Erratum to: A Serpent/OpenFOAM coupling for 3D burnup analysis

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Development of a Reduced Order Model for Fuel Burnup Analysis

Fuel burnup analysis requires a high computational cost for full core calculations, due to the amount of the information processed for the total reaction rates in many burnup regions. Indeed, they reach the order of millions or more by a subdivision into radial and axial regions in a pin-by-pin description. In addition, if multi-physics approaches are adopted to consider the effects of temperature and density fields on fuel consumption, the computational load grows further. In this way, the need to find a compromise between computational cost and solution accuracy is a crucial issue in burnup analysis. To overcome this problem, the present work aims to develop a methodological approach to implement a Reduced Order Model (ROM), based on Proper Orthogonal Decomposition (POD), in fuel burnup analysis. We verify the approach on 4 years of burnup of the TMI-1 unit cell benchmark, by reconstructing fuel materials and burnup matrices over time with different levels of approximation. The results show that the modeling approach is able to reproduce reactivity and nuclide densities over time, where the accuracy increases with the number of basis functions employed

Investigation of SCC of high strength aluminum alloys by means of slow strain rate test and cyclic anodic polarization in combination

The stress corrosion cracking (SCC) behavior of high strength 7075-T6 and 2024-T3 Al alloys in NaCl solutions is investigated by means of slow strain rate test (SSRT) and cyclic anodic polarization in combination. Smooth, dog-bone shaped flat tension test specimens, having gage section areas of 40 mm2 and 32 mm2, respectively, and 90 mm of gage length, were machined in the longitudinal (rolling) direction from the commercial wrought sheets (Aviometal Spa). The tensile test was performed at a constant strain rate (ἐ = 10-7, 10-6 or 10-5 s-1) from a pre-load of about 5 kN until fracture. The electrochemical system consisted in non-connected two Plexiglas cylindrical cells that were fixed at the middle of the opposite surfaces of the tensile specimen (working electrode, surface area at each side of 2 cm2). The variation of the open circuit potential (OCP) during straining was measured with respect to saturated calomel reference electrode (SCE) by connecting the two electrode system to a Gamry potentiostat. Contemporarily, the opposite surface was electrochemically perturbed by imposing consecutive cyclic anodic polarizations with open circuit potential measurements in between (OCP/polarization sequences), using an Ir-coated Ti auxiliary electrode, another SCE and a second Gamry potentiostat. At least two combined experiments for each test condition were carried out for repeatability check. Experiments with no OCP/polarization sequence during straining, and vice versa, were performed for reference purposes. The stress-strain curves of Al 7075-T6 (Fig. 1a) show that the ultimate strength and failure strain decrease in aggressive environment as the strain rate is lowered, regardless the electrochemical perturbation, being in agreement with reported data [1]. More interestingly, quasi-periodic stress relaxation/recovery events above the elastic region in correspondence with the dissolution/repassivation cycle were detected for ἐ ≤ 10-6 s-1 and 0.1667 mV/s of potential scan rate (n). The resolved negative spikes in the stress time derivative curve and the related polarization curves (as log | I | - t) for ἐ = 10-7 s-1, 0.6 M NaCl and n = 0.1667 mV/s are reported in Figure 1b. The spike pattern along the time axe was dependent on ἐ and NaCl concentration. The results from ongoing combined experiments with Al 2024-T3 for verification of the above findings will be presented altogether with empirical data analysis for a quantitative insight into the environmentally assisted failure mechanisms. Please click Additional Files below to see the full abstract

Disease Knowledge Transfer across Neurodegenerative Diseases

We introduce Disease Knowledge Transfer (DKT), a novel technique for transferring biomarker information between related neurodegenerative diseases. DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases. DKT is a joint-disease generative model of biomarker progressions, which exploits biomarker relationships that are shared across diseases. Our proposed method allows, for the first time, the estimation of plausible, multimodal biomarker trajectories in Posterior Cortical Atrophy (PCA), a rare neurodegenerative disease where only unimodal MRI data is available. For this we train DKT on a combined dataset containing subjects with two distinct diseases and sizes of data available: 1) a larger, multimodal typical AD (tAD) dataset from the TADPOLE Challenge, and 2) a smaller unimodal Posterior Cortical Atrophy (PCA) dataset from the Dementia Research Centre (DRC), for which only a limited number of Magnetic Resonance Imaging (MRI) scans are available. Although validation is challenging due to lack of data in PCA, we validate DKT on synthetic data and two patient datasets (TADPOLE and PCA cohorts), showing it can estimate the ground truth parameters in the simulation and predict unseen biomarkers on the two patient datasets. While we demonstrated DKT on Alzheimer's variants, we note DKT is generalisable to other forms of related neurodegenerative diseases. Source code for DKT is available online: https://github.com/mrazvan22/dkt.Comment: accepted at MICCAI 2019, 13 pages, 5 figures, 2 table

A Reduced Order Kalman Filter for Computational Fluid-Dynamics Applications

In the last decade, the importance of numerical simulations for the analysis of complex engineering systems, such as thermo-fluid dynamics in nuclear reactors, has grown exponentially. In spite of the large experimental databases available for validation of mathematical models, in order to identify the most suitable one for the system under investigation, the inverse integration of such data into the CFD model is nowadays an ongoing challenge. In addition, such integration could tackle the problem of propagation of epistemic uncertainties, both in the numerical model and in the experimental data. In this framework, the data-assimilation method allows for the dynamic incorporation of observations within the computational model. Perhaps the most famous among these methods, due to its simple implementation and yet robust nature, is the Kalman filter. Although this approach has found success in fields such as weather forecast and geoscience, its application in Computational Fluid-Dynamics (CFD) is still in its first stages. In this setting, a new algorithm based on the integration between the segregated approach, which is the most common method adopted by CFD applications for the solution of the incompressible Navier-Stokes equations, and a Kalman filter modified for fluid-dynamics problems, while preserving mass conservation of the solution, has already been developed and tested in a previous work. Whereas such method is able to robustly integrate experimental data within the numerical model, its computational cost increases with model complexity. In particular, in high-fidelity realistic scenarios the error covariance matrix for the model, which represents the uncertainties associated with it, becomes dense, thus affecting the efficiency and computational cost of the method. For this reason, due to the promised reduction of computational requirements recently investigated, which combines model reduction and data-assimilation, in this work a combination of reduced order model and mass-conservative Kalman filter within a segregated approach for CFD analysis is proposed. The novelty lies in the peculiar formulation of the Kalman filter and how to construct a low-dimensional manifold to approximate, with sufficient accuracy, the high fidelity model. With respect to literature, in which the full-order Kalman filter is applied to a reduced model, the reduction is performed directly on the integrated model in order to obtain a reduced-order Kalman filter already optimised for fluid-dynamics applications. In order to verify the capabilities of this approach, this reduced-order algorithm has been tested against the lid-driven cavity test case.</jats:p

Lupus mastitis in male mimicking a breast lump

A 43-year-old male, with a 3-month history of a left breast lump underwent clinical evaluation in our Institute. This solid and irregular mass measured 2 2 cm and was located at the upper lateral quadrant with no skin changes. There were no inflammatory signs. However, a lymphadenopathy was presented with a mobile ipsilateral axillary node 1.5 cm in diameter. Computerized tomography demonstrated a hyperplastic lateral cervical lymph nodes reactio

Clinical, genetic, and pathological features of male pseudohermaphroditism in dog

Male pseudohermaphroditism is a sex differentiation disorder in which the gonads are testes and the genital ducts are incompletely masculinized. An 8 years old dog with normal male karyotype was referred for examination of external genitalia abnormalities. Adjacent to the vulva subcutaneous undescended testes were observed. The histology of the gonads revealed a Leydig and Sertoli cell neoplasia. The contemporaneous presence of testicular tissue, vulva, male karyotype were compatible with a male pseudohermaphrodite (MPH) condition

A mass conservative Kalman filter algorithm for computational thermo-fluid dynamics

This paper studies Kalman filtering applied to Reynolds-Averaged Navier-Stokes (RANS) equations for turbulent flow. The integration of the Kalman estimator is extended to an implicit segregated method and to the thermodynamic analysis of turbulent flow, adding a sub-stepping procedure that ensures mass conservation at each time step and the compatibility among the unknowns involved. The accuracy of the algorithm is verified with respect to the heated lid-driven cavity benchmark, incorporating also temperature observations, comparing the augmented prediction of the Kalman filter with the Computational Fluid-Dynamic solution found on a fine grid

Reweighting NNPDFs: the W lepton asymmetry

We present a method for incorporating the information contained in new datasets into an existing set of parton distribution functions without the need for refitting. The method involves reweighting the ensemble of parton densities through the computation of the chi-square to the new dataset. We explain how reweighting may be used to assess the impact of any new data or pseudodata on parton densities and thus on their predictions. We show that the method works by considering the addition of inclusive jet data to a DIS+DY fit, and comparing to the refitted distribution. We then use reweighting to determine the impact of recent high statistics lepton asymmetry data from the D0 experiment on the NNPDF2.0 parton set. We find that the D0 inclusive muon and electron data are perfectly compatible with the rest of the data included in the NNPDF2.0 analysis and impose additional constraints on the large-x d/u ratio. The more exclusive D0 electron datasets are however inconsistent both with the other datasets and among themselves, suggesting that here the experimental uncertainties have been underestimated.Comment: 36 pages, 22 figures: errors in Eqns.12,36,37 corrected and parts of Figs.1,6,10,13,15,19 replace