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Numerically Efficient Water Quality Modeling and Security ApplicationsMann, Angelica 02 October 2013 (has links)
Chemical and biological contaminants can enter a drinking water distribution system through one of the many access points to the network and can spread quickly affecting a very large area. This is of great concern, and water utilities need to consider effective tools and mitigation strategies to improve water network security. This work presents two components that have been integrated into EPA’s Water Security Toolkit, an open-source software package that includes a set of tools to help water utilities protect the public against potential contamination events.
The first component is a novel water quality modeling framework referred to as Merlion. The linear system describing contaminant spread through the network at the core of Merlion provides several advantages and potential uses that are aligned with current emerging water security applications. This computational framework is able to efficiently generate an explicit mathematical model that can be easily embedded into larger mathematical system. Merlion can also be used to efficiently simulate a large number of scenarios speeding up current water security tools by an order of magnitude.
The last component is a pair of mixed-integer linear programming (MILP) formulations for efficient source inversion and optimal sampling. The contaminant source inversion problem involves determining the source of contamination given a small set of measurements. The source inversion formulation is able to handle discrete positive/negative measurements from manual grab samples taken at different sampling cycles. In addition, sensor/sample placement formulations are extended to determine the optimal locations for the next manual sampling cycle. This approach is enabled by a strategy that significantly reduces the size of the Merlion water quality model, giving rise to a much smaller MILP that is solvable in a real-time setting. The approach is demonstrated on a large-scale water network model with over 12,000 nodes while considering over 100 timesteps. The results show the approach is successful in finding the source of contamination remarkably quickly, requiring a small number of sampling cycles and a small number of sampling teams. These tools are being integrated and tested with a real-time response system.
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Backtracking approaches for the delineation of contamination sourcesThomas-Thielsch, Katrin 15 July 2013 (has links)
Verunreinigtes Grundwasser stellt eine ernsthafte Bedrohung für die Trinkwasser-Ressourcen auf der ganzen Welt dar. Verunreinigte Grundwasser können zwar in Brunnen detektiert werden, eine ordnungsgemäße Sanierung ist jedoch häufig nur erfolgreich, wenn die Quelle der Verunreinigung erfasst und entfernt wird. Wenn von Anbeginn eines Sanierungsprojektes ein Schwerpunkt auf die Erkennung und Eingrenzung des Verunreinigungsherdes gelegt wird, kann die Sanierung direkt an dieser Stelle ansetzen und zudem hohe Grundwasser-Sanierungskosten verringert werden.
ModBack ist eine Software, die mehrere bestehende Modellierungs-Werkzeuge in einer, ein-fach zu verwendenden, ESRI ArcGIS 10-basierten Schnittstelle vereinigt und hilft mögliche Schadstoffquelle Zonen im Untergrund abzugrenzen. Diese Software ist in Visual Basic 3.5 geschrieben und verwendet ArcObjects Bibliotheken, um die erforderlichen GIS-Anwendungen zu implementieren. Es kann ohne Änderung auf allen Microsoft Windows-basierten PC‘s mit ausreichend RAM und mindestens Microsoft. NET Framework 3.5 verwendet werden. Die Nutzung von ModBack erfordert zusätzliche Installation der folgenden Software: Processing Modflow Pro 7.0 (PMWin), MODPATH, CSTREAM (Bayer-Raich et al., 2003a, Bayer-Raich et al., 2003b, Bayer-Raich et al., 2004), Golden Software Surfer, Microsoft Excel und NAS (eine Software zur Berechnung des natürlichen Schadstoffabbaus).
Die grafische Benutzeroberfläche (GUI) von ModBack ist in vier Verfahrensschritte Dateneingabe, Grundwassermodellierung, Partikel Backtracking und Analysen getrennt. Geographischen Eingangsdaten werden für eine geografische Übersicht des Testfeldes benötigt. Sie bestehen meist aus georeferenzierten Informationen des Testfeldes und Informationen zur unterirdischen Grundwasserverunreinigungen. Grundwasseranalysen werden entweder durch konventionelle Probennahme aus Grundwassermessstellen oder durch die Durchführung integraler Pumpversuche an Kontrolleben mit eine bestimmten Konzentration/Zeit- Serie (CT-series) gesammelt. Aus den Pumpversuchen resultierende hydraulische Daten werden zusammen mit allen anderen verfügbaren Informationen zur Erstellung eines grundlegenden Grundwasserströmungsmodells des Testfeldes verwendet. Nachfolgende Backtracking Verfahren, als auch die Berechnung von advektivem Schadstofftransport beziehen sich auf die-ses Strömungsfeld und werden entlang einer zuvor definierten Kontrollebene berechnet. Eine Analyse der Backtracking-Ergebnisse erfolgt innerhalb ModBack. Die potenzielle Quelle von Kontaminationen oder deren Abwesenheit werden basierend auf dem Verfahren nach Jarsjö et al. (2005) bestimmt. Die Länge einer Schadstofffahne kann anhand von Fahnenlängen Statistiken und /oder dem Abbau erster Ordnung Abbau Gleichungen oder Berechnungen auf ortsspezifische hydraulischen und chemischen Parametern beruhen. Ferner ist ein analytisches Instrument enthalten, um die Verteilung der Verunreinigungen über eine Steuerebene zu identifizieren. Alle relevanten Ergebnisse können als Vektordaten in ModBack graphisch dargestellt und gespeichert werden und sind somit kompatibel mit weiteren GIS-Software Produkten. ModBack wurde bereits an Testgebieten in Slowenien und Süddeutschland angewendet, um die möglichen Zonen der Verunreinigungsquelle oder deren Abwesenheit zu begrenzen. Auf dem Testgelände in Süddeutschland sind diese Abgrenzungen vergleichbar mit früheren Untersuchungen vor Ort und unterstützt somit die Funktionalität der Software ModBack. Mit ModBack, steht ein Werkzeug zur Verfügung, die bereits jetzt Um-welt-Beratern, Ingenieuren und Umwelt-Agenturen ermöglicht denkbare Quellen der Verunreinigung bei der Planung der Untersuchungen vor Ort und Sanierungsmaßnahmen abzugrenzen, und hilft Kosten deutlich zu senken.
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Modeling Of Contaminant Transport Through Soils And Landfill LinersBharat, Tadikonda Venkata 10 1900 (has links)
Accurate modeling of contaminant transport and sorption processes in the soil and landfill liners is a prerequisite for realistic model simulations of contaminant fate and transport in the environment. These studies are also important for the remediation of soil and groundwater contamination. Modeling of contaminant transport through soils and landfill liners consists of either solving the direct/forward problem or the inverse problem.
In this thesis, an automated time-stepping implicit procedure is developed from the convergence and error studies of explicit and implicit finite-difference solutions for the advection-dispersion transport of contaminants through soil with different sorption mechanisms. This study is further extended for transient through-diffusion (TTD) transport of contaminant in landfills by considering linear sorption mechanism. To validate the numerical solution and also to study the behavior of finite-difference numerical solutions for TTD transport problem, closed-form analytical solution is derived. Further, a new interface condition is proposed based on the finite-volume procedure for stratified soil or landfill liner system. Solvers are developed for the parameter estimation of inverse problem by integrating the developed procedures for the above forward problem with different optimization procedures. Solvers based on Simulated Annealing (SA) and Genetic Algorithm (GA) are developed for TTD transport in the landfill liners and verified with the existing methods of parameter estimation. Novel swarm intelligence based solver is developed for the first time for parameter estimation in contaminant transport inverse problem to overcome some of the limitations of the classical optimization methods and evolutionary methods such as GA. Additionally, the proposed swarm intelligence based algorithms and a new variant is applied to solve ill-posed problem of contaminant source characterization.
The presented work in this dissertation can be unswervingly applied for modeling the contaminant transport in laboratory through-diffusion tests and contaminant transport through landfill liners where the transport is usually considered to be one-dimensional and also diffusion-dominated. Similarly, the advection-dispersion transport through laboratory soil columns can also be modeled with the developed, fast, automated, implicit numerical procedure with very good accuracy. The present study can be applied further for contaminant transport through stratified soil/liner system using fast converging numerical algorithms. Finally, the problems of design parameter estimation and source characterization can be handled accurately by the use of developed automated nature-inspired solvers.
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Stochastic Identification of Pollutant Sources in Aquifers by the Ensemble Kalman FilterChen, Zi 01 February 2021 (has links)
[ES] Como parte de los métodos de asimilacíon de datos, los métodos basados en conjuntos han ganado popularidad en hidrogeología dada su capacidad para manejar grandes cantidades de datos observados simultáneamente. Recientemente, se ha comenzado a emplear este método para la identificacíon de fuentes de contaminacíon en casos sintéticos. Basándonos en estos trabajos anteriores, hemos dado un paso adelante evaluando su rendimiento en experimentos de tanque de laboratorio.
La tesis se puede dividir en cuatro partes. En la primera parte, el filtro de Kalman de conjuntos con reinicio (r-EnKF) se utiliza para la identificacíon espacio-temporal de una fuente puntual de contaminantes en un experimento en tanque de laboratorio, junto con la identificacíon de la posicíon y longitud de una placa vertical insertada en el tanque que modifica la geometría del sistema. Los resultados muestran que el r-EnKF es capaz de identificar tanto la fuente como los parámetros relacionados con la geometría del acuífero.
La segunda parte muestra una aplicacíon del filtro de Kalman de conjuntos con anamorfosis normal y reinicio (NS-EnKF) y con inflacíon de la covarianza en un experimento de laboratorio con conductividad heterogénea. El método se prueba primero utilizando un caso sintético que imita el experimento del tanque para establecer el número mínimo de miembros del conjunto y la mejor técnica para evitar el colapso del filtro. Luego, su aplicacíon a los datos del tanque muestra que el NS-EnKF con reinicio puede beneficiarse de la inflacíon de Bauser para reducir el tama ñ o del conjunto y llegar a una buena identificacíon conjunta tanto de la fuente de contaminantes como de la heterogeneidad espacial de las conductividades.
En la tercera parte, el filtro de Kalman de conjuntos suavizado con asimilacíon múltiple de datos (ES-MDA) se emplea para la identificacíon simultánea de una fuente de contaminantes y la distribucíon espacial de la conductividad hidráulica utilizando el r-EnKF como punto de referencia. El resultado muestra que el ES-MDA puede superar al r-EnKF, marginalmente, para el caso sintético específico analizado con el mismo consumo de CPU, y puede funcionar mucho mejor que el r-EnKF a cambio de un mayor costo de CPU.
La cuarta y última parte investiga el rendimiento del ES-MDA en un problema de identificacíon de una inyeccíon de contaminante que varía en el tiempo. Se analiza la influencia de diferentes intervalos de observacíon y esquemas de inflacíon de la covarianza en la determinacíon de la curva de inyeccíon. El resultado muestra que el ES-MDA funciona muy bien en la identificacíon de la curva de inyeccíon cuando la discretizacíon de la misma no es muy alta, pero encuentra problemas de fluctuacíon en los casos con discretizaciones altas. La frecuencia con la que se muestrean los datos de observacíon es un factor influyente, mientras que el número de iteraciones o los métodos de inflacíon de la covarianza tienen menos efecto. / [CA] Com a part dels mètodes d'assimilació de dades, els mètodes basats en conjunts han guanyat popularitat en hidrogeologia donada la seua capacitat per a manejar grans quantitats de dades observades simultàniament. Recentment, s'ha començat a emprar aquest mètode per a la identificació de fonts de contaminació en casos sintètics. Basant-nos en aquests treballs anteriors, hem fet un pas avant avaluant el seu rendiment en experiments de tanc de laboratori. La tesi es pot dividir en quatre parts.En la primera part, el filtre de Kalman de conjunts amb reinici (r-EnKF) s'utilitza per a la identificació espaciotemporal d'una font puntual de contaminants en un experiment en tanc de laboratori, juntament amb la identificació de la posició i longitud d'una placa vertical inserida en el tanc que modifica la geometria del sistema. Els resultats mostren que el r-EnKF és capaç d'identificar tant la font com els paràmetres relacionats amb la geometria de l'aqüífer.
La segona part mostra una aplicació del filtre de Kalman de conjunts amb anamorfosis normal i reinici (NS-EnKF) i amb inflació de la covariància en un experiment de laboratori amb conductivitat heterogènia. El mètode es prova primer utilitzant un cas sintètic que imita l'experiment del tanc per a establir el nombre mínim de membres del conjunt i la millor tècnica per a evitar el col·lapse del filtre. Després, la seua aplicació a les dades del tanc mostra que el NS-EnKF amb reinici pot beneficiar-se de la inflació de Bauser per a reduir la grandària del conjunt i arribar a una bona identificació conjunta tant de la font de contaminants com de l'heterogeneïtat espacial de les conductivitats.
En la tercera part, el filtre de Kalman de conjunts suavitzat amb assimilació múltiple de dades (ES-MDA) s'empra per a la identificació simultània d'una
font de contaminants i la distribució espacial de la conductivitat hidràulica utilitzant el r-EnKF com a punt de referència. El resultat mostra que l'ES-MDA pot superar al r-EnKF, marginalment, per al cas sintètic específic analitzat amb el mateix consum de CPU, i pot funcionar molt millor que el r-EnKF a canvi d'un major cost de CPU.
La quarta i última part investiga el rendiment de l'ES-MDA en un problema d'identificació d'una injecció de contaminant que varia en el temps. S'analitza la influència de diferents intervals d'observació i esquemes de inflació de la covariància en la determinació de la corba d'injecció. El resultat mostra que l'ES-MDA funciona molt bé en la identificació de la corba d'injecció quan la discretització no és massa alta, però troba problemes de fluctuació amb discretitzacions massa fines. La freqüència amb la qual es mostregen les dades d'observació és un factor influent en aquesta aplicació, mentre que el nombre d'iteracions o els mètodes d'inflació de la covariància tenen menys efecte. / [EN] As part of the data assimilation methods, the ensemble-based methods have gained popularity in hydrogeology given their ability to deal with huge amounts of observed data simultaneously. More recently, researchers have started to employ these methods to deduce contamination source information in synthetic cases. Based on these previous work, we take a step further to evaluate their performance in sandbox experiments. The main objective of this thesis is to verify the capacity of the ensemble-based methods in identifying contaminant sources and complex geological heterogeneity.
The thesis could be divided into four parts. In the first part, the restart ensemble Kalman filter (r-EnKF) is used for the spatiotemporal identification of a point contaminant source in a sandbox experiment, together with the identification of the position and length of a vertical plate inserted in the sandbox that modifies the geometry of the system. The results show that the r-EnKF is capable of identifying both contaminant source information and aquifer-geometry-related parameters.
The second part shows an application of the restart normal-score ensemble Kalman filter (NS-EnKF) with covariance inflation in a heterogenous conductivity laboratory experiment. The method is first tested using a synthetic case that mimics the sandbox experiment to establish the minimum number of ensemble members and the best technique to prevent filter collapse. Then, its application to the sandbox data shows that the restart NS-EnKF can benefit from Bauser's inflation to reduce the ensemble size and to arrive to a good joint identification of both the contaminant source and the spatial heterogeneity of conductivities.
In the third part, the ensemble smoother with multiple data assimilation (ES-MDA) is employed for the simultaneous identification of a contaminant source and the spatial distribution of hydraulic conductivity while using the r-EnKF as a benchmark. The outcome shows that the ES-MDA is able to outperform the r-EnKF, marginally, for the specific synthetic case analyzed with almost the same CPU consumption, and it can perform far better than the r-EnKF just with a cost of larger CPU usage.
The forth and last part investigates the performance of the ES-MDA in a time-varying release history identification problem. The influence of different observation intervals and inflation factor schemes on the determination of the release curve are discussed. The outcome shows that the ES-MDA performs great in recovering release history when the history curve is discretized in not too many steps, and that it fails when the discretization is large. The frequency at which observation data are sampled is an influential factor in this application, while the number of iterations or the inflation scheme have less effect. / Thanks to the institutions that financed my studies. The support to carry out my work was received from the Spanish Ministry of Economy and Competitiveness through project CGL2014-59841-P, and from the Spanish Ministry of Education, Culture and Sports through a fellowship for the mobility of professors in foreign research and higher education institutions to my supervisor, reference PRX17/00150 / Chen, Z. (2020). Stochastic Identification of Pollutant Sources in Aquifers by the Ensemble Kalman Filter [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/160628 / TESIS
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Développement d'une stratégie de localisation d'une source de contaminants en nappe : mesures innovantes et modélisation inverse / Development of a contaminant source localisation strategy in aquifers : innovative measurements and inverse modelingEssouayed, Elyess 08 March 2019 (has links)
La gestion et la dépollution de sites contaminés peuvent être complexe et demandent un investissement important pour localiser les sources de contaminations, zones émettant les flux de polluants les plus importants. Les travaux réalisés proposent une stratégie pour localiser les sources de pollution à partir de mesures in situ de flux massiques et de modélisation inverse. Ainsi, dans le cadre de l’étude, un outil innovant a d’abord été développé afin de mesurer la vitesse des eaux souterraines dans un puits. L’outil appelé DVT (Direct Velocity Tool) a permis de répondre aux contraintes imposées par les outils existants et de mesurer des vitesses d’écoulement très lentes. Des essais en laboratoire et des tests en site réels ont été réalisés et comparés à d’autres outils de mesure. Le DVT permet aussi indirectement de définir la portion de source conduisant au flux de polluant maximum, en le combinant avec une mesure locale de concentration. L’étude présente ensuite l’utilisation de la modélisation inverse pour localiser une source de contaminant et d’estimer les paramètres définissant les caractéristiques du domaine. Pour cela, l'étude s'est faite sur deux cas synthétiques. Pour adapter les méthodes à une véritable gestion de sites pollués, une stratégie itérative est développée en imposant un ajout limité de nouvelles observations à chaque phase de modélisation, basée sur l’approche de type Data Worth. Les résultats de la position de la source sur les deux cas synthétiques ont permis d’évaluer la méthode mise en place et de juger son applicabilité à une problématique réelle. Cette stratégie de localisation de source est par la suite testée sur un site réel à partir (i) de mesures in situ de flux massiques avec les vitesses au DVT et les concentrations et (ii) la modélisation. Les essais ont permis de cibler les forages à mettre en place sur site aidant à localiser la source. Néanmoins, en analysant plus précisément les résultats, le champ de conductivité hydraulique estimé par l'optimisation ne correspond pas à la réalité. De plus, les flux massiques de contaminants ainsi que le ratio des polluants du site, mettent en valeur deux panaches distincts. Une phase finale de modélisation a donc été lancée afin d'estimer (i) la présence potentielle de deux sources et (ii) la chimie de la zone étudiée. Les résultats de la stratégie sont comparés aux mesures geoprobe qui a pu confirmer la présence d’une des deux sources identifiées. / Contaminated sites management and remediation can be complex and require a significant investment to locate the contaminant source, which delivers the higher pollutant mass fluxes. The study proposes a strategy for contaminant source localisation using in situ measurement and inverse modelling. First, an innovative tool was developed to measure groundwater velocity in a well. The developed tool called DVT (Direct Velocity Tool) made it possible to measure a low Darcy flux. Laboratory and field tests were performed with the DVT and compared to other velocity measurement tools. By combining the DVT with a local concentration measurement, it is possible to calculate the mass fluxes passing through wells. Then the thesis present the inverse modeling used for source localisation and parameters estimation. The study was done on two synthetics cases using the non-linear optimisation method. To adapt the method to a real management of polluted sites, an iterative strategy is developed by imposing a limited addition of new observations to each modeling phase. This strategy is base on the Data Worth approach. Source localisation results on the two synthetic cases made it possible to judge the method applicability to a real site problem. The source localisation strategy is then applied to a real site with (i) mass flux measurement with velocities (DVT) and concentrations and (ii) inverse modeling. The modeling phases made it possible to locate the new wells and helped the source localisation. Nevertheless, by analysing the results more precisely, the hydraulic conductivity field estimated by the optimisation did not correspond to reality. In addition, contaminant mass fluxes highlightes two distinct zones of flux. By analysing the pollutant ratio of the site, it appears that two plumes are potentially present. Thus, another inverse modeling phase has been tested (i) to locate the two potential sources and (ii) to estimate the chemistry of the site. Results of the strategy were compared to the geoprobe campaign which confirmed the second source location.
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