A significant improvement can be achieved in the performance of transonic transport aircraft using Multidisciplinary Design Optimization (MDO) by implementing truss-braced wing concepts in combination with other advanced technologies and novel design innovations. A considerable reduction in drag can be obtained by using a high aspect ratio wing with thin airfoil sections and tip-mounted engines. However, such wing structures could suffer from a significant weight penalty. Thus, the use of an external strut or a truss bracing is promising for weight reduction. Due to the unconventional nature of the proposed concept, commonly available wing weight equations for transport aircraft will not be sufficiently accurate. Hence, a bending material weight calculation procedure was developed to take into account the influence of the strut upon the wing weight, and this was coupled to the Flight Optimization System (FLOPS) for total wing weight estimation. The wing bending material weight for single-strut configurations is estimated by modeling the wing structure as an idealized double-plate model using a piecewise linear load method. Two maneuver load conditions 2.5g and -1.0g factor of safety of 1.5 and a 2.0g taxi bump are considered as the critical load conditions to determine the wing bending material weight. From preliminary analyses, the buckling of the strut under the -1.0g load condition proved to be the critical structural challenge. To address this issue, an innovative design strategy introduces a telescoping sleeve mechanism to allow the strut to be inactive during negative g maneuvers and active during positive g maneuvers. Also, more wing weight reduction is obtained by optimizing the strut force, a strut offset length, and the wing-strut junction location. The best configuration shows a 9.2% savings in takeoff gross weight, an 18.2% savings in wing weight and a 15.4% savings in fuel weight compared to a cantilever wing counterpart. / Master of Science

No / This paper focuses on the metamodel-based collaborative optimization (CO). The objective is to improve the computational efficiency of CO in order to handle multidisciplinary design optimization problems utilising high fidelity models. To address these issues, two levels of metamodel building techniques are proposed: metamodels in the disciplinary optimization are based on multi-fidelity modelling (the interaction of low and high fidelity models) and for the system level optimization a combination of a global metamodel based on the moving least squares method and trust region strategy is introduced. The proposed method is demonstrated on a continuous fiber-reinforced composite beam test problem. Results show that methods introduced in this paper provide an effective way of improving computational efficiency of CO based on high fidelity simulation models.

No / This paper presents the development of an Analytical Target Cascading (ATC) Multidisciplinary Design Optimization (MDO) framework for a steady-state engine calibration optimization problem. The implementation novelty of this research is the use of the ATC framework to formulate the complex multi-objective engine calibration problem, delivering a considerable enhancement compared to the conventional 2-stage calibration optimization approach [1]. A case study of a steady-state calibration optimization of a Gasoline Direct Injection (GDI) engine was used for the calibration problem analysis as ATC. The case study results provided useful insight on the efficiency of the ATC approach in delivering superior calibration solutions, in terms of “global” system level objectives (e.g. improved fuel economy and reduced particulate emissions), while meeting “local” subsystem level requirements (such as combustion stability and exhaust gas temperature constraints). The ATC structure facilitated the articulation of engineering preference for smooth calibration maps via the ATC linking variables, with the potential to deliver important time saving for the overall calibration development process.

In the upcoming decade, the proliferation of high-LEO constellations is expected to exceed 20,000 objects, yet comprehensive Post Mission Disposal (PMD) strategies for these constellations are currently lacking. With the inherent challenges of efficiently deorbiting satellites from High-LEO orbits, there arises an urgent need to explore innovative approaches. Building upon insights garnered from the ReDSHIFT project and anticipating the proliferation of high-LEO constellations such as OneWeb, TeleSat, and GuoWang, this thesis delves into the potential viability of the Resonance Corridor Method for PMD. The investigation encompasses key metrics, including deorbit timelines and $\Delta v$ requirements to meet regulatory standards or recommendations, with comparisons drawn against alternative methods like Perigee Decrease and Graveyard Orbit solutions. Through this analysis, scenarios emerge where the Resonance Corridor method demonstrates advantages, offering feasible delta-v values while ensuring compliance with regulatory standards and recommendations. The findings yield categorizations of high-LEO constellation shells into specific disposal feasibility groups, thereby providing valuable insights into how space sustainability practices can be added into spacecraft design to align with evolving space debris mitigation standards. Additionally, certain altitude-inclination combinations are found to naturally align with the resonance corridor method, while others necessitate minor architectural adjustments to optimize effectiveness.

Multidisciplinary design optimization (MDO) refers to the process of designing systems characterized by the interaction of multiple interconnected disciplines. High-fidelity MDO usually requires large computational resources due to the computational cost of achieving multidisciplinary consistent solutions by coupling high-fidelity physics-based solvers. Gradient-based minimization algorithms are generally applied to find local minima, due to their efficiency in solving problems with a large number of design variables. This represents a limitation to performing global MDO and integrating black-box type analysis tools, usually not providing gradient information. The latter issues generally inhibit a wide use of MDO in complex industrial applications. An architecture named multi-criterion adaptive sampling MDO (MCAS-MDO) is presented in the current research for complex simulation-based applications. This research aims at building a global derivative-free optimization tool able to employ high-fidelity/expensive black-box solvers for the analysis of the disciplines. MCAS-MDO is a surrogate-based architecture featuring a variable level of coupling among the disciplines and is driven by a multi-criterion adaptive sampling (MCAS) assessing coupling and sampling uncertainties. MCAS uses the dynamic radial basis function surrogate model to identify the optimal solution and explore the design space through parallel infill of new solutions. The MCAS-MDO is tested versus a global derivative-free multidisciplinary feasible (MDF) approach, which solves fully-coupled multidisciplinary analyses, for two analytical test problems. Evaluation metrics include number of function evaluations required to achieve the optimal solution and sample distribution. The MCAS-MDO outperforms the MDF showing a faster convergence by clustering refined function evaluations in the optimum region. The architecture is applied to a steady fluid-structure interaction (FSI) problem, namely the design of a tapered three-dimensional carbon fiber-reinforced plastic hydrofoil for minimum drag. The objective is the design of shape and composite material layout subject to hydrodynamic, structural, and geometrical constraints. Experimental data are available for the original configuration of the hydrofoil and allow validating the FSI analysis, which is performed coupling computational fluid dynamics, solving the Reynolds averaged Navier-Stokes equations, and finite elements, solving the structural equation of elastic motion. Hydrofoil forces, tip displacement, and tip twist are evaluated for several materials providing qualitative agreement with the experiments and confirming the need for the two-way versus one-way coupling approach in case of significantly compliant structures. The free-form deformation method is applied to generate shape modifications of the hydrofoil geometry. To reduce the global computational expense of the optimization, a design space assessment and dimensionality reduction based on the Karhunen–Loève expansion (KLE) is performed off-line, i.e. without the need for high-fidelity simulations. It provides with a selection of design variables for the problem at hand through basis rotation and re-parametrization. By using the KLE, an efficient design space is identified for the current problem and the number of design variables is reduced by 92%. A sensitivity analysis is performed prior to the optimization to assess the variability associated with the shape design variables and the composite material design variable, i.e. the fiber orientation. These simulations are used to initialize the surrogate model for the optimization, which is carried out for two models: one in aluminum and one in composite material. The optimized designs are assessed by comparison with the original models through evaluation of the flow field, pressure distribution on the body, and deformation under the hydrodynamic load. The drag of the aluminum and composite material hydrofoils is reduced by 4 and 11%, respectively, increasing the hydrodynamic efficiency by 4 and 7%. The optimized designs are obtained by evaluating approximately 100 designs. The quality of the results indicates that global derivative-free MDO of complex engineering applications using expensive black-box solvers can be achieved at a feasible computational cost by minimizing the design space dimensionality and performing an intelligent sampling to train the surrogate-based optimization.

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.

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.