A FILTER-FORCING TURBULENCE MODEL FOR LARGE EDDY SIMULATION INCORPORATING THE COMPRESSIBLE POOR MAN\u27S NAVIER--STOKES EQUATIONS

A new approach to large-eddy simulation (LES) based on the use of explicit spatial filtering combined with backscatter forcing is presented. The forcing uses a discrete dynamical system (DDS) called the compressible ``poor man\u27s\u27\u27 Navier--Stokes (CPMNS) equations. This DDS is derived from the governing equations and is shown to exhibit good spectral and dynamical properties for use in a turbulence model. An overview and critique of existing turbulence theory and turbulence models is given. A comprehensive theoretical case is presented arguing that traditional LES equations contain unresolved scales in terms generally thought to be resolved, and that this can only be solved with explicit filtering. The CPMNS equations are then incorporated into a simple forcing in the OVERFLOW compressible flow code, and tests are done on homogeneous, isotropic, decaying turbulence, a Mach 3 compression ramp, and a Mach 0.8 open cavity. The numerical results validate the general filter-forcing approach, although they also reveal inadequacies in OVERFLOW and that the current approach is likely too simple to be universally applicable. Two new proposals for constructing better forcing models are presented at the end of the work

Lattice Boltzmann Methods for Partial Differential Equations

Lattice Boltzmann methods provide a robust and highly scalable numerical technique in modern computational fluid dynamics. Besides the discretization procedure, the relaxation principles form the basis of any lattice Boltzmann scheme and render the method a bottom-up approach, which obstructs its development for approximating broad classes of partial differential equations. This work introduces a novel coherent mathematical path to jointly approach the topics of constructability, stability, and limit consistency for lattice Boltzmann methods. A new constructive ansatz for lattice Boltzmann equations is introduced, which highlights the concept of relaxation in a top-down procedure starting at the targeted partial differential equation. Modular convergence proofs are used at each step to identify the key ingredients of relaxation frequencies, equilibria, and moment bases in the ansatz, which determine linear and nonlinear stability as well as consistency orders of relaxation and space-time discretization. For the latter, conventional techniques are employed and extended to determine the impact of the kinetic limit at the very foundation of lattice Boltzmann methods. To computationally analyze nonlinear stability, extensive numerical tests are enabled by combining the intrinsic parallelizability of lattice Boltzmann methods with the platform-agnostic and scalable open-source framework OpenLB. Through upscaling the number and quality of computations, large variations in the parameter spaces of classical benchmark problems are considered for the exploratory indication of methodological insights. Finally, the introduced mathematical and computational techniques are applied for the proposal and analysis of new lattice Boltzmann methods. Based on stabilized relaxation, limit consistent discretizations, and consistent temporal filters, novel numerical schemes are developed for approximating initial value problems and initial boundary value problems as well as coupled systems thereof. In particular, lattice Boltzmann methods are proposed and analyzed for temporal large eddy simulation, for simulating homogenized nonstationary fluid flow through porous media, for binary fluid flow simulations with higher order free energy models, and for the combination with Monte Carlo sampling to approximate statistical solutions of the incompressible Euler equations in three dimensions

Relating Spontaneous Activity and Cognitive States via NeuroDynamic Modeling

Stimulus-free brain dynamics form the basis of current knowledge concerning functional integration and segregation within the human brain. These relationships are typically described in terms of resting-state brain networks—regions which spontaneously coactivate. However, despite the interest in the anatomical mechanisms and biobehavioral correlates of stimulus-free brain dynamics, little is known regarding the relation between spontaneous brain dynamics and task-evoked activity. In particular, no computational framework has been previously proposed to unite spontaneous and task dynamics under a single, data-driven model. Model development in this domain will provide new insight regarding the mechanisms by which exogeneous stimuli and intrinsic neural circuitry interact to shape human cognition. The current work bridges this gap by deriving and validating a new technique, termed Mesoscale Individualized NeuroDynamic (MINDy) modeling, to estimate large-scale neural population models for individual human subjects using resting-state fMRI. A combination of ground-truth simulations and test-retest data are used to demonstrate that the approach is robust to various forms of noise, motion, and data processing choices. The MINDy formalism is then extended to simultaneously estimating neural population models and the neurovascular coupling which gives rise to BOLD fMRI. In doing so, I develop and validate a new optimization framework for simultaneously estimating system states and parameters. Lastly, MINDy models derived from resting-state data are used to predict task-based activity and remove the effects of intrinsic dynamics. Removing the MINDy model predictions from task fMRI, enables separation of exogenously-driven components of activity from their indirect consequences (the model predictions). Results demonstrate that removing the predicted intrinsic dynamics improves detection of event-triggered and sustained responses across four cognitive tasks. Together, these findings validate the MINDy framework and demonstrate that MINDy models predict brain dynamics across contexts. These dynamics contribute to the variance of task-evoked brain activity between subjects. Removing the influence of intrinsic dynamics improves the estimation of task effects

Lagrangian Liouville models of multiphase flows with randomly forced inertial particles

Eulerian-Lagrangian models of particle-laden (multiphase) flows describe fluid flow and particle dynamics in the Eulerian and Lagrangian frameworks respectively. Regardless of whether the flow is turbulent or laminar, the particle dynamics is stochastic because the suspended particles are subjected to random forces. We use a polynomial chaos expansion (PCE), rather than a postulated constitutive law, to capture structural and parametric uncertainties in the particles' forcing. The stochastic particle dynamics is described by a joint probability density function (PDF) of a particle's position and velocity and random coefficients in the PCE. We deploy the method of distributions (MoD) to derive a deterministic (Liouville-type) partial-differential equation for this PDF. We reformulate this PDF equation in a Lagrangian form, obtaining PDF flow maps and tracing events and their probability in the phase space. That is accomplished via a new high-order spectral scheme, which traces, marginalizes and computes moments of the high-dimensional joint PDF and comports with high-order carrier-phase solvers. Our approach has lower computational cost than either high-order Eulerian solvers or Monte Carlo methods, is not subjected to a CFL condition, does not suffer from Gibbs oscillations and does not require (order-reducing) filtering and regularization techniques. These features are demonstrated on several test cases

Robust filtering of linear time invariant systems by means of polynomial Lyapunov functions

Orientadores: Pedro Luis Dias Peres, Ricardo Coração de Leão Fontoura de OliveiraDissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Este trabalho apresenta novas condições na forma de desigualdades matriciais lineares para a síntese de filtros robustos H2 e H¥ de ordem completa, para sistemas incertos, contínuos e discretos no tempo. Os parâmetros incertos invariantes no tempo pertencem a um politopo com vértices conhecidos. Graças à existência de um número maior de variáveis de folga e à utilização de relaxações baseadas em matrizes polinomiais homogêneas, desigualdades matriciais lineares podem ser obtidas das condições propostas para o projeto de filtros robustos, com desempenho superior aos métodos existentes. A superioridade e eficiência do método proposto para o projeto dos filtros robustos são ilustradas por meio de comparações numéricas e exemplos da literaturaAbstract: This work presents new convex optimization procedures for full order robust H2 and H? filter design for continuous and discrete-time uncertain linear systems. The time-invariant uncertain parameters are supposed to belong to a polytope with known vertices. Thanks to the use of a larger number of slack variables and homogeneous polynomial relaxations, linear matrix inequalities for the design of robust filters can be derived from the proposed conditions, outperforming the existingmethods. The superiority and efficiency of the proposed method for robust filter design are illustrated by means of numerical comparisons in benchmark examples from the literatureMestradoAutomaçãoMestre em Engenharia Elétric

Estimation and control of non-linear and hybrid systems with applications to air-to-air guidance

Issued as Progress report, and Final report, Project no. E-21-67

Hierarchical feature extraction from spatiotemporal data for cyber-physical system analytics

With the advent of ubiquitous sensing, robust communication and advanced computation, data-driven modeling is increasingly becoming popular for many engineering problems. Eliminating difficulties of physics-based modeling, avoiding simplifying assumptions and ad hoc empirical models are significant among many advantages of data-driven approaches, especially for large-scale complex systems. While classical statistics and signal processing algorithms have been widely used by the engineering community, advanced machine learning techniques have not been sufficiently explored in this regard. This study summarizes various categories of machine learning tools that have been applied or may be a candidate for addressing engineering problems. While there are increasing number of machine learning algorithms, the main steps involved in applying such techniques to the problems consist in: data collection and pre-processing, feature extraction, model training and inference for decision-making. To support decision-making processes in many applications, hierarchical feature extraction is key. Among various feature extraction principles, recent studies emphasize hierarchical approaches of extracting salient features that is carried out at multiple abstraction levels from data. In this context, the focus of the dissertation is towards developing hierarchical feature extraction algorithms within the framework of machine learning in order to solve challenging cyber-physical problems in various domains such as electromechanical systems and agricultural systems. Furthermore, the feature extraction techniques are described using the spatial, temporal and spatiotemporal data types collected from the systems. The wide applicability of such features in solving some selected real-life domain problems are demonstrated throughout this study

Data based identification and prediction of nonlinear and complex dynamical systems

We thank Dr. R. Yang (formerly at ASU), Dr. R.-Q. Su (formerly at ASU), and Mr. Zhesi Shen for their contributions to a number of original papers on which this Review is partly based. This work was supported by ARO under Grant No. W911NF-14-1-0504. W.-X. Wang was also supported by NSFC under Grants No. 61573064 and No. 61074116, as well as by the Fundamental Research Funds for the Central Universities, Beijing Nova Programme.Peer reviewedPostprin