Transonic flow potential method development at Ames research center

This paper describes selected developments in transonic flow simulation technology that have utilized nonlinear potential methods for external aerodynamic applications. In particular, the research efforts in this field at Ames Research Center are highlighted. Included are a review of the various potential equation forms, the pertinent characteristics associated with key potential equation numerical algorithms, and a variety of numerical results for various aerodynamic applications to highlight key discussion points.     

Sensitivity analysis and optimization of stochastic Petri nets

In this paper we define the notion of controlled stochastic Petri net (CSPN), which is a stochastic Petri net with controlled parameters and performance indices. Specifically, transition times and/or conflict resolution rules can depend on controlled parameters and transition times can have arbitrary distribution functions. A method for computing statistical estimates of performance indices and their gradients (sensitivities) with respect to controlled parameters is described. This method, which needs only one simulation of a CSPN, is considerably superior to conventional finite differences both in terms of precision and required amount of simulation and is based on likelihood ratio/score function approach, other possibilities based on extensions of infinitesimal perturbation analysis are outlined. These gradient estimates are used in stochastic optimization algorithms to obtain the optimal value of the aggregated performance function of the CSPN. A combined optimization and simulation tool is developed which includes approaches to the gradient estimation mentioned above. The numerical experiments presented in this paper confirm the efficiency of the proposed techniques.     

Multiobjective backtracking search algorithm: application to FSI

Fluid-structure interaction (FSI) problems play an important role in many technical applications, for instance, wind turbines, aircraft, injection systems, or pumps. Thus, the optimization of such kind of problems is of high practical importance. Optimization algorithms aim to find the best values for a system’s parameters under various conditions. In this paper, we present a new Backtracking Search Optimization Algorithm for multiobjective optimization, named BSAMO, a new evolutionary algorithm (EA) for solving real-valued numerical optimization problems. EAs are popular stochastic search algorithms that are widely used to solve nonlinear, nondifferentiable and complex numerical optimization problems. In order to test the performance of this algorithm, a well known benchmark multiobjective problem has been chosen from the literature, and for FSI optimization, using a partitioned coupling procedure. The method has been tested through a 2D plate and a 3D wing subjected to aerodynamic loads. The obtained Pareto solutions are then presented and compared to those of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The numerical results demonstrate the efficiency of BSAMO and also its best performance in tackling real-world multiphysics problems.

     

Placement and routing in 3D integrated circuits

Three-dimension technologies offer great promise in providing improvements in the overall circuit performance. Physical design plays a major role in the ability to exploit the flexibilities offered in the third dimension, and this article gives an overview of placement and routing methods for FPGA- and ASIC-style designs. We describe CAD techniques for placement and routing in 3D ICs, developed under our 3D analysis and design optimization framework. These approaches address a dichotomy of design styles, both FPGA and ASIC. The factors that are important in each style are different, so that a one-size-fits-all approach is impractical, and therefore, we present separate approaches for 3D physical design for each of these technologies. Hence, our FPGA placement method uses a two-step optimization process that minimizes inter-tier vias first, followed by further optimization within and across tiers. In contrast, the ASIC flow uses cost function weighting to discourage, but not minimize, inter-tier crossings.     

Manifold mapping optimization with or without true gradients

This paper deals with the space mapping optimization algorithms in general and with the manifold mapping technique in particular. The idea of such algorithms is to optimize a model with a minimum number of each objective function evaluations using a less accurate but faster model. In this optimization procedure, fine and coarse models interact at each iteration in order to adjust themselves in order to converge to the real optimum. The manifold mapping technique guarantees mathematically this convergence but requires gradients of both fine and coarse model. Approximated gradients can be used for some cases but are subject to divergence. True gradients can be obtained for many numerical model using adjoint techniques, symbolic or automatic differentiation. In this context, we have tested several manifold mapping variants and compared their convergence in the case of real magnetic device optimization.     

Multi-View AAM Fitting and Construction

Active Appearance Models (AAMs) are generative, parametric models that have been successfully used in the past to model deformable objects such as human faces. The original AAMs formulation was 2D, but they have recently been extended to include a 3D shape model. A variety of single-view algorithms exist for fitting and constructing 3D AAMs but one area that has not been studied is multi-view algorithms. In this paper we present multi-view algorithms for both fitting and constructing 3D AAMs. Fitting an AAM to an image consists of minimizing the error between the input image and the closest model instance; i.e. solving a nonlinear optimization problem. In the first part of the paper we describe an algorithm for fitting a single AAM to multiple images, captured simultaneously by cameras with arbitrary locations, rotations, and response functions. This algorithm uses the scaled orthographic imaging model used by previous authors, and in the process of fitting computes, or calibrates, the scaled orthographic camera matrices. In the second part of the paper we describe an extension of this algorithm to calibrate weak perspective (or full perspective) camera models for each of the cameras. In essence, we use the human face as a (non-rigid) calibration grid. We demonstrate that the performance of this algorithm is roughly comparable to a standard algorithm using a calibration grid. In the third part of the paper, we show how camera calibration improves the performance of AAM fitting. A variety of non-rigid structure-from-motion algorithms, both single-view and multi-view, have been proposed that can be used to construct the corresponding 3D non-rigid shape models of a 2D AAM. In the final part of the paper, we show that constructing a 3D face model using non-rigid structure-from-motion suffers from the Bas-Relief ambiguity and may result in a “scaled” (stretched/compressed) model. We outline a robust non-rigid motion-stereo algorithm for calibrated multi-view 3D AAM construction and show how using calibrated multi-view motion-stereo can eliminate the Bas-Relief ambiguity and yield face models with higher 3D fidelity. Electronic Supplementary Material The online version of this article () contains supplementary material, which is available to authorized users.     

On curvature approximation in 2D and 3D parameter–free shape optimization

Manufacturing constraints considered in shape optimization often need to be expressed in terms of curvature. Within the scope of a sensitivity–based parameter–free shape optimization approach, curvature constraints have to be formulated in terms of the FE node coordinates in order to derive the required first order gradients with respect to the design node coordinates. In this contribution we introduce approaches to approximate the curvature of a FE model using the coordinates of the FE nodes at the boundary of the geometry, as a smooth representation of the design boundary is not available. Therefore, in the 2D case we present two different smooth curves which represent the design boundary and for which the curvature can be computed analytically. In a third 2D, as well as in our 3D approach, we use geometric information of the discretization such as the distance to neighboring boundary nodes and edge normals to approximate the curvature at the respective boundary node under consideration.     

Three dimensional numerical simulations of long-span bridge aerodynamics, using block-iterative coupling and DES

The design of long-span bridges often depends on wind tunnel testing of sectional or full aeroelastic models. Some progress has been made to find a computational alternative to replace these physical tests. In this paper, an innovative computational fluid dynamics (CFD) method is presented, where the fluid-structure interaction (FSI) is solved through a self-developed code combined with an ANSYS-CFX solver. Then an improved CFD method based on block-iterative coupling is also proposed. This method can be readily used for two dimensional (2D) and three dimensional (3D) structure modelling. Detached-Eddy simulation for 3D viscous turbulent incompressible flow is applied to the 3D numerical analysis of bridge deck sections. Firstly, 2D numerical simulations of a thin airfoil demonstrate the accuracy of the present CFD method. Secondly, numerical simulations of a U-shape beam with both 2D and 3D modelling are conducted. The comparisons of aerodynamic force coefficients thus obtained with wind tunnel test results well meet the prediction that 3D CFD simulations are more accurate than 2D CFD simulations. Thirdly, 2D and 3D CFD simulations are performed for two generic bridge deck sections to produce their aerodynamic force coefficients and flutter derivatives. The computed values agree well with the available computational and wind tunnel test results. Once again, this demonstrates the accuracy of the proposed 3D CFD simulations. Finally, the 3D based wake flow vision is captured, which shows another advantage of 3D CFD simulations. All the simulation results demonstrate that the proposed 3D CFD method has good accuracy and significant benefits for aerodynamic analysis and computational FSI studies of long-span bridges and other slender structures.     

下反前掠地效翼三维数值模拟

为研究下反前掠地效翼的气动性能,利用FLUENT求解定常不可压N-S方程和可实现的k-ε湍流模型,建立在地面效应下地效翼流场的三维数值模型,对不同下反角和前掠角的地效翼进行数值模拟.计算结果揭示出下反角和前掠角对地效翼空气动力特性、流场特性和纵向稳定性的影响规律.对下反前掠地效翼的三维数值模拟可以为地效飞行器的设计和优化提供参考.     

Integrated aerodynamic design and analysis of turbine blades

This paper presents an integrated approach for aerodynamic blade design in an MDO (multidisciplinary design optimization) environment. First, requisite software packages and data sources for flow computations and airfoil modeling are integrated into a single cybernetic environment, which significantly enhances their interoperability. Subsequently, the aerodynamic blade design is implemented in a quasi-3D way, supported by sophisticated means of project management, task decomposition and allotment, process definition and coordination. Major tasks of aerodynamic blade design include 1D meanline analysis, streamsurface computations, generation of 2D sections, approximation of 3D airfoils, and 3D flow analysis. After compendiously depicting all the major design/analysis tasks, this paper emphatically addresses techniques for blade geometric modeling and flow analysis in more detail, with exemplar application illustrations.     

Robustness,generality and efficiency of optimization algorithms for practical applications

The design of most engineering systems is a complex and time-consuming process. In addition, the need to optimize such systems where multidisciplinary analysis and design procedures are required can cost additional human and computational resources if proper software and numerical algorithms are not used. Several computational aspects of optimization algorithms and the associated software must be considered while making comparative studies and selecting a suitable algorithm for practical applications. Several parameters, such asaccuracy, generality, robustness, efficiency and ease of use, must be considered while deciding the superiority of an optimization approach. Approximate algorithms without sound mathematical basis can be sometimes more efficient for a specific problem, but fail to satisfy other requirements. They are, therefore, not suitable for general applications. An objective of the paper is to emphasize the critical importance of the above-mentioned parameters in large scalestructural optimization and other applications. Theoretical foundations of two promising approaches, thesequential quadratic programming (SQP) andoptimality criteria (OC), are presented and analysed. Recent numerical experiments and experiences with the SQP algorithm satisfying these requirements are described by solving a variety of structural design problems. An important conclusion of the paper is that the SQP method with a potential constraint strategy is a better choice as compared to the currently prevalent mathematical programming (MP) and OC approaches.     

uFlow: software for rational engineering of secondary flows in inertial microfluidic devices

Approaches to abstract and modularize models of fluid flow in microfluidic devices can enable predictive and rational engineering of microfluidic circuits with rapid designer feedback. The shape of co-flowing streams in the inertial flow regime has become of particular importance for new developments in high throughput microscale manufacturing, biological, and chemical research. In a process known as flow sculpting, the cross-sectional distribution of fluid elements is deformed due to the combined effects of diffusion and transverse advection, which are brought on by interaction with velocity gradients induced by sequences of pillar structures. However, the difficulty in solving the Navier–Stokes equations for complex flow-deforming geometries makes design in this space unintuitive, time-consuming, and costly. To mitigate these issues, we have efficiently embedded flow deformation operations previously relegated to high-performance computing into a free, user-friendly, and cross-platform framework called “uFlow”, to bring flow sculpting to the broader community. uFlow computes flow deformation including both advection and diffusion effects from a single pillar in 25 ms on modern consumer hardware, enabling real-time manual design and exploration of microfluidic devices, and fast visualization of 3D particles fabricated via stop flow lithography or optical transient liquid molding. Advanced numerical routines give instant access to a practically infinite set of flow transformations. We showcase uFlow’s design models, describe their implementation and usage, and validate the algorithms which allow real-time feedback with confocal imaging and cutting-edge microfluidic particle fabrication.     

The comparison of incomplete sensitivities and Genetic algorithms applications in 3D radial turbomachinery blade optimization

In the present work, a centrifugal pump impeller’s blades shape was redesigned to reach a higher efficiency in turbine mode using two different optimization algorithms: one is a local method as incomplete sensitivities–gradient based optimization algorithm coupled by 3D Navier–Stokes flow solver, and another is a global method as Genetic algorithms and artificial neural network coupled by 3D Navier–Stokes flow solver. New impeller was manufactured and tested in the test rig. Comparison of the local optimization method results with the global optimization method results showed that the gradient based method has detected the global optimum point. Experimental results confirmed the numerical efficiency improvement in all measured points. This study illustrated that the developed gradient based optimization method is efficient for 3D radial turbomachinery blade optimization.     

An Overview and Experimental Study of Learning-Based Optimization Algorithms for the Vehicle Routing Problem

The vehicle routing problem(VRP) is a typical discrete combinatorial optimization problem, and many models and algorithms have been proposed to solve the VRP and its variants. Although existing approaches have contributed significantly to the development of this field, these approaches either are limited in problem size or need manual intervention in choosing parameters. To solve these difficulties, many studies have considered learning-based optimization(LBO) algorithms to solve the VRP. This p...     

Image analysis using multigrid relaxation methods

Image analysis problems, posed mathematically as variational principles or as partial differential equations, are amenable to numerical solution by relaxation algorithms that are local, iterative, and often parallel. Although they are well suited structurally for implementation on massively parallel, locally interconnected computational architectures, such distributed algorithms are seriously handi capped by an inherent inefficiency at propagating constraints between widely separated processing elements. Hence, they converge extremely slowly when confronted by the large representations of early vision. Application of multigrid methods can overcome this drawback, as we showed in previous work on 3-D surface reconstruction. In this paper, we develop multiresolution iterative algorithms for computing lightness, shape-from-shading, and optical flow, and we examine the efficiency of these algorithms using synthetic image inputs. The multigrid methodology that we describe is broadly applicable in early vision. Notably, it is an appealing strategy to use in conjunction with regularization analysis for the efficient solution of a wide range of ill-posed image analysis problems.     

Side-to-side 3D coverage path planning approach for agricultural robots to minimize skip/overlap areas between swaths

Automated path planning is an important tool for the automation and optimization of field operations. It can provide the waypoints required for guidance, navigation and control of agricultural robots and autonomous tractors throughout the execution of these field operations. Typical field operations are repetitively required nearly every cropping season and therefore it should be carried out in a manner that maximizes the yield and minimizes operational cost, time and environmental impact taking into account the topographic land features. Current 3D terrain field coverage path planning algorithms are simply 2D coverage path planning projected into 3D through field terrain represented by the field’s Digital Elevation Model (DEM). When projecting 2D coverage plan into its 3D counterpart, the actual distance between adjacent paths on the topographic surface either increases or decreases, and consequently there might be skips or overlaps between adjacent paths on the slopes. In addition, when the machine rolls on slopes the effective width of the implement decreases by a similar amount to double this error and complicates the problem. Skips and overlaps can lead to an inefficient use of land and resources. In this paper, a numerical approach to estimate the total skip/overlap areas is developed and applied to determine the optimum-driving angle that minimizes this impact. Also, a novel side-to-side 3D coverage path planning approach, which ensures zero skips/overlaps regardless of the topographical nature of the field terrain, is developed. The approaches developed in this paper are tested and validated using a hypothetical test field of a tailored terrain and a real experimental field of uneven terrain nature. The proposed approaches illustrated that a significant percentage of uncovered area could be saved if appropriate driving angle is chosen and if a side-to-side 3D coverage is used.     

Complementing computational fluid dynamics methods with classical analytical techniques

New aerospace vehicle designs must have greater performance and versatility at affordable cost. This requires multi-disciplinary analysis and optimization which in turn requires more accurate and efficient numerical simulation tools. The need for greater accuracy and efficiency of computational fluid dynamics (CFD) tools is further amplified by the industry trend toward distributed computing (e.g. workstation clusters) and away from supercomputers. Complementary analytic methods coupled with traditional CFD approaches offer the means for increased simulation capability by incorporating more essential physics into solution algorithms and reducing reliance on grid density for achieving accuracy. McDonnell Douglas Aerospace has a focused activity directed at improving affordability of CFD tools with complementary analytic techniques and has developed a strong capability. Results have proven very successful. Several examples of ongoing work are discussed, including improved far-field boundary conditions for CFD codes and analytic-based aerodynamic analysis and design optimization methods.     

A bias-corrected estimator for nonlinear systems with output-error type model structures

Parametric identification of linear time-invariant (LTI) systems with output-error (OE) type of noise model structures has a well-established theoretical framework. Different algorithms, like instrumental-variables based approaches or prediction error methods (PEMs), have been proposed in the literature to compute a consistent parameter estimate for linear OE systems. Although the prediction error method provides a consistent parameter estimate also for nonlinear output-error (NOE) systems, it requires to compute the solution of a nonconvex optimization problem. Therefore, an accurate initialization of the numerical optimization algorithms is required, otherwise they may get stuck in a local minimum and, as a consequence, the computed estimate of the system might not be accurate. In this paper, we propose an approach to obtain, in a computationally efficient fashion, a consistent parameter estimate for output-error systems with polynomial nonlinearities. The performance of the method is demonstrated through a simulation example.     

A Survey of Simple Geometric Primitives Detection Methods for Captured 3D Data

The amount of captured 3D data is continuously increasing, with the democratization of consumer depth cameras, the development of modern multi‐view stereo capture setups and the rise of single‐view 3D capture based on machine learning. The analysis and representation of this ever growing volume of 3D data, often corrupted with acquisition noise and reconstruction artefacts, is a serious challenge at the frontier between computer graphics and computer vision. To that end, segmentation and optimization are crucial analysis components of the shape abstraction process, which can themselves be greatly simplified when performed on lightened geometric formats. In this survey, we review the algorithms which extract simple geometric primitives from raw dense 3D data. After giving an introduction to these techniques, from the acquisition modality to the underlying theoretical concepts, we propose an application‐oriented characterization, designed to help select an appropriate method based on one's application needs and compare recent approaches. We conclude by giving hints for how to evaluate these methods and a set of research challenges to be explored.     

A pso-based mobile robot for odor source localization in dynamic advection-diffusion with obstacles environment: theory, simulation and measurement

This paper provides a combination of chemotaxic and anemotaxic modeling, known as odor-gated rheotaxis (OGR), to solve real-world odor source localization problems. Throughout the history of trying to mathematically localize an odor source, two common biometric approaches have been used. The first approach, chemotaxis, describes how particles flow according to local concentration gradients within an odor plume. Chemotaxis is the basis for many algorithms, such as particle swarm optimization (PSO). The second approach is anemotaxis, which measures the direction and velocity of a fluid flow, thus navigating "upstream" within a plume to localize its source. Although both chemotaxic and anemotaxic based algorithms are capable of solving overly-simplified odor localization problems, such as dynamic-bit-matching or moving-parabola problems, neither method by itself is adequate to accurately address real life scenarios. In the real world, odor distribution is multi-peaked due to obstacles in the environment. However, by combining the two approaches within a modified PSO-based algorithm, odors within an obstacle-filled environment can be localized and dynamic advection-diffusion problems can be solved. Thus, robots containing this modified particle swarm optimization algorithm (MPSO) can accurately trace an odor to its source