Development of an automated aircraft subsystem architecture generation and analysis tool

Purpose – The purpose of this paper is to present a new computational framework to address future preliminary design needs for aircraft subsystems. The ability to investigate multiple candidate technologies forming subsystem architectures is enabled with the provision of automated architecture generation, analysis and optimization. Main focus lies with a demonstration of the frameworks workings, as well as the optimizers performance with a typical form of application problem. Design/methodology/approach – The core aspects involve a functional decomposition, coupled with a synergistic mission performance analysis on the aircraft, architecture and component levels. This may be followed by a complete enumeration of architectures, combined with a user defined technology filtering and concept ranking procedure. In addition, a hybrid heuristic optimizer, based on ant systems optimization and a genetic algorithm, is employed to produce optimal architectures in both component composition and design parameters. The optimizer is tested on a generic architecture design problem combined with modified Griewank and parabolic functions for the continuous space. Findings – Insights from the generalized application problem show consistent rediscovery of the optimal architectures with the optimizer, as compared to a full problem enumeration. In addition multi-objective optimization reveals a Pareto front with differences in component composition as well as continuous parameters. Research limitations/implications – This paper demonstrates the frameworks application on a generalized test problem only. Further publication will consider real engineering design problems. Originality/value – The paper addresses the need for future conceptual design methods of complex systems to consider a mixed concept space of both discrete and continuous nature via automated methods

An Integrated Method for Determination of the Oswald Factor in a Multi-Fidelity Design Environment

Aircraft conceptual design often focuses on unconventional configurations like for example forward swept wings. Assessing the characteristics of these configurations usually requires the use of physic based analysis modules. This is due to the fact that for unconventional configurations no sucient database for historic based analysis modules is available. Nevertheless, physic based models require a lot of input data and their computational cost can be high. Generating input values in a trade study manually is work-intensive and error-prone. Conceptual design modules can be used to generate sucient input data for physic based models and their results can be re-integrated into the conceptual design phase. In this study a direct link between a conceptual design module and an aerodynamic design module is presented. Geometric information is generated by the conceptual design module and the physic based results, in form of the Oswald factor, are then fed back. Apart from the direct link, an equation for determination of the Oswald factor is derived via a Symbolic Regression Approach

Set-based approach to passenger aircraft family design

Presented is a method for the design of passenger aircraft families. Existing point-based methods found in the literature employ sequential approaches in which a single design solution is selected early and is then iteratively modified until all requirements are satisfied. The challenge with such approaches is that the design is driven toward a solution that, although promising to the optimizer, may be infeasible due to factors not considered by the models. The proposed method generates multiple solutions at the outset. Then, the infeasible solutions are discarded gradually through constraint satisfaction and set intersection. The method has been evaluated through a notional example of a three-member aircraft family design. The conclusion is that point-based design is still seen as preferable for incremental (conventional) designs based on a wealth of validated empirical methods, whereas the proposed approach, although resource-intensive, is seen as more suited to innovative designs

Recent Efforts Enabling Martian Rotorcraft Missions

The Mars Helicopter (MH), launching as a part of the Mars 2020 mission, will begin a new era of planetary exploration. Mars research has historically been conducted through landers, rovers, and satellites. As both government and private industries prepare for human exploration of the Martian surface within two decades, more in depth knowledge of what awaits on the surface is critical. Planetary aerial vehicles increase the range of terrain that can be examined, compared to traditional landers and rovers and have more near surface capability than orbiters. The Jet Propulsion Laboratory (JPL) and NASA Ames are currently exploring possibilities for a Mars Science Helicopter (MSH), a second-generation Mars rotorcraft with the capability of conducting science investigations independently of a lander or rover (although this type of vehicle could also be used assist rovers or landers in future missions). Preliminary designs of coaxial-helicopter and hexacopter configurations have targeted the minimum capability of lifting a payload in the range of two to three kilograms with an overall vehicle mass of approximately twenty kilograms. These MSH designs sizes are constrained by the aeroshell dimensions(currently focused on employing legacy Pathfinder or MSL aeroshells), rather than vehicle structural or aeroperformance limitations. Feasibility of the MSH configurations has been investigated considering packaging/deployment, rotor aerodynamics, and structural analysis studies. Initial findings suggest not only the overall feasibility of MSH configurations but also indicate that improvements up to 11.1 times increase in range or 1.3 times increase in hover time might be achievable, even with an additional science payload, compared to the current design of the MH

An aerothermodynamic design optimization framework for hypersonic vehicles

In the aviation field great interest is growing in passengers transportation at hypersonic speed. This requires, however, careful study of the enabling technologies necessary for the optimal design of hypersonic vehicles. In this framework, the present work reports on a highly integrated design environment that has been developed in order to provide an optimization loop for vehicle aerothermodynamic design. It includes modules for geometrical parametrization, automated data transfer between tools, automated execution of computational analysis codes, and design optimization methods. This optimization environment is exploited for the aerodynamic design of an unmanned hypersonic cruiser flying at M∞=8 and 30 km altitude. The original contribution of this work is mainly found in the capability of the developed optimization environment of working simultaneously on shape and topology of the aircraft. The results reported and discussed highlight interesting design capabilities, and promise extension to more challenging and realistic integrated aerothermodynamic design problems

Metaheuristic Approaches to Solve a Complex Aircraft Performance Optimization Problem

The increasing demands for travelling comfort and reduction of carbon dioxide emissions have been considered substantially in the stage of conceptual aircraft design. However, the design of a modern aircraft is a multidisciplinary process, which requires the coordination of information from several specific disciplines, such as structures, aerodynamics, control, etc. To address this problem with adequate accuracy, the multidisciplinary analysis and optimization (MAO) method is usually applied as a systematic and robust approach to solve such complex design issues arising from industries. Since MAO method is tedious and computationally expensive, genetic programming (GP)-based metamodeling techniques incorporating MAO are proposed as an effective approach to minimize the wing stiffness of a large aircraft subject to aerodynamic, aeroelastic and stability constraints in the conceptual design phase. Based on the linear small-disturbance theory, the state-space equation is employed for stability analysis. In the process of multidisciplinary analysis, aeroelastic response simulations are performed using Nastran. To construct metamodels representing the responses of the interests with high accuracy as well as less computational burden, optimal Latin hypercube design of experiments (DoE) is applied to determine the optimized distribution of sampling points. Following that, parametric optimization is carried out on metamodels to obtain the optimal wing geometry shape, elastic axis positions and stiffness distribution, and then the solution is verified by finite element simulations. Finally, the superiority of the GP-based metamodel technique over genetic algorithm is demonstrated by multidisciplinary design optimization of a representative beam-frame wing structure in terms of accuracy and efficiency. The results also show that GP metamodel-based strategy for solving MAO problems can provide valuable insights to tailoring parameters for the effective design of a large aircraft in the conceptual phase

Mars Science Helicopter Conceptual Design

Robotic planetary aerial vehicles increase the range of terrain that can be examined, compared to traditional landers and rovers, and have more near-surface capability than orbiters. Aerial mobility is a promising possibility for planetary exploration as it reduces the challenges that difficult obstacles pose to ground vehicles. The first use of a rotorcraft for a planetary mission will be in 2021, when the Mars Helicopter technology demonstrator will be deployed from the Mars 2020 rover. The Jet Propulsion Laboratory and NASA Ames Research Center are exploring possibilities for a Mars Science Helicopter, a second-generation Mars rotorcraft with the capability of conducting science investigations independently of a lander or rover (although this type of vehicle could also be used assist rovers or landers in future missions). This report describes the conceptual design of Mars Science Helicopters. The design process began with coaxial-helicopter and hexacopter configurations, with a payload in the range of two to three kilograms and an overall vehicle mass of approximately twenty kilograms. Initial estimates of weight and performance were based on the capabilities of the Mars Helicopter. Rotorcraft designs for Mars are constrained by the dimensions of the aeroshell for the trip to the planet, requiring attention to the aircraft packaging in order to maximize the rotor dimensions and hence overall performance potential. Aerodynamic performance optimization was conducted, particularly through airfoils designed specifically for the low Reynolds number and high Mach number inherent in operation on Mars. The final designs show a substantial capability for science operations on Mars: a 31 kg hexacopter that fits within a 2.5 m diameter aeroshell could carry a 5 kg payload for 10 min of hover time or over a range of 5 km

A Framework for Hyper-Heuristic Optimisation of Conceptual Aircraft Structural Designs

Conceptual aircraft structural design concerns the generation of an airframe that will provide sufficient strength under the loads encountered during the operation of the aircraft. In providing such strength, the airframe greatly contributes to the mass of the vehicle, where an excessively heavy design can penalise the performance and cost of the aircraft. Structural mass optimisation aims to minimise the airframe weight whilst maintaining adequate resistance to load. The traditional approach to such optimisation applies a single optimisation technique within a static process, which prevents adaptation of the optimisation process to react to changes in the problem. Hyper-heuristic optimisation is an evolving field of research wherein the optimisation process is evaluated and modified in an attempt to improve its performance, and thus the quality of solutions generated. Due to its relative infancy, hyper-heuristics have not been applied to the problem of aircraft structural design optimisation. It is the thesis of this research that hyper-heuristics can be employed within a framework to improve the quality of airframe designs generated without incurring additional computational cost. A framework has been developed to perform hyper-heuristic structural optimisation of a conceptual aircraft design. Four aspects of hyper-heuristics are included within the framework to promote improved process performance and subsequent solution quality. These aspects select multiple optimisation techniques to apply to the problem, analyse the solution space neighbouring good designs and adapt the process based on its performance. The framework has been evaluated through its implementation as a purpose-built computational tool called AStrO. The results of this evaluation have shown that significantly lighter airframe designs can be generated using hyper-heuristics than are obtainable by traditional optimisation approaches. Moreover, this is possible without penalising airframe strength or necessarily increasing computational costs. Furthermore, improvements are possible over the existing aircraft designs currently in production and operation