The design of many engineering systems requires multiphysics simulations and can benefit from designoptimization. Two key challenges in multidisciplinary designoptimization (MDO) are coupling the models and computin...
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The design of many engineering systems requires multiphysics simulations and can benefit from designoptimization. Two key challenges in multidisciplinary designoptimization (MDO) are coupling the models and computing analytic derivatives, which are required to solve optimization problems with many design variables. While existing multiphysics frameworks address the challenge of implementing coupled models, none of them compute analytic derivatives for large-scale simulations in a general way. The OpenMDAO framework computes coupled derivatives using analytic methods, but it lacks suitable interfaces for simulation-based coupled models. To address this gap, we introduce MPhys, a modular multiphysics simulation library built with the OpenMDAO framework. MPhys defines standard disciplinary interfaces for coupled multidisciplinary models, enabling the rapid development of coupled multiphysics models for gradient-based MDO. We demonstrate MPhys's modularity and extensibility with two example applications: aerostructural designoptimization using two different aerodynamic solvers and aeropropulsive designoptimization. Since its initial development, MPhys has been successfully used with a wide range of applications with various multidisciplinary coupling strategies and fidelity levels. The MPhys library is poised to significantly accelerate the integration of existing models in multiphysics applications and the development of new multidisciplinary coupling strategies. These developments will enable a wider adoption of MDO in practical engineering design.
An on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is presented, aiming to achieve practical engineering applications to explore high-efficiency and low-noise design for aerodyn...
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An on-the-fly unsteady adjoint-based aerodynamic and aeroacoustic optimization methodology is presented, aiming to achieve practical engineering applications to explore high-efficiency and low-noise design for aerodynamic shapes. Firstly, a novel on-the-fly hybrid CFD-CAA approach is developed with a close integration of unsteady Reynolds-averaged Navier-Stokes equations and a fully viscous time-domain FW-H formulation. Subsequently, an adjoint-based sensitivity analysis method is proposed for unsteady aerodynamic and aeroacoustic problems with either stationary or moving boundaries, wherein a unified architecture for discrete-adjoint sensitivity analysis of both aerodynamics and aeroacoustics is achieved by integrating the on-the-fly hybrid CFD-CAA approach. The on-the-fly approach facilitates direct evaluation of partial derivatives required for solving adjoint equations, eliminating the need for explicitly preprocessing flow and adjoint variables at all time levels in a standalone adjoint CAA solver and consequently substantially reducing memory consumption. The proposed optimization methodology is implemented within an open-source suite SU2. Results show that the proposed on-the-fly adjoint methodology is capable of achieving highly accurate sensitivity derivatives while significantly reducing memory requirements by an order of magnitude, and further demonstrations of single-objective and coupled aerodynamic and aeroacoustic optimizations highlight the potential of the proposed method in exploring high-efficiency and low-noise design for aerodynamic shapes.
Estimation of aerodynamic loads is a significant challenge in complex gusty environments due to the associated complexities of flow separation and strong nonlinearities. In this study, we explore the practical feasibi...
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Estimation of aerodynamic loads is a significant challenge in complex gusty environments due to the associated complexities of flow separation and strong nonlinearities. In this study, we explore the practical feasibility of multilayer perceptron (MLP) for estimating aerodynamic loads in gusts, when confounded by noisy and spatially distributed sparse surface pressure measurements. As a demonstration, a nonslender delta wing experiencing various gusts with different initial and final conditions is considered. Time-resolved lift and drag, and spatially distributed sparse surface pressure measurements are collected in a towing-tank facility. The nonlinear MLP model is used to estimate gust scenarios that are unseen in training progress. A filtering process allows us to examine the fluctuation of the dynamic response from the pressure measurements on the MLP. Estimation results show that the MLP model is able to capture the relationship between surface pressure and aerodynamic loads with a minimum quantity of learning samples, delivering accurate estimations, despite the slightly large errors for the cases at the boundary of the datasets. The results also indicate that the dynamic response of the pressure measurements has an influence on the learning of MLP. We further utilize gradient maps to perform a sensitivity analysis, so as to evaluate the contribution of the pressure data to the estimation of gust loads. This study reveals the significant contribution of the sensors located near the leading edge and at the nose of the delta wing. Our findings suggest the potential for an efficient sensor deployment strategy in data-driven aerodynamic load estimation.
The focus of this research is the optimization of impulsive transfer trajectories in the presence of stochastic effects. A trajectory design method is introduced that accounts for a normally distributed initial state ...
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The focus of this research is the optimization of impulsive transfer trajectories in the presence of stochastic effects. A trajectory design method is introduced that accounts for a normally distributed initial state dispersion and minimizes the sum of nominal impulsive & UDelta;V plus 3s trajectory correction maneuver (TCM) magnitude. Four main problems are presented. First, a deterministic optimal trajectory is developed;in the coplanar cases, the optimal solution is Hohmann transfer. For the second problem, an initial state dispersion and a target position dispersion constraint are introduced. It is shown to be possible to modify the nominal two-impulse trajectory to satisfy the dispersion constraint. In the third problem, a TCM is performed at the optimal point along the deterministic optimal trajectory, resulting in a more efficient method to influence the target position dispersion. Problem 4 is the development of a robust trajectory, where the nominal impulsive maneuvers and 3s TCM dV are simultaneously optimized. The result is a different nominal trajectory and TCM that is less expensive than the total cost of problem 3. The optimal TCM is rapidly computed along each nominal trajectory using the numerically propagated state transition matrix history as a step inside the optimization algorithm.
This paper presents a methodology for the optimal design of intentional mistuning for a mistuned bladed disk with interval uncertainty. For a bladed disk where blades are weakly coupled, presence of random mistuning c...
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ISBN:
(纸本)9780791857182
This paper presents a methodology for the optimal design of intentional mistuning for a mistuned bladed disk with interval uncertainty. For a bladed disk where blades are weakly coupled, presence of random mistuning can easily induce vibration localization. This phenomenon will lead to great amplification in response amplitude of certain blades. To achieve desired reliability of a bladed disk, amplified response must be reduced to certain level, which requires probabilistic or reliability analysis. In this study, it is considered that blades have random distribution and coupling between blades has interval uncertainty. To treat the interval uncertainty appropriately, the worst-case combination of interval couplings is searched first, then probability of failure is evaluated under the worst-case condition. To increase reliability of a bladed disk, intentional mistuning is used in this study. While applying the intentional mistuning, it is also wanted to minimize the degree of intentional mistuning to minimize the cost of implementation. To find optimal combination of intentional mistuning parameters to achieve dual goals, gradient-based design optimization approach is utilized, which is expected to guarantee efficient convergence. To carry out gradient-based design optimization, sensitivities of objective function and probabilistic constraints with respect to intentional mistuning parameters are derived. During the sensitivity analysis, distribution of forced response amplitude is identified through Gaussian fit and eigenvalue perturbation theory is referred to. Monte Carlo simulation is utilized to accurately calculate probability of failure and its sensitivity. The proposed method is demonstrated with numerical examples of two distinct bladed disks.
A new method for gradient-basedoptimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electro...
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A new method for gradient-basedoptimization of electromagnetic systems using parametric sensitivity macromodels is presented. Parametric macromodels accurately describe the parameterized frequency behavior of electromagnetic systems and their corresponding parameterized sensitivity responses with respect to design parameters, such as layout and substrate parameters. A set of frequency-dependent rational models is built at a set of design space points by using the vector fitting method and converted into a state-space form. Then, this set of state-space matrices is parameterized with a proper choice of interpolation schemes, such that parametric sensitivity macromodels can be computed. These parametric macromodels, along with the corresponding parametric sensitivity macromodels, can be used in a gradient-based design optimization process. The importance of parameterized sensitivity information for an efficient and accurate designoptimization is shown in the two numerical microwave examples. Copyright (c) 2012 John Wiley & Sons, Ltd.
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