Multi-objective Design Optimization Framework Research Articles

A Reformulated-Vortex-Particle-Method-Based Aerodynamic Multi-Objective Design Optimization Strategy for Proprotor in Hover and High-Altitude Cruise

An improved multi-objective design optimization framework is proposed for the efficient design of proprotor blades tailored to specific high-altitude mission requirements. This framework builds upon existing methods by leveraging a reformulated Vortex Particle Method (rVPM) and incorporates three key stages: (1) rapid determination of overall proprotor parameters using a semi-empirical model, (2) optimized blade chord and twist distribution bounds based on minimum energy loss theory, and (3) global optimization with a high-fidelity rVPM-based aerodynamic solver coupled with a multi-objective hybrid optimization algorithm. Applied to a small high-altitude tiltrotor, the framework produced Pareto-optimal proprotor designs with a figure of merit of 0.814 and cruise efficiency of 0.896, exceeding mission targets by over 15%. Key findings indicate that large taper ratios and low twist improve hover performance, while elliptical blade planforms with high twist enhance cruise efficiency, and a tip anhedral further boosts overall performance. This framework streamlines the industrial customization of proprotor blades, significantly reducing the design space for advanced optimization while improving performance in demanding high-altitude environments.

Multiobjective Design Optimization Framework for Multicomponent System with Complex Nonuniform Loading

To improve the accuracy and efficiency of multiobjective design optimization for a multicomponent system with complex nonuniform loads, an efficient surrogate model (the decomposed collaborative optimized Kriging model, DCOKM) and an accurate optimal algorithm (the dynamic multiobjective genetic algorithm, DMOGA) are presented in this study. Furthermore, by combining DCOKM and DMOGA, the corresponding multiobjective design optimization framework for the multicomponent system is developed. The multiobjective optimization design of the carrier roller system is considered as a study case to verify the developed approach with respect to multidirectional nonuniform loads. We find that the total standard deviation of three carrier rollers is reduced by 92%, where the loading distribution is more uniform after optimization. This study then compares surrogate models (response surface model, Kriging model, OKM, and DCOKM) and optimal algorithms (neighbourhood cultivation genetic algorithm, nondominated sorting genetic algorithm, archive microgenetic algorithm, and DMOGA). The comparison results demonstrate that the proposed multiobjective design optimization framework is demonstrated to hold advantages in efficiency and accuracy for multiobjective optimization.

A multi-objective design optimization framework for wind turbines under altitude consideration

Wind power is one of the most used renewable energy sources; however, effective wind turbine design remains a challenge. This paper proposes a framework to explore the multi-objective design optimization of wind turbines and find the best compromise solution by considering the altitude. The design objectives are minimization of the cost of energy and maximization of the rated power by considering the rotor radius and hub height with respect to the structural design constraints. The Pareto envelope based selection algorithm II (PESA-II) and two versions of the non-dominated sorting genetic algorithm II and III (NSGA-II & NSGA-III) with a mutation strategy and a constraint handling method are implemented to generate Pareto fronts. Five comparing metrics are used to identify the most efficient optimization technique, and two decision methods are adopted to find the best compromise solution. A case study is solved under two altitude scenarios. The overall results show that NSGA-II performs better than the other algorithms, and the obtained best compromise solution is within the design limits. It is also observed that when the altitude increases, the cost of energy increases, and the rated power decreases.

Materials by Design for Stiff and Tough Hairy Nanoparticle Assemblies.

Matrix-free polymer-grafted nanocrystals, called assembled hairy nanoparticles (aHNPs), can significantly enhance the thermomechanical performance of nanocomposites by overcoming nanoparticle dispersion challenges and achieving stronger interfacial interactions through grafted polymer chains. However, effective strategies to improve both the mechanical stiffness and toughness of aHNPs are lacking given the general conflicting nature of these two properties and the large number of molecular parameters involved in the design of aHNPs. Here, we propose a computational framework that combines multiresponse Gaussian process metamodeling and coarse-grained molecular dynamics simulations to establish design strategies for achieving optimal mechanical properties of aHNPs within a parametric space. Taking poly(methyl methacrylate) grafted to high-aspect-ratio cellulose nanocrystals as a model nanocomposite, our multiobjective design optimization framework reveals that the polymer chain length and grafting density are the main influencing factors governing the mechanical properties of aHNPs, in comparison to the nanoparticle size and the polymer-nanoparticle interfacial interactions. In particular, the Pareto frontier, that marks the upper bound of mechanical properties within the design parameter space, can be achieved when the weight percentage of nanoparticles is above around 60% and the grafted chains exceed the critical length scale governing transition into the semidilute brush regime. We show that theoretical scaling relationships derived from the Daoud-Cotton model capture the dependence of the critical length scale on graft density and nanoparticle size. Our established modeling framework provides valuable insights into the mechanical behavior of these hairy nanoparticle assemblies at the molecular level and allows us to establish guidelines for nanocomposite design.

Simultaneous Optimization of Geometry and Firing Angles for In-Wheel Switched Reluctance Motor Drive

Interests in using rare-earth free motors such as switched reluctance motors (SRMs) for electric and hybrid electric vehicles continue to gain popularity, owing to their low cost and robustness. Optimal design of an SRM, to meet specific characteristics for an application, should involve simultaneous optimization of the motor geometry and control in order to achieve the highest performance with the lowest cost. This paper presents a constrained multiobjective optimization framework for design and control of an SRM based on a nondominated sorting genetic algorithm II. The proposed methodology optimizes SRM operation for high volume traction applications by considering multiple criteria including efficiency, average torque, and torque ripple. Several constraints are defined by the considered application, such as the motor stack length, outer diameter, minimum operating power, minimum desired efficiency, rated speed, rated current, and supply voltage. The outcome of this optimization includes an optimal geometry, outlining variables such as air gap length, rotor inner diameter, stator pole arc angle, rotor pole arc angle, rotor back iron, stator pole height, and stator inner diameter as well as optimal turn-on and turn-off firing angles. Then the machine is manufactured according to the obtained optimal specifications. Comprehensive finite-element analysis and experimental results are provided to validate the theoretical findings.

An approximate stochastic dynamics approach for nonlinear structural system performance-based multi-objective optimum design

A novel approach for structural system optimal design considering life cycle cost is developed. Specifically, a performance-based multi-objective design optimization framework for nonlinear/hysteretic multi-degree-of-freedom (MDOF) structural systems subject to evolutionary stochastic excitation is formulated. In the core of the stochastic structural analysis component of the proposed framework lies an efficient approximate dimension reduction technique based on the concepts of statistical linearization and of stochastic averaging for determining the non-stationary system response amplitude probability density functions (PDFs); thus, computationally intensive Monte Carlo simulations are circumvented. Note that the approach can readily handle stochastic excitations of arbitrary non-separable evolutionary power spectral density (EPSD) forms that exhibit strong variability in both the intensity and the frequency content. Further, approximate closed-form expressions are derived for the non-stationary inter-story drift ratio amplitude PDFs corresponding to each and every DOF. In this regard, considering appropriately defined damage measures structural system related fragility curves are determined at a low computational cost as well. Finally, the structural system design optimization problem is formulated as a multi-objective one to be solved by a genetic algorithm based approach. A building structure comprising the versatile Bouc-Wen (hysteretic) model serves as a numerical example for demonstrating the efficiency of the proposed methodology.

A multiobjective optimization framework for design of integrated biorefineries under uncertainty

A systematic approach for development of a reliable optimization framework to address the optimal design of integrated biorefineries in the face of uncertainty is presented. In the current formulation, a distributed strategy which is composed of different layers including strategic optimization, risk management, detailed mechanistic modeling, and operational level optimization is applied. In the strategic model, a multiobjective stochastic optimization approach is utilized to incorporate the tradeoffs between the cost and the financial risk. Then, Aspen Plus models are built to provide detailed simulation of biorefineries. In the final layer, an evolutionary algorithm is employed to optimize the operating condition. To demonstrate the effectiveness of the framework, a hypothetical case study referring to a multiproduct lignocellulosic biorefinery is utilized. The numerical results reveal the efficacy of the proposed approach; it provides decision makers with a quantitative analysis to determine the optimum capacity plan and operating conditions of the biorefinery. © 2015 American Institute of Chemical Engineers AIChE J, 61: 3208–3222, 2015

Experimental Validation of the EBF3PanelOpt Vibroacoustic Analysis of Stiffened Panels

Vibroacoustic experiments are performed at the Structural Acoustics Loads and Transmission facility of NASA Langley Research Center to measure the sound radiation from and transmission loss of two panels with straight and curvilinear stiffeners, respectively. The focus of the work addressed in this paper is to validate the vibroacoustic analysis methods implemented in a multi-objective design optimization framework, EBF3PanelOpt. The stiffened test panels with six stiffeners have been designed using EBF3PanelOpt for turbulent boundary-layer excitation. To build the confidence in finite-element models, free-vibration tests of the manufactured stiffened panels are also performed. The excited experimental mode shape and eigenfrequencies match quite well with the finite element analysis results for the free-vibration test. A medium-density fiberboard and aluminum frame is used to clamp the stiffened panels in the Structural Acoustics Loads and Transmission loss window. Because of strong coupling of the stiffened test panels with the frame, a finite-element model of the stiffened panels with frame is developed. To capture the effect of the bolted joints in the test setup, bush elements with optimal values of rotational and translational stiffnesses are used. Measurements are also performed to estimate the damping in the stiffened panels with the frame. The numerical prediction of sound radiation from and transmission loss of stiffened panels is in fair agreement with the measured values for both the panels.