ML23279A095

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2023 Ehprg Presentation Use Simulator Data to Assess Uncertainty of Time-Required for Human Reliability Analysis
ML23279A095
Person / Time
Issue date: 09/25/2023
From: Chang Y, Jing Xing
NRC/RES/DRA/HFRB
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Use Simulator Data to Assess Uncertainty of Time-Required for Human Reliability Analysis Y. James Chang, Jing Xing U.S. Nuclear Regulatory Commission Presented at EHPRG2023 September 25 - 28, 2023 Lillehammer, Norway

Problem Statement

  • Human error probability (HEP) includes the contribution of insufficient time to complete the tasks.
  • Calculate the time insufficient HEP requires specifying two time uncertainty distributions:

- Time-Required (Treqd): the time the crew taken to complete the tasks

- Time-Available (Tavil): reaching a system threshold if the tasks are not completed by this time

  • This analysis analyzed simulator data to develop guidance on specifying time uncertainty distributions for NRCs IDHEAS human reliability analysis method.

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Probabilistic Estimates Provide Better Risk Perception than Point Estimates Time-Available Time-Required T(0) Time 3

Specify Time Uncertainties

  • Review literature

- EPRI ORE reports, KAERI HuREX reports and papers, and PNNLs PSA 2023 paper.

  • Analyze simulator data

- Mostly from EPRI ORE and Halden experimental data, supplemented with KAERI and ÚJV ez, a. s.. data.

  • Develop guidance

- Data-informed guidance on specifying time uncertainty ORE: Operator Reliability Experiments; HuREX: Human Reliability data Extraction 4

KAERI: Korea Atomic Energy Research Institute; ÚJV ez, a. s.: Czechia Nuclear Research Institute.

Analyze Simulator Data

  • Use Lognormal distribution to represent uncertainty

- Supported by EPRI ORE, KAERI HuREX, and PNNLs analyses.

  • Determine the parameters values

- Use (median, Error Factor) representation.

- Explore different (median, Error Factor) sets for different types of tasks.

Error Factor (EF) is 95th percentile / 50th percentile OR 5 50th percentile / 5th percentile

Calculate Median and EF

1. 2. 3.

Fit Item 2 data to a (Mean, std)

Sort Normal Distribution. = (6.48, 0.55)

Raw Data Log (Raw Data)

HIP3-3-3 276 312 5.6 5.7 4. EF = SQRT(95th/5th) = 2.5 342 5.8 552 6.3 95th =LOGNORM.INV(0.95,6.48,0.55) = 1613 564 6.3 582 6.4 5th =LOGNORM.INV(0.05,6.48,0.55) = 261 660 6.5 720 6.6 1110 1122 7.0 7.0 5.

1236 7.1 Median =MEDIAN(Data in Item 1) = 621 1428 7.3 2454 Median =LOGNORM.INV(0.5,6.48,0.55) = 649 Outlier (2454) 6. (Median, EF) = (621, 2.5) removed 6

Remove Outlier Data (To be consistent with IDHEASs Pt definition)

Seq/seond HI2B1-1-1 HIB2-2-1 HI1P2-2-2 HI1P2-3-1 HIP2-6-4 HIP2-7-5A HI3B1-2-1 HI3B1-2-4 HI2P1-1-1 HIP3-3-3 INTL-LOF INTL-SGT 1 58 2 18 60 3 105 1 43 7 276 359 8 2 85 2 120 70 6 120 2 60 9 312 497 10 3 110 3 324 73 15 360 3 71 9 342 501 10 4 127 3 784 144 21 614 4 134 13 552 598 11 5 153 4 941 183 28 1311 5 185 13 564 605 13 6 204 5 1529 495 105 8 190 15 582 627 16 7 240 5 8 303 16 660 705 16 8 293 5 12 563 17 720 717 18 9 325 6 24 19 1110 988 19 10 343 8 27 1122 2120 20 11 368 23 38 1236 25 12 686 41 1428 28 13 103 2454 42 14 93 7

Overview of (Median, EF) Distribution (139 Data Points) 16 14 12 10 Error Factor 8

6 4

2 0 1000 2000 3000 Median Time (sec) 8

HEPs are Sensitive to EF

  • Most data points EF are between 1.5 and 4.0.
  • An example: Treqd = 8 min, and Tavail = 20 min

- HEP = 6.1E-5 when EF = 1.5

- HEP = 6.6E-2 when EF = 4.0

  • Wide HEP range, insufficient for developing guidance
  • Classify tasks to reduce EF uncertainty 9

Task Classification Examples

  • EPRI ORE identified five types of post-initiator (C) procedure-driven (P) human actions and provided data for three of them

- CP1: respond to an alarm, e.g., response to a spurious pressurizer spray operation

- CP2: do something only after reaching a certain condition, e.g.,

initiate residual heat removal when the suppression pool (SP) temperature exceeds 95 °C.

- CP3: complete the task before something happen, e.g., initiating Standby Liquid Colling System (SLCS) before SP temperature reaches 110 °F

  • KAERI - on APR 1400:

- Diagnosis

- Execution 10

Error Factors from Previous Analyses EPRI ORE KAERI APR 1400 PNNL Analysis of ORE Data CP1, BWR 3.2 Diagnosis 1.7 Upper Bound 2.4 CP2, BWR 2.6 Execution 1.7 Lower Bound 1.6 CP3, BWR 3.4 CP1, PWR 2.6 CP2, PWR 1.9 CP3, PWR 3.5 11

Applicability Considerations of ORE Data

  • Still in the learning curve of symptom-based EOPs While an improvement over earlier procedures, the

[symptom-based] EOPs used at all plants reportedly had deficiencies in logic and clarity that affected crew response.

  • ORE analysis did not screen out the outliers identified in this analysis
  • ORE analysis non-response equation tends to estimate a larger uncertainty 12

Analyze Halden and ÚJV ez, a. s. Data ID Scenario Cue Response Median (sec) Error Factor Cognitive Type 1 LOFW base scenario Reactor trip Stop RCP 605 1.6 Diagnosis 2 LOFW complex scenario Reactor trip Stop RCP 842 1.9 Diagnosis 3 LOFW Reactor trip First manipul;ltion in ES-0.1 561 1.9 Diagnosis 4 LOFW Start a critical actioReach ES-0.1 Step 18 1716 1.6 Execution 5 LOFW base scenario Stop RCP F&B established 1933 3.7 Execution 6 LOFW complex scenario Stop RCP F&B established 1937 2.5 Execution 7 SGTR base scenario Reactor trip E-3 entered 409 1.6 Diagnosis 8 SGTR complex scenario Reactor trip E-3 entered 1197 1.5 Diagnosis 9 SGTR base scenario E-3 Entered Ruptured SG isolated 886 1.4 Execution 10 SGTR complex scenario E-3 Entered Ruptured SG isolated 1541 1.4 Execution 13

Effects of Removing the Outliers Original Data Outliers Removed

  1. Item 5 (sec) Item6 (sec) Item 5 (sec) Item6 (sec 1 170 441 1183 1258 2 1183 1258 1219 1800 3 1219 1800 1776 1850 4 1776 1850 1910 1908 5 1910 1908 1957 2006 6 1957 2006 2092 2007 7 2092 2007 2162 2032 8 2162 2032 2326 2893 9 2326 2893 2505 3747 10 2505 3747 Median 1933 1957 1957 2006 EF 3.7 2.5 1.5 1.7 14

Average Values of Error Factors EPRI ORE KAERI APR 1400 PNNL Analysis of ORE Data CP1, BWR 3.2 Diagnosis 1.7 Average 2.0 CP2, BWR 2.6 Execution 1.7 CP3, BWR 3.4 CP1, PWR 2.6 This Analysis CP2, PWR 1.9 Diagnosis 1.7 CP3, PWR 3.5 Execution 1.5 15

Task Complexity has Insignificant Impact on EF ID Scenario Cue Response Median (sec) Error Factor Cognitive Type 1A LOFW base scenario Reactor trip Stop RCP 605 1.6 Diagnosis 1B LOFW complex scenario Reactor trip Stop RCP 842 1.9 Diagnosis 2A LOFW base scenario Stop RCP F&B established 1933 1.5 Execution 2B LOFW complex scenario Stop RCP F&B established 1937 1.7 Execution 3A SGTR base scenario Reactor trip E-3 entered 409 1.6 Diagnosis 3B SGTR complex scenario Reactor trip E-3 entered 1197 1.5 Diagnosis 4A SGTR base scenario E-3 Entered Ruptured SG isolated 886 1.4 Execution 4B SGTR complex scenario E-3 Entered Ruptured SG isolated 1541 1.4 Execution 16

Contributions from this analysis

  • The analysis provided data basis for specifying Error Factors in an NRC internal task that used IDHEAS-ECA to calculate the HEPs in NRCs probabilistic risk assessment models

- Reduced HEPs significantly for some human actions comparing to use an EF of 3, a common practices in Human Factors Engineering

  • Support NRC to develop guidance on specifying Time-Required and Time-Available uncertainties.

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