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Design of neuro-computing paradigms for nonlinear nanofluidic systems of MHD Jeffery-Hamel flow

JOURNAL OF THE TAIWAN INSTITUTE OF CHEMICAL ENGINEERS 2018年 91卷 57-85页

作者： Mehmood, Ammara Nouman-Ul-Haq Zameer, Aneela Ling, Sai Ho Raja, Muhammad Asif Zahoor Pakistan Inst Engn & Appl Sci Dept Elect Engn Islamabad Pakistan Riphah Int Univ Dept Phys Islamabad Pakistan PIEAS Dept Comp & Informat Sci Islamabad 45650 Pakistan Univ Technol Dept Engn & IT Ctr Hlth Technol Sch Biomed Engn Sydney NSW Australia COMSATS Inst Informat Technol Dept Elect Engn Attock 43600 Pakistan

In this paper, a neuro-heuristic technique by incorporating artificial neural network models (NNMs) optimized with sequential quadratic programming (SQP) is proposed to solve the dynamics of nanofluidics system based on magneto-hydrodynamic (MHD) Jeffery-Hamel (JHF) problem involving nano-meterials. Original partial differential equations associated with MHD-JHF are transformed into third order ordinary differential equations based model. Furthermore, the transformed system has been implemented by the differential equation NNMs (DE-NNMs) which are constructed by a defined error function using logsigmoid, radial basis and tan-sigmoid windowing kernels. The parameters of DE-NNM of nanofluidics system are optimized with SQP algorithm. To illustrate the performance of the proposed system, MHD-JHF models with base-fluid water mixed with alumina, silver and copper nanoparticles for different Hartman numbers, Reynolds numbers, angles of the channel and volume fractions with three different proposed DE-NNMs are designed to evaluate. For comparison purpose, the proposed results with reference numerical solutions of Adams solver illustrate their worth. Statistical inferences through different performance indices are given to demostrate the accuracy, stability and robustness of the stochastic solvers. (C) 2018 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

关键词： Magneto-hydrodynamic Jeffery-Hamel flow Artificial neural networks Nanofluids Neurocomputing sequential quadratic programming

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Three-Dimensional Trajectory Optimization of Soft Lunar Landings from the Parking Orbit with Considerations of the Landing Site

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INTERNATIONAL JOURNAL OF CONTROL AUTOMATION AND SYSTEMS 2011年第6期9卷 1164-1172页

作者： Park, Bong-Gyun Tahk, Min-Jea Korea Adv Inst Sci & Technol Dept Aerosp Engn Taejon 305701 South Korea

Minimum fuel, three-dimensional trajectory optimization from a parking orbit considering the desired landing site is addressed for soft lunar landings. The landing site is determined by the final longitude and latitude;therefore, a two-dimensional approach is limited and a three-dimensional approach is required. In addition, the landing site is not usually considered when performing lunar landing trajectory optimizations, but should be considered in order to design more accurate and realistic lunar landing trajectories. A Legendre pseudospectral (PS) method is used to discretize the trajectory optimization problem as a nonlinear programming (NLP) problem. Because the lunar landing consists of three phases including a de-orbit burn, a transfer orbit phase, and a powered descent phase, the lunar landing problem is regarded as a multiphase problem. Thus, a PS knotting method is also used to manage the multiphase problem, and C code for Feasible sequential quadratic programming (CFSQP) using a sequential quadratic programming (SQP) algorithm is employed as a numerical solver after formulating the problem as an NLP problem. The optimal solutions obtained satisfy all constraints as well as the desired landing site, and the solutions are verified through a feasibility check.

关键词： Legendre pseudospectral method optimal control pseudospectral knotting method sequential quadratic programming soft lunar landing

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Numerical shape optimization of a confined cavity in natural convection regime

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COMPUTERS & FLUIDS 2013年 75卷 11-21页

作者： Aounallah, M. Belkadi, M. Adjlout, L. Imine, O. LAHN Univ Sci & Technol dOran Mohamed Boudiaf Lab Aerohydrodynam Navale USTOMB El Mnaouar 31000 Oran Algeria LASP Univ Sci & Technol dOran Mohamed Boudiaf Lab Aeronaut & Syst Propulsifs USTOMB El Mnaouar 31000 Oran Algeria

The aim of this work is to optimize the geometric shape in enclosures similar to the square cavity in natural convection regime. The desired configuration differs from the classical cavity by the geometrical form of the hot wall to be defined. The procedure is based on a sequential quadratic programming in which the wall is parameterized by Bezier curves. The numerical optimization method developed is the outcome of combining a flow calculation method and a constrained minimization method. Investigations have been performed for different inclination angles and various Rayleigh numbers. The numerical results show that optimization is relevant since the heat transfer rate decreases considerably by comparison with the square and the undulated cavities. It is also found that the beneficial optimization occurs for high Rayleigh numbers. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Optimization sequential quadratic programming Natural convection Cavity Bezier curves

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An efficient computational intelligence approach for solving fractional order Riccati equations using ANN and SQP

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APPLIED MATHEMATICAL MODELLING 2015年第10-11期39卷 3075-3093页

作者： Raja, Muhammad Asif Zahoor Manzar, Muhammad Anwaar Samar, Raza COMSATS Inst Informat Technol Dept Elect Engn Attock Pakistan Int Islamic Univ Dept Elect Engn Islamabad Pakistan Mohammad Ali Jinnah Univ Dept Elect Engn Islamabad Pakistan

A new computational intelligence technique is presented for solution of non-linear quadratic Riccati differential equations of fractional order based on artificial neural networks (ANNs) and sequential quadratic programming (SQP). The power of feed forward ANNs in an unsupervised manner is exploited for mathematical modeling of the equation;training of weights is carried out with an efficient constrained optimization technique based on the SQP algorithm. The proposed scheme is evaluated on two initial value problems of the Riccati fractional order equation with integer and non-integer derivatives. Comparison of results with the exact solution, and with reference numerical methods demonstrates the correctness of the proposed methodology. Performance of the proposed scheme is also validated using results of statistical analysis based on a sufficiently large number of independent runs. (C) 2014 Elsevier Inc. All rights reserved.

关键词： Riccati equations Nonlinear systems Fractional differential equations Artificial neural networks sequential quadratic programming

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Convergence of successive approximation methods with parameter target sets

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MATHEMATICS OF OPERATIONS RESEARCH 2005年第3期30卷 765-784页

作者： Levy, AB Bowdoin Coll Dept Math Brunswick ME 04011 USA

Successive approximation methods appear throughout numerical optimization, where a solution to an optimization problem is sought as the limit of solutions to a succession of simpler approximation problems. Such methods include essentially any standard penalty method, barrier method, trust region method, augmented Lagrangian method, or sequential quadratic programming (SQP) method, as well as many other methods. The approximation problems on which a successive approximation method is based typically depend on parameters, in which case the performance of the method is related to the corresponding sequence of parameters. For many successive approximation methods, the sequence of parameters might need only approach some parameter target set for the method to have nice convergence properties. Successive approximation methods could be analyzed as examples of a generic inclusion solving method from Levy [23] because the solutions to the approximation problems satisfy necessary optimality inclusions. However, the inclusion solving method from Levy [23] was developed for single-parameter target points. In this paper, we extend the results from Levy [23] to allow parameter target sets and apply these results to the convergence analysis of successive approximation methods. We focus on two important convergence issues: (1) the rate of convergence of the iterates generated by a successive approximation method and (2) the validity of the limit as a solution to the original problem. An augmented Lagrangian method allowing quite general parameter updating is explored in detail to illustrate how the framework presented here can expose interesting new alternatives for numerical optimization.

关键词： constrained optimization numerical optimization penalty methods barrier methods trust region methods augmented Lagrangian methods sequential quadratic programming convergence analysis inclusion solving variational analysis generalized continuity

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General implementation of multilevel parallelization in a gradient-based design optimization algorithm

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AIAA JOURNAL 2006年第9期44卷 1993-2001页

作者： Rajan, S. D. Belegundu, A. D. Damle, A. S. Lau, D. St Ville, J. Arizona State Univ Dept Civil & Environm Engn Tempe AZ 85287 USA Penn State Univ Mech & Nucl Engn Dept University Pk PA 16802 USA Hawthorne & York Int FEA Grp Phoenix AZ 85008 USA

As product designs have become more sophisticated, both the simulation models (e.g., finite element models) and the design optimization models have grown bigger. To keep pace with this increase in problem size, we present and implement an optimization strategy that can run on a computing cluster with demonstrable efficiency. First, parallelism is implemented in the context of gradient calculations using divided differences. Then, parallelism is achieved in the context of both direction-finding and line-search steps. Parallel direction finding improves the convergence rate as opposed to just cutting down the amount of arithmetic. A new algorithm based on method of feasible directions is discussed that obtains better optima and is also computationally faster. Implementation details regarding distribution of computing tasks to improve scalability and load balancing are presented. Numerical examples show the efficiency of the developed methodology on a relatively small computing cluster. Gains of about 7:1 have been obtainable using 16 processors on some test problems. Importantly, the framework presented can be developed by researchers using other gradient-based optimization codes on different computing platforms.

关键词： sequential quadratic programming Finite Element Analysis Parallel Computing Architectures Numerical Optimization Planar Truss Parallel Algorithm Parallel Computing Supercomputers Software Development Parallel Computers

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Onboard Autonomous Targeting for the Trans-Earth Phase of Orion

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JOURNAL OF GUIDANCE CONTROL AND DYNAMICS 2010年第3期33卷 943-956页

作者： Marchand, Belinda G. Weeks, Michael W. Smith, Chad W. Scarritt, Sara Univ Texas Austin Austin TX 78712 USA NASA Lyndon B Johnson Space Ctr Aerosci & Flight Mech Div Code EG6 Houston TX 77058 USA

The present investigation focuses on one aspect of the autonomous targeting process used onboard during the Orion trans-Earth injection phase, specifically, a fast and robust algorithm that identifies a feasible return trajectory, one that meets the entry constraints without exceeding the fuel available. Unlike earlier Apollo missions, Orion seeks to land near the polar regions of the moon. Thus, a substantial plane change maneuver is required before returning to Earth. To reduce the fuel expenditure associated with this plane change, a three-maneuver sequence is employed during the return phase. An autonomous onboard targeting process for precision entry (one that incorporates multiple coordinated trans-Earth maneuvers) is sought in the event of loss of communication with the ground. The latter scenario presents a very unique challenge: one never before required of any Apollo vehicle. The Apollo missions also benefited from flexible entry requirements in contrast to Orion. Precision targeting in multibody regimes has only been previously demonstrated in unmanned sample return missions such as Genesis. The formulation presented here ensures that the entry constraints are met without violating the available fuel budget.

关键词： Earth Trans Earth Injection Targeting Algorithms Moon Apollo Missions Sample Return Missions Flight Path Angle Earth Centered Inertial sequential quadratic programming FORTRAN

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Bayesian Support Vector Regression for Reliability-Based Design Optimization

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AIAA JOURNAL 2021年第12期59卷 5141-5157页

作者： Ling, Chunyan Lu, Zhenzhou Zhang, Wenxin Northwestern Polytech Univ Sch Aeronaut Xian 710072 Shaanxi Peoples R China

Solving reliability-based design optimization (RBDO) by combining surrogate models is a powerful tool to deal with the output variation induced by uncertainties during actual engineering design. This paper aims to develop a strategy for solving RBDO problems by support vector regression (SVR) under the Bayesian inference, referred to as Bayesian SVR (BSVR). The BSVR model possesses the features of original SVR as well as providing the prediction variance to direct the sequential sampling process for probabilistic analysis in RBDO. In the meanwhile, a learning function combined with the sample pool truncation strategy is proposed to select the new training samples to adaptively update the BSVR model, which can accurately and efficiently approximate the important probabilistic constraint boundaries in the desired regions, and the newly selected training samples tend to be far away from the existing training points under the current design to avoid clustering. The computational capability and the good engineering applicability of the proposed method are verified by three numerical examples and two engineering applications about a stiffened rib of the wing edge and a latch-lock mechanism of the cabin hatch.

关键词： Lagrange Multipliers Cumulative Distribution Function sequential quadratic programming Finite Element Analysis Support Vector Machine Robust Design Structural Reliability Design of Experiments Mathematical Models Leading Edge Slat

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AN SQP TRUST REGION METHOD FOR SOLVING THE DISCRETE-TIME LINEAR quadratic CONTROL PROBLEM

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2012年第2期22卷 353-363页

作者： Mostafa, El-Sayed M. E. Alexandria Univ Moharam Bey Fac Sci Dept Math Alexandria 21511 Egypt

In this paper, a sequential quadratic programming method combined with a trust region globalization strategy is analyzed and studied for solving a certain nonlinear constrained optimization problem with matrix variables. The optimization problem is derived from the infinite-horizon linear quadratic control problem for discrete-time systems when a complete set of state variables is not available. Moreover, a parametrization approach is introduced that does not require starting a feasible solution to initiate the proposed SQP trust region method. To demonstrate the effectiveness of the method, some numerical results are presented in detail.

关键词： output feedback control design sequential quadratic programming trust region method

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Optimal rate control under geologic uncertainty: water flood and EOR processes

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OPTIMIZATION AND ENGINEERING 2017年第1期18卷 63-86页

作者： Taware, Satyajit Alhuthali, Ahmed H. Sharma, Mohan Datta-Gupta, Akhil Chevron Energy Technol Co Houston TX 77002 USA Saudi Aramco Dhahran Saudi Arabia Natl IOR Ctr Norway Stavanger Norway Texas A&M Univ College Stn TX USA

Much of the current world oil production comes from mature and partially depleted fields. Increasing the ultimate oil recovery from these fields is critical for meeting growing energy demands in the coming years. Many secondary oil recovery methods have been developed to improve oil recovery after primary depletion. The most widely used is water flooding because it is effective and relatively inexpensive to implement. However, the presence of reservoir heterogeneities, for example high permeability channels, can lead to premature breakthrough and poor sweep efficiency, resulting in reduced oil recovery. One way to improve the sweep efficiency is flood front management via the optimal allocation of production and injection rates. In this paper, we propose an optimal rate control strategy to maximize sweep efficiency and accelerate production during water flooding and EOR processes, in particular, polymer flooding and CO2 injection. We describe our approach and the underlying mathematical formulation, and then we illustrate its power using field-scale examples. We demonstrate the practical feasibility of our method in realistic production scenarios using a 3D synthetic benchmark field example, the Brugge field. We also use stochastic optimization to take geological uncertainty into consideration. The value of using multiple realizations in improving the robustness of the optimization in terms of oil recovery and enhancing NPV is clearly illustrated by the 3D example.

关键词： Reservoir managament Rate optimization Production optimization sequential quadratic programming Enhanced oil recovery Waterflood

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