Este trabalho consiste no desenvolvimento de uma metodologia de otimização multidisciplinar de projeto conceitual de aeronaves. O conceito de aeronave otimizada tem como base o estudo evolutivo de características das categorias imediatas àquela que se propõe. Como estudo de caso, foi otimizada uma aeronave de treinamento militar que faça a correta transição entre as fases de treinamento básico e avançado. Para o estabelecimento dos parâmetros conceituais esse trabalho integra técnicas de entropia estatística, desdobramento da função de qualidade (QFD), aritmética fuzzy e algoritmo genético (GA) à aplicação de otimização multidisciplinar ponderada de projeto (OMPP) como metodologia de projeto conceitual de aeronaves. Essa metodologia reduz o tempo e o custo de projeto quando comparada com as técnicas tradicionais existentes. / This work is concerned with the development of a methodology for multidisciplinary optimization of the aircraft conceptual design. The aircraft conceptual design optimization was based on the evolutionary simulation of the aircraft characteristics outlined by a QFD/Fuzzy arithmetic approach where the candidates in the Pareto front are selected within categories close to the target proposed. As a test case a military trainer aircraft was designed target to perform the proper transition from basic to advanced training. The methodology for conceptual aircraft design optimization implemented in this work consisted on the integration of techniques such statistical entropy, quality function deployment (QFD), arithmetic fuzzy and genetic algorithm (GA) to the weighted multidisciplinary design optimization (WMDO). This methodology proved to be objective and well balanced when compared with traditional design techniques.

A Otimização Multidisciplinar em Projeto (em inglês, Multidisciplinary Design Optimization - MDO) é uma ferramenta de projeto importante e versátil e seu uso está se expandindo em diversos campos da engenharia. O foco desta metodologia é unir disciplinas envolvidas no projeto para que trabalhem suas variáveis concomitantemente em um ambiente de otimização, para obter soluções melhores. É possível utilizar MDO em qualquer fase do projeto, seja a fase conceitual, preliminar ou detalhada, desde que os modelos numéricos sejam ajustados às necessidades de cada uma delas. Este trabalho descreve o desenvolvimento de um código de MDO para o projeto conceitual de asas flexíveis de aeronaves, com restrição quanto ao fenômeno denominado flutter. Como uma ferramenta para o projetista na fase conceitual, os modelos numéricos devem ser razoavelmente precisos e rápidos. O intuito deste estudo é analisar o uso de metamodelos para a previsão do flutter de asas de aeronaves no código de MDO, ao invés de um modelo convencional, o que pode alterar significativamente o custo computacional da otimização. Para este fim são avaliados três técnicas diferentes de metamodelagem, que foram escolhidas por representarem duas classes básicas de metamodelos, a classe de métodos de interpolação e a de métodos de aproximação. Para representá-las foram escolhidos o método de interpolação por funções de base radial e o método de redes neurais artificiais, respectivamente. O terceiro método, que é considerado um método híbrido dos dois anteriores, é chamado de redes neurais por funções de bases radiais e é uma tentativa de acoplar as características de ambos em um único metamodelo. Os metamodelos são preparados utilizando um código para solução aeroelástica baseado no método dos elementos finitos acoplado com um modelo aerodinâmico linear de faixas. São apresentados resultados de desempenho dos três metamodelos, de onde se pode notar que a rede neural artificial é a mais adequada para previsão de flutter. O processo de MDO é realizado com o uso de um algoritmo genético multi-objetivo baseado em não-dominância, cujos objetivos são a maximização da velocidade crítica de flutter e a minimização da massa estrutural. Dois estudos de caso são apresentados para avaliar o desempenho do código de MDO, revelando que o processo global de otimização realiza de fato a busca pela fronteira de Pareto. / The Multidisciplinary Design Optimization, MDO, is an important and versatile design tool and its use is spreading out in several fields of engineering. The focus of this methodology is to put together disciplines involved with the design to work all their variables concomitantly, at an optimization environment to obtain better solutions. It is possible to use MDO in any stage of the design process, that is in the conceptual, preliminary or detailed design, as long as the numerical models are fitted to the needs of each of these stages. This work describes the development of a MDO code for the conceptual design of flexible aircraft wings, with restrictions regarding the phenomenon called flutter. As a tool for the designer at the conceptual stage, the numerical models must be fairly accurate and fast. The aim of this study is to analyze the use of metamodels for the flutter prediction of aircraft wings in the MDO code, instead of a conventional model itself, what may affect significantly the computational cost of the optimization. For this purpose, three different metamodeling techniques have been evaluated, representing two basic metamodel classes, that are, the interpolation and the approximation class. These classes are represented by the radial basis function interpolation method and the artificial neural networks method, respectively. The third method, which is considered as a hybrid of the other two, is called radial basis function neural networks and is an attempt of coupling the features of both in single code. Metamodels are prepared using an aeroelastic code based on finite element model coupled with linear aerodynamics. Results of the three metamodels performance are presented, from where one can note that the artificial neural network is best suited for flutter prediction. The MDO process is achieved using a non-dominance based multi-objective genetic algorithm, whose objectives are the maximization of critical flutter speed and minimization of structural mass. Two case studies are presented to evaluate the performance of the MDO code, revealing that overall optimization process actually performs the search for the Pareto frontier.

Multidisciplinary design optimization (MDO) can be used in computer aided engineering (CAE) to efficiently improve and balance performance of automotive structures. However, large-scale MDO is not yet generally integrated within automotive product development due to several challenges, of which excessive computing times is the most important one. In this thesis, a metamodel-based MDO process that fits normal company organizations and CAE-based development processes is presented. The introduction of global metamodels offers means to increase computational efficiency and distribute work without implementing complicated multi-level MDO methods. The presented MDO process is proven to be efficient for thickness optimization studies with the objective to minimize mass. It can also be used for spot weld optimization if the models are prepared correctly. A comparison of different methods reveals that topology optimization, which requires less model preparation and computational effort, is an alternative if load cases involving simulations of linear systems are judged to be of major importance. A technical challenge when performing metamodel-based design optimization is lack of accuracy for metamodels representing complex responses including discontinuities, which are common in for example crashworthiness applications. The decision boundary from a support vector machine (SVM) can be used to identify the border between different types of deformation behaviour. In this thesis, this information is used to improve the accuracy of feedforward neural network metamodels. Three different approaches are tested; to split the design space and fit separate metamodels for the different regions, to add estimated guiding samples to the fitting set along the boundary before a global metamodel is fitted, and to use a special SVM-based sequential sampling method. Substantial improvements in accuracy are observed, and it is found that implementing SVM-based sequential sampling and estimated guiding samples can result in successful optimization studies for cases where more conventional methods fail.

Space debris growth and an influx in space traffic will create a need for increased space traffic management. Due to orbital population density and likely future growth, the implementation of a slot architecture to Sun-synchronous orbit is considered in order to mitigate conjunctions among active satellites. This paper furthers work done in Sun-synchronous orbit slot architecture design and focuses on two main aspects. First, an in-depth relative motion analysis of satellites with respect to their assigned slots is presented. Then, a method for developing a slot architecture from a specific set of user defined inputs is derived.

Currently, the South Pole has a large data problem. It is estimated that 1.2 TB of data is being produced every day, but less than 500 GB of that data is being uploaded via aging satellites to researchers in other parts of the world. This requires those at the South Pole to analyze the data and carefully select the parts to send, possibly missing out on vital scientific information. The South Pole Carrier Pigeon will look to bridge this data gap. The Carrier Pigeon will be a small unmanned aerial vehicle that will carry a 30 TB solid-state hard drive from the South Pole to various destinations in the Southern Hemisphere, but it has been designed to y to Christchurch, New Zealand. This 87 lb. UAV will be able to y 3,650 nmi. up to 25,000 ft., using a 5.7 hp. engine. It will feature an de-icing system on the leading edge of its 8 ft. span wing to allow it to y through cold, moist climates. It will have a 39 in. long fuselage with a tail boom of 33 in. The aircraft has been designed to be made out of composites, thus reducing both the weight of the aircraft as well as its drag. It has been designed to come apart in order to be shipped successfully to the South Pole. There, it will be assembled and launched via a custom pneumatic launcher. It will y autonomously to 15,000 ft. and cruise climb throughout the flight to 25,000 ft., before descending to its destination. There, it will be caught by a net restraint system, where the hard drive will be extracted. The Carrier Pigeon is truly a unique vehicle for its size, range, and robustness.

This dissertation develops systems engineering and machine learning models for aviation maintenance support. With the constant increase in demand for air travel, aviation organizations compete to maintain airworthy aircraft to ensure the safety of passengers. Given the importance of aircraft safety, the aviation sector constantly needs technologies to enhance the maintenance experience, ensure system safety, and limit aircraft downtime. Based on the current literature, the aviation maintenance sector still relies on outdated technologies to maintain aircraft maintenance documentation, including paper-based technical orders. Aviation maintenance documentation contains a mixture of structured and unstructured technical text, mainly inputted by operators, making them prone to error, misunderstanding communication, and inconsistency. This dissertation intends to develop decision support models based on systems engineering and artificial intelligence models that can automate the maintenance documentation system, extract useful information from maintenance work orders, and predict the aircraft's top degrader signals based on textual data. The first chapter of this dissertation introduces the significant setbacks of the aviation industry and provides a working ground for the following chapters. The dissertation's second chapter develops a system engineering framework using model-based systems engineering (MBSE) methodology to model the aviation maintenance process using the systems engineering language (SysML). The outcome of this framework is the design of an automated maintenance system model that can be used to automate maintenance documentation, making it less prone to error. The third chapter of the dissertation uses textual data in maintenance work orders to develop a hybrid approach that uses natural language processing (NLP) and transformer models to predict the readiness of a legacy aircraft. The model was tested using a real-life case study of the EA-6B military aircraft. The fourth chapter of this dissertation develops an ensemble transformer model based on three different transformer models. The ensemble model leverages the benefits of three different transformer architectures and is used to classify events based on an aviation log-based dataset. This dissertation's final and fifth chapter summarizes key findings, proposes future work directions, and provides the dissertation's limitations.

This paper describes the changes made from Cal Poly’s initial railgun system, the Mk. 1 railgun, to the Mk. 1.1 system, as well as the design, fabrication, and testing of a newer and larger Mk. 2 railgun system. The Mk. 1.1 system is developed as a more efficient alteration of the original Mk. 1 system, but is found to be defective due to hardware deficiencies and failure, as well as unforeseen efficiency losses. A Mk. 2 system is developed and built around donated hardware from the Naval Postgraduate School. The Mk. 2 system strove to implement an efficient, augmented, electromagnetic railgun and projectile system capable of firing an approximate 1g aluminum projectile to speeds exceeding 2 km/s. A novel three part projectile is proposed to mitigate rail and projectile degradation. Projectile and sabot system kinematic equations are derived and the projectile is designed and tested along with Mk. 2 barrel. A numerical electromechanical model is developed to predict the performance of the Mk. 2 system and projectile assembly, and predicts a final velocity for the fabricated system exceeding 3.5 km/s and an efficiency as high as 24%. Testing of the Mk. 2 system showed catastrophic failure of the projectile during initial acceleration, resulting in very short acceleration times and distance, low velocity projectiles, and low efficiencies. During further testing of various projectile configurations, the barrel structure failed due to a large internal arc. Future work for the Mk. 2 system is discussed, a revised external barrel structure suggested, and a solid, more conventional solid chevron projectile design suggested.

Collision with orbital debris poses a serious threat to spacecraft and astronauts. Hypervelocity impacts resulting from collisions mean that objects with a mass less than 1g can cause mission-ending damage to spacecraft. A means of shielding spacecraft against collisions is necessary. A means of testing candidate shielding methods for their efficacy in mitigating hypervelocity impacts is therefore also necessary. Cal Poly’s Electromagnetic Railgun was designed with the goal of creating a laboratory system capable of simulating hypervelocity (≥ 3 km/s) impacts. Due to several factors, the system was not previously capable of high-velocity (≥ 1 km/s) tests. A deficient projectile design is revised, and a new design is tested. The new projectile design is demonstrated to enable far greater performance than the previous design, with a muzzle velocity ≥ 1 km/sbeing verified during testing, and an energy conversion efficiency of 2.7%. A method of improving contact and controlling wear at the projectile/rail interface using silver plating and conductive silver paste is validated. A mechanism explaining the problem of internal arcing within the railgun barrel is proposed, and design recommendations are made to eliminate arcing on the basis of the work done during testing. The primary structural members are found to be deficient for their application and a failure analysis of a failed member, loading analysis of the railgun barrel, and design of new structures is undertaken and presented.

Soft System Approaches have been developed worldwide to help problem-solvers and decision-makers develop solutions to complex problems, such as aerospace systems. Soft System Approaches were designed to help lower the disorder of developing a complex system by increasing understanding of a situation. Four Soft System Approaches were investigated, two from the West and two from the East. Within the context of the paper, the West refers to thought patterns associated with thinkers and scientists in Europe and North America, and the East refers to those from and around China. The two from the West are Peter Checkland’s Soft Systems Methodology and Fran Ackerman and Colin Eden’s Strategic Options Development and Analysis, and from the East they are Qian Xuesen’s Meta-Synthetic Approach and Jifa Gu and Zhichang Zhu’s Wuli-Shili-Renli Approach. The four approaches were selected due to their prevalence and notoriety within the Soft System Approach research. The Western Soft System Approaches were created as a direct response to reductionism and are more holistic than Hard System Approaches; however, they are classified as systematic due to their structure. The approaches are tools that can be deployed for a specific task. Eastern Soft System Approaches rooted in Eastern philosophy went from holism to reductionism to a unity of the two. There is no one-size-fits-all approach; therefore, approaches that seek to balance reductionism and holism and give guidance and structure while inviting in other methods. The Eastern approaches are classified as framework Soft System Approaches, which provide structure and guidance but not a formula. Systematic approaches are better for determining a course of action, while framework approaches are better for guiding a whole program. The Eastern approaches covered can and do incorporate other methods, including each other. Depending on the problem and problem solver, the systematic or framework approach may be better suited. Overall, however, the framework approach will be able to solve the most varied problems due to the lack of prescriptiveness and the use of other approaches. Knowing the difference between the approaches and how they can be used helps manage the development of complex aerospace systems.

The primary objective of this research is to formulate a methodology of assessing the maximum impact loading condition that will incur onto an aircraft’s landing gear system via Finite Element Analysis (FEA) and appropriately determining its corresponding structural and impact responses to minimize potential design failures during hard landing (abnormal impact) and shock absorption testing. Both static and dynamic loading condition were closely analyzed, compared, and derived through the Federal Aviation Administration’s (FAA) airworthiness regulations and empirical testing data. In this research, a nonlinear transient dynamic analysis is developed and established via NASTRAN advanced nonlinear finite element model (FEM) to simulate the worst-case loading condition. Under the appropriate loading analysis, the eye-bar and contact patch region theory were then utilized to simulate the tire and nose wheel interface more accurately. The open geometry of the nose landing gear was also optimized to minimize the effect of stress concentration. The result of this research is conformed to the FAA’s regulations and bound to have an impact on the design and development of small and large aircraft’s landing gear for both near and distant future.