55 research outputs found

    Evaluation of the Impact of Scale in the Well-Test Behaviour of Fissured Reservoirs

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    Evaluation of the Impact of Scale in the Well-Test Behaviour of Fissured Reservoirs

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    Imperial Users onl

    THE EFFECTS OF PARAMETRIC UNCERTAINTIES IN SIMULATIONS OF A REACTIVE PLUME USING A LAGRANGIAN STOCHASTIC MODEL

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    A combined Lagrangian stochastic model with micro mixing and chemical sub-models is used to investigate a reactive plume of nitrogen oxides (NOx) released into a turbulent grid flow doped with ozone (O3). Sensitivities to the model input parameters are explored for high NOx model scenarios. A wind tunnel experiment is used to provide the simulation conditions for the first case study where photolysis reactions are not included and the main uncertainties occur in the parameters defining the turbulence scales, the source size and the reaction rate of NO (nitric oxide) with O3. Using nominal values of the parameters from previous studies, the model gives a good representation of the radial profile of the conserved scalar [NOx] compared to the experiments, although the width of the simulated profile is slightly smaller, especially at longer distances from the source. For this scenario, the Lagrangian velocity structure function coefficient has the largest impact on simulated [NOx] profiles. At the next stage photolysis reactions are included in a chemical scheme consisting of eight reactions between species NO, O, O3 and NO2. The high dimensional model representation (HMDR) method is used to investigate the effects of uncertainties in the various model inputs resulting from the parameterisation of important physical and chemical processes in the reactive plume model, on the simulation of primary and secondary chemical species concentrations. Both independent and interactive effects of the parameters are studied. In total 22 parameters are assumed to be uncertain, among them the turbulence parameters, temperature dependant rate parameters, photolysis rates, temperature, fraction of NO in total NOx at the source and background concentration of O3. Only uncertainties in the mixing time scale coefficient and the structure function coefficient are responsible for the variance in the [NOx] radial profile. On the other hand, the variance in the [O3] profile is caused by parameters describing both physical and chemical processes

    Global uncertainty and sensitivity analysis of a reduced chemical kinetic mechanism of a gasoline, n-butanol blend in a high pressure rapid compression machine.

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    A detailed evaluation of a recently developed combined n-butanol/toluene reference fuel (TRF) reduced chemical kinetic mechanism describing the low temperature oxidation of n-butanol, gasoline and a gasoline/n-butanol blend was performed using both global uncertainty and sensitivity methods with ignition delays as the predicted output for the temperature range 678 - 858 K, and an equivalence ratio of 1 at 20 bar. The results obtained when incorporating the effects of uncertainties in forward rate constants in the simulations, showed that uncertainties in predicting key target quantities for the various fuels studied are currently large but driven by few reactions. Global sensitivity analysis of the mechanism based on predicted ignition delays of stoichiometric TRF mixtures, showed the toluene + OH route = phenol + CH3 to be among the most dominant pathways in terms of the predicted output uncertainties but an update on the mechanism based on data from a recent study led to the toluene + OH hydrogen abstraction reaction becoming the most dominant reaction as expected. For the TRF/n-butanol blend, hydrogen abstraction reactions by OH from n-butanol appear to be key in predicting the effect of blending. Uncertainties in the temperature dependence of relative abstraction rates from the α and γ sites may still be present within current mechanisms, and in particular may affect the ability of the mechanisms to capture the low temperature delay times for n-butanol. Further studies of the product channels for n-butanol + OH for temperatures of relevance to combustion applications could help to improve current mechanisms. At higher temperatures, the reactions of HO2 and that of formaldehyde with OH also became critical and attempts to reduce uncertainties in the temperature dependent rates of these reactions would be useful

    Computational methods and software for the design of inertial microfluidic flow sculpting devices

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    The ability to sculpt inertially flowing fluid via bluff body obstacles has enormous promise for applications in bioengineering, chemistry, and manufacturing within microfluidic devices. However, the computational difficulty inherent to full scale 3-dimensional fluid flow simulations makes designing and optimizing such systems tedious, costly, and generally tasked to computational experts with access to high performance resources. The goal of this work is to construct efficient models for the design of inertial microfluidic flow sculpting devices, and implement these models in freely available, user-friendly software for the broader microfluidics community. Two software packages were developed to accomplish this: uFlow and FlowSculpt . uFlow solves the forward problem in flow sculpting, that of predicting the net deformation from an arbitrary sequence of obstacles (pillars), and includes estimations of transverse mass diffusion and particles formed by optical lithography. FlowSculpt solves the more difficult inverse problem in flow sculpting, which is to design a flow sculpting device which produces a target flow shape. Each piece of software uses efficient, experimentally validated forward models developed within this work, which are applied to deep learning techniques to explore other routes to solving the inverse problem. The models are also highly modular, capable of incorporating new microfluidic components and flow physics to the design process. It is anticipated that the microfluidics community will integrate the tools developed here into their own research, and bring new designs, components, and applications to the inertial flow sculpting platform

    Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

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    We develop a framework for multifidelity information fusion and predictive inference in high-dimensional input spaces and in the presence of massive data sets. Hence, we tackle simultaneously the “big N" problem for big data and the curse of dimensionality in multivariate parametric problems. The proposed methodology establishes a new paradigm for constructing response surfaces of high-dimensional stochastic dynamical systems, simultaneously accounting for multifidelity in physical models as well as multifidelity in probability space. Scaling to high dimensions is achieved by data-driven dimensionality reduction techniques based on hierarchical functional decompositions and a graph-theoretic approach for encoding custom autocorrelation structure in Gaussian process priors. Multifidelity information fusion is facilitated through stochastic autoregressive schemes and frequency-domain machine learning algorithms that scale linearly with the data. Taking together these new developments leads to linear complexity algorithms as demonstrated in benchmark problems involving deterministic and stochastic fields in up to 10⁵ input dimensions and 10⁵ training points on a standard desktop computer

    Global sensitivity analysis of detailed chemical kinetic schemes for DME oxidation in premixed flames

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    Detailed chemical kinetic investigations on dimethylether oxidation in one-dimensional premixed flat flames were performed. Local and global sensitivities of the reaction rate constants within selected chemical kinetic schemes were studied using maximum flame temperature, and peak methane and formaldehyde concentrations as predictive target quantities. The global sensitivity analysis was based on the application of high dimensional model representations using quasi-random sampling. First- and second-order sensitivity indices of important reaction steps were determined for fuel rich (Φ = 1.49) and fuel lean (Φ = 0.67) conditions. Differences in the importance ranking for key reactions were found to exist between the selected schemes, highlighting the influence of differences in the key rate constants. Whilst the peak flame temperature was predicted with fairly low uncertainty by both schemes, significant uncertainties were identified in the prediction of the target minor species. Key reaction rates requiring better quantification in order to improve the prediction of methane and formaldehyde concentrations are identified
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