1,721,097 research outputs found
Monitoring Soil Water Content via Wireless Underground Communication Networks: the Impact of Uncertainty in Soil Textural Parameters
No full textDinamiche di ricarica nella conoide del Trebbia
No full textANALIZZARE LE SERIE TEMPORALI DEI LIVELLI PIEZOMETRICI DELLE ACQUE SOTTERRANEE E DEI LIVELLI DEI CORSI D’ACQUA PERMETTE DI OTTENERE INDICAZIONI PRELIMINARI IN MERITO ALLE DINAMICHE DI RICARICA NATURALE DEI CORPI IDRICI SOTTERRANEI IN PARTICOLARI CONTESTI TERRITORIALI. LO STUDIO SULLA CONOIDE DEL TREBBIA NEL PIACENTINO
Disponibilità idrica dell'acquifero freatico costiero della Regione Emilia-Romagna e analisi dei fattori di rischio
No full textTransfer learning of neural surrogates on multifidelity groundwater simulations
Full text linkComputationally inexpensive surrogates of process-based models, such as deep neural networks, enable ensemble-based computations used in risk assessment, data assimilation, etc. However, generation of large datasets required to train a neural network can be as expensive as the ensemble simulations themselves. We ameliorate this challenge by using data from multifidelity (MF) groundwater simulations and transfer learning (TL) to reduce data generation costs while maintaining model accuracy. As a computational example, we train a deep convolutional neural network (CNN) to reconstruct permeability fields from saturation maps derived from a multiphase flow model. Starting with very low- and low-fidelity data generated on increasingly coarse meshes, we pretrain the CNN, followed by output-layer training and fine-tuning using only a limited number of high-fidelity samples. We demonstrate the surrogate's robustness when interpreting low-quality inputs — such as interpolated maps or data affected by noise — which has strong implications for the applicability in practical hydrogeological scenarios. This multilevel MF-TL strategy achieves a favorable trade-off between computational efficiency and predictive accuracy, significantly outperforming high-fidelity-only approaches under the same computational budget
Tecniche di riduzione dei modelli ed analisi di sensitività globale per la calibrazione di modelli di trasporto in mezzi porosi
No full textDrainage of viscous gravity currents from the edge of a porous or fractured domain with variable properties
No full textTemperature fields induced by geothermal devices
No full textEfficient and sustainable exploitation of low-enthalpy geothermal energy relies on accurate representations of heat transfer processes in the subsurface. An analytical model, which provides such a representation by predicting the dynamics of thermal fields induced by shallow GHEs (ground heat exchangers), is derived. The model accounts for atmospheric temperature fluctuations at the ground surface, an arbitrary geometry of GHEs operating in time-varying heating/cooling modes, and anisotropy and uncertain spatio-temporal variability of thermal conductivity of the ambient soil. To validate the model, its predictions of a thermal field generated by a shallow flat-panel GHEs are compared with experimental data. This comparison demonstrates the model's ability to provide accurate fit-free predictions of soil-temperature fields generated by GHEs. The analysis presented shows that a single horizontal GHE may affect soil temperature by several degrees at distances on the order of 1 m. The volume of influence is expressed in terms of soil thermal properties. Such modeling predictions are invaluable for screening of potential sites and optimal design of geothermal systems consisting of multiple GHEs
Surrogate models provide new insights on metrics based on blood flow for the assessment of left ventricular function
Full text linkRecent developments on the grading of cardiac pathologies suggest flow-related metrics for a deeper evaluation of cardiac function. Blood flow evaluation employs space-time resolved cardiovascular imaging tools, possibly integrated with direct numerical simulation (DNS) of intraventricular fluid dynamics in individual patients. If a patient-specific analysis is a promising method to reproduce flow details or to assist virtual therapeutic solutions, it becomes impracticable in nearly-real-time during a routine clinical activity. At the same time, the need to determine the existence of relationships between advanced flow-related quantities of interest (QoIs) and the diagnostic metrics used in the standard clinical practice requires the adoption of techniques able to generalize evidences emerging from a finite number of single cases. In this study, we focus on the left ventricular function and use a class of reduced-order models, relying on the Polynomial Chaos Expansion (PCE) technique to learn the dynamics of selected QoIs based on a set of synthetic cases analyzed with a high-fidelity model (DNS). The selected QoIs describe the left ventricle blood transit and the kinetic energy and vorticity at the peak of diastolic filling. The PCE-based surrogate models provide straightforward approximations of these QoIs in the space of widely used diagnostic metrics embedding relevant information on left ventricle geometry and function. These surrogates are directly employable in the clinical analysis as we demonstrate by assessing their robustness against independent patient-specific cases ranging from healthy to diseased conditions. The surrogate models are used to perform global sensitivity analysis at a negligible computational cost and provide insights on the impact of each diagnostic metric on the QoIs. Results also suggest how common flow transit parameters are principally dictated by ejection fraction
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