Future of earthquake early warning : quantifying uncertainty and making fast automated decisions for applications

Earthquake early warning (EEW) systems have been rapidly developing over the past decade. Japan Meteorological Agency (JMA) has an EEW system that was operating during the 2011 M9 Tohoku earthquake in Japan, and this increased the awareness of EEW systems around the world. While longer-time earthquake prediction still faces many challenges to be practical, the availability of shorter-time EEW opens up a new door for earthquake loss mitigation. After an earthquake fault begins rupturing, an EEW system utilizes the first few seconds of recorded seismic waveform data to quickly predict the hypocenter location, magnitude, origin time and the expected shaking intensity level around the region. This early warning information is broadcast to different sites before the strong shaking arrives. The warning lead time of such a system is short, typically a few seconds to a minute or so, and the information is uncertain. These factors limit human intervention to activate mitigation actions and this must be addressed for engineering applications of EEW. This study applies a Bayesian probabilistic approach along with machine learning techniques and decision theories from economics to improve different aspects of EEW operation, including extending it to engineering applications.

Existing EEW systems are often based on a deterministic approach. Often, they assume that only a single event occurs within a short period of time, which led to many false alarms after the Tohoku earthquake in Japan. This study develops a probability-based EEW algorithm based on an existing deterministic model to extend the EEW system to the case of concurrent events, which are often observed during the aftershock sequence after a large earthquake.

To overcome the challenge of uncertain information and short lead time of EEW, this study also develops an earthquake probability-based automated decision-making (ePAD) framework to make robust decision for EEW mitigation applications. A cost-benefit model that can capture the uncertainties in EEW information and the decision process is used. This approach is called the Performance-Based Earthquake Early Warning, which is based on the PEER Performance-Based Earthquake Engineering method. Use of surrogate models is suggested to improve computational efficiency. Also, new models are proposed to add the influence of lead time into the cost-benefit analysis. For example, a value of information model is used to quantify the potential value of delaying the activation of a mitigation action for a possible reduction of the uncertainty of EEW information in the next update. Two practical examples, evacuation alert and elevator control, are studied to illustrate the ePAD framework. Potential advanced EEW applications, such as the case of multiple-action decisions and the synergy of EEW and structural health monitoring systems, are also discussed.

Distributed optimization in power networks and general multi-agent systems

The dissertation studies the general area of complex networked systems that consist of interconnected and active heterogeneous components and usually operate in uncertain environments and with incomplete information. Problems associated with those systems are typically large-scale and computationally intractable, yet they are also very well-structured and have features that can be exploited by appropriate modeling and computational methods. The goal of this thesis is to develop foundational theories and tools to exploit those structures that can lead to computationally-efficient and distributed solutions, and apply them to improve systems operations and architecture.

Specifically, the thesis focuses on two concrete areas. The first one is to design distributed rules to manage distributed energy resources in the power network. The power network is undergoing a fundamental transformation. The future smart grid, especially on the distribution system, will be a large-scale network of distributed energy resources (DERs), each introducing random and rapid fluctuations in power supply, demand, voltage and frequency. These DERs provide a tremendous opportunity for sustainability, efficiency, and power reliability. However, there are daunting technical challenges in managing these DERs and optimizing their operation. The focus of this dissertation is to develop scalable, distributed, and real-time control and optimization to achieve system-wide efficiency, reliability, and robustness for the future power grid. In particular, we will present how to explore the power network structure to design efficient and distributed market and algorithms for the energy management. We will also show how to connect the algorithms with physical dynamics and existing control mechanisms for real-time control in power networks.

The second focus is to develop distributed optimization rules for general multi-agent engineering systems. A central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to the given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control on the least amount of information possible. Our work focused on achieving this goal using the framework of game theory. In particular, we derived a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting game-theoretic equilibria and the system level design objective and (ii) that the resulting game possesses an inherent structure that can be exploited for distributed learning, e.g., potential games. The control design can then be completed by applying any distributed learning algorithm that guarantees convergence to the game-theoretic equilibrium. One main advantage of this game theoretic approach is that it provides a hierarchical decomposition between the decomposition of the systemic objective (game design) and the specific local decision rules (distributed learning algorithms). This decomposition provides the system designer with tremendous flexibility to meet the design objectives and constraints inherent in a broad class of multiagent systems. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.

Pulse NMR in solids : chemical shift, lead fluoride and thorium hydride

Part one of this thesis consists of two sections. In the first section the fluorine chemical shift of a single crystal CaF_2 has been measured as a function of external pressure up to 4 kilobar at room temperature using multiple pulse NMR techniques. The pressure dependence of the shift is found to be -1.7 ± 1 ppm/kbar, while a theoretical calculation using an overlap model predicts a shift of -0.46 ppm/kbar. In the second section a separation of the chemical shift tensor into physically meaningful "geometrical" and "chemical" contributions is presented and a comparison of the proposed model calculations with recently reported data on hydroxyl proton chemical shift tensors demonstrates, that for this system, the geometrical portion accounts for the qualitative features of the measured tensors.

Part two of the thesis consists of a study of fluoride ion motion in β-PbF_2 doped with NaF by measurement of the ^(19)F transverse relaxation time (T_2), spin lattice relaxation time (T_1) and the spin lattice relaxation time in the rotating frame (T_(1r)). Measurements over the temperature range of -50°C to 160°C lead to activation energies for T_1, T_(1r) and T_2 of 0.205 ± 0.01, 0.29 + 0.02 and 0.27 ± 0.01 ev/ion, and a T_(1r) minimum at 56°C yields a correlation time of 0.74 μsec. Pressure dependence of T_1 and T_2 yields activation volumes of <0.2 cm^3/g-mole and 1.76 ± 0.05 cm^3/g-mole respectively. These data along with the measured magnetic field independence of T_1 suggest that the measured T_1's are not caused by ^(19)F motion, but by thermally excited carriers.

Part three of the thesis consists of a study of two samples of Th_4H_(15), prepared under different conditions but both having the proper ratio of H/Th (to within 1%). The structure of the Th_4H_(15) as suggested by X-ray measurements is confirmed through a moment analysis of the rigid lattice line shape. T_1 and T_2 measurements above 390 K furnish activation energies of 16.3 ± 1.2 kcal/mole and 18.0 ± 3.0 kcal/mole, respectively. Below 350 K, T_(1r) measurements furnish an activation energy of 10.9 ± 0.7 kcal/mole, indicating most probably more than a single mechanism for proton motion. A time-temperature hysteresis effect of the proton motion was found in one of the two samples and is strongly indicative of a phase change. T_1 at room temperature and below is dominated by relaxation due to conduction electrons with the product T_1T being 180 ± 10 K-sec. Using multiple pulse techniques to greatly reduce homonuclear dipolar broadening, a temperature-dependent line shift was observed, and the chemical shift anisotropy is estimated to be less than 16 ppm.

Time-Domain TeraHertz Spectroscopy and Observational Probes of Prebiotic Interstellar Gas and Ice Chemistry

Understanding the origin of life on Earth has long fascinated the minds of the global community, and has been a driving factor in interdisciplinary research for centuries. Beyond the pioneering work of Darwin, perhaps the most widely known study in the last century is that of Miller and Urey, who examined the possibility of the formation of prebiotic chemical precursors on the primordial Earth [1]. More recent studies have shown that amino acids, the chemical building blocks of the biopolymers that comprise life as we know it on Earth, are present in meteoritic samples, and that the molecules extracted from the meteorites display isotopic signatures indicative of an extraterrestrial origin [2]. The most recent major discovery in this area has been the detection of glycine (NH₂CH₂COOH), the simplest amino acid, in pristine cometary samples returned by the NASA STARDUST mission [3]. Indeed, the open questions left by these discoveries, both in the public and scientific communities, hold such fascination that NASA has designated the understanding of our "Cosmic Origins" as a key mission priority.

Despite these exciting discoveries, our understanding of the chemical and physical pathways to the formation of prebiotic molecules is woefully incomplete. This is largely because we do not yet fully understand how the interplay between grain-surface and sub-surface ice reactions and the gas-phase affects astrophysical chemical evolution, and our knowledge of chemical inventories in these regions is incomplete. The research presented here aims to directly address both these issues, so that future work to understand the formation of prebiotic molecules has a solid foundation from which to work.

From an observational standpoint, a dedicated campaign to identify hydroxylamine (NH₂OH), potentially a direct precursor to glycine, in the gas-phase was undertaken. No trace of NH₂OH was found. These observations motivated a refinement of the chemical models of glycine formation, and have largely ruled out a gas-phase route to the synthesis of the simplest amino acid in the ISM. A molecular mystery in the case of the carrier of a series of transitions was resolved using observational data toward a large number of sources, confirming the identity of this important carbon-chemistry intermediate B11244 as l-C₃H⁺ and identifying it in at least two new environments. Finally, the doubly-nitrogenated molecule carbodiimide HNCNH was identified in the ISM for the first time through maser emission features in the centimeter-wavelength regime.

In the laboratory, a TeraHertz Time-Domain Spectrometer was constructed to obtain the experimental spectra necessary to search for solid-phase species in the ISM in the THz region of the spectrum. These investigations have shown a striking dependence on large-scale, long-range (i.e. lattice) structure of the ices on the spectra they present in the THz. A database of molecular spectra has been started, and both the simplest and most abundant ice species, which have already been identified, as well as a number of more complex species, have been studied. The exquisite sensitivity of the THz spectra to both the structure and thermal history of these ices may lead to better probes of complex chemical and dynamical evolution in interstellar environments.

Real-time load-side control of electric power systems

Two trends are emerging from modern electric power systems: the growth of renewable (e.g., solar and wind) generation, and the integration of information technologies and advanced power electronics. The former introduces large, rapid, and random fluctuations in power supply, demand, frequency, and voltage, which become a major challenge for real-time operation of power systems. The latter creates a tremendous number of controllable intelligent endpoints such as smart buildings and appliances, electric vehicles, energy storage devices, and power electronic devices that can sense, compute, communicate, and actuate. Most of these endpoints are distributed on the load side of power systems, in contrast to traditional control resources such as centralized bulk generators. This thesis focuses on controlling power systems in real time, using these load side resources. Specifically, it studies two problems.

(1) Distributed load-side frequency control: We establish a mathematical framework to design distributed frequency control algorithms for flexible electric loads. In this framework, we formulate a category of optimization problems, called optimal load control (OLC), to incorporate the goals of frequency control, such as balancing power supply and demand, restoring frequency to its nominal value, restoring inter-area power flows, etc., in a way that minimizes total disutility for the loads to participate in frequency control by deviating from their nominal power usage. By exploiting distributed algorithms to solve OLC and analyzing convergence of these algorithms, we design distributed load-side controllers and prove stability of closed-loop power systems governed by these controllers. This general framework is adapted and applied to different types of power systems described by different models, or to achieve different levels of control goals under different operation scenarios. We first consider a dynamically coherent power system which can be equivalently modeled with a single synchronous machine. We then extend our framework to a multi-machine power network, where we consider primary and secondary frequency controls, linear and nonlinear power flow models, and the interactions between generator dynamics and load control.

(2) Two-timescale voltage control: The voltage of a power distribution system must be maintained closely around its nominal value in real time, even in the presence of highly volatile power supply or demand. For this purpose, we jointly control two types of reactive power sources: a capacitor operating at a slow timescale, and a power electronic device, such as a smart inverter or a D-STATCOM, operating at a fast timescale. Their control actions are solved from optimal power flow problems at two timescales. Specifically, the slow-timescale problem is a chance-constrained optimization, which minimizes power loss and regulates the voltage at the current time instant while limiting the probability of future voltage violations due to stochastic changes in power supply or demand. This control framework forms the basis of an optimal sizing problem, which determines the installation capacities of the control devices by minimizing the sum of power loss and capital cost. We develop computationally efficient heuristics to solve the optimal sizing problem and implement real-time control. Numerical experiments show that the proposed sizing and control schemes significantly improve the reliability of voltage control with a moderate increase in cost.