A fundamental investigation and ultrasonic characterisation of coal effective stress behaviour

Coal seam gas is an important energy resource worldwide. The methane extraction from coal seam is often accompanied by CO2 injection to enhance the gas recovery and reduce the greenhouse gas emission footprint. Amongst all processes active in coal seam gas extraction, understanding and characterizing the coal effective stress and its evolution with time need special attention. The reservoir characteristics of coal seam that control its effective stress evolution, however, differ from that of other hydrocarbon resources in granular sediments. In conventional reservoirs, it is the pore volume determining the gas storage, but in coal seams, it is the pore surface area that defines the capacity through adsorption. As naturally fractured reservoirs, coal seams often contain an extensive fracture network called cleats thus, the coupled relationship between the mechanical behaviour and sorption effect can significantly influence the effective stress and in turn permeability. The main aim of this dissertation is thus to investigate and model the interaction of effective stress evolution and adsorption/desorption processes using advanced and innovative laboratory experiments and numerical simulations. This dissertation includes four parts consisting of the modelling and fundamental studies employing novel experimental and numerical techniques. The first part of the dissertation seeks to understand the characteristics of sorption-hydromechanical behaviour through the microscale and non-destructive investigation using micro-computed tomography (μCT), ultrasonic and their combined measurements. These fundamental investigations shed light on the coupled physical processes in different coal samples extracted from the Sydney Basin Australia through tracking 3D internal geometry. Using an X-ray transparent triaxial system, a range of stress-pore pressure boundary conditions are applied on different coals to obtain the 3D internal structure. The different coal components and their fracture patterns are analysed with respect to the bulk, matrix, and fracture compressibilities. In a further step, the coupled stress and swelling strain responses of coal when exposed to carbon dioxide (CO2) and helium (He) are studied by imbedding ultrasonic sensors in the X-ray transparent triaxial system. With the real-time visualisation of fracture porosity due to different adsorption levels, the effects of CO2 adsorption and involved processes on ultrasonic responses are investigated. The combined physical and numerical methods determine the main factor influencing wave propagation in coal to i) assist developing the representative constitutive model and ii) be used in the acoustic-driven parameterisation in the final part of the dissertation. The micro-scale investigation highlights the importance of internal structure affecting the mechanical properties in coal and the acoustic wave velocity allows evaluating the changes in facture characteristics during CO2 adsorption. Using detailed understanding of the physical processes involved in coal multiphysics, a constitutive model based on the continuum mechanics and non-equilibrium thermodynamics is developed in the second part of the dissertation. The model considers changes in gas content in the tight coal matrix through sorption and diffusion processes along with gas leakage from the matrix into fractures where Darcy type flow takes place. Also, the time dependency of coupling processes is accounted for especially the volumetric strains induced by gas sorption and their overall effects on changes in the fracture aperture, hence in the bulk flow conductivity. The novelty of the proposed model specially lies in the derivation of the thermodynamically consistent formulation of time-dependent effective stress law. The next part of the dissertation seeks to validate the developed effective stress law experimentally and to investigate i) the performance of commonly used theoretical models for swelling stress estimation, ii) the validity of thermodynamics coupling coefficient defining the swelling stress and iii) the effect of external stress on coal volumetric strain response. Especially designed experiments on two coal samples are conducted, including time-dependent diffusion and volumetric strain experiments under various stress and CO2 pressure conditions. A new experimental method is proposed to characterize the key input of the model which is the swelling coupling coefficient. Results of these series of experiments also show that the stress induced compression has minor effect on gas desorption. Since the wave velocity and porosity are interrelated, in the last part of the dissertation, some key parameters involved in the set of developed hydromechanical relationships are measured/modelled using acoustic measurement and finite element simulation. First, the effective stress coefficient is predicted using the percolation theory and hydromechanical and ultrasonic laboratory measurements on coal samples. The swelling coefficient representing adsorption induced volumetric strain development is next studied using acoustic simulation. As a newly proposed coupling coefficient in the model development, the relationship between the coefficient and wave velocity are correlated in three pore pressure conditions and its response to each condition is collected and analysed. Finally, the fracture permeability in coal seams is estimated using a novel physics-informed neural network (PINN) technique. In the training of PINN model, a synthetic dataset is built from several ultrasonic measurements and numerical simulations, with input variables of wave velocity and density. This model is successfully applied in a field case study where downhole geophysical logging data is available. In general, the acoustic-driven technique provides a strong and useful pathway to predict model parameters using geophysical logging data in a field setting, where sonic logs are available

Linking quantitative radiology to molecular mechanism for improved vascular disease therapy selection and follow-up

Objective: Therapeutic advancements in atherosclerotic cardiovascular disease have improved the prevention of ischemic stroke and myocardial infarction. However, diagnostic methods for atherosclerotic plaque phenotyping to aid individualized therapy are lacking. In this thesis, we aimed to elucidate plaque biology through the analysis of computed-tomography angiography (CTA) with sufficient sensitivity and specificity to capture the differentiated drivers of the disease. We then aimed to use such data to calibrate a systems biology model of atherosclerosis with adequate granularity to be clinically relevant. Such development may be possible with computational modeling, but given, the multifactorial biology of atherosclerosis, modeling must be based on complete biological networks that capture protein-protein interactions estimated to drive disease progression. Approach and Results: We employed machine intelligence using CTA paired with a molecular assay to determine cohort-level associations and individual patient predictions. Examples of predicted transcripts included ion transporters, cytokine receptors, and a number of microRNAs. Pathway analyses elucidated enrichment of several biological processes relevant to atherosclerosis and plaque pathophysiology. The ability of the models to predict plaque gene expression from CTAs was demonstrated using sequestered patients with transcriptomes of corresponding lesions. We further performed a case study exploring the relationship between biomechanical quantities and plaque morphology, indicating the ability to determine stress and strain from tissue characteristics. Further, we used a uniquely constituted plaque proteomic dataset to create a comprehensive systems biology disease model, which was finally used to simulate responses to different drug categories in individual patients. Individual patient response was simulated for intensive lipid-lowering, anti-inflammatory drugs, anti-diabetic, and combination therapy. Plaque tissue was collected from 18 patients with 6735 proteins at two locations per patient. 113 pathways were identified and included in the systems biology model of endothelial cells, vascular smooth muscle cells, macrophages, lymphocytes, and the integrated intima, altogether spanning 4411 proteins, demonstrating a range of 39-96% plaque instability. Simulations of drug responses varied in patients with initially unstable lesions from high (20%, on combination therapy) to marginal improvement, whereas patients with initially stable plaques showed generally less improvement, but importantly, variation across patients. Conclusion: The results of this thesis show that atherosclerotic plaque phenotyping by multi-scale image analysis of conventional CTA can elucidate the molecular signatures that reflect atherosclerosis. We further showed that calibrated system biology models may be used to simulate drug response in terms of atherosclerotic plaque instability at the individual level, providing a potential strategy for improved personalized management of patients with cardiovascular disease. These results hold promise for optimized and personalized therapy in the prevention of myocardial infarction and ischemic stroke, which warrants further investigations in larger cohorts

Updating the Lambda modes of a nuclear power reactor

[EN] Starting from a steady state configuration of a nuclear power reactor some situations arise in which the reactor configuration is perturbed. The Lambda modes are eigenfunctions associated with a given configuration of the reactor, which have successfully been used to describe unstable events in BWRs. To compute several eigenvalues and its corresponding eigenfunctions for a nuclear reactor is quite expensive from the computational point of view. Krylov subspace methods are efficient methods to compute the dominant Lambda modes associated with a given configuration of the reactor, but if the Lambda modes have to be computed for different perturbed configurations of the reactor more efficient methods can be used. In this paper, different methods for the updating Lambda modes problem will be proposed and compared by computing the dominant Lambda modes of different configurations associated with a Boron injection transient in a typical BWR reactor. (C) 2010 Elsevier Ltd. All rights reserved.This work has been partially supported by the Spanish Ministerio de Educacion y Ciencia under projects ENE2008-02669 and MTM2007-64477-AR07, the Generalitat Valenciana under project ACOMP/2009/058, and the Universidad Politecnica de Valencia under project PAID-05-09-4285.González Pintor, S.; Ginestar Peiro, D.; Verdú Martín, GJ. (2011). Updating the Lambda modes of a nuclear power reactor. Mathematical and Computer Modelling. 54(7):1796-1801. https://doi.org/10.1016/j.mcm.2010.12.013S1796180154

A multiscale strategy for fouling prediction and mitigation in gas turbines

Gas turbines are one of the primary sources of power for both aerospace and land-based applications. Precisely for this reason, they are often forced to operate in harsh environmental conditions, which involve the occurrence of particle ingestion by the engine. The main implications of this problem are often underestimated. The particulate in the airflow ingested by the machine can deposit or erode its internal surfaces, and lead to the variation of their aerodynamic geometry, entailing performance degradation and, possibly, a reduction in engine life. This issue affects the compressor and the turbine section and can occur for either land-based or aeronautical turbines. For the former, the problem can be mitigated (but not eliminated) by installing filtration systems. For what concern the aerospace field, filtration systems cannot be used. Volcanic eruptions and sand dust storms can send particulate to aircraft cruising altitudes. Also, aircraft operating in remote locations or low altitudes can be subjected to particle ingestion, especially in desert environments. The aim of this work is to propose different methodologies capable to mitigate the effects of fouling or predicting the performance degradation that it generates. For this purpose, both hot and cold engine sections are considered. Concerning the turbine section, new design guidelines are presented. This is because, for this specific component, the time scales of failure events due to hot deposition can be of the order of minutes, which makes any predictive model inapplicable. In this respect, design optimization techniques were applied to find the best HPT vane geometry that is less sensitive to the fouling phenomena. After that, machine learning methods were adopted to obtain a design map that can be useful in the first steps of the design phase. Moreover, after a numerical uncertainty quantification analysis, it was demonstrated that a deterministic optimization is not sufficient to face highly aleatory phenomena such as fouling. This suggests the use of robust or aggressive design techniques to front this issue. On the other hand, with respect to the compressor section, the research was mainly focused on the building of a predictive maintenance tool. This is because the time scales of failure events due to cold deposition are longer than the ones for the hot section, hence the main challenge for this component is the optimization of the washing schedule. As reported in the previous sections, there are several studies in the literature focused on this issue, but almost all of them are data-based instead of physics-based. The innovative strategy proposed here is a mixture between physics-based and data-based methodologies. In particular, a reduced-order model has been developed to predict the behaviour of the whole engine as the degradation proceeds. For this purpose, a gas path code that uses the components’ characteristic maps has been created to simulate the gas turbine. A map variation technique has been used to take into account the fouling effects on each engine component. Particularly, fouling coefficients as a function of the engine architecture, its operating conditions, and the contaminant characteristics have been created. For this purpose, both experimental and computational results have been used. Specifically for the latter, efforts have been done to develop a new numerical deposition/detachment model.Le turbine a gas sono una delle pricipali fonti di energia, sia per applicazioni aeronautiche che terrestri. Proprio per questa ragione, esse sono spesso costrette ad operare in ambienti non propriamente puliti, il che comporta l’ingestione di contaminanti solidi da parte del motore. Le principali implicazioni di questo problema sono spesso sottovalutate. Le particelle solide presenti nel flusso d’aria che il motore ingerisce durante il suo funzionamento possono depositarsi o erodere le superfici interne della macchina, e portare a variazioni alla sua aerodinamica, quindi a degrado di performance e, molto probabilmente, alla diminuzione della sua vita utile. Questo problema aflligge sia la parte del compressore che la parte della turbina, e si manifesta sia in applicazioni terrestri che aeronautiche. Per quanto riguarda la prima, la questione può essere mitigata (ma non eliminata) dall’installazione di sistemi di filtraggio all’ingresso della macchina. Per le applicazioni aeronautiche invece, i sistemi di filtraggio non possono essere utilizzati. Questo implica che il particolato presente ad alte quote, magari grazie ad eventi catastrofici quali eruzioni vulcaniche, o a basse quote, quindi ambienti deseritic, entra liberamente nella turbina a gas. Lo scopo principale di questo lavoro di tesi, è quello di proporre differenti metodologieallo scopo di mitigare gli effetti dello sporcamento o predirre il degrado che esso comporta nelle turbine a gas. Per questo scopo, sia la parte del compressore che quella della turbina sono state prese in considerazione. Per quanto riguarda la parte turbina, saranno presentate nuove guide progettuali volte al trovare la geometria che sia meno sensibile possibile al problema dello sporcamento. Dopo di ciò, i risultati ottenuti verranno trattati tramite tecniche di machine learning, ottenendo una mappa di progetto che potrà essere utile nelle prime fasi della progettazione di questi componenti. Inoltre, essendo l’analisi fin qui condotta di tipo deterministico, un’analisi delle principali fonti di incertezza verrà eseguita con l’utilizzo di tecniche derivanti dall’uncertainty quantification. Questo dimostrerà che l’analisi deterministica è troppo semplificativa, e che sarebbe opportuno spingersi verso una progettazione robusta per affrontare questa tipologia di problemi. D’altro canto, per quanto concerne la parte compressore, la ricerca è stata incentrata principalmente sulla costruzione di uno strumento predittivo, questo perchè la scala temporale del degrado dovuto alla deposizione a "freddo" è molto più dilatata rispetto a quella della sezione "calda". La trategia proposta in questo lavoro di tesi è un’insieme di modelli fisici e data-driven. In particolare, si è sviluppato un modello ad ordine ridotto per la previsione del comportamento del motore soggetto a degrado dovuto all’ingestione di particolato, durante un’intera missione aerea. Per farlo, si è generato un codice cosiddetto gas-path, che modella i singoli componenti della macchina attraverso le loro mappe caratteristiche. Quest’ultime vengono modificate, a seguito della deposizione, attraverso opportuni coefficienti di degrado. Tali coefficienti devono essere adeguatamente stimati per avere una corretta previsione degli eventi, e per fare ciò verrà proposta una strategia che comporta l’utilizzo sia di metodi sperimentali che computazionali, per la generazione di un algoritmo che avrà lo scopo di fornire come output questi coefficienti

Tracing back the source of contamination

From the time a contaminant is detected in an observation well, the question of where and when the contaminant was introduced in the aquifer needs an answer. Many techniques have been proposed to answer this question, but virtually all of them assume that the aquifer and its dynamics are perfectly known. This work discusses a new approach for the simultaneous identification of the contaminant source location and the spatial variability of hydraulic conductivity in an aquifer which has been validated on synthetic and laboratory experiments and which is in the process of being validated on a real aquifer

Numerical modelling of the growth and remodelling phenomena in biological tissues

Living biological tissues are complex structures that have the capacity of evolving in response to external loads and environmental stimuli. The adequate modelling of soft biological tissue behaviour is a key issue in successfully reproducing biomechanical problems through computational analysis. This study presents a general constitutive formulation capable of representing the behaviour of these tissues through finite element simulation. It is based on phenomenological models that, used in combination with the generalized mixing theory, can numerically reproduce a wide range of material behaviours. First, the passive behaviour of tissues is characterized by means of hyperelastic and finite-strain damage models. A new generalized damage model is proposed, providing a flexible and versatile formulation that can reproduce a wide range of tissue behaviour. It can be particularized to any hyperelastic model and requires identifying only two material parameters. Then, the use of these constitutive models with generalized mixing theory in a finite-strain framework is described and tools to account for the anisotropic behaviour of tissues are put forth. The active behaviour of tissues is characterized through constitutive models capable of reproducing the growth and remodelling phenomena. These are built on the hyperelastic and damage formulations described above and, thus, represent the active extension of the passive tissue behaviour. A growth model considering biological availability is used and extended to include directional growth. In addition, a novel constitutive model for homeostatic-driven turnover remodelling is presented and discussed. This model captures the stiffness recovery that occurs in healing tissues, understood as a recovery or reversal of damage in the tissue, which is driven by both mechanical and biochemical stimuli. Finally, the issue of correctly identifying the material parameters for computational modelling is addressed. An inverse method using optimization techniques is developed to facilitate the identification of these parameters.Els teixits biològics vius són estructures complexes que tenen la capacitat d'evolucionar en resposta a càrregues externes i estímuls ambientals. El modelat adequat del comportament del teixit biològic tou és un tema clau per poder reproduir amb èxit problemes biomecànics mitjançant anàlisi computacional. Aquest estudi presenta una formulació constitutiva general capaç de representar el comportament d'aquests teixits mitjançant la simulació amb elements finits. Es basa en models fenomenològics que, usats en combinació amb la teoria de mescles generalitzada, permeten reproduir numèricament un ampli ventall de comportaments materials. Primer, el comportament passiu dels teixits es caracteritza amb models hiperelàstics i de dany en grans deformacions. Es proposa un model generalitzat de dany que proporciona una formulació versàtil i flexible per poder reproduir una extensa gamma de conductes de teixits. Pot ser particularitzat amb qualsevol model hiperelàstic i requereix identificar tan sols dos paràmetres materials. Llavors, es descriu l'ús d'aquests models constitutius en conjunt amb la teoria generalitzada de mescles, desenvolupada en el marc de grans deformacions, i es presenten eines que permeten incorporar les propietats anisòtropes dels teixits. El comportament actiu dels teixits es caracteritza mitjançant models constitutius capaços de reproduir els fenòmens de creixement i remodelació. Aquests es construeixen sobre les formulacions d'hiperelasticitat i dany descrites anteriorment i, per tant, suposen l'extensió activa del comportament passiu del teixit. Es fa servir un model de creixement que té en compte la disponibilitat biològica de l'organisme, que després s'amplia per incloure dany direccional en el model. També es presenta i analitza un nou model constitutiu per al remodelat per renovació tendint a l’homeòstasi (homeostatic-driven turnover remodelling). Aquest model captura la recuperació de rigidesa que s'observa en teixits que es guareixen. Aquí, el remodelat s'entén com la recuperació o inversió del dany en el teixit i és motivat tant per estímuls mecànics com bioquímics. Finalment, s'aborda el tema de la identificació correcta dels paràmetres materials per al modelat computacional. Es desenvolupa un mètode invers que fa ús de tècniques d'optimització per facilitar la identificació d'aquests paràmetre

Shear-promoted drug encapsulation into red blood cells: a CFD model and μ-PIV analysis

The present work focuses on the main parameters that influence shear-promoted encapsulation of drugs into erythrocytes. A CFD model was built to investigate the fluid dynamics of a suspension of particles flowing in a commercial micro channel. Micro Particle Image Velocimetry (μ-PIV) allowed to take into account for the real properties of the red blood cell (RBC), thus having a deeper understanding of the process. Coupling these results with an analytical diffusion model, suitable working conditions were defined for different values of haematocrit