Probabilistic Risk Analysis Procedure for Aircraft Overruns · Probabilistic Risk Analysis...

Probabilistic Risk Analysis Procedure for Aircraft Overruns

P. Trucco, M. De Ambroggi, M.G. Gratton Politecnico di Milano – Italy

M.C. Leva Trinity College Dublin - Ireland

3rd Symposium on Games and Decisions in Reliability and Risk (GDRR) Kinsale, Ireland - 8th–10th July 2013

Three possible scenarios:

•  Landing Overrun,

•  Takeoff Overrun

•  Landing Undershoot

Runway Excursion: when the aircraft departs from the runway limits during the operation.

2 INTRODUCTION

GDRR 2013 - Kinsale, Ireland - 8th–10th July 2013

Runway-related accidents represent a relevant fraction of the total number of recorded accidents in airport operations

3

Other 70%

Excursions 29%

Runway incursions

1%

Runway-related

accidents 30%

[Flight Safety Foundation, 2009]

INTRODUCTION


Overrun Probability Models: a comparison

Authors Year # of factors in the model

Input/Output variables

Based on international data (accident / NOD)

NOD

Eddowes et al.

2001 1 Deterministic No No

Kirkland et al.

2004 1 Deterministic No No

Wong et al. 2008 17 Deterministic No Yes

Hall et al. 2008 14 Deterministic Yes Yes

4 STATE OF THE ART REVIEW

FAA - Airport Cooperative Research Program (ACRP) - Report 3 “Analysis of Aircraft Overruns and Undershoots for Runway Safety Areas”


State of the art review and

model selection

Event probability

and location model

Event-Consequence

model

Topological model

“Risk Map” Test case

application

Objective

5 AIM AND SCOPE OF THE STUDY

Development of a Probabilistic Risk Analysis Procedure to design a topological “risk map” of overrun events based on the statistical characterisation of airport operations and context conditions

Methodology

ACRP Model

•  Ops statistical characterisation •  Monte Carlo

•  Kinetic Energy as Severity index


The ACRP Model (Hall, 2008) •  Made of three modules:

•  Accident Probability Model: returns the overrun probability value for a given operation

•  Longitudinal Location Model: returns the conditional probability that the longitudinal excursion is greater than a certain value starting from the runway end

•  Lateral Location Model: returns the conditional probability that the lateral excursion is greater than a certain value starting from the runway centre axis

•  Based on international accident data and NOD in countries with accident rates similar to US. •  Input data are normalised by means of a deceleration model

developed by Kirkland et al. (2003)

6 THE ACRP MODEL


ACRP Accident Probability Model

7

b(Landing Overrun)=-15.456+0.551(Heavy aircraft)-2.113(Commuter Aircraft)-1.064(Medium Aircraft)-0.876(Small Aircraft)+0.445(Turboprop Aircraft)-0.856(Foreign Origin)+1.832(Ceiling Height<1000 ft)+1.639(Ceiling Height 1001-1500 ft)+2.428(Visibility<2 SM)+1.186(Visibility 2-4 SM)+1.741(Visibility 4-6 SM)+0.322(Visibility 6-8 SM)-0.532(Crosswind 2-5 kt)+1.566(Crosswind 5-12 kt)+1.518(Crosswind>12 kt)+0.986(Electrical Storm)+1.926(Icing Conditions)+1.499(Snow)-1.009(Temperature<5°C)-0.631(Temperature 5-5°C)+0.265(Temperature>25°C)+1.006(Non Hub Airport)+0.924(Significant Terrain)

INPUT: Categorical variables of relevant factors

P =

OUTPUT: Overrun Probability for a single movement

Limitation: available excess runway is not considered among the relevant factors

THE ACRP MODEL


ACRP Longitudinal Location Model

8

0 0.2 0.4 0.6 0.8

1

0 500 1000 1500

Pro

babi

lity

(dis

tanc

e >

x)

Distance x from threshold [m]

Raw distances model for Landing Overruns

•  Regression model:

•  Separate models for Landing and Takeoff Overruns

•  Lateral Location Model is very similar (over variable y)

THE ACRP MODEL


Deceleration Model (Kirkland et al. 2003) •  Relates the travelled distance (s) during an excursion to the initial

speed of the aircraft (u)

9

q = constant [ms] s = travelled distance [m] p = coefficient dependent on ground type:

Ground Type p Wet Grass/Dry Grass/Pavement -0.0185 Mud/Gravel -2.8065 Obstacles/Water -8.5365

0 200 400 600 800

0 500 1000 1500

Spe

ed [K

m/h

]

Travelled Distance [m]

THE ACRP MODEL


10

Probabilistic topology of the event occurrence: Probability distribution of each single area to be involved in an overrun event [event/movement]

EVENT PROBABILITY AND LOCATION MODEL


11

Probabilistic topology of the event severity: Iso Kinetic Energy Areas

[KJ/movement]

EVENT CONSEQUENCE MODEL


Expected speed at a certain point beyond the runway end

12 EVENT CONSEQUENCE MODEL


MONTE CARLO SIMULATION Expected kinetic energy at a certain point beyond the runway end


[KJ/movement]

13


[KJ/movement]

EVENT CONSEQUENCE MODEL


Meteorological Data Traffic Data

•  Source: ENAV •  Format: METAR •  Large sample

(sampling frequency of 30 min)

•  Strong seasonality •  Information on Icing

conditions is lacking •  Large uncertainties due

to unstable factors (e.g. wind direction)

•  Source: Airport Operator

•  Format: records from database Businnes Objects ▫  about 40.000

movements/year

•  Landing and takeoff weight information is lacking; ▫  substituted with

Maximum Takeoff Weight of Aircraft (MTOW)

14

Linate Airport (LIN) – Milan, Italy

TEST APPLICATION


•  Seasonality of meteorological data ▫  Chi-square and Friedman tests on single

parameters ▫  Four separated models have been developed

for each season

•  Sensitivity Analysis ▫  To determine the most relevant factors under

different seasons (tornado plot)

•  Correlations ▫  Linear approximation - Pearson’s coefficient

15 TEST APPLICATION

Linate Airport (LIN) – Milan, Italy


•  Major influencing factors: Icing, Snow, Crosswind

•  Major differences between landing and takeoff critical conditions

•  Relevant traffic conditions: Equipment Class and User Class

Sensitivity Analysis

16

1.00

E-0

6

2.00

E-0

6

3.00

E-0

6

4.00

E-0

6

5.00

E-0

6

6.00

E-0

6

7.00

E-0

6

8.00

E-0

6

Jet/Turbo / Factor D Local/Foreign Origin

Visibility / Factor Equipment Class / Fa

Electrical Storm / F Temperature / Factor

Ceiling / Factor Dis Snow / Factor Distri

Crosswind / Factor D Icing / Factor Distr

Mean of Accident Probability for WINTER LANDINGS

Winter Landings, Sensitivity Tornado

0.00

E+0

0

1.00

E-0

7

2.00

E-0

7

3.00

E-0

7

4.00

E-0

7

5.00

E-0

7

6.00

E-0

7

7.00

E-0

7

8.00

E-0

7

9.00

E-0

7

1.00

E-0

6

Visibility / Factor Temperature / Factor

Ceiling / Factor Dis User Class / Factor Fog / Factor Distrib

Equipment Class / Fa Crosswind / Factor D

Icing / Factor Distr Snow / Factor Distri

Mean of Accident Probability for WINTER TAKEOFFS

Winter Takeoffs, Sensitivity Tornado

DISCUSSION OF RESULTS


Expected Probability for each single area to be involved in an overrun event [event/movement]

17

1,00E-‐06

1,00E-‐07

1,00E-‐08

1,00E-‐09

<1,00E-‐10



Event probability - Expected return time [years/event]

18

0-‐50 Years

50-‐100 Years

>100 Years



IKEA (Iso Kinetic Energy Areas): expected kinetic energy distribution [kJ/movement] beyond runway end

19

1,00E-‐02 1,00E-‐03 1,00E-‐04 1,00E-‐05 <1,00E-‐06



Advantages of the proposed approach

•  First attempt to characterise an airport overrun risk −  Probabilistic approach −  Topological representation −  Based on NOD on traffic and meteorological information

•  Easy way of reporting results, understandable by non technical people (e.g. policy makers and population)

•  Can be easily combined with structural vulnerability analyses of buildings and infrastructures to arrive at a full risk assessment of the airport area

•  IKEA information can be directly used to design protection measures and physical barriers

20 CONCLUSIONS


Opportunities for future developments •  On the side of the Probability and Location Models:

▫  to include Available Excess Runway among the factors included in the ACRP model

•  On the side of the Deceleration Model

▫  to enhance the model proposed by Kirkland et al. (2003), to take into consideration a larger variety of ground types and also the contribution of specific protective installations

•  On the side of the Consequence Model

▫  Standardisation of basic structural analysis and characterisation of different airport facilities

▫  Modelling more complex scenarios (e.g. overrun + fire/explosion)

21 CONCLUSIONS


Thank you !

Prof. Paolo Trucco, [email protected] @TruccoPaolo www.ssrm.polimi.it

3rd Symposium on Games and Decisions in Reliability and Risk (GDRR) Kinsale, Ireland - 8th–10th July 2013

Overrun Event Probability

23

•  Fall and Winter are the most critical seasons: −  Due to adverse

meteorological conditions

•  Differences between Landing and Takeoff: −  Means differ of about an

order of magnitude: - PLanding = 1.19x10-6 - PTakeoff = 2.81x10-7

−  Variances differ of about one order of magnitude



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