1 Introduction The TOP-modelPotential applicationsConclusion The Transient Observations-based Particle Model and its potential application in radiation

1

Introduction The TOP-model Potential applications Conclusion

The Transient Observations-based Particle Model

and

its potential application in radiation effects evaluation

S. Benck, M. Cyamukungu, J. Cabrera, V. Pierrard

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The radiation belts

From static empirical models to dynamic models

Radiation Belts

Magnetosphere

The Sun-Earth system

Solar wind

- semi empirical models: numerical solution of a diffusion equation; the observations are the boundary conditions to these models

- empirical model: based on the observation of transient particle fluxes

The advantage of dynamic models- facilitate the investigation of the physical processes

involved in the flux variations

- to complete radiation effects evaluation

- risk evaluation

- estimation of the time fraction during which a device will be disabled due to exceeding fluxes

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The observation of transient particle fluxes was found to be a convenient approach to study the dynamic of the space radiation environment.

This method actually includes the following activities:

The measured steady state particle fluxes

Measurements of particle flux lifetimes as a function of energy, position and pitch angle

Analysis of the event-driven flux variations

First step: The development of a probabilistic model based on probability distribution functions of the flux variations.

Second step: The development of a phenomenological model of the event-driven flux variations as a function of geomagnetic indices and/or solar wind parameters.

Third step: Physics of anomalies (drivers - effects).

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The measured steady state particle fluxesSteady state fluxes are measured during a long period of low geomagnetic activity (from 1 May - 17 July 2004).

They provide lower limit of particle fluxes at a given position and can be used for instrument cross-calibrations.

Steady state fluxes at a given position in space are very stable (likely to be only affected by solar cycle and secular variation of the geomagnetic field).

1 Jan 200400:00:00

1 Jan 200400:20:00

1 Jan 200400:40:00

1 Jan 200401:00:00

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Measurements of particle flux lifetimesIn general, the steady state flux level is reached after determined lifetimes which only depend on particle energy, position in space and particle pitch angle. Cases are observed in which enhanced fluxes do not decay according to the characteristic lifetime: investigation of such behaviours reveals itself to be a powerful tool for the identification of the mechanisms which affect space particle flux variations.

Electron Decay time constants as a function of L for different electron energies as deduced from DEMETER and SAC-C data.

(Benck et al. Ann. Geophys., 28, p.849, 2010)


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Analysis of the event-driven flux variations


deltaF

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Analysis of of the event-driven flux variations


Probability distribution function for the flux variations at a given position for a given energy bin

Probability distribution function for the time interval (time measured between the instant of Dst minimum and instant of flux maximum) use of the mean value

Cumulative distribution function for the flux variations at a given position for a given energy bin

0.00

0.20

0.40

0.60

0.80

1.00

1.20

-50000 0 50000 100000 150000 200000

deltaF (cm-2 s-1 sr-1)

Cu

mu

lati

ve d

istr

ibu

tio

n f

un

ctio

n

CDF E=0.33-0.39MeV L=3.4-3.6 B=0.22-0.46GCDF based on four pointsCDF smoothedsimulated events

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

-50000 0 50000 100000 150000 200000

deltaF (cm-2 s-1 sr-1)

Pro

bab

ilit

y

PDF E=0.33-0.39MeV L=3.4-3.6 B=0.22-0.46G

PDF based on four points

PDF based on four points

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time evolution of the flux (in cm-2/sr-1/s-1) for E=0.5-0.7 MeV represented in geographic coordinate system for LEO orbit (700 km altitude)

• simulation time = 150 days

• time resolution = 3 days

• storm occurrence deduced from the storm waiting time probability distribution (Poisson distribution with Tw=12.5 days for solar max)

• time interval between storm onset and start of flux increase (at Dstmin) = 1.35 days

• time between start of flux increase and moment of maximum flux reached = mean value from PDF

• flux variation = random (seed 3) from CDF

• starting flux value = steady state

no dynamics for SAA

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• simulation time = 150 days

• time resolution = 3 days

• storm occurrence deduced from the storm waiting time probability distribution (Poisson distribution with Tw=12.5 days for solar max)

• time interval between storm onset and start of flux increase (at Dstmin) = 1.35 days

• time between start of flux increase and moment of maximum flux reached = mean value from PDF

• flux variation = random (seed 3) from CDF

• starting flux value = steady state

Animated

time evolution of the flux (in cm-2/sr-1/s-1) for E=0.5-0.7 MeV represented in geographic coordinate system for LEO orbit (700 km altitude)

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Hypothetic case: Simulation of the electron dose absorbed in a small silicon volume shielded by a finite aluminum slab of 0.5 mm thickness

0.5 mm Al

Si

AE8max: 30% lower

TOP-model

___ AE8max

Average differential flux for a LEO orbit (700 km altitude)

for a 3 years mission : TID electron AE8 = 11 krad (Shieldose –2)

TID electron TOP = 16 krad

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Accumulation of the electron dose (rad) during 150 days

Dose accumulated per half day


seed 1 seed 3___ constant dose rate___ variable dose rate

~1 krad

risk evaluation

ELDRS and mixed dose rate

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seed 3

Mean dose rate in rad/h as a function of position

Instant dose rate in rad/h as a function of position and time

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The TOP-model can be combined with measurements from an energetic particle telescope (EPT) to perform space weather forecasting:

- EPT provides real-time particle fluxes (In-flight particle and energy discrimination)

- The TOP model provides the lifetime values

Forecast of quiet conditions may be provided up to 7 days in advance.

- Useful for fluence/flux - driven effects (charging, CCD blurring) Estimation of the time fraction during that a device will be disabled due to exceeding fluxes

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Dynamic radiation belt models allow:

- to further define the physical processes involved in particle flux variations

- to give a more realistic prediction of the fluxes encountered in orbit and their resulting dose accumulation:

The dose rate is very variable within a given mission; this may change the risk evaluation for dose rate sensitive devices

Predict the probability of occurrence of steep dose enhancements per unit time (i.e. for human radioprotection)

Provide an estimation of the time fraction during which a device will be disabled due to exceeding fluxes

Extension of the model to higher altitudes

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Additional diapositives

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extension of the database to complete the coverage of the radiation beltsThemis/SST st

CRRES/MEA ,SACC/ICARE st, ,

DEMETER/IDP ,

SAMPEX/PET st, ,

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High acquisition rates due to digital operation mode (peak flux 107 cm-2 sr-1 s-1)

acquire data in environments where most instruments saturate

1.E+04

1.E+05

1.E+06

1.E+07

01/10/00 13/02/02 28/06/03 09/11/04 24/03/06

UT

flu

x (

cm

-2 s

-1 s

r-1)

0.19-0.25 MeV

0.23-0.29 MeV

0.29-0.35 MeV

0.33-0.39 MeV

0.39-0.45 MeV

0.45-0.51 MeV

Ti

Tf

flux saturation for SAC-C/ICARE in the centre of the SAA (L=1.4-1.6, B=0.16-0.18)

peak flux ~3 106 cm-2 sr-1 s-1

solar max declining

~180 d330 days70 days ~45 d

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seed 1

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seed 1

seed 3

Mean dose rate in rad/h as a function of position

Documents

1 Introduction The TOP-modelPotential applicationsConclusion The Transient Observations-based Particle Model and its potential application in radiation