Sensitivity Analysis of Geological Parameters Influencing a Solute Transport from a Deep Repository of Spent Nuclear Fuel

When evaluating Nuclear Waste DGR Safety, it is necessary to confirm its safety in a long run and above all its safety towards the biosphere which is more precisely that the biosphere will not be in any hazard caused by radioactive substances, With the aid of geologists, a model of a hypothetical area was elaborated and described with the use of geological and hydrogeological parameters. The volume of isotopes released out of the massif at the borderline of the near/far field from the DGR was determined. The paper results showed that ground water flow and transport of substances within the area were first to be determined. The Flow123D SW was used for the determination. The resulting outcome represents a determination of transported substances concentration depending on time. The disadvantage of the model is the fact that all the input parameters were set deterministically. The problem is solved by using the sensitivity analysis （changing the input parameters） or using the Monte Carlo Method. The major results are： calculations of the radionuclide concentrations in the elements depending on time and determination of parameters that have the biggest impact on the sensitivity of the whole model.

Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

Rotor angle stability(RAS)prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems.The wide deployment of phasor measurement units(PMUs)promotes the development of data-driven methods for RAS prediction.This paper proposes a temporal and topological embedding deep neural network(TTEDNN)model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data.The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid.Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered.Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance,scalability,and robustness against measurement uncertainties of the TTEDNN model.Results show that the TTEDNN model performs best among existing deep learning models.Furthermore,the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.