Approach to Effectively Interdict Highly Enriched Uranium Smuggling

GROUP 1: Daniel Boratko (NUEN), Thomas McGonigle (ISEN), Jacob Morphey (ISEN), Cody Orsak (NUEN), Reuben Thadikonda (ECEN)

Faculty Team: Sunil Chirayath (Lead), William Charlton, and David Boyle sunilsc@tamu.edu

Texas A&M University – Dwight Look College of Engineering, College Station, TX 77843-3133

The AggiE Challenge project is separated into two main groups: Cargo Container & Border Crossing Vehicles. Those transportations are

common for smuggling nuclear material into the United States. Outlined above is the Border Crossing Vehicle Scenario.

The strategic network analysis code, SHIELD considers various pathways through which an

adversary could smuggle HEU into the United States in order to determine the success rate

of the adversary. SHIELD uses cargo ports, air ports, train stations, as well as legal and

illegal border crossings as individual nodes which are separately analyzed to determine the

non-detection rate at each node.

Grand Challenge Addressed: The AggiE-Challenge project presented here addresses one of the grand challenges articulated by the National Academy of Engineering, namely, “Prevention of Nuclear Terrorism.” Only a few tens of kilograms of highly enriched uranium (HEU) are required to build a nuclear bomb but more than one million

kilograms of HEU exists in the world. A concern is that HEU could be stolen and smuggled into the U.S., either as HEU or as a nuclear weapon, for acts of nuclear terrorism. Securing the U.S. borders against attempts to transport HEU is a national priority. Current nuclear material detection technology is inadequate for several important HEU

smuggling scenarios. One of the most difficult challenges is the interdiction of shielded HEU being smuggled into the U.S. in cargo containers or border crossing vehicles.

Research Connections: Addressing this threat requires a multi-disciplinary approach of improved inspection policies, systems analyses, advanced algorithms, and advanced detection systems. These advanced systems will incorporate detector arrays that fully integrate all available signal information, potentially including inverse mathematical

analysis simulations, to provide revolutionary improvements in the performance of border monitoring technology. The U.S. Department of Homeland Security (DHS) had funded a multi-year multi-disciplinary research team at TAMUS Nuclear Security Science and Policy Institute (NSSPI) to conduct research in this area. The project ended

on 8/31/2012. The AggiE Challenge team conducted research to build upon these results, in particular by testing and validating the procedures and algorithms developed for improving the global nuclear detection architecture.

BACKGROUND INFORMATION AND INTRODUCTION TO THE AGGIE CHALLENGE PROJECT

STRATEGIC NETWORK ANALYSIS METHOD

A strategic network analysis software tool named SHIELD (sample strategic network shown

in Figure-1) developed by NSSPI is identified by this AggiE challenge team for determining

the success of an adversary in smuggling HEU into the U.S. through an international network,

which depicts trafficking nodes such as ports, border crossings, rail/road networks, etc. The

software is based on the Monte Carlo method of the adversary’s walk through a network from

a starting point to a location in the U.S. Smugglers’ behavior and the results of each move are

determined by sampling using random numbers. By repeating this procedure for thousands or

millions of times, the expected outcome of the smugglers’ success and the associated

uncertainty can be obtained. To do so, specific information on the non-detection probability of

HEU at each of the nodes and along the paths in the network need to be provided to the

software as input. Non-detection probability perceived by the adversary is another input

needed by the software in predicting the adversary’s success. The non-detection probabilities

for each node is determined through a tactical network analysis, which is described below.

PROJECT OUTLINE FLOW CHART

TACTICAL NETWORKING ANALYSIS METHOD

Figure 1: A sample strategic global network selected for analysis using SHIELD to determine

Adversary’s success in smuggling HEU into the United States

INTERDISCIPLINARY RESEARCH GROUPS

Team 1 - Focuses on the Car & Truck scenarios, hardness measure, & tactical queuing network analysis.

Team 2 - Focuses on the Cargo container scenarios, hardness measure, & tactical queuing network analysis

Team 3 – Focuses on the strategic networking analysis, these members calculate the success/failure rates of smugglers.

Team 4 – Working on developing the processes of extracting radiation field detection data for different sensor especially as passive detection

RADIATION TRANSPORT MODELING OF CAR/TRUCK USING ‘MCNP’ To determine the probability of detection and the false alarm rate for cars/trucks crossing the border into the U.S., the main task is to generate the Gaussian

distribution of gamma radiation counts for cars/trucks that don’t have HEU inside it (counts only from radiation background) and subsequently when HEU

is present inside. And, this procedure needs to be repeated for multiple vehicle crossing scenarios. As the density of the materials inside the vehicles

increase, the radiation transport simulations become computationally expensive and hence massively parallel computing methodologies need to be

employed to solve them. MCNP is such a massively parallel radiation transport code developed at Los Alamos National Laboratory and has been identified

as the tool for radiation transport analysis by this AggiE Challenge team. The MCNP code can be used to calculate the expected photon flux entering the

detector volume and can also be used to produce the detector response function by explicitly modeling the detector configuration and material in the

geometry. In doing so, no post processing of the photon flux data is required. This detector response will be in the form of counts per unit counting time

which can then be used as input to determine the probability of detection which is needed for the tactical as well as strategic network analysis. Figure 3

shows a 3D visualization of the car as modeled in MCNP with three 16” x 4” x 2” sodium iodide detectors explicitly modeled adjacent. Figure 4 shows the

simulation of where photon interactions are expected with a) the HEU source in the gas tank of the car and b) the background source starting in the concrete

below the car.

Figure 3: 3D Car model along with the three 16” x 4” x

2” sodium iodide detectors for performing radiation

transport simulation and detection

ACKNOWLEDGEMENTS

CONCLUSIONS

The research project is to determine approaches to prevent highly enriched uranium in

small kilogram quantities from entering the United States. We achieved our goal of

understanding the process and the individual stages of tactical and strategic analysis

(using SHIELD code). This semester through our research, we have learned each aspect

of the process to determine the probability of success for the smuggler and taken steps

to understand and develop tools we have identified as necessary to the combat the

problem. As a team, we worked together to develop a hardness code, passive detector

logic code, queuing network code as well as run simulations of gross counts of HEU

and Background radiation using the MCNP code. As industrial and nuclear engineers,

we broke through the boundaries of our designated majors to collaborate. We have

performed simulations for a both a regular sized car and a small truck with a HEU

source and with background radiation from the concrete without the presence of HEU.

The team would like to acknowledge Texas A&M University Engineering Student Services and Academic Programs Office for giving us the opportunity to expand our horizon and apply our knowledge to current issues. We would also like to place on

record the support we received from our graduate coordinator, Evans Kitcher.

PASSIVE DETECTION ANALYSIS

At the border crossings, vehicles undergo passive radiation detection. Different types of

detectors are available, the detector used will affect the probability of detection. This

team plans to use two types of detectors; high purity germanium (HPGe) and sodium

iodide (NaI) in the analysis to determine the difference in detection probability.

Figure 4a: Particle transport simulation with

Uranium Source (1kg HEU) in Car Gas

Tank

Figure 4b: Particle transport simulation with

Radiation Background source in concrete

Whichever detector (NaI or HPGe) is chosen, simulations are executed to determine

the detector response to the HEU source and the Background. A threshold for observed

counts is set by the inspection based on the natural radiation background. If the

observed counts is above this threshold an alarm is sounded and the vehicle will

undergo a higher level of scrutiny (usually manual inspection).

Figure 2 illustrates the current processing flow stream for the inspection of vehicles at the

land borders. The border crossing inspection process is broken up into three categories; pre-

screening, primary inspection, and secondary inspection. The pre-screening considers the

cargo manifest and conducts a risk assessment using the Automated Targeting System

(ATS). Primary inspection uses the Radiation Portal Monitor to evaluate the radioactive

profile of a vehicle. Secondary inspection includes a non-intrusive imaging inspection,

manual inspection, and additional inspection. Based on the outputs from the primary

inspection and pre-screening, the vehicle will be sent to one of the processes of the

secondary inspection.

Figure 2: Inspection Policy at Border Crossings

MCNP Code

Measurement

235U

(185.7keV)

238U

(1001.1 keV)

40K

(1460.91 keV)

Concrete 38.25 ± 6.2 1.25 ± 1.1 66.75 ± 4.7

Banana 36.5 ± 6.0 3 ± 1.7 199 ± 15

Cat Litter 203 ± 42.8 2 ± 1.4 178 ± 14.28

Uranium 34400 ± 219.5 4110 ± 65.86 172 ± 14.92

Uranium

with bananas 15900 ± 137.46 2800 ± 54.75 143 ± 14.87

Uranium

with cat litter 7140 ± 127.68 1840 ± 45.27 150 ± 15.45

Experimental results for radiation background measurements (radiation counts)

with HPGe detector

Figure 5: Reuben Thadikonda (ECEN) and graduate student coordinator, Evans Kitcher

(NUEN) performing background radiation measurements near TAMU Bonfire

memorial using HPGe Detector.