Method for controlling a nuclear reactor, nuclear reactor and calibration method

Abstract

The invention relates to a control method comprising a control operation for controlling the nuclear reactor comprising a step of generating actuator commands using a digital model of the nuclear reactor, and an estimation operation for estimating at least one estimated numerical coefficient, belonging to the set of numerical coefficients of the digital model, the estimation operation being performed in parallel with the control operation, and comprising a step of determining simulated values for a group of operating parameters using the digital model, for a sliding time window [t0; t0+T].

Description

[0001]TITLE: Method for controlling a nuclear reactor, nuclear reactor and calibration method The invention generally relates to the control of nuclear reactors. The patent applications filed in France under the numbers FR 1850867 and FR 2103869 relate to methods for controlling nuclear reactors implementing a predictive digital model configured to determine the evolution over time of a set of state parameters of the nuclear reactor from an initial state, using the commands provided to the actuators. This digital model makes it possible to determine online, i.e. in real time, the best commands based on the operational instructions imposed on the operators. Patent applications FR 1850867 and FR 2103869 use a model of the nuclear reactor that is both simple and precise, suitable for use in predictive control. The predictive digital model must be as precise as possible, to take advantage of all theoperational margins of the nuclear reactor and ensure a high level of performance. It must be fast, since the controls are updated periodically, for example with a time interval of one minute. The predictive numerical model is used several times during each time interval, during the control optimization operation. In this context, the invention aims to propose a control method which is even more precise. To this end, the invention according to a first aspect relates to a method for controlling a nuclear reactor, the nuclear reactor having a pressure vessel, a core comprising a plurality of nuclear fuel assemblies arranged in the pressure vessel, a primary core cooling circuit in which a primary heat transfer fluid containing a neutron poison circulates, and a plurality of actuators acting on the core or the primary circuit, the method comprising a reactor control operationnuclear reactor with the following steps: S10 / acquisition of the current values ​​of a plurality of acquired operating parameters of the nuclear reactor; S20 / generation of actuator commands using the current values ​​of the acquired operating parameters, at least some commands being determined using a digital model of the nuclear reactor, the digital model being configured to determine an evolution over time of a set of state parameters of the nuclear reactor using the commands provided to the actuators and a set of digital coefficients; S30 / control of the actuators using the generated commands; steps S10 / , S20 / and S30 / being implemented iteratively; the method also comprising an operation of estimating at least one estimated digital coefficient belonging to the set of digital coefficients and / or at least one estimated state parameter belonging to the set of state parameters, implementationiteratively at times tc, in parallel with the control operation, this estimation operation comprising at each iteration the following steps: S40 / generation of a generated value of the at least one estimated numerical coefficient and / or of an initial generated value of the at least one estimated state parameter; S50 / determination of simulated values ​​of a group of simulated operating parameters forming part of the acquired operating parameters, using the numerical model, for a sliding time window [t0; t0+T] with tc, T being the width of the time window, the time window being located in the past with respect to tc, using in the numerical model the generated value of the at least one estimated numerical coefficient and / or considering at t0 the initial generated value of the at least one estimated state parameter; S60 / evaluation of a cost function using the current values ​​and the simulated values ​​of the group of simulated operating parameters;steps S40 / , S50 / and S60 / being repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated numerical coefficient and / or the generated initial value of the at least one estimated state parameter making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated numerical coefficient and / or of the at least one estimated state parameter at time tc. Thus, the invention makes it possible to periodically update the numerical coefficients of the numerical model used for controlling the nuclear reactor and / or to recalibrate certain state parameters. The operation of estimating the numerical coefficients and / or the state parameters is carried out in parallel with the operation of controlling the nuclear reactor, i.e. concomitantly. Indeed, in the state of the art, certain numerical coefficients are constants obtained by calculation, using hypotheses andsimplifications that do not correspond to reality, or that are calibrated for specific operating conditions. During the life of the nuclear reactor, these operating conditions change in such a way that the numerical coefficients are no longer representative. For example, there is progressive wear of the mechanical systems of the nuclear reactor, in particular for the movement of the core reactivity control groups. Furthermore, the nuclear fuel assemblies are partially renewed every 18 months. Fission products accumulate during this period, which has an impact on the operation of the core. The invention makes it possible to periodically re-evaluate these numerical coefficients, so as to guarantee excellent representativeness of the numerical model. This re-evaluation is carried out by comparing the current values ​​recorded for certain operating parameters with the values ​​simulated using the numerical model. The comparisonis performed over a sliding time window. In other words, the estimation operation uses an MHE (Moving Horizon Estimation) type algorithm. Such an approach allows the numerical coefficients to be reconstructed quickly, converging after a few iterations towards precise and realistic values. This method is adapted to the time constant used for the nuclear reactor control operation. The nuclear reactor controls are typically refreshed every minute. The numerical coefficients can also be re-evaluated every minute. The invention allows certain state parameters of the nuclear reactor to be re-evaluated in addition to or instead of the numerical coefficients. These state parameters are typically quantities not available for measurement, such as the concentration of Xenon 135 or Iodine 135 in nuclear fuel assemblies. These parametersstate variables may be available in or calculated by the reactor instrumentation and control system, but are not always. They affect the numerical results provided by the numerical model, and therefore the commands determined by the control operation. The control method may also have one or more of the following characteristics, considered individually or in all technically possible combinations: - the cost function includes a term corresponding to the difference between the current values ​​and the simulated values ​​of the group of operating parameters simulated over the time window; - the cost function includes a term corresponding to the difference between the generated value of the at least one estimated numerical coefficient and the estimate of the at least one estimated numerical coefficient obtained at the previous iteration; - the cost function includes a term corresponding to the difference between the generated initial value of the at least one estimated numerical coefficient and the estimate of the at least one estimated numerical coefficient obtained at the previous iteration;an estimated state parameter and the estimation of the at least one estimated state parameter obtained at the previous iteration; - the numerical model of the nuclear reactor comprises several sub-models, each sub-model modeling a level of the core of the nuclear reactor and comprising at least one equation describing a kinetics of a neutron density at said level and an equation describing a temperature of the primary heat transfer fluid at said level, the numerical model further comprising equations describing neutron exchanges between the levels and equations characterizing a reactivity at each level; - the primary circuit comprises at least one primary loop having a hot branch and a cold branch connected respectively to a primary heat transfer fluid outlet and to a primary heat transfer fluid inlet of the pressure vessel, the nuclear reactor further comprising: - a secondary circuit equipped with a turbine; - a steam generatorfor the or each primary loop, having a first side inserted in the primary loop and a second side inserted in the secondary circuit; - groups of control rods absorbing neutrons; - a mechanism capable of selectively inserting or extracting the groups of control rods in the core of the nuclear reactor; - a unit for injecting neutron poison into the primary heat transfer fluid; and - a unit provided for injecting water into the primary circuit; - the equations characterizing the reactivity at each level take into account one or more of the following effects: - effect due to a variation in the temperature of the primary heat transfer fluid at said level; - effect due to a variation in the power supplied by the core at said level; - effect due to the movements of groups of control rods; - effect due to a variation in the concentration of neutron poison in the primary heat transfer fluid; - effect due to a variation in theconcentration of Xenon 135 in the nuclear fuel assemblies at said level; - the state parameters include one or more of the parameters from the list below: - for each level of the core, neutron density at said level, concentration of Xenon 135 in the nuclear fuel assemblies at said level, concentration of Iodine 135 in the nuclear fuel assemblies at said level; - for each level of the core, temperature of the primary coolant; - temperature of the primary coolant at the inlet and outlet of the core; - temperature of the primary coolant in the hot leg and in the cold leg; - concentration of neutron poison in the primary coolant; - the or each estimated numerical coefficient is chosen from the following list: -̅ ^̅ ̅^^̅^̅^^: coefficient of transmutation by neutron absorption of Xenon 135 into Xenon 136; - l*: average lifetime of neutrons in the core; - D: coefficientof neutron exchange between the levels of the core; - Kbor, Kbank, Kxenon: efficiency coefficients characterizing the contribution in the variation of the reactivity at each level of the core respectively of the variation of the concentration of the neutron poison in the primary heat transfer fluid, of the movements of the groups of control rods, of the variation of the concentration of Xenon 135 in the nuclear fuel assemblies at said level of the core; - ^^ ^^^^ : time constant of the temperature exchange between primary and secondary circuits within the steam generator; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the hot branch; - ^^ ^^^^: time constant for the evolution of the temperature of the primary heat transfer fluid along the cold branch; - the or each estimated state parameter is chosen from the following list: - for each level of the core, concentration of Xenon 135 in the nuclear fuel assemblies at said level; - for each level of the core, concentration of Iodine 135 in the nuclear fuel assemblies at said level, - at step S50 / , the simulated operating parameters are chosen from the following list: - neutron flux at each level of the core; - axial distribution of the neutron flux in the core; - temperature of the primary heat transfer fluid in the hot branch and in the cold branch of the primary circuit; - concentration of neutron poison in the primary heat transfer fluid; - concentration of Xenon 135 in the nuclear fuel assemblies at each level of the core,concentration of Iodine 135 in the nuclear fuel assemblies at each level of the core; - the actuator controls used by the numerical model are chosen from the following list: - movement of the control groups; - injection of neutron poison into the primary circuit; - injection of water into the primary circuit; - power supplied by the turbine; - at each iteration, the optimization operation is treated as an optimization problem aimed at minimizing the cost function, by implementing a numerical integrator and a numerical optimization solver. According to a second aspect, the invention relates to a nuclear reactor comprising a pressure vessel, a core comprising a plurality of nuclear fuel assemblies arranged in the pressure vessel, a primary core cooling circuit in which a primary heat transfer fluid containing a neutron poison circulates,a plurality of actuators acting on the core or the primary circuit, and a control assembly of the nuclear reactor, the control assembly being configured to implement the control method having the above characteristics. The nuclear reactor may also have the following characteristics: - the primary circuit comprises at least one primary loop having a hot branch and a cold branch connected respectively to a primary heat transfer fluid outlet and to a primary heat transfer fluid inlet of the pressure vessel,the nuclear reactor further comprising: - a secondary circuit equipped with a turbine; - a steam generator for the or each primary loop having a first side inserted in the primary loop and a second side inserted in the secondary circuit; - groups of control rods absorbing neutrons; - a mechanism capable of selectively inserting or extracting the groups of control rods in the core of the nuclear reactor; - a unit for injecting neutron poison into the primary heat transfer fluid; and - a unit provided for injecting water into the primary circuit. According to a third aspect, the invention relates to a method for calibrating a numerical model used for controlling a nuclear reactor, the nuclear reactor having a pressure vessel, a core comprising a plurality of nuclear fuel assemblies arranged in the pressure vessel,a primary core cooling circuit in which a primary heat transfer fluid containing a neutron poison circulates, and a plurality of actuators acting on the core or the primary circuit, the control of the nuclear reactor being carried out according to a method comprising the following steps: S10 / acquisition of the current values ​​of a plurality of operating parameters acquired from the nuclear reactor; S20 / generation of actuator commands using the current values ​​of the acquired operating parameters, at least some commands being determined using said digital model, the digital model being configured to determine an evolution over time of a set of state parameters of the nuclear reactor using the commands supplied to the actuators and a set of digital coefficients; S30 / control of the actuators using the generated commands; steps S10 / ,S20 / and S30 / being implemented iteratively; the calibration method comprising an operation of generating reference values ​​of a plurality of reference operating parameters of the nuclear reactor for a time interval I, and an operation of estimating at least one estimated numerical coefficient belonging to the set of numerical coefficients, implemented for a time window [t0; t0+T] within the time interval I, T being the width of the time window, this estimation operation comprising at each iteration the following steps: S70 / generation of a generated value of the at least one estimated numerical coefficient; S80 / determination of simulated values ​​of a group of simulated operating parameters forming part of the reference operating parameters, using the numerical model,for the time window [t0; t0+T] using in the numerical model the generated value of the at least one estimated numerical coefficient; S90 / evaluation of a cost function using the reference values ​​and the simulated values ​​of the group of simulated operating parameters; steps S70 / , S80 / and S90 / being repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated numerical coefficient making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated numerical coefficient at time t0+T. The calibration method may also have one or more of the characteristics below, considered individually or according to all technically possible combinations: - the estimation operation is implemented in a non-iterative manner,the time window [t0; t0+T] corresponding to the time interval I; - the estimation operation is implemented iteratively, the time window [t0; t0+T] sliding within the time interval I, the time window moving at each iteration and sweeping the time interval I. Other characteristics and advantages will emerge from the detailed description given below, for information purposes and in no way limiting, with reference to the appended figures, among which: figure 1 is a schematic representation of a nuclear reactor according to the invention; figure 2 is a step diagram illustrating the control method of the invention; figure 3 is a schematic representation of the numerical model of the reactor core used in the method of figure 2; figures 4 and 5 are curves, showing for an example of application of the invention the coefficients D, determined by the estimation operation, as a function of time; Figures 6 to 9 are curves, showing for another example of application of the invention the coefficients D,̅ ^̅ ̅^^̅^̅^^ KBore, and KXenon determined by the estimation operation, as a function of time; and. Figure 10 is a curve illustrating yet another example of application of the invention, aimed at determining, among other things, a state parameter. The reactor 1 shown in Figure 1 comprises, in a conventional manner, a pressure vessel 2 and a core 3 arranged in the pressure vessel 2. The core 3 itself comprises a plurality of nuclear fuel assemblies 5. The nuclear reactor 1 also comprises a primary circuit 7, provided for cooling the core 3, in which circulates a primary heat transfer fluid containing a neutron poison.The primary circuit 7 comprises at least one primary loop having a hot branch BC and a cold branch BF connected respectively to a primary heat transfer fluid outlet and to a primary heat transfer fluid inlet of the pressure vessel 2. Typically, the primary circuit 7 comprises several primary loops as described above. Each primary loop comprises a primary pump 8. The nuclear reactor 1 further comprises a secondary circuit 9. A secondary heat transfer fluid circulates in the secondary circuit 9. The secondary circuit 9 comprises a turbine 11. The secondary circuit also comprises a condenser 13, a feed tank 15, and secondary pumps 17, 19. The nuclear reactor 1 comprises a steam generator 21 for each primary loop. Its first side is inserted in said primary loop and its second side in the secondary circuit.The hot branch BC connects the outlet of the pressure vessel to the inlet on the first side of the steam generator 21. The cold branch BF connects the discharge of the primary pump 8 to the inlet of the pressure vessel 2. The primary heat transfer fluid transfers its heat to the secondary fluid in the or each steam generator 21. The secondary heat transfer fluid is vaporized in the steam generator 21, under the effect of the heat transferred by the primary heat transfer fluid. The secondary heat transfer fluid in the form of steam circulates from the steam generator 21 to the turbine 11 and is then condensed in the condenser 13. It is then returned in liquid form to the steam generator 21. A valve 23 is inserted on the steam line connecting the steam generator 21 to the turbine 11, and makes it possible to adjust the flow of steam supplying it. The turbine 11 mechanically drives an alternator 25.The electricity generated by the alternator 25 supplies an electrical distribution network 29. The nuclear reactor 1 also includes a unit 31 for injecting neutron poison into the primary heat transfer fluid. The neutron poison is typically boron. A tank 32 containing a concentrated solution of neutron poison, for example boric acid, is connected to the primary circuit 7 via the conduit 33. A pump 35 and a valve 37 are inserted in the conduit 33. The unit 31 selectively increases the concentration of neutron poison in the primary liquid. The nuclear reactor also includes a unit 39 intended to inject water into the primary circuit. The unit 39 includes a tank 41 connected by a line 43 to the primary circuit 7. A pump 45 and a valve 47 are inserted in the line 43. The water is typically pure demineralized water.The unit 39 is designed to inject water into the primary circuit 7, which has the effect of reducing the concentration of neutron poison in the primary heat transfer fluid. Conventionally, the nuclear reactor 1 also comprises groups 49 of control rods and a mechanism 51 capable of selectively inserting or extracting the groups of control rods 49 into the core 3 of the nuclear reactor. The control rods are made of a neutron-absorbing material. The groups of control rods are 49 moved so as to selectively modify the reactivity inside the core 3. The nuclear reactor 1 thus comprises a plurality of actuators acting on the core 3 or the primary circuit 7.In the example described above, these actuators comprise the control rod groups 49, the unit 31 for injecting neutron poison into the primary heat transfer fluid, the unit 39 provided for injecting water into the primary circuit, the valve 23 for adjusting the flow rate of steam supplying the turbine 11. The valve 23 adjusts the power supplied by the turbine 11, which indirectly acts on the power supplied by the core via the core control members 3. The nuclear reactor 1 also comprises instrumentation 53 for directly measuring or determining a plurality of operating parameters of the nuclear reactor.These operating parameters include, for example, the following parameters: - power supplied by the turbine 11; - temperature of the primary heat transfer fluid at the inlet and outlet of the core 3; - temperature of the primary heat transfer fluid in the hot leg BC and in the cold leg BF; - position of the control rod groups 49; - power supplied by the core 3 of the nuclear reactor; - neutron flux at different levels of the core 3; - distribution of the neutron flux in the core 3; - concentration of neutron poison in the primary heat transfer fluid. Advantageously, the current flow rate of neutron poison and the current flow rate of water injected into the primary heat transfer fluid are also measured or determined. The instrumentation 53 includes in particular neutron detectors, placed outside the core 3, and distributed over the entire height of the core. These detectors are known as ex-core chambers.The power supplied by the core is for example obtained by calculation, based on the information provided by the detectors measuring the neutron fluxes outside the core. Alternatively, the power supplied by the core is determined using the measurement of the power supplied by the turbine. The parameter characterizing the distribution of the neutron flux in the core is for example the axial power distribution, or axial offset AO. The axial offset is calculated using the following formula: AO = (Φh-Φb) / (Φh+Φb) where Φh is the neutron flux of the upper half of the core, and Φb the neutron flux of the lower half of the core. The neutron fluxes of the upper and lower halves of the core are typically obtained by the neutron detectors placed outside the core 3. Alternatively, they are obtained by probes placed in the core 3, called in-core probes.Alternatively, the parameter characterizing the distribution of the neutron flux in the core is Φh-Φb, or any other suitable parameter. The instrumentation 53 comprises temperature measuring probes, arranged to measure the temperature of the heat transfer fluid in the hot and cold branches and in the pressure vessel. The instrumentation 53 advantageously comprises a sensor directly or indirectly measuring the mechanical power supplied by the turbine. The position of the groups of control rods 49 is typically determined from information provided by the mechanism 51 for moving the control rods. The nuclear reactor 1 also comprises a nuclear reactor control assembly 55. The control assembly is configured to implement the nuclear reactor control method which will now be described.As illustrated in Figure 2, the method comprising an operation of controlling the nuclear reactor with the following steps: S10 / acquisition of the current values ​​of a plurality of operating parameters acquired from the nuclear reactor; S20 / generation of commands for the actuators using the current values ​​of the operating parameters acquired, at least some commands being determined using a digital model of the nuclear reactor, the digital model being configured to determine an evolution over time of a set of state parameters of the nuclear reactor using the commands provided to the actuators and a set of digital coefficients; S30 / controlling the actuators using the generated commands. Steps S10 / , S20 / and S30 / are implemented iteratively.Advantageously, steps S20 / and S30 / are repeated with a period of less than sixty minutes, preferably less than twenty minutes, and for example equal to one minute. Step S10 / is repeated with the same period. Alternatively, it is repeated with a longer period. This period may be different depending on the acquired operating parameters. The commands are determined to regulate a certain number of regulated operating parameters of the nuclear reactor to setpoint values. The regulated operating parameters are for example the temperature of the primary heat transfer fluid Tmoy, the axial offset AO, the power supplied by the turbine, etc. The actuator commands generated in step S20 / are chosen from the following list: - movement of the control groups; - injection of neutron poison into the primary circuit; - injection of water into the primary circuit; - power supplied by the turbine.Typically, all the commands in the above list are generated in step S20 / . The numerical model is used, for example, to determine all the commands in the list. Alternatively, the numerical model is used to determine only certain commands in the list. The other commands are set manually, or determined in step S20 / using control loops independent of the numerical model. Each control loop is designed in this case to regulate one of the regulated operating parameters, using one of the commands. For example, nuclear reactors operating according to mode A comprise a primary heat transfer fluid temperature control loop. The loop receives as input the current average temperature Tmoy of the primary heat transfer fluid in the core 3 of the reactor. This value corresponds, for example, to the average of the temperature measured at the reactor inlet and the temperature measured at the core outlet.The loop also receives the setpoint temperature. The average temperature of the primary coolant Tmoy in the core is controlled by moving groups of control rods 49, depending on the difference between the current temperature and the setpoint temperature. Four groups of rods, called groups A, B, C, D, can be moved to control the temperature Tmoy. In FR1850867, the numerical model is used to determine the movement of the control groups, the injection of neutron poison and the injection of water. In FR2103869, the numerical model is used to determine the injection of neutron poison and the injection of water. The numerical model in step S20 / can be used in multiple ways. In FR1850867, it is used in combination with a gain-sequenced control algorithm. The numerical model is used to determine commands. The gain-sequenced control algorithm is used to develop corrective commands.The commands actually applied for controlling the actuators are the sum of the commands and the corrective commands. In FR2103869, the numerical model is used to determine, during power transients, optimized sequences for injecting neutron poison or water. Other uses of the numerical model can be considered. The numerical model uses all the commands in the list above. Alternatively, it uses only some of the commands in the list above. The numerical model of the nuclear reactor core is a so-called predictive model. The numerical model of the nuclear reactor core is a nonlinear model. Alternatively, the numerical model is linear. This numerical model is then, for example, obtained by linearizing the nonlinear model described below. This numerical model is illustrated schematically in Figure 3.The numerical model of the core comprises several sub-models, each sub-model modeling a core level 3. In other words, the core is divided, along the vertical direction, into several slices, each sub-model modeling one of the core slices. Typically, the numerical model of the core comprises between two and twenty sub-models, preferably between two and ten sub-models, and comprises for example six sub-models. Each sub-model comprises at least one equation describing a kinetics of a neutron density in said level, and one equation describing a temperature of the primary heat transfer fluid in said level. The temperatures T2 to T7 at the outlet of each level are deduced from the neutron flux in each level. The equations are indicated below: Level 1: Highest level:. with: - n i : neutron density at level i; -ρ i: reactivity at level i - D: neutron exchange coefficient; - l*: average lifetime of neutrons (prompt and delayed); - KT / H: temperature / enthalpy conversion coefficient; - Kn: power / neutron flux conversion coefficient; - Qp: mass flow rate of primary heat transfer fluid in the core. - T1, T7: temperature at the inlet and outlet of the core; - Ti: temperature of the primary heat transfer fluid at each level; The temperature at each level corresponds here to the temperature at the inlet of this level. Thus, each level of the core is modeled using a one-group approximation of point neutron kinetics with, in addition, a coefficient D to account for neutron exchanges between levels. Delayed neutrons and precursors, which have a dynamics faster than the dynamics expected by the process or the operator, are not modeled.These neutrons have a typical dynamic of 10 seconds, for a calculation time step of the order of 60 seconds. The numerical model also includes equations characterizing the reactivity at each level. These equations take into account one or more of the following effects: - effect due to a variation in the temperature of the primary coolant at said level (also called moderating effect); - effect due to a variation in the power supplied by the core at said level (Doppler effect); - effect due to the movement of the control rod groups; - effect due to a variation in the concentration of neutron poison in the primary coolant; - effect due to a variation in the concentration of Xenon 135 in the nuclear fuel assemblies at said level. The equations are:^^^^ = ^^^^0 + ∆^^^^^^^^ + ∆^^^^^^^^^ + ∆^^^^^^^^^ + ∆^^^^^^^^^. avec ∆^^^^^^^^ = ^^^^^^^∆^^^^∆^^^^^^^^^^ = ^^^^^^^^^∆^^^^^^^^^^^∆^^^^^^^^^ = ^^^^^^^∆^^^^^^ where - ρi0: initial reactivity at level i, determined at the previous time step; - Pi: thermal power supplied by level i of the core, assumed to be proportional to the neutron density; - Xei: concentration of Xenon 135 in the nuclear fuel assemblies at level i; - Ii: concentration of Iodine 135 in the nuclear fuel assemblies at level i; - Γ l : production coefficient of Iodine 135 by fission; -λ l : decay constant of Iodine 135 into Xenon 135; - Γ Xe : coefficient of production of Xenon 135 by fission; - λ Xe: decay constant of Xenon 135 ;-̅ ^̅ ̅^^̅^̅^^ : neutron absorption transmutation coefficient of Xenon 135 into Xenon 136- Kmod, Kdop, Kbor, Kbank, Kxenon : efficiency coefficients characterizing the contribution in the variation of reactivity at each level of the core respectively of the variation of temperature, variation of the power supplied by the core, of the variation of concentration of the neutron poison in the primary coolant, of the displacements of the groups of control rods, of the variation of the concentration of Xenon 135 in the nuclear fuel assemblies at said level of the core. In the equations above, Δ denotes a variation compared to the previous time step. The effect due to the variation of the power supplied by the core makes it possible to characterize the effect of a variation of the temperature of the nuclear fuel, also called Doppler effect.The evolution of Xenon 135 at each level is modeled using classical equations taking into account: - the production of Iodine 135 by fission; - the radioactive decay of Iodine 135 into Xenon 135; - the production of Xenon 135 by fission; - the radioactive decay of Xenon 135; - the transmutation of Xenon 135 into Xenon 136 by neutron absorption. ΔPbanki corresponds to the displacement of all the control groups within level i, expressed in steps. This parameter corresponds to the sum of the variations in the number of insertion steps in level i of all the control groups. ΔPi is obtained by the following equation: ΔPi = Kn x Δni, where Kn is a predetermined constant. Furthermore, the numerical model integrates the following general equation:. P being the total power supplied by the core. The evolution over time of the total power supplied by the core is typically imposed by the electricity distribution network operator. The value of ΔPbanki is determined by the numerical model, based on the evolution of the average temperature Tmoy of the primary heat transfer fluid in the core. The numerical model first determines the value of Tmoy according to the following equation: Tmoy = (T1 + T7) / 2. Then, the numerical model determines an average reference temperature Tref, based on parameters such as the desired reactor power. This data is available in the reactor control system. It is typically imposed by the electricity distribution network operator 29. The numerical model then determines the difference ΔTmoy between Tmoy and the average reference temperature Tref.If Tmoy goes outside the temperature deadband, i.e. if ΔTmoy exceeds a predetermined threshold value, the numerical model sets the value of Tmoy at the limit of the deadband and determines that it is necessary to move the control groups. It calculates a value of ΔPbank as a function of ΔTmoy, allowing criticality to be reached. The reference temperature values ​​and the width of the temperature deadband as a function of the reactor power are predetermined values, recorded in the numerical model, or are provided by the control unit 55. ΔCpn is obtained by integrating the quantities of neutron poison and / or water injected into the primary heat transfer fluid.The numerical model uses the following equations for this purpose: dCpn / dt = – Cpn x Qw / Mt for water injection; dCpn / dt = (Crea – Cpn) x Qpn / Mt for neutron poison injection, with Mt total mass of water in the primary circuit, for example 260 tonnes for an N4 unit, Crea neutron poison concentration in the injected neutron poison solution, Cpn current neutron poison concentration in the primary coolant, Qw injected demineralized water flow rate, Qpn injected neutron poison solution flow rate. A delay is considered to evaluate the effect of the injection of demineralized water or neutron poison. The value of Qp, i.e. the primary coolant flow rate in the core, is a predetermined value. Alternatively, it is recovered in the control unit 55. The numerical model also includes equations modeling the evolution of the temperatures of the cold and hot branches:. c =τ − bc - Tbf and Tbc are the temperatures of the primary heat transfer fluid in the cold and hot branches;- Tḃf, Tḃc and T1̇ are the time derivatives of Tbf, Tbc and T1;- K is a constant; - Pvap is the power extracted by the value generators; - ^^ ^^^^ : time constant of the temperature exchange between primary and secondary circuits within the steam generator; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the hot branch; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the cold branch. The numerical coefficients used in the numerical model are therefore K T / H , K n The operating parameters whose current values ​​are acquired at step S10 / are chosen from the following list: - neutron flux at each level n iof the core; - axial distribution of the neutron flux in the core; - temperature of the primary coolant in the hot leg and in the cold leg of the primary circuit; - concentration of neutron poison in the primary coolant; - concentration of Xenon 135 in the nuclear fuel assemblies at each level of the core, concentration of Iodine 135 in the nuclear fuel assemblies at each level of the core. Typically, the current values ​​of all the above parameters are acquired. Alternatively, only the current values ​​of some of these parameters are acquired. The neutron flux at each level of the core is provided directly by the neutron detectors of instrumentation 53 distributed over the height of the core. Alternatively, it is calculated from the information provided by the neutron detectors. The axial distribution of the neutron flux in the core is provided by instrumentation 53.This distribution corresponds for example to the axial offset. The temperature of the primary coolant in the hot leg and in the cold leg of the primary circuit and the concentration of neutron poison in the primary coolant are provided directly by the instrumentation 53. The concentration of Xenon 135 and the concentration of Iodine 135 in the nuclear fuel assemblies at each level of the core are typically recovered in the control assembly 55. This control assembly 55, in certain nuclear reactors, comprises an expert system configured to determine, among other things, these concentrations of Xenon 135 and Iodine 135. This expert system is designated by the acronym CMS (Core Monitoring System). When these concentrations are not available, they are preferably estimated by the estimation operation, as described below.The state parameters comprise one or more of the parameters from the list below: - for each level of the core, neutron density at said level ni, Xenon 135 Xei concentration in the nuclear fuel assemblies at said level, Iodine 135 Ii concentration in the nuclear fuel assemblies at said level; - for each level of the core, temperature of the primary heat transfer fluid Ti; - temperature of the primary heat transfer fluid at the inlet and outlet of the core T1, T7; - temperature of the primary heat transfer fluid in the hot leg and in the cold leg TBC, TBF; - neutron poison concentration in the primary heat transfer fluid Cpn. Typically, all of the above parameters are state parameters. Alternatively, only some of the above parameters are state parameters. In step S30 / , the commands generated in step S20 / are used to control the corresponding actuators.Step S30 / is completely automatic, the actuators being controlled by a computer without operator intervention. Alternatively, at least some of the actuators are controlled manually in step S30 / . The commands generated in step S20 / constitute decision aids, i.e. recommendations, for the operators controlling the nuclear reactor. The method also comprises an operation of estimating at least one estimated numerical coefficient belonging to the set of numerical coefficients and / or at least one estimated state parameter belonging to the set of state parameters, implemented iteratively at times tc, in parallel with the control operation.This estimation operation comprises the following steps at each iteration: S40 / generation of a generated value of the at least one estimated numerical coefficient and / or of an initial generated value of the at least one estimated state parameter; S50 / determination of simulated values ​​of a group of simulated operating parameters forming part of the acquired operating parameters, using the numerical model, for a sliding time window [t0; t0+T] with t. c , T being the width of the time window, the time window being located in the past relative to t c, using in the digital model the generated value of the at least one estimated digital coefficient and / or considering at t0 the generated initial value of the at least one estimated state parameter; S60 / evaluation of a cost function using the current values ​​and the simulated values ​​of the simulated operating parameters. Steps S40 / , S50 / and S60 / are repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated digital coefficient and / or the generated initial value of the at least one estimated state parameter making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated digital coefficient and / or of the at least one estimated state parameter at time tc. The estimation operation is carried out at the same time as the control operation.It allows the numerical coefficients or state variables used in the numerical model for the nuclear reactor control operation to be corrected in real time. The estimation operation is carried out iteratively, with a period of less than sixty minutes, preferably less than twenty minutes, and for example worth one minute. The estimation operation aims to estimate, for example, only at least one numerical coefficient. Alternatively, it aims to estimate belonging to both at least one numerical coefficient and at least one state parameter. Indeed, the accuracy of the numerical model relies partly on a detailed knowledge of the state parameters. It happens that certain state parameters are not available, either via the instrumentation or more generally in the nuclear reactor control system. In this case, one or more of these state parameters are advantageously determined during the estimation operation.According to another, less frequent alternative, only one or more state parameters are determined by the estimation operation. The or each estimated numerical coefficient is preferably chosen from the following list: - ̅ ^̅ ̅^^̅^̅^^: neutron absorption transmutation coefficient of Xenon 135 into Xenon 136; - l*: average lifetime of neutrons (prompt and delayed) in the core; - D: neutron exchange coefficient between the levels of the core; - Kbor, Kbank, Kxenon: efficiency coefficients characterizing the contribution to the variation of the reactivity at each level of the core respectively of the variation of the concentration of the neutron poison in the primary coolant, of the displacements of the groups of control rods, of the variation of the concentration of Xenon 135 in the nuclear fuel assemblies at said level of the core; - ^^. ^^^^: time constant of the temperature exchange between primary and secondary circuits within the steam generator; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the hot branch; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the cold branch. The or each estimated state parameter is preferably chosen from the following list: - for each level of the core, concentration of Xenon 135 Xe i in the nuclear fuel assemblies at said level, - for each level of the core, concentration of Iodine 135 I iin nuclear fuel assemblies at said level. Typically, the estimation operation is configured to estimate all the numerical coefficients in the above list and all the state parameters in the above list. To limit the computation time, the estimation operation alternatively is configured to estimate: - only between 1 and 9 of the above numerical coefficients, preferably between 1 and 7, more preferably between 1 and 4; or - only between 1 and 35 of the above state parameters, preferably between 1 and 8, more preferably between 1 and 4; - both between 1 and 9 of the above numerical coefficients and between 1 and 35 of the above state parameters, preferably between 1 and 7 of the above numerical coefficients and between 1 and 24 of the above state parameters, more preferably between 1 and 4 of the above numerical coefficients and between 1 and 12 of the above state parameters.At step S50 / , the simulated operating parameters are chosen from the following list: - neutron flux at each level ni of the core; - axial distribution of the neutron flux in the core; - temperature of the primary coolant in the hot leg and in the cold leg of the primary circuit; - neutron poison concentration in the primary coolant; - concentration of Xenon 135 in the nuclear fuel assemblies at each level of the core, concentration of Iodine 135 in the nuclear fuel assemblies at each level of the core. Typically, at step S50 / , the group of simulated operating parameters includes all the operating parameters from the list above. Alternatively, the group of simulated operating parameters includes only some of the operating parameters from the list above. The numerical model is used to determine the evolution over time of the simulated values ​​over the time window [t0; t0+T].The width T of the time window is between 0:15 and 24 hours, preferably between 0:30 and 1:30, and is for example 1 hour. The instant t0+T corresponds for example to the instant tc. Alternatively, the instant t0+T is shifted in the past relative to tc, typically by a few minutes. The time window is sliding in the sense that it moves in time with the instant tc. It always has the same time shift relative to the instant tc. As indicated above, the numerical model uses at step S50 / the generated value of the at least one estimated numerical coefficient and / or considers at t0 the initial generated value of the at least one estimated state parameter. The other numerical coefficients used are predetermined constants. The initial values ​​of the other state parameters are typically those obtained by the simulation at the previous iteration.The digital model in step S50 / uses the values ​​of the commands actually applied during the time window [t0; t0+T]. The cost function used in step S60 / includes a term corresponding to the difference between the current values ​​and the simulated values ​​of the group of operating parameters simulated over the time window. Advantageously, the cost function also includes a term corresponding to the difference between the generated value of the at least one estimated digital coefficient and the estimate of the at least one estimated digital coefficient obtained in the previous iteration. This term is only useful when the estimation operation aims to estimate at least one digital coefficient. The cost function preferably includes a term corresponding to the difference between the initial value generated of the at least one estimated state parameter and the estimate of the at least one estimated state parameter obtained in the previous iteration.This term is only useful when the estimation operation aims to estimate at least one state parameter. For example, the cost function is:. Where J is the cost function; x is the vector of state parameters; ^^ is the vector of state parameters, containing the initial value generated for the estimated state parameter(s) and the value at t0 estimated at the previous iteration for the other state parameters; ^^ is the vector of the estimated numerical coefficient(s) obtained at the previous iteration; p is the vector containing the generated values ​​of the estimated numerical coefficient(s); y(τ) is the vector of current values ​​of the group of measured operating parameters; h(x(τ)) is the vector of simulated values ​​of the group of operating parameters simulés ; are standards ‖ ‖ ^^ s such that ^^ ^^^^ = √^^ ^^^^ ^^ , where a is an n-component vector and Sx is an nxn matrix. Sx, Sp and R are matrices used to weight the contribution of the different terms of the cost function. For large Sx, the difference between the state parameters determined at the current problem resolution and the previously calculated state parameters is heavily penalized (used if there is confidence in this estimate). If little confidence is placed in the previous estimate, the weighting may be less important. Sp plays the same role for the numerical coefficients of the numerical model. R penalizes the differences between the operating parameters simulated by the numerical model and the measured operating parameters. R is generally chosen based on considerations relating to the nature of the measurement uncertainties (accuracy of the instrumentation devices, etc.). Sx and Sp are adjusted by the designer to obtain the best possible estimation results.As mentioned above, steps S40 / , S50 / and S60 / are repeated until a convergence criterion of the cost function is satisfied. More precisely, at each iteration, the optimization operation is treated as an optimization problem aiming at minimizing the cost function J, by implementing a numerical integrator and a numerical optimization solver. The optimization problem is first transcribed into a nonlinear programming (NLP) problem by a discretization method. This discretization method is a single-shot method, a multiple-shot method or a direct collocation method. The nonlinear programming problem is solved using a numerical integrator capable of handling so-called stiff systems by implementing an adapted method: collocation, multistep linear method, Euler method, Range-Kutta method, Dormand-Price method.The numerical optimization solver uses the interior point method or sequenced quadratic programming (SQP). The cost function is optimized by the numerical optimization solver. However, the calculation of the cost function requires simulating the model with the numerical model. This simulation uses the numerical integrator. The control assembly 55 will now be described (see also FIG. 1). This control assembly 55 comprises: a / an acquisition unit 57 iteratively acquiring the current values ​​of the acquired operating parameters; b / a calculation member 59 configured to generate the actuator commands using the current values ​​of the acquired operating parameters, at least some commands being determined using the numerical model of the nuclear reactor; c / a unit 61 for controlling the actuators using the generated commands. The acquisition unit 57 is a computer, or part of a computer.Typically, the acquisition unit 57 is configured to implement step S10 / . The calculation unit 59 is configured to implement step S20 / . The digital model is that described above. The control unit 61 is configured to implement step S30 / . The control assembly 55 also comprises a unit 63 for estimating at least one estimated digital coefficient belonging to the set of digital coefficients and / or at least one estimated state parameter belonging to the set of state parameters. The estimation unit 63 is a calculation unit. It is integrated into the calculation unit 59. Alternatively, it is independent. The estimation unit 63 is configured to implement the estimation operation described above. A first example of application of the control method of the invention has been represented in figures 4 and 5. In this example, the digital model comprises six levels, i.e. six sub-models.It compares the values ​​simulated by the numerical model of eight operating parameters with the measurements of these same operating parameters: the neutron densities at the six levels, the temperature of the primary heat transfer fluid in the hot leg and the temperature of the primary heat transfer fluid in the cold leg of the primary circuit. The width of the time window is one hour. The optimization operation aims to determine two numerical coefficients: - D: neutron exchange coefficient between the levels of the core (represented in Figure 4); -̅ ^̅ ̅^^̅̅^^: neutron absorption transmutation coefficient of Xenon 135 into Xenon 136 (represented in Figure 5). The horizontal curve R corresponds to the real value of the numerical coefficient. Curve E corresponds to the successive estimates obtained using the method of the invention. The scenario considered here corresponds to a transition from 100% of the power to 70% with a plateau at this power.Then a rise to 100% of the power and power plateau. Finally, a load reduction to 50% of the power. The two numerical coefficients are initialized to values ​​very different from the real values, but of a plausible order of magnitude. The figures show that the estimates converge quickly towards the real values. The variations in nominal power of the reactor induce small-scale disturbances, but the estimates quickly return to the real values. A second example of application of the control method of the invention is shown in Figures 6 to 9. In this example, the numerical model comprises six levels, i.e. six sub-models.It compares the values ​​simulated by the numerical model of twenty operating parameters with the measurements or simulations of these operating parameters: the neutron densities at the six levels, the concentrations of Xenon 135 in the nuclear fuel assemblies at each level of the core, the concentrations of Iodine 135 in the nuclear fuel assemblies at each level of the core, the temperature of the primary coolant in the hot leg and the temperature of the primary coolant in the cold leg of the primary circuit. The width of the time window is one hour. The optimization operation aims to determine four numerical coefficients: - D: neutron exchange coefficient between the levels of the core (represented in Figure 6) -̅ ^̅ ̅^^̅^̅^^: neutron absorption transmutation coefficient of Xenon 135 into Xenon 136 (represented in Figure 7); - K. bor and K xenon(Figures 8 and 9). The horizontal curve R corresponds to the actual value of the numerical coefficient. Curve E corresponds to the successive estimates obtained using the method of the invention. The scenario considered here is the same as in Figures 4 and 5. The four numerical coefficients are initialized to values ​​that are very different from the actual values, but of plausible orders of magnitude. The figures show that the estimates converge quickly towards the actual values. Variations in the nominal power of the reactor induce small-scale disturbances, but the estimates quickly return to the actual values. A third example of application of the control method of the invention is illustrated in Figure 10. In this example, the numerical model has six levels, i.e. six sub-models.It compares the values ​​simulated by the numerical model of eight operating parameters with the measurements of these operating parameters: the neutron densities at the six levels, the temperature of the primary heat transfer fluid in the hot leg and the temperature of the primary heat transfer fluid in the cold leg of the primary circuit. The width of the time window is one hour. The optimization operation aims to determine the xenon 135 densities in each sub-model of the numerical model, i.e. six xenon densities. The scenario considered is the same as in Figures 4 and 5. The estimates of these operating parameters are initialized to values ​​that are out of range (15% error) but of a plausible order of magnitude. Figure 10 illustrates the error in the estimation of the xenon 135 density, i.e. the difference between the value estimated by the numerical model and the actual value. This difference is expressed as a % of the actual value.The value shown in Figure 10 is the average of the errors for the six levels of the model. The error is on the ordinate, and the time on the abscissa. Figure 10 shows in this case that the estimates converge quickly since after a single optimization, the normalized estimation error for all xenon densities is of the order of a percent, and decreases further over time. According to another aspect, the invention relates to a method for calibrating a numerical model used for controlling a nuclear reactor. The numerical model is that described above. The calibration is carried out independently of the control of the nuclear reactor. This means that the calibration operation is decoupled from the control of the nuclear reactor and can be carried out at any time, for example before the commissioning of the numerical model, or while the nuclear reactor is shut down.The numerical model is calibrated using simulated values, called reference values ​​below, instead of the current values ​​acquired using the sensors equipping the nuclear reactor. This calibration method typically aims to determine the numerical coefficients used by the numerical model at different operating points of the nuclear reactor, in particular during power transients. More precisely, the calibration method comprises an operation of generating reference values ​​of a plurality of reference operating parameters of the nuclear reactor for a time interval I, and an operation of estimating at least one estimated numerical coefficient belonging to the set of numerical coefficients, implemented for a time window [t0; t0+T] within the time interval I, T being the width of the time window.For example, the estimation operation is implemented iteratively for a sliding time window [t0; t0+T] within the time interval I, T being the width of the time window, the time window moving at each iteration and sweeping the time interval I. The generated reference values ​​correspond to the evolution over time of the values ​​of the reference operating parameters over the entire time interval I. The reference operating parameters are for example the same as the acquired operating parameters described above with reference to step S10 / . The time interval I has a duration of several hours, for example 24 hours and preferably 12 hours. The reference values ​​are generated by an expert system, comprising software for simulating the operation of the nuclear reactor. This expert system is frequently referred to as CMS (Core Monitoring System).A CMS of this type, called ARGOS, is integrated in some nuclear reactors. This expert system is known and will not be described in detail here. The reference values ​​are generated by considering a nuclear reactor control scenario during time interval I, for example a power transient, or constant power operation, or any other scenario. The at least one estimated numerical coefficient is as described above. The estimation operation is implemented by iteratively sliding the time window [t0; t0+T] in time interval I. The time window moves at each iteration by a time step smaller than 60 minutes, preferably smaller than 20 minutes, for example 1 minute. The width T of the time window is as described above. If the time interval starts at time T1 and ends at time T2, at the first iteration t0 preferably coincides with T1.At the last iteration, t0+T preferably coincides with T2. In other words, the time window sweeps the entire interval I.The estimation operation comprising at each iteration the following steps: S70 / generation of a generated value of the at least one estimated numerical coefficient; S80 / determination of simulated values ​​of a group of simulated operating parameters forming part of the reference operating parameters, using the numerical model, for the time window [t0; t0+T], using in the numerical model the generated value of the at least one estimated numerical coefficient; S90 / evaluation of a cost function using the reference values ​​and the simulated values ​​of the group of simulated operating parameters; steps S70 / , S80 / and S90 / being repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated numerical coefficient making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated numerical coefficient at time t0+T.Step S70 / is performed like step S40 / described above. Step S80 / is performed like step S50 / described above. Step S90 / is performed like step S60 / described above, using the reference values ​​instead of the current values ​​of the group of simulated operating parameters. The estimation operation therefore makes it possible to determine the evolution of the at least one estimated numerical coefficient in the time interval [T1+T; T2], i.e. practically over the entire time interval I. The value of the at least one estimated numerical coefficient retained to calibrate the numerical model is typically the value determined at the last iteration, i.e. for the instant T2. Alternatively, the estimation operation is implemented in a non-iterative manner, the time window [t0; t0+T] corresponding to the time interval I.In other words, t0 coincides with T1, t0+ T coincides with T2, the width T of the time window being equal to the duration of the time interval I.

Claims

CLAIMS 1. Method for controlling a nuclear reactor, the nuclear reactor (1) having a pressure vessel (2), a core (3) comprising a plurality of nuclear fuel assemblies (5) arranged in the pressure vessel (2), a primary circuit (7) for cooling the core (3) in which a primary heat transfer fluid containing a neutron poison circulates, and a plurality of actuators acting on the core (3) or the primary circuit (7), the method comprising an operation for controlling the nuclear reactor with the following steps: S10 / acquisition of the current values of a plurality of operating parameters acquired from the nuclear reactor;S20 / generation of actuator commands using the current values of the acquired operating parameters, at least some commands being determined using a digital model of the nuclear reactor, the digital model being configured to determine an evolution over time of a set of state parameters of the nuclear reactor using the commands provided to the actuators and a set of digital coefficients; S30 / control of the actuators using the generated commands; steps S10 / , S20 / and S30 / being implemented iteratively;the method also comprising an operation of estimating at least one estimated digital coefficient belonging to the set of digital coefficients and / or at least one estimated state parameter belonging to the set of state parameters, implemented iteratively at times tc, in parallel with the control operation, this estimation operation comprising at each iteration the following steps: S40 / generation of a generated value of the at least one estimated digital coefficient and / or of an initial generated value of the at least one estimated state parameter; S50 / determination of simulated values of a group of simulated operating parameters forming part of the acquired operating parameters, using the digital model, for a sliding time window [t0; t0+T] with t; c , T being the width of the time window, the time window being located in the past relative to t c, using in the numerical model the generated value of the at least one estimated numerical coefficient and / or considering at t0 the initial generated value of the at least one estimated state parameter; S60 / evaluation of a cost function using the current values and the simulated values of the group of simulated operating parameters; steps S40 / , S50 / and S60 / being repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated digital coefficient and / or the generated initial value of the at least one estimated state parameter making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated digital coefficient and / or of the at least one estimated state parameter at time tc.

2. Control method according to claim 1, in which the cost function comprises a term corresponding to the difference between the current values and the simulated values of the group of simulated operating parameters over the time window.

3. Control method according to claim 1 or 2, in which the cost function comprises a term corresponding to the difference between the generated value of the at least one estimated digital coefficient and the estimate of the at least one estimated digital coefficient obtained at the previous iteration. 4.

5. A control method according to any one of claims 1 to 3, wherein the cost function comprises a term corresponding to the difference between the initial value generated of the at least one estimated state parameter and the estimate of the at least one estimated state parameter obtained at the previous iteration.

5. A control method according to any one of the preceding claims, wherein the numerical model of the nuclear reactor comprises several sub-models, each sub-model modeling a level of the core of the nuclear reactor and comprising at least one equation describing kinetics of a neutron density at said level and an equation describing a temperature of the primary heat transfer fluid at said level, the numerical model further comprising equations describing neutron exchanges between the levels and equations characterizing a reactivity at each level. 6.Control method according to claim 5, wherein the primary circuit (7) comprises at least one primary loop having a hot branch (BC) and a cold branch (BF) connected respectively to a primary heat transfer fluid outlet and to a primary heat transfer fluid inlet of the pressure vessel (2), the nuclear reactor (1) further comprising: - a secondary circuit (9) equipped with a turbine (11); - a steam generator (21) for the or each primary loop, having a first side inserted in the primary loop and a second side inserted in the secondary circuit; - groups of control rods (49) absorbing neutrons; - a mechanism (51) capable of selectively inserting or extracting the groups of control rods (49) in the core (3) of the nuclear reactor; - a unit (31) for injecting neutron poison into the heat transfer fluid. primary; and - a unit (39) provided for injecting water into the primary circuit.

7. Control method according to claim 6, in which the equations characterizing the reactivity at each level take into account one or more of the following effects: - effect due to a variation in the temperature of the primary heat transfer fluid at said level; - effect due to a variation in the power supplied by the core at said level; - effect due to the movements of groups of control rods; - effect due to a variation in the concentration of neutron poison in the primary heat transfer fluid; - effect due to a variation in the concentration of Xenon 135 in the nuclear fuel assemblies at said level. 8.A control method according to claim 6 or 7, wherein the state parameters comprise one or more of the parameters from the list below: - for each level of the core, neutron density at said level, Xenon 135 concentration in the nuclear fuel assemblies at said level, Iodine 135 concentration in the nuclear fuel assemblies at said level; - for each level of the core, temperature of the primary heat transfer fluid; - temperature of the primary heat transfer fluid at the inlet and outlet of the core; - temperature of the primary heat transfer fluid in the hot branch and in the cold branch; - neutron poison concentration in the primary heat transfer fluid.

9. A control method according to any one of claims 6 to 8, wherein the or each estimated numerical coefficient is chosen from the following list:. -̅ ^̅ ̅^^̅^̅^^ : coefficient de transmutation par absorption de neutron du Xénon 135 en Xénon136; - l*: average lifetime of neutrons (prompt and delayed) in the core; - D: neutron exchange coefficient between the levels of the core; - Kbor, Kbank, Kxenon: efficiency coefficients characterizing the contribution in the variation of the reactivity at each level of the core respectively of the variation of the concentration of the neutron poison in the primary heat transfer fluid, of the movements of the groups of control rods, of the variation of the concentration of Xenon 135 in the nuclear fuel assemblies at said level of the core; - ^^ ^^^^ : time constant of the temperature exchange between primary and secondary circuits within the steam generator; - ^^ ^^^^ : time constant for the evolution of the temperature of the primary heat transfer fluid along the hot branch; - ^^ ^^^^: time constant for the evolution of the temperature of the primary heat transfer fluid along the cold branch.

10. Control method according to any one of claims 6 to 9, in which the or each estimated state parameter is chosen from the following list: - for each level of the core, concentration of Xenon 135 in the nuclear fuel assemblies at said level, - for each level of the core, concentration of Iodine 135 in the nuclear fuel assemblies at said level. 11.Control method according to any one of claims 6 to 10, in which in step S50 / , the simulated operating parameters are chosen from the following list: - neutron flux at each level of the core; - axial distribution of the neutron flux in the core; - temperature of the primary heat transfer fluid in the hot branch and in the cold branch of the primary circuit; - concentration of neutron poison in the primary heat transfer fluid; - concentration of Xenon 135 in the nuclear fuel assemblies at each level of the core, concentration of Iodine 135 in the nuclear fuel assemblies at each level of the core. 12.Control method according to any one of claims 6 to 11, wherein the actuator commands used by the digital model are chosen from the following list: - movement of the control groups; - injection of neutron poison into the primary circuit; - injection of water into the primary circuit; - power supplied by the turbine.

13. Control method according to any one of the preceding claims, wherein at each iteration, the optimization operation is treated as an optimization problem aimed at minimizing the cost function, by implementing a digital integrator and a digital optimization solver. 14.Nuclear reactor comprising a pressure vessel (2), a core (3) comprising a plurality of nuclear fuel assemblies (5) arranged in the pressure vessel (2), a primary circuit (7) for cooling the core (3) in which a primary heat transfer fluid containing a neutron poison circulates, a plurality of actuators acting on the core (3) or the primary circuit (7), and a set (55) of. control of the nuclear reactor, the control assembly (55) being configured to implement the control method of any one of claims 1 to 13 15. Nuclear reactor according to claim 14, wherein the primary circuit (7) comprises at least one primary loop having a hot branch (BC) and a cold branch (BF) connected respectively to a primary heat transfer fluid outlet and to a primary heat transfer fluid inlet of the pressure vessel (2), the nuclear reactor (1) further comprising: - a secondary circuit (9) equipped with a turbine (11); - a steam generator (21) for the or each primary loop having a first side intercalated in the primary loop and a second side intercalated in the secondary circuit; - groups of control rods (49) absorbing neutrons; - a mechanism (51) capable of selectively inserting or extracting the groups of control rods (49) in the core (3) of the nuclear reactor;- a unit (31) for injecting neutron poison into the primary heat transfer fluid; and - a unit (39) intended to inject water into the primary circuit.

16. Method for calibrating a numerical model used for controlling a nuclear reactor, the nuclear reactor (1) having a pressure vessel (2), a core (3) comprising a plurality of nuclear fuel assemblies (5) arranged in the pressure vessel (2), a primary circuit (7) for cooling the core (3) in which a primary heat transfer fluid containing a neutron poison circulates, and a plurality of actuators acting on the core (3) or the primary circuit (7), the control of the nuclear reactor being carried out according to a method comprising the following steps: S10 / acquisition of the current values of a plurality of operating parameters acquired from the nuclear reactor;S20 / generation of actuator commands using the current values of the acquired operating parameters, at least some commands being determined using said digital model, the digital model being configured to determine an evolution over time of a set of state parameters of the nuclear reactor using the commands provided to the actuators and a set of digital coefficients; S30 / control of the actuators using the generated commands; steps S10 / , S20 / and S30 / being implemented iteratively; the calibration method comprising an operation of generating reference values of a plurality of reference operating parameters of the nuclear reactor for a time interval I, and an operation of estimating at least one coefficient; estimated numerical coefficient belonging to the set of numerical coefficients, implemented for a time window [t0; t0+T] within the time interval I, T being the width of the time window, this estimation operation comprising at each iteration the following steps: S70 / generation of a generated value of the at least one estimated numerical coefficient; S80 / determination of simulated values of a group of simulated operating parameters forming part of the reference operating parameters, using the numerical model, for the time window [t0; t0+T] by using in the numerical model the generated value of the at least one estimated numerical coefficient; S90 / evaluation of a cost function using the reference values and the simulated values of the group of simulated operating parameters;steps S70 / , S80 / and S90 / being repeated until a convergence criterion of the cost function is satisfied, the generated value of the at least one estimated numerical coefficient making it possible to satisfy the convergence criterion being retained as an estimate of the at least one estimated numerical coefficient at time t0+T.

17. Calibration method according to claim 16, in which the estimation operation is implemented in a non-iterative manner, the time window [t0; t0+T] corresponding to the time interval I.

18. Calibration method according to claim 16, in which the estimation operation is implemented in an iterative manner, the time window [t0; t0+T] sliding within the time interval I, the time window moving at each iteration and scanning the time interval I.;

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