Browsing by Author "Samaniego Alvarado, Esteban Patricio"

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A causal flow approach for the evaluation of global climate models
(2020) Vázquez Patiño, Angel Oswaldo; Campozano Parra, Lenin Vladimir; Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio
© 2020 Royal Meteorological Society Global climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in-depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.
A micromechanics-based variational phase-field model for fracture in geomaterials with brittle-tensile and compressive-ductile behavior
(2022) Francois, Stijn
This paper presents a framework for modeling failure in quasi-brittle geomaterials under different loading conditions. A micromechanics-based model is proposed in which the field variables are linked to physical mechanisms at the microcrack level: damage is related to the growth of microcracks, while plasticity is related to the frictional sliding of closed microcracks. Consequently, the hardening/softening functions and parameters entering the free energy follow from the definition of a single degradation function and the elastic material properties. The evolution of opening microcracks in tension leads to brittle behavior and mode I fracture, while the evolution of closed microcracks under frictional sliding in compression/shear leads to ductile behavior and mode II fracture. Frictional sliding is endowed with a non-associative law, a crucial aspect of the model that considers the effect of dilation and allows for realistic material responses with non-vanishing frictional energy dissipation. Despite the non-associative law, a variationally consistent formulation is presented using notions of energy balance and stability, following the energetic formulation for rate-independent systems. The material response of the model is first described, followed by the numerical implementation procedure and several benchmark finite element simulations. The results highlight the ability of the model to describe tensile, shear, and mixed-mode fracture, as well as responses with brittle-to-ductile transition. A key result is that, by virtue of the micromechanical arguments, realistic failure modes can be captured, without resorting to the usual heuristic modifications considered in the phase-field literature. The numerical results are thoroughly discussed with reference to previous numerical studies, experimental evidence, and analytical fracture criteria.
A phase-field model for ductile fracture with shear bands: a parallel implementation
(2021) Rodriguez Manzano, Mario Patricio
Modeling complex material failure with competing mechanisms is a difficult task that often leads to mathematical and numerical challenges. This work contributes to the study of localized failure mechanisms by means of phase fields in a variational framework: in addition to the treatment of brittle and ductile fracture, done in previous work, we consider the case of shear band formation followed by ductile fracture. To achieve this, a new degradation function is introduced, which distinguishes between two successive failure mechanisms: (i) plastic strain localization and (ii) ductile fracture. Specifically, the onset of elastic damage is delayed to allow for the formation of shear bands driven by plastic deformations, thus accounting for the mechanisms that precede the coalescence of voids and microcracks into macroscopic ductile fractures. Once a critical degradation value has been reached, a phase-field model is introduced to capture the (regularized) kinematics of macroscopic cracks. To tackle the issue of potentially high computational cost, we propose a parallel implementation of the phase-field approach based on an iterative algorithm. The algorithm was implemented within the Alya system, a high performance computational mechanics code. Several examples show the capabilities of our implementation. We pay special attention to the ability to capture different failure mechanisms
A simple circular cell method for multilevel finite element analysis
(2012-01-01) Samaniego Alvarado, Esteban Patricio
A simple multiscale analysis framework for heterogeneous solids based on a computational homogenization technique is presented. The macroscopic strain is linked kinematically to the boundary displacement of a circular or spherical representative volume which contains the microscopic information of the material. The macroscopic stress is obtained from the energy principle between the macroscopic scale and the microscopic scale. This new method is applied to several standard examples to show its accuracy and consistency of the method proposed. © Copyright 2012 Hossein Talebi et al.
A staggered approach for the coupling of Cahn–Hilliard type diffusion and finite strain elasticity
(SPRINGER VERLAG, 2016-02-01) Samaniego Alvarado, Esteban Patricio
We develop an algorithm and computational implementation for simulation of problems that combine Cahn–Hilliard type diffusion with finite strain elasticity. We have in mind applications such as the electro-chemo-mechanics of lithium ion (Li-ion) batteries. We concentrate on basic computational aspects. A staggered algorithm is proposed for the coupled multi-field model. For the diffusion problem, the fourth order differential equation is replaced by a system of second order equations to deal with the issue of the regularity required for the approximation spaces. Low order finite elements are used for discretization in space of the involved fields (displacement, concentration, nonlocal concentration). Three (both 2D and 3D) extensively worked numerical examples show the capabilities of our approach for the representation of (i) phase separation, (ii) the effect of concentration in deformation and stress, (iii) the effect of strain in concentration, and (iv) lithiation. We analyze convergence with respect to spatial and time discretization and found that very good results are achievable using both a staggered scheme and approximated strain interpolation.
A study of microgrids through cooperative games including the effect of geographical proximity.
(Institute of Electrical and Electronics Engineers Inc, 2017) Sanango Fernandez, Juan Bautista
Given the growing demand for energy and the diversification of its sources, electric utilities have seen the need to incorporate distributed generation in their systems in order to minimize energy losses and raise quality indicators. In this context, it is important to have mathematical tools to assist the management of energy exchange between all players in an electrical system (customers, generators, and "prosumers"). The theory of cooperative games offers tools that are able to model the exchange among coalitions formed by elements of a system. Specifically, we adopt an existing algorithm that simulates the exchange of energy between microgrids as a cooperative game. In order to avoid the consideration of unrealistic coalition formation scenarios, we modify this algorithm by including the effect of geographical proximity. The resulting strategy was implemented in MatLab and was applied to a simple case study. Both the effect of including this restriction on the reduction of losses and the impossibility of forming the so-called grand coalition are analyzed.
A variational approach to the phase field modeling of brittle and ductile fracture
(2018) Rodríguez , Patricio; Ulloa, Jaciento; Samaniego, Cristóbal; Samaniego Alvarado, Esteban Patricio
The modeling of the post-critical behavior of materials is still a scientific challenge. This is especially true when dealing with materials that undergo complex behavior, in which several mechanisms are combined. This physical complexity is reflected in the mathematics and the numerics of this kind of problems. In this work, we study the modeling of brittle and ductile fracture. We adopt regularized kinematics based on a phase field description of the fracture topology. In order to ensure mathematical soundness, we use a rigorous variational framework for dissipative rate-independent materials. This framework allows to introduce several dissipative mechanisms in a straightforward and clear manner. For instance, gradients for both damage and plasticity are introduced. This implies the existence of two internal length scales that control the degree of ductility of the macroscopic fracture mechanism. A finite element discretization allows to test the possibilities of the proposed model to describe different fracture behaviors with several benchmark numerical experiments. In addition, the variational framework naturally leads to a robust staggered algorithm. Despite the simplicity of the numerical solution, different types of fracture processes can be described as particular cases: quasi-brittle, elasto-plastic brittle, and ductile.
A Variational merging approach to the spatial description of environmental variables
(2018) Ulloa, Jacinto Israel; Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Ballari, Daniela Elisabet
High resolution images of environmental variables are highly valuable sources of information in research and environmental management. Obtaining spatially continuous information from ground observations is challenging due to the wide variety of factors that affect classical interpolation methods. While geostatistical methods have produced noteworthy results, they generally rely on important assumptions and strongly depend on the availability of observed data. In turn, satellite‐based or model‐based gridded images generally represent the global spatial structure of environmental variables, but are subject to bias. With the objective of exploiting the benefits of both sources of information, we propose a new mathematical formulation to merge observed data with gridded images of environmental variables using partial differential equations in a variational setting. With a …
An energy approach to the solution of partial differential equations in computational mechanics via machine learning: concepts, implementation and applications
(2020) Samaniego Alvarado, Esteban Patricio; Anitescu, Cosmin; Goswami, Somdatta; Nguyen Thanh, Vien Minh; Hongwei, Guo; Hamdia, Khader M.; Zhuang, Xiaoying; Rabczuk, Timon
Partial Differential Equations (PDEs) are fundamental to model different phenomena in science and engineering mathematically. Solving them is a crucial step towards a precise knowledge of the behavior of natural and engineered systems. In general, in order to solve PDEs that represent real systems to an acceptable degree, analytical methods are usually not enough. One has to resort to discretization methods. For engineering problems, probably the best-known option is the finite element method (FEM). However, powerful alternatives such as mesh-free methods and Isogeometric Analysis (IGA) are also available. The fundamental idea is to approximate the solution of the PDE by means of functions specifically built to have some desirable properties. In this contribution, we explore Deep Neural Networks (DNNs) as an option for approximation. They have shown impressive results in areas such as visual recognition. DNNs are regarded here as function approximation machines. There is great flexibility to define their structure and important advances in the architecture and the efficiency of the algorithms to implement them make DNNs a very interesting alternative to approximate the solution of a PDE. We concentrate on applications that have an interest for Computational Mechanics. Most contributions explore this possibility have adopted a collocation strategy. In this work, we concentrate on mechanical problems and analyze the energetic format of the PDE. The energy of a mechanical system seems to be the natural loss function for a machine learning method to approach a mechanical problem. In order to prove the concepts, we deal with several problems and explore the capabilities of the method for applications in engineering.
Análisis comparativo de Downscaling estadístico y dinámico en las cuencas de los ríos Paute y Jubones
(2015) Guanuchi Quito, Juan Carlos; Samaniego Alvarado, Esteban Patricio
Global circulation models are a powerful tool for climate prediction. The coarse scale of their results makes them difficult to apply to decision-making processes at local and regional level. Aiming at the incorporation of regional and local information, several downscaling techniques have been devised. Notwithstanding, their application, the results exhibit errors; to tackle this problem, the technique known as Quantile Mapping allows a consistent change on the results distribution, so that they are fitted to the observations distribution. This technique is applied to downscaling results for the Paute and Jubones basins, located in Southern Ecuador. It is observed that they depend on the Quantile Mapping variant applied. Even though it is clear that errors cannot be completely eliminated, improvement of up to 70% has been attained in dynamical downscaling; in statistical downscaling, just a slight improvement is observed. To identify and generate projections of climate change in the basins, Delta method was applied, with their results seasonality, anomalies, and climate variability were analyzed.
Análisis exploratorio de modelos guiados en datos informados por la física como modelos subrogantes en problemas de mecánica de sólidos no lineales
(Universidad de Cuenca, 2025-09-25) Aguilar Valarezo, Emerson Roberto; Merchán Avila, Daniel Esteban; Samaniego Alvarado, Esteban Patricio
This work presents a proof of concept based on a multiscale data-driven (DD) approach to modeling the elastoplastic behavior of materials without using explicit constitutive laws. Two configurations are studied: a one-dimensional case (bar) and a two-dimensional case (plane strain), considering both hardening and softening plasticity. At the one-dimensional and twodimensional microscopic scales (with hardening), synthetic data were generated for the material under tensile loading and for the unit cell subjected to shear loading, respectively. These stress-strain data were fed into a DD model which, at the macroscopic scale, solved the problem without resorting to a constitutive model. Adequate convergence was achieved in both hardening cases (one-dimensional and two-dimensional). However, in the onedimensional softening case, increasing the number of elements (from 3 to 5) caused the softening model to fail to achieve convergence. In the two-dimensional softening case, a Deep Ritz-type variational model was used to perform virtual shear experiments on the unit cell. These experiments used Dirichlet-type boundary conditions and the equilibrium equations of continuum mechanics to obtain data on material behavior. The 2D softening results showed that the DD model presents difficulties in solving the problem, highlighting the need for methodological adjustments in scenarios with strain localization. MATLAB and Python were used alongside deep learning libraries for model training. The study demonstrates the potential of the DD approach to model complex materials without requiring an explicit constitutive formulation.
Aplicabilidad de los modelos NAM y DBM para estimar caudales en subcuencas alto andinas de Ecuador
(Universidad de Cuenca, 2013-12) Quichimbo, A.; Vázquez, R. F.; Samaniego Alvarado, Esteban Patricio; Universidad de Cuenca; Dirección de Investigación de la Universidad de Cuenca
A Data-Based Mechanistic (DBM) model and the Nedbor-Afstromnings Model (NAM) were applied to simulate the rainfall-runoff relationship of two Andean basins, different in size, located in southern Ecuador. This article provides a comparative analysis of both modeling approaches, with emphasis on the evaluation of the model performance. The study revealed that the DBM model better mimics the rainfall-runoff system than the NAM model representing the river basin by a structure composed of three linear reservoirs.
Aplicación de la teoría de la plasticidad unidimensional a la sección de una viga
(2007) Guartasaca Ordoñez, Juan Carlos; Samaniego Alvarado, Esteban Patricio
Causality and climate networks approaches for evaluating climate models, tracing flows, and selecting physically meaningful predictors
(Universidad de Cuenca, 2022-04-14) Vázquez Patiño, Angel Oswaldo; Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir
Climate consists of many components, for example, atmosphere, hydrosphere, cryosphere, and biosphere. All the components act under mechanisms that relate them in a highly non-linear way, making the climate a complex system. This complexity is a challenge to study the climate and its implications at various spatiotemporal scales. However, the dependence of anthropogenic activities on the climate has encouraged its study in order, for example, to anticipate its periodic changes and, as far as possible, extreme events that may have adverse effects. As climate study is an intricate task, several approaches have been used to unravel the underlying processes that dominate its behavior. Those approaches range from linear correlation analysis to complex machine learning-based knowledge discovery analysis. This last approach has become more relevant after the introduction of sophisticated climate simulation models and high-tech equipment (e.g., satellite) that allow a climate record of greater coverage (spatial and temporal) and that, together, have generated ubiquitous large databases. One of the knowledge discovery approaches based on this big data is based on climate networks. Nevertheless, causal reasoning methods have also been used recently to infer and characterize these networks, which are called causal climate networks. Several studies have been carried out with climate networks; however, the recent introduction of causality methods makes the study of climate with causal climate networks an opportunity to explore and exploit them more widely. In addition, the particularities of the climate make it necessary to understand specific operational issues that must be taken into account when applying networks. This thesis aims to propose new methodologies and applications of causal climate networks following as a common thread the characterization of physical phenomena that manifest themselves at different spatial scales. For this, different case studies have been taken. They are the climate in South America and a large part of the Pacific and Atlantic oceans, then, reducing the scale, the surrounding factors that influence the rainfall of Ecuador, and, finally, the selection of predictors for downscaling models in an Andean basin. Among the main results are the following three. First, a methodology for evaluating global climate models based on what is called here as causal flows. Second, an approach that studies causal flows and helps trace influence paths in flow fields. Third, the presentation of evidence that shows the effectiveness of methods based on causality in selecting predictors for downscaling models. The thesis contributes to efforts to bridge the gap between the climate science and causal inference communities. This through the study and application of causal reasoning and taking advantage of the enormous amounts of climate data available today
Climatology and teleconnections of mesoscale convective systems in an andean basin in southern Ecuador: the case of the Paute basin
(2018) Samaniego Alvarado, Esteban Patricio; Campozano Parra, Lenin Vladimir; Célleri Alvear, Rolando Enrique; Albuja Silva, Edgar Cristóbal
Mesoscale convective systems (MCSs) climatology, the thermodynamic and dynamical variables, and teleconnections influencing MCSs development are assessed for the Paute basin (PB) in the Ecuadorian Andes from 2000 to 2009. The seasonality of MCSs occurrence shows a bimodal pattern, with higher occurrence during March-April (MA) and October-November (ON), analogous to the regional rainfall seasonality. The diurnal cycle of MCSs shows a clear nocturnal occurrence, especially during the MA and ON periods. Interestingly, despite the higher occurrence of MCSs during the rainy seasons, the monthly size relative frequency remains fairly constant throughout the year. On the east of the PB, the persistent high convective available potential and low convective inhibition values from midday to nighttime are likely related to the nocturnal development of the MCSs. A significant positive correlation between the MCSs occurrence to the west of the PB and the Trans-Niño index was found, suggesting that ENSO is an important source of interannual variability of MCSs frequency with increasing development of MCSs during warm ENSO phases. On the east of the PB, the variability of MCSs is positively correlated to the tropical Atlantic sea surface temperature anomalies south of the equator, due to the variability of the Atlantic subtropical anticyclone, showing main departures from this relation when anomalous conditions occur in the tropical Pacific due to ENSO.
Comparative Study of UV Radiation Resistance and Reactivation Characteristics of E. coli ATCC 8739 and Native Strains: Implications for Water Disinfection
(2023) Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio; Duque Sarango, Paola Jackeline; Pinos Vélez, Verónica Patricia
In certain countries where fresh water is in short supply, the effluents from wastewater treatment plants are being recycled for other uses. For quality assurance, tertiary disinfection treatments are required. This study aims to evaluate the inactivating efficacy with an ultraviolet (UV) system on fecal bacteria from effluents of urban wastewater treatment facilities and the post-treatment influence of the environmental illumination. The effect from different UV doses was determined for native and standardized lyophilized strains of Escherichia coli right after the irradiation as well as after 24 h of incubation under light or dark conditions. To achieve 3 log-reductions of the initial bacterial concentration, a UV dose of approximately 12 mJ cm−2 is needed for E. coli ATCC 8739 and native E. coli. However, there is a risk of the reactivation of 0.19% and 1.54% of the inactivated organisms, respectively, if the treated organisms are stored in an illuminated environment. This suggests that the post-treatment circumstances affect the treatment success; storing the treated water under an illuminated environment may pose a risk even if an effective inactivation was achieved during the irradiation.
Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador
(2016) Campozano Parra, Lenin Vladimir; Tenelanda Patiño, Daniel Orlando; Sánchez Cordero, Esteban Remigio; Samaniego Alvarado, Esteban Patricio; Feyen, Jan
Downscaling improves considerably the results of General Circulation Models (GCMs). However, little information is available on the performance of downscaling methods in the Andean mountain region. The paper presents the downscaling of monthly precipitation estimates of the NCEP/NCAR reanalysis 1 applying the statistical downscaling model (SDSM), artificial neural networks (ANNs), and the least squares support vector machines (LS-SVM) approach. Downscaled monthly precipitation estimates after bias and variance correction were compared to the median and variance of the 30-year observations of 5 climate stations in the Paute River basin in southern Ecuador, one of Ecuador’s main river basins. A preliminary comparison revealed that both artificial intelligence methods, ANN and LS-SVM, performed equally. Results disclosed that ANN and LS-SVM methods depict, in general, better skills in comparison to SDSM. However, in some months, SDSM estimates matched the median and variance of the observed monthly precipitation depths better. Since synoptic variables do not always present local conditions, particularly in the period going from September to December, it is recommended for future studies to refine estimates of downscaling, for example, by combining dynamic and statistical methods, or to select sets of synoptic predictors for specific months or seasons.
Data-based local rainfall modeling through global climate information
(Universidad de Cuenca, 2022-03-28) Mendoza Sigüenza, Daniel Emilio; Samaniego Alvarado, Esteban Patricio
Climate is a global system whose subsystems interact complexly. Deterministic models are capable of describing the climate phenomena with physical detail around the globe. Nonetheless, the several concurrent global climate patterns make the numerical modeling challenging for tropical regions. This is because of inadequate parameterizations and systematic errors, typical of physics-based models. Additionally, the morphology of the mountains in the tropical Andes generates complex spatial patterns for the fluxes. A strategy to circumvent the climate modeling complexity in tropical mountain systems is based on the following considerations. 1) Although complex, the climate in the tropical Andes is strongly seasonal. 2) The climate is a network system in which global patterns greatly influence that seasonality. Both considerations seem to be rational criteria to devise a simplified but meaningful modeling process. This research thesis is about the modeling of local monthly rainfall signals using global climate patterns. It is assumed that global climate signals are crucial drivers for the local seasonal features. A signals’ decomposition using the well-known Dynamic-HarmonicRegression (DHR) helps determine which global climate signals influence the local climate. The DHR technique allows the rainfall to be separated into non-stationary trends and quasi-periodical signals. On the one hand, trends are used to find out inter-annual connections with global patterns. On the other hand, the non-stationary amplitudes of periodical components allow finding intra-annual connections. In a second stage, the identified global signals are included as exogenous variables in a harmonic model for simulating the monthly local rainfall. Global patterns determine the non-stationary properties of trends and periodical components through non-linear functions. The non-linearity is attained by the State-Dependent (SDP) technique, which infers non-parametrical functions between the harmonic’ parameters and global climate states. A preliminary evaluation reveals a model with abilities to accurately predict monthly rainfall signals, which points to potential fundamental climate mechanisms and conceptual links between the local seasonal behavior and global climate states. Finally, the model is data-driven in principle, synthesizing local seasonal features driven by global climate patterns, providing the model with a process-driven flavor. Because of this, the model’s evaluation requires a more comprehensive perspective, responding to both the data-driven and process-driven nature. In that sense, a predictability evaluation of the proposed model in contrast to other empirical alternatives is carried out. This predictive-based evaluation is typical for data-driven techniques. In addition, this work considers a process-oriented assessment based on the model’s capacity to mimic intrinsic seasonal and temporal characteristics. This reveals a model with better predictive accuracy than its alternatives in statistical terms and other attributes. It is argued that the predictability of the proposed model is attributed to its capacity for mimicking local rainfall features driven by global climate patterns.
Desarrollo de un algoritmo de interrelación para microredes de distribución eléctrica
(2015) Sanango Fernández, Juan Bautista; Samaniego Alvarado, Esteban Patricio
In a power distribution system all the power receiving an electrical substation, from power plants, it is distributed to customers who belong to the area of incidence of the Substation, incurring many cases power losses in conductors and inadequate levels quality and reliability of Electricity. The foregoing makes administrators of distribution networks develop models and techniques to find practical solutions to reduce technical losses of energy and raise their levels of quality and reliability. Distributed generation based on non-conventional energy sources (wind and solar farms, biomass generation, micro-hydro, etc.) and their integration into the Distribution Network has enabled mathematical models, such as the Theory of Cooperative Games, can be used to simulate energy cooperative exchanges. This work focuses on developing an algorithm that allows to use the Game Theory Cooperative Coalition of transferable utility as a tool to reduce energy losses in a distribution network, forming coalitions where every individual sees improved payment (decrease energy loss), leading to consumer benefits and satisfaction of using the available energy from generation sources. Micro Network of the future will be a distribution system designed as an intelligent network capable of operating, controlling and managing, in an automated manner, among its members, the efficient exchange of Electricity.
Determinación de las propiedades elásticas del conglomerado de las formaciones Turi y Terrazas fluvioglacíaricas de la Ciudad de Cuenca a partir de las propiedades de su matriz
(2009) Santacruz Reyes, Karla Johanna; Samaniego Alvarado, Esteban Patricio