Uncertainty quantification for flow and transport in porous media

The major spreading and trapping mechanisms of carbon dioxide in geological media are subject to spatial variability due to heterogeneity of the physical and chemical properties of the medium. For modelling to make a useful contribution to the understanding of carbon dioxide sequestration and its associated risk assessment, the impact of heterogeneity on flow, transport and reaction processes and their uncertainties must be identified, characterised, and its consequences quantified. Complex computer simulation models based on systems of partial differential equations with random inputs are often used to describe the flow of groundwater through this heterogeneous media. The Monte Carlo method is a widely used and effective approach to quantify uncertainty in such systems of partial differential equations with random inputs. This thesis investigates two alternatives to Monte Carlo for solving the equations with random inputs; the first of these are techniques developed for improving the computational performance of Monte Carlo, namely methods such as, multilevel Monte Carlo, quasi Monte Carlo, multilevel quasi Monte Carlo. The second alternative, Gaussian process emulation, is an approach based on Bayesian non parametric modelling, in which we build statistical approximations of the simulator, called emulators. Numerical calculations carried out in this thesis have demonstrated the effectiveness of the proposed alternatives to the Monte Carlo method for solving two dimensional model problems arising in groundwater flow and Carbon capture and storage processes. Multilevel quasi Monte Carlo has been proven to be the more efficient method, in terms of computational resources used, among Monte Carlo, multilevel Monte Carlo and quasi Monte Carlo. Gaussian process emulation has been proven to be a reliable surrogate for these simulators and it has been concluded that the use of Gaussian process emulation is a powerful tool which can be satisfactorily used when the physical processes are modelled through computationally expensive simulators

Uncertainty quantification for flow and transport in porous media

The major spreading and trapping mechanisms of carbon dioxide in geological media are subject to spatial variability due to heterogeneity of the physical and chemical properties of the medium. For modelling to make a useful contribution to the understanding of carbon dioxide sequestration and its associated risk assessment, the impact of heterogeneity on flow, transport and reaction processes and their uncertainties must be identified, characterised, and its consequences quantified. Complex computer simulation models based on systems of partial differential equations with random inputs are often used to describe the flow of groundwater through this heterogeneous media. The Monte Carlo method is a widely used and effective approach to quantify uncertainty in such systems of partial differential equations with random inputs. This thesis investigates two alternatives to Monte Carlo for solving the equations with random inputs; the first of these are techniques developed for improving the computational performance of Monte Carlo, namely methods such as, multilevel Monte Carlo, quasi Monte Carlo, multilevel quasi Monte Carlo. The second alternative, Gaussian process emulation, is an approach based on Bayesian non parametric modelling, in which we build statistical approximations of the simulator, called emulators. Numerical calculations carried out in this thesis have demonstrated the effectiveness of the proposed alternatives to the Monte Carlo method for solving two dimensional model problems arising in groundwater flow and Carbon capture and storage processes. Multilevel quasi Monte Carlo has been proven to be the more efficient method, in terms of computational resources used, among Monte Carlo, multilevel Monte Carlo and quasi Monte Carlo. Gaussian process emulation has been proven to be a reliable surrogate for these simulators and it has been concluded that the use of Gaussian process emulation is a powerful tool which can be satisfactorily used when the physical processes are modelled through computationally expensive simulators

Screening of effective electrolyte additives for zinc-based redox flow battery systems

This is the author accepted manuscript. The final version is available from Elsevier via the DOI in this record.The purpose of this work is to assess the suitability of potential electrolyte additives for zinc morphology control and improved electrochemical performance of the zinc electrode for application in zinc based redox flow battery (RFB) systems. Based on existing literature in the field, sixteen candidates are selected, including four metallic additives, two non-ionic surfactants and ten quaternary ammonium compounds. The electrochemical performance of the zinc electrode is assessed using cyclic voltammetry, linear sweep voltammetry and zinc half-cell cycling tests using chronopotentiometry. Zinc electrodepositions are carried out using chronopotentiometry in order to assess the effect of additives on zinc morphology with scanning electron microscopy. Based on zinc reduction and oxidation reaction potentials, the cycling efficiencies, and the effect on zinc morphology, the most promising additives of those tested are tetraethylammonium hydroxide and tetraethylammonium bromide. Both provide smooth and compact zinc deposits and zinc electrode coulombic efficiencies of 95-97 % without leading to significant changes in the zinc reduction/oxidation overpotentials, yielding anodic and cathodic current densities of 77-78 mA cm-2 and 31-32 mA cm-2 at overpotentials of +/- 50 mV, respectively. In a zinc-nickel flow cell, these additives provide energy efficiencies of 78-79 %, compared with 69 % without an additive.This work was supported by the EPSRC Supergen Energy Storage Project (grant number: EP/P003494/1) entitled ‘Zinc-Nickel Redox Flow Battery for Energy Storage’; the EPSRC PhD studentship as a Doctoral Training Partnership (DTP); and the support from the College of Engineering, Mathematics and Physical Sciences in the University of Exeter

Nanoporous Elements in Microfluidics for Multiscale Manipulation of Bioparticles

Author Manuscript 2011 July 22Solid materials, such as silicon, glass, and polymers, dominate as structural elements in microsystems including microfluidics. Porous elements have been limited to membranes sandwiched between microchannel layers or polymer monoliths. This paper reports the use of micropatterned carbon-nanotube forests confined inside microfluidic channels for mechanically and/or chemically capturing particles ranging over three orders of magnitude in size. Nanoparticles below the internanotube spacing (80 nm) of the forest can penetrate inside the forest and interact with the large surface area created by individual nanotubes. For larger particles (>80 nm), the ultrahigh porosity of the nanotube elements reduces the fluid boundary layer and enhances particle–structure interactions on the outer surface of the patterned nanoporous elements. Specific biomolecular recognition is demonstrated using cells (≈10 μm), bacteria (≈1 μm), and viral-sized particles (≈40 nm) using both effects. This technology can provide unprecedented control of bioseparation processes to access bioparticles of interest, opening new pathways for both research and point-of-care diagnostics.National Institute of Biomedical Imaging and Bioengineering (U.S.) (Grant P41 EB002503)United States. Department of State (Fulbright Science and Technology Award

Gaussian Process Modelling for Uncertainty Quantification in Convectively-Enhanced Dissolution Processes in Porous Media

Numerical groundwater flow and dissolution models of physico-chemical processes in deep aquifers are usually subject to uncertainty in one or more of the model input parameters. This uncertainty is propagated through the equations and needs to be quantified and characterised in order to rely on the model outputs. In this paper we present a Gaussian process emulation method as a tool for performing uncertainty quantification in mathematical models for convection and dissolution processes in porous media. One of the advantages of this method is its ability to significantly reduce the computational cost of an uncertainty analysis, while yielding accurate results, compared to classical Monte Carlo methods. We apply the methodology to a model of convectively-enhanced dissolution processes occurring during carbon capture and storage. In this model, the Gaussian process methodology fails due to the presence of multiple branches of solutions emanating from a bifurcation point, i.e., two equilibrium states exist rather than one. To overcome this issue we use a classifier as a precursor to the Gaussian process emulation, after which we are able to successfully perform a full uncertainty analysis in the vicinity of the bifurcation point

A Neutrophil Timer Coordinates Immune Defense and Vascular Protection

Neutrophils eliminate pathogens efficiently but can inflict severe damage to the host if they over-activate within blood vessels. It is unclear how immunity solves the dilemma of mounting an efficient anti-microbial defense while preserving vascular health. Here, we identify a neutrophil-intrinsic program that enabled both. The gene Bmal1 regulated expression of the chemokine CXCL2 to induce chemokine receptor CXCR2-dependent diurnal changes in the transcriptional and migratory properties of circulating neutrophils. These diurnal alterations, referred to as neutrophil aging, were antagonized by CXCR4 (C-X-C chemokine receptor type 4) and regulated the outer topology of neutrophils to favor homeostatic egress from blood vessels at night, resulting in boosted anti-microbial activity in tissues. Mice engineered for constitutive neutrophil aging became resistant to infection, but the persistence of intravascular aged neutrophils predisposed them to thrombo-inflammation and death. Thus, diurnal compartmentalization of neutrophils, driven by an internal timer, coordinates immune defense and vascular protection.We thank all members of the Hidalgo Lab for discussion and insightful comments; J.M. Ligos, R. Nieto, and M. Viton for help with sorting and cytometric analyses; I. Ortega and E. Santos for animal husbandry; D. Rico, M.J. Gomez, C. Torroja, and F. Sanchez-Cabo for insightful comments and help with transcriptomic analyses; V. Labrador, E. Arza, A.M. Santos, and the Microscopy Unit of the CNIC for help with microscopy; S. Aznar-Benitah, U. Albrecht, Q.-J. Meng, B. Staels, and H. Duez for the generous gift of mice; J.A. Enriquez and J. Avila for scientific insights; and J.M. Garcia and A. Diez de la Cortina for art. This study was supported by Intramural grants from A* STAR to L.G.N., BES-2013-065550 to J.M.A., BES-2010-032828 to M.C.-A, and JCI-2012-14147 to L.A.W (all from Ministerio de Economia, Industria y Competitividad; MEIC). Additional MEIC grants were SAF2014-61993-EXP to C.L.-R.; SAF2015-68632-R to M.A.M. and SAF-2013-42920R and SAF2016-79040Rto D.S. D.S. also received 635122-PROCROP H2020 from the European Commission and ERC CoG 725091 from the European Research Council (ERC). ERC AdG 692511 PROVASC from the ERC and SFB1123-A1 from the Deutsche Forschungsgemeinschaft were given to C.W.; MHA VD1.2/81Z1600212 from the German Center for Cardiovascular Research (DZHK) was given to C.W. and O.S.; SFB1123-A6 was given to O.S.; SFB914-B08 was given to O.S. and C.W.; and INST 211/604-2, ZA 428/12-1, and ZA 428/13-1 were given to A.Z. This study was also supported by PI12/00494 from Fondo de Investigaciones Sanitarias (FIS) to C.M.; PI13/01979, Cardiovascular Network grant RD 12/0042/0054, and CIBERCV to B.I.; SAF2015-65607-R, SAF2013-49662-EXP, and PCIN-2014-103 from MEIC; and co-funding by Fondo Europeo de Desarrollo Regional (FEDER) to A.H. The CNIC is supported by the MEIC and the Pro CNIC Foundation and is a Severo Ochoa Center of Excellence (MEIC award SEV-2015-0505).S

Data from: Surrogate modelling for the prediction of spatial fields based on simultaneous dimensionality reduction of high-dimensional input/output spaces

Time-consuming numerical simulators for solving groundwater flow and dissolution models of physico-chemical processes in deep aquifers normally require some of the model inputs to be defined in high-dimensional spaces in order to return realistic results. Sometimes, the outputs of interest are spatial fields leading also to high-dimensional output spaces. Although Gaussian process emulation has been satisfactorily used for computing faithful and inexpensive approximations of complex simulators, these have been mostly applied to problems defined in low-dimensional input spaces. In this paper, we propose a method for simultaneously reducing the dimensionality of very high-dimensional input and output spaces in Gaussian process emulators for stochastic partial differential equation models while retaining the qualitative features of the original models. This allows us to build a surrogate model for the prediction of spatial fields in such time-consuming simulators. We apply the methodology to a model of convection and dissolution processes occurring during carbon capture and storage

Supporting data for the figures showed in the manuscript

Supporting dat

Data_RSOS-170203

Data_RSOS-17020