Path planning, flow estimation, and dynamic control for underwater vehicles

Underwater vehicles such as robotic fish and long-endurance ocean-sampling platforms operate in challenging fluid environments. This dissertation incorporates models of the fluid environment in the vehicles' guidance, navigation, and control strategies while addressing uncertainties associated with estimates of the environment's state. Coherent flow structures may be on the same spatial scale as the vehicle or substantially larger than the vehicle. This dissertation argues that estimation and control tasks across widely varying spatial scales, from vehicle-scale to long-range, may be addressed using common tools of empirical observability analysis, nonlinear/non-Gaussian estimation, and output-feedback control. As an application in vehicle-scale flow estimation and control, this dissertation details the design, fabrication, and testing of a robotic fish with an artificial lateral-line inspired by the lateral-line flow-sensing organ present in fish. The robotic fish is capable of estimating the flow speed and relative angle of the oncoming flow. Using symmetric and asymmetric sensor configurations, the robot achieves the primitive fish behavior called rheotaxis, which describes a fish's tendency to orient upstream. For long-range flow estimation and control, path planning may be accomplished using observability-based path planning, which evaluates a finite set of candidate control inputs using a measure related to flow-field observability and selects an optimizer over the set. To incorporate prior information, this dissertation derives an augmented observability Gramian using an optimal estimation strategy known as Incremental 4D-Var. Examination of the minimum eigenvalue of an empirical version of this Gramian yields a novel measure for path planning, called the empirical augmented unobservability index. Numerical experiments show that this measure correctly selects the most informative paths given the prior information. As an application in long-range flow estimation and control, this dissertation considers estimation of an idealized pair of ocean eddies by an adaptive Lagrangian sensor (i.e., a platform that uses its position data as measurements of the fluid transport, after accounting for its own control action). The adaptive sampling is accomplished using the empirical augmented unobservability index, which is extended to non-Gaussian posterior densities using an approximate expected-cost calculation. Output feedback recursively improves estimates of the vehicle position and flow-field states

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Self-Evaluation Applied Mathematics 2003-2008 University of Twente

This report contains the self-study for the research assessment of the Department of Applied Mathematics (AM) of the Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS) at the University of Twente (UT). The report provides the information for the Research Assessment Committee for Applied Mathematics, dealing with mathematical sciences at the three universities of technology in the Netherlands. It describes the state of affairs pertaining to the period 1 January 2003 to 31 December 2008

Structural Health Monitoring System for Deepwater Risers with Vortex-Induced Vibration: Nonlinear Modeling, Blind Identification, Fatigue/Damage Estimation and Vibration Control

This study focuses on developing structural health monitoring techniques to detect damage in deepwater risers subjected to vortex-induced vibration (VIV), and studying vibration control strategies to extend the service life of offshore structures. Vibration-based damage detection needs both responses from the undamaged and damaged deepwater risers. Because no experimental data for damaged deepwater risers is available, a model to predict the VIV responses of deepwater risers with given conditions is needed, which is the forward problem. In this study, a new three dimensional (3D) analytical model is proposed considering coupled VIV (in-line and cross-flow) for top-tensioned riser (TTR) with wake oscillators. The model is verified by direct numerical simulations and experimental data. The inverse problem is to detect damage using VIV responses from the analytical models with/without damage, where the change between dynamic properties obtained from riser responses represents damage. The inverse problem is performed in two steps: blind identification and damage detection. For blind identification, a wavelet modified second order blind identification (WMSOBI) method and a complex WMSOBI (CWMSOBI) method are proposed to extract modal properties from output only responses for standing and traveling wave vibration, respectively. Numerical simulations and experiments validate the effectiveness of proposed methods. For damage detection, a novel weighted distribution force change (WDFC) index (for standing wave) and a phase angle change (PAC) index (for traveling wave) are proposed and proven numerically. Experiments confirm that WDFC can accurately locate damage and estimate damage severity. Furthermore, a new fatigue damage estimation method involving WMSOBI, S-N curve and Miner's rule is proposed and proven to be effective using field test data. Vibration control is essential to extend the service life and enhance the safety of offshore structures. Literature review shows that semi-active control devices are potentially a good solution. A novel semi-active control strategy is proposed to tune the damper properties to match the dominant frequency of the structural response in real-time. The effectiveness of proposed strategy in vibration reduction for deepwater risers and offshore floating wind turbines is also validated through numerical studies

Estimation of the Concentration from a Moving Gaseous Source in the Atmosphere Using a Guided Sensing Aerial Vehicle

The estimation of the gas concentration (process-state) associated with a stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gaseous source into the ambient atmosphere is representative of an accidental or deliberate release of chemicals, or a release of gases from biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework presented couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the advection-diffusion equation with variable eddy diffusivities and ambient winds. Cases are considered for a 2D and 3D domain. The state estimator is a modified Luenberger observer with a €�collocated€� filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2D and 3D adaptive, multigrid, multi-step finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation in order to improve accuracy and efficiency. The motion dynamics of the SAV are incorporated into the spatial process and the SAV€™s guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid-refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid-refinement. Extensive numerical experiments serve to demonstrate the effectiveness of the coupled approach

Flow control and sensing using data-driven reduced-order modeling

Transfer operators, such as the Koopman operator, are driving a paradigm shift in how we perform data-driven modeling of fluid flows. Approximations of the Koopman operator provide linear representations even for strongly nonlinear flows, which enables the application of standard linear methods for estimation and control under realistic flow conditions. In the past decade, we have witnessed several breakthroughs in obtaining low-dimensional approximations of the Koopman operators, providing a tractable reduced-order model for complex fluid flows using data from numerical simulations or experiments. In this thesis, we leverage these recent developments in operator-theoretic modeling of fluid flows and provide data-driven solutions to the flow control and sensing problems. The contributions of this thesis can be divided into three parts. In the first part, we introduce a novel method, low-rank Dynamic Mode Decomposition (lrDMD), for data-driven reduced-order modeling of fluid flows. While existing data-driven modeling methods fit an endomorphic linear function on a low-dimensional subspace, lrDMD approximates flow dynamics using a linear map between different subspaces. We show that this approach leads to the design of better reduced-order feedback controllers. We formulate a rank-constrained matrix optimization problem and propose two complementary methods to solve the problem. lrDMD outperforms existing methods in feedback control and optimal actuator placement. In the second part, we present a completely data-driven framework for sensor placement in fluid flows. This framework can be applied in conjunction with any reduced-order modeling technique that constructs a linear model for the flow dynamics. We formulate an optimization problem that minimizes the trace of a data-driven approximation of the estimation error covariance matrix, where the estimates are provided by a Kalman filter. We propose an efficient adjoint-based gradient descent method to solve the optimization problem. We show that sensors placed using our method lead to better performance in important applications, such as flow estimation and control, compared to existing data-driven sensor placement methods. In the third and final part, we propose a new method of interface tracking and reconstruction in multiphase flows using shadowgraphs or back-lit imaging data. First, we show that while traditional modeling methods provide valuable information about the spatio-temporal structure of flow instabilities, they are not able to resolve spatial or temporal discontinuities, such as the liquid-gas interface, in the data. To remedy this, we propose a two-step approach, using data-driven modeling techniques in conjunction with optical flow methods, that preserves sharp interfaces and provides reliable reconstruction and short-time prediction of the flow. We apply our method to an experimental liquid jet with a co-axial air-blast atomizer using back-lit imaging. Our method is able to accurately reconstruct and predict the flow while preserving the sharpness of the liquid-gas interface

A Surrogate Data Assimilation Model for the Estimation of Dynamical System in a Limited Area

We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area. Our model employs a feedforward neural network for online computation, eliminating the need for integrating high-dimensional limited-area models. This approach offers significant computational advantages over traditional DA algorithms. Furthermore, our method avoids the requirement of lateral boundary conditions for the limited-area model in both online and offline computations. The design of our surrogate DA model is built upon a robust theoretical framework that leverages two fundamental concepts: observability and effective region. The concept of observability enables us to quantitatively determine the optimal amount of observation data necessary for accurate DA. Meanwhile, the concept of effective region substantially reduces the computational burden associated with computing observability and generating training data

NASA Tech Briefs, March 2004

Topics covered include: 1) Advanced Signal Conditioners for Data-Acquisition Systems; 2) Downlink Data Multiplexer; 3) Viewing ISS Data in Real Time via the Internet; 4) Autonomous Environment-Monitoring Networks; 5) Readout of DSN Monitor Data; 6) Parallel-Processing Equalizers for Multi-Gbps Communications; 7) AIN-Based Packaging for SiC High-Temperature Electronics; 8) Software for Optimizing Quality Assurance of Other Software; 9) The TechSat 21 Autonomous Sciencecraft Experiment; 10) Software for Analyzing Laminar-to-Turbulent Flow Transitions; 11) Elastomer Filled With Single-Wall Carbon Nanotubes; 12) Modifying Ship Air-Wake Vortices for Aircraft Operations; 13) Strain-Gauge Measurement of Weight of Fluid in a Tank; 14) Advanced Docking System With Magnetic Initial Capture; 15) Blade-Pitch Control for Quieting Tilt-Rotor Aircraft; 16) Solar Array Panels With Dust-Removal Capability; 17) Aligning Arrays of Lenses and Single-Mode Optical Fibers; 18) Automatic Control of Arc Process for Making Carbon Nanotubes; 19) Curved-Focal-Plane Arrays Using Deformed-Membrane Photodetectors; 20) Role of Meteorology in Flights of a Solar-Powered Airplane; 21) Model of Mixing Layer With Multicomponent Evaporating Drops; 22) Solution-Assisted Optical Contacting; 23) Improved Discrete Approximation of Laplacian of Gaussian; 24) Utilizing Expert Knowledge in Estimating Future STS Costs; 25) Study of Rapid-Regression Liquefying Hybrid Rocket Fuels; and 26) More About the Phase-Synchronized Enhancement Method