Optimal uncertainty quantification via convex optimization and relaxation

Many engineering applications face the problem of bounding the expected value of a quantity of interest (performance, risk, cost, etc.) that depends on stochastic uncertainties whose probability distribution is not known exactly. Optimal uncertainty quantification (OUQ) is a framework that aims at obtaining the best bound in these situations by explicitly incorporating available information about the distribution. Unfortunately, this often leads to non-convex optimization problems that are numerically expensive to solve.

This thesis emphasizes on efficient numerical algorithms for OUQ problems. It begins by investigating several classes of OUQ problems that can be reformulated as convex optimization problems. Conditions on the objective function and information constraints under which a convex formulation exists are presented. Since the size of the optimization problem can become quite large, solutions for scaling up are also discussed. Finally, the capability of analyzing a practical system through such convex formulations is demonstrated by a numerical example of energy storage placement in power grids.

When an equivalent convex formulation is unavailable, it is possible to find a convex problem that provides a meaningful bound for the original problem, also known as a convex relaxation. As an example, the thesis investigates the setting used in Hoeffding's inequality. The naive formulation requires solving a collection of non-convex polynomial optimization problems whose number grows doubly exponentially. After structures such as symmetry are exploited, it is shown that both the number and the size of the polynomial optimization problems can be reduced significantly. Each polynomial optimization problem is then bounded by its convex relaxation using sums-of-squares. These bounds are found to be tight in all the numerical examples tested in the thesis and are significantly better than Hoeffding's bounds.

Optimal guidance of low-thrust interplanetary space vehicles

The low-thrust guidance problem is defined as the minimum terminal variance (MTV) control of a space vehicle subjected to random perturbations of its trajectory. To accomplish this control task, only bounded thrust level and thrust angle deviations are allowed, and these must be calculated based solely on the information gained from noisy, partial observations of the state. In order to establish the validity of various approximations, the problem is first investigated under the idealized conditions of perfect state information and negligible dynamic errors. To check each approximate model, an algorithm is developed to facilitate the computation of the open loop trajectories for the nonlinear bang-bang system. Using the results of this phase in conjunction with the Ornstein-Uhlenbeck process as a model for the random inputs to the system, the MTV guidance problem is reformulated as a stochastic, bang-bang, optimal control problem. Since a complete analytic solution seems to be unattainable, asymptotic solutions are developed by numerical methods. However, it is shown analytically that a Kalman filter in cascade with an appropriate nonlinear MTV controller is an optimal configuration. The resulting system is simulated using the Monte Carlo technique and is compared to other guidance schemes of current interest.

Secondary Organic Aerosol Composition Studies Using Mass Spectrometry

Trace volatile organic compounds emitted by biogenic and anthropogenic sources into the atmosphere can undergo extensive photooxidation to form species with lower volatility. By equilibrium partitioning or reactive uptake, these compounds can nucleate into new aerosol particles or deposit onto already-existing particles to form secondary organic aerosol (SOA). SOA and other atmospheric particulate matter have measurable effects on global climate and public health, making understanding SOA formation a needed field of scientific inquiry. SOA formation can be done in a laboratory setting, using an environmental chamber; under these controlled conditions it is possible to generate SOA from a single parent compound and study the chemical composition of the gas and particle phases. By studying the SOA composition, it is possible to gain understanding of the chemical reactions that occur in the gas phase and particle phase, and identify potential heterogeneous processes that occur at the surface of SOA particles. In this thesis, mass spectrometric methods are used to identify qualitatively and qualitatively the chemical components of SOA derived from the photooxidation of important anthropogenic volatile organic compounds that are associated with gasoline and diesel fuels and industrial activity (C12 alkanes, toluene, and o-, m-, and p-cresols). The conditions under which SOA was generated in each system were varied to explore the effect of NO_x and inorganic seed composition on SOA chemical composition. The structure of the parent alkane was varied to investigate the effect on the functionalization and fragmentation of the resulting oxidation products. Relative humidity was varied in the alkane system as well to measure the effect of increased particle-phase water on condensed-phase reactions. In all systems, oligomeric species, resulting potentially from particle-phase and heterogeneous processes, were identified. Imines produced by reactions between (NH₄)₂SO₄ seed and carbonyl compounds were identified in all systems. Multigenerational photochemistry producing low- and extremely low-volatility organic compounds (LVOC and ELVOC) was reflected strongly in the particle-phase composition as well.