In most of the engineering and industrial flow applications, one encounters fluid-structure interaction. This interaction can lead to some undesirable forces acting on the structure, causing its damage or fatigue. The phenomenon, being complex in nature, requires thorough understanding of the flow physics. Analyzing canonical flows, such as the flow past a cylinder, provides fundamental concepts governing the fluid behavior. Despite a simpler geometry, studying such flows are a building block in an effort to comprehend, model, and control complicated flows. For the flow past a circular cylinder, we examine the phenomenon of vortex shedding observed in many bluff body wakes. We develop a parallel computational fluid dynamics (CFD) code to solve the incompressible Navier-Stokes equations on curvilinear coordinates to analyze vortex shedding. The algorithm is implemented on a distributed-memory, message-passing parallel computer, and a domain decomposition technique is employed to partition the grid into various processors. We validate and verify the numerical results with existing experimental and numerical studies. We analyse the performance of the parallel CFD solver by computing the speed-up and efficiency of the solver. We also show that the algorithm is scalable and can be efficiently employed to study other engineering problems requiring larger grid sizes and computational domains. Various other features of the solver, such as the turbulence model, moving boundary techniques, shear, and other canonical flows are also presented. Direct numerical simulations (DNS) are performed to simulate the flow past a circular cylinder to compute the velocity and pressure fields. Based on the flow realizations of the DNS data, we use the proper orthogonal decomposition (POD) tool to determine the minimum degrees of freedom (or modes) required to represent the flow field. For the current nonlinear problem, the dominant POD modes are used in a Galerkin procedure to project the Navier-Stokes equations onto a low-dimensional space, thereby reducing the distributed-parameter problem into a finite-dimensional nonlinear dynamical system in time. We use long-time integration of the reduced-order model to calculate periodic solutions and alternatively use a shooting technique to home on the system limit cycles. We obtain the pressure-Poisson equation by taking the divergence of the Navier-Stokes equation and then project it onto the pressure POD modes. Then, we decompose the pressure into lift and drag components and compare the results with the CFD results. To reduce the fluctuating forces on the structure, we implement full-state feedback control on the low-dimensional model with suction applied aft of the separation point. The control algorithm is successfully simulated using the CFD code and suppression of vortex-shedding is achieved. / Ph. D.

No description available.

This thesis uses Proper Orthogonal Decomposition to model parametric and coupled systems. First, Proper Orthogonal Decomposition and its properties are introduced as well as how to numerically compute the decomposition. Next, a test case was used to show how well POD can be used to simulate and control a system. Finally, techniques for modeling a parametric system over a given range and a coupled system split into subdomains were explored, as well as numerical results. / Master of Science

This thesis proposes a large eddy simulation reduced order model (LES-ROM) framework for the numerical simulation of realistic flows. In this LES-ROM framework, the proper orthogonal decomposition (POD) is used to define the ROM basis and a POD differential filter is used to define the large ROM structures. An approximate deconvolution (AD) approach is used to solve the ROM closure problem and develop a new AD-ROM. This AD-ROM is tested in the numerical simulation of the one-dimensional Burgers equation with a small diffusion coefficient ( ν= 10⁻³). / Master of Science

Proper Orthogonal Decomposition (POD), combined with the Method of Snapshots and Galerkin projection, is a popular method for the model order reduction of nonlinear PDEs. The POD requires the left singular vectors from the singular value decomposition (SVD) of an n-by-m "snapshot matrix" S, each column of which represents the computed state of the system at a given time. However, the direct computation of this decomposition can be computationally expensive, particularly for snapshot matrices that are too large to fit in memory. Hierarchical Approximate POD (HAPOD) (Himpe 2016) is a recent method for the approximate truncated SVD that requires only a single pass over S, is easily parallelizable, and can be computationally cheaper than direct SVD, all while guaranteeing the requested accuracy for the resulting basis. This method processes the columns of S in blocks based on a predefined rooted tree of processors, concatenating the outputs from each stage to form the inputs for the next. However, depending on the selected parameter values and the properties of S, the performance of HAPOD may be no better than that of direct SVD. In this work, we numerically explore the parameter values and snapshot matrix properties for which HAPOD is computationally advantageous over the full SVD and compare its performance to that of a parallelized incremental SVD method (Brand 2002, Brand 2003, and Arrighi2015). In particular, in addition to the two major processor tree structures detailed in the initial publication of HAPOD (Himpe2016), we explore the viability of a new structure designed with an MPI implementation in mind. / Master of Science

Rotordynamic instability problems in turbomachinery have become more important in recent years due to rotordynamic components with higher speeds and higher power densities. These features typically lead to increased instability risk in rotor dynamic components as fluids-structure interactions take place. In addition, critical damage of rotordynamic components can result from high level vibrations of supporting bearing system, where the reduced rotor speed can lead to system operating near the rotor critical speed. Therefore, increased accuracy in modeling of rotordynamic components is required to predict the potential instability issues in high performance rotordynamic design. The instability issue may potentially be eliminated in design stage by varying the characteristics of the unstable components. One such turbomachinery component is the annular pressure seal. The annular pressure seals are specifically designed to prevent the fluid leakage from high pressure stage to low pressure stage in turbomachinery. Typical annular pressure seals have two different flow regions, an annular jet-flow region between the rotor and stator, and cylindrical or circumferential indentions on the stator/rotor surface that serve as cavities where flow recirculation occurs. As the working fluid enters the cavities and recirculates, the kinetic energy is reduced, resulting in a reduction of leakage flow. The current challenge is to model with higher precision the interaction between the rotordynamic components and the working fluid. In this dissertation, this challenge was overcome by developing a hybrid Bulk Flow/CFD method to compute rotordynamic responses for the annular pressure seals. In addition, design of experiments studies were performed to relate the surface patterning with the resulting rotordynamic response for the annular pressure seals, in which several different geometry specifications were investigated. This study on annular pressure seal design generated regression models for rotordynamic coefficients that can be used as optimization guidelines. Research topics related to the annular pressure seals were presented in this dissertation as well. The reduced order model of both hole-pattern seals and labyrinth seals were investigated. The results showed that the flow field representing the flow dynamics in annular pressure seals can be expressed as a combination of first three proper orthogonal decomposition modes. In addition, supercritical state of carbon dioxide (sCO2) process fluid was examined as the working fluid in a preliminary study to better understand the effects on annular pressure seals. The results showed that the performance and stability in the annular pressure seals using sCO2 as process fluid can both be improved. / Doctor of Philosophy / This dissertation focused on understanding the correlations between surface patterning and rotordynamic responses in the annular pressure seals. The annular pressure seals are a specific type of rotordynamic component that was designed to prevent the fluid leakage from high pressure stage to low pressure stage in turbomachinery. As the working fluid enters the cavities and recirculates, the kinetic energy is reduced, resulting in a reduction of leakage flow through the annular pressure seals. Rotordynamic instability becomes an issue that may be related to the annular pressure seals in some cases. In recent years, rotordynamic components with higher rotor speeds and higher power densities are commonly used in industrial applications. These features could lead to increased instability risk in rotor-bearing systems as fluids-structure interactions take place. Therefore, high precision modeling of the rotodynamic components is required to predict the instability issues in high performance rotordynamic design. The instability issue may potentially be eliminated in design stage by varying the characteristics of the potentially unstable components. In this study, the surface patterning and rotordynamic responses were investigated for several different annular pressure seal models with a hybrid Bulk Flow/Computational Fluid Dynamics method. This dissertation provides for the first time regression models for rotordynamic coefficients that can be used as optimization guidelines. Research topics related to the annular pressure seals were presented in this dissertation as well. The reduced order model of both hole-pattern seals and labyrinth seals were investigated. The results showed that the flow field representing the flow dynamics in annular pressure seals can be expressed as a combination of first three proper orthogonal decomposition modes. In addition, supercritical state of carbon dioxide (sCO2) process fluid was examined to better understand the effects of working fluid on annular pressure seals. The results showed that the performance and stability in the annular pressure seals using sCO2 as process fluid can both be improved.

We investigate the effect of spatial filtering on the recently proposed data-driven correction reduced order model (DDC-ROM). We compare two filters: the ROM projection, which was originally used to develop the DDC-ROM, and the ROM differential filter, which uses a Helmholtz operator to attenuate the small scales in the input signal. We focus on the following questions: ``Do filtering and differentiation with respect to space variable commute, when filtering is applied to the diffusion term?'' or in other words ``Do we have commutation error (CE) in the diffusion term?" and ``If so, is the commutation error data-driven correction ROM (CE-DDC-ROM) more accurate than the original DDC-ROM?'' If the CE exists, the DDC-ROM has two different correction terms: one comes from the diffusion term and the other from the nonlinear convection term. We investigate the DDC-ROM and the CE-DDC-ROM equipped with the two ROM spatial filters in the numerical simulation of the Burgers equation with different diffusion coefficients and two different initial conditions (smooth and non-smooth). / M.S. / We propose reduced order models (ROMs) for an efficient and relatively accurate numerical simulation of nonlinear systems. We use the ROM projection and the ROM differential filters to construct a novel data-driven correction ROM (DDC-ROM). We show that the ROM spatial filtering and differentiation do not commute for the diffusion operator. Furthermore, we show that the resulting commutation error has an important effect on the ROM, especially for low viscosity values. As a mathematical model for our numerical study, we use the one-dimensional Burgers equations with smooth and non-smooth initial conditions.

O estudo da dinâmica estrutural de risers oceânicos apresenta instigantes desafios aos pesquisadores da área da engenharia de estruturas, uma vez que os meios tradicionais de análises dinâmicas lineares nem sempre se ajustam às suas complexas particularidades. No atual estágio do desenvolvimento científico da área de engenharia de estruturas, a aplicação de técnicas de análise dinâmica não linear, dentro de determinadas hipóteses, mostra-se como uma das alternativas possíveis e viáveis à tradicional análise dinâmica linear. Com vistas a uma nova abordagem do problema, o presente trabalho adota uma metodologia de análise não linear dinâmica de risers oceânicos em configuração de lançamento de catenária, conjugada a uma técnica de processamento de Modelos de Ordem Reduzida para o estudo dos fenômenos dinâmicos manifestados por risers. Trata-se de um método de modelagem local, restrito à região de contato unilateral do riser com o solo, considerado este último um meio viscoelástico. Os resultados da aplicação desta metodologia são demonstrados nos estudos de caso apresentados com comparações com modelos numéricos (Método dos Elementos Finitos) e modelos físicos. / The dynamic study of offshore risers still demands large efforts from structural engineering researchers, since these systems may behave in a way that is not well modeled and understood using simply linear dynamic theories. Nevertheless, the current development stage of non linear dynamic theories gives hope that their use for the analyses of such systems can be of great value, even though, this must be carefully done specially by the analyst. The present work refers to a non linear dynamic methodology application to offshore risers, particularly steel catenary risers, by a technique known as reduced-order modeling, in the study of dynamic phenomena that these structures may present. The model is local, which means that it represents the touch-down zone of the riser-soil system. The soil modeling was presumed to be viscoelastic. The results obtained in case studies are compared with those from numerical (Finite Element Method) and small scale physical models.

The use of Computational Fluid Dynamics (CFD) tools is crucial for the development of novel and cost-effective combustion technologies and the minimization of environmental concerns at industrial scale. CFD simulations facilitate scaling-up procedures that otherwise would be complicated by strong interactions between reaction kinetics, turbulence and heat transfer. CFD calculations can be applied directly at the industrial scale of interest, thus avoiding scaling-up from lab-scale experiments. However, this advantage can only be obtained if CFD tools are quantitatively predictive and trusted as so. Despite the improvements in the computational capability, the implementation of detailed physical and chemical models in CFD simulations can still be prohibitive for real combustors, which require large computational grids and therefore significant computational efforts. Advanced simulation approaches like Large Eddy Simulation (LES) and Direct Numerical Simulation (DNS) guarantee higher fidelity in computational modeling of combustion at, unfortunately, increased computational cost. However, with adequate, reduced, and cost-effective modeling of physical phenomena, such as chemical kinetics and turbulence-chemistry interactions, and state of the art computing, LES will be the tool of choice to describe combustion processes at industrial scale accurately. Therefore, the development of reduced physics and chemistry models with quantified model-form uncertainty is needed to overcome the challenges of performing LES of industrial systems. Reduced-order models must reproduce the main features of the corresponding detailed models. They feature predictivity and capability of bridging scales when validated against a broad range of experiments and targeted by Validation and Uncertainty Quantification (V/UQ) procedures. In this work, V/UQ approaches are applied for reduced-order modeling of pulverized coal devolatilization and subsequent char oxidation, and furthermore for modeling NOx emissions in combustion systems.For coal devolatilization, a benchmark of the Single First-Order Reaction (SFOR) model was performed concerning the accuracy of the prediction of volatile yield. Different SFOR models were implemented and validated against experimental data coming from tests performed in an entrained flow reactor at oxy-conditions, to shed light on their drawbacks and benefits. SFOR models were chosen because of their simplicity: they can be easily included in CFD codes and are very appealing in the perspective of LES of pulverized coal combustion burners. The calibration of kinetic parameters was required to allow the investigated SFOR model to be predictive and reliable for different heating rates, hold temperatures and coal types. A comparison of several calibration approaches was performed to determine if one-step models can be adaptive and able to bridge scales, without losing accuracy, and to select the calibration method to employ for wider ranges of coal rank and operating conditions. The analysis pointed out that the main drawback of the SFOR models is the assumption of a constant ultimate volatile yield, equal to the value from the coal proximate analysis. To overcome this drawback, a yield model, i.e. a simple functional form that relates the ultimate volatile yield to the particle temperature, was proposed. The model depends on two parameters that have a certain degree of uncertainty. The performances of the yield model were assessed using a collaboration of experiments and simulations of a pilot-scale entrained flow reactor. A consistency analysis, based on the Bound-to-Bound Data Collaboration (B2B-DC) approach, and a Bayesian method, based on Gaussian Process Regression (GPR), were employed for the investigation of experiments and simulations. In Bound-to- Bound Data Collaboration the model output, evaluated at specified values of the model parameters, is compared with the experimental data: if the prediction of the model falls within the experimental uncertainty, the corresponding parameter values would be included in the so-called feasible set. The existence of a non-empty feasible set signifies consistency between the experiments and the simulations, i.e. model-data agreement. Consistency was indeed found when a relative error of 19% for all the experimental data was applied. Hence, a feasible set of the two SFOR model parameters was provided. A posterior state of knowledge, indicating potential model forms that could be explored in yield modeling, was obtained by Gaussian Process Regression. The model form evaluated through the consistency analysis is included within the posterior derived from GPR, indicating that it can satisfactorily match the experimental data and provide reliable estimation in almost every range of temperatures. CFD simulations were carried out using the proposed yield model with first-order kinetics, as in the SFOR model. Results showed promising agreement between predicted and experimental conversion for all the investigated cases.Regarding char combustion modeling, the consistency analysis has been applied to validate a reduced-order model and quantify the uncertainty in the prediction of char conversion. The model capability to address heterogeneous reaction between char carbon and O2, CO2 and H2O reagents, mass transport of species in the particle boundary layer, pore diffusion, and internal surface area changes was assessed by comparison with a large number of experiments performed in air and oxy-coal conditions. Different model forms had been considered, with an increasing degree of complexity, until consistency between model outputs and experimental results was reached. Rather than performing forward propagation of the model-form uncertainty on the predictions, the reduction of the parameter uncertainty of a selected model form was pursued and eventually achieved. The resulting 11-dimensional feasible set of model parameters allows the model to predict the experimental data within almost ±10% uncertainty. Due to the high dimensionality of the problem, the employed surrogate models resulted in considerable fitting errors, which led to a spoiled UQ inverse problem. Different strategies were taken to reduce the discrepancy between the surrogate outputs and the corresponding predictions of the simulation model, in the frameworks of constrained optimization and Bayesian inference. Both strategies succeeded in reducing the fitting errors and also resulted in a least-squares estimate for the simulation model. The variety of experimental gas environments ensured the validity of the consistent reduced model for both conventional and oxy-conditions, overcoming the differences in mass transport and kinetics observed in several experimental campaigns.The V/UQ-aided modeling of coal devolatilization and char combustion was done in the framework of the Predictive Science Academic Alliance Program II (PSAAP-II) funded by the US Department of Energy. One of the final goals of PSAAP-II is to develop high-fidelity simulation tools that ensure 5% uncertainty in the incident heat flux predictions inside a 1.2GW Ultra-Super-Critical (USC) coal-fired boiler. The 5% target refers to the expected predictivity of the full-scale simulation without considering the uncertainty in the scenario parameters. The data-driven approaches used in this Thesis helped to improve the predictivity of the investigated models and made them suitable for LES of the 1.2GW USC coal-fired boiler. Moreover, they are suitable for scale-bridging modeling of similar multi-phase processes involved in the conversion of solid renewable sources, such as biomass.In the final part of the Thesis, the sensitivity to finite-rate chemistry combustion models and kinetic mechanisms on the prediction of NO emissions was assessed. Moreover, the forward propagation of the uncertainty in the kinetics of the NNH route (included in the NOx chemistry) on the predictions of NO was investigated to reveal the current state of the art of kinetic modeling of NOx formation. The analysis was carried out on a case where NOx formation comes from various formation routes, both conventional (thermal and prompt) and unconventional ones. To this end, a lab-scale combustion system working in Moderate and Intense Low-oxygen Dilution (MILD) conditions was selected. The results showed considerable sensitivity of the NO emissions to the uncertain kinetic parameters of the rate-limiting reactions of the NNH pathway when a detailed kinetic mechanism is used. The analysis also pointed out that the use of one-step global rate schemes for the NO formation pathways, necessary when a skeletal kinetic mechanism is employed, lacks the required chemical accuracy and dims the importance of the NNH pathway in this combustion regime. An engineering modification of the finite-rate combustion model was proposed to account for the different chemical time scales of the fuel-oxidizer reactions and NOx formation pathways. It showed an equivalent impact on the emissions of NO than the uncertainty in the kinetics of the NNH route. At the cost of introducing a small mass imbalance (of the order of ppm), the adjustment led to improved predictions of NO. The investigation established a possibility for the engineering modeling of NO formation in MILD combustion with a finite-rate chemistry combustion model that can incorporate a detailed mechanism at affordable computational costs. / Doctorat en Sciences de l'ingénieur et technologie / info:eu-repo/semantics/nonPublished

In this paper we describe an approach to generating low-order models of spiral inductors that accurately capture the dependence on both frequency and geometry (width and spacing) parameters. The approach is based on adapting a multiparameter Krylov-subspace based moment matching method to reducing an integral equation for the three dimensional electromagnetic behavior of the spiral inductor. The approach is demonstrated on a typical on-chip rectangular inductor. / Singapore-MIT Alliance (SMA)