Aerodynamic Design Optimization with Consistently Discrete Sensitivity Derivatives Via the Incremental Iterative Method

In this study which involves advanced fluid-flow codes, an incremental iterative formulation (also known as the delta or correction form), together with the well-known spatially split approximate-factorization algorithm, is presented for solving the large, sparse systems of linear equations that are associated with aerodynamic sensitivity analysis. For the smaller two dimensional problems, a direct method can be applied to solve these linear equations in either the standard or the incremental form, in which case the two are equivalent. However, iterative methods are needed for larger two-dimensional and three dimensional applications because direct methods require more computer memory than is currently available. Iterative methods for solving these equations in the standard form are generally unsatisfactory due to an ill-conditioned coefficient matrix; this problem is overcome when these equations are cast in the incremental form. The methodology is successfully implemented and tested using an upwind cell-centered finite-volume formulation applied in two dimensions to the thin-layer Navier-Stokes equations for external flow over an airfoil. In three dimensions this methodology is demonstrated with a marching-solution algorithm for the Euler equations to calculate supersonic flow over the High-Speed Civil Transport configuration (HSCT 24E). The sensitivity derivatives obtained with the incremental iterative method from a marching Euler code are used in a design-improvement study of the HSCT configuration that involves thickness, camber, and planform design variables

Bio-implantable microdevices and structures for functional electrical stimulation applications

This dissertation describes the development of microstructures and devices for applications in functional electrical stimulation. A nerve cuff electrode design has been developed for applications in neural electrical stimulation and recording, which addresses limitations with existing cuff electrodes. The developed clip-on micro-cuff electrode design consists of a naturally closed cuff with inner diameter in the micro-scale or above. A novel pinch-hinge feature allows a user to easily open the cuff and place it on target nerve tissue for stimulation or recording purposes. Upon release of the pinch-hinge, the cuff assumes its normally closed nature. The device conducts and reads electrical signals in the amplitude and frequency range of typical neural signals. A typical clip-on cuff device with 800 µm inner diameter is opened to its maximum extent by a relatively low force of less than 0.8 N, offering an alternative to other designs requiring application of a force for cuff closure. For applications involving gastric muscle stimulation, a novel gastric pacing electrode is fabricated in biocompatible silicone elastomer. In response to physiological temperature of about 37 ˚C, polyethylene glycol embedded inside the device body melts due to which the structure changes from a more rigid state initially to a more flexible state. This is expected to reduce tissue penetration during and after electrode implantation. A comprehensive piece-wise discrete element equivalent circuit model has been developed to represent an electrode-neural tissue interface. This model addresses internal aspects of both the tissue and the electrode surface and is an improvement over previous models. The equivalent circuit is employed in conjunction with electronic circuit simulation software to study the electrical response of an axon to external stimulus. Simulation results broadly correlate with practical observations reported by others. Lastly, a new percutaneous access device functioning as an interface between implants and the external world is reported here. The device made of silicone elastomer incorporates stress concentration features and shows promise for improved robustness and reliability. The device also incorporates micro-scale porous structures to allow for tissue in-growth to facilitate anchoring of the device

A Study on Assessment and Management of Diabetic Gastropathy

To assess and manage Diabetic gastropathy. Diabetic gastropathy is least concern in developing countries but many patients receiving oral anti diabetics leads to serious gastric problems. This study involves identification of gastric problems and improves compliance, medication adherence among population and also determine the severity of gastric problems due to oral hypoglycemic drugs. In our study, women are more effected (54%) than men (46%). Most effected age group is 40-60 years age with 58% Mild (male-20.9%, female-22.27%) and moderate (male-37.9%, females-39.7%) conditions are the most effected in terms of severity. This is due to poor glycemic control and not using proper medication, diet. Treatment should be focused on improving gastric symptoms by controlling gastric emptying. Prevention of gastric symptoms by following some dietary changes, nutritional and physiological support is effective to patients

Observations on computational methodologies for use in large-scale, gradient-based, multidisciplinary design incorporating advanced CFD codes

How a combination of various computational methodologies could reduce the enormous computational costs envisioned in using advanced CFD codes in gradient based optimized multidisciplinary design (MdD) procedures is briefly outlined. Implications of these MdD requirements upon advanced CFD codes are somewhat different than those imposed by a single discipline design. A means for satisfying these MdD requirements for gradient information is presented which appear to permit: (1) some leeway in the CFD solution algorithms which can be used; (2) an extension to 3-D problems; and (3) straightforward use of other computational methodologies. Many of these observations have previously been discussed as possibilities for doing parts of the problem more efficiently; the contribution here is observing how they fit together in a mutually beneficial way

Identifying poverty-driven need by augmenting census and community survey data

Master of ScienceDepartment of Computing and Information SciencesWilliam H. HsuNeed is a function of both individual household’s ability to meet basic requirements such as food, shelter, clothing, medical care, and transportation, and latent exogenous factors such as the cost of living and available community support for such requirements. Identifying this need driven poverty helps in understanding the socioeconomic status of individuals and to identify the areas of development. This work aims at using georeferenced data from the American Community Survey (ACS) to estimate baseline need based on aggregated socioeconomic variables indicating absolute and relative poverty. In this project, I implement and compare the results of several machine learning classification algorithms such as Random Forest, Support Vector Machine, and Logistic Regression to identify poverty for different block groups in the United State