Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2007. / Includes bibliographical references (p. 75-76). / We intend to try to better our understanding of how the combined L-stable 'Trapezoidal Rule with the second order Backward Difference Formula' (TR-BDF2) integrator and the standard A-stable Trapezoidal integrator perform on systems of coupled non-linear partial differential equations (PDEs). It was originally Professor KlausJiirgen Bathe who suggested that further analysis was needed in this area. We draw attention to numerical instabilities that arise due to insufficient numerical damping from the Crank-Nicolson method (which is based on the Trapezoidal rule) and demonstrate how these problems can be rectified with the TR-BDF2 scheme. Several examples are presented, including an advection-diffusion-reaction (ADR) problem and the (chaotic) damped driven pendulum. We also briefly introduce how the ideas of splitting methods can be coupled with the TR-BDF2 scheme and applied to the ADR equation to take advantage of the excellent modern day explicit techniques to solve hyperbolic equations. / by Sohan Dharmaraja. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 101-104). / Roll-to-Roll (R2R) manufacturing has seen great interest in the recent decade due to the proliferation of personalized and wearable devices for monitoring a variety of biometrics. Given the sensitive nature of the potential applications of these sensors, the throughput of manufacturing due to increased demand, and the scale of the electrical components being manufactured, R2R flexible electronics manufacturing technologies require new sensing and measurement capabilities for defect detection and process control. The work presented herein investigates the use of ultrasound, specifically Lamb and longitudinal waves, as a sensing modality and measurement technique for thin film R2R manufacturing substrates. Contact (transducer-based) and non-contact (photoacoustic) generation methods along with deterministic and probabilistic tomographic reconstruction algorithms were implemented evaluate their suitability for non-destructive evaluation (NDE) and in-line control of surface additions of 76[mu]m aluminum and polyethylene terephthalate (PET) films. The ultrasonic waves were used to ascertain properties of these substrates such as the thickness of substrate, applied load, presence of defects (holes/cracks), size of defects, presence of surface features (fluid drops, multi-layer structures), and nature of surface features (differing chemistries). In addition, surface features alter the behavior of sound waves in the presence of such features. These surface features may then be imaged to create tomographic maps. The results presented show that, currently, a quasi-contact acoustic generation scheme can be used to successfully image defects and surface features on the order of -1mm. Furthermore, the algorithm is able to distinguish qualitatively between surface features of differing physiochemical properties. The authors hope that the information collected from this thesis will be part of a rich data set that can contribute to advanced machine-learning frameworks for predictive maintenance, failure, and process control analysis for the R2R process. / by Rishon Robert Benjamin. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2008. / Includes bibliographical references (leaves 99-102). / This thesis presents a novel algorithm, called the parametric optimized belief roadmap (POBRM), to address the problem of planning a trajectory for controlling a robot with imperfect state information under uncertainty. This question is formulated abstractly as a partially observable stochastic shortest path (POSSP) problem. We assume that the feature-based map of a region is available to assist the robot's decision-making. The POBRM is a two-phase algorithm that combines local and global optimization. In an offline phase, we construct a belief graph by probabilistically sampling points around the features that potentially provide the robot with valuable information. Each edge of the belief graph stores two transfer functions to predict the cost and the conditional covariance matrix of a final state estimate if the robot follows this edge given an initial mean and covariance. In an online phase, a sub-optimal trajectory is found by the global Dijkstra's search algorithm, which ensures the balance between exploration and exploitation. Moreover, we use the iterative linear quadratic Gaussian algorithm (iLQG) to find a locally-feedback control policy in continuous state and control spaces to traverse the sub-optimal trajectory. We show that, under some suitable technical assumptions, the error bound of a sub-optimal cost compared to the globally optimal cost can be obtained. The POBRM algorithm is not only robust to imperfect state information but also scalable to find a trajectory quickly in high-dimensional systems and environments. In addition, the POBRM algorithm is capable of answering multiple queries efficiently. We also demonstrate performance results by 2D simulation of a planar car and 3D simulation of an autonomous helicopter. / by Vu Anh Huynh. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 75-77). / The cryosphere is comprised of about 33 million km³ of ice, which corresponds to 70 meters of global mean sea level equivalent [30]. Simulating continental ice masses, such as the Antarctic or Greenland Ice Sheets, requires computational models capturing abrupt changes in ice sheet dynamics, which are still poorly understood. Input parameters, such as basal drag and topography, have large effects on the applied stress and flow fields but whose direct observation is very difficult, if not impossible. Computational methods are designed to aid in the development of ice sheet models, ideally identifying the relative importance of each parameter and formulating inverse methods to infer uncertain parameters and thus constrain ice sheet flow. Efficient computation of the tangent linear and adjoint models give researchers easy access to model derivatives. The adjoint and tangent linear models enable efficient global sensitivity computation and parameter optimization on unknown or uncertain ice sheet properties, information used to identify model properties having large effects on sea-level. The adjoint equations are not always easily obtained analytically and often require discretizing additional PDE's. Algorithmic differentiation (AD) decomposes the model into a composite of elementary operations (+, -, *, /, etc ... ) and a source-to-source transformation generates code for the Jacobian and its transpose for each operations. Derivatives computed using the tangent linear and adjoint models, with code generated by AD, are applied to parameter estimation and sensitivity analysis of simple glacier models. AD is applied to two examples, equations describing changes in borehole temperature over time and instantaneous ice velocities. Borehole model predictions and data are compared to infer paleotemperatures, geothermal heat flux, and physical ice properties. Inversion using adjoint methods and AD increases the control space, allowing inference for all uncertain parameters. The sensitivities of ice velocities to basal friction and basal topography are compared. The basal topography has significantly larger sensitivities, suggesting it plays a larger role in flow dynamics and future work should seek to invert for this parameter. / by Andrew D. Davis. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 85-89). / In this work novel high-order hybridizable discontinuous Galerkin (HDG) projection methods are further developed for ocean dynamics and geophysical fluid predictions. We investigate the effects of the HDG stabilization parameter for both the momentum equation as well as tracer diffusion. We also make a correction to our singularity treatment algorithm for nailing down a numerically consistent and unique solution to the pressure Poisson equation with homogeneous Neumann boundary conditions everywhere along the boundary. Extensive numerical results using physically realistic ocean flows are presented to verify the HDG projection methods, including the formation of internal wave beams over a shallow but abrupt seamount, the generation of internal solitary waves from stratified oscillatory flow over steep topography, and the circulation of bottom gravity currents down a slope. Additionally, we investigate the implementation of open boundary conditions for finite element methods and present results in the context of our ocean simulations. Through this work we present the hybridizable discontinuous Galerkin projection methods as a viable and competitive alternative for large-scale, realistic ocean modeling. / by Johnathan Hiep Vo. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2010. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (p. 83-86). / The objective of this work is to develop a high efficiency two-dimensional reactive flow solver to investigate perforated-plate stabilized laminar premixed flames. The developed code is used to examine the impact of the operating conditions and the perforated plate design on the steady flame characteristics. It is also used to numerically investigate the response of these flames to imposed inlet velocity perturbations. The two-dimensional simulations are performed using a reduced chemical kinetics mechanism for methane-air combustion, consisting of 20 species and 79 reactions. Heat exchange is allowed between the gas mixture and the solid plate. The physical model is based on a zero-Mach-number formulation of the axi-symmetric compressible conservation equations. The steady results suggest that the flame consumption speed, the flame structure, and the flame surface area depend significantly on the equivalence ratio, mean inlet velocity, the distance between the perforated plate holes and the plate thermal conductivity. In the case of an adiabatic plate, a conical flame is formed, anchored near the corner of the hole. When the heat exchange between the mixture and the plate is finite, the flame acquires a Gaussian shape stabilizing at a stand-off distance, that grows with the plate conductivity. The flame tip is negatively curved; i.e. concave with respect to the reactants. Downstream of the plate, the flame base is positively curved; i.e. convex with respect to the reactants, stabilizing above a stagnation region established between neighboring holes. As the plate's thermal conductivity increases, the heat flux to the plate decreases, lowering its top surface temperature. As the equivalence ratio increases, the flame moves closer to the plate, raising its temperature, and lowering the flame stand-off distance. As the mean inlet velocity increases, the flame stabilizes further downstream, the flame tip becomes sharper, hence raising the burning rate at that location. The curvature of the flame base depends on the distance between the neighboring holes; and the flame there is characterized by high concentration of intermediates, like carbon monoxide. To investigate flame dynamics, linear transfer functions, for low mean inlet velocity oscillations, are analyzed for different equivalence ratio, mean inlet velocity, plate thermal conductivity and distance between adjacent holes. The oscillations of the heat exchange rate at the top of the burner surface plays a critical role in driving the growth of the perturbations over a wide range of conditions, including resonance. The flame response to the perturbations at its base takes the form of consumption speed oscillations in this region. Flame stand-off distance increases/decreases when the flame-wall interaction strengthens/weakens, impacting the overall dynamics of the heat release. The convective lag between the perturbations and the flame base response govern the phase of heat release rate oscillations. There is an additional convective lag between the perturbations at the flame base and the flame tip which has a weaker impact on the heat release rate oscillations. At higher frequencies, the flame-wall interaction is weaker and the heat release oscillations are driven by the flame area oscillations. The response of the flame to higher amplitude oscillations are used to gain further insight into the mechanisms. Key words: Laminar premixed flames, perforated-plate stabilized flames, flame-wall interactions, flame consumption speed, stand-off distance. / by Kushal Sharad Kedia. / S.M.

Thesis: S.M., Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2012. / This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. / Cataloged from student-submitted PDF version of thesis. / Includes bibliographical references (pages 85-87). / Stochastic programming models are large-scale optimization problems that are used to facilitate decision-making under uncertainty. Optimization algorithms for such problems need to evaluate the expected future costs of current decisions, often referred to as the recourse function. In practice, this calculation is computationally difficult as it involves the evaluation of a multidimensional integral whose integrand is an optimization problem. Accordingly, the recourse function is estimated using quadrature rules or Monte Carlo methods. Although Monte Carlo methods present numerous computational benefits over quadrature rules, they require a large number of samples to produce accurate results when they are embedded in an optimization algorithm. We present an importance sampling framework for multistage stochastic programming that can produce accurate estimates of the recourse function using a fixed number of samples. Our framework uses Markov Chain Monte Carlo and Kernel Density Estimation algorithms to create a non-parametric importance sampling distribution that can form lower variance estimates of the recourse function. We demonstrate the increased accuracy and efficiency of our approach using numerical experiments in which we solve variants of the Newsvendor problem. Our results show that even a simple implementation of our framework produces highly accurate estimates of the optimal solution and optimal cost for stochastic programming models, especially those with increased variance, multimodal or rare-event distributions. / by Berk Ustun. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2007. / Includes bibliographical references (p. 105-106). / We present a further study and analysis of an exponential annealing based algorithm for convex optimization. We begin by developing a general framework for applying exponential annealing to conic optimization. We analyze the hit-and-run random walk from the perspective of convergence and develop (partially) an intuitive picture that views it as the limit of a sequence of finite state Markov chains. We then establish useful results that guide our sampling. Modifications are proposed that seek to raise the computational practicality of exponential annealing for convex optimization. In particular, inspired by interior-point methods, we propose modifying the hit-and-run random walk to bias iterates away from the boundary of the feasible region and show that this approach yields a substantial reduction in computational cost. We perform computational experiments for linear and semidefinite optimization problems. For linear optimization problems, we verify the correlation of phase count with the Renegar condition measure (described in [13]); for semidefinite optimization, we verify the correlation of phase count with a geometry measure (presented in [4]). / by Jeremy Chen. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Computation for Design and Optimization Program, 2009. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 75-76). / We consider the problem of recovering a low rank matrix given a sampling of its entries. Such problems are of considerable interest in a diverse set of fields including control, system identification, statistics and signal processing. Although the general low rank matrix completion problem is NP-hard, there exist several heuristic methods that solve the problem approximately by solving the convex relaxation of the original problem. One particularly popular method is to use nuclear norm (sum of singular values) to approximate the rank of the matrix and formulate the problem as a semidefinite program that can be solved efficiently. In this thesis, we propose a local completion algorithm that searches for possible completion in the neighborhood of each unspecified entry given the rank of the matrix. Unlike existing methods, this algorithm requires only local information of the matrix if the rank is known. Critical in all low rank matrix completion algorithms is the sampling density. The denser the matrix is sampled, the more likely it can be recovered accurately. We then propose a condensation process that increases the sampling density in a specific part of the matrix through elementary row and column re-ordering. Hence we can solve a sub-problem of the original low rank matrix completion problem and gain information on the rank of the matrix. Then the local algorithm is readily applicable to recover the entire matrix. We also explore the effect of additional sampling structures on the completion rate of the low rank matrix completion problems. In particular, we show that imposing regularity in the sampling process leads to slightly better completion rates. / (cont.) We also provide a new semidefinite formulation for a particular block sampling structure that reduces the size of the constraint matrix sizes by a factor of 1.5. / by Feng Nan. / S.M.

A significant limitation of the conventional level set method in topology optimization is that it can not create new holes in the design domain. Therefore, the topological derivative approach is proposed to overcome this problem. In this part of the thesis, we investigated the use of the topological derivative in combination with the level set method for topology optimization of solid structures. The topological derivative can indicate the appropriate location to create new holes so that the strong dependency of the optimal topology on the initial design can be alleviated. We also develop an approach to evolve the level set function by replacing the gradient item with a Delta function in the standard Hamilton-Jacobi equation. We find that this handling can create new holes in the solid domain, grow a structure from an empty domain, and improve the convergence rate of the optimization process. The success of our approach is demonstrated by several numerical examples. / Following those methods some numerical implementation issues are discussed, and numerical examples of 2D structural topology optimization problems of minimum compliance design are given and combined with a comparative study where the efficiency, convergence and accuracy of the present methods are highlighted. Finally, conclusions are given. / In the second part of this thesis, we implement another variational level set method, the piecewise constant level set (PCLS) method. This method was first proposed by Lie-Lysaker-Tai in the interface problem field for such tasks as image segmentation and denoising problems. In this approach, by defining a piecewise density function over the whole design domain, the sensitivity of the objective function in respect to the design variable, the level set surface, can be explicitly obtained. Thus, the piecewise density function can be viewed as a bridge establishing the relationship between the implicit level set function and the performance function defined on the design domain. This proposed method retains the advantages of the implicit level set representation, such as the capability of the interface to develop sharp corners, break apart and merge together in a flexible manner. Because the PCLS method is implemented by an implicit iteration differential scheme rather than solving the Hamilton-Jacobi equation, it is not only free of the CFL condition and the reinitialization scheme, but it is also easy to implement. These favorable properties lead to a great timesaving advantage over the conventional level set method. Two other meaningful advantages are the natural nucleation property with which the proposed PCLS method need not incorporate any artificial nucleation scheme and the dependence of the initial design is greatly alleviated. / In the third part of this thesis, we apply a parametric scheme by combining the conventional level set method with radial basis functions (RBFs). This method is introduced because the conventional level set function has no analytical form then the entire design domain must be made discrete in an artificial manner using a rectilinear grid for level set processing - often through a distance transform. The classical level set method for structural topology optimization requires a careful choice of an upwind scheme, extension velocity and a reinitialization algorithm. With the versatile tool, RBF, the original problem can be converted to a parametric optimization problem. Therefore, the costly Hamilton-Jacobi PDE solving procedure can be easily replaced by a standard gradient method or another mature conventional optimization method in the parameter space such as MMA, OC, mathematic programming and so on. / Keywords: structural optimization, level set method, topological derivative, radial basis functions, piecewise constant level set method. (Abstract shortened by UMI.) / The concept of structural optimization has been more and more widely accepted in many engineering fields during the past several decades, because the optimization can result in a much more reasonable and economical structure design with even less material consumption. / Wei Peng. / "June 2007." / Adviser: Yu Michael Wang. / Source: Dissertation Abstracts International, Volume: 69-01, Section: B, page: 0640. / Thesis (Ph.D.)--Chinese University of Hong Kong, 2007. / Includes bibliographical references (p. 166-180). / Electronic reproduction. Hong Kong : Chinese University of Hong Kong, [2012] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Electronic reproduction. [Ann Arbor, MI] : ProQuest Information and Learning, [200-] System requirements: Adobe Acrobat Reader. Available via World Wide Web. / Abstracts in English and Chinese. / School code: 1307.