L'optimisation multidisciplinaire (MDO) à base de gradients est efficace et très utilisée pour le dimensionnement structural d'ailes flexibles. Cependant, dans le contexte de simulations numériques haute-fidélité, le calcul efficace des gradients reste un défi majeur. L'objectif de ce travail est d'étudier les approches les mieux adaptées aux spécificités du calcul de sensibilité des efforts aéroélastiques par rapport à des paramètres structuraux.Deux techniques de calcul de gradient haute-fidélité adaptées aux systèmes aéroélastiques fortement couplés sont proposées. La technique la plus intrusive repose sur les formulations directe et adjointe qui nécessitent un effort d'implémentation logicielle substantiel. Alternativement, nous proposons une approche découplée et non-intrusive, moins lourde à implémenter et cependant capable de fournir une approximation précise des gradients. Ces deux techniques ont été intégrées dans le logiciel CFD elsA de l'Onera.La précision, l'efficience et l'applicabilité de ces méthodes sont démontrées sur le cas-test avion de transport civil Common Research Model (CRM). Nous résolvons un problème inverse dont l'objectif est de retrouver, en conditions de vol de croisière, une loi cible de vrillage voilure. Ces deux méthodes s'avèrent comparables en matière de précision et de coût. Elles offrent ainsi une souplesse supplémentaire de mise en œuvre en fonction du niveau d'intégration recherché dans le processus MDO. / To improve the structural design of flexible wings, gradient based Multidisciplinary Design Optimization (MDO) techniques are effective and widely used. However, gradients calculation is not trivial and can be costly when high-fidelity models are considered. Our objective is to study different suitable approaches to compute gradients of aeroelastic loads with respect to structural design parameters.To this end, two high-fidelity aero-structure gradient computation techniques for strongly coupled aeroelastic systems are proposed. The most intrusive technique includes the well-established direct and adjoint formulations that require substantial implementation effort. In contrast, we propose an alternative uncoupled non-intrusive approach easier to implement and yet capable of providing accurate gradients approximations. Both techniques have been implemented in the Onera elsA CFD software.Accuracy, efficiency and applicability of these methods are demonstrated on the civil transport aircraft Common Research Model (CRM) test-case. More specifically, an inverse design problem is set up with the objective of matching an in-flight target twist law distribution. These two methods prove to be comparable in terms of accuracy and cost. Thus they offer additional operational flexibility depending on the level of integration sought in the MDO process.

This material is based on research sponsored by Air Force Research Laboratory under agreement number FA8650-09-2-3938. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of Air Force Research Laboratory or the U.S. Government. / This work seeks to develop multidisciplinary design optimization (MDO) methods to find the optimal design of a particular aircraft called an Efficient Supersonic Air Vehicle (ESAV). This is a long-range military bomber type of aircraft that is to be designed for high speed (supersonic) flight and survivability. The design metric used to differentiate designs is minimization of the take-off gross weight. The usefulness of MDO tools, rather than compartmentalized design practices, in the early stages of the design process is shown. These tools must be able to adequately analyze all pertinent physics, simultaneously and collectively, that are important to the aircraft of interest. Low-fidelity and higher-fidelity ESAV MDO frameworks have been constructed. The analysis codes in the higher-fidelity framework were validated by comparison with the legacy B-58 supersonic bomber aircraft. The low-fidelity framework used a computationally expensive process that utilized a large design of computer experiments study to explore its design space. This resulted in identifying an optimal ESAV with an arrow wing planform. Specific challenges to designing an ESAV not addressed with the low-fidelity framework were addressed with the higher-fidelity framework. Specifically, models to characterize the effects of the low-observable ESAV characteristics were required. For example, the embedded engines necessitated a higher-fidelity propulsion model and engine exhaust-washed structures discipline. Low-observability requirements necessitated adding a radar cross section discipline. A relatively less costly computational process utilizing successive NSGA-II optimization runs was used for the higher-fidelity MDO. This resulted in an optimal ESAV with a trapezoidal wing planform. The NSGA-II optimizer considered arrow wing planforms in early generations during the process, but these were later discarded in favor of the trapezoidal planform. Sensitivities around this optimal design were computed using the well-known ANOVA method to characterize the surrounding design space. The lower and higher fidelity frameworks could not be combined in a mixed-fidelity optimization process because the low-fidelity was not faithful enough to the higher-fidelity analysis results. The low-fidelity optimum was found to be infeasible according to the higher-fidelity framework and vice versa. Therefore, the low-fidelity framework was not capable of guiding the higher-fidelity framework to the eventual trapezoidal planform optimum. / Air Force Research Laboratory / Ph. D.

Modeling and Simulation is a crucial element of the aerospace engineering design pro- cess because it allows designers to thoroughly test their solution before investing in the resources to create it. The Horizon Simulation Framework (HSF) v3.0 is an aerospace modeling and simulation tool that allows the user to verify system level requirements in the early phases of the design process. A low fidelity model of the system that is created by the user is exhaustively tested within the built-in Day-in-the-Life simulator to provide useful information in the form of failed requirements, system bottle necks and leverage points, and potential schedules of operations. The model can be stood up quickly with Extended Markup Language (XML) input files or can be customly created with Python Scripts that interact with the framework at runtime. The goal of the work presented in this thesis is to progress HSF from v2.3 to v3.0 in order to take advantage of current software development technologies. This includes converting the codebase from C++ and Lua scripting to C♯ and Python Scripting. The particulars of the considerations, benefits, and implementation of the new framework are discussed in detail. The simulation data and performance run time of the new framework were compared to that of the old framework. The new framework was found to produce similar data outputs with a faster run time.

Model-Based Systems Engineering in industry has been constantly increasing its presence within the aerospace industry. SysML is one such MBSE tool that shows complex system organization and relationships. The Horizon Simulation Framework is another MBSE tool, created by Cal Poly students, that gives users the ability to run “day-in-the-life” simulations of systems. Finding a way to link these two tools could allow systems engineers to reap the benefits of both. This thesis investigates the background and design process involved with developing the code that can convert an output file generated in SysML, into a format specifically made for the Horizon Simulation Framework. The goal was to create an interface that can allow users to model a system in SysML, and analyze the model and verify system requirements using HSF. Another goal was to expand the capabilities of the Horizon Simulation Framework by designing and develop a module that would allow users to define and analyze system-level requirements. To evaluate the effectiveness of both codes, the Aeolus example case was used. A SysML model of the system was created as the product of another thesis; SysML based CubeSat Model Design and Integration with the Horizon Simulation Framework. The Aeolus SysML model was converted and used as input in an HSF simulation. The SysML model simulation data was compared against those of the original test case. To test the requirement module, system level requirements were formulated within the Aeolus system and run in simulation, providing an analysis of the results. The results of the analysis confirmed a successful conversion of the SysML model into an equivalent HSF model and a successful analysis of system-level requirements.

Interplanetary space exploration has thus far consisted of single, expensive spacecraft missions. Mission costs are particularly high on missions to the outer planets and while invaluable, finite budgets limit our ability to perform extensive and frequent investigations of the planets. Planetary systems such as Jupiter and Saturn provide extremely complex exploration environments with numerous targets of interest. Exploring these targets in addition to the main planet requires multiple fly-bys and long mission timelines. In LEO, CubeSats have changed the exploration paradigm, offering a fast and low cost alternative to traditional space vehicles. This new mission development philosophy has the potential to significantly change the economics of interplanetary exploration and a number of missions are being developed to utilize CubeSat class spacecraft beyond earth orbit (e.g., NEAScout, Lunar Ice Cube, Marco and BioSentinel). This paper takes the CubeSat philosophical approach one step further by investigating the potential for small satellite swarms to provide extensive studies of the Saturn system. To do this, an architecture was developed to best replicate the Cassini Primary Mission science objectives using swarms of CubeSats. Cassini was chosen because of its complexity and it defines a well-understood baseline to compare against. The paper outlines the overall mission architecture developed and provides a feasible initial design for the spacecraft in the architecture. The number of swarms needed, number of CubeSats per swarm, size of the CubeSats, overall science output and estimated mission cost are all presented. Additional science objectives beyond Cassini's capabilities are also proposed. Significant scientific returns can be achieved by the swarm based architecture and the risk tolerance afforded by the utilization of large numbers of low-cost sensor carriers. This study found a potential architecture that could reduce the cost of replicating Cassini by as much as 63%. The results of this investigation are not constrained to Saturn and can be easily translated to other targets such as Uranus, Neptune or the asteroid belt.

Powering spacecraft with electric propulsion is becoming more common, especially in CubeSat-class satellites. On account of the risk of spacecraft interactions, it is important to have robust analysis and modeling tools of electric propulsion engines, particularly of the plasma plume. The Navier-Stokes equations used in classic continuum computational fluid dynamics do not apply to the rarefied plasma, and therefore another method must be used to model the flow. A good solution is to use the DSMC method, which uses a combination of particle modeling and statistical methods for modeling the simulated molecules. A DSMC simulation known as SINATRA has been developed with the goal to model electric propulsion plumes. SINATRA uses an octree mesh, is written in C++, and is designed to be expanded by further research. SINATRA has been initially validated through several tests and comparisons to theoretical data and other DSMC models. This thesis examines expanding the functionality of SINATRA to simulate charged particles and make SINATRA a DSMC-PIC hybrid. The electric potential is calculated through a 7-point 3D stencil on the mesh nodes and solved with a Gauss-Seidel solver. It is validated through test cases of charged particles to demonstrate the accuracy and capabilities of the model. An ambipolar diffusion test case is compared to a neutral diffusion case and the electric field is shown to stabilize the diffusion rate. A steady state flow test case shows the simulation is able to stabilize and solve the electric potential for a plume-like scenario. It includes additional features to simplify further research including a comprehensive user manual, industry-standard version control, text file inputs, GUI control, and simple parallelism of the simulation. Compilation and execution are standardized to be simple and platform independent to allow longevity of the code base. Finally, the execution bottlenecks of linking particles to cells and particle moving were removed to reduce the simulation time by 95%.

The CubeSat Astronomy Network is a proposed system of multiple CubeSat spacecraft capable of performing follow-up observations of astronomical targets of interest. The system is intended to serve as a space-borne platform that can complement existing systems utilized for astronomical research by undergraduate and high school students. Much research and development work has been performed to develop model-based system engineering methodologies and products for CubeSat missions, including the Horizon Simulation Framework. The Horizon Simulation Framework enables the development of system models using the Extended Markup Language (XML), and its simulation program can generate system simulations over model-specified timespans. System requirements and constraints, as well as subsystem dependencies and functions, can also be directly specified in these models. Previous work using the framework has been performed to characterize “day-in-the-life” operations for Earth-observing spacecraft. A similar goal is intended for modeling the CubeSat Astronomy Network: simulating mission operations during nominal conditions to validate system and subsystem requirements. By developing this model, system and subsystem requirements derived in the course of preliminary design for the Network can be analyzed, modelled, and evaluated for feasibility. These results can then be used to inform design decisions related to system architecture and concept of operations at the early stages of design, while the models themselves can grow and mature alongside project development and be re-used for future design work.

Space debris has become an increasingly hazardous obstacle to continued spaceflight operations. In an effort to mitigate this problem an investigation of the feasibility of a microsatellite active debris removal system was conducted. Through proposing a novel concept of operation, utilizing a grapple-and-tug system architecture, and by analyzing each resultant mission phase in the frame of a representative example, it was found that microsatellite scale systems are capable of fulfilling the active debris removal mission. Analysis of rendezvous, docking, control and deorbit mission requirements determined that the design of a grapple-and-tug system will be driven by sizing of the propellant required to deorbit the target vehicle. Further sensitivity analysis determined that target altitude and mass are critical factors in determining the capabilities of a microsatellite mission. Preliminary sizing demonstrated that hardware considerations for both satellite core and mission related activities do not impede microsatellite feasibility. Further investigation of microsatellite debris removal missions including detailed design analysis and engineering is suggested.

With the More Electric Aircraft paradigm, commercial commuter aircraft are increasing the size and complexity of electrical power systems by increasing the number of electrical loads. With this increase in complexity comes a need to analyze electrical power systems using new tools. The Hybrid Power System Optimizer (HyPSO) developed by Airbus SAS is a simulator designed to analyze new aircraft power systems. This thesis project will first provide a method to assess the reliability of complex aircraft electrical power systems before and after failure and reconfiguration events. Next, an add-on to HyPSO is developed to integrate the previously developed reliability calculations. Proof-of-concepts including new data visualizations are performed and provided.

This work proposes a new method for using reduced order models in lieu of high fidelity analysis during the sensitivity analysis step of gradient based design optimization. The method offers a reduction in the computational cost of finite difference based sensitivity analysis in that context. The method relies on interpolating reduced order models which are based on proper orthogonal decomposition. The interpolation process is performed using radial basis functions and Grassmann manifold projection. It does not require additional high fidelity analyses to interpolate a reduced order model for new points in the design space. The interpolated models are used specifically for points in the finite difference stencil during sensitivity analysis. The proposed method is applied to an airfoil shape optimization (ASO) problem and a transport wing optimization (TWO) problem. The errors associated with the reduced order models themselves as well as the gradients calculated from them are evaluated. The effects of the method on the overall optimization path, computation times, and function counts are also examined. The ASO results indicate that the proposed scheme is a viable method for reducing the computational cost of these optimizations. They also indicate that the adaptive step is an effective method of improving interpolated gradient accuracy. The TWO results indicate that the interpolation accuracy can have a strong impact on optimization search direction.