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.

Automotive engines are becoming increasingly technically complex and associated legal emissions standards more restrictive, making the task of identifying optimum actuator settings to use significantly more difficult. Given these challenges, this research aims to develop a process for engine calibration optimisation by exploiting advanced mathematical methods. Validation of this work is based upon a case study describing a steady-state Diesel engine calibration problem. The calibration optimisation problem seeks an optimal combination of actuator settings that minimises fuel consumption, while simultaneously meeting or exceeding the legal emissions constraints over a specified drive cycle. As another engineering target, the engine control maps are required as smooth as possible. The Multidisciplinary Design Optimisation (MDO) Frameworks have been studied to develop the optimisation process for the steady state Diesel engine calibration optimisation problem. Two MDO strategies are proposed for formulating and addressing this optimisation problem, which are All At Once (AAO), Collaborative Optimisation. An innovative MDO formulation has been developed based on the Collaborative Optimisation application for Diesel engine calibration. Form the MDO implementations, the fuel consumption have been significantly improved, while keep the emission at same level compare with the bench mark solution provided by sponsoring company. More importantly, this research has shown the ability of MDO methodologies that manage and organize the Diesel engine calibration optimisation problem more effectively.

The use of dispersed (virtual) teams is growing rapidly in the engineering profession. To help prepare students for work in this type of industry, university engineering courses are requiring students to work in teams. Industry leaders and university faculty are interested in improving and measuring the performance of these distributed teams. Surveys, interviews, and observations from the AerosPACE Partners for the Advancement of Collaborative Engineering (AerosPACE) capstone design course are examined to demonstrate how different collaboration tools can be used to best enhance a distributed design team's effectiveness. Collaboration tools to which distributed design teams should give extra consideration at different stages of the product development process are identified and presented in a model. Teams that follow this model will be more effective in their communication patterns. This study also consists of examining whether peer ratings can accurately predict team effectiveness (as defined by task and relational effectiveness) within a dispersed multidisciplinary, design team. The hypotheses predict that peer ratings will not be unidimensional over time, and will have a positive, significant relationship with team effectiveness. A longitudinal study was conducted on data gathered form the same capstone design course. Confirmatory factor analysis (CFA) was first used to test unidimensionality of peer ratings and structural equation modeling (SEM) was used to model the data and determine any predictive relationships. Model fit statistics are reported to confirm adequate fit for each model. Results showed that while peer ratings are unidimensional at individual time points, they don't behave equally over time and should be considered separately. The structural equation models yielded mixed results, with some parts of peer ratings significantly predicting relational effectiveness and with yet failing to predict task effectiveness. As such, by examining peer assessments, supervisors and faculty will be able to determine and predict relational effectiveness of teams working at different locations, but should use other methods to predict task 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.

The Multiple Cooling Multi-Objective Simulated Annealing Algorithm is used for the conceptual design optimization of a supersonic Unmanned Combat Aerial Vehicle (UCAV). Single and multiobjective optimization problems are addressed while limiting performance requirements between desired bounds to obtain viable aircraft configurations. A conceptual aircraft design code was prepared for planned but flexible combat missions. The results demonstrate that the optimization technique employed is an effective tool for the conceptual design of aircrafts.

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.