CEA Cadarache

In France, alluvial aquifers provide 45% of the freshwater used for drinking, agriculture, and industry (Maréchal and Rouillard, 2020). These aquifers are often hydraulically connected to rivers and are particularly vulnerable due to their proximity to the surface. This proximity makes them sensitive to anthropogenic pressures both quantitatively and qualitatively. The current study aims to better understand groundwater-surface water interactions in an anthropized alluvial aquifer in the lower Rhône Valley. This goal will be achieved using multivariate statistical methods on hydrogeochemical and stable water isotope data. Two datasets were analyzed. One dataset contained inert tracers (Cl−, Br−, δ2H and δ18O) and included 667 water samples (40 rainwater, 110 surface water and 517 groundwater). The other dataset contained major and minor ions (Ca2+, Mg2+, K+, Na+, Cl−, SO42-, alkalinity, NO3−, Br− and U(VI)) and stable water isotopes (δ2H and δ18O) data., and included 374 water samples (37 surface water and 337 groundwater). First, a hierarchical cluster analysis (HCA) was applied to both datasets to better understand groundwater recharge and the geochemical processes that control groundwater chemistry in the study area. We used the recently developed t-distributed stochastic neighbor embedding (t-SNE) (Van Der Maaten and Hinton, 2008) method and principal component analysis (PCA) to assist with the cluster analysis and visualization. When HCA, PCA, and t-SNE were applied to inert tracers dataset, the results first revealed the distribution of groundwater on the study site between two recharge sources: the Rhône River and rainfall. Water samples collected along the Rhône River boundary are characterized by highly depleted δ2H and δ18O signatures, indicating the significant influence (up to 80%) of the Rhône on the recharge of the alluvial aquifer in this area. In contrast, groundwater samples collected in the northern and northwestern parts of the site showed highly enriched δ2H and δ18O signatures, similar to those of rainfall indicating dominant recharge from local precipitation. Others water samples are characterized by intermediate δ2H and δ18O signatures, falling between the signatures of rainfall and the Rhône River, indicating mixed recharge. These water samples are predominantly distributed in the southern part of the site. Second, the results revealed the response of the alluvial groundwater to exceptional climatic events. In 2022, particular isotopic signatures with higher deuterium excess and high chloride concentrations were observed in rainwater and the Rhône River, which are reflected in groundwater. This suggests an influence of continental air masses originating from the Sahara Desert (Xu-Yang et al., 2025). When HCA, PCA, and t-SNE were applied to the second dataset containing hydrogeochemical and stable water isotope data, the results identified water samples with high uranium and chloride concentrations, likely due to historical pollution. This study shows that t-SNE is a promising tool for assessing groundwater-surface water interactions in an alluvial aquifer when used to assist in cluster analysis. Compared with PCA, t-SNE can better identify hidden information and perform much better with complex, nonlinear hydrogeochemical, and stable water isotope data. ReferencesMaréchal and Rouillard, 2020.https://doi.org/10.1007/978-3-030-32766-8_2Van Der Maaten and Hinton, 2008.Res.9,2579–2625.Xu-Yang, et al., 2025.https://doi.org/10.1126/sciadv.adr9192

The EU-DEMO design is exploring several blanket models, including the Water Cooled Lithium Lead blankets. In this blanket model, liquid LiPb will be used as both the tritium breeder and multiplier. This liquid will experience a spatially varying neutron field as it moves across the tokamak. The production of radioisotopes would depend on the flow parameters of LiPb across this spatially varying neutron field. In this study, we have explored the production of radioisotopes in such a field and compared it with an equivalent, static, non-flowing simulation. While static calculations are simpler and computationally less expensive, we find that the predicted production of a radionuclide deviates from flowing fluid simulations to varying degrees, depending on the radionuclide’s half-life. We also demonstrate how the computational methodology developed can be used to explore online tritium extraction parameters and their impact on the End-of-Life tritium inventory. • Simulations of flowing fluid demonstrate that the production of a radionuclide can deviate from equivalent predictions without flow, depending on its half-life. • A computational methodology has been developed to explore the impact of tritium extraction parameters on the end-of-life inventory. • In LiPb, 210 Po is produced continuously via beta decay, and so its concentration is strongly influenced by the flow path • The short half-life of 207m Pb means that its predicted concentration is the same in both the static and flowing scenarios.

Abstract This paper reports on the generic prediction capability of full electromagnetic plasma initiation modelling with DYON, which was carried out for the first time in fusion research by the joint modelling of the International Tokamak Physics Activity—Integrating Operation Scenario group. The following devices were included in the experiment database: VEST (spherical torus, copper coils, Stainless steel wall, R / a = 0.3 m/0.2 m, V v = 3.7 m 3 ), MAST-U (spherical torus, copper coils, C wall, R / a = 0.7 m/0.5 m, V v = 55 m 3 ), EAST (conventional tokamak, superconducting coils, metallic wall, R / a = 1.85 m/0.5 m, V v = 38 m 3 ), DIII-D (conventional tokamak, copper coils, C wall, R / a = 1.67 m/0.65 m, V v = 35 m 3 ), and KSTAR (conventional tokamak, superconducting coils, C wall, R / a = 1.8 m/0.5 m, V v = 55 m 3 ). Despite the different hardware features of the devices, the required operating spaces of the loop voltage induction and prefill gas pressure for inductive plasma initiation in each device were successfully reproduced by the predictive simulations with DYON using only the individual hardware design and the control room input data for each discharge. This successful validation across multiple machines demonstrates that the full electromagnetic DYON modelling can capture the essential physics of inductive plasma initiation. The simulation settings commonly employed for all modelling and the modifications necessary to account for the discrepancies between individual devices are reported. Predictions for ITER based on the multi-machine validation indicate that a wide range of prefill gas pressures exists for the Townsend breakdown and the plasma burn-through (0.01–1.5 mPa).

Abstract Nuclear heating plays an important aspect in design and deployment of both fission and fusion reactors and experimental devices in terms of cooling requirements. Two experimental campaigns in the framework of a collaboration project between the French Atomic and Alternative Energy Commission (CEA) and Jožef Stefan Institute (JSI), Slovenia, have been performed at the JSI TRIGA reactor for the experimental assessment of nuclear heating in fission and fusion-relevant materials by the differential calorimetry technique, based on the CALMOS and CARMEN differential calorimeters, previously developed at CEA. The results of the first campaign performed at reactor powers between 100 and 250 kW have already been reported, highlighting some measurement difficulties. Therefore, the second campaign was performed at a lower reactor power of 30 kW to overcome these issues. Moreover, a computational analysis of the experiments was performed using the JSIR2S code package to calculate the nuclear heating levels. Both experiments and their reproduction by simulations are described in detail. We present a comparison of the previously reported measured nuclear heating values of the first campaign with the computational results, with consistent underestimation by simulations by 8–35%. We report the experimental and computational results for the second experimental campaign performed at a reactor power of 30 kW. The simulated heating values were in agreement with the measurements within the measured heating uncertainty, with simulated heating 2.7–11.3% lower than the experimental values.

Abstract Understanding the interaction between turbulence and zonal flows is critical for modeling turbulence transport in fusion plasmas, often described through predator-prey dynamics. However, traditional deterministic models like the Lotka–Volterra equations simplify this interaction and fail to capture the small fluctuations in simulation data. In this study, we develop a neural network (NN) model based on stochastic differential equations to represent the predator-prey dynamics using limited data from simulations of the modified Hasegawa–Wakatani system. We extract the drift and diffusion terms via NNs, incorporating physical constraints and employing the Unscented Transform to mitigate challenges brought by limited data. The model accurately reproduces key dynamical features, including stagnation phenomena and energy exchange mechanisms, and the state density distribution generated from the model shows a low KL divergence with the simulation data. A parameter scan reveals that zonal flow shearing efficiency decreases with amplitude, and predator-prey oscillations damp in the absence of stochasticity. These findings underscore the value of integrating physical insight into data-driven approaches for complex plasma systems.