Creation of Realistic Radiation Transport Models of Radiation Portal

Chapter 14

Creation of Realistic Radiation Transport Models of Radiation Portal Monitors for Homeland Security Purposes

Downloaded by COLUMBIA UNIV on August 6, 2012 | http://pubs.acs.org Publication Date: November 16, 2006 | doi: 10.1021/bk-2007-0945.ch014

S. M . Robinson, R . Kouzes, R . J . M c C o n n Jr., R. Pagh, J . E. Schweppe, and E . R. Siciliano Pacific Northwest National Laboratory, P.O. Box 999, P8-50, Richland, W A 99352

Much of the data used to analyze and calibrate alarm algorithms for radiation portal monitor (RPM) systems has come from actual measurements of vehicles passing through RPMs. Due to the inherent limitations and expense of taking data with controlled radioactive sources, the majority of these data contain no sources except for naturally occurring radioactive material (NORM) cargo sources in the presence of natural background. Advances in computing capabilities have made it feasible to simulate "in-the-field" detector responses from a wide variety of source/cargo configurations, and to produce data matching that generated in the field. Computational models have been developed by the Radiation Portal Monitor Project (RPMP) for many detectors, vehicles, cargo configurations, and sources. These models are being used to simulate R P M responses to complicated source/cargo configurations for vehicles with and without sources. The simulated data is, and will be used to 1) complement existing field data, 2) help guide the progress of future data taking, 3) improve our ability to calibrate and refine alarm algorithms, 4) verify the causes of effects seen in the field, and 5) look for unknown effects not corresponding to theoretical models. A large set of simulated data that has been validated against field data will allow for in-depth testing of detection alarm © 2007 American Chemical Society

In Applied Modeling and Computations in Nuclear Science; Semkow, T., et al.; ACS Symposium Series; American Chemical Society: Washington, DC, 2006.

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196 algorithms for a variety of source scenarios. A n overview of the P N N L / R P M Modeling System will be presented. Highlights from selected results already obtained and compared to data will be shown, and a set of ongoing cases being simulated will be outlined.

Introduction Downloaded by COLUMBIA UNIV on August 6, 2012 | http://pubs.acs.org Publication Date: November 16, 2006 | doi: 10.1021/bk-2007-0945.ch014

Status of Field Data Although some data from actual Radiation Portal Monitor (RPM) deployments are available to authorized researchers and laboratories, these data are limited in their use to any individual researcher. They are difficult to analyze systematically, mainly because of differences in backgrounds and N O R M / T E N O R M cargo rates at individual installation locations. Furthermore, data involving actual targeted sources (as opposed to only N O R M cargo or natural backgrounds) are difficult to obtain, and small in number. This has led researchers to conduct individual tests on desired source scenarios, but these data are not kept in a standard location or format. There is a great need for a large and standardized data set, containing many targeted source scenarios. This would allow for analysis of data characteristics in a well-understood environment. Also, this would facilitate testing of alarm algorithms and source detectability. 1

Overview of Large-Scale Simulation System A comprehensive data production system has been created to simulate the results from in-field R P M configurations. This system includes a variety of models of vehicles, cargos and sources. Each vehicle is stepped horizontally through simulated R P M installations, to model drive-throughs at various speeds. To provide a high-degree of realism, the dimensions and composition of all

'Naturally occurring radioactive material. Road Salt and Granite are two common N O R M sources. One subclass of N O R M is T E N O R M (Technologically enhanced N O R M ) . For example, alkaline batteries and some salts are rich in natural K due to chemical processing. 4 0


197 components comprising these models are verified for accuracy against actual vehicles and R P M units. As shown in Fig. 1, these models can be used to create simulated data sets which behave like actual field data, giving confidence that they can then be used in evaluating algorithm responses to source scenarios. The ability to test detection algorithms against numerous source scenarios is expected to greatly aid in optimization of future algorithms, including the use of multiple algorithms together in "hybrid" algorithm schemes.

Numerical Modeling Downloaded by COLUMBIA UNIV on August 6, 2012 | http://pubs.acs.org Publication Date: November 16, 2006 | doi: 10.1021/bk-2007-0945.ch014

Cargo and Vehicle Models A set of very specific and detailed models have been constructed for use in the simulation of P O V (Personally Owned Vehicle) and Cargo lanes, commonly found at border crossing R P M installations. O f particular interest is the so-called "Ε-Van Model" (/). This model was constructed to simulate the shielding effects an averaged-sized privately-owned vehicle (POV) would have on a small, but finite radioactive source being transported by the vehicle. It was based on the geometry of a Ford Econoline™ van, with the specific component dimensions representing a composite of the Econoline™ models E-150/250/350 for the model year 2000. The Ε-Van development objective was to include with sufficient detail all the structural features of a realistic van that would provide "intrinsic" shielding. Also of interest is the "Cargo Truck" lane model (Fig. 2). In this model, a large cargo vehicle is filled with standard cargo boxes, which can be filled with a variety of materials. Materials of disparate densities, hydrogen content and atomic number, such as air, polyethylene and concrete are considered, and modeled in the M C N P input decks. Like the Ε-van model, the Cargo Truck model also contains specific details of vehicle construction, but is also meant to be a general model for all vehicles carrying standard configurations of commercial cargo. Wooden cargo pallets are present, along with a choice of several cargo box heights and configurations. As in the field, this vehicle model is simulated to pass through a cargo lane, whereas the Ε-Van model is simulated to pass through a POV lane.

Detector Models For this work, it is also necessary to simulate the specific detection systems in deployment, and those under consideration for future use. A variety of models for PolyVinyl-Toluene (PVT) and Thallium-doped Sodium-Iodide (Nal(Tl)) based detection systems have been created, verified and validated (see below).



Figure 1. The proposed RPM simulation system.

Calibrated Data Simulation/Algorithm Analysis System



199

Figure 2. The "Cargo Truck Model. "


200 In the data sets produced by this research, the responses of each of these systems will be computed.


Verification and Validation of Models A n important step in ensuring the reliability of simulated data is to "verify" the accuracy of all model components, and "validate" the results whenever possible. In generic terms, verification means the encoded instructions solve the model problem correctly, whereas validation indicates the degree to which the model output represents the real world. In the context of the R P M P models, verification is used to mean the dimensions and compositions of the encoded model components represent the corresponding real-world components correctly, and validation means the simulated responses compare well to measured responses. This work is currently in progress, and results have shown that the cargo model exhibits the expected effects on source detectablity, including radiation attenuation by dense cargo, detection asymmetry of a source placed on one side of the vehicle, and "channeling" of gamma rays through less dense cargo areas. As an example, the source is placed either in the middle of the vehicle, or toward the passenger's side, and results are calculated for the detector panels on the top and bottom of either side of the R P M lane (Fig. 3). Once models are verified, the results of the simulations must be validated, meaning that a high level of confidence must be established that the models produce results in keeping with actual quantitative measurements. Measurements have been made on standard sources to validate the simulated detector responses against the actual responses, in the case of P V T and Nal-based portal monitor systems (Fig. 4). Verification and validation of these models are important steps in establishing confidence in the results of the simulations using them.

Computing Environment - M C N P The calculations use the general-purpose Monte Carlo N-Particle Transport code, M C N P (2) with model components constructed to simulate the gamma-ray detection system, a vehicle (in which sources may be located), cargo carried by this vehicle, and the remaining environment (pavement and atmosphere). Simulations must therefore be made to represent all of these parameters, and to estimate the detector response from a vehicle passing through an R P M system. For this purpose, the M C N P environment will be "wrapped" by a set of scripts designed to produce a series of M C N P input files representing the desired scenario. After the vehicle model, cargo, potential sources ( N O R M and otherwise) and speed of vehicle are specified, an "input script" will take these


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characteristics and turn them into a time-stepped series of input files, each one representing the vehicle's position at some point in time. These input files are run as a batch, generating the detector's response to the vehicle at every point in time. Then an "output script" will be run on the collection of M C N P outputs, reading the tallied detector responses, and assembling the results into a timeseries o f data that would be seen by the R P M software in the field. These data are the "Simulated Field Results" of Fig. 1.

Figure 3. Arbitrary RPM response (Passenger Side, Driver Side and Totals) to source positions in a cargo truck.



202

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Figure 4. Real and simulated Ρ VT and Nal responses to a Ba source.

Computing Environment - Multiprocessing It is estimated that many tens of thousands of processor hours will be necessary to produce the datasets in this work, and so parallel computational techniques recently evaluated at P N N L will be employed. Because o f the statistical independence o f each Monte-Carlo "trajectory," the time required to run M C N P calculations can be reduced significantly by the use of parallel processing, and pre-compiled versions of M C N P that enable such processing are included in its distribution (3,4). A t P N N L , a prototype system of 10 heterogeneous desktop computers (the "Heterogeneous Windows Cluster") has been constructed for this purpose, and several smaller clusters have been installed and configured for use by individual researchers. These systems afford large speed increases over traditional desktop hardware alone (Fig. 5), and the


203 use of parallel processing solutions makes generation of the proposed data possible in a reasonable time frame.


Heterogeneous Windows Cluster Parallel Performance

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Figure 5. Proportional MCNP speedup of the HWC system in use at PNNL.

A Comprehensive Model As mentioned earlier, the system for constructing and managing the simulated data is handled by several scripts that "wrap" the M C N P application, providing the desired functionality. A script called Pstudy, written in the Perl language (5) has been used in the past for generating this kind of variation in M C N P input decks, for parameter studies. A n extension to the Pstudy scripts has been recently written, allowing for entire pieces of code to be inserted or removed automatically. This functionality allows for the iterative replacement of sources and cargos, as well as dynamic time-stepped movement of the vehicle model. In this way, a simple system is established to simulate R P M response from a given vehicle, source(s), velocity and cargo. A further script will be utilized to perform permutations on this parameter space, and automatically provide each set of results. Each scenario will also be repeated several times with a different initial random seed, for greater statistics.


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Database of Results A database will be maintained to hold all of the results from each scenario, and to be accessible and searchable for these results. A "standard set" of scenarios will be kept in database format to provide a standardized set of data for R P M analyses/Each scenario consists of a vehicle driving through the appropriate R P M detector, with some cargo configuration and contained sources. Each scenario also includes many M C N P runs representing different time stepped "snapshots" of the vehicle as it passes through the R P M . In addition to gross counts reported from the R P M system, full spectral information is kept from each run, recorded over a set of narrow energy bins between 0 and 3 M e V . These results will allow for interrogation of energy dependent alarm algorithms. Several repetitions of each such simulation will be made, with a different initial random number, to establish good statistics on the results. With 5 repetitions for each scenario, preliminary results show that the data size of this database will be around 250 Megabytes per scenario.

Alarm Algorithm Testing An immediate use for these data will be to test the behavior of algorithms deployed in the field. Algorithms will be run directly on simulated field results to test the response to source (as well as non-source) scenarios. In this way, alarm algorithm responses can be further verified, and several pertinent results (such as alarm response to source configuration) can be gained. This will also provide a framework for testing more complex algorithm schemes. Since simulated time series of data from each scenario will be quickly and uniformly accessible, current and future algorithms can be easily tested. It will also be possible to test any "hybrid" alarming scheme, in which the results from multiple algorithms are used together to generate a single alarm decision. A dense collection of source scenario results will make testing and optimization of such complex alarm algorithms possible.

Preliminary Results Some preliminary work has been completed, with scenarios created and run on the parallel computing infrastructure at P N N L . In one particular case, a B a "check source" is placed at an offset location between the pallets of the cargo truck model in Fig. 2. The truck is then "driven" through the R P M system with the source, at 1 mph. It should be noted that this speed is slower than those observed in the field, and is used here for the sake of reproducing a large l 3 3


205 number of time steps in the figure. It is expected that more repetitions o f each scenario will be needed as vehicle speed increases, and few detections are made per time step. Results from the drive-through simulation show the expected increase o f detection rate when the source is near the detector, as well as the asymmetry of detection of a source located to one side of the vehicle (see Figs. 6, 7). Significant expected statistical fluctuations are present, necessitating repetition of these time-histories for good statistical coverage.


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Figure 6. Position of the offset Ba source.

Conclusions The hardware and software infrastructure present at P N N L is capable o f being used to produce a wealth o f data pertinent to source simulation and algorithm analysis. Insofar as it matches data from actual R P M systems, and provides a dense collection of targeted source scenarios, this data will be useful to evaluate and optimize the algorithms currently used in the field. It will be possible for friture algorithms to be characterized with this system, enabling continued improvement of total system performance.



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References 1.

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4. 5.

Siciliano, E . R.; McConn, R. J. Jr.; Schweppe, J. E. The Ε-Van Vehicle Model for M C N P Simulations. Report PIET-43741-TM-059; Pacific Northwest National Laboratory: Richland, W A , 2005. MCNP - A General Purpose Monte Carlo N-Particle Transport Code; Version 5. Report LA-UR-03-1987; Los Alamos National Laboratory: Los Alamos, NM, 2003. Goorley, T.; Brown, F. B ; Cox, L . J. M C N P Improvements for Windows PCs. Report LA-UR-02-7162; Los Alamos National Laboratory: Los Alamos, N M , 2002. Brown, F. B.; Martin, W. R. High performance computing and Monte Carlo. Trans. Am. Nucl. Soc. 2004, 91. Brown F.; Sweezy, J. E.; Hayes, R. B . Monte Carlo Parameter Studies and Uncertainty Analyses with M C N P 5 ; PHYSOR, 2004.


Creation of Realistic Radiation Transport Models of Radiation Portal

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