**3.2. Monte Carlo simulations**

GEANT 3.21 [20] is a method of detector description and simulation tools, whit characteristics presented in **Figure 8**.

**Figure 8.** GEANT 3.21 characteristics.

In essence, the utilization of the Monte Carlo simulation method for the detection efficiencies evaluation for large samples such as waste drum is nearly the same as in the situation of small samples. Considering the practical aspects, it appears a large difference regarding the calcu‐ lation time. In the case of big samples, in which the majority of emission points are located far away from the detector, the fraction from the number of emitted photons that contributes to the detector signal is very small. Consequently, the number of photons that should be followed until a statistically significant number of signals will be reached should be very large, resulting in a long computation time.

In the experiment presented in this section, 4.32E+10 photons were simulated. All the details of the source, measurement geometry and detector were implemented in the GEANT 3.21 code. To explain the simulation process, **Figure 9** was created.

**Figure 9.** Simulation process.

**Figure 7.** ISOCART (left) and Segmented Gamma Scanner WS1100 (right) gamma-ray spectrometry systems.

cylinder was 50 cm for both geometries.

**3.2. Monte Carlo simulations**

**Figure 8.** GEANT 3.21 characteristics.

presented in **Figure 8**.

128 Nuclear Material Performance

The volume source considered in simulation was a 2201 radioactive waste drum typically used for conditioning of radioactive waste in Romania. Several studies were reported using this kind of sample [29–32]. The source matrix was considered to be concrete with standard composition, and the axis of the detector was perpendicular on the axis of the cylinder. The distance from the center of the coordinate system associated to the detector to the center of the

GEANT 3.21 [20] is a method of detector description and simulation tools, whit characteristics

In essence, the utilization of the Monte Carlo simulation method for the detection efficiencies evaluation for large samples such as waste drum is nearly the same as in the situation of small samples. Considering the practical aspects, it appears a large difference regarding the calcu‐ lation time. In the case of big samples, in which the majority of emission points are located far The simulations were done for the main gamma-ray photons (12 energies) emitted by 152Eu. In the case of Geom1, 1.5E+09 photons were followed for each energy, totalizing 1.8E+10 photons for all energies. In the case of Geom2, 2.1E+09 photons were simulated for each energy, representing 2.52E+10 photons in total for all energies. Individual spectra were recorded in separate files, and in the end, all spectra were combined with weights according to the emission probability of each gamma-ray [33]. The resulting spectra are presented in **Figure 10**.

**Figure 10.** The final spectrum for Geom1 and Geom2 geometries.

#### **3.3. The detection efficiency evaluation**

The detector intrinsic efficiency commonly is conditioned mainly by the material of the detector, the radiation energy and the physical thickness of the detector in the direction of the incident radiation [34]. A small dependence on source-detector distance is present due to the average path length of the radiation through the detector will amend somewhat with this area. The counting efficiencies can be classified by the nature of the events recorded. If all phenom‐ ena from the detector will be recorded, then the total efficiency will be of interest. Therefore, all interactions, no matter if the energies are small, are considered to be recorded. The peak efficiency presumes that only those interactions that deposit the full energy of the incident radiation are recorded. If the total area under the peak is integrated, then the number of full energy events can be achieved. In **Figure 11**, it represents the FEP and total peak efficiencies obtained from the simulated spectra, for Geom1 and Geom2 geometries. The fact that the efficiency in Geom2 is smaller than in Geom1 even if the second detector has a higher intrinsic efficiency is due to the smaller collimator acceptance in the case of Geom2.

**Figure 11.** The peak efficiencies and total efficiencies simulated with GEANT 3.21 for Geom1 and Geom2.
