Multidisciplinary Design Optimisation of Unmanned Aerial Vehicles (UAV) using Multi-Criteria Evolutionary Algorithms (original) (raw)
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A growing area in aerospace engineering is the use and development of Unmanned Aerial Vehicles (UAVs) for military and civilian applications. There are difficulties in the design of these vehicles because of the varied and non-intuitive nature of the configurations and missions that can be performed. Similar to their manned counterparts, the challenge is to develop trade-off studies of optimal configurations to produce a high performance aircraft that satisfy mission requirements The goal in the present study is to address these issues from a multi-criteria and multidisciplinary design optimisation (MDO) standpoint.
This paper reviews recent progress made in Evolutionary Algorithms (EAs) for single, multi-objective and Multidisciplinary Design Optimisation (MDO) problems. Specifically we discuss the integration and implementation of a Hierarchical Asynchronous Evolutionary Algorithm (HAPEA) to solve complex engineering problems which can be multi-modal, involve non-linear approximations that are non-differentiable or involve multiple objectives. The algorithm is based upon traditional evolution strategies with the incorporation of an asynchronous function evaluation for the solution. The algorithm is adaptable for multiple population of EAs with variable fidelity models and use the concepts of Game Theory to handle multi-objective problems. Initially we give some examples of the performance of the algorithm for representative single and multi-objective analytical test functions, which involve multiple local minima, discontinuous Pareto fronts or constraints and then two cases related to aircraft design are analyzed. Result indicate that the method is robust and efficient on its application for real world problems.
2006
This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of Unmanned Aerial Vehicles (UAVs). The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser, several design modules, mesh generators and post-processing capabilities in an integrated platform. Traditional deterministic optimisation techniques for MDO are effective when applied to specific problems and within a specified range. A new class of optimisation techniques named Hierarchical Asynchronous Parallel Evolutionary Algorithms (HAPEAs) have shown to be robust as they require no derivatives or gradients of the objective function, have the capability of finding globally optimum solutions amongst many local optima, can be executed asynchronously in parallel and adapted easily to arbitrary solver codes without major modifications. The application of the methodology is illustrated on multi-criteria and multidisciplinary design problems. Results indicate the practicality and robustness of the method in finding optimal solutions and Pareto trade-offs between the disciplinary analyses and producing a set of non dominated individuals.
Metamodel-based Multidisciplinary Design Optimization of a General Aviation Aircraft
12th World Congress on Structural and Multidisciplinary Optimisation, 2017
Computational burden is still a significant challenge in the in multidisciplinary design optimization (MDO) of complex engineering systems. This challenge can be arising from the curse of dimensionality of the design space and the multiplicity of disciplines involved in the design problem. Tremendous efforts have been made to improve the computational efficiency, especially in the field of MDO. Meta-modeling is one of the powerful tools to facilitate this problem and has been received increasing attention in the past decades. Meta-models are used to provide simpler models instead of the complex original models and by admitting a small percentage of error reduces computing time of the problem. Kriging meta-model, due to its high efficiency in medium dimension problems has been attracted the attention of many researchers. Due to lack of continuity in the complex design problems, creating a comprehensive and appropriate meta-model with acceptable accuracy to cover the entire design space is difficult and almost impossible. This paper proposed a strategy to improve the accuracy of the created meta-models using the elimination of outlier data from sampled points and re-designing the effective Kriging meta-model parameters. The proposed strategy is applied to the conceptual design of a General Aviation Aircraft (GAA) using MDO methodology and appropriate Kriging meta-model. Meta-models of the design disciplines including propulsion, aerodynamics, weight and sizing, performance criteria and stability disciplines are created and integrated based on Multidisciplinary Design Feasibility (MDF) structure to improve the aircraft performance. The gross weight of the aircraft and cruise phase range are considered as the objective functions. The NSGA-II multi-objective evolutionary optimization algorithm is utilized to demonstrate a set of possible answers in the form of the Pareto front.
Multidisciplinary optimization applications in preliminary design - Status and directions
38th Structures, Structural Dynamics, and Materials Conference, 1997
Multidisciplinary design optimization (MDO) has played an important role in aircraft preliminary design for 30 years, yet it is far from a mature field. This paper discusses the increasingly widespread use of MDO for aircraft design, describing the evolution of computational tools and strategies, and summarizing some current research directions. The objective of this review is not to provide a comprehensive survey of MDO methods and applications, but rather to highlight some interesting aspects that suggest how this field is developing. • From a recently-retired senior design engineer, describing events at his aerospace company, "For fifteen years I beat my head against a stone wall...
12th AIAA Aviation Technology, Integration, and Operations (ATIO) Conference and 14th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference, 2012
This paper examines the evolutionary approach for aircraft design optimization. Several niching and elitist models are first applied to Multiple-Objective Genetic Algorithms (MOGAs). Numerical results suggest that the combination of the fitness sharing and the best-N selection leads to the best performance. The resulting MOGA is then applied to multidisciplinary design optimization problems of transonic and supersonic wing planform shapes. The results confirm the feasibility of the present approach. Wing weight (lb) Drag (lb) Aspect ratio Minimum drag Minimum aspect ratio Minimum weight Center of Pareto surface
Multidisciplinary design optimization with mixed categorical variables for aircraft design
AIAA SCITECH 2022 Forum, 2022
Multidisciplinary design optimization methods aim at adapting numerical optimization techniques to the design of engineering systems involving multiple disciplines. In this context, a large number of mixed continuous, integer and categorical variables might arise during the optimization process and practical applications involve a large number of design variables. Recently, there has been a growing interest in mixed variables constrained Bayesian optimization but most existing approaches severely increase the number of the hyperparameters related to the surrogate model. In this paper, we address this issue by constructing surrogate models using less hyperparameters. The reduction process is based on the partial least squares method. An adaptive procedure for choosing the number of hyperparameters is proposed. The performance of the proposed approach is confirmed on analytical tests as well as two real applications related to aircraft design. A significant improvement is obtained compared to genetic algorithms.
Investigation of Multi-Disciplinary Optimisation for Aircraft Preliminary Design
SAE Technical …, 2011
The ACARE 2020 vision for commercial transport aircraft targets a 50% reduction per passenger kilometer in fuel consumption and CO2 emissions, with a 20-25% reduction to be achieved through airframe improvements. This step change in performance is dependent on the successful integration and down-selection of breakthrough technologies at early stage of aircraft development process, supported by advanced multidisciplinary design capabilities. Conceptual design capabilities, integrating more disciplines are routinely used at Future Project Office. The challenge considered here is to transition smoothly from conceptual to preliminary design whilst maintaining a true multidisciplinary approach. The design space must be progressively constrained, whilst at the same time increasing the level of modelling fidelity and keeping as many design options open for as long as possible. Failing to account for all relevant design constraints may result in wasteful investigations into infeasible parts of the design space or design modifications which do not improve the overall aircraft performance. Several multidisciplinary design capability approaches, ranging from rapid trade-off studies to true multidisciplinary analysis and optimisation (MDA/MDO), are investigated to understand their ability to increase the robustness of the preliminary design data and to realize the overall aircraft performance objectives within the required timescales. A prerequisite for such approach is the existence of efficient and fully integrated processes. These trade-off studies being performed in the preliminary design phase while there remains much design freedom, they can yield significant weight savings and performance gains. Enabling such a concurrent engineering process, MDO should be an efficient decision support tool for the designer. The paper will review the objectives and key ideas of the approach intended to develop multidisciplinary integrated processes and will present recent-and still under progress in dedicated research programs-activities aiming at developing medium to high fidelity MDO methodologies.