Structural Dynamics

1985 ◽  
Vol 38 (10) ◽  
pp. 1287-1289
Author(s):  
F. C. Moon ◽  
E. H. Dowell
Keyword(s):  
Structural Dynamics ◽  
Optimization Problems ◽  
Acoustic Noise ◽  
Flexible Structures ◽  
Computer Software ◽  
Nonlinear Problems ◽  
Great Promise ◽  
Bluff Bodies ◽  
Building Structures ◽  
Dynamics And Control

While much of the linear theory of structural dynamics has been codified in numerous computer software, important problems remain such as inverse methods (modal synthesis or system identification) and optimization problems. Nonlinear problems, however, are a fertile ground for new research, especially those involving large deformations (e.g., crash simulation) and material nonlinearities. Structure interaction problems will continue to be a fruitful area of research including fluid-structure dynamics and interaction with acoustic noise, thermal fields, soils, and electromagnetic forces. For example, new knowledge about unsteady flows around bluff bodies is needed to make significant progress with dynamic interaction problems with bridge and building structures in unsteady winds. A new field which shows great promise for application is the theory of feedback control of flexible structures. Advances in this area could pay off in near-space engineering and robotics. The training of new researchers with backgrounds in both structural dynamics and control theory and experience is a high priority for the control-structure field, however.

Nonlinear Dynamics and Control of Flexible Structures

1989 ◽  
Author(s):  
Francis C. Moon ◽  
Peter Gergely ◽  
James S. Thorp ◽  
John F. Abel
Keyword(s):  
Nonlinear Dynamics ◽  
Flexible Structures ◽  
Dynamics And Control ◽  
And Control ◽  
Nonlinear Dynamics And Control

A Comprehensive Investigation of Ensembles of Gaussian-Based and Gradient-Based Transformed Reliability Analyses: When and How to Use Them

2016 ◽  
Author(s):  
Po Ting Lin ◽  
Wei-Hao Lu ◽  
Shu-Ping Lin
Keyword(s):  
Design Optimization ◽  
Design Space ◽  
Optimization Problems ◽  
Nonlinear Problems ◽  
Kernel Functions ◽  
Sensitivity Analyses ◽  
Normal Space ◽  
Highly Nonlinear ◽  
Standard Normal ◽  
Gradient Based

In the past few years, researchers have begun to investigate the existence of arbitrary uncertainties in the design optimization problems. Most traditional reliability-based design optimization (RBDO) methods transform the design space to the standard normal space for reliability analysis but may not work well when the random variables are arbitrarily distributed. It is because that the transformation to the standard normal space cannot be determined or the distribution type is unknown. The methods of Ensemble of Gaussian-based Reliability Analyses (EoGRA) and Ensemble of Gradient-based Transformed Reliability Analyses (EGTRA) have been developed to estimate the joint probability density function using the ensemble of kernel functions. EoGRA performs a series of Gaussian-based kernel reliability analyses and merged them together to compute the reliability of the design point. EGTRA transforms the design space to the single-variate design space toward the constraint gradient, where the kernel reliability analyses become much less costly. In this paper, a series of comprehensive investigations were performed to study the similarities and differences between EoGRA and EGTRA. The results showed that EGTRA performs accurate and effective reliability analyses for both linear and nonlinear problems. When the constraints are highly nonlinear, EGTRA may have little problem but still can be effective in terms of starting from deterministic optimal points. On the other hands, the sensitivity analyses of EoGRA may be ineffective when the random distribution is completely inside the feasible space or infeasible space. However, EoGRA can find acceptable design points when starting from deterministic optimal points. Moreover, EoGRA is capable of delivering estimated failure probability of each constraint during the optimization processes, which may be convenient for some applications.

Nonlinear Hydrodynamic Forces on Flexible Structures: A Combined-Force Model

2004 ◽  
Vol 126 (1) ◽  
pp. 78-83 ◽  
Author(s):  
Iftekhar Anam ◽  
Jose M. Roesset
Keyword(s):  
Flexible Structures ◽  
Nonlinear Problems ◽  
Nonlinear Effects ◽  
Diffraction Theory ◽  
Computational Effort ◽  
New Method ◽  
Force Model ◽  
Order Difference ◽  
The Individual ◽  
Better Than

A new combined-force method is suggested to approximate the second-order difference frequency forces from diffraction theory (Φ2 theory) with less computational effort. The new method is formulated by combining two limiting cases of the Φ2 theory; i.e., Newman’s approximation and the slender Φ2 theory. Numerical results show that the new method reproduces the individual nonlinear effects of the Φ2 theory better than the existing approximations. Results of this work also show the limitations of Morison’s equation as the slender-body counterpart of the diffraction theory for nonlinear problems.

Multiagent Coordination Optimization Based Model Predictive Control Strategy With Application to Balanced Resource Allocation

2014 ◽  
Author(s):  
Haopeng Zhang ◽  
Qing Hui
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Strategy ◽  
Large Scale ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Nonlinear Problems ◽  
Pso Algorithm ◽  
Network Resource ◽  
Multiagent Coordination

Model predictive control (MPC) is a heuristic control strategy to find a consequence of best controllers during each finite-horizon regarding to certain performance functions of a dynamic system. MPC involves two main operations: estimation and optimization. Due to high complexity of the performance functions, such as, nonlinear, non-convex, large-scale objective functions, the optimization algorithms for MPC must be capable of handling those problems with both computational efficiency and accuracy. Multiagent coordination optimization (MCO) is a recently developed heuristic algorithm by embedding multiagent coordination into swarm intelligence to accelerate the searching process for the optimal solution in the particle swarm optimization (PSO) algorithm. With only some elementary operations, the MCO algorithm can obtain the best solution extremely fast, which is especially necessary to solve the online optimization problems in MPC. Therefore, in this paper, we propose an MCO based MPC strategy to enhance the performance of the MPC controllers when addressing non-convex large-scale nonlinear problems. Moreover, as an application, the network resource balanced allocation problem is numerically illustrated by the MCO based MPC strategy.

Airworthiness aspects of new technologies: Interaction between aircraft structural dynamics and control systems

1998 ◽  
Vol 212 (5) ◽  
pp. 309-317 ◽  
Author(s):  
M Hockenhull
Keyword(s):  
Control Systems ◽  
Structural Dynamics ◽  
Flight Control ◽  
Closed Loop ◽  
New Technologies ◽  
Loop Control ◽  
Transport Aircraft ◽  
Control Load ◽  
Dynamics And Control ◽  
And Control

The application of electrical flight control systems to civil transport aircraft has directed attention to the need for improved airworthiness regulation. In this paper, the scope and interpretation of a new FAR/JAR Part 25 regulation in preparation is discussed, applicable to aircraft that have closed-loop control systems for flight control, load alleviation or stability augmentation, and have the potential to interact with the aircraft's structural dynamics.

Distributed Parameter-Dependent Modeling and Control of Flexible Structures

2004 ◽  
Vol 127 (2) ◽  
pp. 230-239 ◽  
Author(s):  
Fen Wu ◽  
Suat E. Yildizoglu
Keyword(s):  
Optimization Problems ◽  
Flexible Structures ◽  
Synthesis Condition ◽  
Distributed Model ◽  
Linear Matrix ◽  
Distributed Parameter ◽  
Modeling And Control ◽  
L2 Norm ◽  
And Control ◽  
Parameter Dependent

In this paper, distributed parameter-dependent modeling and control approaches are proposed for flexible structures. The distributed model is motivated from distributed control design, which is advantageous in reducing control implementation cost and increasing control system reliability. This modeling approach mainly relies on a central finite difference scheme to capture the distributed nature of the flexible system. Based on the proposed distributed model, a sufficient synthesis condition for the design of a distributed output-feedback controller is presented using induced L2 norm as the performance criterion. The controller synthesis condition is formulated as linear matrix inequalities, which are convex optimization problems and can be solved efficiently using interior-point algorithms. The distributed controller inherits the same structure as the plant, which results in a localized control architecture and a simple implementation scheme. These modeling and control approaches are demonstrated on a non-uniform cantilever beam problem through simulation studies.

OPTIMAL TESTING RESOURCE ALLOCATION FOR MODULAR SOFTWARE CONSIDERING COST, TESTING EFFORT AND RELIABILITY USING GENETIC ALGORITHM

2009 ◽  
Vol 16 (06) ◽  
pp. 495-508 ◽  
Author(s):  
P. K. KAPUR ◽  
ANU. G. AGGARWAL ◽  
KANICA KAPOOR ◽  
GURJEET KAUR
Keyword(s):  
Genetic Algorithm ◽  
Resource Allocation ◽  
Software Development ◽  
Large Scale ◽  
Optimization Problems ◽  
Low Cost ◽  
Computer Software ◽  
Critical Issue ◽  
Software Systems ◽  
The Cost

The demand for complex and large-scale software systems is increasing rapidly. Therefore, the development of high-quality, reliable and low cost computer software has become critical issue in the enormous worldwide computer technology market. For developing these large and complex software small and independent modules are integrated which are tested independently during module testing phase of software development. In the process, testing resources such as time, testing personnel etc. are used. These resources are not infinitely large. Consequently, it is an important matter for the project manager to allocate these limited resources among the modules optimally during the testing process. Another major concern in software development is the cost. It is in fact, profit to the management if the cost of the software is less while meeting the costumer requirements. In this paper, we investigate an optimal resource allocation problem of minimizing the cost of software testing under limited amount of available resources, given a reliability constraint. To solve the optimization problem we present genetic algorithm which stands up as a powerful tool for solving search and optimization problems. The key objective of using genetic algorithm in the field of software reliability is its capability to give optimal results through learning from historical data. One numerical example has been discussed to illustrate the applicability of the approach.

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