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---
name: nasa-pra
description: "Knowledge base from the NASA Probabilistic Risk Assessment Procedures Guide (NASA/SP-2011-3421, 2nd ed.). Use for quantitative PRA of aerospace and safety-critical systems: the risk triplet and scenario logic stack (MLD, ESD, event trees, fault trees, minimal cut sets), Bayesian data collection and parameter estimation, aleatory/epistemic uncertainty modeling and common-cause failure (Alpha Factor, beta-factor, CCBE), human reliability analysis (THERP, CREAM, NARA, SPAR-H), context-based software risk (CSRM), physics-based and structural/phenomenological models (stress-strength, limit states, FORM/SORM, NASGRO, range safety), uncertainty propagation and importance measures (F-V, RAW, Birnbaum, DIM), and launch-abort modeling with worked PRA examples. This is the QUANTITATIVE engine beneath NASA's risk doctrine — for the qualitative RIDM/CRM decision framework use the nasa-risk pack. Thin on programme management, organisational risk governance, and non-aerospace regulatory contexts; aerospace-focused and anchored to the 2011 second edition."
---

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# NASA PRA Procedures Guide (NASA/SP-2011-3421, 2nd ed.)
**Source**: NASA (US Government work, public domain) | **Chapters**: 8

## How to Use This Skill
- **Without arguments** — load the core frameworks below; they are the working toolkit, not a summary.
- **With a topic** — ask about fault trees, Bayesian updating, common-cause failure, human error probabilities, software risk, stress-strength models, uncertainty propagation, importance measures, or launch abort; the relevant chapter is read on demand.
- **With a chapter** — ask for `ch03` (data + Bayesian estimation) or `ch08` (launch abort + worked examples).
- **Supporting files** — `glossary.md` (terms), `patterns.md` (named techniques with trade-offs), `cheatsheet.md` (selection tables, tells & smells).

**Prerequisites:** none — plain Markdown; no MCP server, API key, or licence tier needed at runtime.

## Core Frameworks & Mental Models

### The Risk Triplet (Kaplan & Garrick)
PRA represents risk as a set of triplets — **scenario, likelihood, consequence** — answering *what can go wrong, how likely is it, what are the consequences*, each with uncertainty made explicit. A single expected-value summary is rejected because prevention requires understanding the *elements* of each adverse scenario. A scenario's frequency is the initiating-event frequency times the conditional probability of reaching its end state.

### The Scenario Logic Stack
Risk is decomposed through a chain of graphical Boolean models:
- **Master Logic Diagram (MLD)** — deductively generates a comprehensive initiating-event list (terminating at *pinch points*), supported by FMEA reasoning.
- **Event Sequence Diagram (ESD)** — inductive, success-oriented flowchart, one per initiator, mapping paths to end states; communication-friendly, built first.
- **Event Tree (ET)** — the quantitative tree whose mutually exclusive paths are scenarios; the conventional "down" branch is failure.
- **Fault Tree (FT)** — deductive decomposition of a top event (the Boolean *complement* of a system success criterion) through AND/OR gates to basic events. Shared basic events across pivotal-event fault trees are how conditional dependencies are captured.
- **Minimal Cut Sets (MCSs)** — the irreducible joint-failure combinations; the target of Boolean reduction and the basis for quantification. Prevent every MCS, and prevent each MCS by defeating any one member.

### Two Layers of Uncertainty
**Aleatory** = irreducible stochastic variability of observables (the "model of the world"). **Epistemic** = reducible limit of knowledge about parameters and assumptions. They are kept separate. Epistemic uncertainty is moved by **Bayesian updating** (prior + likelihood → posterior) and propagated to the risk metric, typically by sampling. Results are a *fan of percentile curves*, not a single line — the width of the fan is the epistemic uncertainty.

### Bayesian Estimation as the Default Engine
Parameters (failure rates, demand probabilities, initiator frequencies, HEPs, software conditional probabilities) are estimated by fitting a prior and updating with evidence through a likelihood matched to the data-generating process: **Poisson** for time-based counts, **Binomial** for demand-based counts, **lognormal** for handbook/expert values. **Conjugate pairs** (Gamma–Poisson, Beta–Binomial) give closed-form posteriors; mismatches need numerical or MCMC solutions. Sequential updates compound (today's posterior is tomorrow's prior) and are order-independent.

### Dependence: Common-Cause Failure
Ignoring positive dependence underestimates risk. A CCF needs a **root cause** plus a **coupling factor** (shared design, hardware, function, people, procedures, location, environment). Treatment is two-phase: conservative qualitative + quantitative screening, then detailed analysis with **common-cause basic events (CCBEs)** expanded into the fault trees and parameters from the recommended **Alpha Factor** model (beta-factor and Basic Parameter are simpler precursors). The trick is to make the dependence an explicit basic event, after which ordinary independent-event math applies.

### Humans and Software as First-Class Contributors
**HRA** turns human-system interactions into basic events with **human error probabilities** and uncertainty bounds, via a five-step process and method selection (THERP, CREAM, NARA, SPAR-H; time-critical actions via a Time Response Curve). **Software risk is design risk** — compliant code responding wrongly to an unanticipated condition. The **Context-based Software Risk Model (CSRM)** splits software failure into unconditional/routine (failure-rate-quantifiable) and conditional/triggered (failure-on-demand-quantifiable) contributions, marks each recoverable or critical, and risk-informs testing.

### Physics-Governed Branch Points
Many branch outcomes are set by physics, not random failures. The unifying device is the **stress-strength formulation**: the probability that capability exceeds demand, both random variables, via a **limit-state function** (failure when g ≤ 0). Solve analytically for normal/lognormal cases, otherwise by Monte Carlo / LHS / AIS / FORM / SORM / AMV. Range-safety models (inert debris, blast, ray-focusing, plume) translate accidents into public consequence, judged by **Casualty Expectation** against EWR 127-1.

### Results, Importance, and the Safety Case
Uncertainty is propagated (LHS preferred over crude Monte Carlo) with a convergence procedure. **Importance measures** rank what drives the risk: F-V/RRW (where improvement pays off), Birnbaum (sensitivity), RAW (worst-case failure), DIM (parameters, additive). Because complex-system risk cannot be *proven*, credibility rests on completeness, scrutability, peer review, and a **Risk-Informed Safety Case** of evidence plus operating commitments.

### The Graded Approach
Match modeling depth to the stakes and life-cycle phase. Start parametric and coarse with honest uncertainty ranges; add fidelity only where existing models become insufficient or where uncertainty exposes a risk-driving design weakness. A coarse model with honest uncertainty beats a detailed one built on placeholder data.

## Chapter Index

| # | Title | Key content |
|---|---|---|
| [ch01](chapters/ch01-nasa-pra-foundations-risk-management-and-pra-framework.md) | Foundations — Risk Management and the PRA Framework | Risk triplet; RIDM/CRM context; scenario logic chain; minimal cut sets; aleatory vs. epistemic; RISC; graded approach |
| [ch02](chapters/ch02-nasa-pra-scenario-development-and-logic-modeling.md) | Scenario Development and Logic Modeling | MLD, ESD, event trees, fault trees; success criteria; initiating events; linking; tree-style selection; reduction to MCS |
| [ch03](chapters/ch03-nasa-pra-data-collection-and-bayesian-parameter-estimation.md) | Data Collection and Bayesian Parameter Estimation | Two-phase database process; failure taxonomy; priors/likelihoods/posteriors; conjugacy; sequential updating; population variability |
| [ch04](chapters/ch04-nasa-pra-uncertainty-modeling-and-common-cause-failures.md) | Uncertainty Modeling and Common Cause Failures | Model of the world; epistemic models; lognormal/gamma/beta; beta-factor, Basic Parameter, Alpha Factor; CCBE expansion; Impact Vector |
| [ch05](chapters/ch05-nasa-pra-human-reliability-and-software-risk-assessment.md) | Human Reliability and Software Risk Assessment | HRA five-step; THERP, CREAM, NARA, SPAR-H; HEP and PSFs; time-reliability; CSRM; DFM; software reliability growth |
| [ch06](chapters/ch06-nasa-pra-physical-structural-and-phenomenological-models.md) | Physical, Structural, and Phenomenological Models | Phenomenological modeling; stress-strength; range safety (LARA, blast, re-entry); MMOD; fire PRA; limit states; FORM/SORM; NASGRO/PFM |
| [ch07](chapters/ch07-nasa-pra-uncertainty-propagation-and-results-presentation.md) | Uncertainty Propagation and Results Presentation | Two-step propagation; crude Monte Carlo vs. LHS; convergence; epistemic dependency; importance measures; exceedance curves |
| [ch08](chapters/ch08-nasa-pra-launch-abort-modeling-and-worked-pra-examples.md) | Launch Abort Modeling and Worked PRA Examples | Abort assessment chain; manifestation/environment; triggers; blast P-I; GoldSim integration; Appendices A–E (Boolean algebra, hardware models, conjugate updating, worked PRAs) |

## Topic Index
- **Risk triplet** → ch01, ch02
- **Scenario development** → ch02
- **Initiating events** → ch02, ch08
- **Master Logic Diagram** → ch02, ch08
- **Event sequence diagram** → ch02, ch08
- **Event tree** → ch02, ch08
- **Fault tree** → ch01, ch02
- **Minimal cut sets** → ch02, ch08
- **Success criteria** → ch02, ch08
- **Linking** → ch02
- **Data collection** → ch03
- **Bayesian parameter estimation** → ch03
- **Prior and posterior** → ch03, ch04, ch08
- **Likelihood** → ch03, ch04
- **Conjugate priors** → ch03, ch04, ch08
- **Sequential updating** → ch03
- **Failure taxonomy** → ch03
- **Population variability** → ch03
- **Aleatory uncertainty** → ch04, ch07
- **Epistemic uncertainty** → ch04, ch07
- **Model of the world** → ch04
- **Lognormal distribution** → ch04, ch07
- **Common cause failure** → ch04
- **Alpha factor model** → ch04
- **Beta factor model** → ch04, ch08
- **Coupling factor** → ch04
- **Human reliability analysis** → ch05
- **Human error probability** → ch05
- **Performance shaping factors** → ch05
- **THERP** → ch05
- **CREAM** → ch05
- **SPAR-H** → ch05
- **Software risk** → ch05
- **CSRM** → ch05
- **Conditional software failure** → ch05
- **Phenomenological models** → ch06
- **Stress strength** → ch06
- **Limit state** → ch06
- **Range safety** → ch06
- **Casualty expectation** → ch06
- **MMOD** → ch06
- **Fire PRA** → ch06
- **NASGRO fracture mechanics** → ch06
- **FORM and SORM** → ch06
- **Uncertainty propagation** → ch07
- **Latin hypercube sampling** → ch07
- **Monte Carlo** → ch07, ch08
- **Convergence** → ch07
- **Epistemic dependency** → ch07
- **Importance measures** → ch07
- **Fussell-Vesely** → ch07
- **Risk achievement worth** → ch07
- **Birnbaum measure** → ch07
- **Differential importance measure** → ch07
- **Exceedance curve** → ch07
- **Launch abort** → ch08
- **Abort effectiveness** → ch08
- **Failure manifestation** → ch08
- **Warning time** → ch08
- **Blast overpressure** → ch08
- **Pressure impulse curve** → ch08
- **Structure function** → ch08
- **Worked PRA examples** → ch08

## Supporting Files
- [glossary.md](glossary.md) — ~50 terms with chapter references, from Aleatory Uncertainty to Warning Time
- [patterns.md](patterns.md) — 14 named patterns with When/How/Trade-offs, covering the scenario logic stack to importance-driven resource allocation
- [cheatsheet.md](cheatsheet.md) — PRA task flow, model-style and HRA-method selection tables, likelihood-matching, importance-measure guide, and tells & smells

---

## Scope & Limits
This skill covers the *Probabilistic Risk Assessment Procedures Guide for NASA Managers and Practitioners* (NASA/SP-2011-3421, Second Edition, December 2011), a US Government public-domain work. It is the **quantitative** counterpart to NASA's risk doctrine: it supplies the scenario logic, data-and-Bayesian estimation, uncertainty and dependence modeling, human and software risk, physics-based models, and abort modeling that put defensible, uncertainty-bearing numbers on risk. It is a *guide of recommended procedures*, not an exhaustive textbook, so individual methods (fault-tree construction, Bayesian inference, CCF) are pointed to companion references — the *Fault Tree Handbook with Aerospace Applications* (2002), *Bayesian Inference for NASA Risk and Reliability Analysis* (NASA/SP-2009-569), NUREG/CR-4780 (CCF), the *NASA System Safety Handbook* (NASA/SP-2010-580), and the *NASA Risk Management Handbook* (NASA/SP-2011-3422, the **nasa-risk** pack). It does **not** cover the qualitative RIDM/CRM decision and governance framework in depth (see nasa-risk), programme-management or organisational-risk requirements, or non-aerospace regulatory contexts; its examples are space-flight-centric (launch vehicles, crewed missions, range safety, lunar/planetary missions) and anchored to the 2011 second edition.