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Optimal location of FG actuator/sensor patches on an FG rotating conical shell for active control of vibration

ACTA MECHANICA 2022年第12期233卷 5335-5357页

作者： Niasar, Mohammad Jafari Rahaghi, Mohsen Irani Jafari, Ali Asghar KN Toosi Univ Technol Fac Mech Engn Tehran Iran Univ Kashan Fac Mech Engn Dept Solid Mech Kashan Iran

In this paper, the optimization of piezoelectric patch positions is conducted in order to improve vibration performance of an FG-truncated conical shell. This investigation is done based upon a new optimization trend on a rotating cone for the first time, as well as, the piezoelectric material has been considered functionally graded. The vibration model is based on the classical theory, and the governing equation is obtained using the Lagrange equation. The sensor voltage change rate is selected as feedback signal to vibration control. Four different piezoelectric sets considered with different numbers of piezoelectric Patches. The settling time of the system and position of the piezoelectric patches in longitudinal direction is assumed as an objective function and optimization variable, respectively. Optimization is carried out using sequential quadratic programming and pattern search algorithms. Also, the symmetrical and asymmetrical layouts of the piezoelectric patches in order to study the settling time have been considered. Then, the effect of piezoelectric lengths and arcs on the settling time has been studied. The results show that the best position for piezoelectric placement is in a range between the middle and the base of the cone.

关键词： sequential quadratic programming Mathematical optimization Pattern search Actuator

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Point Mascon Global Lunar Gravity Models

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2022年第5期45卷 815-829页

作者： McArdle, Sean Russell, Ryan P. Univ Texas Austin Aerosp Engn 2617 Wichita St Austin TX 78712 USA

Lunar gravitational models are generated using sets of point mass potentials (mascons) fit to the GRGM1200A representation derived from GRAIL mission data. Point mass ensembles allow for simple modeling of a gravity potential and its partial derivatives. The new point mascon models are intended to be low- to medium-resolution replacements for spherical harmonics models in mission planning applications or onboard flight software. The design space of how to parameterize the mascons is explored. The chosen configuration uses equally weighted point mass potentials and requires the successive solutions to thousands of nonlinear optimization problems that adjust the mascon locations. A database of models that vary in resolution is generated and disseminated as supplementary material, with the highest-fidelity model demonstrating equivalent accuracy and memory footprint to a 60 degree/order spherical harmonics truncation. Gravitational acceleration and gravity gradient evaluation runtimes are roughly two to eight times faster for the new mascon models when compared with spherical harmonics models with equivalent error, depending on the model fidelity and the computational environment.

关键词： Spherical Harmonics Flight Software Database Model GRAIL Mission Sparse Nonlinear Optimizer Polar Orbit FORTRAN Earth sequential quadratic programming Spacecraft Dynamics

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Surrogate Model Based Aerodynamic Shape Optimization of a Hydrogen Powered Aircraft

Surrogate Model Based Aerodynamic Shape Optimization of a Hy...

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AIAA Aviation Forum

作者： Kiely, Michael Agarwal, Ramesh K. Washington Univ St Louis St Louis MO 63130 USA

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

This paper describes both the low and high fidelity methods used in implementation of machine learning based optimization techniques for aerodynamic shape optimization of hydrogen powered commercial aircraft. Using vortex lattice method based aerodynamic performance estimations in conjunction with empirical solvers used to estimate an arbitrarily generated aircraft's overall structural weight and drag, a Bayesian optimization algorithm is applied to optimize the wing to maximize the aircraft's overall range. This method was applied to a test case of the Boeing 737-800 where it was seen to predict the planform shape of the real world wing with a high degree of accuracy. This methodology is then applied to the design of transonic supercritical airfoils in which the airfoil is parameterized and optimized to reduce drag at a specified lift coefficient. The resultant airfoils are seen to closely resemble modern supercritical airfoils and reduce the drag significantly. Lastly a method of airfoil surrogate modelling using convolutional neural networks to predict aerodynamic performance of airfoils at a fraction of the computational cost is examined. This method is seen to provide highly accurate estimates for the drag coefficient of airfoils, however is seen to fall short of being able to fully optimize the airfoils. Ultimately the previously described methodologies are all used in conjunction to create a computer program which is able to fully optimize the geometry and airfoil distribution of a wing with high degree of accuracy at the maximum possible computational efficiency.

关键词： Surrogate Model Hydrogen Powered Aircraft Aerodynamic Shape Optimization Supercritical Airfoils Convolutional Neural Network Bayesian Optimization Aerodynamic Performance Vortex Lattice Method sequential quadratic programming Computer Programs

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Discretization Error Guided Optimal Mesh Adaptation in One-dimensional Steady Problems

Discretization Error Guided Optimal Mesh Adaptation in One-d...

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AIAA Aviation Forum

作者： Carvalho, Eduardo de Oliveira Moura, Rodrigo Costa de Castro da Silva, Andre Fernando Aeronaut Inst Technol ITA Dept Aerodynam Praca Marechal Eduardo Gomes 50 BR-12228900 Sao Jose Dos Campos SP Brazil

ISBN: (数字)9781624107047

ISBN: (纸本)9781624107047

When solving differential equations, one must often use spatial discretization. However, this process introduces errors that are mesh dependent. Thus, improving solution quality while saving computational resources requires adequate spatial resolution. One way of doing so is to treat this issue as an optimization problem that targets the reduction of discretization error. The current work presents an approach to mesh optimization using r-adaptation and the adjoint method for one-dimensional steady equations. The two equations selected to display this methodology are the heat equation with a forcing term and the viscous burgers equation. The discretization method is a second-order finite differences scheme. The results present a substantial reduction in discretization error when the optimized meshes are employed.

关键词： Inhomogeneous Heat Equation Finite Difference Scheme sequential quadratic programming Space Domain Computing Dirichlet Boundary Condition Implicit Euler Method MATLAB Kinematic Viscosity Mathematical Models

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FMNSICS: Fractional Meyer neuro-swarm intelligent computing solver for nonlinear fractional Lane-Emden systems

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NEURAL COMPUTING & APPLICATIONS 2022年第6期34卷 4193-4206页

作者： Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Umar, Muhammad Shoaib, Muhammad Baleanu, Dumitru Hazara Univ Dept Math & Stat Mansehra Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Elect & Comp Engn Attock Campus Attock 43600 Pakistan COMSATS Univ Islamabad Dept Math Attock Campus Attock 43600 Pakistan Cankaya Univ Dept Math Ankara Turkey Inst Space Sci Magurele Romania

The fractional neuro-evolution-based intelligent computing has substantial potential to solve fractional order systems represented with Lane-Emden equation arising in astrophysics including Newtonian self-gravitating, spherically symmetric and polytropic fluid. The present study aimed to present a neuro-swarm-based intelligent computing solver for the solution of nonlinear fractional Lane-Emden system (NFLES) using by exploitation of fractional Meyer wavelet artificial neural networks (FMW-ANNs) and global optimization mechanism of particle swarm optimization (PSO) combined with rapid local search of sequential quadratic programming (SQP), i.e., FMW-ANN-PSO-SQP. The motivation for the design of FMW-ANN-PSO-SQP intelligent computing comes with an objective of presenting an accurate, reliable, and viable framworks to deal with stiff nonlinear singular models represented with NFLES involving both fractional and integer derivative terms. The designed algorithm is tested for six different variants of NFLESs. The obtained numerical outcomes obtained by the proposed FMW-ANN-PSO-SQP are compared with the exact results to authenticate the correctness, efficacy, and viability, and these aspects are further endorsed statistical observations.

关键词： Particle swarm optimization Meyer wavelets Fractional Lane-Emden model Artificial neural networks sequential quadratic programming Intelligent computing

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A dynamic optimization method for power distribution network operation with high ratio photovoltaics

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IET GENERATION TRANSMISSION & DISTRIBUTION 2022年第21期16卷 4417-4432页

作者： Liu, Keyan Zhan, Huiyu Wei, Yuhan Kang, Tianyuan China Elect Power Res Inst 15 Xiaoying E Rd Beijing 100192 Peoples R China

With the high ratio distributed photovoltaics (PVs) penetration to the distribution network (DN), the bearing capacity, integration capacity, consuming capacity and controlling capacity of DN for PVs are all facing great challenges. Enhancing the flexible regulation and operation optimization capabilities of DN have become key factors to satisfy these new challenges. For the operation optimization problem of high ratio PVs penetration, a novel quasi-equal curtailment ratio (QCR) constraint of PVs is proposed in the dynamic optimization method for DN operation. Firstly, the uncertainty of high ratio PVs and their active power deviations are discussed. And a dynamic optimization framework of PVs based on optimal power flow (OPF) has been proposed. Secondly, the optimization model for high ratio PVs is formulated with multi-objective of maximum PVs output, minimum voltage deviation and minimum line loss, under the equality and inequality constraints of power flow, PVs power generation limit, QCR of PVs and so on. Then the multi-objective optimization problem (MOP) is transformed into a single-objective optimization problem (SOP) by weighting coefficients, and solved by sequential quadratic programming (SQP) with trust-region (TR) searching algorithm. Finally, the proposed method is tested, verified and compared with the primal dual interior point (PDIP) and traditional SQP algorithms in IEEE 33-bus, PG&E 69-bus, 292-bus and 1180-bus test cases. The experimental results show the rapidity and robustness of the proposed method.

关键词： search problems PV power generation limit operation optimization problem optimal power flow SQP algorithm high ratio distributed photovoltaic penetration quadratic programming QCR multiobjective optimization problem single-objective optimization problem dynamic optimization framework minimum line loss primal dual interior point dynamic optimization method active power deviation bearing capacity trust-region searching algorithm controlling capacity photovoltaic power systems high ratio PVs penetration power generation economics IEEE 292-bus test case power distribution network operation IEEE 33-bus test case minimum voltage deviation distributed power generation PG&E 69-bus test case IEEE 1180-bus test case novel quasiequal curtailment ratio constraint optimization model sequential quadratic programming integration capacity consuming capacity load flow demand side management flexible regulation DN operation power distribution economics PDIP

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A novel computing stochastic algorithm to solve the nonlinear singular periodic boundary value problems

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INTERNATIONAL JOURNAL OF COMPUTER MATHEMATICS 2022年第10期99卷 2091-2104页

作者： Sabir, Zulqurnain Baleanu, Dumitru Ali, Mohamed R. Sadat, R. Hazara Univ Dept Math & Stat Mansehra Pakistan Cankaya Univ Dept Math Ankara Romania Inst Space Sci Magurele Romania Future Univ Fac Engn & Technol Cairo Egypt Benha Univ Fac Engn Benha Dept Basic Sci Banha Egypt Zagazig Univ Zagazig Fac Engn Dept Math Zagazig Egypt

In this work, a class of singular periodic nonlinear differential systems (SP-NDS) in nuclear physics is numerically treated by using a novel computing approach based on the Gudermannian neural networks (GNNs) optimized by the mutual strength of global and local search abilities of genetic algorithms (GA) and sequential quadratic programming (SQP), i.e. GNNs-GA-SQP. The stimulation of offering this numerical computing work comes from the aim of introducing a consistent framework that has an effective structure of GNNs optimized with the backgrounds of soft computing to tackle such thought-provoking systems. Two different problems based on the SPNDS in nuclear physics will be examined to check the proficiency, robustness and constancy of the GNNs-GA-SQP. The outcomes obtained through GNNs-GA-SQP are compared with the true results to find the worth of designed procedures based on the multiple trials.

关键词： Periodic singular systems nuclear physics genetic algorithm sequential quadratic programming Gudermannian neural networks

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Multi-objective optimal trajectory planning of customized industrial robot based on reliable dynamic identification for improving control accuracy

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2022年第6期49卷 1156-1168页

作者： Hou, Renluan Niu, Jianwei Guo, Yuliang Ren, Tao Han, Bing Yu, Xiaolong Ma, Qun Wang, Jin Qi, Renjie Beihang Univ Hangzhou Innovat Inst Hangzhou Peoples R China Zhejiang Univ Sch Mech Engn Key Lab Adv Mfg Technol Zhejiang Prov Hangzhou Zhejiang Peoples R China Beihang Univ Sch Comp Sci & Engn State Key Lab Virtual Real Technol & Syst Beijing Peoples R China Zhejiang Univ Sch Mech Engn State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Sch Mech Engn Engn Res Ctr Design Engn & Digital Twin Zhejiang Hangzhou Peoples R China

Purpose The purpose of this paper is to enhance control accuracy, energy efficiency and productivity of customized industrial robots by the proposed multi-objective trajectory optimization approach. To obtain accurate dynamic matching torques of the robot joints with optimal motion, an improved dynamic model built by a novel parameter identification method has been proposed. Design/methodology/approach This paper proposes a novel multi-objective optimal approach to minimize the time and energy consumption of robot trajectory. First, the authors develop a reliable dynamic parameters identification method to obtain joint torques for formulating the normalized energy optimization function and dynamic constraints. Then, optimal trajectory variables are solved by converting the objective function into relaxation constraints based on second-order cone programming and Runge-Kutta discrete method to reduce the solving complexity. Findings Extensive experiments via simulation and in real customized robots are conducted. The results of this paper illustrate that the accuracy of joint torque predicted by the proposed model increases by 28.79% to 79.05% over the simplified models used in existing optimization studies. Meanwhile, under the same solving efficiency, the proposed optimization trajectory consumes a shorter time and less energy compared with the existing optimization ones and the polynomial trajectory. Originality/value A novel time-energy consumption optimal trajectory planning method based on dynamic identification is proposed. Most existing optimization methods neglect the effect of dynamic model reliability on energy efficiency optimization. A novel parameter identification approach and a complete dynamic torque model are proposed. Experimental results of dynamic matching torques verify that the control accuracy of optimal robot motion can be significantly improved by the proposed model.

关键词： Path planning Dynamic identification Time-energy consumption optimization sequential quadratic programming Industrial robot Control Offline programming

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Physics-Informed Neural Networks for Optimal Planar Orbit Transfers

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JOURNAL OF SPACECRAFT AND ROCKETS 2022年第3期59卷 834-849页

作者： Schiassi, Enrico D'Ambrosio, Andrea Drozd, Kristofer Curti, Fabio Furfaro, Roberto Univ Arizona Dept Syst & Ind Engn Syst & Ind Engn 1127 East James E Rogers Way Tucson AZ 85721 USA Univ Arizona Dept Syst & Ind Engn 1127 East James E Rogers Way Tucson AZ 85721 USA Univ Roma La Sapienza Sch Aerosp Engn Via Salaria 851 I-00138 Rome Italy

This paper presents a novel framework, combining the indirect method and Physics-Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal planar orbit transfer problems. According to the indirect method, the optimal control is retrieved by directly applying the Pontryagin minimum principle, which provides the first-order necessary optimality conditions. The necessary conditions result in a two-point boundary value problem (TPBVP) in the state-costate pair, constituting a system of ordinary differential equations, representing the physics constraints of the problem. More precisely, the goal is to model a neural network (NN) representation of the state-costate pair for which the residuals of the TPVBP are as close to zero as possible. This is done using PINNs, which are particular NNs where the training is driven by the problem's physics constraints. A particular PINN method will be used, named Extreme Theory of Functional Connections (X-TFC), which is a synergy of the classic PINN and the Theory of Functional Connections. With X-TFC, the TPBVP's boundary conditions are analytically satisfied. This avoids having unbalanced gradients during the network training. The results show the feasibility of employing PINNs to tackle this class of optimal control problems for space applications.

关键词： Neural Networks Transversality Condition Boundary Value Problems Solar Sail Earth Adaptive Mesh Refinement Nonlinear programming Astronomical Unit sequential quadratic programming Particle Swarm Optimization

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Surrogate-Assisted Differential Evolution Using Knowledge-Transfer-Based Sampling for Expensive Optimization Problems

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AIAA JOURNAL 2022年第5期60卷 3251-3266页

作者： Long, Teng Ye, Nianhui Shi, Renhe Wu, Yufei Tang, Yifan Beijing Inst Technol Sch Aerosp Engn Beijing Peoples R China Beijing Inst Technol Key Lab Dynam & Control Flight Vehicle Minist Educ Beijing 100081 Peoples R China Simon Fraser Univ Sch Mech Syst Engn Surrey BC V3T 0A3 Canada

To sufficiently reuse the knowledge from previous optimization efforts, a surrogate-assisted differential evolution using knowledge-transfer-based sampling (denoted as SADE-KTS) method is proposed for solving expensive black-box optimization problems. In SADE-KTS, a novel knowledge-transfer-based sampling method is integrated with the differential evolution framework to generate promising initial sample points. In this way, a least-squares support vector machine classifier is constructed based on the prior optimization knowledge database to calibrate the initial sample points adaptively, which improves the exploration performance via transferring the existed optimization efforts to the current optimization task. Moreover, the radial basis function and kriging surrogates are employed to replace the expensive simulation models for evolutionary operations, where the tailored differential evolution operators are cooperated with the sequential quadratic programming optimizer to lead the search to the global optimum efficiently. A number of numerical benchmarks are tested to illustrate the optimization capacity of SADE-KTS compared with several competitive optimization algorithms. Finally, SADE-KTS is applied to an airfoil aerodynamic knowledge-based optimization problem considering the existed optimization knowledge, which demonstrates the practicality and effectiveness of the proposed SADE-KTS in engineering practices.

关键词： Airfoil sequential quadratic programming Support Vector Machine Black Box Numerical Simulation Multi Objective Evolutionary Algorithms Lagrange Multipliers Lift Coefficient Finite Element Analysis Multidisciplinary Design Optimization

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