A presente pesquisa teve por objetivo propor uma estrutura de modelagem matemática para a localização ótima de unidades de tratamento térmico de resíduos com recuperação energética. Para tal, o ferramental utilizado foi o método de programação inteira mista, sendo a modelagem desenvolvida aplicada para a Região Metropolitana da Baixada Santista e Litoral Norte. Foi considerada como premissa básica que o processo de incineração seria operado pelo poder público; todos os municípios geradores de Resíduos Sólidos Domiciliares foram considerados como potenciais localidades para a instalação da unidade de tratamento térmico de resíduos; todos os aterros sanitários que atendiam os Municípios estudados foram considerados para a recepção das escórias e cinzas provenientes do processo de incineração. Foram especificados quatro cenários para tal análise, que abordaram competitividade em relação ao uso de aterros sanitários e a presença de eficiência na coleta seletiva dos Municípios. Os resultados apontaram para que a unidade de tratamento térmico de resíduos se localize no entorno dos Municípios de Santos, Praia Grande e São Vicente. Mesmo com a opção do uso de aterros sanitários, a implantação da unidade de tratamento térmico de resíduos se apresentou como uma alternativa mais favorável, tendo sido levados em conta, na modelagem proposta, aspectos tanto ambientais como econômicos. / This study aimed to propose a mathematical modeling framework for optimal location of units of the thermal treatment of waste with energy recovery. To this end, the tool used was the method of mixed integer programming, and the developed modeling applied to the Santos Metropolitan Region and North Coast. It was considered as the basic premise that the incineration process would be operated by the Government; all municipalities solid waste generators were considered as potential locations for the installation of the unit thermal treatment of waste, all landfills that serve municipalities studied were considered for receipt of slag and ash from the incineration process. Four scenarios were specified for this analysis that addressed competitiveness in relation to the use of landfills and the presence of selective collection efficiency in the municipalities. Results showed that the unit of thermal treatment of waste should be located in the vicinity of the cities of Santos, São Vicente and Praia Grande. Even with the option of using landfill, the deployment of the unit of thermal treatment of waste is presented as an alternative more favorable, having been taken into account in the proposed model, both environmental and economic aspects.

<p>The main accomplishments of this research are </p> <p>(1) developed a high fidelity computational methodology based on large eddy simulation to capture lean blowout (LBO) behaviors of different fuels; </p> <p>(2) developed fundamental insights into the combustion processes leading to the flame blowout and fuel composition effects on the lean blowout limits; </p> <p>(3) developed artificial intelligence-based models for early detection of the onset of the lean blowout in a realistic complex combustor. </p> <p>The methodologies are demonstrated by performing the lean blowout (LBO) calculations and statistical analysis for a conventional (A-2) and an alternative bio-jet fuel (C-1).</p> <p>High-performance computing methodology is developed based on the large eddy simulation (LES) turbulence models, detailed chemistry and flamelet based combustion models. This methodology is employed for predicting the combustion characteristics of the conventional fuels and bio-derived alternative jet fuels in a realistic gas turbine engine. The uniqueness of this methodology is the inclusion of as-it-is combustor hardware details such as complex hybrid-airblast fuel injector, thousands of tiny effusion holes, primary and secondary dilution holes on the liners, and the use of highly automated on the fly meshing with adaptive mesh refinement. The flow split and mesh sensitivity study are performed under non-reacting conditions. The reacting LES simulations are performed with two combustion models (finite rate chemistry and flamelet generated manifold models) and four different chemical kinetic mechanisms. The reacting spray characteristics and flame shape are compared with the experiment at the near lean blowout stable condition for both the combustion models. The LES simulations are performed by a gradual reduction in the fuel flow rate in a stepwise manner until a lean blowout is reached. The computational methodology has predicted the fuel sensitivity to lean blowout accurately with correct trends between the conventional and alternative bio-jet fuels. The flamelet generated manifold (FGM) model showed 60% reduction in the computational time compared to the finite rate chemistry model. </p> <p>The statistical analyses of the results from the high fidelity LES simulations are performed to gain fundamental insights into the LBO process and identify the key markers to predict the incipient LBO condition in swirl-stabilized spray combustion. The bio-jet fuel (C-1) exhibits significantly larger CH₂O concentrations in the fuel-rich regions compared to the conventional petroleum fuel (A-2) at the same equivalence ratio. It is observed from the analysis that the concentration of formaldehyde increases significantly in the primary zone indicating partial oxidation as we approach the LBO limit. The analysis also showed that the temperature of the recirculating hot gases is also an important parameter for maintaining a stable flame. If this temperature falls below a certain threshold value for a given fuel, the evaporation rates and heat release rated decreases significantly and consequently leading to the global extinction phenomena called lean blowout. The present study established the minimum recirculating gas temperature needed to maintain a stable flame for the A-2 and C-1 fuels. </p> The artificial intelligence (AI) models are developed based on high fidelity LES data for early identification of the incipient LBO condition in a realistic gas turbine combustor under engine relevant conditions. The first approach is based on the sensor-based monitoring at the optimal probe locations within a realistic gas turbine engine combustor for quantities of interest using the Support Vector Machine (SVM). Optimal sensor locations are found to be in the flame root region and were effective in detecting the onset of LBO ~20ms ahead of the event. The second approach is based on the spatiotemporal features in the primary zone of the combustor. A convolutional autoencoder is trained for feature extraction from the mass fraction of the OH ( data for all time-steps resulting in significant dimensionality reduction. The extracted features along with the ground truth labels are used to train the support vector machine (SVM) model for binary classification. The LBO indicator is defined as the output of the SVM model, 1 for unstable and 0 for stable. The LBO indicator stabilized to the value of 1 approximately 30 ms before complete blowout.

<p>  </p> <p>Organic-inorganic halide perovskites are at the brink of commercialization as the next generation of light-absorbing materials for solar energy harvesting devices. Perovskites have large absorption coefficients, long charge-carrier lifetimes and diffusion lengths, and a tunable absorption spectrum. Furthermore, these materials can be low-temperature solution-processed, which transfers to low-cost manufacturing and cost-competitive products. The remarkable material properties of perovskites enable a broad product-market fit, encompassing traditional and new applications for solar technology. Perovskites can be deposited on flexible substrates for flexible solar cells, applied in thermochromic windows for power generation and building cooling, or tuned for tandem solar cell application to include in high-performance solar panels. However, perovskites are intrinsically unstable, which has so far prevented their commercialization. Despite large research efforts, including over two thousand publications per year, perovskite solar cells degrade in under one year of operation. In a saturated research field, new ideas are needed to inspire alternative approaches to solve the perovskite stability problem. In this dissertation, we detail research efforts surrounding the concept of a self-healing perovskite solar cell.</p> <p>     A self-healing perovskite solar cell can be classified with two distinctions: mechanically healing and molecularly healing. First, mechanically self-healing involves the material’s ability to recover its intrinsic properties after mechanical damage such as tares, lacerations, or cracking. This type of healing was unique to the organic polymer community and ultra-rare in semiconducting materials. By combining a self-healing polymer with perovskite material, we developed a self-healing semiconducting perovskite composite material which can heal using synergistic grain growth and solid-state diffusion processes at slightly elevated temperatures. The material is demonstrated in flexible solar cells with improved bending durability and a power conversion efficiency reaching 10%. The addition of fluidic polymer enables macroscopic perovskite material movement, which is otherwise brittle and rigid. The results inspire the use of polymer scaffolds for mechanically self-healing solar cells.</p> <p>     The second type of healing, molecular healing, involves healing defects within the rigid crystal domains resulting from ion migration. The same phenomenon which leads to device degradation, also assists the recovery of the device performance after resting the device in the dark. During device operation, perovskite ions diffuse in the perovskite lattice and accumulate at the device interfaces where they undergo chemical reactions or leave the perovskite layer, ultimately consuming the perovskite precursors. The photovoltaic performance can be recovered if irreversible degradation is limited. Ideally, degradation and recovery can match day and night cycling to dramatically extend the lifetime of perovskite solar cells. In this dissertation, we introduce the application of chalcogenide chemistry in the fabrication of perovskite solar cells to control the thin film crystallization process, ultimately to reduce defects in the perovskite bulk and introduce surface functionality which extends the device stability. This new strategy will help improve molecularly self-healing perovskite solar cell by reducing irreversible degradation. Lastly, we present a few other new ideas to inspire future research in perovskite solar cells and assist in the commercialization of the next generation of photovoltaics.</p>

<p>Two studies incorporated providing the groundwork for a blueprint to design sodium sulfur cells from electrode fabrication to choices in electrolyte such as DME, DEGDME, TEGDME and two different salts NaClO4 and NaPF6. First study describes role of the binder within the system comparing carboxymethyl cellulose and carboxymethyl cellulose with a styrene butadiene elastomer addition. The second study focuses on methods to prevent polysulfide shuttling within room temperature sodium sulfur system</p>

<p>This work focuses on the collection and analysis of Lithium-ion battery operational and temperature data during nail penetration testing through two different experimental approaches. Raman spectroscopy, machine learning, and internal temperature sensors are used to collect and analyze data to further investigate the effects on cell operation during and after nail penetrations, and the feasibility of using this data to predict future performance.</p> <p><br></p> <p>The first section of this work analyzes the effects on continued operation of a small Lithium-ion prismatic cell after nail penetration. Raman spectroscopy is used to examine the effects on the anode and cathode materials of cells that are cycled for different amounts of time after a nail puncture. Incremental capacity analysis is then used to corroborate the findings from the Raman analysis. The study finds that the operational capacity and lifetime of cells is greatly reduced due to the accelerated degradation caused by loss of material, uneven current distribution, and exposure to atmosphere. This leads into the study of using the magnitude and corresponding voltage of incremental capacity peaks after nail puncture to forecast the operation of damaged cells. A Gaussian process regression is used to predict discharge capacity of different cells that experience the same type of nail puncture. The results from this study show that the method is capable of making accurate predictions of cell discharge capacity even with the higher rate of variance in operation after nail puncture, showing the method of prediction has the potential to be implemented in devices such as battery management systems.</p> <p><br></p> <p>The second section of this work proposes a method of inserting temperature sensors into commercially-available cylindrical cells to directly obtain internal temperature readings. Characterization tests are used to determine the effect on the operability of the modified cells after the sensors are inserted, and lifetime cycle testing is implemented to determine the long-term effects on cell performance. The results show the sensor insertion causes a small reduction in operational performance, and lifetime cycle testing shows the cells can operate near their optimal output for approximately 100-150 cycles. Modified cells are then used to monitor internal temperatures during nail penetration tests and how the amount of aging affects the temperature response. The results show that more aging in a cell causes higher temperatures during nail puncture, as well as a larger difference between internal and external temperatures, due mostly to the larger contribution of Joule heating caused by increased internal resistance.</p>

<p>An emerging technology in the field of transient thermal management is thermal energy storage, or TES, which enables temporary, on-demand heat rejection via storage as latent heat in a phase-change material.  Latent TES devices have enabled advances in many thermal management applications, including peak load shifting for reducing energy demand and cost of HVAC systems and providing supplemental heat rejection in transient thermal management systems.  However, the design of a transient thermal management system with integrated storage comprises many challenges which are yet to be solved.  For example, design approaches and performance metrics for determining the optimal dimensions of the TES device have only recently been studied.  Another area of active research is estimation of the internal temperature state of the device, which can be difficult to directly measure given the transient nature of the thermal storage process.  Furthermore, in contrast to the three main functions of a thermal-fluid system--heat addition, thermal transport, and heat rejection--thermal storage introduces the need for active, real-time control and automated decision making for managing the operation of the thermal storage device. </p> <p>In this thesis, I present the design process for integrating thermal energy storage into a single-phase thermal management system for rejecting transient heat loads, including design of the TES device, state estimation and control algorithm design, and validation in both simulation and experimental environments. Leveraging a reduced-order finite volume simulation model of a plate-fin TES device, I develop a design approach which involves a transient simulation-based design optimization to determine the required geometric dimensions of the device to meet transient performance objectives while maximizing power density.  The optimized TES device is integrated into a single-phase thermal-fluid testbed for experimental testing.  Using the finite volume model and feedback from thermocouples embedded in the device, I design and experimentally validate a state estimator based on the state-dependent Riccati equation approach for determining the internal temperature distribution to a high degree of accuracy.  Real-time knowledge of the internal temperature state is critical for making control decisions; to manage the operation of the TES device in the context of a transient thermal management system, I design and test, both in simulation and experimentally, a logic-based control strategy that uses fluid temperature measurements and estimates of the TES state to make real-time control decisions to meet critical thermal management objectives. Together, these advances demonstrate the potential of thermal energy storage technology as a component of thermal management systems and the feasibility of logic-based control strategies for real-time control of thermal management objectives.</p>

<p dir="ltr">Evidence for climate change-related impacts and risks is already widespread globally, affecting not only the ecosystems but also the economy and health of our communities. Data-driven predictive modeling approaches such as machine learning and deep learning have emerged to be powerful tools for interpreting large and complex non-linear datasets such as meteorological variables from weather stations or the distribution of infectious droplets produced in a cough. However, the strength of these data-driven models can be further optimized by complementing them with foundational knowledge of the physical processes they represent. By understanding the core physics, one can enhance the reliability and accuracy of predictive outcomes. The effectiveness of these combined approaches becomes particularly feasible and robust with the recent advancements in the High-Performance Computing field. With improved processing speed, algorithm design, and storage capabilities, modern computers allow for a deeper and more precise examination of the data. Such advancements equip us to address the diverse challenges presented by climate change more effectively.</p><p dir="ltr">In particular, this document advances research in mitigating and preventing the consequences of global warming by implementing data-driven predictive models based on statistical, machine learning, and deep learning methods via two phases. In the first phase, this dissertation proposes frameworks consisting of machine and deep learning algorithms to increase the resilience of small-scale renewable energy systems, which are essential for reducing greenhouse gas emissions in the ecosystems. The second phase focuses on using data from physics-based models, i.e., computational fluid dynamics (CFD), in data-driven predictive models for improving the design of air cleaning technologies, which are crucial to reducing the transmission of infectious diseases in indoor environments. </p><p dir="ltr">Specifically, this work is an article-based collection of published (or will be published) research articles. The articles are reformatted to fit the thesis's structure. The contents of the original articles are self-contained. </p>

<p dir="ltr">Structural instability, traditionally deemed undesirable in engineering, can be leveraged for beneficial outcomes through intelligent design. One notable instance is elastic buckling, often leading to structures with two stable equilibria (bistable). Connecting bistable elements to form multistable mechanical metamaterials can enable the discretization and offer tunability of mechanical properties without the need for continuous energy input.<i> </i>In this work, we study the physics of these multistable metamaterials and utilize their state and property alterations along with snap-through instabilities resulting from state change for engineering applications. These materials hold potential for diverse applications, including mechanical and thermo-mechanical defrosting, energy absorption, energy harvesting, and mechanical storage and computation.</p><p dir="ltr">Focusing on defrosting, we find that the energy-efficient mechanical method using embedded bistable structures in heat exchanger fins significantly outperforms the thermal methods. The combination of manufacturing methods, material choice, boundary conditions, and actuation methodologies is systematically investigated to enhance defrosting performance. A purely mechanical strategy is effective against solid, glaze-like ice accumulations; however, performance is substantially diminished for low-density frost. To address this limitation, we study frost formation on the angular shape morphing fins and subsequently introduce a thermo-mechanical defrosting strategy. This hybrid approach focuses on the partial phase transition of low-density frost to solid ice through thermal methods, followed by mechanical defrosting. We experimentally validate this approach on a multistable heat exchanger fin pack.</p><p dir="ltr">Recent advancements have led to a new paradigm of reusable energy-absorbing materials, known as Phase Transforming Cellular Materials (PXCM) that utilize multiple negative stiffness elements connected in series. We explore the feasibility of this multistable metamaterial as frequency up-conversion material and utilize these phase transformations for energy harvesting. We experimentally demonstrate the energy-harvesting capabilities of a phase-transforming unit-cell-spring configuration and investigate the potential of multicell PXCM as an energy harvesting material.</p><p dir="ltr">The evolution towards intelligent matter, or physical intelligence, in the context of mechanical metamaterials can be characterized into four distinct stages: static, responsive, adaptive, and intelligent mechanical metamaterials. In the pursuit of designing intelligent mechanical metamaterials, there has been a resurgence in the field of mechanical computing. We utilize multistable metamaterials to develop mechanical storage systems that encode memory via bistable state changes and decode it through a global stiffness readout. We establish upper bounds for maximum memory capacity in elastic bit blocks and propose an optimal stiffness distribution for unique and identifiable global states. Through both parallel and series configurations, we realize various logic gates, thereby enabling in-memory computation. We further extend this framework by incorporating viscoelastic mechano-bits, which mimic the decay of neuronal action potentials. This allows for temporal stiffness modulation and results in increased memory storage via non-abelian behavior, for which we define a fundamental time limit of detectability. Additionally, we investigate information entropy in both elastic and viscoelastic systems, showing that temporal neural coding schemes can extend the system’s entropy beyond conventional limits. This is experimentally validated and shown to not only enhance memory storage but also augment computational capabilities.</p><p dir="ltr">The work in this thesis establishes multistability as a key design principle for developing responsive, adaptive, and intelligent materials, opening new avenues for future research in the field of multistable metamaterials.</p>

<p dir="ltr">Interest in using detonation-based combustion cycles for use propulsion and power generation has gained considerable attention in the last 10 years or so. The rotating detonation engine (RDE), in particular, has garnered the most attention as a possible replacement for current generation combustion systems. RDEs are continuous flow devices that typically operate in a non-premixed fashion. Reactants are injected into an annular combustion chamber that is usually several millimeters wide. One or more detonation waves propagate azimuthally around the annulus, consuming the reactants. The products then expand out of the combustor where it can produce thrust or be passed into a turbine. The detonation wave front in RDEs travel at speeds between 1-3 km/s which poses additional complexity beyond traditional combustors. There are large gaps in the research community for RDEs that use one or more liquid based propellants. Questions regarding liquid breakup, atomization, breakup, recovery all remain unanswered both experimentally and numerically. This work seeks to understand these fundamental physical phenomena that drive these devices by applying advanced, high-speed laser and other optical diagnostics. </p><p dir="ltr"> A 120 mm nominal diameter rotating detonation combustor that operates on non-premixed hydrogen-air was modified to remove a hydrogen orifice and was replaced with a single liquid fuel injector. This simple, yet important, modification enables the study of a one-way coupling between a liquid fuel jet and a detonation wave at relevant spatio-temporal scales. Planar laser-induced fluorescence was performed at rates up to 1 MHz to quantify the quasi-steady jet dynamics and the recovery behavior of the single liquid jet. Long-duration PLIF imaging lasting 30-40 detonation periods at 300 kHz was also performed for statistical significance. A diesel liquid-in-crossflow injector was observed to breakup or be removed from the PLIF plane within only a few microseconds. After the detonation wave passes through the spray there is a significant dwell period can last between 20-40% of the detonation period before the new fuel is issued into the channel. The quasi-steady liquid jet trajectory was also compared to a jet-in-crossflow from literature and there is decent agreement in the jet near-field. </p><p dir="ltr"> The same hardware scheme with a different liquid fuel injector was tested in conjunction with an alternative imagine scheme. The first technique was able to capture details in the radial-axial plane but could not resolve any motion in the azimuthal direction. A volume-based illumination scheme was used for LIF to image a liquid fuel jet in the azimuthal-axial plane. For this experiment the location of the liquid fuel jet was moved into a different position and as a result experiences significantly different behavior than the jet in crossflow. The breakup and evaporation process takes place over a much longer period of time and there is no pause of liquid fuel injection. Similarly, LIF was performed at 300 kHz for 30 detonation cycles to enable sadistically quantification and phase averaging. Filtered OH* and CH* chemiluminescence imaging was also performed over the same field of view as the LIF imaging. Estimation of the velocity field was calculated using optical flow from the Jet-A LIF images. The velocity results agree well with the recovery analysis from the PLIF measurements.</p><p dir="ltr"> Using the same liquid fuel injection scheme, Jet-A droplet diameter and velocity was measured <i>in-situ</i> during a hot-fire experiment using phase Doppler interferometry (PDI). Although a point technique, PDI was used to measure thousands of droplets during a single test at multiple locations and with multiple conditions. As a means of comparison, cold flow experiments were performed with water in the exit plume. Droplet diameters were measured between 1-20 µs in both cases. PDI results were compared with the optical flow results and there is agreement in median velocities and some differences in the minimum and maximum velocity values. Possible sources of error in the diameter measurement are discussed as well.</p>

<p>The present work is focused on the synthesis of small-scale (modular processes) to produce olefins from light alkane resources in shale gas.</p> <p>Olefins, which are widely used to produce important chemicals and everyday consumer products, can be produced from light alkanes - ethane, propane, butanes etc. Shale gas is comprised of light alkanes in significant proportion; and is available in abundance. Meanwhile, shale gas wells are small sized in nature and are distributed over many different areas or regions. In this regard, using shale gas as raw material for olefin production would require expensive transportation infrastructure to move the gas from the wells or local gas gathering stations to large central processing facilities. This is because existing technologies for natural gas conversions are particularly suited for large-scale processing. One possible way to take advantage of the abundance of shale resource for olefins production is to place small-sized or modular processing plants at the well sites or local gas gathering stations.</p> <p>In this work, new process concepts are synthesized and studied towards developing simple technologies for on-site and modular processing of light alkane resources in shale gas for olefin production. Replacing steam with methane as diluent in conventional thermal cracking processes is proposed to eliminate front-end separation of methane from the shale gas processing scheme. Results from modeling studies showed that this is a promising approach. To eliminate the huge firebox volume associated with thermal cracking furnaces and allow for a compact cracking reactor system, the use of electricity to supply heat to the cracking reactor is considered. Synthesis efforts led to the development of two electrically powered reactor configurations that have improved energy efficiency and reduced carbon footprints over and compare to conventional thermal cracking furnace configurations.</p> <p>The ideas and results in the present work are radical in nature and could lead to a transformation in the utilization of light alkanes, natural gas and shale resources for the commercial production of fuels and chemicals.</p>