Optimisation of PM scheduling for multi-component systems – a simulated annealing approach

Proper planning of preventive maintenance (PM) is crucial in many industries such as oil transmission pipelines, automotive and food industries. A critical decision in the PM plans is to determine frequencies and types of maintenance actions in order to achieve a certain level of system availability with a minimum total cost. In this paper, we consider the problem of obtaining availability-based non-periodic optimal PM planning for systems with deteriorating components. The objective is to sustain a certain level of availability with the minimal total maintenance-related costs. In the proposed approach, the planning horizon is divided into some inspection periods of equal intervals. For any given interval, a decision must be made to perform one of the three actions on each component; inspection, preventive repair and preventive replacement. Any of these activities has different effects on the reliability of the components and the corresponding distinct costs based on the required recourses. The cost function includes the cost for repair, replacement, system downtime and random failures. System availability and PM resources are the main constraints considered. Since the proposed model is combinatorial in nature involving non-linear decision variables, a simulated annealing algorithm is employed to provide good solutions within a reasonable time.     

PASTA: a power-aware solution to scheduling of precedence-constrained tasks on heterogeneous computing resources

Power efficiency is one of the main challenges in large-scale distributed systems such as datacenters, Grids, and Clouds. One can study the scheduling of applications in such large-scale distributed systems by representing applications as a set of precedence-constrained tasks and modeling them by a Directed Acyclic Graph. In this paper we address the problem of scheduling a set of tasks with precedence constraints on a heterogeneous set of Computing Resources (CRs) with the dual objective of minimizing the overall makespan and reducing the aggregate power consumption of CRs. Most of the related works in this area use Dynamic Voltage and Frequency Scaling (DVFS) approach to achieve these objectives. However, DVFS requires special hardware support that may not be available on all processors in large-scale distributed systems. In contrast, we propose a novel two-phase solution called PASTA that does not require any special hardware support. In its first phase, it uses a novel algorithm to select a subset of available CRs for running an application that can balance between lower overall power consumption of CRs and shorter makespan of application task schedules. In its second phase, it uses a low-complexity power-aware algorithm that creates a schedule for running application tasks on the selected CRs. We show that the overall time complexity of PASTA is $O(p.v^{2})$ where $p$ is the number of CRs and $v$ is the number of tasks. By using simulative experiments on real-world task graphs, we show that the makespan of schedules produced by PASTA are approximately 20 % longer than the ones produced by the well-known HEFT algorithm. However, the schedules produced by PASTA consume nearly 60 % less energy than those produced by HEFT. Empirical experiments on a physical test-bed confirm the power efficiency of PASTA in comparison with HEFT too.     

Strong Dependency of Ion Acceleration on Ion Beam Divergency in Magnetized Collisionless Plasma Sheath

The ion acceleration inside the collisionless plasma sheath is investigated at the presence of external magnetic field. By using the fluid model, the number and momentum equations of the ions and the Boltzmann and Poisson equations are solved numerically in the case that the ion beam has a small divergency at the plasma sheath boundary. It is shown that the kinetic energy of the ions has a strong dependency to the magnitude of divergency when the magnetic field has a small component parallel to the sheath boundary.     

Robust non-aggressive three-axis attitude control of spacecraft: dynamic sliding mode approach

Conventional sliding mode control (SMC) has been extensively applied in controlling spacecrafts because of its appealing characteristics such as robustness and a simple design procedure. Several methods such as second-order sliding modes and discontinuous controllers are applied for the SMC implementation. However, the main problems of these methods are convergence and error tracking in a finite amount of time. This paper combines an improved dynamic sliding mode controller and model predictive controller for spacecrafts to solve the chattering phenomenon in traditional sliding mode control. To this aim, this paper develops dynamic sliding mode control for spacecraft’s applications to omit the chattering issue. The proposed approach shows robust attitude tracking by a set of reaction wheels and stabilizes the spacecraft subject to disturbances and uncertainties. The proposed method improves the performance of the SMC for spacecraft by avoiding chattering. A set of simulation results are provided that show the advantages and improvements of this approach (in some sense) compared to SMC approaches.     

New procedure to calculate the Hansen solubility parameters of polymers

The simplest experimental method to determine the Hansen solubility parameters (HSPs) for a polymer is to evaluate whether or not it dissolves in selected solvents. Those solvents dissolving the polymer will have HSPs closer to those of the polymer than those that do not. A computer program or graphical method can then be used to find the HSP for the polymer. In this work, an improved method for calculating the HSP of polymers, based on the Nelder–Mead optimization algorithm, is presented. The results of this program fit the data very well. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 103: 31–36, 2007     

Alternative fuel and gasoline in an SI engine: A comparative study of performance and emissions characteristics

The strict regulation of environmental laws, the price of oil and its restricted resources, has made engine manufacturers use other energy resources instead of oil and its products. Despite the fact that nowadays alternative fuels are not currently widely used in vehicular applications, using these kinds of fuels will be definitely inevitable in the future. In this paper, a computer code is developed in Matlab environment and then its results are validated with experimental data. This simulated engine model could be used as an powerful tool to investigate the performance and emission of a given SI engine fueled by alternative fuels including hydrogen, propane, methane, ethanol and methanol. Also, the superior of alternative fuels is shown by comparing the performance and emissions of alternative fueled engines to those in conventional fueled engines. Eventually, it is concluded that volumetric efficiency of the engine working on hydrogen is the lowest (28% less that gasoline fueled engine), gasoline produce more power than the all being tested alternative fuels and BSFC of methanol is 91% higher than that of gasoline while BSFC of hydrogen is 63% less than gasoline.