Inverse problems can be applied to aircraft in many areas. One of the disciplines within the aerospace industry with the most openly published data is in the area of aircraft performance. Many aircraft manufacturers publish performance claims, flight manuals and Standard Aircraft Characteristics (SAC) charts without any mention of the more fundamental technical information of the drag and engine data. With accurate tools, generalized aircraft models and a few curve-fitting techniques, it is possible to evaluate vehicle performance and estimate the drag, thrust and fuel consumption (TSFC) with some accuracy. This thesis is intended to research the use of aircraft performance information to deduce these aircraft--specific drag and engine models. The proposed method incorporates models for each performance metric, modeling options for drag, thrust and TSFC, and an inverse method to match the predicted performance to the actual performance. Each of the aircraft models is parametric in nature, allowing for individual parameters to be varied to determine the optimal result. The method discussed in this work shows both the benefits and pitfalls of using performance data to deduce engine and drag characteristics. The results of this method, applied to the McDonnell Douglas DC-10 and Northrop F-5, highlight many of these benefits and pitfalls, and show varied levels of success. A groundwork has been laid to show that this concept is viable, and extension of this work to additional aircraft is possible with recommendations on how to improve this technique.

Submitted by Silvana Teresinha Dornelles Studzinski (sstudzinski) on 2017-03-16T12:44:59Z No. of bitstreams: 1 Daniel Augusto Kestering_.pdf: 4709572 bytes, checksum: bd1166e3946f589fd86f700a714928c2 (MD5) / Made available in DSpace on 2017-03-16T12:44:59Z (GMT). No. of bitstreams: 1 Daniel Augusto Kestering_.pdf: 4709572 bytes, checksum: bd1166e3946f589fd86f700a714928c2 (MD5) Previous issue date: 2016-10-31 / CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / PROSUP - Programa de Suporte à Pós-Gradução de Instituições de Ensino Particulares / A investigação dos modelos de arrasto gás-sólido é fundamental para se obter bons resultados de fluidização utilizando dinâmica dos fluidos computacional. A tecnologia de fluidização é muito utilizada para conversão térmica de combustíveis sólidos e tem como principal vantagem a boa mistura entre gás e sólido. O presente trabalho utiliza dois softwares para simulação de leitos fluidizados, MFIX e Ansys Fluent, para comparar os modelos de arrasto de Syamlal e O`Brien (1987) e Di Felice (1994). A abordagem utilizada para modelagem do problema é o modelo de dois fluidos (Two Fluid Model, TFM), juntamente com a teoria cinética para escoamento laminar (Kinetic Theory for Granular Flow, KTGF). Um método para ajuste do modelo de DF (DI FELICE, 1994), baseado no trabalho de Esmaili e Mahinpey (2011), é sugerido, assim como o modelo de SO (SYAMLAL; O`BRIEN,1987) é ajustado utilizando dados em condição de mínima fluidização. Foram conduzidos experimentos para obtenção de velocidade e fração de vazios em condição de mínima fluidização a fim de ajustar ambos os modelos. As partículas utilizadas nos experimentos foram esferas de vidro de 1,21 mm, 0,8 mm e areia de fundição de 0,29 mm. O método proposto representa de forma adequada os dados obtidos em mínima fluidização das três partículas. Com os modelos de arrasto ajustados, simulações numéricas em regime de fluidização foram conduzidas em domínio bidimensional e tridimensional. Os resultados obtidos nestas simulações apresentam boa concordância com resultados experimentais em queda de pressão do leito e borbulhamento. Concomitantemente, um código para obtenção de modelo de arrasto utilizando o algoritmo EMMS/Bubbling foi desenvolvido e simulações numéricas bidimensionais foram conduzidas, para teste e validação. Os resultados do código mostram que o modelo segue a mesma tendência de Shi, Wang e Li (2011), que desenvolveram o modelo EMMS/Bubbling. / The investigation of gas-solid drag models is a key to obtain good results of fluidization by using computational fluid dynamic tools. The fluidization technology is used for solid fuel thermal conversion and its main advantage is the high gas-solid mixture. The present effort uses two software for fluidized beds simulation, MFIX and Ansys Fluent, in order to compare the drag models of Syamlal and O`Brien (1987) and Di Felice (1994). Two Fluid Model is the approach used to model together with Kinetic Theory for Granular flow. A method to adjust DF drag model (DI FELICE, 1994), based on Esmaili and Mahinpey (2011), is suggested, as well as SO drag model (SYAMLAL; O’BRIEN, 1987) is adjusted using data obtained from minimum fluidization condition. Experiments were realized to obtain velocity and void fraction at minimum fluidization condition in order to adjust both models. Glass beads with diameter of 1,21 mm and 0,8 mm and sand with diameter of 0,29 mm were used on experiments. The purposed method fits the data obtained on minimum fluidization condition of the three particles, in accordance with experimental data. With the models adjusted, numerical simulation were conducted using drag models for two- and three-dimensional domain. The results of this simulations agrees with experimental data of pressure drop and bubble formation. Simultaneously, a code to obtain a drag model using EMM/Bubbling algorithm was developed and numerical simulation were conducted. Results of EMMS show that the model have the same tendency of results of Shi, Wand and Li (2011), who developed EMMS/Bubbling model.

This dissertation focused on development and utilization of numerical and experimental approaches to improve the CFD modeling of fluidization flow of cohesive micron size particles. The specific objectives of this research were: (1) Developing a cluster prediction mechanism applicable to Two-Fluid Modeling (TFM) of gas-solid systems (2) Developing more accurate drag models for Two-Fluid Modeling (TFM) of gas-solid fluidization flow with the presence of cohesive interparticle forces (3) using the developed model to explore the improvement of accuracy of TFM in simulation of fluidization flow of cohesive powders (4) Understanding the causes and influential factor which led to improvements and quantification of improvements (5) Gathering data from a fast fluidization flow and use these data for benchmark validations. Simulation results with two developed cluster-aware drag models showed that cluster prediction could effectively influence the results in both the first and second cluster-aware models. It was proven that improvement of accuracy of TFM modeling using three versions of the first hybrid model was significant and the best improvements were obtained by using the smallest values of the switch parameter which led to capturing the smallest chances of cluster prediction. In the case of the second hybrid model, dependence of critical model parameter on only Reynolds number led to the fact that improvement of accuracy was significant only in dense section of the fluidized bed. This finding may suggest that a more sophisticated particle resolved DNS model, which can span wide range of solid volume fraction, can be used in the formulation of the cluster-aware drag model. The results of experiment suing high speed imaging indicated the presence of particle clusters in the fluidization flow of FCC inside the riser of FIU-CFB facility. In addition, pressure data was successfully captured along the fluidization column of the facility and used as benchmark validation data for the second hybrid model developed in the present dissertation. It was shown the second hybrid model could predict the pressure data in the dense section of the fluidization column with better accuracy.