京都大学 / 新制・課程博士 / 博士(工学) / 甲第25013号 / 工博第5190号 / 新制||工||1991(附属図書館) / 京都大学大学院工学研究科機械理工学専攻 / (主査)教授 泉井 一浩, 教授 小森 雅晴, 教授 井上 康博 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

This thesis discusses a methodology for probabilistic design optimization of aircraft structures subject to a multidisciplinary set of requirements originating from the desire to minimize structural weight while fulfilling the demands for quality, safety, producibility, and affordability. With this design methodology as the framework, a software is developed, which is capable of performing design optimization of metallic built-up beam structures where the material properties, external load, as well as the structural dimensions are treated as probabilistic random variables. The structural and failure analyses are based on analytical and semi-empirical methods whereas the component reliability analysis is based on advanced first-order second moment method. Metrics-based analytical models are used for the manufacturability analysis of individual parts with the total manufacturing cost estimated using models derived from the manufacturing cost / design guide developed by the Battelle¡¯s Columbus Laboratories. The resulting optimization problem is solved using the method of sequential quadratic programming. A wing spar design optimization problem is used as a demonstrative example including a comparison between non-buckling and buckling web design concepts. A sensitivity analysis is performed and the optimization results are used to highlight the tradeoffs among weight, reliability, and manufacturing cost.

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<p> This thesis contributes significantly towards the development of a robust algorithm for design sensitivity analysis and the optimization of microwave structures. Based on the frequency-domain finite-element method, the approach provides accurate sensitivity information using both 2-D and 3-D formulations. It also significantly accelerates the optimization process.</p> <p> The design sensitivity analysis method greatly influences the efficiency and accuracy of gradient-based optimization by providing the response gradient (response Jacobians) for the whole range of parameter values. However, common commercial electromagnetic simulators provide only specific engineering responses, such as Z- or S-parameters. No sensitivity information is made available for further exploration of the design-parameter space. It is common to compute the design sensitivities from the response themselves using finite-difference or higher-order approximations at the response level. Consequently, for each design parameter of interest, at least one additional full-wave analysis is performed. However, when the number of design parameters becomes large, the simulation time becomes prohibitive for electromagnetic design procedures.</p> <p> The self-adjoint sensitivity analysis (SASA) is so far the most efficient way to extract the sensitivity information for the network parameters with the finite-element method. As an improvement of the adjoint-variable method (AVM), it eliminates the additional (adjoint) system analyses. With one single full-wave analysis, the sensitivities with respect to all design parameters are computed. This significantly improves the efficiency of the sensitivity computations. Through our proposed method, the finite-difference frequency-domain self-adjoint sensitivity analysis (FDFD-SASA), the process is further improved by eliminating the need for exporting the system matrix, thus improving both compatibility and computation time. The only requirement for integrating the sensitivity solver with the commercial EM simulators is the ability to access the field solution at the user-defined grid points. The sensitivity information is obtained by simple manipulation of the field solution as a post-process and hence, it adds little or no overhead to the simulation time.</p> <p> We explore the feasibility of implementing our newly proposed method using field solutions from a frequency-domain commercial solver HFSS v 11. We confirm the accuracy of the FDFD-SASA for shape parameters of metallic structures. Both 2-D and 3-D examples are presented, where the reference results are provided through the traditional finite-difference approximations. Also, the efficiency of the FDFD-SASA is validated by a filter design example, exploiting gradient-based optimization algorithm.</p> / Thesis / Master of Applied Science (MASc)

Through this research, a robust aircraft design methodology is developed for analysis and optimization of the Air Vehicle (AV) segment of Unmanned Aerial Vehicle (UAV) systems. The analysis functionality of the AV design is integrated with a Genetic Algorithm (GA) to form an integrated Multi-disciplinary Design Optimization (MDO) methodology for optimal AV design synthesis. This research fills the gap in integrated subsonic fixed-wing UAV AV MDO methods. No known single methodology captures all of the phenomena of interest over the wide range of UAV families considered here. Key advancements include: 1) parametric Low Reynolds Number (LRN) airfoil aerodynamics formulation, 2) UAV systems mass properties definition, 3) wing structural weight methods, 4) self-optimizing flight performance model, 5) automated geometry algorithms, and 6) optimizer integration. Multiple methods are provided for many disciplines to enable flexibility in functionality, level of detail, computational expediency, and accuracy. The AV design methods are calibrated against the High-Altitude Long-Endurance (HALE) Global Hawk, Medium-Altitude Endurance (MAE) Predator, and Tactical Shadow 200 classes, which exhibit significant variations in mission performance requirements and scale from one another. Technology impacts on the design of the three UAV classes are evaluated from a representative system technology year through 2025. Avionics, subsystems, aerodynamics, design, payloads, propulsion, and structures technology trends are assembled or derived from a variety of sources. The technology investigation serves the purposes of validating the effectiveness of the integrated AV design methods and to highlight design implications of technology insertion through future years. Flight performance, payload performance, and other attributes within a vehicle family are fixed such that the changes in the AV designs represent technology differences alone, and not requirements evolution. The optimizer seeks to minimize AV design gross weight for a given mission requirement and technology set. All three UAV families show significant design gross weight reductions as technology improves. The predicted design gross weight in 2025 for each class is: 1) 12.9% relative to the 1994 Global Hawk, 2) 6.26% relative to the 1994 Predator, and 3) 26.3% relative to the 2000 Shadow 200. The degree of technology improvement and ranking of contributing technologies differs among the vehicle families. The design gross weight is sensitive to technologies that directly affect the non-varying weights for all cases, especially payload and avionics/subsystems technologies. Additionally, the propulsion technology strongly affects the high performance Global Hawk and Predator families, which have high fuel mass fractions relative to the Tactical Shadow 200 family. The overall technology synergy experienced 10-11 years after the initial technology year is 6.68% for Global Hawk, 7.09% for Predator, and 4.22% for the Shadow 200, which means that the technology trends interact favorably in all cases. The Global Hawk and Shadow 200 families exhibited niche behavior, where some vehicles attained higher aerodynamic performance while others attained lower structural mass fractions. The high aerodynamic performance Global Hawk vehicles had high aspect ratio wings with sweep, while the low structural mass fraction vehicles had straight, relatively low aspect ratios and smaller wing spans. The high aerodynamic performance Shadow 200 vehicles had relatively low wing loadings and large wing spans, while the lower structural mass fraction counterparts sought to minimize physical size. / Ph. D.

Microphone arrays are becoming increasingly integrated into every facet of life. From sonar to gunshot detection systems to hearing aids, the performance of each system is enhanced when multi-sensor processing is implemented in lieu of single sensor processing. Head mounted microphone arrays have a broad spectrum of uses that follow the rigorous demands of human hearing. From noise cancellation to focused listening, from localization to classification of sound sources, any and all attributes of human hearing may be augmented through the use of microphone arrays and signal processing algorithms. Placing a set of headphones on a human provides several desirable features such as hearing protection, control over the acoustic environment (via headphone speakers), and a means of communication. The shortcoming of headphones is the complete occlusion of the pinnae (the ears), disrupting auditory cues utilized by humans for sound localization. This thesis presents the underlying theory in designing microphone arrays placed on diffracting bodies, specifically the human head. A progression from simple to complex geometries chronicles the effect of diffracting structures on array manifold matrices. Experimental results validate theoretical and computational models showing that arrays mounted on diffracting structures provide better beamforming and localization performance than arrays mounted in the free field. Data independent, statistically optimal, and adaptive beamforming methods are presented to cover a broad range of goals present in array applications. A framework is developed to determine the performance potential of microphone array designs regardless of geometric complexity. Directivity index, white noise gain, and singular value decomposition are all utilized as performance metrics for array comparisons. The biological basis for human hearing is presented as a fundamental attribute of headset array optimization methods. A method for optimizing microphone locations for the purpose of the recreation of HRTFs is presented, allowing transparent hearing (also called natural hearing restoration) to be performed. Results of psychoacoustic testing with a prototype headset array are presented and examined. Subjective testing shows statistically significant improvements over occluded localization when equipped with this new transparent hearing system prototype. / Ph. D.

Thermophysical characterization of anisotropic composite materials is extremely important in the control of today fabrication processes and in the prediction of structure failure due to thermal stresses. Accuracy in the estimation of the thermal properties can be improved if the experiments are designed carefully. However, on one hand, the typically used parametric study for the design optimization is tedious and time intensive. On the other hand, commonly used gradient-based estimation methods show instabilities resulting in nonconvergence when used with models that contain correlated or nearly correlated parameters. The objectives of this research were to develop systematic and reliable methodologies for both Experimental Design Optimization (EDO) used for the determination of thermal properties, and Simultaneous Parameter Estimation (SPE). Because of their advantageous features, Genetic Algorithms (GAs) were investigated for use as a strategy for both EDO and SPE. The EDO and SPE approaches used involved the maximization of an optimality criterion associated with the sensitivity matrix of the unknown parameters, and the minimization of the ordinary least squares error, respectively. Two versions of a general-purpose genetic-based program were developed: one is designed for the analysis of any EDO / SPE problems for which a mathematical model can be provided, while the other incorporates a control-volume finite difference scheme allowing for the practical analysis of complex problems. The former version was used to illustrate the genetic performance on the optimization of a difficult mathematical test function. Two test cases previously solved in the literature were first analyzed to demonstrate and assess the GA-based {EDO/SPE} methodology. These problems included the optimization of one and two dimensional designs for the estimation at ambient temperature of two and three thermal properties, respectively (effective thermal conductivity parallel and perpendicular to the fibers plane and effective volumetric heat capacity), of anisotropic carbon/epoxy composite materials. The two dimensional case was further investigated to evaluate the effects of the optimality criterion used for the experimental design on the accuracy of the estimated properties. The general-purpose GA-based program was then successively applied to three advanced studies involving the thermal characterization of carbon/epoxy anisotropic composites. These studies included the SPE of successively three, seven and nine thermophysical parameters, with for the latter case, a two dimensional EDO with seven experimental key parameters. In two of the three studies, the parameters were defined to represent the dependence of the thermal properties with temperature. Finally, the kinetic characterization of the curing of three thermosetting materials (an epoxy, a polyester and a rubber compound) was accomplished resulting in the SPE of six kinetic parameters. Overall, the GA method was found to perform extremely well despite the high degree of correlation and low sensitivity of many parameters in all cases studied. This work therefore validates the use of GAs for the thermophysical characterization of anisotropic composite materials. The significance in using such algorithms is not only the solution to ill-conditioned problems but also, a drastically cost savings in both experimental and time expenses as they allow for the EDO and SPE of several parameters at once. / Ph. D.