Proyectos

North-central Chile and south-east France are facing semi-arid and Mediterranean climates, respectively. In addition to being historically exposed to water supply challenges, these regions are sensitive to climate change, which is increasing aridity. Thus, the pressure on water resources to meet domestic, agricultural and industrial demands is high and growing. As precipitation in these regions is low and highly variable, the water stored underground is a key adjustment variable to mitigate years with low precipitation. Hence, groundwater resources need to be carefully managed in order to secure water supply in the years and decades to come. To tackle this challenge, this project aims to develop geophysical characterization and groundwater modeling approaches to support sustainable groundwater management. The main objective of the project is to produce key information about groundwater for water resources planners in semi-arid and Mediterranean areas such as in north-central Chile and southeast France. This objective will be achieved by training students and young researchers in state-of-the-art technologies such as the Magnetic Resonance Sounding (MRS) geophysical method and the WEAP-MODFLOW integrated water resources modeling framework.

Proyecto de investigación básica en procesos de arrastre estocásticos.

Society is facing an unprecedented challenge in terms of combining sustainability, economic growth and technological development. The industry has tackled these demands by developing novel products and innovative service strategies, taking the maximum advantage of the installed capabilities and cutting edge technologies. Steel industry has taken the lead by supporting internal research and scientific collaborations worldwide, enabling an ever increasing number of scientific developments. Steel plays a major role as the backbone material of civilization for a number of reasons, namely (i) abundance, (ii) relatively cheap, (iii) wide range of properties and applications, (iv) 100% recyclable, (v) potential to improve in-service performance. In the framework of (v), the current proposal aims to provide new grades of steel by means of chemical patterning of austenite. The concept of austenite patterning consists in producing layers in the microstructure with a chemical composition different from the bulk composition, via specific alloying elements and thermal cycles. These layers, after fully austenitization, deliver transformation products on cooling different than expected from the average austenite, allowing a new degree of freedom for tailoring of microstructures. So far there is only one scientific paper on the subject, which has reported outstanding mechanical strength (ultimate) of ca. 2 GPa, with uniform elongations of 7% in a lamellar martensite-austenite microstructure in a single 0.51C-4.35Mn steel. The present proposal sets a detailed working plan to investigate the impact of the initial microstructure and thermal path upon the chemical patterning of austenite in a number of different steel chemistries. The aim is two-folded: to analize the evolution of the phase transformations at different stages of the process as a function of the initial microstructure and heat-treatment parameters, and to gain fundamental insights on the mechanical behavior of the new steel grades. It is hypothesized that the correct interplay of the parameters mentioned above can yield optimized final microstructures with enhanced in-service performance.The methodology incorporates up-to-date assessment tools of thermodynamic equilibria and kinetics (ThermoCalc & Dictra) in selected steel chemistries, accurate tracking of phase transformations via Dilatometry experiments, in-depth characterization of the microstructure and mechanical properties and insitu/ex-situ ultrasound probing of tensile test specimens to better understand the hardening mechanisms. The experimental results will be compared with modeling strategies for both phase transformations and mechanical behavior. The expected results of the proposal will be of interest to the scientific community due to the novelty of the experimental concept and the potential contribution to the understanding of structure-property relations. Else, the findings will be of significance for the design of structural parts, such as high strength and impact toughness for car body crash worthiness. In the case of Chilean mining industry, wear and impact wear resistance are potential applications of the new steel grades to be tested. The proposal is lay out within a novel cooperation framework between a group of specialists on specific aspects of materials science (phase transformations in steel, constitutive modeling, ultrasound probing), oriented to contribute to the fundamental understanding of the microstructure-property relations resulting from chemical patterning of austenite. Additionally, three universities and one industrial partner (University of Twente, The Nederland, Gent University, Belgium; University of Alberta, Canada; and ME Elecmetal, Chile-US, respectively) are supporting the proposal with resources such as workshops, sample preparation, specific characterization techniques, software for post-processing, among others.

Ensure a secure and sustainable energy and mineral supply is a major challenge for the 21th century. Understanding the nature and evolution of hydrothermal systems can contribute to that aim by improving the effectiveness of exploration strategies for geothermal energy and precious metal epithermal deposits. Studies bridging together geochemistry and structural geology have shown that fault activity plays a critical role on fluid circulation and fluid chemical composition in hydrothermal systems. Despite the relevance of processes affecting the chemical and physical dimensions in such dynamic systems, little is known about the residence times of fluids and its metal budget in different structural contexts. Into this framework, fundamental questions arise regarding the optimal conditions leading to the development of high enthalpy geothermal resources and the formation of epithermal deposits: What is the structural control on the sources and concentration of base metals in hydrothermal fluids? How does the structural context affect the water residence times in hydrothermal systems? How does the meteoric recharge affect the geothermal systems in different structural domains? An ideal natural laboratory to address these questions is the Andean Cordillera of Central-Southern Chile, where hydrothermal systems occur in close spatial relationship with active volcanism as well as major seismically-active fault systems. Recent studies based on fluid geochemistry and noble gases isotopic composition have shown that the intersection of structural features promotes both the accumulation formation of magmatic/hydrothermal reservoir in the upper crust exerting a first-order control on hydrothermal fluid composition by conditioning residence times of magmas, promoting magma differentiation and separation of magmatic vapors. However, how similar processes are involved on residence times and metal budget of hydrothermal fluids remains unconstrained. We propose a geochemical and multi-isotopic study that integrates state-of-the-art analytical techniques to unravel the circulation times and base metals contribution from magma degassing and water- rock interaction in two volcano-tectonic settings in Southern Andes, i) the arc parallel strike-slip Liquiñe- Ofqui Fault System (LOFS) and ii) the intersection between the LOFS structures and the Andean Transverse Fault (ATF). I will integrate major and trace elements (e.g., Cu, Pb, Zn, among others) geochemistry of hot springs with water dating systems (3H-3He, 14C, U-Th/4He), noble gas (3He/4He; 40Ar/36Ar, 4He/20Ne), strontium (87Sr/86Sr) and water stable (𝛿!"𝑂, 𝛿 #𝐻) isotopes to identify the circulation times, recharge condition and metal budget of hydrothermal systems. This study will be the first to directly measure the residence times and metals contents of hydrothermal fluids in the Southern Andes of Chile. The results from the study will contribute to a better understanding of the fundamental geological and environmental controls on the evolution of hydrothermal systems. The data will directly impact the community exploring for geothermal energy in the Andes because it will help them to better constrain the formation and recharge times of geothermal reservoirs. In addition, this will be an original contribution that will impact the general geochemical science community, as no data exists on the links between residence times of hydrothermal fluids and the structural context.

This project will consider the design of Stochastic MPC strategies based Computational Intelligence techniques such as fuzzy models and neural networks, but focusing on achieving theoretical properties such as stability and feasibility, increasing computation speed, and can use control policies. This is novel in that these properties are hardly ever obtained when using nonlinear models in SMPC. Additionally, we will aim to systematize the stability and convergence analyses. These developments will be validated on applications such as irrigation systems, climatization systems, microgrids. This is expected to improve the performance of existing control methods for systems with stochastic uncertainty, and enable the design of advanced control systems where the lack of guarantees or slow computation does not permit the implementation of advanced control systems. Additionally, the development of ad-hoc controllers based on SMPC with RL for Electric Vehicle Routing will be tackled in this research. This controller will be tackled following principles particularly selected for this application since stability is not a major concern here because of the finite horizon for every day of operation.

PROPOSAL ABSTRACT: Cyber-attacks in modular multilevel converters (MMCs) Cyber-attacks on electrical systems are a major concern to many countries as they can have significant impacts on social well-being and economic prosperity. Recent examples of such attacks are (i) the cyber-attacks that targeted the supervisory control data acquisition (SCADA) system of the Ukrainian power grid, causing a power outage that affected approximately 225,000 customers, (ii) the cyber-attack that occurred in 2019 in Venezuela, affecting the power supply of Caracas city, and (iii) the cyber-attacks that affected battery control systems in Korea, resulting in fire and damage. These examples show that cyber-attacks can significantly affect the normal operation of electrical systems and have motivated much research around the world. Much of the published research deals with cyber-attack issues in electrical systems such as microgrids, smart grids and modern power systems. However, cyber-attacks in modular multilevel converters (MMCs) have received limited attention: a recent letter (2021, see introduction section) is the only article published in this area so far. The MMC is deemed to be a prominent solution for medium to high-voltage and high-power applications, with several commercial converters adopting this approach and numerous projects worldwide using this topology. This project aims to investigate cyber-attack issues in the MMC control system and addresses the lack of investigation in this area. In particular, this research will consider the MMC in the context of high voltage direct current (HVDC) transmission systems since this topology is a promising solution to transfer power over long distances. It has been widely used in commercial projects (Trans Bay Cable, Dolwin2, Nano3-terminal DC grid, etc.). In this project, distributed control schemes for controlling the MMC are considered. For this control approach, low-level control tasks are distributed among local controllers placed in the MMC submodules, while high-level control tasks are undertaken by a central controller. The computational burden for the central controller is therefore reduced. The distributed architecture is chosen since the MMC for HVDC applications requires a high number of submodules (SMs). In this case, if the traditional centralised control scheme is used, where a central controller is in charge of processing all the information required for implementing the whole control system, the execution time may not be sufficient for each control cycle to perform all the control tasks, limiting the modularity, flexibility and expandability of this topology (in terms of software development). This project will investigate and quantify the impacts, in terms of operation and stability, of the type of cyber-attack named “false data injection attacks” (FDIAs ) on distributed control systems used for MMCs. Particular focus will be centred on designing strategies for detecting these cyber-attacks and locating the vulnerable sub-modules in the MMC. Also, novel cyber-attack-resilient distributed control schemes will be proposed. It must be pointed out that all the methods derived in this project will be validated through simulation and experimental results. To this end, the cyber-attack detection methods will be based on state observers (Kalman filter, particle filter, etc.) and artificial intelligence (AI) based observers. Note that these detection methods should operate in a distributed architecture, meaning the state estimation should be performed based on partial information of the system. They should be able to be implemented on the SMs local controllers. Finally, once the cyber-attacks are identified, control schemes to neutralise those attacks will be investigated, generating cyber-attack-resilient distributed control schemes for the MMC. The objectives of this proposal are (i) Analyse and quantify the effects of cyber-attacks on the standard distributed control strategies proposed for MMCs and the ones recently proposed based on the consensus theory, (ii) Design distributed methods for the detection of the cyber-attacks considered in this project, (iii) Design of cyber-attacks-resilient distributed control schemes for the MMC, and (iv) Implement an experimental rig for testing and validate the proposals derived from this research. Since there is no literature regarding cyber-attacks in MMCs, the literature review will be focused on work dealing with cyber-attacks on other electrical systems such as microgrids, smart grid and modern power systems. The aim will be to determine if the techniques proposed for these systems can be adapted for the MMC. A similar approach will be followed to address cyber-attack-resilient distributed control schemes. The main contributions of this project will be: 1. The project will provide the foundation for research into cyber-attacks in MMC, as so far, there is very little information in the literature on this topic. Note that cyber-attacks are a hot topic in other electrical systems. Thus it should be investigated for the MMC. 2. Novel distributed methods for detecting cyber-attacks in MMCs will be proposed and validated through simulations and experiment. 3. Novel cyber-attack-resilient distributed control schemes for the MMC will be proposed and validated through simulations and experiment. 4. An experimental rig to analyse and validate the points mentioned above will be designed and built. It will be composed of a central controller and local controllers placed in each SM of the MMC.

Estudio de problemas de optimización y juegos con incertidumbre dependiente de decisiones. Estudio a nivel teórico y algorítmico. Estudio de aplicaciones.

Numerous real-life decision-making processes involve solving a task with uncertain input that can be estimated from historic data. There is a growing interest in decision-focused learning methods (a.k.a. smart predict-then-optimize) whose goal is to find models that fit the data while considering how the predicted input will perform in a particular task. For example, the task can be a shortest path problem that uses predictions on travel times in the objective function. Fitting the data and ignoring the task may lead to sub-optimal decisions. Sometimes, uncertainty is involved in the constraints of the model. In this case, ignoring the task would lead to infeasible decisions. The goal of this project is to develop efficient exact algorithms and new applications to train a Machine Learning models that perform well in one or several tasks using mathematical programming (MP) tools. In this context, the typical measure of a predictor is the regret: the excess cost incurred when making a suboptimal decision due to an imprecise predictor. This problem is bilevel in nature: the top-level decision consists in determining a predictor that minimizes a regret while considering that the predictions will affect a task, e.g. an optimization problem in a lower level. This structure is typically non-convex and non-continuous, making the problem difficult to solve for realistic instances. However, several recent advances in bilevel-tailored approaches exploit this structure and can solve large scale problems. There are two main ways of estimating task-oriented predictors: 1) stochastic gradient-based methods; and 2) MP reformulations of the problem. Stochastic gradient-based methods replace the non-differentiable regret for some differentiable surrogate loss function approximating the real loss. Due to advances in neural network implementations and stochastic gradient-descent this approach is the most applied among researchers and practitioners in the Machine Learning community. MP for data science has attracted the attention of researchers and practitioners in different areas as mathematics, operations research, computer science during the last years. It provides some degree of flexibility, being able to model desirable considerations for predictions models. For instance, MP has been successfully used to train sparse models yielding improved explainability and/or fairness. Moreover, MP models are in many cases solvable by any off-the-shelf solver. However, for the decision-focused learning problem there are still many gaps using MP formulations. To the best of our knowledge, MP formulations have been used only for surrogate loss functions. Behind the low usage of MP tools is the scalability. A typical ML setting involves data sets involving thousands of observations. In consequence, the training task becomes more difficult. To tackle this issue, decomposition methods such as cut and column generation can help to solve problems at scale. We aim to provide efficient exact methods that can return either optimal solutions, or optimality guarantees for large scale instances. During the last years, decision-focused learning has been widely applied to combinatorial optimization problems. However, this approach can also be used in many other applications such as Markov decision processes (MDPs) or game theory. In the first case, we can take algorithmic advantage of well-known existing algorithms for MDPs such as value iteration or policy iteration, or approximation techniques such as Q-learning. The game-theoretic setting is more challenging: depending on the notion of equilibrium the loss function would change. For instance, the definition of regret can be applied straightforwardly to Stackelberg equilibria (or leader-follower equilibria), a concept widely applied to energy markets, security and transportation. In the case of Nash equilibrium, the definition of regret is not direct anymore. We hope to develop models that adapt the decision-focused learning paradigm to this broader context. The study of this topic requires the use and development of MP of tools along with their algorithmic analysis. During the project, we expect to develop efficient of algorithms that can be used by decision making, as well as contribute in the understanding of the theoretical aspects of decision-focused learning.

Esta investigación se centra en el conocimiento del profesor de matemáticas de educación básica en ejercicio y se sitúa como la continuación de un proyecto de investigación adjudicado en 2020 por el IR (La construcción del pensamiento algebraico en la Formación Inicial Docente: un estudio exploratorio). El interés de este proyecto de iniciación en investigación 2022 está puesto en indagar cómo los docentes consideran el pensamiento matemático de sus estudiantes para interpretar situaciones de enseñanza-aprendizaje y tomar decisiones en el momento. Dado que los contextos de enseñanza de las matemáticas son complejos y dinámicos, los programas o cursos de Desarrollo Profesional son considerados un espacio para comprender y enriquecer el conocimiento profesional de los docentes, los cuales deben estar al servicio del aprendizaje y rendimiento de los estudiantes. Como una forma de abordar el conocimiento profesional de los docentes se encuentra la competencia del Noticing, la cual permite resolver tareas profesionales y se considera un elemento crucial de la experticia del profesor de Matemáticas. Sin embargo, la especificidad en el conocimiento del profesor hace necesario profundizar en el desarrollo del Noticing a través de diferentes dominios de conocimiento de las matemáticas escolares. De esta forma, esta investigación indagará en describir cómo profesores de matemáticas de educación básica en ejercicio promueven el pensamiento algebraico de sus estudiantes. Así, el tema de este proyecto extiende la investigación previa que se ha centrado, principalmente, en la formación inicial docente y en alguno de los aspectos del álgebra (como los patrones). Los objetivos generales de investigación son: • Caracterizar el conocimiento profesional evidenciado por docentes de educación básica sobre el fomento del pensamiento algebraico al participar de un curso de DP. • Identificar las acciones desarrolladas en un curso de DP que fomentan la competencia del Noticing en los docentes de educación básica. Conceptualmente, la competencia del Noticing se aborda desde el componente “Atender al pensamiento matemático de los estudiantes” (Jacobs, Lamb y Philipp, 2010) y su propósito central es comprender las características del pensamiento de los estudiantes, interpretarlo y tomar decisiones informadas. Este componente involucra tres habilidades interrelacionadas: (a) prestar atención a las estrategias de los estudiantes; (b) interpretar sus comprensiones; y (c) decidir cómo responder sobre la base de dichas comprensiones. Adicionalmente, este proyecto se basa en el marco conceptual del álgebra propuesto por Kaput (2008), el cual organiza la enseñanza del álgebra a través de cuatro prácticas (generalización, representación, justificación y razonamiento) que se encuentran presente en cada uno de los enfoques al pensamiento algebraico (aritmética generalizada; equivalencia, expresiones, ecuaciones e inecuaciones; y pensamiento funcional). De manera general, este proyecto sigue las directrices metodológicas de la Investigación de Diseño (Swan, 2020). Participarán 20 profesores de educación básica que realizan clases de matemáticas en colegios municipales con los cuales la Institución Patrocinante tiene convenios. Para indagar cómo se desarrolla la habilidad del Noticing en el curso de Desarrollo Profesional sobre pensamiento algebraico, los docentes participarán del curso, así como de entrevistas individuales semiestructuradas y de grabaciones de clases. El foco de análisis de la información estará centrado en las producciones orales y escritas de los docentes, los cuales permitirán analizar cómo se va desarrollada la competencia descrita. Este proyecto contempla la ejecución de cuatro etapas a desarrollar en dos años: (a) diseño inicial del curso de Desarrollo Profesional; (b) recogida de la información; (c) organización y análisis de datos; y (d) difusión de resultados. El impacto esperado de los resultados influirá en tres áreas principales: (a) investigación e internacionalización, consolidando y posicionando una línea de investigación novedosa en el campo de la educación matemática; (b) docencia en pre y posgrado, enriqueciendo la formación inicial y continúa de profesores; y (c) redes de colaboración, lo cual permitirá seguir estableciendo un trabajo conjunto con investigadores nacionales e internacionales interesados en la mejora de las prácticas matemáticas que se realizan en el aula. Referencias Jacobs, V. R., Lamb, L. L. y Philipp, R. A. (2010). Professional noticing of children’s mathematical thinking. Journal for Research in Mathematics Education, 2(41), 169-202. Kaput, J. (2008). What is algebra? What is the algebraic reasoning? En J. . Kaput, D. W. Carraher y M. L. Blanton (Eds.), Algebra in the early grades (pp. 5-17). Nueva York, NY: Lawrence Erlbaum Associates. Swan, M. (2020). Design Research in Mathematics Education. En S. Lerman (Ed.), Encyclopedia of Mathematics Education (pp. 192-195). Springer. https://doi.org/10.1007/978-3-030-15789-0_180.