Indian Institute of Science Bangalore
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Discrete Velocity Boltzmann Schemes with Efficient Multidimensional Models
No full textTraditional CFD algorithms have achieved a high degree of sophistication for modelling fluid flows in the past five decades. This sophistication can be seen clearly in one dimensional modelling, with a wide variety of successful algorithms tuned for capturing the discontinuities in the flows, with the focus on accuracy and robustness. The modelling of multidimensional flows, however, has not been as sophisticated. Most of the industrial flow solvers are based on the 1-D models extended to multi-dimensions in a rather simple way, as in the popular cell-centred finite volume methods, with the inherent limitation of locally 1-D modelling. As a result, these algorithms are grid-dependent, with the discontinuities aligned with the grid lines being captured crisply while the discontinuities oblique to the grid lines are diffused. A few good multi-dimensional models introduced by the researchers have not met with the success of
industrial applications, as compared to the earlier 1-D physics based models. Thus, there is a need for designing better multi-dimensional models. In this work, a new multi-dimensional kinetic theory based algorithm is introduced for simulating compressible flows.
Modelling multidimensional compressible flows at the macroscopic level is beset with the mathematical difficulties of dealing with the non-commuting flux Jacobian matrices, together with the algorithms based strongly on eigen-structure. Alternative modelling based on kinetic theory is thus simpler. The main advantage comes from the linearity of the convection terms in Boltzmann equation (together with the nonlinear collision term), the moments of which lead to the nonlinear hyperbolic conservation equations of the macroscopic model. Thus, developing truly multidimensional algorithms is also expected to be simpler in this elegant framework. A multidimensional kinetic theory based algorithm is proposed in this thesis, with a neat separation of different physical aspects of multidimensional flows and their appropriate numerical treatment.
The new algorithm begins with the separation of fluid and peculiar velocities in the convec tion terms of the Boltzmann equation, which naturally leads to macroscopic convection-pressure splitting. With the identification of the unidirectional information propagation for the fluid velocity part, an appropriate streamline upwinding method is proposed. Based on the multi directional information propagation for the peculiar velocity part (correspondingly the pressure part at the macroscopic level), a fractional update based kinetic flux difference splitting method, which generates an algorithm at the macroscopic level, is introduced. Higher order accuracy is achieved using k-exact reconstruction, which suits the multidimensional features of the scheme well. The new multidimensional kinetic scheme is tested on several typical
benchmark test cases for Euler equations and is shown to yield superior results when compared to the corresponding grid-aligned finite volume based kinetic scheme
Experimental Investigations on Ramp-induced Large Separation Bubble in a Hypersonic Flow
No full textShockwave Boundary Layer Interactions (SBLIs) are ubiquitous in supersonic/
hypersonic flows and often lead to the separation of the boundary layer. These
separations can be broadly classified into small and large separation bubbles based on
their lengths compared to the boundary layer thickness. While small bubbles have
minimal influence on the outer flow conditions, large bubbles significantly impact the
outer flow, causing changes in pressure distribution. These large separation bubbles
have been observed to be unsteady and thus can adversely affect the performance of
aerodynamic devices. Moreover, these unsteady pressure loads can lead to vibrations
and fatigue failure of the structure. Therefore, understanding the flow physics within
the separated region and its influence on the outer flow is essential for efficient
aerodynamic design.
The research gap in the field lies in the accurate prediction of the onset of
unsteadiness in Ramp-induced Shockwave Boundary Layer Interactions (R-SBLIs)
and the lack of controlled experimental data on unsteady flows with separation
occurring at the leading edge. Previous studies have primarily focused on ramp angles
below 30°, neglecting higher angles that could lead to detached shock solutions. These
gaps in the literature motivate the present study, which aims to investigate the
different flow regimes, mechanisms, and sources of low-frequency unsteadiness in
R-SBLI. The investigation includes incipient separation, steady separated flow,
unsteady separated flow, and the identification of flow topology.
A comprehensive approach is proposed for the identification and characterisation
of different flow regimes encountered in compression corner flows. The flow regime
depends on the pressure ratio imposed by the ramp angle. Shock polar analysis helps
identify the nature of shock-shock interactions, which determines the pressure
variation along the ramp surface. The oscillation and pulsation modes are identified
based on the location of the shear layer impingement on the ramp. A
conditional-based algorithm is developed for flow regime identification. This approach
provides a systematic understanding of the flow topology for a given set of freestream
conditions and test models
Chemistry of bimetallic, chalcogenide and highly reactive metal nanoparticles
No full textThe remarkable modifications in the characteristics of materials on a nanoscale, caused by surface effects, quantum confinement, and dependence on shape, leads to diverse applications of nanomaterials, such as in catalysis, environmental applications, energy conversion and storage, etc. Research on nanomaterials is extensively concentrated on metal nanoparticles, particularly those made of noble metals and various transition metal elements, as well as systems that utilize these metals. However, research on systems based on post-transition elements, such as Sn, has remained relatively underdeveloped. This is because these elements have a strong affinity for oxygen and a low affinity towards most surfactants, making controlled synthesis of their size and shape challenging. Besides that, researchers have also been drawn to various other types of nanomaterials, including alloys, intermetallics, chalcogenides, and more. In addition, the synthesis of nanomaterials with a particular emphasis on their usage in energy storage applications is a significant area of research. Achieving a controlled and scalable synthesis of nanomaterials is the primary challenge in the field of nanoscience. Out of the many techniques available, solution-based chemical synthesis strategies provide an effective and straightforward approach to producing nanomaterials. The solution-based synthesis offers control over size and shape of nanomaterials by providing a convenient medium for their growth and carries the advantage of greater flexibility compared to the dry synthetic routes. In this direction, the digestive ripening technique in combination with solvated metal atom dispersion method (SMAD) is one of the exceptional solution-based synthesis methods for creating nanomaterials. This thesis is dedicated to demonstrating a solution-based synthesis of a broad range of nanomaterials, including alloys, intermetallics, and chalcogenides, and to investigate their potential for various applications. The research also delves into the synthesis and characterization of highly reactive nanomaterials, such as magnesium-carbon composites, which are essential for hydrogen energy storage
CodeQueries: Benchmarking Query Answering over Source Code
No full textSoftware developers often make queries about the security, performance effectiveness, and maintainability of their code. Through an iterative debugging process, developers analyze the code to find answers to these queries. The process can be seen as a question-answering task that requires developers to identify code spans satisfying certain properties. Many of these queries can be answered by existing code analysis tools such as CodeQL. However, using such tools requires design, implementation, and verification efforts.
In this work, we propose an alternative to the code analysis tools by formulating the task of query answering over source code as a span prediction problem. In the proposed approach, a neural model is designed to predict appropriate answer spans in a code in response to a query. The required supporting-facts to justify the predicted answers are also identified by the model. Pre-trained language models for code are fine-tuned on a newly prepared challenging dataset, CodeQueries, for query answering over source code. We demonstrate that the proposed approach performs well on the query answering over source code task when only relevant code blocks are provided as input to the model. Experiments conducted on the dataset demonstrate that the proposed neural approach is robust to noisy span labeling and can even handle code with minor syntax errors. Although large-sized code and limited training examples adversely affect the model performance, we suggest methods to address these issues. Based on our study, we believe that the proposed neural approach will be an additional tool in a developer's toolbox for query answering over source code
Towards metal additive manufacturing using alternate powders
No full textMetal powders are predominantly produced using processes derived from atomization, which, apart from being highly energy intensive, are also inflexible and very material specific. These limitations are particularly acute when viewed in the context of additive manufacturing (AM), especially with the use of high-performance structural alloys, refractories, and emerging materials such as high entropy alloys.
This work explores an alternative method for making metal powders for AM applications using mechanical deformation and is presented in two parts.
In the first part, we develop an abrasion-based process for making powders in plain carbon and stainless steel. The curious occurrence of perfectly sphere-shaped particles in a grinding configuration is studied in detail as a potential source of spherical powders. A new hypothesis is forwarded for their formation based on theoretical four-body heat partition calculations and coupled infrared thermography measurements. The particles are postulated to form via heating at the abrasive-substrate contact zone, followed by exothermic oxidation leading to melting, and finally rapid solidification to ambient temperature. Given the difficulty in directly assessing these phenomena using in situ techniques, each step of the formation process is analyzed using a combination of physical models and postmortem characterization (XRD, HRTEM and EDS). The result is a comprehensive analytical framework to predict when and how perfectly spherical particles may be obtained via abrasion.
Building on these fundamental studies, a protocol was then developed for collecting and evaluating the produced powders for use in AM applications. This was done in three steps. Firstly, bulk powder flowability was evaluated post and prior to size segregation via sieving. Secondly, the elimination of surface oxide layers was undertaken using a novel reduction kinetics analysis under a hydrogen atmosphere. Finally, energetics calculations of the entire process were used to quantify its potential use vis-à-vis atomization-derived powders. Based on these post-process investigations, a stand-alone tool was developed for producing powders at scale using commercial CNC machines.
In the second part of this work, we explore the use of these powders, both as unaided feedstock for AM and as potential blending candidates with other commercial powders. For this purpose, we designed and developed an open architecture directed energy deposition (DED) based AM system. Unlike existing DED systems, the powder handling module of the developed system is customized to handle non-standard metal powders. To demonstrate the capabilities of this system, extensive benchmarking tests were first performed, and an ‘operating map’ was determined for its use with a given metal powder.
Using this DED system, the powders produced in the first part of the work were used for making standard test geometries. Corresponding microstructural, elemental, and mechanical evaluations were carried out to analyze the properties of the final printed parts and were found to be comparable to those obtained with commercial powders. In summary, this work demonstrates a plausible alternative route for making metal powders specifically for metal AM applications
Multimodal sleep staging and diagnosis of sleep disorders
No full textSleep is not a single uniform state, but rather a cyclical pattern involving multiple stages such as rapid eye movement (REM) and non-REM (NREM stages N1, N2, and N3). Polysomnography (PSG) is considered as the gold standard for sleep scoring; however, it requires experts to visually inspect overnight data of about 8 hrs and annotate as per the standard guidelines. This manual scoring is tedious, time-consuming, and expensive, and also suffers from inter-rater variability. Among the different signals recorded in PSG, electroencephalogram (EEG) is considered to be the most useful and informative for differentiating the different stages of sleep. Electrooculogram (EOG) and electromyogram (EMG) are also used in PSG to record eye movements and muscle activities, respectively, which can help distinguish NREM from REM sleep stages. This thesis explores the modalities of EEG, EOG, and EMG for accurately classifying the multiple stages of sleep. Also, it aims to diagnose different sleep disorders by extracting effective features from the overnight sleep recordings of patients.
In the first part, the results of our studies on the binary classification of sleep and wake states are presented. We have evaluated the performance of single-channel EEG using handcrafted features, such as bandpower ratios, Lempel-Zev complexity, sample entropy, and Hjorth's parameters. We also performed Poincare plot (PP) analysis and derived various features such as length and width of PP, area, ratio of length to width, asymmetry index of PP, and entropy of gridded PP. We then investigated the utility of single-channel EOG or EMG in classifying sleep from wake states in healthy subjects as well as clinical population. With an EMG signal, we obtained an average classification accuracy of 85% on 10 healthy controls and 70% on 25 patients with different sleep disorders. An adaptive data-driven approach called ensemble empirical mode decomposition is used to obtain the mode functions of the EOG signal. Mean, mode, standard deviation, kurtosis, and skewness are computed for each of the intrinsic mode functions (IMFs) and are used as features. Apart from these, we also calculate the instantaneous frequency and energy of IMFs using Hilbert Huang transform. For feature selection, we have used mutual information criteria to rank the features according to their importance and considered top K features (value of K is selected such that after including these top K features, there is no further improvement in classification performance), which are consistently present across all the runs. By using the single channel EOG, we obtain an accuracy of about 95% on healthy controls and 91% on patients with sleep disorders. These studies show that a single-channel EMG or EOG provides results at par with EEG and can be used to identify sleep and wake states in a clinical setting.
In part two of the thesis, we have considered the multi-class classification of sleep stages using a single-channel EEG. In this work, we used the handcrafted features that were investigated in our earlier work on binary classification of sleep-wake states. We utilized random undersampling with boosting technique (RUSBoost) to deal with the class imbalance issue, which is innate to the sleep stage classification problem. This RUSBoost classifier uses a decision tree as the base classifier. We have evaluated the performance of the proposed method on three publicly available datasets of overnight PSG recordings of healthy individuals. The results are reported using two different approaches: (i)subject-independent testing (SIT) with leave-one-subject-out and 50%-holdout strategies and (ii)subject-dependent testing (SDT). The proposed approach is able to outperform most of the existing studies in the literature on automated speech stage classification (ASSC).
In the third part of the thesis, we aim to further improve the performance of ASSC by a) decomposing the multi-class classification problem into multiple binary classification tasks and b) combining multiple modalities, i.e., EEG, EMG, and EOG. This work proposes a hierarchical model (HM) in which the different levels of hierarchy are designed such that a) each level deals with only one binary classification task, and b) sleep stages that are difficult to disambiguate are passed through multiple levels before reaching the final decision. In this work, we also investigate the effectiveness of temporal context (TC) and data augmentation (DA) in the classification performance of the proposed HM. The model is evaluated on seven publicly available datasets comprising healthy subjects as well as patients with diverse sleep disorders. We found that the combination of DA and TC provides a consistent improvement across all the datasets. We also reported the results of the cross-dataset evaluation in which the model is trained and tested on different datasets. The proposed model achieves average accuracies of 83.1%, 90.0%, 84.4%, 82.1%, 81.5%, 79.9%, and 73.7% on Sleep-EDF, Exp Sleep-EDF, ISRUC-S1, S2 and S3, DRMS-SUB, and DRMS-PAT datasets, respectively. For all the datasets except DRMS-SUB, the proposed method outperforms the state-of-the-art approaches for the automated identification of sleep stages.
In the last part of the thesis, we aim to classify various sleep disorders, namely insomnia, narcolepsy (NAR), periodic leg movement syndrome (PLM), nocturnal frontal lobe epilepsy (NFLE), REM behavior disorder (RBD), bruxism and sleep-disordered breathing (SDB) using EEG, EOG and/or EMG signals. We used gradient boosting decision tree model called LightGBM to classify healthy controls and different sleep disorders, and compared its performance with that of SVM. The proposed approach is evaluated on the publicly available CAP dataset of 108 subjects, including healthy controls and seven sleep disorders. We first addressed the problem of binary classification of specific pathologies from healthy controls. A single feature called gridded distribution entropy derived from Poincare plots of EEG signal is able to provide 100% accuracy in distinguishing healthy controls from each pathology, except insomnia and PLM. However, by using EOG channel, these two groups are distinguished from healthy controls with 100% accuracy, indicating the efficacy of EOG channel in disambiguating insomnia and PLM. An accuracy of 83.3% is obtained for the seven-class classification of sleep disorders using only an EOG channel. We have further utilized the binary EEG classifiers for disambiguating the classes that are misclassified by the seven-class EOG classifier. The proposed approach is referred to as the LightGBM-EOG-EEG (LEE) method since it first utilizes the EOG channel for performing multi-class classification (healthy controls and six sleep disorders) and then the EEG channel (P4-O2) for correcting the confused classes. The performance improves from 83.3% to 93.3% by using the LEE method, which combines the powers of EOG and EEG channels. Finally, we used simple threshold-based postprocessing to resolve the confusion between NAR and SDB, taking the accuracy to 94.4%, the best in the literature. This threshold is based on the value of the ratio of the duration of two sleep stages
A multi-physics-based modelling approach to predict mechanical and thermo-mechanical behaviour of cementitious composite in a multi-scale framework
No full textConcrete is a heterogeneous material whose constituents (e.g., cement paste, aggregate etc.) range from a characteristic length-scale dimension of a nanometre to a metre. Owing to the heterogeneity of concrete and the contrasting nature of its constituent’s (cement paste, aggregate) response at ambient and high temperatures, applying a homogeneous macroscopic model to predict concrete’s mechanical and thermo-mechanical performance is questionable. Hence, in this thesis, multiple physical and chemical processes that occur within the concrete constituents at different length scales are considered, and a multi-scale model is developed to study the mechanical and thermo-mechanical behaviour of concrete in a hygral-thermal-chemical-mechanical (HTCM) framework. Firstly, the governing equations of HTCM processes are described at meso-scale, a length-scale where coarse aggregate is explicitly modelled in a binding medium called mortar. After that, a hierarchical homogenization approach is employed, and the evolution of mechanical properties etc., are upscaled (from micro to meso) and used at the meso-scale.
The proposed methodology is then used to predict the evolution of mechanical properties (e.g., compressive strength) and time-dependent deformation (e.g., shrinkage and creep) of cement paste, mortar and concrete for a wide variety of factors (e.g., type and content of constituents, different curing conditions, etc.). Like ambient conditions, the developed model is used to simulate thermo-mechanical responses (e.g., in terms of spalling, deformation, residual capacity, etc.) of both plain and reinforced concrete structural elements. Further, the effect of several other meso and macroscopic parameters (e.g., interfacial transition zone, aggregate shape, random configurations of aggregates etc.) on concrete’s mechanical and thermo-mechanical behaviour is studied numerically at the meso-scale.
Validation of the proposed methodology with the available experimental results at both ambient and high temperatures for a wide variety of cases highlights the general applicability of the model. It has been shown that on several occasions, existing macro, meso or multi-scale models unable to reproduce the mechanical and thermos-mechanical behaviour of concrete structures. Such limitations can be overcome with the present developed approach. Further, empiricism in several calibrated parameters in the existing thermal-hygral-mechanical macroscopic models (associated with elasticity, strength, shrinkage, and creep prediction) can be avoided by using the present developed multi-scale and multi-physics-based methodology.
Similarly, simulated results at high temperatures highlight several crucial aspects related to obtaining a more precise residual capacity of a concrete structure, which is impossible to reproduce with a homogenized macroscopic model. For instance, spalling out of random concrete parts at different times during high-temperature exposure cannot be simulated with a homogenized assumption. Further, unlike macroscopic models, a mesoscopic model does not require transient creep strain to be specified explicitly in the analysis. The primary influencing mechanisms behind this transient creep strain are implicitly taken into account in the present developed meso-scale model that results in such advantages.Ministry of Human Resource and Development, Government of Indi
Investigation of Growth, Structural and Optical properties of different phases of Ga2O3
No full textAmong the semiconducting sesquioxides, Ga2O3 has attracted considerable research attention
in recent years due to its excellent properties, including direct ultra wide band gap, optical
transparency, high excitonic binding energy. These properties makes it a potential candidate for
deep UV optoelectronics and power electronics applications. The Ga2O3 exhibits polymorphism
which includes at least α-, β-, γ- and ϵ-/κ- phases. Among these phases most of the research has
been carried out on thermodynamically stable β-polyphase, whose highly asymmetric crystal
structure imparts highly non-isotropic optical and electronic properties. Aside from the fact that
β-(AlxGa1–x)2O3 alloy is limited to an Aluminium mole fraction of 71 % thereby impeding the
bandgap tuning, its non-polar crystal symmetry pose some challenges or would add additional
steps to the device development process. These factor make it imperative to investigate other
meta-stable polymorphs. There is a critical need for cost-effective and high-throughput methods
for the deposition of semiconducting thin films in a wide range of industrial applications.
In this research work optical and structural properties of metastable phases of Ga2O3 have
been investigated which were deposited using cost-effective, easy to use and high-throughput
techniques. In particular, an approach involving microwave-irradiation was employed to deposit
polycrystalline thin films at sub 200 oC temperatures, and mist-CVD method was developed to
achieve epitaxial thin films of high crystallinity at atmospheric pressure. The work begins by
understanding the structural properties and optical reponse of the cubic spinel γ-Ga2O3. The
polycrystalline film was deposited on the sapphire substrate at various microwave powers, following which the sample deposited at 300 W microwave power was annealed and a comparative
study vis-` a-vis the optical and structural properties was done on annealed and as-deposited
sample. A planar geometry MSM photodetector was fabricated with decent response. Finally,
the carrier transport mechanism was investigated by analyzing temperature dependent I-V curves with Thermionic emission models at low electric field and hopping conduction mechanism at high electric field regime. The outcomes of the investigation renders microwave as the
method of choice for deposition of conformal, high quality polycrystalline optical films. These
results persuaded us to deposit Ga2O3 poly-film on GaN/AlGaN-HEMT stack for the realization of dual-band/broadband photodetector. In the final section of this part, nanocrystalline
(In0.26Ga0.74)2O3 film was realized, and its defect span was studied using Urbach’s rule. This
film demonstrated the high responsivity of ∼ 17 WA .
In general, microwave irradiation is suitable for the fabrication of highly conformal polycrystalline thin film; however, deposition of epitaxial thin films present a great challenge. Low-defect
epitaxial films are imperative for manufacturing highly efficient photodetectors, sensors, transistors and diodes. In addition, they make it possible to observe specific optical excitations,
such as free excitons. A polycrystalline film could assist in converting free excitons into trapped
excitons, thus hindering our ability to observe the existence of free excitons in the film. Nevertheless, one could detect these free excitons in poly films at cryogenic temperatures. To address
these issues, this stage of the research involves building a hot-walled mist-CVD reactor and
deciphering its underlying growth mechanism. The highly epitaxial α-Ga2O3 film was stabilized at a relatively lower temperature of 350 oC. The crystallinity of the films were studied
using series of rocking curve scans and pole figure measurements. Through the application of
Elliott-Toyozawa theory, optical charcterization of the film with emphasis on excitonic properties was conducted. Eventually, an MSM photodetector was fabricated on the film deposited
at 450 oC and its optical response was studied. This is the first time excitonic fingerprint has
been observed in spectral responsivity measurements.
While hot-walled mist-CVD reactors are quite capable of depositing α-Ga2O3 films, they
suffer from specific growth-related issues. The film deposition rates are slow in conjunction
with high thickness variability over the substrate. Furthermore, a large tube diameter promotes
homogeneous nucleation, facilitating germination of high-density denuded regions. Considering
these factors when depositing films conducive to high-quality devices is essential. As a method
of alleviating the problems mentioned above, a fine channel mist-CVD (FCM) reactor was
developed. This reactor was employed to deposit κ-Ga2O3 film with high crystallinity of ∼
104 arcsec FWHM of on-axis Rocking Curve (RC); the dilemma about its crystal structure was
resolved with the help of diffraction simulation coupled with a pole figure scan of the uncommon
pole germane to orthorhombic symmetry. Ultimately, an MSM device was fabricated on the κ-
phase and its spectral response was studied within the framework of the parabolic WKB model
to extract depletion width, unintentional doping level, and built-in electric field. Later, the work evaluated spectra of various optical functions such as refractive index, dielectric function,
and high-frequency dielectric constant. This study was concluded by an in-depth investigation
of Urbach’s tail using Codi’s rule.
Hitherto the ongoing research focussed on the deposition of high-quality pure phase κ-Ga2O3.
These objectives were achieved at low precursor flow rates leading to slow deposition rates. Low
film thickness obstructs fully utilizing the optical potential of the material. To deal with these
hurdles, a thin buffer layer of (111) oriented cubic MgO was employed in addition to a high
precursor flow rate for realizing a thicker film of κ-Ga2O3 on the sapphire substrate. The asdeposited film possesses a high absorbance in conjunction with high film thickness, owing to a
significant deposition rate. The fabricated photodetector on this film demonstrates ultra-high
responsivity of ∼ 920 WA with rapid transients. Finally, the thermal stability of the films was
assessed using temperature-dependent XRD measurements and an RSM scan. The film was
found to be thermally stable until at least 950 oC
Abstractions and Optimizations for Data-driven Applications Across Edge and Cloud
No full textModern data driven applications have a novel set of requirements. Advances in deep neural networks (DNN) and computer vision (CV) algorithms have made it feasible to extract meaningful insights from large-scale deployments of urban cameras and drone video feeds. These data driven applications, usually composed as workflows, tend to have high bandwidth and low latency requirements in order to extract timely results from large data sources. Other applications may necessitate the use of multiple geographically distributed resources. Such requirements may be driven by data privacy regulations such as the General Data Protection Regulation (GDPR) of the European Union, need for specialized hardware, or as a means of avoiding vendor lock-ins.
To support these modern applications, a diverse computing landscape has emerged over the last decade. We have witnessed increasingly powerful Edge computing resources be available in network proximity to the data sources for these applications. The number of Cloud Service Providers (CSPs) has increased along with the regions in which they operate. And finally, the CSPs have supplemented Infrastructure as a Service (IaaS) offerings with modern serverless compute offerings which promise cost benefits as well as lower operational overheads.
The availability of choices in compute resources makes it challenging for application developers to manage the lifecycle of their applications – from programming the application, to optimizing it for performance, and finally deploying it. Typically, developers rely on platforms that promise ease of programmability coupled with scalability with minimal developer effort. However, the combination of application requirements and compute resource characteristics makes it challenging for platform designers to make design choices that optimizes the application for programmability and performance. A thorough revisit of existing platforms, abstractions, and optimizations is essential for addressing these challenges.
In this thesis, we tackle these challenges with three distinct but related research contributions on scalable platforms, distributed algorithms and system optimizations:
(1) We propose Anveshak, a platform that provides a domain specific programming model and a distributed runtime for efficiently tracking entities in a multi-camera network; (2) We design algorithms and heuristics to solve MSP, which co-schedules the flight routes of a drone fleet to visit and record video at waypoints, and perform subsequent on-board Edge analytics; and (3) We develop XFaaS, a platform that allows “zero touch” deployment of functions and workflows across multiple clouds and Edges by automatically generating code wrappers, Cloud queues, and coordinating with the native FaaS engine of a CSP.
These platforms, abstractions and optimizations solve different combinations of the problem dimensions, are motivated through real-world applications, and the solutions are validated through detailed experiments on distributed systems. Taken together, this suite of contributions addresses the key gaps highlighted in this dissertation, and help bridge the gap between modern computing resource characteristics and modern application requirements
Metal-Organic Framework (MOF) Compounds: Synthesis, Structure and Catalytic Studies
No full textThe metal-organic framework (MOF) compounds exhibit interesting
developments in coordination chemistry, materials science and other branches of
chemistry. These compounds have potential applications in diverse fields that include
gas separation, storage, catalysis, conductivity and water purification, etc. The MOFs are
a class of crystalline inorganic-organic hybrid compounds formed with metal nodes and
organic linking units. Such compounds can be prepared either by one-pot synthesis or by
sequential synthesis. In one-pot synthesis, the organic linkers with binding sites are
directly allowed to interact with the metal salts in a single pot. On the other hand, in
sequential synthesis, the pre-prepared metalloligand as precursor mixture interacts with
the second metal ions in a sequential manner.
In the current work, the synthesis of new metal-organic framework (MOF)
compounds by employing one-pot as well as sequential synthesis has been attempted.
The structures of the compounds have been determined by employing the single-crystal
X-ray diffraction technique. The prepared compounds possess both Lewis acidic and
basic centers, which were exploited for catalytic studies. The catalytically active sites
were employed to catalyze single-step as well as multiple-step reactions in a tandem
fashion. Some of the compounds exhibit water adsorption and proton conductivity
behavior. In addition, the thermochromic behavior of the framework compounds has
been described in detail.
Chapter 1 presents a brief overview of metal-organic framework (MOF)
compounds and their various important properties.
In chapter 2, the synthesis, structure and Lewis acid catalytic studies of
heterometallic metal-organic framework compounds using Cu(I)/Ag(I) based
metalloligand, [M6(2-mna)6]
6-
(M = Cu+
/Ag+
; H2mna = 2-mercaptonicotinic acid) and
Cd2+ are presented.
In chapter 3, the synthesis, structure, thermochromism and Lewis acid catalytic
behavior of [Cu6(2-mna)6]6-based heterometallic metal-organic framework compound
are presented.
In chapter 4, we present the synthesis, structure and Lewis acid-base catalytic
studies of 1,4-naphthalene dicarboxylic acid based metal-organic framework compounds.
In chapter 5, the synthesis, structure and one-pot tandem catalytic studies of bi functional metal-organic framework compounds are presented.
In chapter 6, the synthesis, structure and multi-step tandem catalytic studies of 3-
amino triazole based metal-organic framework compounds are presented