Theses - Mechanical Engineering
http://hdl.handle.net/1721.1/7847
Wed, 20 Jun 2018 14:54:16 GMT2018-06-20T14:54:16ZThe effect of cooling on boundary layer transition in a gas
http://hdl.handle.net/1721.1/115811
The effect of cooling on boundary layer transition in a gas
Kline, S. J. (Stephen Jay), 1922-
Thesis (Sc.D.) Massachusetts Institute of Technology. Dept. of Mechanical Engineering, 1952.; Vita.; Bibliography: leaves 53-56.
Tue, 01 Jan 1952 00:00:00 GMThttp://hdl.handle.net/1721.1/1158111952-01-01T00:00:00ZLow-cost, high performance solar vapor generation
http://hdl.handle.net/1721.1/115737
Low-cost, high performance solar vapor generation
Ni, George (George Wei)
Sustainable access to energy and access to water are two of the defining technological problems that society currently faces. Threats of climate change and depletion of fossil fuel reserves are forcing a shift towards more renewable sources of energy, such as solar energy and others. At the same time, water resources are becoming scarcer, caused by unsustainable extraction of ground water resources. Current projections show that by 2025, the population of people living in water-stressed areas is expected to increase to 3.9 billion. Exacerbating this problem is continuing urbanization, which stresses local water supplies further. The two problems of energy and water are inextricably tied together. Water processing, such as desalination and wastewater management, fundamentally requires energy inputs, while energy production often requires water for operational cooling. This thesis focuses on developing technologies for low-intensity utilization of solar energy for desalination and wastewater management. Traditional solar thermal technologies collect sunlight, and use motorized optical concentrators to concentrate the weak solar flux to create high temperature steam, often 400'C or higher. These optical concentrators are costly and require maintenance that are unattractive in many small-scale and low-intensity applications. These applications include distributed desalination, medical sterilization, wastewater management, and more. In this thesis, the research has focused on 1) evaporation mechanisms in nanofluids for solar applications, 2) a solar steam generation structure that operates without optical concentrators, and 3) a floating solar still that produces water without the need for periodic cleaning of excess salts, and has a material cost of $3 to supply individual daily drinking water needs, which can be paid back quickly for some regions like the Maldive. One of the first approaches to solar vapor generation was to use nanoparticles suspended in water, or nanofluids, to localize solar absorption to near the evaporation surface. This approach reduces the temperature drop between the heat generation site and the evaporation surface, increasing the evaporation rate. This thesis first explores the vapor generation mechanisms in nanofluid-based solar vapor generation, and develops a small-scale nanofluid-based solar receiver that could generate vapor at 70% efficiency. A theory was developed to show how nanoparticle suspension could affect the nanofluid transient performance. This thesis next demonstrates a small-scale floating solar steam generator, that does not require optical concentration. This was achieved by further extending the heat localization concept, using various widely available materials to reduce radiative, convective, and conductive losses. By reconfiguring the device, steam at 100°C or vapor at 70% efficiency could be produced. The basic steam generator was then improved and adapted to reject excess salts left behind from vapor formation. The salt rejecting structure was coupled with a condensation cover, to form a floating solar still that was demonstrated to operate in the ocean, simultaneously producing drinkable water and rejecting the excess salts. Salt rejection experiments were conducted to prove the long-term ability of the structure to operate in saline waters.
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references (pages 163-170).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1157372018-01-01T00:00:00ZDevelopment and validation of a novel framework for designing and optimizing passive prosthetic feet using lower leg trajectory
http://hdl.handle.net/1721.1/115734
Development and validation of a novel framework for designing and optimizing passive prosthetic feet using lower leg trajectory
Olesnavage, Kathryn M
This thesis presents a novel framework to optimize the design of passive prosthetic feet to best replicate physiological lower leg trajectory under typical ground reaction forces. The goal of developing this framework is ultimately to design a low cost, mass manufacturable prosthetic foot for persons with amputations living in the developing world. Despite a vast body of literature on prosthetic foot design, there is a dearth of knowledge regarding how the mechanical characteristics of passive prosthetic feet affect their biomechanical performance. Without understanding this relationship, the design of a prosthetic foot cannot be optimized for peak performance as measured by gait symmetry, metabolic cost of walking, or subjective feedback. The approach to designing prosthetic feet introduced here involves predicting the lower leg trajectory for a given prosthetic foot under typical loading and comparing this modeled trajectory to target physiological gait kinematics with a novel metric called the Lower Leg Trajectory Error (LLTE). The usefulness of this design approach was demonstrated by optimizing three simple conceptual models of prosthetic feet, each with two degrees of freedom. An experimental prosthetic foot with variable ankle stiffness was built based on one of these analytical models and tested by a subject with unilateral transtibial amputation in a gait lab under five different ankle stiffness conditions. Across five prosthetic-side steps with each of the five ankle stiffness conditions, the constitutive model used in the optimization process accurately predicted the horizontal and vertical position of the knee throughout stance phase to within an average of 1.0 cm and 0.3 cm, respectively, and the orientation of the lower leg segment to within 1.5°. After validating the theory behind this approach with the simple conceptual foot models, a method was developed to implement the same approach in optimizing the shape and size of a single-part compliant foot, resulting in a lightweight, easy to manufacture, low cost prosthetic foot. The optimal prosthetic foot design was built and tested qualitatively on six subjects in India with unilateral transtibial amputations with promising preliminary results..
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.; Cataloged from PDF version of thesis.; Includes bibliographical references.
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1157342018-01-01T00:00:00ZProbabilistic regional ocean predictions : stochastic fields and optimal planning
http://hdl.handle.net/1721.1/115733
Probabilistic regional ocean predictions : stochastic fields and optimal planning
Narayanan Subramani, Deepak
The coastal ocean is a prime example of multiscale nonlinear fluid dynamics. Ocean fields in such regions are complex, with multiple spatial and temporal scales and nonstationary heterogeneous statistics. Due to the limited measurements, there are multiple sources of uncertainties, including the initial conditions, boundary conditions, forcing, parameters, and even the model parameterizations and equations themselves. To reduce uncertainties and allow long-duration measurements, the energy consumption of ocean observing platforms need to be optimized. Predicting the distributions of reachable regions, time-optimal paths, and risk-optimal paths in uncertain, strong and dynamic flows is also essential for their optimal and safe operations. Motivated by the above needs, the objectives of this thesis are to develop and apply the theory, schemes, and computational systems for: (i) Dynamically Orthogonal ocean primitive-equations with a nonlinear free-surface, in order to quantify uncertainties and predict probabilities for four-dimensional (time and 3-d in space) coastal ocean states, respecting their nonlinear governing equations and non-Gaussian statistics; (ii) Stochastic Dynamically Orthogonal level-set optimization to rigorously incorporate realistic ocean flow forecasts and plan energy-optimal paths of autonomous agents in coastal regions; (iii) Probabilistic predictions of reachability, time-optimal paths and risk-optimal paths in uncertain, strong and dynamic flows. For the first objective, we further develop and implement our Dynamically Orthogonal (DO) numerical schemes for idealized and realistic ocean primitive equations with a nonlinear free-surface. The theoretical extensions necessary for the free-surface are completed. DO schemes are researched and DO terms, functions, and operations are implemented, focusing on: state variable choices; DO norms; DO condition for flows with a dynamic free-surface; diagnostic DO equations for pressure, barotropic velocities and density terms; non-polynomial nonlinearities; semi-implicit time-stepping schemes; and re-orthonormalization consistent with leap-frog time marching. We apply the new DO schemes, as well as their theoretical extensions and efficient serial implementation to forecast idealized-to-realistic stochastic coastal ocean dynamics. For the realistic simulations, probabilistic predictions for the Middle Atlantic Bight region, Northwest Atlantic, and northern Indian ocean are showcased. For the second objective, we integrate data-driven ocean modeling with our stochastic DO level-set optimization to compute and study energy-optimal paths, speeds, and headings for ocean vehicles in the Middle Atlantic Bight region. We compute the energy-optimal paths from among exact time-optimal paths. For ocean currents, we utilize a data-assimilative multiscale re-analysis, combining observations with implicit two-way nested multi-resolution primitive-equation simulations of the tidal-to-mesoscale dynamics in the region. We solve the reduced-order stochastic DO level-set partial differential equations (PDEs) to compute the joint probability of minimum arrival-time, vehicle-speed time-series, and total energy utilized. For each arrival time, we then select the vehicle-speed time-series that minimize the total energy utilization from the marginal probability of vehicle-speed and total energy. The corresponding energy-optimal path and headings be obtained through a particle backtracking equation. For the missions considered, we analyze the effects of the regional tidal currents, strong wind events, coastal jets, shelfbreak front, and other local circulations on the energy-optimal paths. For the third objective, we develop and apply stochastic level-set PDEs that govern the stochastic time-optimal reachability fronts and paths for vehicles in uncertain, strong, and dynamic flow fields. To solve these equations efficiently, we again employ their dynamically orthogonal reduced-order projections. We develop the theory and schemes for risk-optimal planning by combining decision theory with our stochastic time-optimal planning equations. The risk-optimal planning proceeds in three steps: (i) obtain predictions of the probability distribution of environmental flows, (ii) obtain predictions of the distribution of exact time-optimal paths for the forecast flow distribution, and (iii) compute and minimize the risk of following these uncertain time-optimal paths. We utilize the new equations to complete stochastic reachability, time-optimal and risk-optimal path planning in varied stochastic quasi-geostrophic flows. The effects of the flow uncertainty on the reachability fronts and time-optimal paths is explained. The risks of following each exact time-optimal path is evaluated and risk-optimal paths are computed for different risk tolerance measures. Key properties of the risk-optimal planning are finally discussed. Theoretically, the present methodologies are PDE-based and compute stochastic ocean fields, and optimal path predictions without heuristics. Computationally, they are several orders of magnitude faster than direct Monte Carlo. Such technologies have several commercial and societal applications. Specifically, the probabilistic ocean predictions can be input to a technical decision aide for a sustainable fisheries co-management program in India, which has the potential to provide environment friendly livelihoods to millions of marginal fishermen. The risk-optimal path planning equations can be employed in real-time for efficient ship routing to reduce greenhouse gas emissions and save operational costs.
Thesis: Ph. D. in Mechanical Engineering and Computation, Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018.; Cataloged from PDF version of thesis. "Submitted to the Department of Mechanical Engineering and Center for Computational Engineering."; Includes bibliographical references (pages 253-268).
Mon, 01 Jan 2018 00:00:00 GMThttp://hdl.handle.net/1721.1/1157332018-01-01T00:00:00Z