Nonlinear large amplitude structural and aeroelastic behavior of composite rotor blades at large static deflection

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 1992.Includes bibliographical references (leaves 206-211).by Taehyoun Kim.Ph.D

Toward a Holistic Delay Analysis of EtherCAT Synchronized Control Processes

This paper analyzes the end-to-end delay of EtherCAT-based control processes that use the events of message frames and global clock for synchronized operation. With the end-to-end delay defined as the time interval between the start of a process cycle and the actual input or output, we develop a holistic delay model for control processes in EtherCAT, by taking into account the time for in-controller processing, message delivery, and slave-local handling. Based on the measurements from a real EtherCAT control system, we discuss the average and deviation of the process delay as we vary the number of slaves and process cycle time. The experiment results show that the output delays are mainly increased by the average controller delay, whereas the input delays are more affected by the deviation rather than the average of the controller delay. Our in-depth analysis on the controller reveals that DMA time chiefly enlarges the controller delay for increasing number of slaves, while task release jitter is the main cause of the increased delay for longer cycle time. The presented delay model and evaluation results can be essentially used for the design of EtherCAT-based automation that requires highly synchronized operations, such as for coordinated motion and high-precision data sensing

Formal Modeling and Verification of Motor Drive Software for Networked Motion Control Systems

Abstract: This paper presents a model-based approach to the design and verification of motor drive software for networked motion control systems. We develop a formal model for an Ethernetbased motion system, where, using timed automata, we describe the concurrent and synchronized behaviors of the components, i.e., motion controller, motor drives, and communication links. The drive, in particular, is modeled in enough detail to accurately reflect the software implementation used in a real drive. We use the design of multitasked drive software with fixed-priority preemptive scheduling. With UPPAAL model checking, we verify the precision and accuracy of the rendered motion in terms of the requirements on the actuation delay at each drive and the actuation deviation between different drives, respectively. The analysis results demonstrate the benefits of our model-based approach in the safety verification and design space exploration of motor drive software. We show that it is possible to verify deadlock freeness and real-time schedulability in an early design phase. And, for varying number of drives and size of messages, we can successfully determine the combination of task periods that leads to the best precision and accuracy

GT2008-50479 A TIME-DOMAIN FLUID-STRUCTURE INTERACTION ANALYSIS OF FAN BLADES

ABSTRACT This paper presents aerodynamic and aeromechanical analyses for an entire row of fan blades (i.e. tens of blades with a finite aspect ratio) subject to a uniform incoming flow. In this regard, a new unsteady three-dimensional vortex lattice model has been developed for multiple blades in discrete time domain. Using the new model, the characteristics of the unsteady aerodynamic forces on vibrating blades, including their temporal development, are examined. Also, the new aerodynamic model is applied to examine the aeromechanical behavior of fan blades by using a standard eigenvalue analysis. For this analysis, the fan blades have been modeled as threedimensional plates, and, increasing the number of blades (or solidity) is predicted to destabilize the fan blade row. INTRODUCTION Fluid-structure interaction phenomena occur in many scientific and engineering applications, including aircraft wings and turbomachinery blades. Understanding fluid structure interactions in turbomachinery is important because such interactions lead to fatigue and ultimately structural damage of blades. The fluid structure interaction analyses require modeling of unsteady aerodynamics which can be conducted in either time or frequency domain. In the time domain analyses, Euler and Navier-Stokes solvers are used to calculate unsteady aerodynamic forces on turbomachinery blades. At each time step, for a given geometry, the flow field is solved to determine the aerodynamic forces acting on the blades. Subsequently, the forces are used to analyze the blades' motions and update their displacements. The entire process is then repeated. Such time domain analyses require extensive computation time and cost [1,2]. On the other hand, unsteady aerodynamic analysis can also be carried out in the frequency domain, and the frequency domain approaches have been applied to calculate unstead

Analysis of recent climate change over the Arctic using ERA-Interim reanalysis data

This study investigates recent climate change over the Arctic and its link to the mid-latitudes using the ERA-Interim global atmospheric reanalysis data from the European Center for Medium-Range Weather Forecast (ECMWF). Since 1979, substantial surface warming, associated with the increase in anthropogenic greenhouse gases, has occurred over the Arctic. The greatest warming in winter has taken place offshore in the Kara-Barents Sea, and is associated with the increase in turbulent heat fluxes from the marginal ice zone. In contrast to the marked warming over the Arctic Ocean in winter, substantial cooling appears over Siberia and eastern Asia, linked to the reduction of Arctic sea ice during the freezing season (September–March). However, in summer, very little change is observed in surface air temperature over the Arctic because increased radiative heat melts the sea ice and the amount of turbulent heat gain from the ocean is relatively small. The heat stored in the upper ocean mixed layer in summer with the opening of the Arctic Ocean is released back to the atmosphere as turbulent heat fluxes during the autumn and through to the following spring. This warming of the Arctic and the reduced sea ice amplifies surface cooling over Siberia and eastern Asia in winter

Guaranteeing Isochronous Control of Networked Motion Control Systems Using Phase Offset Adjustment

Guaranteeing isochronous transfer of control commands is an essential function for networked motion control systems. The adoption of real-time Ethernet (RTE) technologies may be profitable in guaranteeing deterministic transfer of control messages. However, unpredictable behavior of software in the motion controller often results in unexpectedly large deviation in control message transmission intervals, and thus leads to imprecise motion. This paper presents a simple and efficient heuristic to guarantee the end-to-end isochronous control with very small jitter. The key idea of our approach is to adjust the phase offset of control message transmission time in the motion controller by investigating the behavior of motion control task. In realizing the idea, we performed a pre-runtime analysis to determine a safe and reliable phase offset and applied the phase offset to the runtime code of motion controller by customizing an open-source based integrated development environment (IDE). We also constructed an EtherCAT-based motion control system testbed and performed extensive experiments on the testbed to verify the effectiveness of our approach. The experimental results show that our heuristic is highly effective even for low-end embedded controller implemented in open-source software components under various configurations of control period and the number of motor drives

Parallel Dislocation Model Implementation for Earthquake Source Parameter Estimation on Multi-Threaded GPU

Graphics processing units (GPUs) have been in the spotlight in various fields because they can process a massive amount of computation at a relatively low price. This research proposes a performance acceleration framework applied to Monte Carlo method-based earthquake source parameter estimation using multi-threaded compute unified device architecture (CUDA) GPU. The Monte Carlo method takes an exhaustive computational burden because iterative nonlinear optimization is performed more than 1000 times. To alleviate this problem, we parallelize the rectangular dislocation model, i.e., the Okada model, since the model consists of independent point-wise computations and takes up most of the time in the nonlinear optimization. Adjusting the degree of common subexpression elimination, thread block size, and constant caching, we obtained the best CUDA optimization configuration that achieves 134.94×, 14.00×, and 2.99× speedups over sequential CPU, 16-threads CPU, and baseline CUDA GPU implementation from the 1000×1000 mesh size, respectively. Then, we evaluated the performance and correctness of four different line search algorithms for the limited memory Broyden–Fletcher–Goldfarb–Shanno with boundaries (L-BFGS-B) optimization in the real earthquake dataset. The results demonstrated Armijo line search to be the most efficient one among the algorithms. The visualization results with the best-fit parameters finally derived by the proposed framework confirm that our framework also approximates the earthquake source parameters with an excellent agreement with the geodetic data, i.e., at most 0.5 cm root-mean-square-error (RMSE) of residual displacement

Search Space Reduction for Determination of Earthquake Source Parameters Using PCA and k-Means Clustering

The characteristics of an earthquake can be derived by estimating the source geometries of the earthquake using parameter inversion that minimizes the L2 norm of residuals between the measured and the synthetic displacement calculated from a dislocation model. Estimating source geometries in a dislocation model has been regarded as solving a nonlinear inverse problem. To avoid local minima and describe uncertainties, the Monte-Carlo restarts are often used to solve the problem, assuming the initial parameter search space provided by seismological studies. Since search space size significantly affects the accuracy and execution time of this procedure, faulty initial search space from seismological studies may adversely affect the accuracy of the results and the computation time. Besides, many source parameters describing physical faults lead to bad data visualization. In this paper, we propose a new machine learning-based search space reduction algorithm to overcome these challenges. This paper assumes a rectangular dislocation model, i.e., the Okada model, to calculate the surface deformation mathematically. As for the geodetic measurement of three-dimensional (3D) surface deformation, we used the stacking interferometric synthetic aperture radar (InSAR) and the multiple-aperture SAR interferometry (MAI). We define a wide initial search space and perform the Monte-Carlo restarts to collect the data points with root-mean-square error (RMSE) between measured and modeled displacement. Then, the principal component analysis (PCA) and the k-means clustering are used to project data points with low RMSE in the 2D latent space preserving the variance of original data as much as possible and extract k clusters of data with similar locations and RMSE to each other. Finally, we reduce the parameter search space using the cluster with the lowest mean RMSE. The evaluation results illustrate that our approach achieves 55.1~98.1% reductions in search space size and 60~80.5% reductions in 95% confidence interval size for all source parameters compared with the conventional method. It was also observed that the reduced search space significantly saves the computational burden of solving the nonlinear least square problem