The objectives of this study are (1) to develop an accurate and efficient fatigue analysis procedure that can be used in reliability analysis and reliability-based design optimization (RBDO) of composite wind turbine blades; (2) to develop a wind load uncertainty model that provides realistic uncertain wind load for the reliability analysis and the RBDO process; and (3) to obtain an optimal composite wind turbine blade that satisfies target reliability for durability under the uncertain wind load. The current research effort involves: (1) developing an aerodynamic analysis method that can effectively calculate detailed wind pressure on the blade surface for stress analysis; (2) developing a fatigue failure criterion that can cope with non-proportional multi-axial stress states in composite wind turbine blades; (3) developing a wind load uncertainty model that represents realistic uncertain wind load for fatigue reliability of wind turbine systems; (4) applying the wind load uncertainty model into a composite wind turbine blade and obtaining an RBDO optimum design that satisfies a target probability of failure for a lifespan of 20 years under wind load uncertainty. In blade fatigue analysis, resultant aerodynamic forces are usually applied at the aerodynamic centers of the airfoils of a blade to calculate stress/strain. However, in reality the wind pressures are applied on the blade surface. A wind turbine blade is often treated as a typical beam-like structure for which fatigue life calculations are limited in the edge-wise and/or flap-wise direction(s). Using the beam-like structure, existing fatigue analysis methods for composite wind turbine blades cannot cope with the non-proportional multi-axial stress states that are endured by wind turbine blades during operation. Therefore, it is desirable to develop a fatigue analysis procedure that utilizes detailed wind pressures as wind loads and considers non-proportional multi-axial stress states in fatigue damage calculation. In this study, a 10-minute wind field realization, determined by a 10-minute mean wind speed V10 and a 10-minute turbulence intensity I10, is first simulated using Veers’ method. The simulated wind field is used for aerodynamic analysis. An aerodynamic analysis method, which could efficiently generate detailed quasi-physical blade surface pressures, has been developed. The generated pressures are then applied on a high-fidelity 3-D finite element blade model for stress and fatigue analysis. The fatigue damage calculation considers the non-proportional multi-axial complex stress states. A detailed fatigue damage contour, which indicates the fatigue failure locally, can be obtained using the developed fatigue analysis procedure. As the 10-minute fatigue analysis procedure is deterministic in this study, the calculated 10-minute fatigue damage is determined by V10 and I10. It is necessary to clarify that the rotational speed of the wind turbine blade is assumed to be constant (12.1 rpm) and the pitch angle is fixed to be 0 degree for different wind conditions, since the rotational speed control and pitch angle control have not been considered in this study. For predicting the fatigue life of a wind turbine, a fixed Weibull distribution is widely used to determine the percentage of time the wind turbine experiences different mean wind speeds during its life-cycle. Meanwhile, fixed turbulence intensities are often used based on the designed wind turbine types. These simplifications, i.e., fixed Weibull distribution and fixed turbulence intensities, ignore the realistic uncertain wind load when designing a reliable wind turbine system. In the real world, both the mean wind speed and turbulence intensity vary constantly over one year, and their annual distributions are different at different locations and in different years. Thus, it is necessary to develop a wind load uncertainty model that can provide a realistic uncertain wind load for designing reliable wind turbine systems. In this study, 249 groups of measured wind data, collected at different locations and in different years, are used to develop a dynamic wind load uncertainty model. The dynamic wind load uncertainty model consists of annual wind load variation and wind load variation in a large spatiotemporal range, i.e., at different locations and in different years. The annual wind load variation is represented by the joint probability density function of V10 and I10. The wind load variation in a large spatiotemporal range is represented by the probability density functions of five parameters, C, k, a, b, and τ, which determine the joint probability density function of V10 and I10. In order to obtain the RBDO optimum design efficiently, a deterministic design optimization (DDO) procedure of a composite wind turbine blade has been first carried out using averaged percentage of time (probability) for each wind condition. A wind condition is specified by two terms: 10-minute mean wind speed and 10-minute turbulence intensity. In this research, a probability table, which consists of averaged probabilities corresponding to different wind conditions, is referred as a mean wind load. The mean wind load is generated using the dynamic wind load uncertainty model. During the DDO process, the laminate thickness design variables are tailored to minimize the total cost of composite materials while satisfying the target fatigue lifespan of 20 years. It is found that, under the mean wind load condition, the fatigue life of the initial design is only 0.0004 year. After the DDO process, even though the cost at the DDO optimum design is increased by 31.5% compared to that at the initial design, the predicted fatigue life at the DDO optimum design is significantly increased to 19.9995 years. Reliability analyses of the initial design and the DDO optimum design have been carried out using the wind load uncertainty model and Monte Carlo simulation. The reliability analysis results show that the DDO procedure reduces the probability of failure from 100% at the initial design to 49.9% at the DDO optimum design considering only wind load uncertainty. In order to satisfy the target 2.275% probability of failure, it is necessary to further improve the fatigue reliability of the composite wind turbine blade by RBDO. Reliability-based design optimization of the composite wind turbine blade has been carried out starting at the DDO optimum design. Fatigue hotspots for RBDO are identified among the laminate section points, which are selected from the DDO optimum design. Local surrogate models for 10-minute fatigue damage have been created at the selected hotspots. Using the local surrogate models, both the wind load uncertainty and manufacturing variability has been included in the RBDO process. It is found that the probability of failure is 50.06% at the RBDO initial design (DDO optimum design) considering both wind load uncertainty and manufacturing variability. During the RBDO process, the normalized laminate thickness design variables are tailored to minimize the total cost of composite materials while satisfying the target 2.275% probability of failure. The obtained RBDO optimum design reduces the probability of failure from 50.06% at the DDO optimum design to 2.28%, while increasing the cost by 3.01%.

<p>Safety-critical applications have to function correctly even in presence of faults. This thesis deals with techniques for tolerating effects of transient and intermittent faults. Reexecution, software replication, and rollback recovery with checkpointing are used to provide the required level of fault tolerance. These techniques are considered in the context of distributed real-time systems with non-preemptive static cyclic scheduling.</p><p>Safety-critical applications have strict time and cost constrains, which means that not only faults have to be tolerated but also the constraints should be satisfied. Hence, efficient system design approaches with consideration of fault tolerance are required.</p><p>The thesis proposes several design optimization strategies and scheduling techniques that take fault tolerance into account. The design optimization tasks addressed include, among others, process mapping, fault tolerance policy assignment, and checkpoint distribution.</p><p>Dedicated scheduling techniques and mapping optimization strategies are also proposed to handle customized transparency requirements associated with processes and messages. By providing fault containment, transparency can, potentially, improve testability and debugability of fault-tolerant applications.</p><p>The efficiency of the proposed scheduling techniques and design optimization strategies is evaluated with extensive experiments conducted on a number of synthetic applications and a real-life example. The experimental results show that considering fault tolerance during system-level design optimization is essential when designing cost-effective fault-tolerant embedded systems.</p>

Within the scope of this thesis study, an optimization tool for the ballistic design of three-dimensional grains in solid propellant rocket motors is developed. The modeling of grain geometry and burnback analysis is performed analytically by using basic geometries like cylinder, cone, sphere, ellipsoid, prism and torus. For the internal ballistic analysis, a quasi-steady zero-dimensional flow solver is used. Genetic algorithms have been studied and implemented to the design process as an optimization algorithm. Lastly, the developed optimization tool is validated with the predesigned rocket motors.

In January 2005, President Bush announced the Vision for Space Exploration. This vision involved a progressive expansion of human capabilities beyond Low Earth Orbit beginning with a return to the moon no later than 2020. Current design processes utilized to meet this vision employ performance based trade studies to determine the lowest cost, highest reliability solution. The methodology implemented in this dissertation focuses on a concurrent evaluation of the performance, cost, and reliabilities of lunar architectures. This process directly addresses the top level requirements early in the design process and allows the decision maker to evaluate the highest reliability, lowest cost lunar architectures without being distracted by the performance details of the architecture. To achieve this methodology of bringing optimal cost and reliability solutions to the decision maker, parametric performance, cost, and reliability models are created to model each vehicle element. These models were combined using multidisciplinary optimization techniques and response surface equations to create parametric vehicle models which quickly evaluate the performance, reliability, and cost of the vehicles. These parametric models, known as ROSETTA models, combined with a life cycle cost calculator provide the tools necessary to create a lunar architecture simulation. The integration of the tools into an integrated framework that can quickly and accurately evaluate the lunar architectures is presented. This lunar architecture selection tool is verified and validated against the Apollo and ESAS lunar architectures. The results of this lunar architecture selection tool are then combined into a Pareto frontier to guide the decision maker to producing the highest reliability architecture for a given life cycle cost. With this presented methodology, the decision maker can transparently choose a lunar architecture solution based upon the high level design discriminators. This method can achieve significant reductions in life cycle costs (over 40%) keeping the same architecture reliability as a traditional design process. This methodology also allows the decision maker to choose a solution which achieves a significant reduction in failure rate (over 50%) while maintaining the same life cycle costs as the point solution of a traditional design process.

Accuracy of the finite difference sensitivity calculations are improved by calculating the optimum finite difference interval sizes. In an aerodynamic inverse design algorithm, a compressor cascade geometry is perturbed by shape functions and finite differences sensitivity derivatives of the flow variables are calculated with respect to the base geometry flow variables. Sensitivity derivatives are used in an optimization code and a new airfoil is designed verifying given design characteristics. Accurate sensitivities are needed for optimization process. In order to find the optimum finite difference interval size, a method is investigated. Convergence error estimation techniques in iterative solutions and second derivative estimations are investigated to facilitate this method. For validation of the method, analytical sensitivity calculations of Euler equations are used and several applications are performed. Efficiency of the finite difference sensitivity calculations is improved by parallel computing. Finite difference sensitivity calculations are independent tasks in an inverse aerodynamic design algorithm and can be computed separately. Sensitivity calculations are performed on parallel processors and computing time is decreased.

The Flux Jacobian matrices, the elements of which are the derivatives of the flux vectors with respect to the flow variables, are needed to be evaluated in implicit flow solutions and in analytical sensitivity analyzing methods. The main motivation behind this thesis study is to explore the accuracy of the numerically evaluated flux Jacobian matrices and the effects of the errors in those matrices on the convergence of the flow solver, on the accuracy of the sensitivities and on the performance of the design optimization cycle. To perform these objectives a flow solver, which uses exact Newton&rsquo / s method with direct sparse matrix solution technique, is developed for the Euler flow equations. Flux Jacobian is evaluated both numerically and analytically for different upwind flux discretization schemes with second order MUSCL face interpolation. Numerical flux Jacobian matrices that are derived with wide range of finite difference perturbation magnitudes were compared with analytically derived ones and the optimum perturbation magnitude, which minimizes the error in the numerical evaluation, is searched. The factors that impede the accuracy are analyzed and a simple formulation for optimum perturbation magnitude is derived. The sensitivity derivatives are evaluated by direct-differentiation method with discrete approach. The reuse of the LU factors of the flux Jacobian that are evaluated in the flow solution enabled efficient sensitivity analysis. The sensitivities calculated by the analytical Jacobian are compared with the ones that are calculated by numerically evaluated Jacobian matrices. Both internal and external flow problems with varying flow speeds, varying grid types and sizes are solved with different discretization schemes. In these problems, when the optimum perturbation magnitude is used for numerical Jacobian evaluation, the errors in Jacobian matrix and the sensitivities are minimized. Finally, the effect of the accuracy of the sensitivities on the design optimization cycle is analyzed for an inverse airfoil design performed with least squares minimization.

Decision making is necessary to provide a synthesis scheme to design activities and identify the most preferred design alternative. There exist several methods that address modeling designer preferences in a graphical manner to aid the decision making process. For instance, the Conjoint Analysis has been proven effective for various multi-attribute design problems by utilizing a ranking- or rating-based approach along with the graphical representation of the designer preference. However, the ranking or rating of design alternatives can be inconsistent from different users and it is often difficult to get customer responses in a timely fashion. The high number of alternative comparisons required for complex engineering problems can be exhausting for the decision maker. In addition, many design objectives can have interdependencies that can increase complexity and uncertainty throughout the decision making process. The uncertainties apparent in the attainment of subjective data as well as with system models can reduce the reliability of decision analysis results. To address these issues, the use of a new technique, the Improved Conjoint Analysis, is proposed to enable the modeling of designer preferences and trade-offs under the consideration of uncertainty. Specifically, a simulation-based ranking scheme is implemented and incorporated into the traditional process of the Conjoint Analysis. The proposed ranking scheme can reduce user fatigue and provide a better schematic decision support process. In addition, the incorporation of uncertainty in the design process provides the capability of producing robust or reliable products. The efficacy and applicability of the proposed framework are demonstrated with the design of a cantilever beam, a power-generating shock absorber, and a mesostructured hydrogen storage tank.

Industrial and military vehicles, including trucks, tanks and others, employ cooling systems that address passenger cooling and auxiliary cooling loads ranging from a few Watts to 50 kW or more. Such systems are typically powered using vapor-compression cooling systems that either directly supply cold air to the various locations, or cool an intermediate single-phase coolant closed loop, which in turn serves as the coolant for the passenger cabins and auxiliary loads such as electronics modules. Efforts are underway to enhance the performance of such systems, and also to develop more light weight and compact systems that would remove high heat fluxes. The distributed cooling configuration offers the advantage of a smaller refrigerant system package. The heat transfer between the intermediate fluid and air or with the auxiliary heat loads can be fine tuned through the control of flow rates and component sizes and controls to maintain tight tolerances on the cooling performance. Because of the additional loop involved in such a configuration, there is a temperature penalty between the refrigerant and the ultimate heat sink or source, but in some configurations, this may be counteracted through judicious design of the phase change-to-liquid coupled heat exchangers. Such heat exchangers are inherently smaller due to the high heat transfer coefficients in phase change and single-phase liquid flow compared to air flow. The additional loop also requires a pump to circulate the fluid, which adds pumping power requirements. However, a direct refrigerant-to-heat load coupling system might in fact be suboptimal if the heat loads are distributed across large distances. This is because of the significantly higher pressure drops (and saturation temperature drops) incurred in transporting vapor or two-phase fluids through refrigerant lines across long plumbing elements. An optimal system can be developed for any candidate application by assessing the tradeoffs in cooling capacity, heat exchanger sizes and configurations, and compression, pumping and fan power. In this study, a versatile simulation platform for a wide variety of direct and indirectly coupled cooling systems was developed to enable comparison of different component geometries and system configurations based on operating requirements and applicable design constraints. Components are modeled at increasing levels of complexity ranging from specified closest approach temperatures for key components to models based on detailed heat transfer and pressure drop models. These components of varying complexity can be incorporated into the system model as desired and trade-off analyses on system configurations performed. Employing this platform as a screening, comparison, and optimization tool, a number of conventional vapor-compression and distributed cooling systems were analyzed to determine the efficacy of the distributed cooling scheme in mobile cooling applications. Four systems serving approximately a 6 kW cooling duty, two with air-coupled evaporators and two with liquid-coupled evaporators, were analyzed for ambient conditions of 37.78°C and 40% relative humidity. Though the condensers and evaporators are smaller in liquid-coupled systems, the total mass of the heat exchangers in the liquid-coupled systems is larger due to the additional air-to-liquid heat exchangers that the configuration requires. Additionally, for the cooling applications considered, the additional compressor power necessitated by the liquid-coupled configuration and the additional power consumed by the liquid-loop pumps result in the coefficient of performance being lower for liquid-coupled systems than for air-coupled systems. However, the use of liquid-coupling in a system does meet the primary goal of decreasing the system refrigerant inventory by enabling the use of smaller condensers and evaporators and by eliminating long refrigerant carrying hoses.

The expected growth of civil aviation over the next twenty years places significant emphasis on revolutionary technology development aimed at mitigating the environmental impact of commercial aircraft. As the number of technology alternatives grows along with model complexity, current methods for Pareto finding and multiobjective optimization quickly become computationally infeasible. Coupled with the large uncertainty in the early stages of design, optimal designs are sought while avoiding the computational burden of excessive function calls when a single design change or technology assumption could alter the results. This motivates the need for a robust and efficient evaluation methodology for quantitative assessment of competing concepts. This research presents a novel approach that combines Bayesian adaptive sampling with surrogate-based optimization to efficiently place designs near Pareto frontier intersections of competing concepts. Efficiency is increased over sequential multiobjective optimization by focusing computational resources specifically on the location in the design space where optimality shifts between concepts. At the intersection of Pareto frontiers, the selection decisions are most sensitive to preferences place on the objectives, and small perturbations can lead to vastly different final designs. These concepts are incorporated into an evaluation methodology that ultimately reduces the number of failed cases, infeasible designs, and Pareto dominated solutions across all concepts. A set of algebraic samples along with a truss design problem are presented as canonical examples for the proposed approach. The methodology is applied to the design of ultra-high bypass ratio turbofans to guide NASA's technology development efforts for future aircraft. Geared-drive and variable geometry bypass nozzle concepts are explored as enablers for increased bypass ratio and potential alternatives over traditional configurations. The method is shown to improve sampling efficiency and provide clusters of feasible designs that motivate a shift towards revolutionary technologies that reduce fuel burn, emissions, and noise on future aircraft.

A Bayesian adaptive sampling method is developed for highly coupled multidisciplinary design problems. The method addresses a major challenge in aerospace design: exploration of a design space with computationally expensive analysis tools such as computational fluid dynamics (CFD) or finite element analysis. With a limited analysis budget, it is often impossible to optimize directly or to explore a design space with off-line design of experiments (DoE) and surrogate models. This difficulty is magnified in multidisciplinary problems with feedbacks between disciplines because each design point may require iterative analyses to converge on a compatible solution between different disciplines. Bayesian Collaborative Sampling (BCS) is a bi-level architecture for adaptive sampling that simulataneously - concentrates disciplinary analyses in regions of a design space that are favorable to a system-level objective - guides analyses to regions where interdisciplinary coupling variables are probably compatible BCS uses Bayesian models and sequential sampling techniques along with elements of the collaborative optimization (CO) architecture for multidisciplinary optimization. The method is tested with the aero-structural design of a glider wing and the aero-propulsion design of a turbojet engine nacelle.