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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Misra, Gaurav Bai, Xiaoli Rutgers State Univ Dept Mech & Aerosp Engn Piscataway NJ 08854 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

In this paper, a convex optimization based approach is proposed for solving the relative spacecraft guidance problem with non-convex obstacle avoidance constraints akin to sequential convex programming approaches proposed in literature, which require solving a sequence of simpler convex programs. However, contrary to existing approaches, the method proposed in this paper ensures that the iterates remain feasible as long as the first iterate is feasible and achieves monotonic convergence of the objective function. Significantly, the proposed methodology leads to anytime solutions, i.e., the computation can be aborted whenever needed and still return a quality solution, which is highly desirable for real-time operations due to potential unexpected situations and limited computation resources. However, most existing methods only guarantee the correctness of the solution when they converge, whereas preconvergence solution obtained can be useless or even dangerous when applied. The underlying methodology assumes linear dynamics with polynomial path constraints and relies on the convex-concave procedure and sum-of-squares programming to obtain tractable inner convex approximations. To validate the efficacy of the proposed method, a relative spacecraft fuel optimal guidance problem under Clohessy-Hill-Wiltshire (CHW) dynamics is studied under plume impingement, keep-out zones, and obstacle avoidance constraints.

关键词： Nonlinear programming Spacecraft Trajectory Optimization Model Predictive Control Optimal Control Algorithms Convergence sequential quadratic programming Pseudo Spectral Method Boundary Value Problems Guidance Laws Rockets

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A B-Spline-based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization

A B-Spline-based Generative Adversarial Network Model for Fa...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Du, Xiaosong He, Ping Martins, Joaquim R. R. A. Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

Airfoil aerodynamic optimization is of great importance in aircraft design;however, it relies on high-fidelity physics-based models that are computationally expensive to evaluate. In this work, we provide a methodology to reduce the computational cost for airfoil aerodynamic optimization. Firstly, we develop a B-spline based generative adversarial networks (BSplineGAN) parameterization method to automatically infer design space with sufficient shape variability. Secondly, we construct multi-layer neural network (MNN) surrogates for fast predictions on aerodynamic drag, lift, and pitching moment coefficients. The BSplineGAN has a relative error lower than 1% when fitting to UIUC database. Verification of MNN surrogates shows the root means square errors (RMSE) of all aerodynamic coefficients are within the range of 20%-40% standard deviation of testing points. Both normalized RMSE and relative errors are controlled within 1%. The proposed methodology is then demonstrated on an airfoil aerodynamic optimization. We also verified the baseline and optimized designs using a high-fidelity computational fluid dynamic solver. The proposed framework has the potential to enable web-based fast interactive airfoil aerodynamic optimization.

关键词： Airfoil Databases Generative Adversarial Network Aerodynamic Shape Optimization Cumulative Distribution Function Aircraft Design Surrogate Model Computational Fluid Dynamics Lift Coefficient Shape Control sequential quadratic programming

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Comparison of Algorithms for High-Order, Metric-Based Mesh Optimization

Comparison of Algorithms for High-Order, Metric-Based Mesh O...

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AIAA Science and Technology Forum and Exposition (AIAA SciTech)

作者： Sanjaya, Devina P. Fidkowski, Krzysztof J. Murman, Scott M. Univ Tennessee Dept Mech Biomed & Aerosp Engn Knoxville TN 37996 USA Univ Michigan Dept Aerosp Engn Ann Arbor MI 48109 USA NASA Ames Res Ctr Moffett Field CA 94035 USA

ISBN: (数字)9781624105951

ISBN: (纸本)9781624105951

We assess the performance of two iterative mesh optimization algorithms for generating high-order, metric-conforming meshes appropriate for use with high-order, finite-element methods. The three key steps in these metric-based approaches are: 1) obtain the desired high-order, Riemannian metric field, 2) generate a linear, metric-conforming mesh, and 3) place the high-order geometry nodes such that the mesh-implied metric conforms to the desired metric field. The procedure to obtain the desired high-order metric field is similar to the Mesh Optimization via Error Sampling and Synthesis (MOESS) developed by Yano(1) for linear meshes. In this work, we consider two approaches to obtain the desired, higher-order metric field: 1) performing MOESS on a refined mesh, and 2) extending native MOESS to high-order MOESS (HOMES). The mesh regeneration procedures used to obtain the metric-conforming property are also described in detail. The accuracy benefits offered by these two proposed algorithms are presented for a series of one-dimensional problems. A two-dimensional proof of concept is also provided to establish the value of extending the proposed metric-based optimization algorithm to higher dimensions and to demonstrate the feasibility of the proposed mesh regeneration procedure in higher dimensions.

关键词： sequential quadratic programming Extended Finite Element Method Computational Fluid Dynamics Mesh Generation Peclet Number Computer Aided Design Discontinuous Galerkin Method Angle of Attack Shear Layers High Performance Computing Systems

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Design of evolutionary cubic spline intelligent solver for nonlinear Painleve-I transcendent

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INTERNATIONAL JOURNAL OF MODERN PHYSICS B 2021年第29期35卷

作者： Ali, Sharafat Ahmad, Iftikhar Raja, Muhammad Asif Zahoor Ahmad, Siraj ul Islam Shoaib, Muhammad Univ Gujrat Dept Math Gujrat 50700 Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan COMSATS Univ Islamabad Dept Phys Islamabad 44000 Pakistan COMSATS Univ Islamabad Dept Math Attock Campus Attock 43600 Pakistan

In this research paper, an innovative bio-inspired algorithm based on evolutionary cubic splines method (CSM) has been utilized to estimate the numerical results of nonlinear ordinary differential equation Painleve-I. The computational mechanism is used to support the proposed technique CSM and optimize the obtained results with global search technique genetic algorithms (GAs) hybridized with sequential quadratic programming (SQP) for quick refinement. Painleve-I is solved by the proposed technique CSM-GASQP. In this process, variation of splines is implemented for various scenarios. The CSM-GASQP produces an interpolated function that is continuous upto its second derivative. Also, splines proved to be stable than a single polynomial fitted to all points, and reduce wiggles between the tabulated points. This method provides a reliable and excellent procedure for adaptation of unknown coefficients of splines by searching globally exploiting the performance of GA-SQP algorithms. The convergence, exactness and accuracy of the proposed scheme are examined through the statistical analysis for the several independent runs.

关键词： Painleve-I cubic splines method computational hybrid mechanism genetic algorithm sequential quadratic programming numerical computing

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Computationally- and Memory-Efficient One-Shot Approach for Unsteady Adjoint-Based Design Optimization

Computationally- and Memory-Efficient One-Shot Approach for ...

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AIAA Aviation Forum

作者： Djeddi, Reza Ekicit, Kivanc Univ Tennessee Dept Mech Aerosp & Biomed Engn Knoxville TN 37996 USA

ISBN: (数字)9781624105982

ISBN: (纸本)9781624105982

A computationally efficient "one-shot" approach with a low memory footprint is presented for unsteady design optimization. The proposed technique is based on a novel and unique approach that combines "local-in-time" and fixed-point iteration methods to advance the unconverged primal and adjoint solutions forward and backward in time to evaluate the sensitivity of the globally time-integrated objective function. This is in some ways similar to the "piggyback" iterations where primal and adjoint solutions are evaluated at a frozen design. During each cycle, the primal, adjoint, and design update problems are solved to advance the optimization problem. This new coupled approach is shown to provide significant savings in the memory footprint while reducing the computational cost of primal and adjoint evaluations per design cycle. The method is first applied to an inverse design problem for the unsteady lid-driven cavity. Following this, vortex suppression and mean drag reduction for a circular cylinder in cross-flow is considered. Both of these objectives are achieved by optimizing the rotational speeds for steady or periodically oscillating excitations. For all cases presented in this work, the proposed technique is shown to provide significant reductions in memory as well as computational time. It is also shown that the unsteady design optimization problem converges to the same optimal solution obtained using a conventional approach.

关键词： Karman Vortex Street Aerodynamic Shape Optimization Magnus Effect Computational Fluid Dynamics sequential quadratic programming Navier Stokes Equations Optimal Control Incompressible Flow Lagrange Multipliers High Performance Computing

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sequential quadratic programming based differential evolution algorithm for optimal power flow problem

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IET GENERATION TRANSMISSION & DISTRIBUTION 2011年第11期5卷 1149-1154页

作者： Sivasubramani, S. Swarup, K. S. Indian Inst Technol Dept Elect Engn Chennai 600036 Tamil Nadu India

This study proposes a hybrid algorithm combining sequential quadratic programming (SQP) and differential evolution (DE) algorithm for solving the optimal power flow (OPF) problem. In this hybrid method, SQP is used to generate an individual, which is a member of an initial population, for DE algorithm. Having generated an individual by SQP, which will be nearer to the optimal solution, DE algorithm can reach the optimal solution more effectively than the classical evolutionary algorithms can. The proposed method has been used to solve the OPF problem on the standard IEEE 30- and IEEE 118-bus test systems to validate the effectiveness. Two different objectives, namely fuel cost considering valve-point effects and the transmission line losses, have been considered. The simulation results obtained from the proposed hybrid method reveal that this algorithm gives better solution for the problem having more non-convexity.

关键词： evolutionary computation differential evolution algorithm quadratic programming sequential quadratic programming DE algorithm Power transmission, distribution and supply load flow valve-point effect transmission line loss IEEE 118-bus test system OPF problem SQP IEEE 30-bus test system optimal power flow problem Optimisation techniques

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A novel study of Morlet neural networks to solve the nonlinear HIV infection system of latently infected cells

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RESULTS IN PHYSICS 2021年 25卷 104235-104235页

作者： Umar, Muhammad Sabir, Zulqurnain Raja, Muhammad Asif Zahoor Baskonus, Haci Mehmet Yao, Shao-Wen Ilhan, Esin Hazara Univ Dept Math & Stat Mansehra Pakistan Natl Yunlin Univ Sci & Technol Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Harran Univ Dept Math & Sci Educ Sanliurfa Turkey Henan Polytech Univ Sch Math & Informat Sci Jiaozuo 454000 Henan Peoples R China Kirsehir Ahi Evran Univ Kirsehir Turkey

The aim of this study is to provide the numerical outcomes of a nonlinear HIV infection system of latently infected CD4+ T cells exists in bioinformatics using Morlet wavelet (MW) artificial neural networks (ANNs) optimized initially with global search of genetic algorithms (GAs) hybridized for speedy local search of sequential quadratic programming (SQP), i.e., MW-ANN-GA-SQP. The design of an error function is presented by designing the MW-ANN models for the differential equations along with the initial conditions that represent the HIV infection system involving latently infected CD4+ T cells. The precision and persistence of the presented approach MW-ANN-GA-SQP are recognized through comparative studies from the results of the Runge-Kutta numerical scheme for solving the HIV infection spread system in case of single and multiple trails of the MW-ANN-GA-SQP. Statistical estimates with 'Theil's inequality coefficient' and 'root mean square error' based indices further validate the sustainability and applicability of proposed MW-ANN-GA-SQP solver.

关键词： Morlet wavelets HIV infection models Genetic algorithms Neural networks sequential quadratic programming Bioinformatics

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Optimal designs for mixed continuous and binary responses with quantitative and qualitative factors

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JOURNAL OF MULTIVARIATE ANALYSIS 2021年 182卷 104712-104712页

作者： Kao, Ming-Hung Khogeer, Hazar Arizona State Univ Sch Math & Stat Sci Tempe AZ 85287 USA

This work is concerned with optimal designs for multivariate regression of responses of mixed variable types (continuous and binary) on quantitative and qualitative factors. New complete class results with respect to the Loewner ordering, and relevant Chebyshev systems are derived to identify a small class of designs, within which locally optimal designs can be found for a group of models and optimality criteria. The complete class results facilitate the search of optimal designs via some general-purpose optimization techniques. Extensions of some previous results for characterizing optimal designs are also provided. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Approximate design Chebyshev system Complete class Optimality criteria sequential quadratic programming

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A Convex OPF Approximation for DC Networks Considering Voltage-Dependent Load Models

A Convex OPF Approximation for DC Networks Considering Volta...

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2020 IEEE Andescon Conference

作者： Montoya, Oscar Gil-Gonzalez, Walter Garces, A. Casilimas-Pena, A. Garrido, V. M. Univ Dist Francisco Jose de Caldas Engn Fac Bogota Colombia Inst Univ Pascual Bravo Fac Ingn Medellin Colombia Univ Tecnol Pereira Dept Elect Power Engn Pereira Colombia Univ Tecnol Bolivar Smart Energy Lab Cartagena Colombia

ISBN: (纸本)9781728193656

This paper addresses the problems of power flow and optimal power flow a nalysis considering voltage-dependent load models from the convex point of view. First, Taylor series expansion method is employed for linearizing the power flow equations generating a set of affine hyperplanes. Second, the sequential quadratic programming (SQP) approach is employed for adjusting the linearization point to eliminate the voltage estimation error between the exact and proposed convex models recursively. Two voltage-dependent load models are considered in our power flow and optimal power flow proposals which based on the exponential and polynomial models. General algebraic modeling system (GAMS) and its nonlinear optimization packages are employed for comparison purposes. Two DC-test systems with 6 and 21 nodes are used to validate the performance of the SQP proposed. The proposed SQP approach is implemented in MATLAB software with quadprog toolbox.

关键词： Direct-current networks optimal power flow analysis sequential quadratic programming Taylor's based series expansion method

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An inverse method for the reconstruction of thermal boundary conditions of semitransparent medium

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 134卷 574-585页

作者： Sun, Shuangcheng Wang, Guangjun Chen, Hong Zhang, Daqian Chongqing Univ Sch Energy & Power Engn 174 Shazheng St Chongqing 400044 Peoples R China Chongqing Univ Key Lab Low Grade Energy Utilizat Technol & Syst Minist Educ Chongqing 400044 Peoples R China

The sequential quadratic programming (SQP) algorithm is applied to reconstruct the time- and space-dependent boundary conditions in 1D and 2D radiative-conductive systems in this study. The coupled radiation-conduction heat transfer in absorbing, scattering and emitting media is solved by the discrete ordinate method combined with finite volume method, and the simulated boundary temperature is served as input for the inverse analysis. The SQP algorithm is employed as the optimization technique, through which the time-dependent boundary heat flux in 1D heat transfer problems and the space dependent boundary heat flux and convective heat transfer coefficient in 2D heat transfer problems are recovered. No prior information on the functional forms of the unknown boundary conditions is needed for inverse analysis. All the retrieval results show that the SQP algorithm is robust to the reconstruction of thermal boundary conditions in coupled radiative-conductive systems. The present inverse technique is proved to be more efficient and accurate than the conjugate gradient method, Levenberg-Marquardt method and particle swarm optimization algorithm. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Inverse heat transfer sequential quadratic programming Heat flux Convective heat transfer coefficient Coupled radiation and conduction

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