operations personnel participate in and
systematically examine all aspects of the system design and conceptof operations to identify potential risks.
Perform concurrent design, create environment that fosters open
communication, and listen to all team members.
Risk Identification Decisions:How Do I Identify Risks?
Daniel Guggenheim
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Early Risk Identification Enables Good Design Decisions.
Daniel GuggenheimS h l f Risk Analysis Decisions:
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Risk Exposure = Probability x Impact
Probabilityof
Occurrenc
e
Impact if Occurs
Low Medium High
Low
Medium
High
Risk Analysis Decisions:How Do I Assess and Prioritize Risks?
How do you decide level of
impacts for prioritization? Impact against what?
> FOMs!
This is multi-attributedecision problem
Level of analysis depends onresources and importance
> From pros/cons to MAUT
> From expert judgment toquantitative analysis
Need integrated systems
analysis capability How do you decide level of
probability for prioritization?
Experience/Databases
Expert Elicitation
QRA/PRA
Daniel GuggenheimS h l f Risk Analysis Decisions:
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Risk Analysis Decisions:Probability Assessment
Probability
Rating
Ordinal
Value
Description
Very Low 1 Qualitative: Very unlikely to occur, management not required in most cases. Strong controls in place.
Quantitative: P< 10-5 (for risks with primary impact on Safety) or P
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Risk Analysis Decisions:Impact Assessment
Daniel GuggenheimSchool of Risk Analysis Decisions:
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Risk Analysis Decisions:Impact Assessment
Daniel GuggenheimSchool of Example: ELP Top Project Risks
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RankIRMAID No. Risk Title (Owner)
RiskType
1 1154 Launch Vehicle Operability (J. Reuter) Perf
2 1118Ability for CLV to Meet PerformanceRequirements (J. Reuter) Perf
3 1128 J2X Development Schedule (J. Snoddy) Sch
4 1113 Requirements Maturation (J. Reuter) Sch
5 1155 Enhanced Flight Termination System (R. Burt)Cost/S
ch
6 1151Human Space Flight Development Summary (A.Priskos) Sch
7 1158 Fault Tolerance Requirements (J. Reuter) Sch
8 1156 Vehicle Controllability (J. Reuter) Perf
9 1159Inability to meet Earth Departure Stage (EDS)loiter time requirements (P. Sumrall) Perf
10 1152Ability of Heritage Hardware to Meet New CLVRequirements (R. Burt)
Cost/Sch
11 1114 Transition Between CLV and SSP (D. Dumbacher)Cost/S
ch
12 1116 Engineering Tools, Models and Processes (N.Otte) Perf
Example: ELP Top Project RisksAugust 10, 2006
Top Directorate Risk (TDR)
Proposed Top Director Risk (P-TDR)
Top Program Risk (TPR)
Proposed Top Program Risk (P-TPR)
Top Project Risk (TProjR)
Proposed Top Project Risk (P-TProjR)
Top Element Risk (TER)
Proposed Top Element Risk (P-TER)
1116
1114
1152
112811131151
11541118
1158
1155
Performance Cost
Schedule Safety
11561159
Likelihood
Consequences
5
4
3
2
1
1 2 3 4 5
812
11
10
4
3,5, 6
1,2
9
7
Daniel GuggenheimSchool of
E l CX IRMA Ri k 1118 S R t
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School ofAerospace Engineering Example: CX IRMA Risk: 1118 Summary Report
Open Date: 7/10/2006 Status as of 10/30/2006 ECD: 1/1/2007
Risk Title: Ability for CLV to Meet Performance Requirements
Escalation Level: TPR
Risk Rank: 2
Owning WBS Element:ARES_I_VEH_INT
Risk Owner: James Reuter
Risk Statement:
Given the history of vehicle and payload growth; there is a possibility that the inability tomaintain the performance and margins needed to meet performance requirements.Children - ARM # 1596, 1563, 1358, 1099, 1606
5 - Likelihood
Consequence(s)
0 - Safety
4 - Performance
0 - Schedule
3 - Cost
Context:
(Imported from ARM Risk 1006) The CLV may not be able to meet mass and
performance requirements. These requirements are not yet well defined, other technicalrequirements may impact this further.
Flights Affected:
Status:
10/4/2006 The Performance Enhancement Team (PET) has began a trades optionanalysis study in order to mitigate this risk. The study results will provide the bestmitigation strategy to buy down this risk.
WBS Element Affected:
CLV
Daniel GuggenheimSchool of
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School ofAerospace Engineering Continuous Risk Management Process
Make Decisions Under Uncertainty at Every Step in Process!
Daniel GuggenheimSchool of Risk Planning Decisions: How Do I
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School ofAerospace Engineering
Examine approaches to reduce BOTH the probability and impact of
identified risk Determine potential impact on any FOMs if risk occurs (e.g. payload)
Systematically look at mitigation options for offsetting effects on FOMs
Systematically examine all other design variables/assumptions, requirements,
or other control parameters that affect FOM (e.g., influence diagram) Must also look at effects of employing options on OTHER FOMs
Select option(s) that offset impact on desired FOM with minimal impact on
other FOMs including risk
This is a multi-attribute decision problem!
Also examine options to reduce probability that risk will occur Systematically examine all events in schedule/mission timeline or other
assumptions that affect probability (e.g., PRA, IMS, influence diagram)
Again, must also look at effects of employing options on ALL FOMs
Scope level of effort/methods to resources and criticality
Risk Planning Decisions: How Do IDecide Best Approach to Mitigate Risks?
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School ofAerospace Engineering
gELO Performance Risk Mitigation
ELO #2 Risk: Ares I Ability to Meet Performance Requirements
Given the history of vehicle and payload growth, the concern is the
inability to maintain the performance and margins needed to meetpayload requirements and affordability goals.
Ares I configuration evolved from 4-Segment, SSME to 5-SegmentBooster, J2-X (January 2006)
Received challenge by Cx to provide additional payload capability
Developed weight allocation challenges to elements to meetperformance requirements while retaining performance margins
LI
Likeliho
od
Consequences
L = Lunar
I = ISS
DAC-1 conducted for SRD requirements feasibility and design maturation Weight allocations not yet met
Payload performance met only by use of performance margin
Performance Enhancement Team (PET) established to reduce or mitigate Ares I
Performance Risk Tasked to identify and evaluate candidate design refinements geared to meet or
exceed payload requirements without significant impact to system safety, cost,schedule, operability
Provide recommendations to the Project to support the DAC-2 configuration decision
Identify threats of successfully meeting technical and programmatic requirements withthe reference configuration
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Aerospace Engineering
gELO Performance Risk Mitigation
Daniel GuggenheimSchool of
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Of Technology
Aerospace Engineering Continuous Risk Management Process
Make Decisions Under Uncertainty at Every Step in Process!
Daniel GuggenheimSchool of Risk Tracking and Control Decisions:
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Aerospace Engineering
Continue risk identification, assessment/prioritization, and mitigation
planning process Continuous Risk Management Keep and update prioritized list and assess progress at reducing top
risks on regular basis in database (e.g., ARM)
Decide to reduce or increase risk exposure score as necessary using
processes discussed above Continually track progress at meeting requirements through allocated/
decomposed TPMs and integrated/validated systems analysis tools
Continually track readiness of critical technologies through TPMs
Use systems engineering database (e.g. Cradle) to link risks to TPMs
and requirements
Use logic-linked integrated master schedule
Use earned value management system to evaluate cost vs. budget
Use expert elicitation approaches to evaluate progress (see below)
Risk Tracking and Control Decisions:How Do I Track and Control Risks?
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Aerospace EngineeringRisk Tracking and Control Decisions:
Sample Technical Performance Measures
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Aerospace EngineeringRisk Tracking and Control Decisions:
Sample TPM Tracking Approaches
Daniel GuggenheimSchool ofA E i i
Risk Tracking and Control Decisions:
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Aerospace Engineering
Technical Approach Overview for Technology Project
Tech AssessmentGuidelinesFrom theProgram
TechnologyProject DataReleased toExpert Team
TechnologyProject Personnel
Input toTeam Discussions
ReportResults
DefineRisk Assessment
Process andProvide SW Tool
Form IndependentExpert RiskAssessment
Teams
Establish RiskAssessmentCriteria andCollect Data
Expert TeamReaffirmsTPMs &
Reviews/DiscussesAvailable Data
Expert Team ProvidesCollaborative
Risk AssessmentUsing ITRACS
Process Expert Input Expert Team Reviews
DataITRACSInternet AccessibleSoftware
TeleconferencingSystem
Expert Elicitation Process (UAH/SAIC)
Daniel GuggenheimSchool ofA E i i
Risk Tracking and Control Decisions:
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Aerospace Engineering
Mechanics of the Process
Select Technology to be assessed Select Technical or Programmatic Risk Metric to be assessed
Assessing the selected TPM or Programmatic Risk Metric:Given the available information and data on the Technology Development, and considering allthe risk assessment criteria, what numerical interval is most likely to contain the outcome tobe achieved for this metric? And what is the relative likelihood of the other potential outcome
intervals compared to the most likely interval?
Metric Interval Most Likely Relative Likelihood
20 to 25 units 5% as likely as 35 to 40
25 to 30 25% as likely as 35 to 40
30 to 35 75% as likely as 35 to 40
35 to 40 100% (most likely interval)
40 to 45 10% as likely as 35 to 40
Expert Elicitation Process (UAH/SAIC)
Daniel GuggenheimSchool ofAerospace Engineering
Risk Tracking and Control Decisions:
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Aerospace Engineering
Mechanics of the Process
Metric Interval Probability Distribution
20 to 25 units .02425 to 30 .09530 to 35 .357
35 to 40 .47640 to 45 .0481.000
ITRACS combines all the individual evaluators inputs to produce anormalized collaborative probability distribution:
The collaborative probability distribution coupled with the metric goal, isused to calculate the estimated risk of not achieving the development goal:
.6
.4
.2
020 25 30 35 40 45
Metric Value
Metric Goal
Risk Area (24%)
< 38
Expert Elicitation Process (UAH/SAIC)
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Risk Tracking and Control Decisions:
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Aerospace Engineering
Expert Elicitation Process (UAH/SAIC)
Perform initial assessment at project start (or during prioritization)
Use process to perform project audits at scheduled review periods
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Risk Tracking and Control Decisions:
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Aerospace Engineering
Probability of Success
Expected Value Mean oraverage value of the
estimated probability
distribution. It is the value
of the metric expected by
the evaluators
Expected Value Deviation
Deviation of the EV from the
goal, calculated as follows:
Absolute Value: EV Goal
Goal
A minus sign in front of the
calculated value indicates that
the EV is worse than the goal.
Assumption: The Low to High range contains
100% of the possible values of the metric.
SAICITRACS
Expert Elicitation Process (UAH/SAIC)
Daniel GuggenheimSchool ofAerospace Engineering
Steps in Expert Elicitation Processes
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Aerospace Engineering
(General)
EPA Expert Elicitation Task Force White Paper January 2009
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Expert Elicitation Processes
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Aerospace Engineering
(Cooke Approach)
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Aerospace Engineering
Uncertainty and Margin in
Design
Daniel GuggenheimSchool ofAerospace Engineering Margin and Risk
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Aerospace Engineering
Judicious use of MARGIN is the most important risk mitigation strategy!
Includes cost, schedule, and performance/weight margins
Use of margin is necessary but not sufficient for risk management
Still need to identify and mitigate root causes of risk
Increasing margin decreases risk, but at the expense of other FOMs
May decrease performance/payload or increase cost at some point Eventually reaches diminishing returns in customer value proposition
Finding the right balance between margin/risk and other FOMs is, once
again, a multi-attribute decision problem
How do I know how much increasing margin decreases risk?
Through probabilistic analysis using historical data regression or expertelicitation coupled with Monte Carlo simulation
How do I know how much increasing margin affects other FOMs?
Through integrated systems analysis capability or expert elicitation
Margin and Risk
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p g g
Weight/Performance Margins and
Uncertainty
Daniel GuggenheimSchool ofAerospace Engineering Uncertainty Risk and Weight
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p g g Uncertainty, Risk, and Weight
Weight is a key control variable during system design/development
Key performance driver (e.g., payload, range)
Can be traded for other FOMs (e.g., higher factors of safety, moreredundancy for reliability, cheaper but heavier materials for cost)
Finding the right balance between weight margin/risk and other FOMsis, once again, a multi-attribute decision problem
Why dont things weigh what I predicted? Inadequate model fidelity and human errors (I forgots)
Weight growth due to integration effects during development
> Brackets, welds, joints, integrated acoustics/vibration/thermal loads
Manufacturing tolerances and constraints on manufacturability
Ground operations requirements not modeled (access panels, space) Uncertainty/inadequate modeling of operational environment
Modifications to balance other FOMs (cost, safety, risk)
Changing requirements
Daniel GuggenheimSchool ofAerospace Engineering Determining Weight Margins
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Determining Weight Margins
How do I mitigate risk of weight growth?
Gather as much relevant historical data as possible to improve models Use high-fidelity models to capture integrated weights and loads
Include manufacturing and operations personnel as an integral part of thedesign team and LISTEN to them
Gather as much data as possible on operational environment
Find the right balance between weight margin/risk and other FOMs upfront through integrated systems analysis
Spend adequate time up front defining requirements and DONT change
THEN, provide adequate weight margins!
How do I decide on adequate weight margins?
Depends on level of analysis/modeling used to derive weight prediction Gather as much relevant historical weight and growth data as possible
Include margin allocations for contingency for non-modeled items, weightgrowth during development, uncertainty in operating environments/loads,manufacturing/operations tolerances, life and factors of safety
Use probabilistic methods to capture historic knowledge
Daniel GuggenheimSchool ofAerospace Engineering
Spacecraft Weight DefinitionSPEC MIL M 38310A
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MAXIMUM
Limit
Nominal
Target
Verified Uncertainty Manufacturing Variation
Allowance for adverseconditions
Criteria Changes
Growth
Contingency
Estimates Parametric StudiesBased on AssumedDesign Criteria
WeightIncrement
SPEC MIL-M-38310A
Daniel GuggenheimSchool ofAerospace Engineering Mass Margin Definition (JPL)
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g ( )
Dry massCurrent Best Estimatetaking into accounteverything known
Spacecraft Dry MassCurrent Best Estimate
Spacecraft Mass Margin
SpacecraftDry Mass Allocation
Propellant(s) Sized for SpacecraftDry mass allocation
SpacecraftWet (Gross) Massallocation
Payload allocationAvailable fro Launch Vehicle
Launch Vehicle Margin (may be zero)
SpacecraftMass(normalized)
Daniel GuggenheimSchool ofAerospace Engineering
Mass Properties Control (JPL)*
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Of Technology*Design, Verification/Validation and Operations Principles for Flight Systems (D-17868), Rev. 2Neil Yarnell, Mar 03, 2003
Ample margins enable risk management- balanced risk management is necessary to enable success.- prudent to have ample mass and power resources to account for and accommodate
uncertainties and expected growth.- ample mass and power resources in conjunction with ample funding resources provide flexibility
to resolve developmental and operational issues, and enable timely,
balanced risk management decisions without having to perform time-consuming trade studiesto micro-manage every kg. and watt.
Preliminary Missions
and Systems Review
Preliminary
DesignReview
Critical
DesignReview
Mass
(kg)
Launch
Mass GrowthMass Allocation
98% Mass Allocation
Mass Current Best Estimate
Mass Margin Requirement- Accommodates
mass growth forknowns and unknowns
30%
20%
10%
2%
Daniel GuggenheimSchool ofAerospace Engineering Mass Properties Control (AIAA)*
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Mass Properties Control (AIAA)
*
Recommended Practice for Mass Properties Control for Satellites, Missiles,and Launch Vehicles, AIAA/ANSI R-020A-1999
Design Maturity
Structure
ThermalControl
Propulsion
Batteries
WireHarness
Mechanisms
Instrumentation
ElectricalCompo
nents
Estimated
(preliminary sketches)18 18 18 20 50 18 50 15
Layout(or major modification of
existing hardware)12 12 12 15 30 12 30 15
Pre-Release Drawings
(or minor modification of
existing hardware)8 8 8 10 25 8 25 10
Released Drawings
(calculated value)4 4 4 5 5 4 5 5
Existing Hardware(acutal mass from another
program)2 2 2 3 3 2 3 3
Actual Mass
(measured flight hardware)0 0 0 0 0 0 0 0
Customer Supplied
Equipment0 0 0 0 0 0 0 0
Percent Mass Growth Allowance
Daniel GuggenheimSchool ofAerospace Engineering
(AIAA Mass Properties Control for Space Systems, 2006)
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G i I i
Daniel GuggenheimSchool ofAerospace Engineering
NASA SpacecraftWeight Growth History
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Of TechnologyWeight Growth History
Pre-Phase A 25-35%
Phase A 25-35%
Phase B 20-30%Phase C 15-25%
NASA Design Margins
for Spacecraft
0 1 2 3 4 5 60.9
1.0
1.1
1.2
1.3
1.4
1.5
Mercury
X-20
Apollo LMX-15
Apollo CSM
Gemini
Time, years
S
tatusWeight/OriginalWeight
0 1 2 3 4 5 60.9
1.0
1.1
1.2
1.3
1.4
1.5
Mercury
X-20
Apollo LMX-15
Apollo CSM
Gemini
Time, years
S
tatusWeight/OriginalWeight
G i I t i t t
Daniel GuggenheimSchool ofAerospace Engineering
Aerospace VehicleWeight Growth History
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Of TechnologyWeight Growth History
1) Apollo CM 20) MGS
2) Apollo LM 21) Peacekeeper3) ASSET 22) PILOT4) Atlas III 23) PRIME5) Atlas V 24) Shuttle ET6) B-9U 25) Shuttle Orbiter7) Classified Program A 26) Skylab8) Classified Program B 27) Titan I9) Classified Program C 28) Titan II SLV10) Classified Program D 29) Titan III B
11) Classified Program E 30) Titan III C12) Classified Program F 31) Titan III D/E13) Classified Program G 32) Titan 34 D14) Classified Program H 33) Titan IV15) Clemintine 34) Viking16) Gemini 35) X-15A-217) H-33 36) X-3318) L-1011 37) X-34
19) Mercury 38) XB-70A
Lowest Weight Growth = 7%Average Weight Growth = 27.5%
Highest Weight Growth = 55%
G i I t i t t
Daniel GuggenheimSchool ofAerospace Engineering
Aerospace VehicleWeight Growth History By Category
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Of TechnologyWeight Growth History By Category
0
10
20
30
40
50
60
CommercialAircraft
Fighters LaunchVehicles
HumanIn-Space
X-Vehicles
C-131F-106DC-10
DC-8DC-9
Concorde F-111
F-102
F-101
Saturn I S-I
Saturn V S-IISaturn V S-IV
STS OrbiterX-37
X-20
XB-70X-33
Apollo LM
Gemini
Apollo CSMMercury
Skylab
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering
Aerospace Vehicle Mass GrowthCumulative Probability Distribution
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Of TechnologyCumulative Probability Distribution
Mass Growth, Percent
0
0.2
0.4
0.6
0.8
1
0 10 20 30 40 50 60
Probability
50% Probability or less -> 28%
60% Probability or less -> 30%
70% Probability or less -> 34%
80% Probability or less -> 39%
< = . 5
95.0%
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Of TechnologyWeight Growth History
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering
Space Shuttle GrowthPhase C/D (1972-1983)
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Wing 0.27Tail 0.14
LH Tank 0.13
LOX Tank 0.13
Body 0.03Gear 0.06
TPS 0.01
Propulsion 0.12Subsystems 0.50
Isp, sec -2.5
Phase C/D (1972-1983)
Percent
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering
Historical Weight EstimatingRelationship (Wing)
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y = 3079.7x0.5544
10000
100000
1 10 100 1000
Weight,
lbs
Wing Weight =30790.554
(1+.20)
Shuttle
H-33, Phase B Shuttle
NAR, Phase B Shuttle
747
C-5
L-1011
737
727-200
707
DC-8-17%
+20%
-
.17
Design Weight*Maneuver Load*Safety Factor*Structural Span
Root Chord
( )
Relationship (Wing)
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering Weight Uncertainty Models
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Normal(0, .113) Trunc(-.4,+inf) Shift=+.015X
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Of TechnologyTriangular Distribution
Triang(-.17, 0, .2)X
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Of TechnologyMonte Carlo Simulation
Triang(-.17, 0, .2)X
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g
Of Technology
Dry Weight = 339Klbs 25% with 90% Confidence
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering Weight Uncertainty Impacts
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g
Of Technology
0%
20%
40%
60%
80%
100%
250000 300000 350000 400000 450000
Dry Weight, lbs
CumulativeProbability
Mean = 340Klbs
95% = 426Klbs
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering
Probabilistic Weight Tracking High Speed Research Program
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g
Of TechnologyHigh Speed Research Program
96 97 98 99 00 01
R
elativeMTOW
Weight
Year
0.8
1.0
1.2
1.4
Assessment Baseline
5-95th PercentileUncertainty Band
Benchmark
Georgia Inst i tu t e
Daniel GuggenheimSchool ofAerospace Engineering Performance Margins Other Than Weight
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Flight Performance Reserves
Specific Impulse Margin Mixture Ratio Bias
Maximum Temperature Margin
Acoustic/Vibration Margins
Maximum Pressure (Yield and Burst) Margin
Maximum Loiter Time
Launch Window/Availability Margin
Flight Control Margins
Power Margins
Delta-V Margins Payload Margin
Tank Ullage Margin
Boundary Layer Transition Margins
Etc.
Georgia Inst i tu t e
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Cost Margins and Uncertainty
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Daniel GuggenheimSchool ofAerospace Engineering Methods of Developing Cost Estimates
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Bottoms-Up Detailed Engineering Build-up
The separate elements are identified in great detail and summedinto the total cost.
Very complex for new systems since costs of development andproduction are unknown
Top-Down Parametric or Statistical Regression analysis is used to establish relations between cost
and initial parameters of the system, e.g. weight, size, speed,power, SLOC, etc.
where xi are the parameters, c and the exponents are determinedby regression of historical data.
Used in conceptual design Analogy
Future costs of a new project are based on costs of old projectswith allowances for cost escalation and complexity differencesbased on simple multiplication factors.
Cost, new = (x * Cost, old)
1 2Exponent Exponent
component 1 2Cost = c x x
Georgia Inst i tu t e
Of T h l
Daniel GuggenheimSchool ofAerospace Engineering
NASA/Air Force Cost Model (NAFCOM)
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Parametric cost model based on122 NASA and Air Force space
flight hardware projects
Launch Vehicles
Robotic Satellites
Human-Rated Spacecraft
Space Shuttle
Recent updates based onbenchmarking activity withcontractors, internal assessment
NAFCOM customers
MSFC, NASA HQ, IPAO, other NASAcenters
NAFCOM is used by over 800 civilservants and government contractors
NASA/Air Force Cost Model (NAFCOM)
Georgia Inst i tu t e
Of T h l
Daniel GuggenheimSchool ofAerospace Engineering NAFCOM CER Complexity Modeling
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Complexity Generator CERs Multi-variable equations based on sophisticated statistical
analysis of the NAFCOM data base
Identified 73 key technical and programmatic cost drivers,such as
> Funding availability
> Risk management> Integration complexity
> Pre-Development study
> New design
> Weight
> Structural efficiency
> Output Power
> Number of Transmitters
> Stabilization type
> Etc.
Georgia Inst i tu t e
Of T h l
Daniel GuggenheimSchool ofAerospace Engineering
Modeling Cost Risk With CERs
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Of Technologyg
$
Cost Driver (Weight)
Cost = a + bXc
Inputvariable
CostEstimate
Historical data point
Cost estimating relationship
Standard percent error boundsTechnical Uncertainty
Combined CostModeling and Technical
Uncertainty
Cost ModelingUncertainty
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Cost Cumulative Distribution
Function (CDF)
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
55 65 75 85 95 105 115 125 135 145 155 165 175 185 195 205 215 225
$M
Cumulativ
eProbability
Function (CDF)
80th
percentile$146M
95th percentile$184M
50% probability of cost coming in at or below $115M45% probability of cost coming in between $115M and $184M
20% probability of cost exceeding $146M5% probability of cost exceeding $184M
Mean
$120M
50th percentile$115M
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Operations Cost Risk Hidden Costs
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Direct (Visible) WorkTip of the Iceberg
Indirect (Hidden)
Support (Hidden)
+
+
Recurr ing Ops $$s
Direct (Most Visible) Work Drives Massive(and Least Visible) Technical &
Administrative Support Infrastructure Example: Direct Unplanned Repair Activity
Drives Ops Support Infra, Logistics,Sustaining Engineering, SR&QA and FlightCertification
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Life Cycle Cost Gets Locked In Earlyusing only Systems Engineering Decomposition
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Requirements Cost Risk
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Requirements Cost/Program Cost, percent
0 5 10 15 20
200
160
80
40
0
120
GRO78OMV
TDRSSIRAS
Gali
TETH
EDO (recent start)
ERB77
HST
LAND76
COBESTSLAND78
GRO82ERB80
VOYAGER HEAO
GOES I-MCEN
MARSACTS
CHA.REC
SEASAT UARS
DE
SMM PIONVEN
Ulysses
IUE
ISEE
EUVE/EP
PAY NOWOR
PAY LATER
Targ
etCostOverrun,
Percent
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Requirements
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Schedule Margins and Uncertainty
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Uncertainty, Risk, and Schedule
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Why is it never on time?
I forgots Unaccounted-for interdependencies and temporal linkages
Test failures
Hardware/software integration
Requirements changes
Programmatic, organizational, and funding issues How do I reduce schedule uncertainty and risk?
Gather as much relevant historical relevant schedule data as possibleand use to anchor bottoms-up predictions
Include integration, test, manufacturing and operations personnel in
schedule development and LISTEN to them Use logic-linked, integrated master schedule software (e.g., Primavera)
Focus on critical path and top events that could get on critical path
Perform probabilistic analysis using historical data or expert elicitation
Provide adequate schedule margin based on probabilistic data
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
NASA Program Schedule DurationsFrom Red Star Database
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gy
0 20 40 60 80 100 120 140
Mars Exploration Rover
Gemini - Manned
Skylab Workshop - Manned
Centaur-G' - Launch Vehicle
Voyager - Unmanned
Viking Lander - Planetary
Magellan - Planetary
Viking Orbiter - Unmanned
Apollo LM - MannedS-IVB - Launch Vehicle
Apollo CSM - Manned
Mars Observer - Unmanned
Skylab Airlock - Manned
S-II - Launch Vehicle
External TankShuttle Orbiter - Manned
Spacelab - Manned
Months
PDRCDR
DDTE
PDRCDRFirst Flight
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
NASA Programs/Projects DurationProbability Distribution
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gy
InvGauss(5.4174, 18.7886) Shift=+0.88261
0.00
0.05
0.10
0.15
0.20
0.25
2 4 6 810
12
14
< >5.0%90.0%
2.98 11.91
All ProgramsMean = 6.3 years
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
NASA Programs/Projects DurationProbability Distributions
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gy
ExtValue(8.1224, 1.7630)
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
5 6 7 8 910
11
12
13
< >5.0% 90.0%6.188 13.359
Gamma(1.9244, 1.5663) Shift=+2.3128
0.00
0.05
0.10
0.15
0.20
0.25
0.30
2 4 6 810
12
14
>5.0%90.0%2.82 9.55
Manned ProgramsMean = 9.1 years
Unmanned ProgramsMean = 5.3 years
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Perform Probabilistic Critical PathAnalysis on Logic-Linked IMS
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Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Cost and Schedule Interactions
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Program decision makers need understanding of how uncertainties in
costs and schedule interact Might choose a high risk schedule to meet a hard cost target
Might be willing to have higher costs to ensure meeting a launch date
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Capturing Cost andSchedule Uncertainties
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Difference between conditional median cost of ($107.8M) given a
schedule of 53 months and conditional median cost ($87.4M) given ahigh-risk schedule of 43 months is over $20M
This could be very significant to a decision maker who wished totrade cost, schedule, and risk
Use joint probability models to analyze cost-schedule interactions
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
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Technology Risk Mitigation
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Technology Risk Mitigation Approach
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Select Technologies that are Evolutionary -- Not Revolutionary
No Major Breakthroughs Required
Define Low-Risk Back-Ups for Each Technology Project
Includes Fall-Back and Fall-Up Positions (e.g., RLV Composite Tanks)
Mature Key Technologies to TRL Levels 6 or 7 Before ATP Decision
Define TPMs for Each Technology Task and Use to Track Progress
Conduct Risk/Progress Evaluations at Major Technology DevelopmentMilestone Reviews
Track Technology Development Progress Through Changes to TPMs
Evaluate Impact of Technology Progress on System Requirements
Evaluate Technology Development Risk and Take Corrective Actions
> Develop Detailed Risk Mitigation Plans> Introduce Back-up Technologies/Approaches as Needed
> Reallocate Funding as Required
Tools Exist to Facilitate the Process (e.g., Active Risk Manager)
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
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Risk Leveling
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Risk Leveling
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How much should we try to reduce a given risk? How safe is safeenough? Is a human life priceless?
In design, one requirement/constraint should not be a significantlylarger driver than others
Question requirementshow cost/benefit of relaxing requirement toDecision Maker
Good design has multiple simultaneous driving requirements/constraints
Process of requirements leveling
Same is true in risk analysismust perform risk leveling
Dont let one or two risk sources dominate or go unaddressed
Dont spend scarce resources trying to reduce one risk to a lower level ororder-of-magnitude than others
Make it safe enough and no safer
How do we know when the risks are leveled
Must have integrated systems analysis capability to model and asses risks
Can perform probabilistic risk analysis (PRA)
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
ESAS Mission ModeLoss of Crew FOM Results
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Sources of Loss of Crew Risk forESAS Lunar Mission
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Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
Radiation Shielding Design ApproachPrior to ESAS
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0
0.25
0.5
0.75
1
0 5 10 15Added Poly Shield Amount, g/cm^2
Gy-Eq(BFO)
4 X Aug 72 4 X Sep 89 Current LEO Limit
0
0.25
0.5
0.75
1
0 5 10 15Added Poly Shield Amount, g/cm^2
Gy-Eq(BFO)
4 X Aug 72 4 X Sep 89 Current LEO Limit
Based onGraphite (60%)-Epoxy (40%)Vehicle of samemass
Organ Dose 4Xs 1972-SPE Aluminum LSAM LSAM + Poly 5g/cm2
Skin (Gy-Eq) 5.49 5.78 4.05 0.86 0.91 0.67
Eye (Gy-Eq) 4.79 5.05 3.56 0.83 0.88 0.65
BFO (Gy-Eq) 0.86 0.91 0.67 0.24 0.26 0.20
Effective Dose (Sv) 1.08 1.14 0.84 0.28 0.29 0.23
Organ Dose 4Xs 1989-SPE Aluminum LSAM LSAM + Poly 5g/cm2
Skin (Gy-Eq) 0.91 0.96 0.72 0.29 0.31 0.26
Eye (Gy-Eq) 0.82 0.86 0.66 0.29 0.31 0.26
BFO (Gy-Eq) 0.29 0.30 0.26 0.17 0.18 0.16
Effective Dose (Sv) 0.30 0.32 0.26 0.17 0.18 0.16
Organ Dose 4Xs 1972-SPE Composite LSAM LSAM + Poly 5g/cm2
Skin (Gy-Eq) 4.23 4.45 3.09 0.74 0.78 0.56
Eye (Gy-Eq) 3.81 4.01 2.80 0.72 0.76 0.55
BFO (Gy-Eq) 0.73 0.77 0.56 0.21 0.23 0.17
Effective Dose (Sv) 0.90 0.95 0.69 0.25 0.26 0.20
Organ Dose 4Xs 1989-SPE Composite LSAM LSAM + Poly 5 g/cm2
Skin (Gy-Eq) 0.73 0.77 0.58 0.27 0.28 0.24
Eye (Gy-Eq) 0.68 0.72 0.55 0.27 0.28 0.24
BFO (Gy-Eq) 0.27 0.28 0.23 0.16 0.17 0.15
Effective Dose (Sv) 0.27 0.29 0.24 0.16 0.17 0.15
Based onAluminumVehicle
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
ESAS Analysis Cycle 2Radiation Risk Assessment
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Solar particle event design environmentsbasis and considerations: 99% event for mortality risks (acute or
chronic risks) The August 72 event is generallyaccepted as the benchmark solarparticle event in measurable history.
Ones confidence of not exceeding the72 event fluence level above 30 MeVon a one year mission near the solarmaximum is about 97%.
To achieve 99.5% confidence levelabove 30 MeV one must assume afluence of 4 times the August 72event.
Radiation limits outside LEO do not currentlyexist - being developed by NCRP and theCHMO
LEO Career Limit Probability of 3% additional risk of lifetimelethal cancer within a 95% confidence interval
LEO Blood-Forming Organs (BFO) Short-term Limits 30-day limit - 25 cGy-Eq Annual limit 50 cGy-Eq
N x 1972 Event
2 4
%RiskofFatalCancer
0
4
8
12
16
20
EX CEV baseline
CEV with 5 g/cm2
poly shield
Risk Limit
Females 45-yr (no prior missions)
Polyethylene Augmentation Shield, g/cm2
0 2 4 6 8 10
%RiskofFatalCanc
er
0
4
8
12
16
20
Female 45-yr
Risk Limit
4x1972 Event for EX-CEV Design
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
ESAS Analysis Cycle 2CEV Acute and Late Risks
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Estimated probability of an SPE that could cause debilitation+ (1.5X Aug 1972event)
Estimated probability of catastrophic event (4X Aug 1972 event)
Recommend maximum of 2 g/cm2 CEV shielding based upon risk leveling for a 16day maximum mission (0.04 year), 0.005 P (exceeding 72 levels), and riskprobabilities given in table below
HDPE Depth (g/cm2) % Acute Death* % Sickness % REID**
0 9.5 54.0 9.1 [3.2,17.3]
2 (0.02) (2.9) 3.8 [1.3,10.5]
5 0 0 1.5 [0.5,4.3]
HDPE Depth (g/cm2) % Acute Death* % Sickness % REID**
0 3.0 34.4 7.6 [2.7,16.7]
2 (0.01) (1.9) 3.4 [1.2,9.6]
5 0 0 1.4 [0.4,3.9]
Aluminum Vehicle, 4X 1972 SPE
Graphite-Epoxy Vehicle, 4X 1972 SPE
Death at 60-days with minimal medical treatment** Risk of Cancer death for 45-yr Females
+Debilitating event identified as dose that would cause vomiting within 2 days in 50% of total population
(99.5% confidence of not exceeding the 72 event fluence levelabove 30 MeV on a one year mission near the solar maximum)
(99.5% confidence of not exceeding the 72 event fluence levelabove 30 MeV on a one year mission near the solar maximum)
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
ESAS Analysis Cycle 3 SPE Risks versusProbability of SPE Occurrence in a 9-day Mission
2
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Nx1972
Event F(>30 MeV)
%Probability for
9 day mission Acute Death Acute Sickness
Career Limit
Violation
30-Day Limit
Violation
4X 2x10
10
0.02
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70.2
66.5
23.4
21.0
60
62
64
66
68
72
0 1 2 3 4 5
CEV Supplemental Radiation Protection (g/cm2)
TLIInjectedMass(t)
20
21
22
23
24
25
26
27
28
29
30
CEVMass(t)
CEV Mas s
I n jec ted Mass
RECOMMENDATION:
Eliminate supplementalradiation shielding
70 Injected Mass Sensitivity:~740 kg per g/cm2
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering
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Probabilistic Risk
Assessment
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Probabilistic Risk Assessment
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The human mind cannot grasp the causes of phenomena in
the aggregate. But the need to find these causes is inherent inmans soul. And the human intellect, without investigating the
multiplicity and complexity of the conditions of phenomena,
any one of which taken separately may seem to be the cause,
snatches at the first, the most intelligible approximation to acause, and says: This is the cause!
Leo Tolstoy,
War and Peace
Scenario Development is used in risk analysis to facilitate thesystematic search for the causes of risk.
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Probabilistic Risk Assessment (PRA)
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PRA provides thorough, quantitative scenario-based approach toassessing the probability that a risk will occur and its consequences
Used on Space Shuttle (Fragola) and ISS (Futron) Flow between process steps
Master Logic Diagram> Identifies how hazards are controlled
Functional Event Sequence Diagram
> Shows how the system responds to off normal events Event Trees
> Inductively models that represent the way pivotal events can combine inresponse to specific initiating events
Fault Trees> Deductive models that generate logical combinations of failures that can
cause a specific high level pivotal event Each technique addresses a part of the risk assessment problem
In combination, allow analyst to accurately and completely representsmajority of risk
Joseph Fragola NIA Risk-Based Design Short Course
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Joseph Fragola NIA Risk-Based Design Short Course
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Daniel GuggenheimSchool ofAerospace Engineering Probabilistic Risk Assessment
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High Mixture RatioNot Detected
Failure in Channel A
Erroneous Signal inChannel A
Harness Failure inChannel A
Logic Control Failurein Channel A
Failure in Channel B
Erroneous Signal inChannel B
Harness Failure inChannel B
Logic Control Failurein Channel B
Loss-of-Vehicle
LOV due toOrbiter Failure
LOV due toSolid Rocket
Booster Failure
LOV due toMain Engine
Failure
Loss ofContainment
Loss ofPropulsion
Loss of
Hydrogen Flow
High MixtureRatio in the
Fuel Preburner
Loss ofPressure in the
MCC
Loss of GrossHydrogen
Flow
Lower flowratetriggers active
computer controlsequence
Controller IncreasesOxidizer Flow to Fuel
PreburnerYes Yes
High Mixture RatioDetected
High Mixture Ratio inthe Fuel Preburner
No
High Mixture Ratio inthe Both Preburners
Yes S/D
No
LOV
Master Logic Diagram
Functional Event Sequence Diagram
Event Tree
Fault TreeJoseph Fragola NIA Risk-Based Design Short Course
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Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Probabilistic Risk Assessment
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Steps in PRA Process
Joseph Fragola NIA Risk-Based Design Short Course
Georgia Inst i tu t e
Of Technology
Daniel GuggenheimSchool ofAerospace Engineering Probabilistic Risk Assessment
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Where does data come from?