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This master thesis covers the realization of a CFD calibration study regarding the numerical simulation of the ground vortex phenomenon. As part of InVIGO project (EU CleanSky2), wind tunnel test campaigns were performed using a nacelle model combined with a movable raised floor. Those tests allowed the gathering of an unprecedented amount of experimental data regarding this phenomenon, via the implementation of pressure measurements and stereo-PIV acquisition. Measured velocity fields were also post-processed to compute vortex characterization indices. In the context of this study, several Python scripts are developed to process this data through the computation of total pressure and velocity maps. A calculation matrix of 28 representative points is chosen among test cases and the corresponding nacelle geometry is prepared to generate a series of high resolution structured mesh (18.22 million cells) using an existing ICEM macro. For every case, corresponding CFD calculations are implemented on elsA using the RANS resolution method associated with the Spalart-Allmaras turbulence model. In parallel, a series of CFD post-processing scripts are developed using Cassiopée modules, in order to extract pressure/velocity maps and compute both distortion and convergence indices. Also, a Safran AE in-house script dedicated to vortex characterization is not only implemented to compute vortex indices, but also upgraded with an additional vortex profile extraction functionality. The cross-analysis of numerical and experimental data thus collected is performed, allowing the assessment of the great calculation representativeness regarding the overall inlet flow topology as well as the vortex apparition. The comparison of vortex characterization indices displays common tendencies, however disparities regarding the implemented processing methodologies along with convergence difficulties encountered for some vortex cases highlights the need to put the relevance of those results into perspective.

Yes / Technological systems are not merely designed with a narrow function in mind. Good designs typically aim at reducing operational costs, e.g. through achieving high energy efficiency and improved dependability (i.e. reliability, availability and maintainability). When there is a choice of alternative design options that perform the same function, it makes sense to compare alternatives so that the variant that minimises operational costs can be selected. In this paper, we examine this issue in the context of the design of Waste Heat Recovery Systems (WHRS) for main engines of large commercial freight vessels. We propose a method that can predict the operational cost of a WHRS via thermodynamic analysis which shows costs related to energy utilisation, and dependability analysis which shows costs related to system unavailability and repair. Our approach builds on recent advances in thermodynamic simulation and compositional dependability analysis techniques. It is a model-based approach, and allows reuse of component libraries, and a high degree of automation which simplify application of the method. Our case study shows that alternative designs can be explored in fast iterations of this method, and that this facilitates the evidence-based selection of a design that minimises operational costs.

An effective medium approximation and a refined Monte Carlo simulation procedure for solving the stochastic groundwater flow problem are presented. The effective medium approximation permits one to solve the stochastic groundwater flow problem in a single run to generate the expected pressure head field. The proposed effective hydraulic conductivity expression for the effective medium is of the same form as the local Gardner’s equation and is easy to use. The refined Monte Carlo simulation procedure uses analytical means to estimate the sample size by controlling the error incurred in using the sample average in place of its population mean at a chosen confidence level. This estimator consistently performs well. Also, a variance reducing estimator which is different from the simple average for pressure head is developed. This estimator takes advantage of the correlation between the saturated conductivity and the pressure head distribution to reduce the output variance and is unbiased. This reduced variance results in a smaller width of uncertainty about the predicted pressure head. Both the effective medium approximation and the Monte Carlo approaches perform well when applied to several problems. / Ph. D.

A groundwater flow model was constructed for the Rincon Valley area and Mesilla Basin. The system is dominated by the complex interaction of the Rio Grande, canals, laterals, and drains with groundwater pumping. The primary purpose of the model was to aid the New Mexico -Texas Water Commission in assessing options for water resources development in the Lower Rio Grand Basin from Caballo Reservoir in New Mexico to El Paso, Texas. One such assessment was to evaluate the effect of secondary irrigation releases from Caballo Reservoir on the water budget. In addition, the model will eventually be linked to a surface water model (BESTSM) being utilized by the New Mexico -Texas Water Commission to evaluate water supply alternatives for El Paso, Texas. Stress periods were specified on a seasonal basis, a primary irrigation season from March through October and a secondary irrigation season from November through February. Analysis of model output indicates that groundwater pumping decreases Rio Grande flows, secondary irrigation season releases do not alter the water budget significantly, and that recharge and discharge from aquifer storage are strongly related to the season.

The dynamic solution of multiphase flow through porous media is of special interest to several fields of science and engineering, such as petroleum, geology and geophysics, bio-medical, civil and environmental, chemical engineering and many other disciplines. A natural application is the modeling of the flow of two immiscible fluids (phases) in a reservoir. Others, that are broadly based and considered in this work include the hydrodynamic dispersion (as in reactive transport) of a solute or tracer chemical through a fluid phase. Reservoir properties like permeability and porosity greatly influence the flow of these phases. Often, these vary across several orders of magnitude and can be discontinuous functions. Furthermore, they are generally not known to a desired level of accuracy or detail and special inverse problems need to be solved in order to obtain their estimates. Based on the physics dominating a given sub-region of the porous medium, numerical solutions to such flow problems may require different discretization schemes or different governing equations in adjacent regions. The need to couple solutions to such schemes gives rise to challenging domain decomposition problems. Finally, on an application level, present day environment concerns have resulted in a widespread increase in CO₂capture and storage experiments across the globe. This presents a huge modeling challenge for the future. This research work is divided into sections that aim to study various inter-connected problems that are of significance in sub-surface porous media applications. The first section studies an application of mortar (as well as nonmortar, i.e., enhanced velocity) mixed finite element methods (MMFEM and EV-MFEM) to problems in porous media flow. The mortar spaces are first used to develop a multiscale approach for parabolic problems in porous media applications. The implementation of the mortar mixed method is presented for two-phase immiscible flow and some a priori error estimates are then derived for the case of slightly compressible single-phase Darcy flow. Following this, the problem of modeling flow coupled to reactive transport is studied. Applications of such problems include modeling bio-remediation of oil spills and other subsurface hazardous wastes, angiogenesis in the transition of tumors from a dormant to a malignant state, contaminant transport in groundwater flow and acid injection around well bores to increase the permeability of the surrounding rock. Several numerical results are presented that demonstrate the efficiency of the method when compared to traditional approaches. The section following this examines (non-mortar) enhanced velocity finite element methods for solving multiphase flow coupled to species transport on non-matching multiblock grids. The results from this section indicate that this is the recommended method of choice for such problems. Next, a mortar finite element method is formulated and implemented that extends the scope of the classical mortar mixed finite element method developed by Arbogast et al [12] for elliptic problems and Girault et al [62] for coupling different numerical discretization schemes. Some significant areas of application include the coupling of pore-scale network models with the classical continuum models for steady single-phase Darcy flow as well as the coupling of different numerical methods such as discontinuous Galerkin and mixed finite element methods in different sub-domains for the case of single phase flow [21, 109]. These hold promise for applications where a high level of detail and accuracy is desired in one part of the domain (often associated with very small length scales as in pore-scale network models) and a much lower level of detail at other parts of the domain (at much larger length scales). Examples include modeling of the flow around well bores or through faulted reservoirs. The next section presents a parallel stochastic approximation method [68, 76] applied to inverse modeling and gives several promising results that address the problem of uncertainty associated with the parameters governing multiphase flow partial differential equations. For example, medium properties such as absolute permeability and porosity greatly influence the flow behavior, but are rarely known to even a reasonable level of accuracy and are very often upscaled to large areas or volumes based on seismic measurements at discrete points. The results in this section show that by using a few measurements of the primary unknowns in multiphase flow such as fluid pressures and concentrations as well as well-log data, one can define an objective function of the medium properties to be determined, which is then minimized to determine the properties using (as in this case) a stochastic analog of Newton’s method. The last section is devoted to a significant and current application area. It presents a parallel and efficient iteratively coupled implicit pressure, explicit concentration formulation (IMPEC) [52–54] for non-isothermal compositional flow problems. The goal is to perform predictive modeling simulations for CO₂sequestration experiments. While the sections presented in this work cover a broad range of topics they are actually tied to each other and serve to achieve the unifying, ultimate goal of developing a complete and robust reservoir simulator. The major results of this work, particularly in the application of MMFEM and EV-MFEM to multiphysics couplings of multiphase flow and transport as well as in the modeling of EOS non-isothermal compositional flow applied to CO₂sequestration, suggest that multiblock/multimodel methods applied in a robust parallel computational framework is invaluable when attempting to solve problems as described in Chapter 7. As an example, one may consider a closed loop control system for managing oil production or CO₂sequestration experiments in huge formations (the “instrumented oil field”). Most of the computationally costly activity occurs around a few wells. Thus one has to be able to seamlessly connect the above components while running many forward simulations on parallel clusters in a multiblock and multimodel setting where most domains employ an isothermal single-phase flow model except a few around well bores that employ, say, a non-isothermal compositional model. Simultaneously, cheap and efficient stochastic methods as in Chapter 8, may be used to generate history matches of well and/or sensor-measured solution data, to arrive at better estimates of the medium properties on the fly. This is obviously beyond the scope of the current work but represents the over-arching goal of this research. / text

The Canadian Precipitation Analysis (CaPA) system has been developed by Environment and Climate Change Canada (ECCC) to optimally combine different sources of information to estimate precipitation accumulation across Canada. The system combines observations from different networks of weather stations and radar measurements with the background information generated by ECCC's Regional Deterministic Prediction System (RDPS), derived from the Global Environmental Multiscale (GEM) model. The main scope of this study is to assess the importance of weather stations when combined with the background information for hydrological modeling. A new approach to meteorological network design, considered to be a stochastic hydro-geostatistical scheme, is proposed and investigated which is particularly useful for augmenting data-sparse networks. The approach stands out from similar approaches of its kind in that it is comprised of a data assimilation component included based on the paradigm of an Observing System Simulation Experiment (OSSE), a technique used to simulate data assimilation systems in order to evaluate the sensitivity of the analysis to new observation network. The proposed OSSE-based algorithm develops gridded stochastic precipitation and temperature models to generate synthetic time-series assumed to represent the 'reference' atmosphere over the basin. The precipitation realizations are used to simulate synthetic observations, associated with hypothetical station networks of various densities, and synthetic background data, which in turn are assimilated in CaPA to realize various pseudo-analyses. The reference atmosphere and the pseudo-analyses are then compared through hydrological modeling in WATFLOOD. By comparing the flow rates, the relative performance of each pseudo-analysis associated with a specific network density is assessed. The simulations show that as the network density increases, the accuracy of the hydrological signature of the CaPA precipitation products improves hyperbolically to a certain limit beyond which adding more stations to the network does not result in further accuracy. This study identifies an observation network density that can satisfy the hydrological criteria as well as the threshold at which assimilated products outperforms numerical weather prediction outputs. It also underlines the importance of augmenting observation networks in small river basins to better resolve mesoscale weather patterns and thus improve the predictive accuracy of streamflow simulation. / May 2017

Discrete-event simulations are increasingly being used to solve problems and to aid in decision making which are proving useful in the manufacturing industry. The main aim for this thesis was to compare the current production line and how implementing changes for a future state as a supporting basis for making the decision. The theoretical framework focused on the Lean philosophy merged with simulation-based methods. The simulation model was build using the collected data. A time study was conducted in order to verify the process and setup times since these were only estimated at the time. Two simulation models were built for the current production line and the future state. The future state was based on the current one since no changes in the process and setup times would be made during the two simulation-model. Experiments were then done to compare the different states, one with batch and the other single-piece flow. The parameters were set on equal terms and the compared values were throughput (TH), lead-time (LT) and work in process (WIP). The conclusion drawn from the results is that the future state would be more efficient.

Composite materials have traditionally been used in high-end aerospace parts and low-end consumer parts. The reason for this separation in markets is the wide gap in technology between pre-preg materials processed in an autoclave and chop strand fiberglass blown into an open mold. Liquid composite molding has emerged as a bridge between inexpensive tooling and large, technical parts. Processes such as vacuum infusion have made it possible to utilize complex layups of reinforcement materials in an open mold style set-up, creating optimal conditions for composites to penetrate many new markets with rapid innovation. Flow simulation for liquid composite molding is often performed to assist in process optimization, and requires the permeability of the reinforcement to be characterized. For infusion under a flexible membrane, such as vacuum infusion, or for simulation of a part with non-uniform thickness, one must test the permeability at various levels of compaction. This process is time consuming and often relies on interpolation or extrapolation around a few experimental permeability measurements. To accelerate the process of permeability characterization, a small number of methodologies have been previously presented in the literature, in which the permeability may be tested at multiple fiber volume contents in a single test. Some of the methods even measure the permeability over a continuous range of thicknesses, thus requiring no later interpolation of permeability values. A novel method is presented here for the rapid measurement of permeability over a continuous range of fiber volume content, in a single unidirectional vacuum infusion flow experiment. The thickness gradient across the vacuum bag, as well as the fluid pressure at several locations in the mold, were concurrently measured to calculate the fabric compressibility. An analytical flow model, which accounts for the compressibility, is then used by iterating the fitting constant in a permeability model until the predicted flow front progression matches empirical measurement. The method is demonstrated here for two reinforcement materials: 1) a fiberglass unbalanced weave and 2) a carbon bi-ax non-crimped fabric. The standard deviation of calculated permeabilities across the multiple infusion experiments for each material and flow orientation ranged from 12.8% to 29.7%. Validation of these results was performed by comparing the resulting permeability with multiple non-continuous permeability measurement methods.

<p>The overall aim of this project is to study the heat transfer inside the reaction chamber of the Hot-Wire Chemical Vapor Deposition (HWCVD) system with a commercial software package FLUENT6.3 / it is one of the most popular Computational Fluid Dynamics solvers for complex flows ranging from incompressible to mildly compressible to even highly compressible flows. The wealth of physical models in FLUENT allows us to accurately predict laminar and turbulent flows, various modes of heat transfer, chemical reactions, multiphase flows and other phenomena with complete mesh flexibility and solution-based mesh adaptation. In this study the 3-D HWCVD geometry was measured and created in GAMBIT which then generates a mesh model of the reaction chamber for the calculation in FLUENT. The gas flow in this study was characterized as the steady and incompressible fluid flow due to the small Mach number and assumptions made to simplify the complexity of the physical geometry. This thesis illustrates the setups and solutions of the 3-D geometry and the chemically reacting laminar and turbulent gas flow, wall surface reaction and heat transfer in the HWCVD deposition chamber.</p>