The performance of particle damper is strongly nonlinear, and the energy dissipation is derived from a combination of mechanisms including plastic collisions and friction between the particles and the walls and between the particles themselves. An optimized support vector regression model is built to predict the damping ratio of cantilever beam with particle damper. Then, the optimal parameters are adopted to construct the support vector regression models. In addition, genetic algorithm is used to select the optimal variables so as to improve the predictive ability of the models. Cross validation combined with support vector regression is used in this research and is compared with the genetic algorithm-support vector regression method. Genetic algorithm-support vector regression as research object to compare with the combination of cross validation and support vector regression. The experimental results demonstrate that the proposed genetic algorithm-support vector regression model provides better prediction capability. Therefore, the genetic algorithm-support vector regression model is proven to be an effective approach to predict the damping ratio of cantilever beam with particle damper.

AbstractThis paper considers the distributed resource allocation problem over a network of n agents with individual optimization functions and coupled resource constraints. The optimization variables are bounded for each agent. We propose an asynchronous distributed gradient descent based method which is able to tackle time-varying communication networks and communication problems. The main ideas are twofold. First, the degree of freedom of the network is reduced to deal with the global constraint. Second, we utilize delay compensation to counteract the delay of asynchronous communication. We prove that the convergence rate is O(1/k) and analyze both the communication complexity and the time complexity. This method is applied into the parallel pump control problem in HVAC system and shows good numerical results.

Motivated by the importance and challenges of the energy saving problem of parallel-connected pumps in heating, ventilation, and air-conditioning (HVAC) systems, we propose a distributed optimal control algorithm for on-off status and flow rate set points of parallel-connected pumps in HVAC systems. The proposed algorithm consists of two parts: First, in order to process the network information, we apply the breadth first search algorithm to construct a tree for exchanging messages. Second, all nodes coordinate with each other and randomly sample the speed ratios. To our best knowledge, the algorithm proposed in this paper is the first effort to address the challenges of existing studies at the same time. The algorithm solves the pump optimization problem in a distributed manner, achieves the minimum pump energy consumption and has the convergence guarantee. Even if some of the pumps break down, the whole system can still be working and have great flexibility. Simulation experiments on six parallel-connected pumps are provided for different working cases to demonstrate the effectiveness of the proposed algorithm and compare with the other four methods. The results show that our algorithm strictly satisfies the demand constraints and presents good energy saving potential, the convergence guarantee, and flexibility. The maximum energy saving can be up to 29.92%.

Reducing the energy consumption of a variable air volume (VAV) system in heating, ventilation, and air-conditioning (HVAC) systems attracts many attentions. In this letter, a novel method, namely, optimal dynamic duct static pressure (ODSP) method is proposed to find the globally optimal solutions of the original non-convex multi-zone VAV system energy optimization problem. Our method is based on the primal dual interior point method to solve an equivalent convex optimization problem of the multi-zone VAV system energy optimization problem. Different from existing methods, our method is a real-time optimization method and explicitly considers the duct network model to find the energy saving strategy. Numerical simulations illustrate the effectiveness of our method compared with current methods, which show that our method provides better energy efficiency and nearly 20% energy is saved. Our method also has the guarantee to find the optimal solutions.

The increasing rate of vehicle population leads to traffic congestion and gas emission. Frequent traffic congestion increases commuting time and affects efficiency in work. Raising the proportion of public transportation is a solution to the issue. Compared with conventional transportation, Individualized Public Transportation System (IPTS) is more flexible and can be customized for every passenger. This paper proposes a four-stage system design method. Scheduling, dispatching and order responding algorithm are design to meet the demand of IPTS. We define the concept of Available Area for each bus, in order to recognize whether a new passenger order can be added to the bus without detour. We present the results in two scenarios: uniform distributed passengers and commuting passengers. IPTS reduces total travel time and waiting time compared to conventional transportation.