An inverse method for 3d aerodynamic design of wing shape

In aerodynamic shape optimization, the convergence and computational cost are greatly affected by the representation capacity and compactness of the design space. Previous research has demonstrated that using a deep generative model to parameterize two-dimensional (2D) airfoils achieves high representation capacity/compactness, which significantly benefits shape optimization. In this paper, we propose a deep generative model, Free-Form Deformation Generative Adversarial Networks (FFD-GAN), that provides an efficient parameterization for three-dimensional (3D) aerodynamic/hydrodynamic shapes like aircraft wings, turbine blades, car bodies, and hulls. The learned model maps a compact set of design variables to 3D surface points representing the shape. We ensure the surface smoothness and continuity of generated geometries by incorporating an FFD layer into the generative model. We demonstrate FFD-GAN's performance using a wing shape design example. The results show that FFD-GAN can generate realistic designs and form a reasonable parameterization. We further demonstrate FFD-GAN's high representation compactness and capacity by testing its design space coverage, the feasibility ratio of the design space, and its performance in design optimization. We demonstrate that over 94% feasibility ratio is achieved among wings randomly generated by the FFD-GAN, while FFD and B-spline only achieve less than 31%. We also show that the FFD-GAN leads to an order of magnitude faster convergence in a wing shape optimization problem, compared to the FFD and the B-spline parameterizations.

The most widely used method applied in the context of off-line dynamic demand calibration is Simultaneous Perturbation Stochastic Approximation (SPSA). In the research following the SPSA approach single origin-destination (O-D) demand components were mostly considered as calibration parameters. However, basic SPSA, especially in high dimensions, shows convergence issues, as proven by various authors. To overcome this drawback, some authors suggested modifications of basic SPSA to improve its performance. In this paper, we investigate various techniques and approaches to improve the SPSA performance, and overcome, or at least alleviate, its shortcomings. We concentrate our analysis mostly on SPSA coefficients and gradient control. The comparison of investigated settings is conducted on a real-world network. This establishes a path to identify critical aspects that influence the calibration process and suggests an optimal SPSA configuration for practice. The contribution of this paper is to provide a detailed analysis of the SPSA behavior in cases its configuration is subject to various modifications. The findings are primarily intended for the offline context. However, the insights can also be used for the selection of the most efficient SPSA configuration given time constraint, particularly suitable for on-line applications.

In recent years, simulation optimization has attracted a great deal of attention because simulation can model the real systems in fidelity and capture complex dynamics. Among numerous simulation optimization algorithms, Simultaneous Perturbation Stochastic Approximation (SPSA) algorithm is an attractive approach because of its simplicity and efficiency. Although SPSA has been applied in several problems, it does not converge for some. This research proposes Augmented Simultaneous Perturbation Stochastic Approximation (ASPSA) algorithm in which SPSA is augmented to include presearch, ordinal optimization, non-uniform gain, and line search. Performances of ASPSA are tested on complex discrete supply chain inventory optimization problems. The tests results show that ASPSA not only achieves speed up, but also improves solution quality and converges faster than SPSA. Experiments also show that ASPSA is comparable to Genetic Algorithms in solution quality (6% to 15% worse) but is much more efficient computationally (over 12x faster).Request access from your librarian to read this chapter's full text.

Considering the problem of the highly dependence on the accuracy model information of the industrial control process, an online PID parameter tuning method is proposed based on the simultaneous perturbation stochastic approximation (SPSA) algorithm and the traditional PID control only by the input and output data of the system. Interfering with each component of the PID controller parameters simultaneously, and the gradient information of the control performance loss function is estimated via the output value after disturbance by the global optimization method of stochastic approximation algorithm. The PID controller parameter is iterative updated according to the gradient estimation value. Finally, we applied this method to the control process of cement grinding particle size, and the simulation results by MATLAB shown it can lead a high control precision which proves the feasibility and effectiveness of this adaptive PID control method.

B034 PARALLEL WELL LOCATION OPTIMIZATION USING STOCHASTIC ALGORITHMS ON THE GRID COMPUTATIONAL FRAMEWORK 1 Hector Klie 1 Wolfgang Bangerth 1 2 Mary F. Wheeler 1 Manish Parashar 3 and Vincent Matossian 3 1 Center for Subsurface Modeling The University of Texas at Austin Austin TX. 2 Institute for Geophysics The University of Texas at Austin Austin TX. 3 The Applied Software Systems Laboratory Rutgers University Piscataway NJ. Abstract The determination of optimal well locations is a challenging problem in oil production since it depends on geological and fluid properties as well as on economic parameters. This work addresses the efficient

This study proposes a data-driven PID tuning for liquid slosh suppression based on enhanced stochastic approximation. In particular, a new version of Simultaneous Perturbation Stochastic Approximation (SPSA) based on memory type function is introduced. This memory-based SPSA (M-SPSA) algorithm has the capability to obtain a better optimization accuracy than the conventional SPSA since it is able to keep the best design parameter during the tuning process. The effectiveness of this algorithm is tested to data-drive PID tuning for liquid slosh problem. The achievement of the M-SPSA based algorithm is assessed in terms of trajectory tracking of trolley position, slosh angle reduction and also computation time. The outcome of this study shows that the PID-tuned M-SPSA is able to provide better control performance accuracy than the other variant of SPSA based method.

This paper describes the implementation of a self- optimizing embedded control scheme for an induction motor drive. The online design problem is formulated as a search problem and solved with a stochastic optimization algorithm. The objective function takes in account the tracking error, and is directly measured on the hardware bench. The online optimization is performed with the simultaneous perturbation stochastic approximation (SPSA) algorithms, which offer a very effective tradeoff between simplicity of implementation, speed of convergence and quality of the final solutions. Among the known SPSA algorithms considered in this paper, we also propose a novel variant inspired to the concept of elitism frequently used in evolutionary computation. To assess the relative performances of the various algorithms, the paper carries out a comprehensive analysis of a control scheme for an induction motor drive subject to time-varying load disturbances.

Epidemiological models have tremendous potential to forecast disease burden and quantify the impact of interventions. Detailed models are increasingly popular, however these models tend to be stochastic and very costly to evaluate. Fortunately, readily available high-performance cloud computing now means that these models can be evaluated many times in parallel. Here, we briefly describe PSPO, an extension to Spall's second-order stochastic optimization algorithm, Simultaneous Perturbation Stochastic Approximation (SPSA), that takes full advantage of parallel computing environments. The main focus of this work is on the use of PSPO to maximize the pseudo-likelihood of a stochastic epidemiological model to data from a 1861 measles outbreak in Hagelloch, Germany. Results indicate that PSPO far outperforms gradient ascent and SPSA on this challenging likelihood maximization problem.

The minimization of a convex function defined over the grid Z/sup p/ is considered. A few relevant mathematical devices such as integer convexity, Markov chain Monte Carlo (MCMC) methods, including stochastic comparison (SC), and simultaneous perturbation stochastic approximation (SPSA) are summarized. A truncated fixed gain SPSA method is proposed and investigated in combination with devices borrowed from the MCMC literature. The main contribution of the paper is the development and testing a number of devices that may eventually improve the convergence properties of the algorithm, such as various truncation techniques, averaging and choices of acceptance probabilities. The basis for comparison of performances is accuracy vs. number of function evaluations. We present experimental evidence for the superiority of an SC method allowing moves in wrong directions with small probability, where the underlying method is an SPSA method using averaging and adaptive truncation.

Calibration and validation have long been a significant topic in traffic model development. In fact, when moving to dynamic traffic assignment (DTA) models, the need to dynamically update the demand and supply components creates a considerable burden on the existing calibration algorithms, often rendering them impractical. These calibration approaches are mostly restricted either due to non-linearity or increasing problem dimensionality. Simultaneous perturbation stochastic approximation (SPSA) has been proposed for the DTA model calibration, with encouraging results, for more than a decade. However, it often fails to converge reasonably with the increase in problem size and complexity. In this paper, we combine SPSA with principal components analysis (PCA) to form a new algorithm, we call, PC–SPSA. The PCA limits the search area of SPSA within the structural relationships captured from historical estimates in lower dimensions, reducing the problem size and complexity. We formulate the algorithm, demonstrate its operation, and explore its performance using an urban network of Vitoria, Spain. The practical issues that emerge from the scale of different variables and bounding their values are also analyzed through a sensitivity analysis using a non-linear synthetic function.

In this paper we consider a possible application of simultaneous perturbation stochastic approximation (SPSA) method to problems of control of educational processes. SPSA is an efficient tool for optimization problems and uncertain parameters estimation. What is more important, such SPSA procedures actually work under arbitrary external observation noise, while most other optimization methods work only with Gaussian noises. The illustrative example is also presented.

This paper addresses optimization for hybrid electric vehicle (HEV). This project is using a single agent method to optimize the power losses under a specific driving cycle which is simultaneous perturbation stochastic approximation (SPSA) based method. For optimization process, four gain are added in four main parts of the HEV system. Those main parts are engine, motor, generator and battery. These four gain is controlled the output for each component to give the minimum power losses. The design method is applied to free model of HEV by using Simulink/MATLAB software while M-File/MATLAB is used to apply the SPSA method. The result from design method achieved a minimum reduction of power losses compared to original system. Thus, the comparison of result has been done to show the different before and after optimization.

Various aspects of the neutron spectroscopy problem are studied in this research. Theoretically, the neutron emission problem transforms into a limited angle Radon transform problem. In order to solve this ill posed problem, various algorithms are tested spanning two different techniques; algebraic reconstruction as well as Monte-Carlo methods. The developed algorithms are Stochastic Gradient Approximation (SGA) method, Simultaneous Perturbation Stochastic Approximation (SPSA) method, and Time of Flight (TOF) method.

We examine the theoretical and numerical global convergence properties of a certain ldquogradient freerdquo stochastic approximation algorithm called the ldquosimultaneous perturbation stochastic approximation (SPSA)rdquo that has performed well in complex optimization problems. We establish two theorems on the global convergence of SPSA, the first involving the well-known method of injected noise. The second theorem establishes conditions under which ldquobasicrdquo SPSA without injected noise can achieve convergence in probability to a global optimum, a result with important practical benefits.

Arriving at optimal signal timing parameters to improve the efficiency of traffic flow has been one of the major challenges faced by traffic engineers. The choice of a robust optimization framework and an accurate traffic model plays a significant role in determining optimal signal timing parameters. Though traffic flow is intuitively stochastic, few studies incorporate stochasticity in their optimization framework for traffic signal design. This study proposes two simulation-based stochastic optimization algorithms—an evolutionary algorithm-based framework and a simultaneous perturbation stochastic approximation (SPSA) algorithm-based framework for the optimal signal design of an isolated intersection using a calibrated microsimulation environment with reasonable accuracy. A software-in-the loop approach is used to control the traffic signals in the microsimulation environment. SPSA is a gradient descent algorithm with a powerful approach for approximating the gradient with just two function evaluations per gradient approximation. To evaluate the performance of the two frameworks, the study optimizes the signal timings for a case study on an isolated intersection in an urban arterial in Chennai. On comparing the two algorithms, it is found that SPSA performed better and took 100 function evaluations less than that taken by GA. A better (near optimal) initial solution is found to yield a faster rate of convergence for both algorithms. As the proposed optimization framework incorporates the stochastic nature of traffic in the optimization algorithm, it can accommodate the temporal variations in traffic and thereby provide traffic engineers a robust signal control strategy for improving the efficiency of traffic flow.