Decoding complex flow phenomena via Dynamic Mode Decomposition

In this thesis, the viability of the Dynamic Mode Decomposition (DMD) as a technique to analyze and model complex dynamic real-world systems is presented. This method derives, directly from data, computationally efficient reduced-order models (ROMs) which can replace too onerous or unavailable high-fidelity physics-based models. Optimizations and extensions to the standard implementation of the methodology are proposed, investigating diverse case studies related to the decoding of complex flow phenomena. The flexibility of this data-driven technique allows its application to high-fidelity fluid dynamics simulations, as well as time series of real systems observations. The resulting ROMs are tested against two tasks: (i) reduction of the storage requirements of high-fidelity simulations or observations; (ii) interpolation and extrapolation of missing data. The capabilities of DMD can also be exploited to alleviate the cost of onerous studies that require many simulations, such as uncertainty quantification analysis, especially when dealing with complex high-dimensional systems. In this context, a novel approach to address parameter variability issues when modeling systems with space and time-variant response is proposed. Specifically, DMD is merged with another model-reduction technique, namely the Polynomial Chaos Expansion, for uncertainty quantification purposes. Useful guidelines for DMD deployment result from the study, together with the demonstration of its potential to ease diagnosis and scenario analysis when complex flow processes are involved.

Design of wireless sensor networks for fluid dynamic applications

In fluid dynamics research, pressure measurements are of great importance to define the flow field acting on aerodynamic surfaces. In fact the experimental approach is fundamental to avoid the complexity of the mathematical models for predicting the fluid phenomena. It’s important to note that, using in-situ sensor to monitor pressure on large domains with highly unsteady flows, several problems are encountered working with the classical techniques due to the transducer cost, the intrusiveness, the time response and the operating range. An interesting approach for satisfying the previously reported sensor requirements is to implement a sensor network capable of acquiring pressure data on aerodynamic surface using a wireless communication system able to collect the pressure data with the lowest environmental–invasion level possible. In this thesis a wireless sensor network for fluid fields pressure has been designed, built and tested. To develop the system, a capacitive pressure sensor, based on polymeric membrane, and read out circuitry, based on microcontroller, have been designed, built and tested. The wireless communication has been performed using the Zensys Z-WAVE platform, and network and data management have been implemented. Finally, the full embedded system with antenna has been created. As a proof of concept, the monitoring of pressure on the top of the mainsail in a sailboat has been chosen as working example.

Studio dei metodi di valutazione della pericolosità associata ai "debris flow" in ambiente alpino

The present work concerns with the study of debris flows and, in particular, with the related hazard in the Alpine Environment. During the last years several methodologies have been developed to evaluate hazard associated to such a complex phenomenon, whose velocity, impacting force and inappropriate temporal prediction are responsible of the related high hazard level. This research focuses its attention on the depositional phase of debris flows through the application of a numerical model (DFlowz), and on hazard evaluation related to watersheds morphometric, morphological and geological characterization. The main aims are to test the validity of DFlowz simulations and assess sources of errors in order to understand how the empirical uncertainties influence the predictions; on the other side the research concerns with the possibility of performing hazard analysis starting from the identification of susceptible debris flow catchments and definition of their activity level. 25 well documented debris flow events have been back analyzed with the model DFlowz (Berti and Simoni, 2007): derived form the implementation of the empirical relations between event volume and planimetric and cross section inundated areas, the code allows to delineate areas affected by an event by taking into account information about volume, preferential flow path and digital elevation model (DEM) of fan area. The analysis uses an objective methodology for evaluating the accuracy of the prediction and involve the calibration of the model based on factors describing the uncertainty associated to the semi empirical relationships. The general assumptions on which the model is based have been verified although the predictive capabilities are influenced by the uncertainties of the empirical scaling relationships, which have to be necessarily taken into account and depend mostly on errors concerning deposited volume estimation. In addition, in order to test prediction capabilities of physical-based models, some events have been simulated through the use of RAMMS (RApid Mass MovementS). The model, which has been developed by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL) in Birmensdorf and the Swiss Federal Institute for Snow and Avalanche Research (SLF) takes into account a one-phase approach based on Voellmy rheology (Voellmy, 1955; Salm et al., 1990). The input file combines the total volume of the debris flow located in a release area with a mean depth. The model predicts the affected area, the maximum depth and the flow velocity in each cell of the input DTM. Relatively to hazard analysis related to watersheds characterization, the database collected by the Alto Adige Province represents an opportunity to examine debris-flow sediment dynamics at the regional scale and analyze lithologic controls. With the aim of advancing current understandings about debris flow, this study focuses on 82 events in order to characterize the topographic conditions associated with their initiation , transportation and deposition, seasonal patterns of occurrence and examine the role played by bedrock geology on sediment transfer.

Refined Estimation of Earthquake Source Parameters: Methods, Applications and Scaling Relationships

The objective of this work of thesis is the refined estimations of source parameters. To such a purpose we used two different approaches, one in the frequency domain and the other in the time domain. In frequency domain, we analyzed the P- and S-wave displacement spectra to estimate spectral parameters, that is corner frequencies and low frequency spectral amplitudes. We used a parametric modeling approach which is combined with a multi-step, non-linear inversion strategy and includes the correction for attenuation and site effects. The iterative multi-step procedure was applied to about 700 microearthquakes in the moment range 1011-1014 N•m and recorded at the dense, wide-dynamic range, seismic networks operating in Southern Apennines (Italy). The analysis of the source parameters is often complicated when we are not able to model the propagation accurately. In this case the empirical Green function approach is a very useful tool to study the seismic source properties. In fact the Empirical Green Functions (EGFs) consent to represent the contribution of propagation and site effects to signal without using approximate velocity models. An EGF is a recorded three-component set of time-histories of a small earthquake whose source mechanism and propagation path are similar to those of the master event. Thus, in time domain, the deconvolution method of Vallée (2004) was applied to calculate the source time functions (RSTFs) and to accurately estimate source size and rupture velocity. This technique was applied to 1) large event, that is Mw=6.3 2009 L’Aquila mainshock (Central Italy), 2) moderate events, that is cluster of earthquakes of 2009 L’Aquila sequence with moment magnitude ranging between 3 and 5.6, 3) small event, i.e. Mw=2.9 Laviano mainshock (Southern Italy).

CFD modeling of two-phase boiling flows in the slug flow regime with an interface capturing technique

The objective of this thesis was to improve the commercial CFD software Ansys Fluent to obtain a tool able to perform accurate simulations of flow boiling in the slug flow regime. The achievement of a reliable numerical framework allows a better understanding of the bubble and flow dynamics induced by the evaporation and makes possible the prediction of the wall heat transfer trends. In order to save computational time, the flow is modeled with an axisymmetrical formulation. Vapor and liquid phases are treated as incompressible and in laminar flow. By means of a single fluid approach, the flow equations are written as for a single phase flow, but discontinuities at the interface and interfacial effects need to be accounted for and discretized properly. Ansys Fluent provides a Volume Of Fluid technique to advect the interface and to map the discontinuous fluid properties throughout the flow domain. The interfacial effects are dominant in the boiling slug flow and the accuracy of their estimation is fundamental for the reliability of the solver. Self-implemented functions, developed ad-hoc, are introduced within the numerical code to compute the surface tension force and the rates of mass and energy exchange at the interface related to the evaporation. Several validation benchmarks assess the better performances of the improved software. Various adiabatic configurations are simulated in order to test the capability of the numerical framework in modeling actual flows and the comparison with experimental results is very positive. The simulation of a single evaporating bubble underlines the dominant effect on the global heat transfer rate of the local transient heat convection in the liquid after the bubble transit. The simulation of multiple evaporating bubbles flowing in sequence shows that their mutual influence can strongly enhance the heat transfer coefficient, up to twice the single phase flow value.

Temperature Variation Aware Energy Optimization in Heterogeneous MPSoCs

Thermal effects are rapidly gaining importance in nanometer heterogeneous integrated systems. Increased power density, coupled with spatio-temporal variability of chip workload, cause lateral and vertical temperature non-uniformities (variations) in the chip structure. The assumption of an uniform temperature for a large circuit leads to inaccurate determination of key design parameters. To improve design quality, we need precise estimation of temperature at detailed spatial resolution which is very computationally intensive. Consequently, thermal analysis of the designs needs to be done at multiple levels of granularity. To further investigate the flow of chip/package thermal analysis we exploit the Intel Single Chip Cloud Computer (SCC) and propose a methodology for calibration of SCC on-die temperature sensors. We also develop an infrastructure for online monitoring of SCC temperature sensor readings and SCC power consumption. Having the thermal simulation tool in hand, we propose MiMAPT, an approach for analyzing delay, power and temperature in digital integrated circuits. MiMAPT integrates seamlessly into industrial Front-end and Back-end chip design flows. It accounts for temperature non-uniformities and self-heating while performing analysis. Furthermore, we extend the temperature variation aware analysis of designs to 3D MPSoCs with Wide-I/O DRAM. We improve the DRAM refresh power by considering the lateral and vertical temperature variations in the 3D structure and adapting the per-DRAM-bank refresh period accordingly. We develop an advanced virtual platform which models the performance, power, and thermal behavior of a 3D-integrated MPSoC with Wide-I/O DRAMs in detail. Moving towards real-world multi-core heterogeneous SoC designs, a reconfigurable heterogeneous platform (ZYNQ) is exploited to further study the performance and energy efficiency of various CPU-accelerator data sharing methods in heterogeneous hardware architectures. A complete hardware accelerator featuring clusters of OpenRISC CPUs, with dynamic address remapping capability is built and verified on a real hardware.

Spatial Disparity and Dynamic Trajectory of Convergence in China: Construction and Application of a Composite Index of Regional Development

China is a large country characterized by remarkable growth and distinct regional diversity. Spatial disparity has always been a hot issue since China has been struggling to follow a balanced growth path but still confronting with unprecedented pressures and challenges. To better understand the inequality level benchmarking spatial distributions of Chinese provinces and municipalities and estimate dynamic trajectory of sustainable development in China, I constructed the Composite Index of Regional Development (CIRD) with five sub pillars/dimensions involving Macroeconomic Index (MEI), Science and Innovation Index (SCI), Environmental Sustainability Index (ESI), Human Capital Index (HCI) and Public Facilities Index (PFI), endeavoring to cover various fields of regional socioeconomic development. Ranking reports on the five sub dimensions and aggregated CIRD were provided in order to better measure the developmental degrees of 31 or 30 Chinese provinces and municipalities over 13 years from 1998 to 2010 as the time interval of three “Five-year Plans”. Further empirical applications of this CIRD focused on clustering and convergence estimation, attempting to fill up the gap in quantifying the developmental levels of regional comprehensive socioeconomics and estimating the dynamic convergence trajectory of regional sustainable development in a long run. Four clusters were benchmarked geographically-oriented in the map on the basis of cluster analysis, and club-convergence was observed in the Chinese provinces and municipalities based on stochastic kernel density estimation.

Estimation of Quasi-Rational DSGE Models

Small-scale dynamic stochastic general equilibrium have been treated as the benchmark of much of the monetary policy literature, given their ability to explain the impact of monetary policy on output, inflation and financial markets. One cause of the empirical failure of New Keynesian models is partially due to the Rational Expectations (RE) paradigm, which entails a tight structure on the dynamics of the system. Under this hypothesis, the agents are assumed to know the data genereting process. In this paper, we propose the econometric analysis of New Keynesian DSGE models under an alternative expectations generating paradigm, which can be regarded as an intermediate position between rational expectations and learning, nameley an adapted version of the "Quasi-Rational" Expectatations (QRE) hypothesis. Given the agents' statistical model, we build a pseudo-structural form from the baseline system of Euler equations, imposing that the length of the reduced form is the same as in the `best' statistical model.