3D radiological modelling techniques...3D radiological modelling techniques Presented by István...
Transcript of 3D radiological modelling techniques...3D radiological modelling techniques Presented by István...
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3D radiological modelling techniques Presented by István Sző[email protected] for Energy Technology, Halden, Norway
Interregional Workshop on Optimization of Technology Selection for Decommissioning of Large and Small Nuclear Installations
2019 Sep 9-13 Miami
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Digital support concepts
in nuclear environments
(since 1996 till today)
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Radiation transport and dosimetry
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Real-time (Point Kernel)
radiation transprot
Radiation transport and dosimetry
Atmospheric dispersionInterpolation,
Geostatistics
Source deconvolution
MC radiation trasport
(MCNP, GEANT4)
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𝐹𝑙𝑢𝑥 =𝑁𝑢𝑚𝑏𝑒𝑟 𝑜𝑓 𝑝𝑎𝑟𝑡𝑖𝑐𝑙𝑒𝑠
𝑆𝑢𝑟𝑓𝑎𝑐𝑒 𝑎𝑟𝑒𝑎 𝑜𝑓 𝑠𝑝ℎ𝑒𝑟𝑒 (𝑐𝑚2)
𝐷𝑜𝑠𝑒 = 𝑓𝑙𝑢𝑥 ∗ 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟𝑒𝑛𝑒𝑟𝑔𝑦
Source Detector
Point Kernel radiation transport
VACUM
Distance
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𝐷𝑜𝑠𝑒 = 𝑓𝑙𝑢𝑥 ∗ 𝑐𝑜𝑛𝑣𝑒𝑟𝑠𝑖𝑜𝑛 𝑓𝑎𝑐𝑡𝑜𝑟𝑒𝑛𝑒𝑟𝑔𝑦∗ attenuation* scatter (build-up)
Source Detector
Point Kernel radiation transport
INFINITE HOMOGENIOUS
MATERIAL
Optical thickness
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Point Kernel radiation transport
𝐷 𝑟, 𝐸 = 𝑄 𝐸 ∙1
4𝜋𝑟2∙ 𝑒−Σ𝑟 ∙ 𝑑 𝐸 ∙ 𝐵𝑑 Σ𝑟, 𝐸
𝑄(E) source strength
Σ𝑟=µ×r optical thickness of the shield (mfp)
r shield thickness
𝜇 linear attenuation coefficient
𝑑 𝐸 flux-to-dose conversion
𝐵𝑑 Σ𝑟, 𝐸 buildup factor
𝑟
Point isotropic detector
(measuring position)
Point Kernel (point source)
Infinite homogeneous medium
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𝐷 𝑟, 𝐸 = 𝑄 𝐸 ∙1
4𝜋𝑟2∙ 𝑒−Σ𝑟 ∙ 𝑑 𝐸 ∙ 𝐵𝑑 Σ𝑟, 𝐸
Our implementation
𝒄𝒐𝒏𝒔𝒕 ∙ 𝑬 ∙ Τ𝝁𝒆𝒏 𝝆
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Wall
Optical thickness
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Our implementation – extended sources
point kernel (point source)
𝑟1
Source decomposition
Virtual point-isotropic
detector on chest height
Ray tracing
A source can be considers as a point if distance to it’s centre is 10X longer than it’s diameter
• Extended sources are decomposed to point kernels – dose is summed up
• Uniform decomposition vs. adaptive decomposition e.g. based on distance to the ‘detector’ – speed and accuracy trade-offs
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Optical thickness
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Slant penetration
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Multiple shields
𝐷 𝑟, 𝐸 =𝑄 𝐸
4𝜋𝑟2∙ 𝑒−Σ𝑟 ∙ 𝑑 𝐸 ∙ 𝐵𝑑 Σ𝑟, 𝐸
𝑒−Σ𝑟= 𝑒−(Σ𝑟1+Σ𝑟2…)
𝐵𝑑 Σ𝑟, 𝐸 = 𝐵𝑑 Σ𝑟1, 𝐸 +𝐵𝑑 Σ𝑟2, 𝐸 …
air
𝑟1
Shields Virtual point-isotropic
detector on chest height
Ray tracingprimitive
complex
airair
point kernel (point isotropic source)
𝑟2
𝐷 =
𝑖
𝐷 𝑟, 𝐸𝑖
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The real situation
airComplex Source(with unknowns)
shield
shield
“reflection” sky-shine
self absorption/attenuation
Human body
Heavy structure
Background (measurements)
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Benchmarking
Point source
Plane source
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Normally incident radiation
Positive numbers mean that point-kernel is overestimating the dose rate
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Obliquely incident radiation
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Water Concrete
Iron Lead
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Shield modelling
Evaluation of radiological shield intersection algorithms
Source Detector
Infinite homogeneous
material
Source Detector
‘Infinite’ homogeneous
slab
Source Detector
Slant penetration
Source Detector
Multi layers
Source DetectorComplex shape, composition, …
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Surface Mesh
• Based on Boundary Representation (B-rep) tech
• Triangles define the boundary between inside and outside of the model
• The set of faces must form a complete, closed skin of the model (with no intersecting faces)
• Must be tessellated for round objects => not 100 % accurate
• Brute force method - every triangle is checked against every ray
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Bounding Volume Hierarchy (BHV)
Optimized version of the Surface Mesh
• A BV (in our case an axis aligned bounding box) is created around every triangle
• BVs are grouped under parent BVs - tree hierarchy
• If a ray does not hit a BV, all surfaces in the children BVs are skipped in the ray-tracing
BV = Bounding Volume
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Error when intersecting edges or vertexes
Both Surface Mesh and BVH uses the even-odd rule to determine if the particle is inside or outside the object. The state is flipped between inside and outside when intersecting a surface of a mesh. At edges and vertices multiple intersections can be detected when penetrating the skin of the model.
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Voxel Grid
• Shields are modelled as a three-dimensional grid of voxels
• Each voxel defines if the volume is inside or outside the shield
• Compex (round) shapes need to be tesselated => not 100% accureate
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Constructive Solid Geometry (CSG)
• Combines primitives (spheres, cylinders, boxes,…) using Boolean operations.
• Boolean operations are organized in tree structure (results of an operation can be input to another operation)
Boolean operations
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Asymptotic analysis
Letter Represents
n the number of nodes in a CSG tree
c the number of cells along the longest edge of a Voxel Grid
t the number of triangles within a mesh
i the number of triangles within a mesh that were intersected
Algorithm Worst-case Average-case Best-case
CSG 𝑂(𝑛2) Ω(n)
Voxel Grid 𝑂(𝑐) ϴ(c) Ω(𝑐)
Surface Mesh 𝑂(𝑡 log 𝑡) ϴ(𝑡 i log𝑖) Ω(t)
BVH 𝑂(𝑡 log 𝑡) ϴ(log𝑡 + 𝑖 log 𝑖) Ω(1)
Algorithm Worst-case
CSG 𝑂 𝑛
Voxel Grid 𝑂(𝑐3)
Surface Mesh 𝑂 𝑡
BVH 𝑂 𝑡
Runtime complexity Memory requirement
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Shield shapes:
Test case:• 30x30x30 grid• 10 point isotropic source emitting 270 000
rays (lines) in total
Benchmarking
building(rooms, hallway, door
openings) pool pipe sphere cube
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Mesh and voxel tessellated to give error less than 1%26
buildingpoolpipe spherecube
CSG
Voxel
Brute force
BHV
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buildingpoolpipe spherecube
CSG
Voxel
Brute force
BHV
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Limitations
• Extreme photons energies (available input on energy absorption is for 10keV - 10MeV)
• Extreme optical thicknesses (available input on buildup)
• Complex dose distribution in the body (representation of human body)
• Shielding composition (applied buildup data and calculation)
• High contribution of “reflections” (usually not considered in PK models)
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Limitations (cont’d)
• Variations in input data availability (model needs source description)
• Accuracy of dose calculations (model is designed for conservative estimations)
• Large extended sources and close detectors (model applies adaptive source decomposition, but needs verification)
• Complex source with high self scatter (model applies buildup for scatter inside the source)
• Exposure pathways other than external –PK model is not applicable for inhalation, ingestion and skin dose from deposited material on clothing/skin)
• Type of radiation (model is applicable to gamma and X-ray only )
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Some solutions
• Combination with MC (Mote Carlo) radiation transport for complex irradiation situations - Interface with MCNP
• Easy production of input for MCNP simulations
• Enables MC radiological assessments for whole jobs (not static)
• Geostatistical & other interpolation techniques & combination with rad. transport
• Source deconvolution techniques
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Interpolation
• Nearest Neighbour: Returns the value in the nearest point from the known measurement. Not really an interpolation method (included as reference)
• Inverse Distance Weighting (IDW): The result is the weighted average of the values in the surrounding points weighted with a function of the inverse distance of these points.
• Radial Basis Function Neural Net (RBF Net): The interpolation is done with a radial basis function neural net using the known points as centres for the radial basis function.
• Ordinary Kriging: Originating from geostatistics where the interpolated value is computed using a covariance function to give weights to the known points
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Interpolation – test cases
Kriging
IDW
RBF
Nearest Neighbour
ChNPP 725 days after accident
Kriging
IDW
RBF
Nearest
Neighbour
Fukushima Daiichi NPP
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Interpolation – Cross-validation
RMSE = Root Mean Squared ErrorMAE = Mean Absolute ErrorME = Mean Error
10 fold cross-validation repeated 10 times
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Iguchi method
(07/10/99 Yukihiro Iguchi, JAEA)
Based on measures + additional input information:
measuring positions =location of sources (hot spots) + 1 background measurement
Will give false information in case of unknown sources!
Linear equation systemsolved by Gaussian Elimination
Use this to calculate 𝛼e.g. by Bisection Method
Sample mapping in an area containing 6 sources
Equation for arbitrary points (P)
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Source deconvolution
A. Input: 1. Set of shields (incl. air) 2. set of measurements 3. set of point sources (positions and isotope vectors)
Method: Adjusts source strengths so that calculated values match measurements
Tech: Custom genetic algorithm to solve a global optimisation problem where source strengths are found to optimise ‘cost’
cost = square sum of the absolute difference between the measured and calculated
B. Position of the sources are unknown (prototype)
Method: Generate a high number of point sources along the surfaces (e.g. walls)
Tech: Same global optimisation algorithm
A.
B.
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Source point clouds – gamma camera support
3D gamma scanners generate clouds of points with associated rad. properties (…)
How to use this in PK calculations?
Oct tree to achieve (near) real-time speed
Sources are combined based on distance from ‘detector’
Inaccuracy if a combined source is detected as shielded while in reality it’s only partly shielded
Could be used to see simulate decontamination.
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Single scatter ALBEDO
source
test case
Method: Semi-empirical formula for single scatter albedo
Gusev et. al. 198. Shielding from Ionizing Radiation. Vol.1. Physical Fundamentals for Shielding from Radiation (ISBN 5-283-02971-9)
Tech: R script
Conclusions:
Dose from ‘reflection’ = 2% of direct dose with no shielding
Dose from ‘reflection’ is comparable with direct dose with shields > cm lead
Method is sensitive to resolution i.e. nr of faces of the polyhedron representing the source
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Monte Carlo radiation transport - MCNP
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Human phantoms
ICRP - Phys. Med. Biol. 58 (2013) 6985–7007
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Inhalation
222Rn
218Po
214Pb
5.49 MeV
6 MeV
3.823 d 3.05 perc
26.8 perc
214Po
214Bi
19.7 perc
164 s
210Tl
1.32 perc
5.5 MeV
99.98%
0,02%
(stabil)
210Pb
210Bi
210Po
206Po
7.69 MeV 5.3
MeV
21 év 5.01 nap
138.4 nap
19.7 perc
1.32 perc
26.8 perc 3.05 perc 3.823 nap
5.3 MeV
5.49 MeV 6 MeV
0.02 %
99.8 %
164 s
7.69 MeV
6 MeV
138.4 nap 5.01 nap
days min min min
min
days days years(stable)
Overall dose
Effective dose
mSv
Risk
(e.g. cancer risk)
Details of rad. burden
Regional (e.g. lobe,
airway specific)
exposure rate, events
on cellular level Risk
(e.g. cancer risk)
Epidemiology
Modelling and in-vitro experiments
Supra-linear
Linear No Threshold
Threshold
Hormetic
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lokálisan (karina régió) sűrű rács
légút falának közelében sűrű rács
decays
Cell transformation probability
direct effect
Geometry - Medical
imaging, CAD
Air & particle transport (and deposition) – custom CFD
Epithelium modelling, Own-code α – cell interactions - Own-code, lit. data
Inhalation
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Publications• Szőke et al. New software tools for dynamic radiological characterisation and monitoring in nuclear sites. Workshop on Radiological
Characterisation for Decommissioning; 2012 April 17-19; Nyköping (Sweden). http://www.oecd-nea.org/rwm/wpdd/rcd-workshop/
• Szőke et al. Human-centred technologies for nuclear decommissioning. NKS-R Decommissioning Seminar; 2013 November 6-7; Halden
http://projects.hrp.no/nks-decom-2013/files/2013/01/Human_centred_techologies_for_nuclear_decommissioning_ife_szoeke_ppt_nks2013.pdf
• Szőke & Johnsen Human-centred radiological software techniques supporting improved nuclear safety. Nuclear Safety and Simulation. 2013; 4 3:
219-25. http://www.ijnsweb.com/?type=subscriber&action=articleinfo&id=176
• Szőke et al. Real-time 3D radiation risk assessment supporting simulation of work in nuclear environments. Journal of Radiological Protection.
2014; 34 2: 389–416. http://iopscience.iop.org/0952-4746/34/2/389/
• Szőke et al. Comprehensive support for nuclear decommissioning based on 3D simulation and advanced user interface technologies. Journal of
Nuclear Science and Technology. 2014; http://www.tandfonline.com/doi/full/10.1080/00223131.2014.951704
• Chizhov et al. 3D simulation as a tool for improving safety culture during the remediation work in the Andreeva Bay. Journal of Radiological
Protection. 2014; 34(4): 755-73. http://iopscience.iop.org/0952-4746/34/4/755/ - Winner of 2014 Bernard Wheatley Award!
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