Radial Turbine Thermo-Mechanical Stress Optimization by Multidisciplinary Discrete Adjoint Method

This paper addresses the problem of the design optimization of turbomachinery components under thermo-mechanical constraints, with focus on a radial turbine impeller for turbocharger applications. Typically, turbine components operate at high temperatures and are exposed to important thermal gradients, leading to thermal stresses. Dealing with such structural requirements necessitates the optimization algorithms to operate a coupling between fluid and structural solvers that is computationally intensive. To reduce the cost during the optimization, a novel multiphysics gradient-based approach is developed in this work, integrating a Conjugate Heat Transfer procedure by means of a partitioned coupling technique. The discrete adjoint framework allows for the ecient computation of the gradients of the thermo-mechanical constraint with respect to a large number of design variables. The contribution of the thermal strains to the sensitivities of the cost function extends the multidisciplinary outlook of the optimization and the accuracy of its predictions, with the aim of reducing the empirical safety factors applied to the design process. Finally, a turbine impeller is analyzed in a demanding operative condition and the gradient information results in a perturbation of the grid coordinates, reducing the stresses at the rotor back-plate, as a demonstration of the suitability of the presented method

Inferring unknown unknowns: Regularized bias-aware ensemble Kalman filter

Because of physical assumptions and numerical approximations, low-order models are affected by uncertainties in the state and parameters, and by model biases. Model biases, also known as model errors or systematic errors, are difficult to infer because they are `unknown unknowns', i.e., we do not necessarily know their functional form a priori. With biased models, data assimilation methods may be ill-posed because either (i) they are 'bias-unaware' because the estimators are assumed unbiased, (ii) they rely on an a priori parametric model for the bias, or (iii) they can infer model biases that are not unique for the same model and data. First, we design a data assimilation framework to perform combined state, parameter, and bias estimation. Second, we propose a mathematical solution with a sequential method, i.e., the regularized bias-aware ensemble Kalman Filter (r-EnKF), which requires a model of the bias and its gradient (i.e., the Jacobian). Third, we propose an echo state network as the model bias estimator. We derive the Jacobian of the network, and design a robust training strategy with data augmentation to accurately infer the bias in different scenarios. Fourth, we apply the r-EnKF to nonlinearly coupled oscillators (with and without time-delay) affected by different forms of bias. The r-EnKF infers in real-time parameters and states, and a unique bias. The applications that we showcase are relevant to acoustics, thermoacoustics, and vibrations; however, the r-EnKF opens new opportunities for combined state, parameter and bias estimation for real-time and on-the-fly prediction in nonlinear systems.Comment: 22 Figure

I sistemi elettorali sotto la lente costituzionale

Il volume intende approfondire gli aspetti problematici che hanno interessato negli ultimi decenni la materia elettorale in Italia: tanto nel rapporto con le fonti del diritto, quanto in ordine ai principali elementi di “ingegneria elettorale” susseguitisi in sede legislativa. Evitando “imbrigliature” ideologiche ed assumendo, quali precipui punti di riferimento, la Carta fondamentale e le sue esegesi più autorevoli – dottrinali e giurisprudenziali – si tenterà infine di individuare un modello elettorale “costituzionalmente preferibile”

Convolutional autoencoder for the spatiotemporal latent representation of turbulence

Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obtain a latent description of turbulent flows. However, current approaches are often limited by either the need to use some form of thresholding on quantities defining the isosurfaces to which the flow structures are associated or the linearity of traditional modal flow decomposition approaches, such as those based on proper orthogonal decomposition. This problem is exacerbated in flows that exhibit extreme events, which are rare and sudden changes in a turbulent state. The goal of this paper is to obtain an efficient and accurate reduced-order latent representation of a turbulent flow that exhibits extreme events. Specifically, we employ a three-dimensional multiscale convolutional autoencoder (CAE) to obtain such latent representation. We apply it to a three-dimensional turbulent flow. We show that the Multiscale CAE is efficient, requiring less than 10% degrees of freedom than proper orthogonal decomposition for compressing the data and is able to accurately reconstruct flow states related to extreme events. The proposed deep learning architecture opens opportunities for nonlinear reduced-order modeling of turbulent flows from data

Modelling spatiotemporal turbulent dynamics with the convolutional autoencoder echo state network

The spatiotemporal dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate and stable reduced-order models challenging. The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state for a reduced-order representation of the dynamics. We divide the turbulent flow into a spatial problem and a temporal problem. First, we compute the latent space, which is the manifold onto which the turbulent dynamics live (i.e., it is a numerical approximation of the turbulent attractor). The latent space is found by a series of nonlinear filtering operations, which are performed by a convolutional autoencoder (CAE). The CAE provides the decomposition in space. Second, we predict the time evolution of the turbulent state in the latent space, which is performed by an echo state network (ESN). The ESN provides the decomposition in time. Third, by assembling the CAE and the ESN, we obtain an autonomous dynamical system: the convolutional autoncoder echo state network (CAE-ESN). This is the reduced-order model of the turbulent flow. We test the CAE-ESN on a two-dimensional flow. We show that, after training, the CAE-ESN (i) finds a latent-space representation of the turbulent flow that has less than 1% of the degrees of freedom than the physical space; (ii) time-accurately and statistically predicts the flow in both quasiperiodic and turbulent regimes; (iii) is robust for different flow regimes (Reynolds numbers); and (iv) takes less than 1% of computational time to predict the turbulent flow than solving the governing equations. This work opens up new possibilities for nonlinear decompositions and reduced-order modelling of turbulent flows from data

Turbocharger design and optimization by adjoint method coupled with CHT analysis

The design of turbochargers for modern automotive applications requires the solution of a highly constrained problem in which targets of extended operative range and performance are coupled with the need of improved synergies with surrounding engine subsystems. The optimization of Variable Geometry Turbines (VGT) demands a multidisciplinary and multipoint approach aimed at improving the machine efficiency while complying with the restrictions imposed by the wide thermal spectrum experienced during various driving conditions. The present work focuses on the application of CHT analysis to the optimization of VGT turbochargers by introduction of thermal evaluations in an adjoint-based shape optimization framework

A Microwave-Assisted Synthesis of Zinc Oxide Nanocrystals Finely Tuned for Biological Applications

Herein we report a novel, easy, fast and reliable microwave-assisted synthesis procedure for the preparation of colloidal zinc oxide nanocrystals (ZnO NCs) optimized for biological applications. ZnO NCs are also prepared by a conventional solvo-thermal approach and the properties of the two families of NCs are compared and discussed. All of the NCs are fully characterized in terms of morphological analysis, crystalline structure, chemical composition and optical properties, both as pristine nanomaterials or after amino-propyl group functionalization. Compared to the conventional approach, the novel microwave-derived ZnO NCs demonstrate outstanding colloidal stability in ethanol and water with long shelf-life. Furthermore, together with their more uniform size, shape and chemical surface properties, this long-term colloidal stability also contributes to the highly reproducible data in terms of biocompatibility. Actually, a significantly different biological behavior of the microwave-synthesized ZnO NCs is reported with respect to NCs prepared by the conventional synthesis procedure. In particular, consistent cytotoxicity and highly reproducible cell uptake toward KB cancer cells are measured with the use of microwave-synthesized ZnO NCs, in contrast to the non-reproducible and scattered data obtained with the conventionally-synthesized ones. Thus, we demonstrate how the synthetic route and, as a consequence, the control over all the nanomaterial properties are prominent points to be considered when dealing with the biological world for the achievement of reproducible and reliable results, and how the use of commercially-available and under-characterized nanomaterials should be discouraged in this view

Influencia de un período de stress hídrico y de algunos reguladores del crecimiento sobre el grado de nodulación de dos cultivares de soja

El presente trabajo tuvo por objeto estudiar la evolución de sustancias inhibidoras en follaje y raíz de plantas del cv. Halesoy 71 sometidas o no a un período de stress hídrico, y la posible alteración de los patrones evolutivos de tales sustancias por acción del ácido giberélico (AG3) aplicado por vía foliar. En una segunda etapa se trató de reproducir el efecto detrimental de un período de sequía sobre la nodulación, mediante aplicaciones por vía foliar de ABA en plantas del cv. Lee crecidas continuamente en capacidad de campo (c. c.). Por último, se trató de establecer si dicho presunto efecto inhibidor podía ser revertido mediante aspersiones simultáneas de AG3.Academia Nacional de Agronomía y Veterinari

YKL-40/c-Met Expression in Rectal Cancer Biopsies Predicts Tumor Regression following Neoadjuvant Chemoradiotherapy: A Multi-Institutional Study

BACKGROUND:Neoadjuvant chemo-radiotherapy (CRT) followed by surgical resection is the standard treatment for locally advanced rectal cancer, although complete tumor pathological regression is achieved in only up to 30% of cases. A clinicopathological and molecular predictive stratification of patients with advanced rectal cancer is still lacking. Here, c-Met and YKL-40 have been studied as putative predictors of CRT response in rectal cancer, due to their reported involvement in chemoradioresistance in various solid tumors. MATERIAL AND METHODS:A multicentric study was designed to assess the role of c-Met and YKL-40 expression in predicting chemoradioresistance and to correlate clinical and pathological features with CRT response. Immunohistochemistry and fluorescent in situ hybridization for c-Met were performed on 81 rectal cancer biopsies from patients with locally advanced rectal adenocarcinoma. All patients underwent standard (50.4 gy in 28 fractions + concurrent capecitabine 825 mg/m2) neoadjuvant CRT or the XELOXART protocol. CRT response was documented on surgical resection specimens and recorded as tumor regression grade (TRG) according to the Mandard criteria. RESULTS:A significant correlation between c-Met and YKL-40 expression was observed (R = 0.43). The expressions of c-Met and YKL-40 were both significantly associated with a lack of complete response (86% and 87% of c-Met and YKL-40 positive cases, p< 0.01 and p = 0.006, respectively). Thirty of the 32 biopsies co-expressing both markers had partial or absent tumor response (TRG 2-5), strengthening their positive predictive value (94%). The exclusive predictive role of YKL-40 and c-Met was confirmed using a multivariate analysis (p = 0.004 and p = 0.007 for YKL-40 and c-Met, respectively). TRG was the sole morphological parameter associated with poor outcome. CONCLUSION:c-Met and YKL-40 expression is a reliable predictor of partial/absent response to neoadjuvant CRT in rectal cancer. Targeted therapy protocols could take advantage of prior evaluations of c-MET and YKL-40 expression levels to increase therapeutic efficacy