Utilisation de sources et d'adjoints dragon pour les calculs TRIPOLI

Numerical simulation is an essential part of reactor physics in order to understand the behaviour of neutrons inside and outside nuclear reactors. The objective is to solve the neutron transport equation in order to know the neutron flux and the interactions between neutrons and materials. We use neutronic simulation codes in order to solve this equation for criticallity problem, where we have a neutron multiplying environment, and shielding problems. There are two different types of numerical simulation techniques. Deterministic methods solve directly the transport equation using some approximations. The energy domain is divided in regions called groups, we use a spatial mesh for the geometry treatment, transport operator may also be simplified. Those approximations invole an inherent error. However these methods provide high computation time performances. Monte Carlo or stochastic methods follow explicitly a large number of neutrons as they travel through materials minimizing approximations. Continuous-energy and multigroup treatment are both available. Quantities calculated are random variables to which are associated statistical error called standard deviations. We have to simulate a very large number of neutrons if we want the calculation to converge and the results to be precise enough. As a matter of fact, computation time of these methods can be excessively large and represent their main weakness.;The objective of this study is to set up a chaining method from a deterministic code to a Monte Carlo code, in order to improve the convergence of Monte Carlo calculations performed by the code TRIPOLI. We want to use datas calculated by the deterministic code DRAGON and use them in TRIPOLI. We will develop two methods. The first one will calculate source distribution in DRAGON and implement them in TRIPOLI as initial sources of a criticallity calculation. The objective is to accelerate the convergence of the neutrons sources, and save the first batches that are usually non significant. The second method is to use of the adjoint neutron flux calculated by DRAGON as an importance function for Monte Carlo biaising in TRIPOLI. The objective is to improve the figure of merit of the detector response located far away of the neutron source.;The neutron source initialisation of a TRIPOLI calculation required to develop the development of a module in DRAGON that generates a list of sources in the TRIPOLI syntaxe, including for each source, its intensity, its position and the energy domain it covers. We tested our method on a complete 17×17 PWR-UOX assembly and on a reduced 3×3 model. We first verified that the DRAGON and TRIPOLI models were consistent in order to ensure that TRIPOLI receives a coherent source distribution. Then we tested the use of DRAGON sources in TRIPOLI with neutron flux and the effective multiplying coefficient (keff). We observe slightly better standard deviations, of an order of 10 pcm, on keff for simulations using DRAGON sources distributions as compared to simulations with less precise initial sources. Flux convergence is also improved. However some incoherence were also observed in the results, some flux converging slower with DRAGON sources when fewer neutrons per batch are considered. In addition, a very large number of sources is too heavy to insert in TRIPOLI. It seems that our method is perfectible in order to improve implementation and convergence. Study of more complex geometries, with less regular sources distributions (for instance using MOX or irradiated fuel) may provide better performances using our method.;For biaising TRIPOLI calculations using the DRAGON adjoint flux we created a module that produces importance maps readable by TRIPOLI. We tested our method on a source-detector shielding problem in one dimension. After checking the coherence of DRAGON and TRIPOLI models, we biaised TRIPOLI simulations using the DRAGON adjoint flux, and using INIPOND, the internal biaising option of TRIPOLI. We observed a good improvement of the figure of merit of a calculation biaised with the DRAGON adjoint flux. However the use of INIPOND is often more efficient. INIPOND seems to be more sensitive to the energy mesh considered for biaising than the DRAGON adjoint flux, but INIPOND is more stable than the deterministic adjoint with respect to spatial mesh biaising. The initial objective of improving the figure of merit of a TRIPOLI calculation using the DRAGON adjoint flux is reached in this case.