Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2015. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 193-199). / In this thesis, we develop methods for solving the linearized Boltzmann transport equation (BTE) in the relaxation-time approximation for describing small-scale solidstate heat transfer. We first discuss a Monte Carlo (MC) solution method that builds upon the deviational energy-based Monte Carlo method presented in [J.-P. Péraud and N.G. Hadjiconstantinou, Physical Review B, 84(20), p. 205331, 2011]. By linearizing the deviational Boltzmann equation we formulate a kinetic-type algorithm in which each computational particle is treated independently; this feature is shown to be consequence of the energy-based formulation and the linearity of the governing equation and results in an "event-driven" algorithm that requires no time discretization. In addition to a much simpler and more accurate algorithm (no time discretization error), this formulation leads to considerable speedup and memory savings, as well as the ability to efficiently treat materials with wide ranges of phonon relaxation times, such as silicon. A second, complementary, simulation method developed in this thesis is based on the adjoint formulation of the linearized BTE, also derived here. The adjoint formulation describes the dynamics of phonons travelling backward in time, that is, being emitted from the "detectors" and detected by the "sources" of the original problem. By switching the detector with the source in cases where the former is small, that is when high accuracy is needed in small regions of phase-space, the adjoint formulation provides significant computational savings and in some cases makes previously intractable problems possible. We also develop an asymptotic theory for solving the BTE at small Knudsen numbers, namely at scales where Monte Carlo methods or other existing computational methods become inefficient. The asymptotic approach, which is based on a Hilbert expansion of the distribution function, shows that the macroscopic equation governing heat transport for non-zero but small Knudsen numbers is the heat equation, albeit supplemented with jump-type boundary conditions. Specifically, we show that the traditional no-jump boundary condition is only applicable in the macroscopic limit where the Knudsen number approaches zero. Kinetic effects, always present at the boundaries, become increasingly important as the Knudsen number increases, and manifest themselves in the form of temperature jumps that enter as boundary conditions to the heat equation, as well as local corrections in the form of kinetic boundary layers that need to be superposed to the heat equation solution. We present techniques for efficiently calculating the associated jump coefficients and boundary layers for different material models when analytical results are not available. All results are validated using deviational Monte Carlo methods primarily developed in this thesis. We finally demonstrate that the asymptotic solution method developed here can be used for calculating the Kapitza conductance (and temperature jump) associated with the interface between materials. / by Jean-Philippe Péraud. / Ph. D.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2018. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 115-123). / Recent advances in micro-to-nanoscale heat transfer have led to tremendous research interests in the high spatial resolution thermal characterization techniques. Although great improvement has been made in temperature probe, heat flux measurement and thermophysical properties characterization especially for solid-state materials and structures, precision thermal characterization is still challenging due to the presence of multiphysics coupling, the limitation of resolution and the restriction of materials that can be studied. The goal of this thesis is to explore more possible opportunities for advanced thermal measurement techniques. Specifically, this thesis mainly focuses on the development of Raman spectroscopy and phase imaging and demonstrates their applications to micro-to-nanoscale heat transfer. Due to the superior spatial resolution and the non-contact nature, micro-Raman spectroscopy has been widely applied for local temperature measurement. However, the presence of multiphysics coupling to the optical phonon modes and the necessity to have Raman signature for the test materials limit the application of micro-Raman thermometry to simple solid-state devices. In this thesis, we present several advancements which extend the capability of Raman spectroscopy to multiphysics coupling systems, Raman-inactive materials and nanoscale thermometry. Specifically, we simultaneously measured the temperature, stress and electric field in GaN HEMTs and the linear thermal expansion coefficient of MoS2 monolayer flake using the multiple peaks fit method. We presented a method to interface micro-Raman system with a phase change heat transfer test setup and used this integrated setup to study the thin film evaporation on structured surfaces. To measure the temperature of Raman-inactive materials, we used nanoparticles as the Raman agent. We measured the temperature distribution of the optically transparent and thermally insulated silica aerogel. Additionally, this thesis also proposed a concept of nanoscale Raman thermometry using plasmon enhanced gold-silicon nanoparticles. The electric field concentration properties and in situ measurement capability were proven using simulation and experiments. Attributed to the high sensitivity to geometrical structures and refractive index of materials, phase imaging techniques were useful for weakly scattering systems. Although the property of imaging transparent materials has been well-demonstrated, the application of nanoscale detection using phase imaging is lacking. In this thesis, we developed robust phase imaging method based on transport of intensity equation and depth scanning technique and proved the ultrahigh sensitivity of phase in nanoscale inspection. This developed technology was validated through a number of simulations and experiments, including detecting the deep subwavelength defects on 9 nm semiconductor wafers. The thesis finally shows the opportunity of using phase imaging to study micro-to-nanoscale phase change heat transfer. The dynamic interactions and growth of condensing droplets were investigated using the phase imaging enhanced environmental scanning electron microscopy. / by Lenan Zhang. / S.M.

Massachusetts Institute of Technology. Dept. of Mechanical Engineering. Thesis. 1974. M.S. / MICROFICHE COPY ALSO AVAILABLE IN BARKER ENGINEERING LIBRARY. / Includes bibliographical references. / M.S.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2003. / Includes bibliographical references (p. 186-187). / This thesis deals with a method of functional lung imaging using Positron Emission Tomography (PET). In this technique, a radioactive tracer, nitrogen-13, is dissolved in saline solution, and injected into a peripheral vein. By analysis of the tracer kinetics through the lung, measured using PET, a three-dimensional image of perfusion and ventilation can be generated. In the first part of this thesis, a new tracer-preparation system, suitable for use in human subjects, is described. The system is remotely operated, highly automated, and incorporates numerous redundant safeguards to protect the patient. The second part of the thesis details a formal approach to the analysis of the experimental data. A model of the tracer in the right heart and lungs is developed, and used to estimate physiological parameters for large to medium-sized regions of diseased lung. As regions of interest are made smaller, the amount of imaging noise in PET data increases. Consequently parameter estimates become less reliable as finer resolution is used. In order to retain as much spatial information as possible, a new approach is explored, in which voxels with similar kinetics are grouped together, and parameters are estimated for the whole group; in this way, spatial resolution is conserved at the expense of parametric discretization. The viability of the approach is demonstrated by high-resolution analysis of ventilation dysfunction in asthmatic subjects. / by Dominick Layfield. / Ph.D.

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1997. / Includes bibliographical references (p. 91-96). / by Miltos E. Kambourides. / M.S.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2011. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 89-98). / In this thesis, I perform intensity-based tomographic phase imaging in two ways. First, I utilize the paraxial transport of intensity equation (TIE) to construct phase maps of a phase object at multiple projection angles and reconstruct the object 3- dimensionally using basic tomographic principles. Then, I use an Intensity Diffraction Tomography (IDT) approach to improve the quality of reconstruction by accounting for diffraction effects under 1st order Rytov Approximation. I improve both approaches by applying compressive sensing techniques to estimate missing points in the undersampled data. Finally, I compare I-DT with single-shot, Gabor-type digital holography (also integrating use of compressive sensing principles) and discuss improvements and extensions of the presented implementation of IDT. / by Justin Wu Lee. / S.M.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2012. / Cataloged from PDF version of thesis. / Includes bibliographical references (p. 91-94). / This thesis investigates ways to obtain consumer preferences for technology products to help designers identify the key attributes that contribute to a product's market success. A case study of residential solar PV panels is conducted in the context of the California, USA market within the 2007-2011 time span. First, interviews are conducted with solar panel installers to gain a better understanding of the solar industry. Second, a revealed preference method is implemented using actual market data and technical specifications to extract preferences. The approach is explored with three machine learning methods: Artificial Neural Networks, Random Forest decision trees, and Gradient Boosted regression. Finally, a stated preference self-explicated survey is conducted, and the results using the two methods compared. Three common critical attributes are identified from a pool of 34 technical attributes: power warranty, panel efficiency, and time on market. From the survey, additional non-technical attributes are identified: panel manufacturer's reputation, name recognition, and aesthetics. The work shows that a combination of revealed and stated preference methods may be valuable for identifying both technical and non-technical attributes to guide design priorities. / by Heidi Qianyi Chen. / S.M.

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1998. / Includes bibliographical references (leaves 112-119). / by Shane Michael Farritor. / Ph.D.

Thesis: S.B., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (page 29). / Parachutes are commonly used in space mission landings. With increasing payloads, parachutes are getting larger, bearing larger loads and operating at faster speeds. Designing these devices requires a knowledge of aerodynamics, fluid flow, and the mechanical properties of cloth. A brief overview of the basics of parachute design are described. There are many possible failure modes, but catastrophic failures are caused by failures at the weakest point of the parachute, the seam. Seams are characterized by a seam efficiency, a percentage of the strength of the cloth used to stitch the seam together. In this study, seams and cloth were tested to failure on an Instron 5582 to experimentally determine their respective Ultimate Tensile Strengths (UTS). The ratio of the seam UTS to the cloth UTS was used to determine seam efficiency. Results indicated no clear relationship between strain rate and seam efficiency in the range tested. However, a strong relationship between stitch concentration and seam efficiency was established. A best fit curve was developed and with an an R2=0.80. In order to better understand the failure mode, the open source Matlab function Ncorr was also used to provide a visualization of the strain on the coupons during testing. The results of the digital correlation analysis performed by Ncorr are also reported, and indicate the importance of transverse and shear strain in causing catastrophic failure. / by Pavlina Karafillis. / S.B.

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2017. / Cataloged from PDF version of thesis. / Includes bibliographical references (pages 171-183). / Quantifying extreme events is a central issue for many technological processes and natural phenomena. As extreme events, we consider transient responses that push the system away from its statistical steady state and that correspond to large excursions. Complex systems exhibiting extreme events include dynamical systems found in nature, such as the occurrence of anomalous weather and climate events, turbulence, formation of freak waves in the ocean and optics, and dynamical systems in engineering applications, including mechanical components under environmental loads, ship rolling and capsizing, critical events in power grids, as well as chemical reactions and conformational changes in molecules. It has been recognized that extreme events occur more frequently than Gaussian statistics suggest and thus occur often enough that they have practical consequences, and sometimes catastrophic outcomes, that are important to understand and predict. A hallmark characteristic of extreme events in complex dynamical systems is non-Gaussian statistics (e.g. heavy-tails) in the probability density function (pdf) describing the response of their observables. For engineers and applied mathematicians, a central issue is how to efficiently and accurately describe this non-Gaussian behavior. For random dynamical systems with inherently nonlinear dynamics, expressed through intermittent events, nonlinear energy transfers, broad energy spectra, and large intrinsic dimensionality, it is largely the case that we are limited to (direct) Monte-Carlo sampling, which is too expensive to apply in real-world applications. To address these challenges, we present both direct and adaptive (sampling based) strategies designed to quantify the probabilistic aspects of extreme events in complex dynamical systems, effectively and efficiently. Specifically, we first develop a direct quantification framework that involves a probabilistic decomposition that separately considers intermittent, extreme events from the background stochastic attractor of the dynamical system. This decomposition requires knowledge of the dynamical mechanisms that are responsible for extreme events and partitions the phase space accordingly. We then apply different uncertainty quantification schemes to the two decomposed dynamical regimes: the background attractor and the intermittent, extreme-event component. The background component, describing the 'core' of the pdf, although potentially very high-dimensional, can be efficiently described by uncertainty quantification schemes that resolve low-order statistics. On the other hand, the intermittent component, related to the tails, can be described in terms of a low-dimensional representation by a small number of modes through a reduced order model of the extreme events. The probabilistic information from these two regimes is then synthesized according to a total probability law argument, to effectively approximate the heavy-tailed, non-Gaussian probability distribution function for quantities of interest. The method is demonstrated through numerous applications and examples, including the analytical and semi-analytical quantification of the heavy-tailed statistics in mechanical systems under random impulsive excitations (modeling slamming events in high speed craft motion), oscillators undergoing transient parametric resonances and instabilities (modeling ship rolling in irregular seas and beam bending), and extreme events in nonlinear Schrodinger based equations (modeling rogue waves in the deep ocean). The proposed algorithm is shown to accurately describe tail statistics in all of these examples and is demonstrated to be many orders of magnitude faster than direct Monte-Carlo simulations. The second part of this thesis involves the development of adaptive, sampling based strategies that aim to accurately estimate the probability distribution and extreme response statistics of a scalar observable, or quantity of interest, through a minimum number of experiments (numerical simulations). These schemes do not require specialized knowledge of the dynamics, nor understanding of the mechanism that cause or trigger extreme responses. For numerous complex systems it may not be possible or very challenging to analyze and quantify conditions that lead to extreme responses or even to obtain an accurate description of the dynamics of all the processes that are significant. To address this important class of problems, we develop a sequential algorithm that provides the next-best design point (set of experimental parameters) that leads to the largest reduction in the error of the probability density function estimate for the scalar quantity of interest when the adaptively predicted design point is evaluated. The proposed algorithm utilizes Gaussian process regression to infer dynamical properties of the quantity of interest, which is then used to estimate the desired pdf along with uncertainty bounds. We iteratively determine new design points through an optimization procedure that finds the optimal point in parameter space that maximally reduces uncertainty between the estimated bounds of the posterior pdf estimate of the observable. We provide theorems that guarantee convergence of the algorithm and analyze its asymptotic behavior. The adaptive sampling method is illustrated to an example in ocean engineering. We apply the algorithm to estimate the non-Gaussian statistics describing the loads on an offshore platform in irregular seas. The response of the platform is quantified through three-dimensional smoothed particle hydrodynamics simulations. Because of the extreme computational cost of these numerical models, quantification of the extreme event statistics for such systems has been a formidable challenge. We demonstrate that the adaptive algorithm accurately quantifies the extreme event statistics of the loads on the structure through a small number of numerical experiments, showcasing that the proposed algorithm can realistically account for extreme events in the design and optimization processes for large-scale engineering systems. / by Mustafa A. Mohamad. / Ph. D.