Hiroyuki Morino

Multidisciplinary Design Optimization and Data Mining for Transonic Regional-Jet Wing

A large-scale, real-world application of evolutionary multi-objective optimization is reported. The multidisciplinary design optimization among aerodynamics, structures, and aeroelasticity of the wing of a transonic regional-jet aircraft was performed using high-fidelity evaluation models. Euler and Navier-Stokes solvers were employed for aerodynamic evaluation. The commercial software NASTRAN was coupled with a computational fluid dynamics solver for the structural and aeroelastic evaluations. An adaptive range multi-objective genetic algorithm was employed as an optimizer. The objective functions were minimizations of block fuel and maximum takeoff weight in addition to drag divergence between transonic and subsonic flight conditions. As a result, nine nondominated solutions were generated and used for tradeoff analysis among three objectives. Moreover, all solutions evaluated during the evolution were analyzed using a self-organizing map as a data mining technique to extract key features of the design space. One of the key features found by data mining was the nongull wing geometry, although the present multidisciplinary design optimization results showed the inverted gull wings as nondominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6% improvement in the block fuel compared to the original geometry designed in the conventional manner.

High-Fidelity Multidisciplinary Design Optimization of Aerostructural Wing Shape for Regional Jet

A large-scale, real-world application of Evolutionary Multi-Objective Optimization is reported. The Multidisciplinary Design Optimization among aerodynamics, structures, and aeroelasticity of the wing of a transonic regional jet aircraft was performed using highfidelity evaluation models. Euler and Navier-Stokes solvers were employed for aerodynamic evaluation. The commercial software NASTRAN was coupled with a Computational Fluid Dynamics solver for the structural and aeroelastic evaluations. Adaptive Range MultiObjective Genetic Algorithm was employed as an optimizer. The objective functions were minimizations of block fuel and maximum takeoff weight in addition to drag divergence between transonic and subsonic flight conditions. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. Moreover, all solutions evaluated during the evolution were analyzed using a Self-Organizing Map as a Data Mining technique to extract key features of the design space. One of the key features found by Data Mining was the non-gull wing geometry, although the present MDO results showed the reverse-gull wings as non-dominated solutions. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance and to achieve 3.6 percent improvement in the block fuel compared to the original geometry designed in the conventional manner.

High-Fidelity multidisciplinary design optimization of wing shape for regional jet aircraft

A large-scale, real-world application of Evolutionary Multi- Criterion Optimization (EMO) is reported in this paper. The Multidisciplinary Design Optimization among aerodynamics, structures and aeroelasticity for the wing of a transonic regional jet aircraft has been performed using high-.delity models. An Euler/Navier-Stokes (N-S) Computational Fluid Dynamics (CFD) solver is employed for the aerodynamic evaluation. The NASTRAN, a commercial software, is coupled with a CFD solver for the structural and aeroelastic evaluations. Adaptive Range Multi-Objective Genetic Algorithm is employed as an optimizer. The objective functions are minimizations of block fuel and maximum takeo. weight in addition to di.erence in the drag between transonic and subsonic .ight conditions. As a result, nine non-dominated solutions have been generated. They are used for tradeo. analysis among three objectives. One solution is found to have one percent improvement in the block fuel compared to the original geometry designed in the conventional manner. All the solutions evaluated during the evolution are analyzed by Self-Organizing Map to extract key features of the design space.

Multi-Objective Design Exploration and its Applications

Multi-objective design exploration (MODE) and its applications are reviewed as an attempt to utilize numerical simulation in aerospace engineering design. MODE reveals the structure of the design space based on trade-off information. A self-organizing map (SOM) is incorporated into MODE as a visual data mining tool for the design space. SOM divides the design space into clusters with specific design features. This article reviews existing visual data mining techniques applied to engineering problems. Then, we discuss three applications of MODE: multidisciplinary design optimization for a regional-jet wing, silent supersonic technology demonstrator and centrifugal diffusers.

Data mining for multidisciplinary design space of regional-jet wing

The data mining technique is an important facet of solving multi-objective optimization problem because it is one of the effective manners to discover the design knowledge in the multi-objective optimization problem which obtains large data. In the present study, two data mining techniques have been performed for a large-scale, real-world multidisciplinary design optimization (MDO) to provide knowledge regarding the design space. The MDO among aerodynamics, structures, and aeroelasticity of the regional-jet wing was carried out using high-fidelity evaluation models on adaptive range multi-objective genetic algorithm. As a result, nine non-dominated solutions were generated and used for tradeoff analysis among three objectives. All solutions evaluated during the evolution were analyzed for the influence of design variables using a self-organizing map (SOM) and a functional analysis of variance (ANOVA) to extract key features of the design space. SOM and ANOVA compensated with the respective disadvantages, and then the design knowledge could be obtained more clearly by the combination between them. Although the MDO results showed the inverted gull-wings as non-dominated solutions, one of the key features found by data mining was the non-gull wing geometry. When this knowledge was applied to one optimum solution, the resulting design was found to have better performance compared with the original geometry designed in the conventional manner.

Nonlinear Aeroelastic Analysis of Control Surface with Freeplay Using Computational-Fluid-Dynamics-Based Reduced-Order Models

A computational-fluid-dynamics-based aeroelastic analysis method is proposed to simulate control-surface limit-cycle oscillation (LCO) induced by freeplay gap. The present method is based on the previously proposed aeroelastic reduced-order model (ROM), in which an unsteady aerodynamic state-space model is generated from aerodynamic responses to step excitation of individual mode using the eigensystem realization algorithm, and connected to a structural dynamic state-space model within the MATLAB/SIMULINK environment. The aeroelastic ROM is extended to treat structural nonlinearity due to the control-surface freeplay by generating an additional feedback line of generalized residual forces in the SIMULINK model. To reduce the problem size and the computation time, a fictitious-mass modal approach is used, which can afford the possible local change of stiffness. The present method is first validated for its capability to simulate aeroelastic responses of a regional-jet horizontal-tail (HT) model without fre...

Efficient Aeroelastic Analysis Using Unstructured CFD Method and Reduced-Order Unsteady Aerodynamic Model

Flutter computations are presented for the AGARD 445.6 wing model and the WingPylon-Nacelle configuration model using a fully implicit aeroelastic unstructured-mesh Euler solver coupled with a linear structural dynamics solver and Reduced-Order Model (ROM) based on the Volterra theory. The ROM is used for the rapid evaluation of nonlinear generalized unsteady aerodynamic forces (GAFs) both in a time-domain and a frequencydomain. The convolved time-domain GAFs agree well with direct computation results, and the frequency-domain GAFs at distinct values of reduced frequency are computed from the time-domain GAF responses to unit-amplitude harmonic excitation of elastic modes using a simple convolution scheme. The frequency-domain GAFs are processed using the Roger’s approximation method in order to generate an unsteady aerodynamic state-space ROM, which is used for creating an aeroelastic state-spa ce model. The aeroelastic state-space model is solved as a complex eigenvalue problem using MATLAB’s ® Toolbox to obtain V-g plots. The ROM-based flutter analysis is rapid and yields accurate results compared with experiment, direct simulation results and linear ae rodynamics results by MSC/NASTRAN.

Calculation of Unsteady Control Surface Aerodynamics using Reduced-Order Models

This paper presents computation of unsteady aerodynamics in response to oscillation of control surface using fully implicit unstructured-m esh Euler / Navier-Stokes (N-S) solver and reduced-order model (ROM) based on Volterra theory. This solver was applied to the Benchmark Active Control Technology (BACT) wing with dynamically deflected aileron. The Euler and N-S computation results showed qualitative agreement with experiment both in the steady and unsteady pressure coefficients, a lthough there were some small quantitative discrepancies both in the magnitude an d phase angle of pressure coefficient. The Volterra-based ROM approach with the N-S solver was also applied to the BACT wing with harmonically oscillating aileron. The comparison of unsteady pressure coefficients between direct and convolved results showed excellent agreement. It has been shown that the ROM approach resulted in good and rapid prediction of the unsteady aerodynamic responses to the aileron oscillation.