Tungsten fibre reinforced Zr-based bulk metallic glass composites

A Zr-based bulk metallic glass (BMG) alloy with the composition (Zr55Al10Ni5Cu30)98.5Si1.5 was used as the base material to form BMG composites. Tungsten fiber reinforced BMG composites were successfully fabricated by pressure metal infiltration technique, with the volume fraction of the tungsten fiber ranging from 10% to 70%. Microstructure and mechanical properties of the BMG composites were investigated. Tungsten reinforcement significantly increased the material’s ductility by changing the compressive failure mode from single shear band propagation to multiple shear bands propagation, and transferring stress from matrix to tungsten fibers

Flexible operation and maintenance optimization of aging cyber-physical energy systems by deep reinforcement learning

Cyber-Physical Energy Systems (CPESs) integrate cyber and hardware components to ensure a reliable and safe physical power production and supply. Renewable Energy Sources (RESs) add uncertainty to energy demand that can be dealt with flexible operation (e.g., load-following) of CPES; at the same time, scenarios that could result in severe consequences due to both component stochastic failures and aging of the cyber system of CPES (commonly overlooked) must be accounted for Operation & Maintenance (O&M) planning. In this paper, we make use of Deep Reinforcement Learning (DRL) to search for the optimal O&M strategy that, not only considers the actual system hardware components health conditions and their Remaining Useful Life (RUL), but also the possible accident scenarios caused by the failures and the aging of the hardware and the cyber components, respectively. The novelty of the work lies in embedding the cyber aging model into the CPES model of production planning and failure process; this model is used to help the RL agent, trained with Proximal Policy Optimization (PPO) and Imitation Learning (IL), finding the proper rejuvenation timing for the cyber system accounting for the uncertainty of the cyber system aging process. An application is provided, with regards to the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED)

A sequential decision problem formulation and deep reinforcement learning solution of the optimization of O&M of cyber-physical energy systems (CPESs) for reliable and safe power production and supply

The Operation & Maintenance (O&M) of Cyber-Physical Energy Systems (CPESs) is driven by reliable and safe production and supply, that need to account for flexibility to respond to the uncertainty in energy demand and also supply due to the stochasticity of Renewable Energy Sources (RESs); at the same time, accidents of severe consequences must be avoided for safety reasons. In this paper, we consider O&M strategies for CPES reliable and safe production and supply, and develop a Deep Reinforcement Learning (DRL) approach to search for the best strategy, considering the system components health conditions, their Remaining Useful Life (RUL), and possible accident scenarios. The approach integrates Proximal Policy Optimization (PPO) and Imitation Learning (IL) for training RL agent, with a CPES model that embeds the components RUL estimator and their failure process model. The novelty of the work lies in i) taking production plan into O&M decisions to implement maintenance and operate flexibly; ii) embedding the reliability model into CPES model to recognize safety related components and set proper maintenance RUL thresholds. An application, the Advanced Lead-cooled Fast Reactor European Demonstrator (ALFRED), is provided. The optimal solution found by DRL is shown to outperform those provided by state-of-the-art O&M policies

A two-phase tabu-evolutionary algorithm for the 0–1 multidimensional knapsack problem

The 0–1 multidimensional knapsack problem is a well-known NP-hard combinatorial optimization problem with numerous applications. In this work, we present an effective two-phase tabu-evolutionary algorithm for solving this computationally challenging problem. The proposed algorithm integrates two solution-based tabu search methods into the evolutionary framework that applies a hyperplane-constrained crossover operator to generate offspring solutions, a dynamic method to determine search zones of interest, and a diversity-based population updating rule to maintain a healthy population. We show the competitiveness of the proposed algorithm by presenting computational results on the 281 benchmark instances commonly used in the literature. In particular, in a computational comparison with the best algorithms in the literature on multiple data sets, we show that our method on average matches more than twice the number of best known solutions to the harder problems than any other method and in addition yields improved best solutions (new lower bounds) for 4 difficult instances. We investigate two key ingredients of the algorithm to understand their impact on the performance of the algorithm

Solving large scale Max Cut problems via tabu search

In recent years many algorithms have been proposed in the literature for solving the Max-Cut problem. In this paper we report on the application of a new Tabu Search algorithm to large scale Max-cut test problems. Our method provides best known solutions for many well-known test problems of size up to 10,000 variables, although it is designed for the general unconstrained quadratic binary program (UBQP), and is not specialized in any way for the Max-Cut problem

Multiple phase tabu search for bipartite boolean quadratic programming with partitioned variables

The Bipartite Boolean Quadratic Programming Problem with Partitioned Variables (BBQP-PV) is an NP-hard problem with many practical applications. In this study, we present an effective multiple phase tabu search algorithm for solving BBQP-PV. The algorithm is characterized by a joint use of three key components: two tabu search phases that employ a simple neighborhood and a very large-scale neighborhood to achieve search intensification, and a hybrid perturbation phase that adaptively chooses a greedy perturbation or a recency-based perturbation for search diversification. Experimental assessment on 50 standard benchmarks indicates that the proposed algorithm is able to obtain improved lower bounds for 5 instances and match the previously best solutions for most instances, while achieving this performance within competitive time. Additional analysis confirms the importance of the innovative search components

CMBR Constraint on a Modified Chaplygin Gas Model

In this paper, a modified Chaplygin gas model of unifying dark energy and dark matter with exotic equation of state $p=B\rho-\frac{A}{\rho^{\alpha}}$ which can also explain the recent accelerated expansion of the universe is investigated by the means of constraining the location of the peak of the CMBR spectrum. We find that the result of CMBR measurements does not exclude the nonzero value of parameter $B$, but allows it in the range $-0.35\lesssim B\lesssim0.025$.Comment: 4 pages, 3 figure

Influence of Fermion Velocity Renormalization on Dynamical Mass Generation in QED3_3

We study dynamical fermion mass generation in (2+1)-dimensional quantum electrodynamics with a gauge field coupling to massless Dirac fermions and non-relativistic scalar bosons. We calculate the fermion velocity renormalization and then examine its influence on dynamical mass generation by using the Dyson-Schwinger equation. It is found that dynamical mass generation takes place even after including the scalar bosons as long as the bosonic compressibility parameter $\xi$ is sufficiently small. In addition, the fermion velocity renormalization enhances the dynamically generated mass.Comment: 6 pages, 3 figures, Chinese Physics Letter, Vol 29, page 057401(2012