Since aircraft design is an inherently multidisciplinary undertaking, the quest for increasingly optimized solutions can only be comprehensively successful by implementing multilevel or system design optimization architectures to step up disciplinary optimizations. In this study, a methodology which uses the Enhanced Collaborative Optimization (ECO) multilevel architecture with the purpose of developing a multidisciplinary design optimization methodology within the context of the preliminary design stage of unmanned aerial vehicles is presented. The concepts of weighting coefficient and dynamic compatibility parameter are presented and assessed for the ECO architecture. A routine that calculates the aircraft performance for the mission profile and vehicle's performance metrics under consideration has been implemented using low fidelity models for the aerodynamics, stability, propulsion, weight, balance and flight performance. A benchmarking case study of two different mission profiles for evaluating the advantage of using a variable span wing within the optimization methodology developed is also featured. Nomenclatureb = wingspan, m c = win mean chord, m C l = airfoil lift coefficient d prop = propeller diameter, inch e = Oswald efficiency number E to = take-off energy, J E cb = climb energy, J E cz = cruise energy, J E dt = descent energy, J ECO = enhanced collaborative optimization FW = fixed wing h min = minimum altitude for each mission stage, m h max = maximum altitude for each mission stage, m h to = take-off altitude for each mission stage, m MDO = multidisciplinary design optimization Opt. = optimization p prop = propeller pitch, inch R = range, m RoC = rate-of-climb, m/s VSW = variable span wing V = velocity, m/s V wind = wind velocity, m/s * PhD Student, Aerospace Sciences Department, AIAA Student Member. † Assistant Professor, Aerospace Sciences Department, AIAA Senior Member. AIAA Aviation W ene = energy weight, N W pay = payload weight, N W str = structural weight, N W sys = systems weight, N W total = design take-off weight, N δ = throttle fraction ∆t = time interval, s ∆x = distance interval, m
The present work describes an aircraft design methodology that uses the wingspan and its mean aerodynamic chord as main design parameters. In the implemented tool, low fidelity models have been developed for the aerodynamics, stability, propulsion, weight, balance and flight performance. A Fortran® routine that calculates the aircraft performance for the user defined mission and vehicle’s performance requirements has been developed. In order to demonstrate this methodology, the results for a case study using the design specifications of the Air Cargo Challenge 2013 are shown.
Abstract:The present work describes the development and final result of a graphical user interface tailored for a mission-based parametric aircraft design optimization code which targets the preliminary design phase of unmanned aerial vehicles. This development was built from the XFLR5 open source platform and further benefits from two-dimensional aerodynamic data obtained from XFOIL. For a better understanding, the most important graphical windows are shown. In order to demonstrate the graphical user interface interaction with the aircraft designer, the results of a case study which maximizes payload are presented.
Given an array (or matrix) of values for a function of one or more variables, it is often desired to find a value between two given points. Multivariable interpolation and approximation by radial basis functions are important subjects in approximation theory that have many applications in Science and Engineering fields. During the last decades, radial basis functions (RBFs) have found increasingly widespread use for functional approximation of scattered data. This research work aims at benchmarking two different approaches: an approximation by radial basis functions and a piecewise linear multivariable interpolation in terms of their effectiveness and efficiency in order to conclude about the advantages and disadvantages of each approach in approximating the aerodynamic coefficients of airfoils. The main focus of this article is to study the main factors that affect the accuracy of the multiquadric functions, including the location and quantity of centers and the choice of the form factor. It also benchmarks them against piecewise linear multivariable interpolation regarding their precision throughout the selected domain and the computational cost required to accomplish a given amount of solutions associated with the aerodynamic coefficients of lift, drag and pitching moment. The approximation functions are applied to two different multidimensional cases: two independent variables, where the aerodynamic coefficients depend on the Reynolds number (Re) and the angle-of-attack (α), and four independent variables, where the aerodynamic coefficients depend on Re, α, flap chord ratio (cflap), and flap deflection (δflap).
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