Currently, there is an interest in using thorium based fuels in nuclear reactors. Thorium is widely distributed in nature and is approximately three times as abundant as uranium. However, ThO2 does not have any fissile elements to fission with thermal neutrons. Consequently, ThO2 must be used in combination with a "driver" fuel (e. g., UO2 or UC), which has 235U as its initial fissile elements. The presence of a "driver" fuel such as UO2 in a nuclear-reactor core results in the production of enough neutrons, which in turn start the thorium cycle. In this cycle, 232Th is converted into 233Th, which decays to 233Pa. The latter element eventually results in the formation of 233U, which is a fissile element (Cochran and Tsoulfanidis, 1999).

In regards to PT reactors, there are two possibilities when ThO2 is used. One option is to place ThO2 and a "driver" fuel in different fuel channels. The separation between ThO2 fuel and the "driver" fuel allows ThO2 fuel to stay longer inside the core. The second option is to
enclose ThC>2 and the "driver" in same fuel bundles, which are placed inside the fuel channels throughout the reactor core. This option requires the enrichment of the "driver" fuel since it has to be irradiated as long as ThO2 fuel stays inside the core (IAEA, 2005). Nevertheless, the current study considers the thermal aspects of one single fuel channel, which consists of ThO2 fuel bundles (i. e., first Option). However, this assumption does not suggest that the whole core is composed of fuel channels containing ThO2.

The use of thorium based fuels in nuclear reactors requires information on the thermophysical properties of these fuels, especially thermal conductivity. Jain et al. (2006) conducted experiments on thorium dioxide (ThO2). In their analysis, the thermal conductivity values were calculated based on Eq. (3), which requires the measured values of the density, thermal diffusivity, and specific heat of ThO2. These properties were measured for temperatures between 100 and 1500°C (Jain et al., 2006). In the current study, the correlation developed by Jain et al. (2006), which is shown as Eq. (4), has been used.

k = apcp (3)

1

kThC2 0.0327+1.603X10-4 T ^

M. Hashemi-Tilehnoee1,* and F. Javidkia2

1Department of Engineering, Aliabad Katoul Branch, Islamic Azad University, Golestan 2School of Mechanical Engineering, Shiraz University, Shiraz

Iran

1. Introduction

In this chapter, different methods for monitoring and controlling power in nuclear reactors are reviewed. At first, some primary concepts like neutron flux and reactor power are introduced. Then, some new researches about improvements on power-monitoring channels, which are instrument channels important to reactor safety and control, are reviewed. Furthermore, some new research trends and developed design in relation with power monitoring channel are discussed. Power monitoring channels are employed widely in fuel management techniques, optimization of fuel arrangement and reduction in consumption and depletion of fuel in reactor core. Power reactors are equipped with neutron flux detectors, as well as a number of other sensors (e. g. thermocouples, pressure and flow sensors, ex-vessel accelerometers). The main purpose of in-core flux detectors is to measure the neutron flux distribution and reactor power. The detectors are used for flux mapping for in-core fuel management purposes, for control actions and for initiating reactor protection functions in the case of an abnormal event (IAEA, 2008). Thus, optimization on power monitoring channel will result in a better reactor control and increase the safety parameters of reactor during operation.

An abstract group G is a set of elements for which a law of composition or "product" is defined.

For illustrative purposes let us consider a simple set of three elements {E, A, B}. A law of composition for these three elements can be expressed in the form of a multiplication table, see Table 1. In position i, j of Table 1. we find the product of element i and element j with the numbering 1 ^ E,2 ^ A,3 ^ B. From the table we can read out that B = AA because element 2, 2 is B and the third line contains the products AE, AA, AB. The table is symmetric therefore AB = BA. Such a group is formed for example by the even permutations of three objects: E = (a, b, c), A = (c, a, b), B = (b, c, a). The multiplication table reflects four necessary

Table 1. Multiplication table for elements { E, A, B}

conditions that a set of elements must satisfy to form a group G. These four conditions are:

1. The product of any two elements of G is also an element of G. Such as for example AB = E.

2. Multiplication is associative. For example (AB)E = A(BE).

3. G contains a unique element E called the identity element, such that for example AE = EA = A and the same holds for every element of G.

4. For every element in G there exists another element in G, such that their product is the identity element. In our example AB = E therefore B is called the inverse of A and is denoted B = A-1.

The application of group theory to physical problems arises from the fact that many characteristics of physical problems, in particular symmetries and invariance, conform to the definition of groups, and thereby allows us to bring to bear on the solution of physical problems the machinery of abstract group theory.

For example, if we consider a characteristic of an equilateral triangle we observe the following with regard to the counter clockwise rotations by 120 degrees. Let us give the operations the following symbols: E-no rotations, Сэ-rotation by 120°, C3C3 = C2-rotation by 240°. The group operation is the sequential application of these operations, the leftmost operator should be applied first. The reader can easily check the multiplication table 2. applies to the

Table 2. Multiplication table of G = {E, C3, C^}

Table 3. Multiplication table of G3

Table 4. Multiplication table of a permutation group

group G = {E, C3,C2}. We see immediately that the multiplication table of the rotations of an equilateral triangle is identical to the multiplication table of the previous abstract group G = {E, C3, C2}. Thus there is a one-to-one correspondence (called isomorphism) between the abstract group of the previous example, and its rules.

During the test term, the aqueous K2MoO4 solution was chemically stable, and the precipitation or the deposit was not generated in the solution. Then the molybdenum concentration of the solution was almost constant before and after the test, and the concentrations before and after the test were 396.2 mg/ml and 384.0 mg/ml respectively. The concentrations were measured with an Inductively Coupled Plasma Atomic Emission Spectrometer (ICP-AES). The pH of the solution was also almost constant at pH9.5-9.7.

2. Conclusion

In the 99Mo production system with the solution irradiation method, a static or flowing aqueous molybdenum solution in a capsule is irradiated with neutrons in a testing reactor, and 99Mo is produced by the 98Mo (n, y) 99Mo reaction. The system aims to provide 100% of the 99Mo imported into Japan. As a part of the technology development, aqueous (NH4)6Mo7O24 4H2O and K2MoO4 solutions were selected as candidates for the irradiation target of the system, and compatibility between the static two solutions and the structural materials of the capsule and pipes in the system, the chemical stability, the radiolysis and the у heating of the solutions were investigated. As a result, it was found that the solutions are promising as the target. In addition, compatibility between a flowing aqueous K2MoO4 solution, which was the first candidate for the irradiation target in terms of a 99Mo production rate, and the structural material and the chemical stability of the flowing solution were investigated. As a result, it was found that stainless steel SUS304 has good compatibility with a flowing aqueous K2MoO4 solution and that the solution is chemically stable. The fundamental characteristics of the selected aqueous molybdate solutions became clear, and SUS304 can be used as the structural material of the capsule and the pipes.

In the future, a neutron irradiation test will be carried out as an overall test of 99Mo production system with the solution irradiation method, and 99Mo production, the separation of activation by-products, the quantity of radiolysis gas, nuclear heating and so on will be investigated.

Aiming at the domestic production of 99Mo in Japan, the development of 99Mo production with the solution irradiation method is kept going.

3. Acknowledgment

The author would like to thank Dr. Tsuchiya, K. and Mr. Ishida, T. of JAEA and Mr. Ishikawa, K. of KAKEN. Inc. for their valuable comments.

[1]CHF/actual local heat flux

Fig. 22. Critical heat flux as a function of the coolant inlet temperature

The minimum DNBR for IPR-R1 TRIGA (DNBR=8.5) is much larger than other TRIGA reactors. The 2 MW McClellen TRIGA calculated by Jensen and Newell (1998) had a DNBR=2.5 and the 3 MW Bangladesh TRIGA has a DNBR=2.8 (Huda and Rahman, 2004).

[2] Rectangular channel with hydraulic diameter from 2.6 to 6.4 mm.

• Pressure from 1.10 to 1.7 bar.

• Heat flux from 0.66 to 3.4 MW/m2.

• Inlet temperature from 35 to 75°C.

• Velocity from 0.6096 to 9.144 m/s.

[3] Frost(1963)

[4] Cox and Cronenberg (1977)

[5] Lundberg and Hobbins (1992)

[6] at nitrogen pressure > 0.25 MPa

[7] Leitnaker & Godfrey (1967)

[8] UN(s)=U(l)+0.5№(g), Gingerich (1969)

[9] UN(s)=U(g)+0.5№(g), Gingerich (1969)

[10]at 1000°C, Bowman et al.(1965;1966)

[11] Faced-Centered Cubic (FCC)

[12] It might be as high as 850°C.

[13]The views expressed are those of the authors and do not reflect those of any government agency or any part thereof.

[14] Static eigenvalue problem: When the flux does not depend on t, the left hand side is zero, and (4.1) has a nontrivial solution only if the cross-sections are interrelated. To this end, we free keff and the static diffusion equation is put in the form of an eigenvalue problem:

[15] As matrix O is orthogonal, its inverse is just its transpose.

[16] Images of V0 cover V.

[17] We obtain reactions rates also from the Green’s function.

[18] Actually a discretized eigenfunction, i. e. x.

[19] Since keff depends on the entering currents, the problem is non-linear.

[20] After normalization of the row vectors, the П matrices become orthogonal: П+П is the unit matrix.

[21] Here Ent is the integer division.

[22]Mo production in JMTR will start by using the solid irradiation method. JMTR aims to provide 99Mo of 37 TBq/w (1,000 Ci/w), and it will cover about 20% of the 99Mo imported into Japan (Inaba et al., 2011).

[23] The generation and reduction of 99Mo by the neutron capture reaction of 98Mo (n, y) 99Mo and 99Mo (n, у) 100Mo and the radioactive decay of 99Mo are evaluated.

• 99Mo doesn’t exist in the initial stage of the calculation.

• The decay of neutron flux due to the inner and outer tubes of the capsule is considered.

• The circulation of the two irradiation targets is not considered.

Equation (26) can be used to calculate the fuel centerline temperature. The thermal conductivity in Eq. (26) is the average thermal conductivity, which varies as a function of temperature. In order to increase the accuracy of the analysis, the radius of the fuel pellet has been divided into 20 rings. Initially, the inner-surface temperature is not known, therefore, an iteration loop should be created to calculate the outer-surface temperature of the fuel and the thermal conductivity of the fuel based on corresponding average temperatures.

16N is one of the radioactive isotopes of nitrogen, which is produced in reactor coolant (water) emitting a Gamma ray with energy about 6 MeV and is detectable by out-core instruments. In this section, a 16N instrument channel in relation to reactor power measurement will be studied. The reactor power and the rate of production of 16N have a linear relation with good approximation. A research type of 16N power monitoring channel subjected to use in Tehran Research Reactor (TRR). Tehran Research Reactor is a 5 MW pool-type reactor which use a 20% enriched MTR plate type fuel. When a reactor is operating, a fission neutron interacts with oxygen atom (16O) present in the water around the reactor core, and convert the oxygen atom into radioactive isotope 16N according to the following (n, p) reaction. Also another possible reaction is production of 19O by 1|O (n, y) 1gO reaction.

Of course, water has to be rich of 1|O for at least 22% to have a significant role in 19O producing, but 18O is exist naturally (0.2%)

Jn + *|0 ^ ^0 + y(2.8 MeV) (2)

£n + “0 ^P + “N* (3)

16N* is produced and radiate gamma rays (6MeV) and в particles during its decay chain.

r6N* igo + _op + y(6.13 MeV) (4)

In addition to “0 (99.76%) and ^0 (0.2%), other isotope of oxygen is also exist naturally in water, including 1g0 (0.04%). 1yN (0.037%) produced from 1g0 by the (n, p) reaction which will decayed through beta emission.

Since activity ratio of 16N to 17N is 257/1, thus activity of 17N does not count much and is negligible. Primary water containing this radioactive 16N is passed through the hold-up tank (with capacity of 384.8 m3, maximum amount of water that can pour to the hold-up tank is 172 m3 and reactor core flow is 500 m3 h-1), which is placed under the reactor core and water flow from core down to this tank by gravity force. The hold-up tank delays the water for about 20.7 min. During this period activity of the short lived 16N (T1/2 = 7.4 s) decays down to low level. The decay tank and the piping connection to the reactor pool are covered with heavy concrete shielding in order to attenuate the energy of gamma emitted by the 16N nuclei. To investigate the amount of 16N in Tehran Research Reactor by direct measurements of gamma radiation and examine the changes with reactor power, the existing detectors in the reactor control room used and experiment was performed. To assess gamma spectrum for the evaluation of 16N in reactor pool a portable gamma spectroscopy system which includes a sodium-iodide detector is used. The sodium-iodide (NaI) detector which is installed at reactor outlet water side is used for counting Gamma rays due to decay of 16N which depends directly on the amount of 16N. Some advantages of the power measurement using 16N system:

— Power measurement by 16N system uses the gamma from decay of 16N isotope only, so other gammas from impurities do not intervened the measurements.

— Since 16N system installed far from the core, fission products and its gamma rays would not have any effects on the measurements.

— Energy dissipation of heat exchanged with surroundings would not intervene, because water temperature would not use in this system for reactor power measurements.

It is expected that the amount of 16N which is produced in reactor water has linear relation with the reactor power. Comparison of theory and experience is shown in Figure 6.

Based on graph which resulted from experimental data and the straight line equation using least squire fit, it is appear that the experimental line deviated from what it expected; it means that the line is not completely straight. It seems this small deviation is due to the increasing water temperature around the core in higher power, density reduction and outlet water flow reduction which cause 16O reduction and so 16N. At the same time the amount of 16N production decreases and thus decreasing gamma radiations, this will reduce the number of counting, but on the other hand, since the number of fast neutron production in reactor can increase according to reactor power and moderator density became less, the possibility of neutron interaction with water would increased. During past years, linearity of the curve as the experimental condition and the measurements were improved. Now that this linearity is achieved, by referring to the graph, it could conclude that 16N system is suitable to measure the reactor power. Safety object of the new channel is evaluated by the radiation risk of 16N, dose measurement performed in the area close to the hold-up tank for gamma and beta radiations. The dose received in these areas (except near the hold-up tank charcoal filter box which is shielded) are below the recommended dose limits for the radiation workers (0.05 Sv/year), therefore it can be seen that the radiation risk of 16N is reduced due to design of the piping system and hold-up tank which is distanced from the core to overlap the decay time. Thus, 16N decay through the piping and hold-up tank is reduced to a safe working level. It could be seen that 16N system is able to measure the reactor power enough accurately to be used as a channel of information. For the pool type research reactor which has only one shut down system also could be used to increase the reactor safety (Sadeghi, 2010).

In a given node, the response matrices are determined based on the analytical solution (4.19). We need an efficient recipe for decomposing the entering currents into irreps and reconstructing the exiting currents on the faces. Since the only approximation in the procedure requires the continuity of the partial currents, we need to specify the representation of the partial currents and how to represent them. The simplest is a representation by discrete points along the boundary, the minimal number is four, the maximal number depends on the computer capacity. An alternative choice is to represent the partial currents by moments over the faces. Usually average, first and second moment suffice to get the accuracy needed by practice. The representation fixes the number of points we need on a side and the number of points (n) on the node boundary.

To project the irreps, we may use (Mackey, 1980) the cos((k — 1)2п/n), k = 1,…, n/2 and sin((k)2n/n), k = 1,…,n/2 vectors (after normalization). The following illustration shows

4, i. e. one value per face. In a square node we need the following matrix

1111 1 -1 1 -1 10 -10 0 10 -1

to project the irreducible components from the side-wise values. As (2.20) shows, irreducible components are linear combinations of the decomposable quantity [20]. The coefficients are given as rows in matrices П4.

In a regular n-gonal node the response matrix has[21] Ent [(n + 2) /2] free parameters. The response matrix also has to be decomposed into irreps, this is done by a basis change. Let the response matrix give

J = RI

Multiply this expression by П from the left:

nj = (nRQ-1) Ш, (4.31)

and we see that for irreducible representations the response matrix is given by ORO+. In a square node:

r t1 t2 t1

(4.32)

t1 t2 t1 r

and the irreducible representation of R4 is diagonal:

A 0 0 0 0 B 0 0 0 0 C 0 0 0 0 C

where

A = r + 2t + t2, B = r — 2t + t2, C = r — t2.

We summarize the following advantages of applying group theory:

• Irreducible components of various items play a central role in the method. The irreducible representations often have a physical meaning and make the calculations more effective (e. g. matrices transforming one irreducible component into another are diagonal).

• The irreducible representations of a given quantity are linearly independent and that is exploited in the analysis of convergence.

• The usage of linearly independent irreducible components is rather useful in the analysis of the iteration of a numerical process.

• In several problems of practical importance, the problem is almost symmetric, some perturbations occur. This makes the calculation more effective.

• It is more efficient to break up a problem into parts and solve each subproblem independently. Results have been reported for operational codes (Gado et al., 1994).

The above considerations dealt with the local symmetries. However, if we decompose the partial currents into irreps, we get a decomposition of the global vector x in equation (4.28) as well. We exploit the linear independence of the irreducible components further on the global scale.

For most physical problems we have a priori knowledge about the solution to a given boundary value problem in the form of smoothness and boundedness. This is brought to bear through the choice of solution space. In the following, we introduce via group theoretical principles the additional information of the particular geometric symmetry of the node. This allows the decomposition of the solution space into irreducible subspaces, and leads, for a given geometry, not only to a rule for choosing the optimum combination of polynomial expansions on the surface and in the volume, but also elucidates the subtle effect that the geometry of the physical system can have on the algorithm for the solution of the associated mathematical boundary problem.

Consider the iteration (4.29) and decompose the iterated vector into irreducible component

x = £ X (4.34)

a

where because of the orthogonality of the irreducible components

xe+ xa = 0

when a = в. The convergence of the iteration means that

XN+k1 — XN+k2 = 0 (4.35)

for any ki, k2. But that entails that as the iteration proceeds, the difference between two iterated vector must tend to zero. In other words, the iteration must converge in every irreducible subspace. This observation may be violated when the iteration process has not been carefully designed.

Let us assume a method, see (Palmiotti, 1995), in which N basis functions are used to expand the solution along the boundary of a node and M basis functions to expand the solution inside the node. It is reasonable to use the approximation of same order along each face, hence, in a square node N is a multiple of four. For an Mth order approximation inside the node, the number of free coefficients is (M + 1)(M + 2)/2. It has been shown that an algorithm (Palmiotti, 1995) with a linear (N = 1) approximation along the four faces, with 8 free coefficients, of the boundary did not result in convergent algorithm unless M = 4 quartic polynomial, with 15 free coefficients, was used inside the node.

In such a code each node is considered to be homogeneous in composition. Central to the accuracy of the method are two approximations. In the first, we assume the solution on the boundary surface of the node to be expanded in a set of basis functions (f (£); i = 1,…, N). In the second, the solution inside the volume is expanded in another set of basis functions (Fj (r); j = 1,…, M). Clearly the independent variable £ is a limit of the independent variable r.

Any iteration procedure, in principle, connects neighboring nodes through continuity and smoothness conditions. For an efficient numerical algorithm it is therefore desirable to have

the same number of degrees of freedom (i. e. coefficients in the expansion) on the surface of the node as within the node. With the help of Table 6, for the case of a square node, we compare the required number of coefficients for different orders of polynomial expansion. A linear approximation along the four faces of the square has at least one component in each irreducible subspace. At the same time the first polynomial contributing to the second irrep is fourth order. Convergence requires the convergence in each subspace thus the approximation inside the square must be at least of fourth order. There is no linear polynomial approximation that would use the same number of coefficients on the surface as inside the volume. The appropriate choice of order of expansion is thus not straightforward but it is important to the accuracy of the solution, because a mismatch of degrees of freedom inside and on the surface of the node is likely to lead to a loss of information in the computational step that passes from one node to the next. A lack of convergence has been observed, see (Palmiotti, 1995), in the case of calculations with a square node when using first order polynomials on the surface. A convergent solution is obtained only with fourth or higher order polynomial interpolation inside the node. Similar relationships apply to nodes of other geometry. For a hexagonal node that there is no polynomial where the number of coefficients on the surface matches the number of coefficients inside the node.

In a hexagonal node in (Palmiotti, 1995), the first convergent solution with a linear approximation on the surface requires at least a sixth order polynomial expansion within the node. Thus, in the case of a linear approximation on the surface, in the case of a square node a third order polynomial within the node does not lead to a convergent solution, although the number of coefficients is greater than those on the surface. In the case of the regular hexagonal node, a convergent solution is obtained only for the sixth order polynomial expansion in the node, while both a fourth and a fifth order polynomial have a greater number of coefficients inside the node than on the surface. It appears that some terms of the polynomial expansion contain less information than others, and are thus superfluous in the computational algorithm. If these terms can be "filtered out", a more efficient and convergent solution should result. The explanation becomes immediately clear from the decomposition of the trial functions inside the volume and on the boundary. In both the square and hexagon nodes, the first order approximation on the boundary is sufficient to furnish all irreducible subspaces whereas this is true for the interpolating polynomials inside V for surprisingly high order polynomials.

There are several controlling factors in nuclear reactors such as:

1. Coolant flow rate.

2. Movement of control rods.

3. Concentration of boric acid.

4. Reaction rate.

5. Error function.

6. Temperature of core.

7. Power of reactor.

8. Core expansion.

9. Fission poisons like Xe and Sm.

10. Fission fragments and fission products.

11. Burn up.

12. Power demand.

13. The kind of fuel.

14. Energy of neutrons.

15. Doppler’s effect.

16. Value of p.

If each system variable as a mathematics variable is considered then can write:

X(t) = Ax(t) + Bu(t) (5)

and:

y(f) = Cx(t)

Where:

x(t) is: variable of system, X(t) is: derivative of system’s variable, y(t) is: output, A is: system’s matrix, B is: control’s matrix, C is: matrix of output and u(t) is: control’s variable.

By taking the laplace conversions from two sides of above equations can write:

sX(s) = AX(s) + Bu(s) (7)

and:

Y(s) = CX(s) (8)

So two last equations that are based on Laplace conversions can be converted to following form:

(sI — A)X(s) = Bu(s)

and:

Y (s) = CX(s) (10)

Therefore:

X(s) = (sI — A)-1 Bu(s) (11)

and:

Y(s) = C(sI — A)-1 Bu(s) (12)

In order to define the transfer function, it will be deduced as shown below:

G(s) = Щ = C(sI — A)-1 B u(s)

The new developed on-line monitoring method which is based on a temperature difference between an instrumented fuel element and the pool water below a research reactor in practice, as known power measuring by thermal balance is as following. The reactor core is cooled by natural convection of demineralized light water in the reactor pool. Heat is removed from the reactor pool and released into the atmosphere through the primary cooling loop, the secondary cooling loop and the cooling tower. Pool temperature depends on reactor power, as well as external temperature, because the latter affects heat dissipation in the cooling tower. The total power is determined by the thermal balance of cooling water flowing through the primary and secondary loops added to the calculated heat losses. These losses represent a very small fraction of the total power (about 1.5% of total). The inlet and outlet temperatures are measured by four platinum resistance thermometers (PT-100) positioned at the inlet and at the outlet pipes of the primary and secondary cooling loops. The flow rate in the primary loop is measured by an orifice plate and a differential pressure transmitter. The flow in the secondary loop is measured by a flow-meter. The pressure transmitter and the temperature measuring lines were calibrated and an adjusted equation was added to the data acquisition system. The steady-state is reached after some hours of reactor operation, so that the power dissipated in the cooling system added with the losses should be equal to the core power. The thermal power dissipated in the primary and secondary loops were given by:

Чсооі = m. Cp. AT (10)

where qC00{ is the thermal power dissipated in each loop (kW), m is the flow rate of the coolant water in the loop (kg. s-1), cP is the specific heat of the coolant (kJ kg-1 °C-1), and ДТ is the difference between the temperatures at loop the inlet and outlet (°C). Figure 21 shows the power evolution in the primary and secondary loops during one reactor operation.

8. Acknowledgement

The authors are thankful InTech publication for their scientific effort to develop the sciences. We also, thank Mrs. M. Mohammadi for her effort to preparing the manuscript.

9. Conclusion

Power monitoring channels play a major role in retaining a safe reliable operation of nuclear reactors and nuclear power plants. Accurate power monitoring using advanced developed channels could make nuclear reactors a more reliable energy source and change public mind about this major energy resource. Regarding harsh accidents such as Chernobyl, Three-Mile Island and the recent accidents in Fukushima nuclear power plants and their dangerous effects on the environment and human life, the importance of developing reactor safety system like power monitoring channels are more attended. New generations of nuclear power plants are much safer than their predecessors because of their new accurate safety systems and more reliable monitoring channels. They produce energy from nuclear fission and are the cleanest, safe and environment-friendly source of energy among many investigated power resources (Javidkia et al., 2011).

There is no doubt that nuclear power is the only feasible green and economic solution for today’s increasing energy demand. Therefore, studying, researches and more investments on the power monitoring systems and channel in nuclear reactors will help to create an inexhaustible source of safe and clean energy.

The most important element of the solution irradiation method is the aqueous molybdenum solution as the irradiation target. The solution with a high concentration near the saturation is used for efficient 99Mo production, and the solution always is in contact with the structural materials of the capsule and the pipes in the 99Mo production system under irradiation. Aqueous molybdate solutions are promising candidates for the irradiation target. The effect of the solutions on metals such as the structural materials has been researched, and molybdates are known as corrosion inhibitors (Kurosawa & Fukushima, 1987; Lu et al, 1989; McCune et al, 1982; Saremi et al, 2006). However, the behavior of aqueous molybdenum solutions including the aqueous molybdate solutions under such the conditions is not well understood. Therefore, the following subjects about the fundamental characteristics of the solutions should be investigated:

1. Selection of the aqueous molybdenum solutions as candidates for the irradiation target

2. Compatibility between the solutions and the structural materials

3. Chemical stability of the solutions

4. Effect of у ray and neutron irradiation on the solutions such as the radiolysis, the у heating and the activation by-products.

The some subjects described above had already investigated (Inaba et al., 2009), and the progress of the development made thus far is explained as below: