@article{rivas_delipei_davis_bhongale_hou_2024, title={A system diagnostic and prognostic framework based on deep learning for advanced reactors}, volume={170}, ISSN={["1878-4224"]}, DOI={10.1016/j.pnucene.2024.105114}, abstractNote={To meet the projected energy demand in the next 30 years, advanced reactor designers are looking to maximize system capacity factor to increase economic competitiveness. To maximize capacity factor, operators must minimize the system downtime due to forced shutdowns from transients. To accomplish this, the objective of this work is to develop a System level Diagnostic/Prognostic (SDP) framework based on state-of-the-art Machine Learning Models (MLM) to support operators by detecting and diagnosing anomalous behaviors and predicting the onset of exceeding safety limits. This Accident Management Support Tool (AMST) consists of a Long Short Term Memory Autoencoder (LSTM-AE) model to identify if an anomaly is present, a Convolutional Neural Network (CNN) diagnostic model to characterize that anomaly, and a Long Short Term Memory Dense layered (LSTM-D) model to provide Remaining Useful Life (RUL) predictions. These models were trained on data from various system wide transients that occur at different power levels and at different rates using a digital twin of the Xe-100 Pebble-Bed High Temperature Gas Reactor (PB-HTGR) developed in SimuPACT. This framework’s capability is showcased with a water ingress constant reactivity insertion event that caused the reactor outlet temperature to exceed its safety threshold. This study showed that as the transient progresses, the LSTM-AE detects an anomaly within 20 s of event initiation, the CNN characterization stays steady throughout the transient with a 60 s delay, and the LSTM-D is able to accurately predict the time to threshold as the reactor outlet temperature approaches its safety threshold 720 s after fault initiation.}, journal={PROGRESS IN NUCLEAR ENERGY}, author={Rivas, Andy and Delipei, Gregory Kyriakos and Davis, Ian and Bhongale, Satyan and Hou, Jason}, year={2024}, month={May} }
 @article{rivas_martin_bays_palmiotti_xu_hou_2022, title={Nuclear data uncertainty propagation applied to the versatile test reactor conceptual design}, volume={392}, ISSN={["1872-759X"]}, url={http://dx.doi.org/10.1016/j.nucengdes.2022.111744}, DOI={10.1016/j.nucengdes.2022.111744}, abstractNote={The Versatile Test Reactor (VTR) currently under development is a 300 MWth sodium-cooled fast reactor (SFR) fueled with ternary metal alloy fuel, which aims to accelerate the testing of advanced nuclear fuels, materials, instrumentation, and sensors in high flux environments that are necessary to license the next generation of advanced reactor concepts. To support the VTR design process, uncertainties associated with the nuclear data has been propagated through the reactor core neutronics calculation to global parameters of interest, such as the core multiplication factor, kinetic parameters, and various reactivity feedback coefficients, following the sensitivity based uncertainty propagation approach. By folding the sensitivity coefficients, separately computed by the generalized perturbation theory code PERSENT and Monte Carlo code Serpent 2, with the variance–covariance matrices from COMMARA-2.0, we obtain the reaction-wise, isotope-wise, and overall uncertainties for each response of interest due to nuclear data uncertainty. With Serpent 2, the statistical error of the uncertainty is obtained by propagating the statistical error of the sensitivity coefficients through the same process using a newly developed uncertainty propagation method. From both codes, the overall top uncertainty contributors are found to be the cross section of Fe-56 elastic scattering, Na-23 elastic scattering, and U-238 inelastic scattering. The large contributions of the Fe-56 elastic scattering cross sections to global parameters are due to its relatively large relative uncertainty of 5–10% in nuclear data and the large volume of Fe-containing reflector assemblies in the fairly compact VTR core design. Both codes agreed well for the overall uncertainty estimates of all responses of interest, except the delayed neutron fraction, prompt neutron generation time, and the coolant density feedback coefficient, where Serpent 2 yielded a much larger value than PERSENT due to the large statistical error of sensitivity coefficients. The calculated uncertainties are also compared to those associated with other SFR cores. Another outcome of this study is a variance–covariance matrix of reactivity coefficients, which can be used in the subsequent uncertainty propagation to the system level to investigate the impact of identified uncertainties on system responses in the safety analysis.}, journal={NUCLEAR ENGINEERING AND DESIGN}, publisher={Elsevier BV}, author={Rivas, Andy and Martin, Nicolas P. and Bays, Samuel E. and Palmiotti, Giuseppe and Xu, Zhiwen and Hou, Jason}, year={2022}, month={Jun} }
 @article{rivas_delipei_hou_2022, title={Predictions of component Remaining Useful Lifetime Using Bayesian Neural Network}, volume={146}, ISSN={["1878-4224"]}, url={http://dx.doi.org/10.1016/j.pnucene.2022.104143}, DOI={10.1016/j.pnucene.2022.104143}, abstractNote={The Machine Prognostics and Health Management (PHM) are concerned with the prediction of the Remaining Useful Lifetime (RUL) of assets. Accurate real-time RUL predictions are necessary when developing an efficient predictive maintenance (PdM) framework for equipment health assessment. If correctly implemented, a PdM framework can maximize the interval between maintenance operations, minimize the cost and number of unscheduled maintenance operations, and improve overall availability of the large facilities like nuclear power plants (NPPs). This is especially important for nuclear power facilities to maximize capacity factor and reliability. In this work, we propose a data-driven approach to make predictions of both the RUL and its uncertainty using a Bayesian Neural Network (BNN). The BNN utilizes the Bayes by backprop algorithm with variational inference to estimate the posterior distribution for each trainable parameter so that the model output is also a PDF from which one can draw the mean prediction and the associated uncertainty. To learn the correlations between various time-series sensor data measurements, a time window approach is implemented with a two-stage noise filtering process for incoming sensor measurements to enhance the feature extraction and overall model performance. As a proof of concept, the NASA Commercial Modular Aero Propulsion System Simulation (C-MAPPS) datasets are utilized to assess the performance of the BNN model. The modeled system can be treated as a surrogate for turbine generators used in NPPs due to the similar mode of operation, degradation, and measurable variables. Comparisons against other state-of-the-art algorithms on the same datasets indicate that the BNN model can not only make predictions with comparable level of accuracy, but also offer the benefit of estimating uncertainty associated with the prediction. This additional uncertainty, which can be continuously updated as more measurement data are collected, can facilitate the decision-making process with a quantifiable confidence level within a PdM framework. Additional advantages of the BNN are showcased, such as providing component maintenance ranges and model executing frequency, with an example of how the BNN estimated uncertainty can be used to support the continuous predictive maintenance. A PdM framework based on a BNN will allow for utilities to make more informed decisions on the optimal time for maintenance so that the loss of revenue can be minimized from planned and unplanned maintenance outages.}, journal={PROGRESS IN NUCLEAR ENERGY}, publisher={Elsevier BV}, author={Rivas, Andy and Delipei, Gregory Kyriakos and Hou, Jason}, year={2022}, month={Apr} }
 @article{rivas_stauff_sumner_hou_2021, title={Propagating neutronic uncertainties for FFTF LOFWOS Test #13}, volume={375}, ISSN={["1872-759X"]}, DOI={10.1016/j.nucengdes.2020.111047}, abstractNote={The safety evaluation conducted for licensing a Sodium-cooled Fast Reactor (SFR) may require modeling transients with best-estimate calculation tools that must first be validated against real-world measurements. To provide the community with a valuable benchmarking opportunity for validating SFR analysis tools and methods, the International Atomic Energy Agency (IAEA) initiated a coordinated research project (CRP) in 2018 for the analysis of the Fast Flux Test Facility (FFTF) Loss of Flow Without Scram (LOFWOS) Test #13. The impact of nuclear data uncertainties on neutronics parameters was previously investigated based on the COMMARA-2.0 covariance matrix. Since the transient simulation results are very sensitive to certain reactivity coefficients, it was decided to employ rigorous uncertainty propagation methods to quantify the impact of nuclear data uncertainties on the best-estimate predication of FFTF LOFWOS Test #13. The DAKOTA code is used to propagate neutronic uncertainties through SAS4A/SASSYS-1 transient simulations, while taking into account spatial and reaction-wise correlations within these uncertainties. This study shows that the remaining discrepancies observed between the Argonne National Laboratory (ANL) best-estimate results and the experimental measurements can be partly explained by the uncertainty associated with Gas Expansion Module (GEM) worth, which contributes the majority of the overall nuclear data uncertainty on the output from the FFTF LOFWOS Test #13 transient simulation. This study also confirmed the importance of including spatial and reaction-wise correlations of nuclear data uncertainties on feedback coefficients in the uncertainty propagation to avoid under-estimating their impact during the transient simulations.}, journal={NUCLEAR ENGINEERING AND DESIGN}, publisher={Elsevier BV}, author={Rivas, Andy and Stauff, Nicolas and Sumner, Tyler and Hou, Jason}, year={2021}, month={Apr} }