The computational model is developed using the SERPENT Monte Carlo Algorithm. Three types of blanket mock-ups are designed. The first one is designed using a uranium multiplier, the second one is designed using a lead multiplier, and the third one is designed without any multiplier.

The design parameters for each type of blanket mock-up configuration are taken from the experimental facility (Afanasiev et al. 1996). The dimension of each layer is given in the Table 1. The lithium zones in the blanket mock-up are divided into two types of blocks. One is 2 cm thick and another is 4 cm thick. Every block contains a different atomic density. The density of the LiAl alloy for every zone is shown in Table 2 considering 2.45% lithium by mass (Joshi 2007). The structural diagram showing each layer of the blanket mock-up with a uranium multiplier is given in Fig. 1. To construct this computational model some simplified assumptions are being considered. They are:

Figure 1. 

Simplified diagram of hybrid blanket mock-up.

• The source from where fusion neutron is emitted is considered as a point source since the geometry of the external source is complex. The considered point source is 15 cm away from the first wall of the blanket
• The detectors holder part which are used to hold detector in different coordinate position to measure reaction rate, and tritium production rate at the intersection of walls are not taken into consideration.

During the computational design of fusion reactors, validating design parameters involves verifying neutron codes and data through calculations based on integral benchmark experiments using a 14.75 MeV neutron source and all the simplification assumptions are taken to into consideration in respect to the experimental facility.

Subsequent calculations of the computational model about the tritium generation rate and neutron activation rate of various isotopes will be considered if the calculated values show an error percentage of less than 10% (Afanasiev et al. 1996). The simulation workflow is given in Fig. 2. The detector’s position for this computation is regarded as relative to the blanket wall. All the calculation is conducted by considering the first wall at x = 0 position with vacuum boundary condition. For the blanket mockup model, the vacuum boundary condition is also set. This simulation tracks 900,000,000 neutrons over 1,000 cycles and the cross-section data are sourced from ENDF/B.VII.1 library.

Figure 2. 

Work flow for this study.

The computational model is validated with respect to the experimental value. The comparison between experimental data and computational data is provided with respect to the relative activation rate of Cu-63 and Cu-65 isotope provided in Figs 3, 4. The relative activation rate is calculated by taking ratio for every obtained value of detector coordinate with respect to the initial value obtained at initial position of detector. The average uncertainty obtained for Cu-63 activation is 3.63% while it is 6% for the activation of Cu-65 isotope. Since the error percentage is less than 10%, the computational model validated the experimental data (Afanasiev et al. 1996; Tonghua et al. 2019; Thompson et al. 2021).

Figure 3. 

Relative activation rate of Cu-63(n,2n).

Figure 4. 

Relative activation rate of Cu-65(n,2n).

Copper activation has served as a conventional method for calculating neutron yields in DT fusion experiments for over 30 years. The primary focus is on the ⁶³Cu(n,2n)⁶²Cu and ⁶⁵Cu(n,2n)⁶⁴Cu reaction (Glebov et al. 2006).

There are three separate lithium zones after the neutron generated from fusion passes the multiplier zone. The work of the lithium zone is to produce tritium using the reaction,

⁶Li + n → ³H + α (2)

The neutron multiplier is used to enhance the number of neutrons so that the tritium production rate can be increased. Since the prompt fission cross-section of U-238 is 0.644 barns with a neutron energy of 14.75 MeV is much higher compared to the elastic cross-section for Pb-208, which is 4.9 × 10^-21 barns at the same energy level, the neutron generation rate for uranium multiplier will be highest (Soppera et al. 2014). Thus, the tritium generation rate for the use of the uranium multiplier would also be higher. Fig. 5 advocates the argument that the normalized neutron flux over the neutron energy range is the highest in the case of the uranium multiplier. Due to a higher cross-section, this study observes a peak in the neutron number for the use of uranium multiplier in the 0.1 to 10 MeV energy range. The average fission neutron energy is 0.1 to 10 MeV. With higher neutron flux, the uranium multiplier will avail more neutrons to the Li-zone which will increase overall tritium production.

Figure 5. 

Normalized neutron flux for the use of different multipliers.

Fig. 6 represents that the cross-section for ⁶Li(n,α)T reaction is the highest within the energy range 200–300 KeV of neutrons. That is why it is important to slow down the neutrons that are emitted after the reaction with the multiplier. Thus, the moderator is used to maximize the tritium production rate.

Figure 6. 

⁶Li(n,α)T reaction cross section (Soppera et al. 2014).

Table 2 shows that the three different lithium zones are divided into different regions of LiAl with varying atomic densities of Li-6, Li-7, and Al isotopes. Lower density of alloy means higher atomic density for Li-6 and Li-7 isotopes. Since the share of lithium is considered a constant value for every LiAl region. That is the reason why the U-shaped curve is observed in the case of observing the tritium production rate Fig. 7. Furthermore, the excessive moderation decreased the tritium production rate in the third lithium zone, though the densities of LiAl are not much higher in that zone. In the first lithium zone, the tritium production rate is the highest in the region where LiAl density is the lowest. This is similar for the second and third lithium zones as well. There is another factor that influences the tritium production rate, which is the width of the lithium-containing regions. Increased width increases the probability of interaction between neutrons and lithium atoms. For this reason, in the second lithium zone, the tritium production rate is the highest cumulatively.

Figure 7. 

Tritium production rate in 3 separate Li zones.

In the case of a uranium multiplier, when a neutron reacts with uranium fission occurs. Since the amount of energy from the prompt fission reaction remains in the range of 100 KeV to 10 MeV the average prompt neutron energy is 700 KeV (Madland 1982). At that range the ⁶Li(n,α)T reaction probability is also high. Therefore, in the first Li zone, the tritium production rate is maximum. Though it is reduced in the second it is reduced in the second and third Li zone due to moderation of neutrons and LiAl regions with high densities.

The excitation energy for the Pb-208 isotope is 2.6125 MeV for neutrons. The higher energetic fusion neutrons can occur inelastic collision and thereby split into smaller nuclei or can produce new neutrons. Since the emitted neutron energy is higher after the inelastic collision, the tritium production rate is lower in the lithium zones in comparison with the situation when the uranium multiplier is used.

When no multiplier is used, the tritium production rate solely depends upon the moderator region. The multiplier is not used therefore neutrons generated from fusion pass the lower number of neutrons compared to the situation when uranium and lead multiplier are used. The neutrons need to be moderated to increase the production of tritium. Because the ⁶Li(n,α)T reaction cross section is low with high energetic neutrons. That is why, the tritium production rate is the highest in the third lithium zone due to better moderation compared to the 1^st and 2^nd lithium zone.

Therefore, the use of a uranium multiplier enhances the overall tritium production rate in three different lithium zones cumulatively. Further study is conducted focusing on the materials’ effect of neutron-induced radiation.

Fig. 8 represents the relative activation rate for different types of neutron-induced disintegration reactions for various detectors from the neutron-producing target layer. The result from the detectors can be used to analyze the effect of material interactions to a single neutron as well as ensure the simulation or analysis matches what would happen in real-world scenarios (Madland 1982; Glebov et al. 2006; Soppera et al. 2014; Jakhar et al. 2015; Tonghua et al. 2019). Table 3 shows cross-section values at the neutron energy level of 14.75 MeV as well as the threshold values for the corresponding neutron-induced disintegration reactions. The relative activation rate is calculated by dividing the activation value measured at each detector position by the activation value measured at the first detector position.

Figure 8. 

Relative activation rate distribution in the axial channel of the blanket mock up with uranium multiplier.

In Fig. 8 and the corresponding relation with Table 3, the study shows that the relative activation rate of In-115(n,n’) is the highest since the threshold energy for this reaction is the lowest. Though with the energy of 14.75 MeV for neutrons ejected from D-T reactions, the cross-section for the ²⁰⁴Pb(n,n’)^204mPb reaction is 0.306 barns, much higher than the cross-section of the ²⁷Al(n,p)²⁷Mg reaction which is 0.07205 barns, that is why the activation rate for the reaction ²⁰⁴Pb(n,n’)^204mPb is higher even though the threshold energy is higher compared to the ²⁷Al(n,p)²⁷Mg reaction (Soppera et al. 2014). For the same reason, the activation rate is higher for the ⁵⁶Fe(n,p)⁵⁶Mn reaction with lower threshold energy compared to the ⁶³Cu(n,2n)⁶²Cu, ⁶⁵Cu(n,2n)⁶⁴Cu, and ¹⁰⁷Ag(n,2n)^106mAg. Though ⁶³Cu(n,2n)⁶²Cu and ⁶⁵Cu(n,2n)⁶⁴Cu reaction have better cross section. The activation rate of ⁶⁴Zn(n,2n)⁶³Zn reaction is the lowest with a relatively lower cross-section value and with the highest threshold energy.