Research developments in vibration control of structures using passive tuned mass dampers

Particle damping, an effective passive vibration control technology, is developing dramatically at the present stage, especially in the aerospace and machinery fields. The aim of this paper is to provide an overview of particle damping technology, beginning with its basic concept, developmental history, and research status all over the world. Furthermore, various interpretations of the underlying damping mechanism are introduced and discussed in detail. The theoretical analysis and numerical simulation, together with their pros and cons are systematically expounded, in which a discrete element method of simulating a multi-degree-of-freedom structure with a particle damper system is illustrated. Moreover, on the basis of previous studies, a simplified method to analyze the complicated nonlinear particle damping is proposed, in which all particles are modeled as a single mass, thereby simplifying its use by practicing engineers. In order to broaden the applicability of particle dampers, it is necessary to implement the coupled algorithm of finite element method and discrete element method. In addition, the characteristics of experimental studies on particle damping are also summarized. Finally, the application of particle damping technology in the aerospace field, machinery field, lifeline engineering, and civil engineering is reviewed at length. As a new trend in structural vibration control, the application of particle damping in civil engineering is just at the beginning. The advantages and potential applications are demonstrated, whereas the difficulties and deficiencies in the present studies are also discussed. The paper concludes by suggesting future developments involving semi-active approaches that can enhance the effectiveness of particle dampers when used in conjunction with structures subjected to nonstationary excitation, such as earthquakes and similar nonstationary random excitations.

Particle impact dampers: Past, present, and future

Study on adaptive-passive multiple tuned mass damper with variable mass for a large-span floor structure

Experimental and numerical studies of a novel track bistable nonlinear energy sink with improved energy robustness for structural response mitigation

MOCS-based optimum design of TMD and FTMD for tall buildings under near-field earthquakes including SSI effects

Dimension adaptive finite difference decomposition using multiple sparse grids for stochastic computation

Abstract. High fidelity models for uncertainty quantification of large structures in finite element framework are computationally exhaustive. Thus, there is a constant demand for efficient algorithm that uses optimal computational cost without compromising with the quality of the end results. With this in view, present study aims to develop a sequentially evolving stochastic response surface using Hermite family of orthogonal polynomials whose support points are generated in sparse grid framework. Using the values of the original model at these support points, unknown coefficients of the stochastic response surface are optimized by moving least square technique. It helps to reduce the number of original function evaluations to determine the coefficients as compared to other deterministic or random sampling techniques. Besides sparse grid scheme for support point generation, they are also populated sequentially as the optimization progresses in every iteration. The uniqueness of the proposed scheme is its adaptability by changing the order of the polynomials and the level of the sparse grid to minimize the overall computational cost. Multiple optima present in the original response can be identified by introducing additional penalty functions whenever they are required. Once the global response surface is ready, Monte-Carlo simulation or its advance version (e.g. Latin Hypercube Sampling) is adopted to generate the probability density functions for the output variables. Numerical studies are presented to prove the efficiency and accuracy of the proposed scheme as compared to other techniques available in the literature.

/pdf/mls-based-sequential-srsm-in-sparse-grid-framework-for-flqcblodrl.pdf

Mls based sequential srsm in sparse grid framework for efficient uncertainty quantification

In cloud computing, a shared, configurable pool of computing resources is made accessible online and allocated to users according to their needs. It is essential for a cloud provider to schedule jobs in the cloud to keep up service quality and boost system efficiency. In this paper, we present a scheduling technique based on modified particle swarm optimization to combat the issues of excessively long scheduling time and high computation costs associated with scheduling jobs in a cloud environment. The modified PSO is used to allocate the jobs to virtual machines in order to minimize the objective function consisting of cost and makespan. The algorithm relies on biological changes that occur in organisms to regulate premature convergence and improve local search capability. The technique is analyzed and simulated using CloudSim, and the simulation results demonstrate that the proposed approach decreases makespan and cost effectively as compared to standard PSO.

https://downloads.hindawi.com/journals/mpe/2023/3916735.pdf

Amit Kumar Rathi

Papers

Dimension adaptive finite difference decomposition using multiple sparse grids for stochastic computation

Mls based sequential srsm in sparse grid framework for efficient uncertainty quantification

Modified Particle Swarm Optimization Based on Aging Leaders and Challengers Model for Task Scheduling in Cloud Computing