MIT Libraries
http://hdl.handle.net/1721.1/7581
2017-02-20T13:28:42ZOptimal bioeconomic management of changing marine resources
http://hdl.handle.net/1721.1/106966
Optimal bioeconomic management of changing marine resources
Moberg, Emily Alison
Marine populations are increasingly subjected to changing conditions whether through harvest or through broad-scale habitat change. Historically, few models have accounted for such trends over time, and even fewer have been used to study how trends affect optimal harvests. I developed and analyzed several models that explore, first, endogenous change caused by harvest and, second, exogenous change from factors (such as rising ocean temperatures) outside harvesters' control. In these models, I characterized the profit-or yield-maximizing strategy when harvesting damages habitat in a multispecies fishery, when harvest creates a selective pressure on dispersal, and when rising temperatures cause changes in vital rates. I explore this last case in both deterministic and stochastic environments, and also allow the harvester to learn about unknown parameters of the stock recruitment model while harvesting. I also develop an unambiguous definition of and describe a statistical test for a shift in a species' spatial distribution. My results demonstrate that optimal harvesting strategies in a changing environment differ in important ways from optimal strategies in a constant environment.
Thesis: Ph. D., Joint Program in Oceanography/Applied Ocean Science and Engineering (Massachusetts Institute of Technology, Department of Biology; and the Woods Hole Oceanographic Institution), 2016.; Cataloged from PDF version of thesis.; Includes bibliographical references.
2016-01-01T00:00:00ZMethods for design optimization using high fidelity turbulent flow simulations
http://hdl.handle.net/1721.1/106965
Methods for design optimization using high fidelity turbulent flow simulations
Talnikar, Chaitanya Anil
Design optimization with high-fidelity turbulent flow simulations can be challenging due to noisy and expensive objective function evaluations. The noise decays slowly as computation cost increases, therefore is significant in most simulations. It is often unpredictable due to chaotic dynamics of turbulence, in that it can be totally different for almost identical simulations. This thesis presents a modified parallel Bayesian optimization algorithm designed for performing optimization with high-fidelity simulations. It strives to find the optimum in a minimum number of evaluations by judiciously exploring the design space. Additionally, to potentially augment the optimization algorithm with the availability of a gradient, a massively parallel discrete unsteady adjoint solver for the compressible Navier-Stokes equations is derived and implemented. Both the methods are demonstrated on a large scale transonic fluid flow problem in a turbomachinery component.
Thesis: S.M., Massachusetts Institute of Technology, School of Engineering, Center for Computational Engineering, Computation for Design and Optimization Program, 2015.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 75-79).
2015-01-01T00:00:00ZSecure electric power grid operation
http://hdl.handle.net/1721.1/106964
Secure electric power grid operation
Foo, Ming Qing
This thesis examines two problems concerning the secure and reliable operation of the electric power grid. The first part studies the distributed operation of the electric power grid using the power flow problem, which is vital to the operation of the grid. The power flow problem is a feasibility problem for finding an assignment of complex bus voltages that satisfies the power flow equations and is within operational and safety limits. For reliability and privacy reasons, it is desirable to solve the power flow problem in a distributed manner. Two novel distributed algorithms are presented for solving convex feasibility problems for networks based on the Method of Alternating Projections (MAP) and the Projected Consensus algorithm. These algorithms distribute computation among the nodes of the network and do not require any form of central coordination. The original problem is equivalently split into small local sub-problems, which are coordinated locally via a thin communication protocol. Although the power flow problem is non-convex, the new algorithms are demonstrated to be powerful heuristics using IEEE test beds. Quadratically Constrained Quadratic Programs (QCQP), which occur in the projection sub-problems, are studied and methods for solving them efficiently are developed. The second part addresses the robustness and resiliency of state estimation algorithms for cyber-physical systems. The operation of the electric power grid is modeled as a dynamical system that is supported by numerous feedback control mechanisms, which depend heavily on state estimation algorithms. The electric power grid is constantly under attack and, if left unchecked, these attacks may corrupt state estimates and lead to severe consequences. This thesis proposes a novel dynamic state estimator that is resilient against data injection attacks and robust to modeling errors and additive noise signals. By leveraging principles of robust optimization, the estimator can be formulated as a convex optimization problem and its effectiveness is demonstrated in simulations of an IEEE 14-bus system.
Thesis: S.M., Massachusetts Institute of Technology, School of Engineering, Center for Computational Engineering, Computation for Design and Optimization Program, 2015.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 87-91).
2015-01-01T00:00:00ZApproximation algorithms for rapid evaluation and optimization of architectural and civil structures
http://hdl.handle.net/1721.1/106963
Approximation algorithms for rapid evaluation and optimization of architectural and civil structures
Tseranidis, Stavros
This thesis explores the use of approximation algorithms, sometimes called surrogate modelling, in the early-stage design of structures. The use of approximation models to evaluate design performance scores rapidly could lead to a more in-depth exploration of a design space and its trade-offs and also aid in reducing the computation time of optimization algorithms. Six machine-learning-based approximation models have been examined, chosen so that they span a wide range of different characteristics. A complete framework from the parametrization of a design space and sampling, to the construction of the approximation models and their assessment and comparison has been developed. New methodologies and metrics to evaluate model performance and understand their prediction error are introduced. The concepts examined are extensively applied to case studies of multi-objective design problems of architectural and civil structures. The contribution of this research lies in the cohesive and broad framework for approximation via surrogate modelling with new novel metrics and approaches that can assist designers in the conception of more efficient, functional as well as diverse structures. Key words: surrogate modelling, conceptual design, structural design, structural optimization.
Thesis: S.M., Massachusetts Institute of Technology, School of Engineering, Center for Computational Engineering, Computation for Design and Optimization Program, 2015.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 109-111).
2015-01-01T00:00:00Z