Spent nuclear fuel | EPFL Graph Search

Spent nuclear fuel, occasionally called used nuclear fuel, is nuclear fuel that has been irradiated in a nuclear reactor (usually at a nuclear power plant). It is no longer useful in sustaining a nuclear reaction in an ordinary thermal reactor and, depending on its point along the nuclear fuel cycle, it will have different isotopic constituents than when it started. Nuclear fuel rods become progressively more radioactive (and less thermally useful) due to neutron activation as they are fissioned, or "burnt" in the reactor. A fresh rod of low enriched uranium pellets (which can be safely handled with gloved hands) will become a highly lethal gamma emitter after 1-2 years of core irradiation, unsafe to approach unless under many feet of water shielding. This makes their invariable accumulation and safe temporary storage in spent fuel pools a prime source of high level radioactive waste and a major ongoing issue for future permanent disposal. Nature of

Structural performance and mechanical properties investigation of spent nuclear fuel rods under static and dynamic bending loads

Efstathios Vlassopoulos

Currently, most Spent Nuclear Fuel (SNF) is kept safely in storage either at on-site facilities or at centralized interim storage sites. Moreover, many countries face delays in implementing their waste management programmes for SNF and high-level waste disposal. As storage pools approach their capacity, an increasing demand for interim dry-storage solutions is foreseen in the near future and, is associated with emerging regulatory issues. Storage prolongation in interim facilities for time periods far beyond than what originally envisaged, requires license renewal of the facility, as well as of transport and storage casks. At the same time, the safety of SNF operation activities (transportation and handling) at surface encapsulation facilities must be ensured. In order to evaluate the SNF response under normal or accident scenarios, in different stages of the back-end of the nuclear fuel cycle, it is essential to assess the mechanical properties of fuel rods, as well as their evolution during and after irradiation. Several previous studies have identified mechanisms that could affect such properties during dry-storage. However, data generated from experiments with irradiated SNF are extremely rare. This PhD complements previous studies by generating new data for the structural performance of SNF, in areas where technical gaps have been identified. In particular, the mechanical properties of SNF as function of burnup have been investigated both experimentally and numerically, under quasi-static and dynamic bending loads. The experimental activities were conducted at the hot-cell facilities of JRC-Karlsruhe and include three-point bending and gravitational impact tests performed at room temperature on pressurized samples from commercially used PWR UO2 rods. Their dynamic response was studied by successfully applying a new Image Analysis methodology developed in this work. Preliminary tests were conducted on surrogate rodlets consisting of fresh and hydrogenated Zircaloy-4 cladding tubes filled with alumina pellets. The results showed that the sampleâs ductility decreases with increasing hydrogen concentration in the cladding. Five SNF rods, with a wide range of burnup, were selected for the tests on irradiated fuel. High burnup samples showed higher toughness due to irradiation damage on the cladding, and less ductile behavior. Overall, strain rate had marginal influence on ductility for high-burnup samples, whereas for low burnup ones, ductility decreased significantly with strain rate. The flexural mechanical properties of the claddings as function of burnup were derived from the 3-point bending tests using the Euler-Bernoulli beam theory for beam elements with hollow-circular profile. A series of post-test examinations concerning the rod failure processes showed that fuel release in case of rod fracture is very limited, amounting to less than the mass of one pellet. Finite Element Analysis was performed in order to simulate the experiments and the rodsâ bending behavior under the examined load conditions. An extensive sensitivity analysis was performed to minimize modeling uncertainties and the final model was calibrated against experimental data from the 3-point bending tests. Good agreement was observed between numerically predicted and experimentally derived mechanical properties, and the model can be used to simulate the mechanical behavior of SNF rods under different loading configurations.

EPFL2021

Data-Driven Predictive Models: Calculational Bias in Characterization of Spent Nuclear Fuel

Characteristics of the spent nuclear fuel (SNF) are typically calculated, requiring validation a priori. The validation process relies on the difference between calculations and measurements, namely the bias. Usually, predicting the bias based on benchmarks is essential, which motivated the present research, focusing on SNF decay heat and Cs-137, U-235, and Pu-239 concentrations.The validation benchmarks are from open-literature, i.e., SNF design and irradiation specifications, as well as the measurements of their characteristics. For the decay heat, they correspond to 262 measurements, conducted at the Clab and the GE-Morris facilities. For the radionuclide concentrations, they are 285 post-irradiation-examination samples, obtained from the SFCOMPO database. The calculations rely on the SCALE code system, namely the Polaris code and the SCALE-based nuclear data.Uncertainties of nuclear data and SNF design and operational history are propagated to the calculated quantities, for two purposes: (1) to assess if the biases are statistically significant, given the calculated uncertainties, and (2) to obtain correlation matrices between the benchmarks. Statistical analyses, resampling and z-tests, are applied on the validation and uncertainty analyses data. They indicate that the biases in several of the analyzed characteristics are significant with respect to uncertainties in the calculated values. For the decay heat case, the biases are considered not significant considering both the calculated and experimental uncertainties. It is also shown that it is crucial to include the correlations between the benchmarks into the hypothesis testing.Then, a novel approach is followed, by applying machine learning (ML) methods to predict the bias of calculated SNF characteristics. The predictive performance is analyzed by comparing the ML-based bias predictions and the validation-based biases. The analyzed ML models predict the bias using highly similar benchmarks or neighbors of the benchmarks, namely Random Forests (RF) and Weighted k-Nearest Neighbors (KKNN). Also, the linear model is analyzed.This research shows that the bias of the decay heat and Pu-239 concentration can be predicted with a reasonable accuracy, relying on specific features of validation benchmarks, or their correlations. The predicted biases bear statistically significant similarities to the observed ones from the validation procedure, using both the RF and the KKNN models. The variances in the original validation data are significantly reduced. The models predict the bias using the spectral index for the decay heat and the hydrogen-to-fissile atom ratio for the Pu-239 concentration. Also, the correlation matrices show that they are informative in predicting the bias of both characteristics. In the case of the U-235 and Cs-137 concentrations, biases could not be satisfactorily predicted. Additionally, the linear models have shown unsatisfactory performance.

EPFL2022

High-Fidelity Determination of Nuclide Inventories for Radioactive Waste Disposal

Valentyn Bykov

The general license application (RBG) for the Swiss deep geological repository will be submitted in 2024. Furthermore, the decommissioning of the nuclear power plants (NPPs) is coming soon, with the Mühleberg NPP (KKM) scheduled to shut down in 2019. In support of the associated analyses and planning, availability of accurate descriptions of the nuclear waste streams is of paramount importance. The work described in this thesis represents a significant improvement of Nagra's most important radioactive waste characterization methodologies: for spent fuel, NPP decommissioning waste, and reactor waste. The resulting high-fidelity nuclide inventories will decisively support the future waste disposal research and development, as well as serve the NPP decommissioning planning in general. The spent fuel characterization demonstrated is based on Polaris. This approach is compared against the code sequence used in the industry (Studsvik CMS), in order to confirm that, when the necessary fuel data is available, either one of these codes can be used to provide detailed nuclide vector for the spent fuel. Afterwards, possible ways to produce the desired nuclide vectors in the future are identified. For decommissioning waste, every step of the old characterization methodology has been further developed and improved upon. This includes a new, detailed, fully three-dimensional approach to NPP modeling and the development of a new activation sequence (based on the ORIGEN depletion solver), which outputs a highly-resolved component-wise activation distribution and visualization. Additionally, the methodology is extended to apply the method's results for algorithm-optimized packaging concepts and waste volume minimization through decay storage and free release analyses. At the end, recommendations for the future improvements are presented. The decommissioning waste characterization methodology is then expanded to accommodate reactor waste, and the more complex nature of the non-stationary components of this waste stream (such as the control rods). A systematic general approach, based additionally on the component dose rate measurements, allows for a more efficient and flexible characterization of all reactor waste. These developments serve as an important foundation in the ongoing transition from a conservative approach towards a best-estimate plus uncertainty (BEPU) waste characterization, which is vital for accurate safety analysis studies, as well as waste and cost minimization. The results produced by these methodologies will form the basis for the next version of MIRAM (the Swiss Model Inventory of Radioactive Materials), MIRAM2020, which is to be used for the RBG. They will also be used as part of the next Swiss NPP decommissioning cost study, KS2021. At the same time, the obtained results are already being provided to the Swiss NPPs planning their decommissioning, specifically KKM and Beznau (KKB), allowing detailed component-wise segmentation strategies and packaging concepts decision making, finally leading to notable cost reduction for the utilities.

EPFL2019