Analysis of fixed-pitch straight-bladed VAWT with asymmetric airfoils

Selection of the airfoil is crucial for better aerodynamic performance and dimensions of a smaller-capacity fixed-pitch SB-VAWT. Most of the earlier research works with SB-VAWT mainly utilized symmetric airfoils as its blade shape, but several research works indicated that the performance of fixed pitch SB-VAWT with asymmetric blades have the potential to exhibit superior characteristics at low Reynolds numbers (RN). However, currently there is lack of comprehensive information in the public domain regarding the desirable aerodynamic and geometric features of prospective asymmetric airfoils for SB-VAWTs. Against this backdrop, this research has been undertaken with an objective to perform detail systematic investigative analysis with asymmetric airfoils appropriate for smaller-capacity fixed-pitch SB-VAWT with optimum design configuration. A computational method has been developed in the present study after identifying and considering the main aerodynamic challenges of smaller-capacity SB-VAWT using theoretical coefficients rather than using rarely available expensive experimental results. After conducting literature survey and detail performance analyses with available asymmetric airfoils, it has been found that there is a need for designing special-purpose airfoils for smaller-capacity SB-VAWT. Under this circumstance, a new airfoil “MI-VAWT1” has been designed and it has been found that its performance is much superior to other prospective asymmetric airfoils and conventionally used symmetric NACA 0015 at low RN and low tip speed ratio ranges. Another airfoil, named as “MI-STRUT1”, has been designed for blade supporting struts to reduce the detrimental parasitic drag losses. After considering the design parameters and detailed sensitivity analyses with selected important parameters, a new class of 3kW SB-VAWT (named as “MI-VAWT 3000”) has been proposed

A Practical Framework for Storing and Searching Encrypted Data on Cloud Storage

Security has become a significant concern with the increased popularity of cloud storage services. It comes with the vulnerability of being accessed by third parties. Security is one of the major hurdles in the cloud server for the user when the user data that reside in local storage is outsourced to the cloud. It has given rise to security concerns involved in data confidentiality even after the deletion of data from cloud storage. Though, it raises a serious problem when the encrypted data needs to be shared with more people than the data owner initially designated. However, searching on encrypted data is a fundamental issue in cloud storage. The method of searching over encrypted data represents a significant challenge in the cloud. Searchable encryption allows a cloud server to conduct a search over encrypted data on behalf of the data users without learning the underlying plaintexts. While many academic SE schemes show provable security, they usually expose some query information, making them less practical, weak in usability, and challenging to deploy. Also, sharing encrypted data with other authorized users must provide each document's secret key. However, this way has many limitations due to the difficulty of key management and distribution. We have designed the system using the existing cryptographic approaches, ensuring the search on encrypted data over the cloud. The primary focus of our proposed model is to ensure user privacy and security through a less computationally intensive, user-friendly system with a trusted third party entity. To demonstrate our proposed model, we have implemented a web application called CryptoSearch as an overlay system on top of a well-known cloud storage domain. It exhibits secure search on encrypted data with no compromise to the user-friendliness and the scheme's functional performance in real-world applications.Comment: 146 Pages, Master's Thesis, 6 Chapters, 96 Figures, 11 Table

METABOLIC MODELING AND OMICS-INTEGRATIVE ANALYSIS OF SINGLE AND MULTI-ORGANISM SYSTEMS: DISCOVERY AND REDESIGN

Computations and modeling have emerged as indispensable tools that drive the process of understanding, discovery, and redesign of biological systems. With the accelerating pace of genome sequencing and annotation information generation, the development of computational pipelines for the rapid reconstruction of high-quality genome-scale metabolic networks has received significant attention. These models provide a rich tapestry for computational tools to quantitatively assess the metabolic phenotypes for various systems-level studies and to develop engineering interventions at the DNA, RNA, or enzymatic level by careful tuning in the biophysical modeling frameworks. in silico genome-scale metabolic modeling algorithms based on the concept of optimization, along with the incorporation of multi-level omics information, provides a diverse array of toolboxes for new discovery in the metabolism of living organisms (which includes single-cell microbes, plants, animals, and microbial ecosystems) and allows for the reprogramming of metabolism for desired output(s). Throughout my doctoral research, I used genome-scale metabolic models and omics-integrative analysis tools to study how microbes, plants, animal, and microbial ecosystems respond or adapt to diverse environmental cues, and how to leverage the knowledge gleaned from that to answer important biological questions. Each chapter in this dissertation will provide a detailed description of the methodology, results, and conclusions from one specific research project. The research works presented in this dissertation represent important foundational advance in Systems Biology and are crucial for sustainable development in food, pharmaceuticals and bioproduction of the future. Advisor: Rajib Sah

Developing Video Measurement of Strain for Polymers Using Labview

The purpose of this study was to develop a computer assisted video extensometer to measure strain, primarily for transparent polymers such as PET. Two markers, which were attached to the specimen, were tracked by the video system. Different techniques were proposed to track these markers implemented in NI LabVIEW software. The accuracy of video extensometry depends on the template creation procedure, shapes of the markers, algorithm and camera resolution. Comparison of the different techniques was discussed in this study. The technique with two different templates in two separate regions provided 15% to 25% more accuracy compared to other techniques discussed in this study. The accuracy of the proposed video extensometer was also shown. The accuracy of the proposed technique was 99% using a camera with 640x480 resolution. Moreover, comparison with a Laser Extensometer and the proposed video extensometer were also shown. Fringes were also captured when the PET was in tensile stress and under polarized light. The pattern-changes of these fringes were recorded by NI LabVIEW software. The disappearance of fringes before the yield point in tension indicated permanent plastic deformation in PET.Mechanical & Aerospace Engineerin

Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling

Background. The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. Results. Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment- incubated vs. synthetic condition. Conclusion. In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues

Li Diffusion in Various Polymorphs of LiTiS2: Insights from Theory

In the present contribution, the stoichiometric and defect properties in 1T, c and 3R polymorphs of lithium titanium disulphide (LixTiS2) are investigated theoretically with periodic quantum chemical methods

Investigation of microbial community interactions between Lake Washington methanotrophs using genome-scale metabolic modeling

Background. The role of methane in global warming has become paramount to the environment and the human society, especially in the past few decades. Methane cycling microbial communities play an important role in the global methane cycle, which is why the characterization of these communities is critical to understand and manipulate their behavior. Methanotrophs are a major player in these communities and are able to oxidize methane as their primary carbon source. Results. Lake Washington is a freshwater lake characterized by a methane-oxygen countergradient that contains a methane cycling microbial community. Methanotrophs are a major part of this community involved in assimilating methane from lake water. Two significant methanotrophic species in this community are Methylobacter and Methylomonas. In this work, these methanotrophs are computationally studied via developing highly curated genome-scale metabolic models. Each model was then integrated to form a community model with a multi-level optimization framework. The competitive and mutualistic metabolic interactions among Methylobacter and Methylomonas were also characterized. The community model was next tested under carbon, oxygen, and nitrogen limited conditions in addition to a nutrient-rich condition to observe the systematic shifts in the internal metabolic pathways and extracellular metabolite exchanges. Each condition showed variations in the methane oxidation pathway, pyruvate metabolism, and the TCA cycle as well as the excretion of formaldehyde and carbon di-oxide in the community. Finally, the community model was simulated under fixed ratios of these two members to reflect the opposing behavior in the two-member synthetic community and in sediment-incubated communities. The community simulations predicted a noticeable switch in intracellular carbon metabolism and formaldehyde transfer between community members in sediment- incubated vs. synthetic condition. Conclusion. In this work, we attempted to predict the response of a simplified methane cycling microbial community from Lake Washington to varying environments and also provide an insight into the difference of dynamics in sediment-incubated microcosm community and synthetic co-cultures. Overall, this study lays the ground for in silico systems-level studies of freshwater lake ecosystems, which can drive future efforts of understanding, engineering, and modifying these communities for dealing with global warming issues

Enhancing lean supply chain through traffic light quality management system

Lean is a continuous journey to grow and excel the company. Any company want to develop and cope with the world pace must adopt lean. However, in most of the organizations the management culture or people’s mentality is not so good to embrace change. They have predestined mind set where no change is normally allowed. Lean is a cooperative way of working that involves all departments and all personnel to work together in a team for the betterment of the entire company. Without providing fixed solution of any problem it suggests the best way that people willingly accept to do. Lean normally deals with highest quality, shorter lead time and lowest cost. In Bangladesh, most of the garment manufacturing companies are experiencing a massive quality problem. We describe a case where traffic light, a tool of lean quality system was adopted to a garment manufacturing company in Bangladesh. We also provide the charts to contrast the before and after scenario in detail, in order to illustrate the company benefits. After the traffic light system being implemented, the quality status was improved, production capacity was increased; significant days were saved that enhanced the lead time and thus strengthen the supply chain

Development of a Mechanistic Chamber Model of a Novel Peristaltic Compressor for Air-conditioning and Refrigeration Applications

The air-conditioning and refrigeration industry are constantly searching for efficiency improvements to vapor compression refrigeration systems. A valve-less compressor with variable volume ratios can significantly increase efficiency and add flexibility to these systems. The novel peristaltic compressor is introduced, which has the ability to operate at variable volume ratios and without valves. This compressor operates by means of a progressively actuated diaphragm that compresses vapor and stimulates flow through a cylindrical chamber. In this study, we will present a discretized thermodynamic model of a peristaltic compressor by splitting the mechanism into a series of conjoined segments. Mass and energy balance equations for each compression segment analyzed to create a mechanistic chamber model of the compressor with the segments interacting through flow of mass between them. This model includes a geometric model of the compression chamber created by the interface between the flexible diaphragm and the cylinder. The geometric model is coupled with the mass and energy balance to predict the compressor performance metrics. The preliminary model results show that the peristaltic compressor presents unique attributes that will be explored in future work

Studies on gait control using a portable pneumatically powered ankle-foot orthosis (PPAFO) during human walking

A powered ankle-foot orthosis (AFO) can be very useful for people with neuromuscular injury. Control of powered AFOs will be more efficient to provide assistance to individuals with lower limb muscle impairments if we can identify different gait events during walking. A walking or gait cycle can be divided into multiple phases and sub-phases by proper gait event detection, and these phases/sub-phases are associated with one of the three main functional tasks during the gait cycle: loading response, forward propulsion, and limb advancement. The gait cycle of one limb can also be characterized by examining the limb’s behavior over one stride, which can be quantified as 0% to 100% of a gait cycle (GC). One easy approach to identify gait events is by checking whether sensor signals go above/below a predetermined threshold. By estimation of a walker’s instantaneous state, as represented by a specific percentage of the gait cycle (from states 0 to 100, which correlate with 0% to 100% GC), we can efficiently detect the various gait events more accurately. Our Human Dynamics and Controls Laboratory previously developed the portable pneumatically powered ankle-foot orthosis (PPAFO), which was capable of providing torque in both plantarflexion and dorsiflexion directions at the ankle. There were three types of sensor attached with the PPAFO (two force sensitive resistors and an angle sensor). In this dissertation, three aspects of effective control strategies for the PPAFO have been proposed. In the first study, two improved and reliable state estimators (Modified Fractional Time (MFT) and Artificial Neural Network (ANN)) were proposed for identifying when the limb with the PPAFO was at a certain percentage of the gait cycle. A correct estimation of percentage of gait cycle will assist with detecting specific gait events more accurately. The performance of new estimators was compared to a previously developed Fractional Time state estimation technique. To control a powered AFO using these estimators, however, detection of proper actuation timing is necessary. In the second study, a supervised learning algorithm to classify the appropriate start timing for plantarflexor actuation was proposed. Proper actuation timing has only been addressed in the literature in terms of functional efficiency or metabolic cost during walking. In this study, we will explore identifying the plantarflexor actuation timing in terms of biomechanics outcomes of human walking using a machine learning based algorithm. The third study investigated the recognition of different gait modes encountered during walking. The actuation scheme plays a significant role in walking on level ground, stair descent or stair ascent modes. The wrong actuation scheme for a given mode can cause falls or trips. A gait mode recognition technique was developed for detecting these different modes by attaching an inertial measurement unit and using a classifier based on artificial neural networks. This new algorithm improves upon the current one step delay limitation found as a drawback of a previously developed technique. Overall, this dissertation focused on addressing some important issues related to control of powered AFO that ultimately will help to assist people wearing the device in daily life situations during walking. The proposed approaches and algorithms introduced in this dissertation showed very promising results that proved that these methods can successfully improve the control system of powered AFOs