Multidisciplinary Analysis And Optimization Research Articles

Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO) for Early Design of Helicopter Primary Parameters

The design of a helicopter involves multiple disciplines and a large number of technical parameters with conflicting objectives, leading to the challenges of multidisciplinary coupling in design and optimization. In the context of the application of Model-Based Systems Engineering (MBSE) in complex product design, how to bridge the gap between MBSE and Multidisciplinary Design Analysis and Optimization (MDAO) to accelerate the early design process of helicopters remains an urgent problem to be addressed. The paper proposed an optimization framework that links MBSE and MDAO models for the design optimization problem of helicopters. On the MBSE side, the design optimization problem of helicopters was defined through the descriptive SysML model. On the MDAO side, the specific domain analysis model and optimization analysis workflow were established. The integration of design data was achieved through stereotype plugins and design components, bridging the gap between MBSE and MDAO, and providing accurate engineering data for system design verification. The paper provided an optimization case of a helicopter, with the optimal mission performance of the helicopter as the objective function. Through MDAO analysis, the design optimization of fuel consumption, take-off weight, and rotor dynamics parameters was completed, achieving an integrated process of system design and optimization.

MDAO and Aeroelastic Analyses of Small Solar-Powered UAVs with Box-Wing and Tandem-Wing Architectures

The market of solar-powered Unmanned Aerial Vehicles (UAVs) for defence purposes and drone services is expected to grow by a factor of more than 2 in the next decade. From an aircraft design perspective, the main challenge is the scalability of the proposed architectures, which is needed to increase the payload capabilities. Beside some successful examples of wing-tail UAVs, some newcomers are developing prototypes with tandem-wing architectures, hence enlarging the possible design. The present paper aims to introduce a further step in this direction, taking also the box-wing architecture into account to show how the presence of wing tip joiners can provide benefits from the aeroelastic point of view. UAVs with take-off mass within 25 kg are considered and the main tools adopted are presented. These are an in-house developed Multi-Disciplinary Analysis and Optimization (MDAO) code called SD2020 and the open source aeroelastic code ASWING, both presented together with an assessment of their accuracy by means of higher fidelity numerical results. SD2020 results are presented for the case of small box-wing solar UAVs optimized to achieve the longest endurance, focusing on the strategy implemented to achieve feasible solutions under an assigned set of constraints. Further results are presented for comparable box-wing and tandem-wing UAVs from both the aerodynamic and aeroelastic standpoints. Whereas the aerodynamic advantages introduced by the box-wing are marginal, significant advantages result from the aeroelastic analyses which indicate that, if the joiners are removed from the box-wing configuration, safety margin from flutter speed is halved and the bending-torsion divergence occurs at relatively low speed values.

Optimization Workflows for Linking Model-Based Systems Engineering (MBSE) and Multidisciplinary Analysis and Optimization (MDAO)

Developing modern products involves numerous domains (controlling, production, engineering, etc.) and disciplines (mechanics, electronics, software, etc.). The products have become increasingly complex while their time to market has decreased. These challenges can be overcome by Model-Based Systems Engineering (MBSE), where all development data (requirements, architecture, etc.) is stored and linked in a system model. In an MBSE system model, product requirements at the system level can lead to numerous technical variants with conflicting objectives at the parameter level. To determine the best technical variants or tradeoffs, Multidisciplinary Analysis and Optimization (MDAO) is already being used today. Linking MBSE and MDAO allows for mutually beneficial synergies to be expected that have not yet been fully exploited. In this paper, a new approach to link MBSE and MDAO is proposed. The novelty compared to existing approaches is the reuse of existing MBSE system model data. Models developed during upstream design and test activities already linked to the MBSE system model were integrated into an MDAO problem. Benefits are reduced initial and reconfiguration efforts and the resolution of the MDAO black-box behavior. For the first time, the MDAO problem was modeled as a workflow using activity diagrams in the MBSE system model. For a given system architecture, this workflow finds the design variable values that allow for the best tradeoff of objectives. The structure and behavior of the workflow were formally described in the MBSE system model with SysML. The presented approach for linking MBSE and MDAO is demonstrated using an example of an electric coolant pump.

Active Manifold and Model-Order Reduction to Accelerate Multidisciplinary Analysis and Optimization

A computational framework is proposed for efficiently solving multidisciplinary analysis and optimization (MDAO) problems in a relatively high-dimensional design parameter space. It relies on projection-based reduced-order models (PROMs) and a new concept of an active manifold (AM) to mitigate the curse of dimensionality during the training of the PROMs. The AM is discovered using a deep convolutional autoencoder for dimensionality reduction: it is proposed as a superior alternative to the concept of active subspace whose capabilities are limited by the associated affine approximation. The computational framework also blends the concept of a global PROM as a surrogate model of a nonlinear partial differential equation (PDE)-based behavior function with that of a database of local, linear PROMs for approximating a linear PDE-based behavior function. It is demonstrated for the solution of a flexible instance of NASA’s Common Research Model for transport aircraft, where the objective function pertains to aerodynamics, the constraint relates to flutter, and the design space contains 58 structural and shape parameters. The obtained results illustrate the potential of the AM concept for mitigating the aforementioned curse of dimensionality. They also demonstrate the feasibility of the proposed computational framework for realistic MDAO problems and its ability to reduce solution time.

Model Reduction Framework with a New Take on Active Subspaces for Optimization Problems with Linearized Fluid‐Structure Interaction Constraints

SummaryIn this paper, a new take on the concept of an active subspace for reducing the dimension of the design parameter space in a multidisciplinary analysis and optimization (MDAO) problem is proposed. The new approach is intertwined with the concepts of adaptive parameter sampling, projection‐based model order reduction, and a database of linear, projection‐based reduced‐order models equipped with interpolation on matrix manifolds, in order to construct an efficient computational framework for MDAO. The framework is fully developed for MDAO problems with linearized fluid‐structure interaction constraints. It is applied to the aeroelastic tailoring, under flutter constraints, of two different flight systems: a flexible configuration of NASA's Common Research Model; and NASA's Aeroelastic Research Wing #2 (ARW‐2). The obtained results illustrate the feasibility of the computational framework for realistic MDAO problems and highlight the benefits of the new approach for constructing an active subspace in both terms of solution optimality and wall‐clock time reduction.

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.