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- All Subjects: Mechanical Engineering
- All Subjects: Turbulence
- Member of: ASU Electronic Theses and Dissertations
Description
Climate change has been one of the major issues of global economic and social concerns in the past decade. To quantitatively predict global climate change, the Intergovernmental Panel on Climate Change (IPCC) of the United Nations have organized a multi-national effort to use global atmosphere-ocean models to project anthropogenically induced climate changes in the 21st century. The computer simulations performed with those models and archived by the Coupled Model Intercomparison Project - Phase 5 (CMIP5) form the most comprehensive quantitative basis for the prediction of global environmental changes on decadal-to-centennial time scales. While the CMIP5 archives have been widely used for policy making, the inherent biases in the models have not been systematically examined. The main objective of this study is to validate the CMIP5 simulations of the 20th century climate with observations to quantify the biases and uncertainties in state-of-the-art climate models. Specifically, this work focuses on three major features in the atmosphere: the jet streams over the North Pacific and Atlantic Oceans and the low level jet (LLJ) stream over central North America which affects the weather in the United States, and the near-surface wind field over North America which is relevant to energy applications. The errors in the model simulations of those features are systematically quantified and the uncertainties in future predictions are assessed for stakeholders to use in climate applications. Additional atmospheric model simulations are performed to determine the sources of the errors in climate models. The results reject a popular idea that the errors in the sea surface temperature due to an inaccurate ocean circulation contributes to the errors in major atmospheric jet streams.
ContributorsKulkarni, Sujay (Author) / Huang, Huei-Ping (Thesis advisor) / Calhoun, Ronald (Committee member) / Peet, Yulia (Committee member) / Arizona State University (Publisher)
Created2014
A level set approach for denoising and adaptively smoothing complex geometry stereolithography files
Description
Stereolithography files (STL) are widely used in diverse fields as a means of describing complex geometries through surface triangulations. The resulting stereolithography output is a result of either experimental measurements, or computer-aided design. Often times stereolithography outputs from experimental means are prone to noise, surface irregularities and holes in an otherwise closed surface.
A general method for denoising and adaptively smoothing these dirty stereolithography files is proposed. Unlike existing means, this approach aims to smoothen the dirty surface representation by utilizing the well established levelset method. The level of smoothing and denoising can be set depending on a per-requirement basis by means of input parameters. Once the surface representation is smoothened as desired, it can be extracted as a standard levelset scalar isosurface.
The approach presented in this thesis is also coupled to a fully unstructured Cartesian mesh generation library with built-in localized adaptive mesh refinement (AMR) capabilities, thereby ensuring lower computational cost while also providing sufficient resolution. Future work will focus on implementing tetrahedral cuts to the base hexahedral mesh structure in order to extract a fully unstructured hexahedra-dominant mesh describing the STL geometry, which can be used for fluid flow simulations.
A general method for denoising and adaptively smoothing these dirty stereolithography files is proposed. Unlike existing means, this approach aims to smoothen the dirty surface representation by utilizing the well established levelset method. The level of smoothing and denoising can be set depending on a per-requirement basis by means of input parameters. Once the surface representation is smoothened as desired, it can be extracted as a standard levelset scalar isosurface.
The approach presented in this thesis is also coupled to a fully unstructured Cartesian mesh generation library with built-in localized adaptive mesh refinement (AMR) capabilities, thereby ensuring lower computational cost while also providing sufficient resolution. Future work will focus on implementing tetrahedral cuts to the base hexahedral mesh structure in order to extract a fully unstructured hexahedra-dominant mesh describing the STL geometry, which can be used for fluid flow simulations.
ContributorsKannan, Karthik (Author) / Herrmann, Marcus (Thesis advisor) / Peet, Yulia (Committee member) / Frakes, David (Committee member) / Arizona State University (Publisher)
Created2014
Description
With a ground-based Doppler lidar on the upwind side of a wind farm in the Tehachapi Pass of California, radial wind velocity measurements were collected for repeating sector sweeps, scanning up to 10 kilometers away. This region consisted of complex terrain, with the scans made between mountains. The dataset was utilized for techniques being studied for short-term forecasting of wind power by correlating changes in energy content and of turbulence intensity by tracking spatial variance, in the wind ahead of a wind farm. A ramp event was also captured and its propagation was tracked.
Orthogonal horizontal wind vectors were retrieved from the radial velocity using a sector Velocity Azimuth Display method. Streamlines were plotted to determine the potential sites for a correlation of upstream wind speed with wind speed at downstream locations near the wind farm. A "virtual wind turbine" was "placed" in locations along the streamline by using the time-series velocity data at the location as the input to a modeled wind turbine, to determine the extractable energy content at that location. The relationship between this time-dependent energy content upstream and near the wind farm was studied. By correlating the energy content with each upstream location based on a time shift estimated according to advection at the mean wind speed, several fits were evaluated. A prediction of the downstream energy content was produced by shifting the power output in time and applying the best-fit function. This method made predictions of the power near the wind farm several minutes in advance. Predictions were also made up to an hour in advance for a large ramp event. The Magnitude Absolute Error and Standard Deviation are presented for the predictions based on each selected upstream location.
Orthogonal horizontal wind vectors were retrieved from the radial velocity using a sector Velocity Azimuth Display method. Streamlines were plotted to determine the potential sites for a correlation of upstream wind speed with wind speed at downstream locations near the wind farm. A "virtual wind turbine" was "placed" in locations along the streamline by using the time-series velocity data at the location as the input to a modeled wind turbine, to determine the extractable energy content at that location. The relationship between this time-dependent energy content upstream and near the wind farm was studied. By correlating the energy content with each upstream location based on a time shift estimated according to advection at the mean wind speed, several fits were evaluated. A prediction of the downstream energy content was produced by shifting the power output in time and applying the best-fit function. This method made predictions of the power near the wind farm several minutes in advance. Predictions were also made up to an hour in advance for a large ramp event. The Magnitude Absolute Error and Standard Deviation are presented for the predictions based on each selected upstream location.
ContributorsMagerman, Beth (Author) / Calhoun, Ronald (Thesis advisor) / Peet, Yulia (Committee member) / Huang, Huei-Ping (Committee member) / Krishnamurthy, Raghavendra (Committee member) / Arizona State University (Publisher)
Created2014
Description
Lidar has demonstrated its utility in meteorological studies, wind resource assessment, and wind farm control. More recently, lidar has gained widespread attention for autonomous vehicles.
The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.
The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018).
The first part of the dissertation begins with an application of a coherent Doppler lidar to wind gust characterization for wind farm control. This application focuses on wind gusts on a scale from 100 m to 1000 m. A detecting and tracking algorithm is proposed to extract gusts from a wind field and track their movement. The algorithm was implemented for a three-hour, two-dimensional wind field retrieved from the measurements of a coherent Doppler lidar. The Gaussian distribution of the gust spanwise deviation from the streamline was demonstrated. Size dependency of gust deviations is discussed. A prediction model estimating the impact of gusts with respect to arrival time and the probability of arrival locations is introduced. The prediction model was applied to a virtual wind turbine array, and estimates are given for which wind turbines would be impacted.
The second part of this dissertation describes a Time-of-Flight lidar simulation. The lidar simulation includes a laser source module, a propagation module, a receiver module, and a timing module. A two-dimensional pulse model is introduced in the laser source module. The sampling rate for the pulse model is explored. The propagation module takes accounts of beam divergence, target characteristics, atmosphere, and optics. The receiver module contains models of noise and analog filters in a lidar receiver. The effect of analog filters on the signal behavior was investigated. The timing module includes a Time-to-Digital Converter (TDC) module and an Analog-to-Digital converter (ADC) module. In the TDC module, several walk-error compensation methods for leading-edge detection and multiple timing algorithms were modeled and tested on simulated signals. In the ADC module, a benchmark (BM) timing algorithm is proposed. A Neyman-Pearson (NP) detector was implemented in the time domain and frequency domain (fast Fourier transform (FFT) approach). The FFT approach with frequency-domain zero-paddings improves the timing resolution. The BM algorithm was tested on simulated signals, and the NP detector was evaluated on both simulated signals and measurements from a prototype lidar (Bhaskaran, 2018).
ContributorsZhou, Kai (Author) / Calhoun, Ronald (Thesis advisor) / Chen, Kangping (Committee member) / Tang, Wenbo (Committee member) / Peet, Yulia (Committee member) / Krishnamurthy, Raghavendra (Committee member) / Arizona State University (Publisher)
Created2019
Description
This work helps to explain the drag reduction mechanisms at low and moderate turbulent Reynolds numbers in pipe flows. Through direct numerical simulation, the effects of wall oscillations are observed on the turbulence in both the near wall and the bulk region. Analysis of the average Reynolds Stresses at various phases of the flow is provided along with probability density functions of the fluctuating components of velocity and vorticity. The flow is also visualized to observe, qualitatively, changes in the total and fluctuating field of velocity and vorticity. Linear Stochastic Estimation is used to create a conditional eddy (associated with stress production) in the flow and visualize the effects of transverse wall oscillations on hairpin growth, auto-generation and structure.
ContributorsCoxe, Daniel (Author) / Peet, Yulia (Thesis advisor) / Adrian, Ronald (Thesis advisor) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2019
Description
Autonomic closure is a new general methodology for subgrid closures in large eddy simulations that circumvents the need to specify fixed closure models and instead allows a fully- adaptive self-optimizing closure. The closure is autonomic in the sense that the simulation itself determines the optimal relation at each point and time between any subgrid term and the variables in the simulation, through the solution of a local system identification problem. It is based on highly generalized representations of subgrid terms having degrees of freedom that are determined dynamically at each point and time in the simulation. This can be regarded as a very high-dimensional generalization of the dynamic approach used with some traditional prescribed closure models, or as a type of “data-driven” turbulence closure in which machine- learning methods are used with internal training data obtained at a test-filter scale at each point and time in the simulation to discover the local closure representation.
In this study, a priori tests were performed to develop accurate and efficient implementations of autonomic closure based on particular generalized representations and parameters associated with the local system identification of the turbulence state. These included the relative number of training points and bounding box size, which impact computational cost and generalizability of coefficients in the representation from the test scale to the LES scale. The focus was on studying impacts of these factors on the resulting accuracy and efficiency of autonomic closure for the subgrid stress. Particular attention was paid to the associated subgrid production field, including its structural features in which large forward and backward energy transfer are concentrated.
More than five orders of magnitude reduction in computational cost of autonomic closure was achieved in this study with essentially no loss of accuracy, primarily by using efficient frame-invariant forms for generalized representations that greatly reduce the number of degrees of freedom. The recommended form is a 28-coefficient representation that provides subgrid stress and production fields that are far more accurate in terms of structure and statistics than are traditional prescribed closure models.
In this study, a priori tests were performed to develop accurate and efficient implementations of autonomic closure based on particular generalized representations and parameters associated with the local system identification of the turbulence state. These included the relative number of training points and bounding box size, which impact computational cost and generalizability of coefficients in the representation from the test scale to the LES scale. The focus was on studying impacts of these factors on the resulting accuracy and efficiency of autonomic closure for the subgrid stress. Particular attention was paid to the associated subgrid production field, including its structural features in which large forward and backward energy transfer are concentrated.
More than five orders of magnitude reduction in computational cost of autonomic closure was achieved in this study with essentially no loss of accuracy, primarily by using efficient frame-invariant forms for generalized representations that greatly reduce the number of degrees of freedom. The recommended form is a 28-coefficient representation that provides subgrid stress and production fields that are far more accurate in terms of structure and statistics than are traditional prescribed closure models.
ContributorsKshitij, Abhinav (Author) / Dahm, Werner J.A. (Thesis advisor) / Herrmann, Marcus (Committee member) / Hamlington, Peter E (Committee member) / Peet, Yulia (Committee member) / Kim, Jeonglae (Committee member) / Arizona State University (Publisher)
Created2019
Description
The goal of this paper was to do an analysis of two-dimensional unsplit mass and momentum conserving Finite Volume Methods for Advection for Volume of Fluid Fields with interfaces and validating their rates of convergence. Specifically three unsplit transport methods and one split transport method were amalgamated individually with four Piece-wise Linear Reconstruction Schemes (PLIC) i.e. Unsplit Eulerian Advection (UEA) by Owkes and Desjardins (2014), Unsplit Lagrangian Advection (ULA) by Yang et al. (2010), Split Lagrangian Advection (SLA) by Scardovelli and Zaleski (2003) and Unsplit Averaged Eulerian-Lagrangian Advection (UAELA) with two Finite Difference Methods by Parker and Youngs (1992) and two Error Minimization Methods by Pilliod Jr and Puckett (2004). The observed order of accuracy was first order in all cases except when unsplit methods and error minimization methods were used consecutively in each iteration, which resulted in second-order accuracy on the shape error convergence. The Averaged Unsplit Eulerian-Lagrangian Advection (AUELA) did produce first-order accuracy but that was due to a temporal error in the numerical setup. The main unsplit methods, Unsplit Eulerian Advection (UEA) and Unsplit Lagrangian Advection (ULA), preserve mass and momentum and require geometric clipping to solve two-phase fluid flows. The Unsplit Lagrangian Advection (ULA) can allow for small divergence in the velocity field perhaps saving time on the iterative solver of the variable coefficient Poisson System.
ContributorsAnsari, Adil (M.S.) (Author) / Herrmann, Marcus (Thesis advisor) / Peet, Yulia (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2019
Numerical simulation of environmental flow over urban landscape for applications to renewable energy
Description
Development of renewable energy solutions has become a major interest among environmental organizations and governments around the world due to an increase in energy consumption and global warming. One fast growing renewable energy solution is the application of wind energy in cities. To qualitative and quantitative predict wind turbine performance in urban areas, CFD simulation is performed on real-life urban geometry and wind velocity profiles are evaluated. Two geometries in Arizona is selected in this thesis to demonstrate the influence of building heights; one of the simulation models, ASU campus, is relatively low rise and without significant tall buildings; the other model, the downtown phoenix model, are high-rise and with greater building height difference. The content of this thesis focuses on using RANS computational fluid dynamics approach to simulate wind acceleration phenomenon in two complex geometries, ASU campus and Phoenix downtown model. Additionally, acceleration ratio and locations are predicted, the results are then used to calculate the best location for small wind turbine installments.
ContributorsYing, Xiaoyan (Author) / Huang, Huei-Ping (Thesis advisor) / Peet, Yulia (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2015
Description
A numerical study of chemotaxis in 3D turbulence is presented here. Direct Numerical
Simulation were used to calculate the nutrient uptake for both motile and non-motile bacterial
species and by applying the dynamical systems theory the effect of flow topology on the
variability of chemotaxis is analyzed. It is done by injecting a highly localized patch of nutrient
in the turbulent flow, and analyzing the evolution of reaction associated with the observed
high and low stretching regions. The Gaussian nutrient patch is released at different locations
and the corresponding nutrient uptake is obtained. The variable stretching characteristics of
the flow is depicted by Lagrangian Coherent Structures and the roles they play in affecting the
uptake are analyzed. The Lagrangian Coherent Structures are quantified by the Finite Time
Lyapunov Exponents which is a measure of the average stretching experienced by the flow in
finite time. It is found that in high stretching regions, the motile bacteria are attracted to the
nutrient patch very quickly, but also dispersed quickly; whereas in low stretching regions the
bacteria respond slower towards the nutrient patch. However the total uptake is intricately
determined by stretching history. These reaction characteristics are reflected in the several
realizations of simulations. This helps in understanding turbulence intensity and how it affects
the uptake of the nutrient.
Simulation were used to calculate the nutrient uptake for both motile and non-motile bacterial
species and by applying the dynamical systems theory the effect of flow topology on the
variability of chemotaxis is analyzed. It is done by injecting a highly localized patch of nutrient
in the turbulent flow, and analyzing the evolution of reaction associated with the observed
high and low stretching regions. The Gaussian nutrient patch is released at different locations
and the corresponding nutrient uptake is obtained. The variable stretching characteristics of
the flow is depicted by Lagrangian Coherent Structures and the roles they play in affecting the
uptake are analyzed. The Lagrangian Coherent Structures are quantified by the Finite Time
Lyapunov Exponents which is a measure of the average stretching experienced by the flow in
finite time. It is found that in high stretching regions, the motile bacteria are attracted to the
nutrient patch very quickly, but also dispersed quickly; whereas in low stretching regions the
bacteria respond slower towards the nutrient patch. However the total uptake is intricately
determined by stretching history. These reaction characteristics are reflected in the several
realizations of simulations. This helps in understanding turbulence intensity and how it affects
the uptake of the nutrient.
ContributorsGeorge, Jino (Author) / Tang, Wenbo (Thesis advisor) / Peet, Yulia (Thesis advisor) / Calhoun, Ronald (Committee member) / Arizona State University (Publisher)
Created2017
Description
The effect of reduced frequency on dynamic stall behavior of a pitching NACA0012 airfoil in a turbulent wake using Direct Numerical Simulations is presented in the current study. Upstream turbulence with dynamically oscillating blades and airfoils is associated with ambient flow unsteadiness and is encountered in many operating conditions. Wake turbulence, a more realistic scenario for airfoils in operation, is generated using a small solid cylinder placed upstream, the vortices shed from which interact with the pitching airfoil affecting dynamic stall behavior.
A recently developed moving overlapping grid approach is used using a high-order Spectral Element Method (SEM) for spatial discretization combined with a dynamic time-stepping procedure allowing for up to third order temporal discretization. Two cases of reduced frequency (k = 0:16 and 0:25) for airfoil oscillation are investigated and the change in dynamic stall behavior with change in reduced frequency is studied and documented using flow-fields and aerodynamic coefficients (Drag, Lift and Pitching Moment) with a focus on understanding vortex system dynamics (including formation of secondary vortices) for different reduced frequencies and it’s affect on airfoil aerodynamic characteristics and fatigue life. Transition of the flow over the surface of an airfoil for both undisturbed and disturbed flow cases will also be discussed using Pressure coefficient and Skin Friction coefficient data for a given cycle combined with a wavelet analysis using Morse wavelets in MATLAB.
A recently developed moving overlapping grid approach is used using a high-order Spectral Element Method (SEM) for spatial discretization combined with a dynamic time-stepping procedure allowing for up to third order temporal discretization. Two cases of reduced frequency (k = 0:16 and 0:25) for airfoil oscillation are investigated and the change in dynamic stall behavior with change in reduced frequency is studied and documented using flow-fields and aerodynamic coefficients (Drag, Lift and Pitching Moment) with a focus on understanding vortex system dynamics (including formation of secondary vortices) for different reduced frequencies and it’s affect on airfoil aerodynamic characteristics and fatigue life. Transition of the flow over the surface of an airfoil for both undisturbed and disturbed flow cases will also be discussed using Pressure coefficient and Skin Friction coefficient data for a given cycle combined with a wavelet analysis using Morse wavelets in MATLAB.
ContributorsGandhi, Anurag (Author) / Peet, Yulia (Thesis advisor) / Huang, Huei-Ping (Committee member) / Herrmann, Marcus (Committee member) / Arizona State University (Publisher)
Created2017