Shape-optimization of 2D hydrofoils using an Isogeometric BEM solver

In this paper, an optimization procedure, based on an Isogeometric BEM solver for the potential flow, is developed and used for the shape optimization of hydrofoils. The formulation of the exterior potential-flow problem reduces to a Boundary-Integral Equation (BIE) for the associated velocity potential exploiting the null-pressure jump Kutta condition at the trailing edge. The numerical solution of the BIE is performed by an Isogeometric Boundary-Element Method (BEM) combining a generic B-splines parametric modeler for generating hydrofoil shapes, using a set of eight parameters, the very same basis of the geometric representation for representing the velocity potential and collocation at the Greville abscissas of the knot vector of the hydrofoil’s B-splines representation. Furthermore, the optimization environment is developed based on the geometric parametric modeler for the hydrofoil, the Isogeometric BEM solver and an optimizer employing a controlled elitist genetic algorithm. Multi-objective hydrofoil shape optimization examples are demonstrated with respect to the criteria (i) maximum lift coefficient and (ii) minimum deviation of the hydrofoil area from a reference area.     

Multi-objective shape optimization using ant colony coupled computational fluid dynamics solver

An adaptation of a parametric ant colony optimization (ACO) to multi-objective optimization (MOO) is presented in this paper. In this algorithm (here onwards called MACO) the concept of MOO is achieved using the reference point (or goal vector) optimization strategy by applying scalarization. This method translates the multi-objective optimization problem to a single objective optimization problem. The ranking is done using ?-dominance with modified L_p metric strategy. The minimization of the maximum distance from the goal vector drives the solution close to the goal vector. A few validation test cases with multi-objectives have been demonstrated. MACO was found to out perform R-NSGA-II for the test cases considered. This algorithm was then integrated with a meshless computational fluid dynamics (CFD) solver to perform aerodynamic shape optimization of an airfoil. The algorithm was successful in reaching the optimum solutions near to the goal vector on one hand. On the other hand the algorithm converged to an optimum outside the boundary specified by the user for the control variables. These make MACO a good contender for multi-objective shape optimization problems.     

Computation of the real structured singular value via pole migration

The paper introduces a new computationally efficient algorithm to determine a lower bound on the real structured singular value μ. The algorithm is based on a pole migration approach where an optimization solver is used to compute a lower bound on real μ independent of a frequency sweep. A distinguishing feature of this algorithm from other frequency independent one‐shot tests is that multiple localized optima (if they exist) are identified and returned from the search. This is achieved by using a number of alternative methods to generate different initial conditions from which the optimization solver can initiate its search from. The pole migration algorithm presented has also been extended to determine lower bounds for complex parametric uncertainties as well as full complex blocks. However, the results presented are for strictly real and repeated parametric uncertainty problems as this class of problem is the focus of this paper and are in general the most difficult to solve. Copyright © 2014 John Wiley & Sons, Ltd.     

Optimization of a serpentine flow field with variable channel heights and widths for PEM fuel cells

The present study proposes a modified serpentine flow field design in which the channel heights vary along each straight flow path to enhance reactant transport and liquid water removal. An optimization approach, combining a simplified conjugate-gradient method (inverse solver) and a three-dimensional, two-phase, non-isothermal fuel cell model (direct solver), has been developed to optimize the key geometric parameters. The optimal design has tapered channels for channels 1, 3 and 4 and increasing heights f...     

The simpler GMRES method combined with finite volume method for simulating viscoelastic flows on triangular grid

An efficient solver integrating the restarted simpler generalized minimal residual method (SGMRES(m)) with finite volume method (FVM) on triangular grid is developed to simulate the viscoelastic fluid flows. In particular, the SGMRES(m) solver is used to solve the large-scale sparse linear systems, which arise from the course of FVM on triangular grid for modeling the Newtonian and the viscoelastic fluid flows. To examine the performance of the solver for the nonlinear flow equations of viscoelastic fluids, we consider two types of numerical tests: the Newtonian flow past a circular cylinder, and the Oldroyd-B fluid flow in a planar channel and past a circular cylinder. It is shown that the numerical results obtained by the SGMRES(m) are consistent with the analytical solutions or empirical values. By comparing CPU time of different solvers, we find our solver is a highly efficient one for solving the flow equations of viscoelastic fluids.     

A numerical approach for shape optimization of fluid flow domains

A numerical method for the shape optimization of fluid flow domains is presented and analyzed. The procedure is based on a flow solver, a mathematical optimization tool, and a technique for shape variation, which are combined into an integrated procedure. The flow solver relies on the discretization of the incompressible Navier–Stokes equations by means of the finite-volume method for block-structured, boundary-fitted grids with multi-grid acceleration. The optimization tool is an implementation of a trust region based derivative-free method. It is designed to minimize smooth functions whose evaluations are considered expensive and whose derivatives are not available or not desirable to approximate. The shape variation is obtained by deforming the computational grid employed by the flow solver. For this purpose, displacement fields scaled by the design variables are added to the initial grid. The displacement vectors are computed once before starting the optimization cycle by using a free-form deformation technique. Applications illustrating the functionality and the properties of the method are presented for some examples of engineering interest, such as the minimization of a pressure drop, the maximization of a lift force, and the optimization of a wall temperature.     

Hydrodynamic design using a derivative-free method

A derivative-free shape optimization tool for computational fluid dynamics (CFD) is developed in order to facilitate the implementation of complex flow solvers in the design procedure. A modified Rosenbrocks method is used, which needs neither gradient evaluations nor approximations. This approach yields a robust and flexible tool and gives the capability of performing optimizations involving complex configurations and phenomena. The flow solver implemented solves the Reynolds-averaged Navier–Stokes equations (RANSE) on unstructured grids, using near-wall, low-Reynolds-number turbulence models. Free surface effects are taken into account by a pseudosteady surface tracking method. A mesh deformation strategy based on both lineal and torsional springs analogies is used to update the mesh while maintaining the quality of the grid near the wall for two-dimensional problems. A free-form-deformation technique is used to manage the mesh and the shape perturbations for three-dimensional cases. Two hydrodynamic applications are presented, concerning first the design of a two-dimensional hydrofoil in relation with the free-surface elevation and then the three-dimensional optimization of a hull shape, at full scale.     

Incompressible flow computations over moving boundary using a novel upwind method

In this paper a novel method for simulating unsteady incompressible viscous flow over a moving boundary is described. The numerical model is based on a 2D Navier–Stokes incompressible flow in artificial compressibility formulation with Arbitrary Lagrangian Eulerian approach for moving grid and dual time stepping approach for time accurate discretization. A higher order unstructured finite volume scheme, based on a Harten Lax and van Leer with Contact (HLLC) type Riemann solver for convective fluxes, developed for steady incompressible flow in artificial compressibility formulation by Mandal and Iyer (AIAA paper 2009-3541), is extended to solve unsteady flows over moving boundary. Viscous fluxes are discretized in a central differencing manner based on Coirier’s diamond path. An algorithm based on interpolation with radial basis functions is used for grid movements. The present numerical scheme is validated for an unsteady channel flow with a moving indentation. The present numerical results are found to agree well with experimental results reported in literature.     

An automatic Euler solver using the unstructured upwind method

A fully-automatic grid generator and an unstructured upwind method for the Euler equations are developed in order to achieve automation in flow computations. The unstructured grid is generated using two techniques: a geometry-adaptive refinement; and a solution-adaptive refinement. The former introduces information about the flowfield geometry into the grid, and the latter introduces the fluid physics. A combination of these two techniques enables the generation of a grid in a fully-automatic manner. Flowfields are solved by an unstructured upwind solver, which is an extension of the flux-vector splitting method of Van Leer for use on arbitrary-shaped unstructured meshes. This robust flow solver with the automatic grid generator can be a useful CFD tool for routine engineering work. The method is applied to external and internal flow problems to demonstrate its capability.     

Algebraic and Parametric Solvers for the Power Flow Problem: Towards Real-Time and Accuracy-Guaranteed Simulation of Electric Systems

The power flow model performs the analysis of electric distribution and transmission systems. With this statement at hand, in this work we present a summary of those solvers for the power flow equations, in both algebraic and parametric version. The application of the Alternating Search Direction method to the power flow problem is also detailed. This results in a family of iterative solvers that combined with Proper Generalized Decomposition technique allows to solve the parametric version of the equations. Once the solution is computed using this strategy, analyzing the network state or solving optimization problems, with inclusion of generation in real-time, becomes a straightforward procedure since the parametric solution is available. Complementing this approach, an error strategy is implemented at each step of the iterative solver. Thus, error indicators are used as an stopping criteria controlling the accuracy of the approximation during the construction process. The application of these methods to the model IEEE 57-bus network is taken as a numerical illustration.     

Domain decomposition based coupling between the lattice Boltzmann method and traditional CFD methods – Part II: Numerical solution to the backward facing step flow

The lattice Boltzmann method (LBM) and traditional finite difference methods have separate strengths when solving the incompressible Navier–Stokes equations. The LBM is an explicit method with a highly local computational nature that uses floating-point operations that involve only local data and thereby enables easy cache optimization and parallelization. However, because the LBM is an explicit method, smaller grid spacing requires smaller numerical time steps during both transient and steady state computations. Traditional implicit finite difference methods can take larger time steps as they are not limited by the CFL condition, but only by the need for time accuracy during transient computations. To take advantage of the strengths of both methods, a multiple solver, multiple grid block approach was implemented and validated for the 2-D Burgers’ equation in Part I of this work. Part II implements the multiple solver, multiple grid block approach for the 2-D backward step flow problem. The coupled LBM–VSM solver is found to be faster by a factor of 2.90 (2.87 and 2.93 for Re = 150 and Re = 500, respectively) on a single processor than the VSM for the 2-D backward step flow problem while maintaining similar accuracy.     

Approximated set-valued mapping approach for handling multiobjective bilevel problems

A significant amount of research has been done on bilevel optimization problems both in the realm of classical and evolutionary optimization. However, the multiobjective extensions of bilevel programming have received relatively little attention from researchers in both the domains. The existing algorithms are mostly brute-force nested strategies, and therefore computationally demanding. In this paper, we develop insights into multiobjective bilevel optimization through theoretical progress made in the direction of parametric multiobjective programming. We introduce an approximated set-valued mapping procedure that would be helpful in the development of efficient evolutionary approaches for solving these problems. The utility of the procedure has been emphasized by incorporating it in a hierarchical evolutionary framework and assessing the improvements. Test problems with varying levels of complexity have been used in the experiments.     

Fast convergence of inviscid fluid dynamic design problems

A new procedure for robust and efficient design optimization of inviscid flow problems has been developed and implemented on a wide variety of test problems. The methodology involves the use of an accurate flow solver to calculate the objective function and an approximate, dissipative flow solver, which is used only in the solution of the discrete quasi-time-dependent adjoint problem. The resulting design sensitivities are very robust even in the presence of noise or other non-smoothness associated with objective functions in many high-speed flow problems. The design problem is solved using what we term progressive optimization, whereby a sequence of a partially converged flow solution, followed by a partially converged adjoint solution followed by an optimization step is performed. This procedure is performed using a sequence of progressively finer grids for the solution of the flow field, while only using coarser grids for the adjoint equation solution.This approach has been tested on numerous inverse and direct (constrained) design problems involving two- and three-dimensional transonic nozzles and airfoils as well as supersonic blunt bodies. The methodology is shown to be robust and highly efficient, with a converged design optimization produced in no more than the amount of computational work to perform from 0.5 to 2.5 fine-mesh flow analyses.     

基于Globus平台的网格服务实现

广义上来讲,网格就是一个集成的计算和资源环境,或者说是一个计算资源池[1]。本文首先介绍了网格技术的发展,以及当前普遍使用的网格环境Globus及其框架,然后介绍了实现网格环境的工具GlobusToolkit以及执行的两个主要标准:OGSA(OpenGridServicesArchitecture)和OGSI(OpenGridServicesInfrastructure)。最后,通过实例说明了网格服务的具体实现过程。     

Large-scale parallel topology optimization using a dual-primal substructuring solver

Parallel computing is an integral part of many scientific disciplines. In this paper, we discuss issues and difficulties arising when a state-of-the-art parallel linear solver is applied to topology optimization problems. Within the topology optimization framework, we cannot readjust domain decomposition to align with material decomposition, which leads to the deterioration of performance of the substructuring solver. We illustrate the difficulties with detailed condition number estimates and numerical studies. We also report the practical performances of finite element tearing and interconnection/dual–primal solver for topology optimization problems and our attempts to improve it by applying additional scaling and/or preconditioning strategies. The performance of the method is finally illustrated with large-scale topology optimization problems coming from different optimal design fields: compliance minimization, design of compliant mechanisms, and design of elastic surface wave-guides. The authors acknowledge the support of the Air Force Office of Scientific Research (AFOSR) under grant FA9550-05-1-0046. The computational facility was obtained under the grant AFOSR-DURIP FA9550-05-1-0291.     

Using Verified Data-Flow Analysis-based Optimizations in Attribute Grammars

Building verified compilers is difficult, especially when complex analyses such as type checking or data-flow analysis must be performed. Both the type checking and program optimization communities have developed methods for proving the correctness of these processes and developed tools for using, respectively, verified type systems and verified optimizations. However, it is difficult to use both of these analyses in a single declarative framework since these processes work on different program representations: type checking on abstract syntax trees and data-flow analysis-based optimization on control flow or program dependency graphs.We present an attribute grammar specification language that has been extended with constructs for specifying attribute-labelled control flow graphs and both CTL and LTL-FV formulas that specify data-flow analyses. These formulas are model-checked on these graphs to perform the specified analyses. Thus, verified type rules and verified data-flow analyses (verified either by hand or with automated proof tools) can both be transcribed into a single declarative framework based on attribute grammars to build a high-confidence language implementations. Also, the attribute grammar specification language is extensible so that it is relatively straight-forward to add new constructs for different temporal logics so that alternative logics and model checkers can be used to specify data-flow analyses in this framework.     

Design and optimization of a modified Sierpinski fractal antenna for broadband applications

Immense understanding of antenna designers illustrate that a general microstrip antenna demonstrate low efficiency. Various techniques have been adopted to improve the performance characteristics of microstrip antenna. This paper deals with the optimization of Sierpinski fractal antenna structure by utilizing the particle swarm optimization (PSO) and curve fitting method. The required data for optimization and fitting the curve has been obtained by varying different design parameters of the proposed antenna. Electromagnetic solver Ansoft HFSS 13.0 is used for generating the parametric data. The MATLAB curve fitting tool is referred for developing the equations which exhibits the relations between the parameters of proposed antenna design. Particle swarm optimization technique is then applied to find the optimum values of the design parameters for the bandwidth enhancement of proposed antenna. Curve fitting based optimized design represents the remarkable improvement in the bandwidth of conventional microstrip line fed Sierpinski fractal antenna for broadband applications.     

Memetic algorithm using multi-surrogates for computationally expensive optimization problems

In this paper, we present a multi-surrogates assisted memetic algorithm for solving optimization problems with computationally expensive fitness functions. The essential backbone of our framework is an evolutionary algorithm coupled with a local search solver that employs multi-surrogate in the spirit of Lamarckian learning. Inspired by the notion of ‘blessing and curse of uncertainty’ in approximation models, we combine regression and exact interpolating surrogate models in the evolutionary search. Empirical results are presented for a series of commonly used benchmark problems to demonstrate that the proposed framework converges to good solution quality more efficiently than the standard genetic algorithm, memetic algorithm and surrogate-assisted memetic algorithms.     

A topology-preserving optimization algorithm for polycube mapping

We present an effective optimization framework to compute polycube mapping. Composed of a set of small cubes, a polycube well approximates the geometry of the free-form model yet possesses great regularity; therefore, it can serve as a nice parametric domain for free-form shape modeling and analysis. Generally, the more cubes are used to construct the polycube, the better the shape can be approximated and parameterized with less distortion. However, corner points of a polycube domain are singularities of this parametric representation, so a polycube domain having too many corners is undesirable. We develop an iterative algorithm to seek for the optimal polycube domain and mapping, with the constraint on using a restricted number of cubes (therefore restricted number of corner points). We also use our polycube mapping framework to compute an optimal common polycube domain for multiple objects simultaneously for lowly distorted consistent parameterization.     

On optimization of expertise matching with various constraints

This paper studies the problem of expertise matching with various constraints. Expertise matching, which aims to find the alignment between experts and queries, is a common problem in many applications such as conference paper-reviewer assignment, product-reviewer alignment, and product-endorser matching. Most existing methods formalize this problem as an information-retrieval problem and focus on finding a set of experts for each query independently. However, in real-world systems, various constraints are often needed to be considered. For example, in order to review a paper, it is desirable that there is at least one senior reviewer to guide the reviewing process. An important question is: “Can we design a framework to efficiently find the optimal solution for expertise matching under various constraints?” This paper explores such an approach by formulating the expertise matching problem in a constraint-based optimization framework. In the proposed framework, the problem of expertise matching is linked to a convex cost flow problem, which guarantees an optimal solution under various constraints. We also present an online matching algorithm to support incorporating user feedbacks in real time. The proposed approach has been evaluated on two different genres of expertise matching problems, namely conference paper-reviewer assignment and teacher-course assignment. Experimental results validate the effectiveness of the proposed approach. Based on the proposed method, we have also developed an online system for paper-reviewer suggestions, which has been used for paper-reviewer assignment in a top conference and feedbacks from the conference organizers are very positive.