Improving the Reliability of Decision-Support Systems for Nuclear Emergency Management Using Software Diversity Methods
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1 Improving the Reliability of Decision-Support Systems for Nuclear Emergency Management Using Software Diversity Methods Tudor B. Ionescu, Eckart Laurien, Walter Scheuermann Institute of Nuclear Technology and Energy Systems Stuttgart, Germany 16th International Conference on Harmonisation within Atmospheric Dispersion Modelling for Regulatory Purposes 8-11 September 2014, Varna, Bulgaria
2 Taking Counter-Measures in Nuclear Emergencies Resources are limited Time is critical Emission data is scarce... (unknown factors) Responsibility of BW government: 100 km (ca km 2, 100Ks of people) Can decision-makers trust the results of a simulationbased DSNE system in emergency situations? Fukushima: SPEEDI system was not used effectively Source: Japanese Diet s Report on Fukushima
3 Motivation NASA-STD B standard for safety-critical software, paragraph a: As soon as the software processes data or analyzes trends that lead directly to safety decisions it must be considered safety critical DSNE systems are safety-critical From a Software Engineering point of view Complexity is the main cause of software faults Model refinements introduce more complexity into the simulation codes (Example: from 2D to 3D) Codes contain an unknown number of software faults How to improving the reliability of dispersion codes? How to increasing the trustworthiness of dispersion simulation results?
4 Software Reliability Engineering (SRE) Single version software reliability methods Fault prevention Coding standards, software metrics Fault removal Unit and integration testing Initial verification of codes Fault prevention & removal Multi-version software reliability methods Error detection and confinement Acceptance tests Recovery Blocks Continuous verification of codes Continuous Verification
5 a.) Recovery Blocks and b.) N-Version Programming Rationale: software build differently is more likely to fail differently. Adjudicator (or voter)
6 From Models to Simulation Codes Gaussian Plume Model Lagrange Particle Model Puff Models Simulation code C 1 Simulation code C 2 Simulation code C 3 Input data and parameters: [P, d P ] Results C 1 [R 1, d R ] Results C 2 [R 2, d R ] Results C 3 [R 3, d R ]
![2 Simulation code C 3 Input data and parameters: [P, d P ]](/docs-images/43/1072918/images/page_6.jpg "Results C 1 [R 1, d R ] Results C 2 [R 2, d R ] Results C 3 [R")
7 Disperison codes and N-Version Programming Example: RODOS (Real-time Online Decision Support System for nuclear emergency management) ATSTEP (Karlsruhe Institute of Technology) Puff model with time-integrated elongated puffs DIPCOT (National Centre for Scientific Research Demokritos, Greece) Lagrange particle model RIMPUFF (Risø DTU National Laboratory, Denmark) Puff model Prerequisites for N-Version programming: Developed by completely independent teams Attempt to solve the transport equation numerically (same software requirements)
8 Comparing Dispersion Simulation Results Problem: How to compare individual results? Inherent model differences Continuous mathematical space Idea: Compare taxonomies of results instead of individual results Discrete mathematical space RIMPUFF DIPCOT
9 Reference Input Case Ensemble 24 input cases with varying parameters Wind speed (1 8 m/s) and direction (0 360 ) Diffusion (A-F) and release category (F1 F8) KKP-2 Diffusion category Wind direction Release category Wind speed
10 A Voter for Dispersion Simulation Results The transport equation is a deterministic model (monotonicity, distance preservation) C k : [P, d p ] [R k, d R ] Isometric map (distance preserving function) Taxonomies of results will be identical if and only if codes correctly implement models
![] [R k, d R ] Isometric map (distance preserving function) Taxonomies](/docs-images/43/1072918/images/page_10.jpg "of results will be identical if and only if codes correctly implement")
11 A Metric for Dispersion Simulation Results Dispersion simulation results = distributions of continuous variables in a finite space Residual sum of squares: j Observed values (Results of C 1 ) j Estimated values (Results of C 2 ) i i r 1 (t) r 2 (t) Generalized RSS: d R (r 1,r 2 ) = 0 < p 1 p < 0.5 concentration of emitted substance is dominant p 0.5 wind speed and direction are dominant
12 Building and Comparing Taxonomies of Results 1. Run simulations on reference input case ensemble using different simulation codes (e.g., ATSTEP, DIPCOT, RIMPUFF) 2. Compute distance matrix using GRSS on dispersion simulation results 3. Hierarchical clustering (centroid method) taxonomies of results 4. Compute the topological distance between taxonomic trees: RF(T 1,T 2 ) max RF(N) = 2N-6 max RF(24) = 42 (Lagrange model) T 1 T 2 (Puff model) 24x24 distance matrix computed using the GRSS metric for the gamma submersion dose results from RIMPUFF
13 A Taxonomy-Based Voter for Dispersion Simulation Results Simulation code ATSTEP_v1(v2) Simulation code DIPCOT_v1(v2) Simulation code RIMPUFF_v1(v2) Input data and parameters: A270B180 A270B180 A270B180 A270B180 Strict Consensus
14 A Taxonomy-Based Voter for Dispersion Simulation Results Simulation code ATSTEP_v1(v2) Simulation code DIPCOT_v1(v2) Simulation code RIMPUFF_v1(v2) Input data and parameters: A270B180 A270B180 A270B180 A270B180 Majority Consensus
15 A Taxonomy-Based Voter for Dispersion Simulation Results Simulation code ATSTEP_v1(v2) Simulation code DIPCOT_v1(v2) Simulation code RIMPUFF_v1(v2) Input data and parameters: A270B180 A270B180 A270B180 A270B180 Disagreement
16 An Acceptance Test for Dispersion Simulation Results Source: Leitfaden für den Fachberater Strahlenschutz der Katastrophenschutzleitung bei kerntechnischen Notfällen Gausian boundary condition 7/10 4/10 2/10 1/10 Maximum dispersion factor
17 An Acceptance Test for Dispersion Simulation Results Weibull distribution: Acceptance test: Check if empirical distribution (from results) fits the hypothetical distribution by means of a goodness of fit test (Kolmogorov-Smirnov).
18 Experimental Validation of the Approach Two versions of the RODOS system RODOS_v1 (ATSTEP_v1, DIPCOT_v1, RIMPUFF_v1) Bug fixing and improvements brought to the system RODOS_v2 (ATSTEP_v2, DIPCOT_v2, RIMPUFF_v2) Reference input case ensemble (24 cases) Extended input case ensemble with rotating wind (24 cases)
19 Results: Kolmogorov-Smirnov Test RODOS_v2 Improves over RODOS_v1 in 4 out of 6 cases RIMPUFF handles wind rotation better than constant wind direction
20 Results: Taxonomy-Based Voter RODOS_v2 No cases of disagreement More cases of strict and majority consensus
21 Results: Taxonomy Voter & Acceptance Test RODOS_v2 No cases of disagreement Many more cases of consensus
22 Conclusion The acceptance test and the taxonomy-based voter are sensitive to the improvements brought to the RODOS system Fully automated software metrics for the continuous verification of dispersion simulation codes Complementary to visual inspection and other means of verification Can be used for Model/code inter-comparisons based on many input cases Ensemble-based dispersion modelling systems Source code available
23 Visualization of Kolmogorov-Smirnov Test Results Generated in R (statistical computing) Quantile-quantile plot
24 Workflow for a dispersion forecasting system supporting functionally redundant simulation codes and continuous verification of results.
25 Monotonicity of Time-Integrated Doses
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