Development and Validation of an Advanced Actuator Line Model for Wind Turbines

As wind turbine technology proceeds towards the development of more advanced and complex machines, modelling tools with fidelity higher than the ubiquitous Blade Element Momentum (BEM) method are needed. Among them, the Actuator Line Method (ALM) stands out in terms of accuracy and computational cost. Moving from this background, an advanced ALM method has been developed within the commercial solver CONVERGE®. As elements of novelty, this tool features a Lagrangian method for sampling the local inflow velocity and a piece-wise smearing function for the force projection process. Various sub-models for both Horizontal Axis Wind Turbines (HAWTs) (e.g. the Shen tip loss correction) and Vertical Axis Wind Turbines (VAWTs) (e.g. the MIT dynamic stall model) has also been included. Aim of the research is to address the new challenges posed by modern machines. HAWTs are in fact getting larger and larger, shifting the research focus on the interaction of increasingly deformable blades with the atmosphere at the micro- and mesoscale level. VAWTs on the other hand, whose popularity has arisen in the last years, thanks to their advantages in non-conventional applications, e.g. floating offshore installations, are extremely complex machines to study, due to their inherently unsteady aerodynamics. The approach has been validated on selected test cases, i.e. the DTU 10MW turbine and a real 2-blade H-rotor, for which both high-fidelity CFD and experimental data are available.

Efficient Algorithm for the Traffic Assignment Problem with Side Constraints

The standard traffic assignment problem (TAP) is often augmented with additional constraints to address non-standard applications. These models are called TAP with side constraints (TAPSC). Despite the rising significance of TAPSC models, the ability to efficiently solve them to satisfactory precision remains limited in real-world applications. The purpose of this paper is to fill this gap by integrating a recently developed high performance TAP solver, known as the path-based Greedy algorithm, with the augmented Lagrangian multiplier (ALM) method. This paper examines how precisely the subproblems in the ALM method should be solved to optimize the overall convergence performance. It is found that insufficiently converged subproblem solutions sometimes lead to catastrophic failures, although pursuing extremely high precision could also be counterproductive. Accordingly, it is proposed to adjust the precision required to solve the subproblems based on an approximate gap measured by the augmented Lagrangian. Results of numerical experiments show that adaptively adjusting the subproblem precision limit produces a 25% speed-up compared with the algorithm with a fixed limit.

A Parallel Splitting Augmented Lagrangian Method for Two-Block Separable Convex Programming with Application in Image Processing

The augmented Lagrangian method (ALM) is one of the most successful first-order methods for convex programming with linear equality constraints. To solve the two-block separable convex minimization problem, we always use the parallel splitting ALM method. In this paper, we will show that no matter how small the step size and the penalty parameter are, the convergence of the parallel splitting ALM is not guaranteed. We propose a new convergent parallel splitting ALM (PSALM), which is the regularizing ALM’s minimization subproblem by some simple proximal terms. In application this new PSALM is used to solve video background extraction problems and our numerical results indicate that this new PSALM is efficient.

JointL1/2-Norm Constraint and Graph-Laplacian PCA Method for Feature Extraction

Principal Component Analysis (PCA) as a tool for dimensionality reduction is widely used in many areas. In the area of bioinformatics, each involved variable corresponds to a specific gene. In order to improve the robustness of PCA-based method, this paper proposes a novel graph-Laplacian PCA algorithm by adoptingL1/2constraint (L1/2gLPCA) on error function for feature (gene) extraction. The error function based onL1/2-norm helps to reduce the influence of outliers and noise. Augmented Lagrange Multipliers (ALM) method is applied to solve the subproblem. This method gets better results in feature extraction than other state-of-the-art PCA-based methods. Extensive experimental results on simulation data and gene expression data sets demonstrate that our method can get higher identification accuracies than others.

Sparse time-frequency decomposition based on dictionary adaptation

In this paper, we propose a time-frequency analysis method to obtain instantaneous frequencies and the corresponding decomposition by solving an optimization problem. In this optimization problem, the basis that is used to decompose the signal is not known a priori . Instead, it is adapted to the signal and is determined as part of the optimization problem. In this sense, this optimization problem can be seen as a dictionary adaptation problem, in which the dictionary is adaptive to one signal rather than a training set in dictionary learning. This dictionary adaptation problem is solved by using the augmented Lagrangian multiplier (ALM) method iteratively. We further accelerate the ALM method in each iteration by using the fast wavelet transform. We apply our method to decompose several signals, including signals with poor scale separation, signals with outliers and polluted by noise and a real signal. The results show that this method can give accurate recovery of both the instantaneous frequencies and the intrinsic mode functions.

On the transmission efficiency of a spring-type operating kinematic mechanism for an SF6 gas-insulated circuit breaker

The purpose of this paper is to study the transmission efficiency of a spring-type operating kinematic mechanism for an SF6 gas-insulated circuit breaker. This mechanism has two degrees of freedom, which, when operating in the open, close, or return mode, becomes a single degree of freedom by fixing a link that adjoins the frame to achieve specific requirements. Firstly, the vector-loop method is employed for position analysis of the links of the mechanism. Subsequently, by using coordinate transformation and the geometry of the cam and the follower, the relationships between cam rotation and the position of the linkage follower in the close operation, as well as cam rotation and the position of the motor-switch follower are established. Based on the transmission angle of the linkage and the pressure angle of the cam mechanism, the transmission efficiencies of the mechanism are evaluated and discussed. The mechanism is also computer simulated and the transmission angle is optimized using the augmented Lagrange multiplier (ALM) method. The results show that the optimized parameters noticeably improve the transmission angles of the present design.

Mechanism Design with MP-Neural Networks

The prevalent Mathematical Programming Neural Network (MPNN) models are surveyed, and MPNN models have been developed and applied to the unconstrained optimization of mechanisms. Algorithms which require Hessian inversion and those which build up a variable approach matrix, are investigated. Based upon a comprehensive investigation of the Augmented Lagrange Multiplier (ALM) method, new algorithms have been developed from the combination of ideas from MPNN and ALM methods and applied to the constrained optimization of mechanisms. A relationship between the weighted least square minimization of design equation error residuals and the mini-max norm of the structure error for function generating mechanisms is developed and employed in the optimization process; as a result, the computational difficulties arising from the direct usage of the complex structural error function have been avoided. The paper presents relevant theory as well as some numerical experience for four MPNN algorithms.

Mathematical Programming Neural Networks (MPNN) for Mechanism Design

The prevalent Mathematical Programming Neural Network (MPNN) models are surveyed, and MPNN models have been developed and applied to the unconstrained optimization of mechanisms. Algorithms which require Hessian inversion and those which build up a variable approach matrix, are investigated. Based upon a comprehensive investigation of the Augmented Lagrange Multiplier (ALM) method, new algorithms have been developed from the combination of ideas from MPNN and ALM methods and applied to the constrained optimization of mechanisms. A relationship between the weighted least square minimization of design equation error residuals and the mini-max norm of the structure error for function generating mechanisms is developed and employed in the optimization process; as a result, the computational difficulties arising from the direct usage of the complex structural error function have been avoided. The paper presents relevant theory as well as some numerical experience for four MPNN algorithms.