Thesis (M.S.)--Mississippi State University. Department of Mechanical Engineering. / Title from title screen. Includes bibliographical references.

Complex and chronic health conditions with multiple diagnoses and lacking in clinical practice guidelines often require a multidisciplinary care management scheme. Research has shown that the domain knowledge for these conditions is multidisciplinary, inconsistent, nonstandardized and poorly categorized making them heterogeneous and consequently challenging for collaborative work. The application of the boundary objects approach has come to the forefront as a way of closing communication gaps in collaborative work. There are limited research efforts in the application of boundary objects in the health care field and almost none in the area of complex chronic conditions. Research investigation of the application of boundary objects in heterogeneous domains is also limited. The primary objective(s) of this thesis is (are) to develop, test and evaluate a model and a methodology for creating boundary objects in the heterogeneous domain of complex chronic conditions. The methodology in this research applies a two-staged approach for enabling interoperability in the domain. The first stage is the development of a controlled vocabulary as a boundary object and the second stage of the two-staged approach is the development of an ontology as a boundary object to generate syntactic, semantic and pragmatic levels of interoperability in the dynamic domain. Towards these objectives, the boundary objects developed in the study satisfy certain unique requirements, namely to, have pragmatic boundaries, be dynamic in nature and be in standardized forms. To the best of our knowledge, this research is the first to investigate the development of boundary objects in the heterogeneous domain of complex chronic conditions. The outcome of this research is the development of a model for the generation of boundary objects to enhance communication among multidisciplinary clinicians. The model is developed in the heterogeneous domain of two complex chronic health conditions, namely, multiple chemical sensitivity and chronic pain. A testing and an evaluation process conducted in this research demonstrates that a high percentage of clinicians (>80%) agree on the overall usefulness of the boundary objects developed in this research. The results from the research are promising in terms of the potential applications of boundary objects in closing communication gaps in the multidisciplinary management of complex conditions.

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For years social workers have been involved in the investigation of sexual abuse of children. This involvement of social workers is therefore no new concept. In the involvement of social workers in this field there has been a close relationship between social workers and especially the judiciary for several years. Apart from the legal profession other professional role players are also involved in the investigation of the sexually molested child. This research focuses particularly on the different roles of the members of the multidisciplinary team during the investigation of sexual abuse of children. A multidisciplinary team approach brings together different role players in this way to make use of the knowledge and strengths of all to the benefit of the victim so that effective service can be rendered. The researcher is of the opinion that if the multidisciplinary team acts in a more efficient way, more prosecution of sexual crimes committed against children could be brought about. / Thesis (M.A. (MW))--North-West University, Potchefstroom Campus, 2010.

For years social workers have been involved in the investigation of sexual abuse of children. This involvement of social workers is therefore no new concept. In the involvement of social workers in this field there has been a close relationship between social workers and especially the judiciary for several years. Apart from the legal profession other professional role players are also involved in the investigation of the sexually molested child. This research focuses particularly on the different roles of the members of the multidisciplinary team during the investigation of sexual abuse of children. A multidisciplinary team approach brings together different role players in this way to make use of the knowledge and strengths of all to the benefit of the victim so that effective service can be rendered. The researcher is of the opinion that if the multidisciplinary team acts in a more efficient way, more prosecution of sexual crimes committed against children could be brought about. / Thesis (M.A. (MW))--North-West University, Potchefstroom Campus, 2010.

A series of novel algorithms for performing aerostructural shape and topology optimization are introduced and applied to the design of aircraft wings. An isoparametric level set method is developed for performing topology optimization of wings and other non-rectangular structures that must be modeled using a non-uniform, body-fitted mesh. The shape sensitivities are mapped to computational space using the transformation defined by the Jacobian of the isoparametric finite elements. The mapped sensitivities are then passed to the Hamilton-Jacobi equation, which is solved on a uniform Cartesian grid. The method is derived for several objective functions including mass, compliance, and global von Mises stress. The results are compared with SIMP results for several two-dimensional benchmark problems. The method is also demonstrated on a three-dimensional wingbox structure subject to fixed loading. It is shown that the isoparametric level set method is competitive with the SIMP method in terms of the final objective value as well as computation time. In a separate problem, the SIMP formulation is used to optimize the structural topology of a wingbox as part of a larger MDO framework. Here, topology optimization is combined with aerodynamic shape optimization, using a monolithic MDO architecture that includes aerostructural coupling. The aerodynamic loads are modeled using a threedimensional panel method, and the structural analysis makes use of linear, isoparametric, hexahedral elements. The aerodynamic shape is parameterized via a set of twist variables representing the jig twist angle at equally spaced locations along the span of the wing. The sensitivities are determined analytically using a coupled adjoint method. The wing is optimized for minimum drag subject to a compliance constraint taken from a 2g maneuver condition. The results from the MDO algorithm are compared with those of a sequential optimization procedure in order to quantify the benefits of the MDO approach. While the sequentially optimized wing exhibits a nearly-elliptical lift distribution, the MDO design seeks to push a greater portion of the load toward the root, thus reducing the structural deflection, and allowing for a lighter structure. By exploiting this trade-off, the MDO design achieves a 42% lower drag than the sequential result.

This thesis presents new tools and techniques developed to address the challenging problem of high-fidelity aerostructural optimization with respect to large numbers of design variables. A new mesh-movement scheme is developed that is both computationally efficient and sufficiently robust to accommodate large geometric design changes and aerostructural deformations. A fully coupled Newton-Krylov method is presented that accelerates the convergence of aerostructural systems and provides a 20% performance improvement over the traditional nonlinear block Gauss-Seidel approach and can handle more flexible structures. A coupled adjoint method is used that efficiently computes derivatives for a gradient-based optimization algorithm. The implementation uses only machine accurate derivative techniques and is verified to yield fully consistent derivatives by comparing against the complex step method. The fully-coupled large-scale coupled adjoint solution method is shown to have 30% better performance than the segregated approach. The parallel scalability of the coupled adjoint technique is demonstrated on an Euler Computational Fluid Dynamics (CFD) model with more than 80 million state variables coupled to a detailed structural finite-element model of the wing with more than 1 million degrees of freedom. Multi-point high-fidelity aerostructural optimizations of a long-range wide-body, transonic transport aircraft configuration are performed using the developed techniques. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300000 DOF. Two design optimization problems are solved: one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each optimization uses a multi-point formulation with 5 cruise conditions and 2 maneuver conditions. The optimization problems have 476 design variables are optimal results are obtained within 36 hours of wall time using 435 processors. The TOGW minimization results in a 4.2% reduction in TOGW with a 6.6% fuel burn reduction, while the fuel burn optimization resulted in a 11.2% fuel burn reduction with no change to the takeoff gross weight.

A series of novel algorithms for performing aerostructural shape and topology optimization are introduced and applied to the design of aircraft wings. An isoparametric level set method is developed for performing topology optimization of wings and other non-rectangular structures that must be modeled using a non-uniform, body-fitted mesh. The shape sensitivities are mapped to computational space using the transformation defined by the Jacobian of the isoparametric finite elements. The mapped sensitivities are then passed to the Hamilton-Jacobi equation, which is solved on a uniform Cartesian grid. The method is derived for several objective functions including mass, compliance, and global von Mises stress. The results are compared with SIMP results for several two-dimensional benchmark problems. The method is also demonstrated on a three-dimensional wingbox structure subject to fixed loading. It is shown that the isoparametric level set method is competitive with the SIMP method in terms of the final objective value as well as computation time. In a separate problem, the SIMP formulation is used to optimize the structural topology of a wingbox as part of a larger MDO framework. Here, topology optimization is combined with aerodynamic shape optimization, using a monolithic MDO architecture that includes aerostructural coupling. The aerodynamic loads are modeled using a threedimensional panel method, and the structural analysis makes use of linear, isoparametric, hexahedral elements. The aerodynamic shape is parameterized via a set of twist variables representing the jig twist angle at equally spaced locations along the span of the wing. The sensitivities are determined analytically using a coupled adjoint method. The wing is optimized for minimum drag subject to a compliance constraint taken from a 2g maneuver condition. The results from the MDO algorithm are compared with those of a sequential optimization procedure in order to quantify the benefits of the MDO approach. While the sequentially optimized wing exhibits a nearly-elliptical lift distribution, the MDO design seeks to push a greater portion of the load toward the root, thus reducing the structural deflection, and allowing for a lighter structure. By exploiting this trade-off, the MDO design achieves a 42% lower drag than the sequential result.

This thesis presents new tools and techniques developed to address the challenging problem of high-fidelity aerostructural optimization with respect to large numbers of design variables. A new mesh-movement scheme is developed that is both computationally efficient and sufficiently robust to accommodate large geometric design changes and aerostructural deformations. A fully coupled Newton-Krylov method is presented that accelerates the convergence of aerostructural systems and provides a 20% performance improvement over the traditional nonlinear block Gauss-Seidel approach and can handle more flexible structures. A coupled adjoint method is used that efficiently computes derivatives for a gradient-based optimization algorithm. The implementation uses only machine accurate derivative techniques and is verified to yield fully consistent derivatives by comparing against the complex step method. The fully-coupled large-scale coupled adjoint solution method is shown to have 30% better performance than the segregated approach. The parallel scalability of the coupled adjoint technique is demonstrated on an Euler Computational Fluid Dynamics (CFD) model with more than 80 million state variables coupled to a detailed structural finite-element model of the wing with more than 1 million degrees of freedom. Multi-point high-fidelity aerostructural optimizations of a long-range wide-body, transonic transport aircraft configuration are performed using the developed techniques. The aerostructural analysis employs Euler CFD with a 2 million cell mesh and a structural finite element model with 300000 DOF. Two design optimization problems are solved: one where takeoff gross weight is minimized, and another where fuel burn is minimized. Each optimization uses a multi-point formulation with 5 cruise conditions and 2 maneuver conditions. The optimization problems have 476 design variables are optimal results are obtained within 36 hours of wall time using 435 processors. The TOGW minimization results in a 4.2% reduction in TOGW with a 6.6% fuel burn reduction, while the fuel burn optimization resulted in a 11.2% fuel burn reduction with no change to the takeoff gross weight.

A survey of the existing literature indicates that optimization on the power management logic of hybrid electric vehicle is mostly performed after the design of the powertrain architecture or the power source components are finalized. The goal of this research is to utilize Multidisciplinary Design Optimization (MDO) to automate and optimize the vehicle’s powertrain component sizes, while simultaneously determining the optimal power management logic in developing the most cost-effective system solution. A generic, modular, and flexible vehicle model utilizing a backward-looking architecture is created using scalable powertrain components. The objective of the research work is to study the energy efficiency of the vehicle system, where the dynamics of the vehicle is not of concern; a backward-looking architecture could be used to compute the power consumption and the overall efficiency accurately while minimizing the required computing resource. An optimization software platform utilizing multidisciplinary design optimization approach is implemented containing the generic vehicle model and an optimizer of the user’s choice. The software model is created in the MATLAB/Simulink environment, where the optimization code and the powertrain component properties are implemented using m-files, and the power consumption calculations of the vehicle system are performed in Simulink. Furthermore, a feature-based optimization technique is developed with the motivation of significantly reducing the simulation run-time. To demonstrate the capabilities of the developed approach and contributions of the research, two optimization case studies are undertaken: (i) series hybrid electric vehicles, and (ii) police vehicle anti-idling system. As the first case study, a plug-in battery-only series hybrid electric vehicle with similar power components as the Chevrolet Volt is created, where the battery size and the power management logic are simultaneously optimized. The objective function of the optimizer is defined from the financial cost perspective, where the objective is to minimize the initial cost of batteries, gasoline and electricity consumption over a period of five years, and the carbon tax as a penalty function for fuel emissions. The battery-only series hybrid electric vehicle is subsequently extended to include ultracapacitors, and the optimization process is expanded to the rest of the powertrain components and power management logic. A comparison between the optimization algorithms found that only genetic algorithm (GA) was capable of finding the optimal solution during a full simulation, while simulated annealing and pattern search were not able to converge to any solution after a 24-hour period. A comparison between the full genetic algorithm optimization and the feature-based (FB) method with secondary optimization found that although the final cost function of the FB methodology is higher than that of the full GA optimization, the total simulation run-time is approximately ten times less using the FB method. The behaviour of the solutions found via both methods exhibited almost identical characteristics, further confirming the validity of the feature-based methodology. Finally, a benchmarking comparison found that with more accurate manufacturers’ component data and additional appropriate performance requirements, the proposed software platform will be capable of predicting a solution that is comparable to the Chevrolet Volt. The second case study involves optimizing an anti-idling system for police vehicles using the same optimization algorithm and generic vehicle model. The goal of the optimization study is to select an additional battery and determine the power management logic to reduce the engine idling time of a police vehicle. It is found that depending on the SOC threshold, the duration of time over which the engine is activated varies in a non-linear fashion, where local minima and maxima exist. A design study confirmed that by utilizing the anti-idling system, significant cost reduction can be realized when compared to one without the anti-idling system. A comparison between the various optimization algorithms showed that the feature-based optimization can obtain a relatively accurate solution while reducing simulation time by approximately 90%. This significant reduction in simulation time warrants the feature-based optimization technique a powerful tool for vehicle design. Due to the high cost of the electrical energy storage components, it is currently still more cost-effective to use the fossil fuel as the primary energy source for transportation. However, given the rise of fuel cost and the advancement in the electrical energy storage technology, it is inevitable that the cost of the electrical and chemical energy storage method will reach a balance point. The proposed optimization platform allows the user the capability and flexibility to obtain the optimal vehicle solution with ease at any given time in the future.