“…DFT calculations [17,20] and experiments [10,23,42] suggest that the desorption energy of H from the W(110) surface depends on the H surface coverage. This dependency has already been implemented in rate equation models by using a continuous function E des (θ D ) [15,24,[43][44][45]. In MHIMS, this function had initially the form of a Fermi-Dirac distribution [24].…”
Rate equation modelling is performed to simulate D2 and D2+D2
+ exposure of the W(110) surface with varying coverage of oxygen atoms (O) from the clean surface up to 0.75 monolayer of O. Density functional Theory (DFT) calculated energetics are used as inputs for the surface processes and desorption energies are optimized to best reproduce the thermal desorption spectrometry (TDS) experiments obtained for D2 exposure. For the clean surface, the optimized desorption energies (1.10 eV to 1.40 eV) are below the DFT ones (1.30 eV to 1.50 eV). For the O covered surface, the main desorption peak is reproduced with desorption energies of 1.1 eV and 1.0 eV for 0.50 and 0.75 monolayer of O respectively. This is slightly higher than the DFT predicted desorption energies. In order to simulate satisfactorily the total retention botained experimentally for D2+D2
+ exposure, a sputtering process needs to be added to the model, describing the sputtering of adsorbed species (D atoms) by the incident D ions. The impact of the sputtering process on the shape of the TDS spectra, on the total retention and on the recycling of D from the wall is discussed. In order to better characterize the sputtering process, especially its products and yields, atomistic calculations such as molecular dynamics are suggested as a next step for this study.
“…DFT calculations [17,20] and experiments [10,23,42] suggest that the desorption energy of H from the W(110) surface depends on the H surface coverage. This dependency has already been implemented in rate equation models by using a continuous function E des (θ D ) [15,24,[43][44][45]. In MHIMS, this function had initially the form of a Fermi-Dirac distribution [24].…”
Rate equation modelling is performed to simulate D2 and D2+D2
+ exposure of the W(110) surface with varying coverage of oxygen atoms (O) from the clean surface up to 0.75 monolayer of O. Density functional Theory (DFT) calculated energetics are used as inputs for the surface processes and desorption energies are optimized to best reproduce the thermal desorption spectrometry (TDS) experiments obtained for D2 exposure. For the clean surface, the optimized desorption energies (1.10 eV to 1.40 eV) are below the DFT ones (1.30 eV to 1.50 eV). For the O covered surface, the main desorption peak is reproduced with desorption energies of 1.1 eV and 1.0 eV for 0.50 and 0.75 monolayer of O respectively. This is slightly higher than the DFT predicted desorption energies. In order to simulate satisfactorily the total retention botained experimentally for D2+D2
+ exposure, a sputtering process needs to be added to the model, describing the sputtering of adsorbed species (D atoms) by the incident D ions. The impact of the sputtering process on the shape of the TDS spectra, on the total retention and on the recycling of D from the wall is discussed. In order to better characterize the sputtering process, especially its products and yields, atomistic calculations such as molecular dynamics are suggested as a next step for this study.
“…An example of such a code is TMAP7 [41] widely used for simulations of the tritium thermal desorption from the plasma-facing components during LID [5,[42][43][44]. Recently, the commercial packages, such as COMSOL Multiphysics also started to attract more attention for LID modeling [45,46].…”
The accurate assessment of the local tritium concentration in the tokamak first wall by means of the laser-induced desorption (LID) diagnostics is sought as one the key solutions to monitoring the local radioactive tritium content in the first wall of the fusion reactor ITER. Numerical models of gas desorption from solids used for LID simulation are usually closed with the one-dimensional transport models. In this study, the temperature and particle dynamics in the target irradiated by a short laser pulse during LID are analyzed by means of the two-dimensional model to assess the validity of using one-dimensional approximation for recovering the diagnostics signal. The quantitative estimates for the parameters governing the heat and particle transfer are presented. The analytical expressions for the sample spatiotemporal temperature profiles driven by the target irradiation with a Gaussian laser beam with the trapezoid temporal shape are derived. The obtained relations are used to simulate tritium desorption from a tungsten sample driven by pulsed heating. It is shown that depending of the ratio between the laser spot radius and the heat diffusion length, the one-dimensional approach can noticeably overestimate the sample temperature in the limit of small laser spot radius (estimated for tungsten as ∼ 0.5 − 1.0 mm), resulting in more than 100% larger amounts of tritium desorbed from the target, compared to the two-dimensional approximation. In the limit of large laser spot radius (≥ 1.5 mm), both approaches yield comparable amounts of tritium desorbed from the sample.
“…the necessity to check the conditions of thermodynamic equilibrium and plume opacity, and require only the registration of the particle flow, released from the target. The main difficulty of the LID-QMS technique lies in the rather limited amount of diagnostic data: the desorption flux time signals [23,29,30] and/or the total amount of hydrogen desorbed in one laser pulse [21,31,32]. Such a constraint, coupled with a large number of parameters characterizing the process of gas desorption from a solid (the presence of traps of various types with different concentrations and binding energies, the presence of energy barriers for hydrogen transport in the volume and on the surface of the material, the presence of oxidized layers, etc.…”
The possibility of using machine learning methods for solving the inverse problem of the laser-induced desorption quadrupole mass-spectrometry (LID-QMS) diagnostic is studied. The formulation of the problem is given, and a general scheme of its solution is proposed. A test model of gas transport in a solid body is considered, which is used to construct a database of gas transport parameters in the sample. The application of the synthetic data and machine learning methods, viz. the interpolation technique, the method of K nearest neighbors, and the neural networks, for solving the LID-QMS inverse problem is investigated. The advantages and disadvantages of each approach are discussed.
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