Optimization of profile and damping layer of plates embedded with acoustic black hole indentations for broadband energy dissipation

Acoustic Black Hole (ABH) plate structure has shown promising potentials of vibration suppression above a cut on frequency. For energy dissipation below the cut on frequency, however, the ABH is less effective due to the absence of wave focusing effect. This work reports a simultaneous optimization of ABH plates for broadband energy dissipation. Two sets of design variables of ABH plates, that is, geometry of the profile and topology of the damping layer, are optimized in an alternatively nested procedure. A novel objective function, namely the upper limit of kinetic energy, is proposed. Modeling of ABH structures is implemented and dynamic characteristic is solved using finite element method. A rectangular plate embedded with two ABH indentations is presented as a numerical example. Influence of frequency ranges in the calculation and mass ratios of the damping layer on results are discussed. The achieved optimal arrangement of the damping layer is found to cover equally, if not more, above the non-ABH (uniform) part of the plate than the ABH area. This is inconsistent with the conventional believe that damping layers should cover as much of the ABH area as possible. Mechanism of the broadband energy dissipation by the optimal solution is demonstrated.

Development of a Method for the Measurement of Human Scent Samples Using Comprehensive Two-Dimensional Gas Chromatography with Mass Detection

Every human body is a source of a unique scent, which can be used for medical or forensic purposes. Human skin scent is a complex mixture of more or less volatile compounds with different chemical and physical properties, which often differ significantly in their concentrations. The most efficient technique for separating such complex samples is comprehensive two-dimensional gas chromatography (GC × GC). This work aimed to find the optimal arrangement of a two-dimensional chromatographic system and define a suitable chromatographic method for non-targeted analysis of human scent samples. Four different chromatographic columns (non-polar Rxi-5MS and TG-5HT, medium polar Rxi-17Sil MS and Rtx-200MS) and their different configurations were tested. The best system was the 30 m primary column Rtx-200MS (with the 2 m pre-column Rtx-200MS) and the 1 m secondary column TG-5HT in a reverse configuration. This system achieved the highest theoretical and conditional peak capacities, optimal resolution, and the lowest number of coelutions.

An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA).

Energy/Economic Analysis and Optimization of On-Grid Photovoltaic System Using CPSO Algorithm

Today, the use of renewable energy is increasing day by day, and this development requires the optimization of these technologies in various dimensions. Solar systems have a higher acceptance due to their high availability and accessibility; the most common solar technology is photovoltaic cell. In this research, modeling was done to achieve the most economically optimal arrangement of photovoltaic panels, inverters, and module placement to generate more electrical energy by considering economic parameters, for which the CPSO algorithm was used. Four different combinations of module and inverter were studied in this research, among which the second combination, which included PV module type one and inverter type two, was the best case. One of the significant results of the present study is 191,430 kWh of electrical energy during the studied year by the solar cell connected to the grid, which requires $42,792,727 to produce.

Optimal Placement of Viscoelastic Vibration Dampers for Kirchhoff Plates Based on PSO Method

The main subject of this study is to determine the optimal position of a fixed number of viscoelastic dampers on the surface of a thin (Kirchhoff-Love) plate. It is assumed that the dampers are described according to the generalized Maxwell model. In order to determine the optimal position of the dampers, a metaheuristic optimization method is used, called the particle swarm optimization method. The non-dimensional damping ratio of the first mode of the plate vibrations is assumed as an objective function in the task. The dynamic characteristics of the plate with dampers are determined by solving the non-linear eigenproblem using the continuation method. The finite element method is used to determine the stiffness matrix and the mass matrix occurring in the considered eigenproblem. The results of exemplary numerical calculations are also presented, where the final optimal arrangement of dampers on the surface of sample plates with different boundary conditions is shown graphically.

Poka Yoke Meets Deep Learning: a Proof of Concept for an Assembly Line Application

In this paper we present the re-engineering process of an assembly line that features speed reducers and multipliers for agricultural applications. The product operates as an interface between an input torque, typically supplied by an agricultural vehicle, and an output torque, generally moving specific equipment placed on a trolley equipped with a tow hook. The "as-is'' (initial version) line was highly inefficient due to several critical issues, including the high age of the machines, a non-optimal arrangement of them in the shop floor, and the absence of controls and process standards. These critical issues were analysed with the tools offered by Lean Manufacturing, which made it possible to identify irregularities and operations that require effort (Mura), overload (Muri), and waste (Muda). The definition of the "to-be'' (new version) assembly line included actions to update the department layout, to modify the assembly process and to design the line feeding system in compliance with the well-known concepts of Golden Zone and Strike Zone. The whole process addressed, in particular, the problem of the incorrect assembly of the oil seals. The registered error was mainly caused by the difficulty in visually identifying the correct side of the assembled oil seal, and by the mental fatigue of operators at the end of the shift. The solution studied in this paper resulted in a Poka-Yoke solution, which, leveraging the modern technologies and methods of deep learning and computer vision, monitors the process flow of the operators through a camera, preventing its completion in the event of an assembly error.

Multi-objective Optimization of a Mechatronic Solar Tracking Mechanisms

In this work, the multi-objective optimization of a dual-axis solar tracker is carried out by using a virtual prototyping software platform that integrates CAD (Computer Aided Design) – CATIA, MBS (Multi-Body Systems) – ADAMS and DFC (Design for Control) – EASY5 computer aided engineering programs. The solar tracking mechanism performs the bi-axial orientation of a PV panel, with the purpose to increase of the amount of incident solar radiation captured by the conversion system, thus improving its energy output. The optimization study aims to determine the optimal arrangement of the linear actuators that control the two degrees of freedom of the system (i.e. the diurnal and elevation movements of the PV panel) and the optimal tuning of their control elements (controllers), so that to minimize the energy consumption for performing the tracking and the tracking errors (relative to the imposed orientation program), while complying with the functional and constructive requirements/constraints coming from the type of actuator used in the application (which is a real/existing one). The tracking mechanism is approached/designed in mechatronic concept (i.e. concurrent engineering), by integrating the mechanical and control subsystems at the virtual prototype level.

Wind Farm Layout Optimization with Different Hub Heights in Manjil Wind Farm Using Particle Swarm Optimization

Nowadays, optimizing wind farm configurations is one of the biggest concerns for energy communities. The ongoing investigations have so far helped increasing power generation and reducing corresponding costs. The primary objective of this study is to optimize a wind farm layout in Manjil, Iran. The optimization procedure aims to find the optimal arrangement of this wind farm and the best values for the hubs of its wind turbines. By considering wind regimes and geographic data of the considered area, and using the Jensen’s method, the wind turbine wake effect of the proposed configuration is simulated. The objective function in the optimization problem is set in such a way to find the optimal arrangement of the wind turbines as well as electricity generation costs, based on the Mossetti cost function, by implementing the particle swarm optimization (PSO) algorithm. The results reveal that optimizing the given wind farm leads to a 10.75% increase in power generation capacity and a 9.42% reduction in its corresponding cost.

Application of biomimetic design in concept of small terrain electric vehicle, MODULO

Biomimetic Design is based on basic natural principles. One of the basic natural principle is principle of maximum and minimum. Biological structures or systems can achieve maximum performance and durability with minimal material and energy consumption. More developed system in nature, means the more favorable ratio of this principle. Therefore, this natural principle of maximum and minimum is the basis for trends in biomimetic design. Trends of biomimetic design (functional morphology, optimal arrangement, determination of functionality, multi-functionality, energy use, and so on) are presented for concept of small terrain electric vehicle in this paper. The electric vehicle model has 2 parts: front and rear module with a choice of accumulator size, and with a choice of 4x2 or 4x4 drives. The transport of people (including injured people) or transport of cargo to hard-to-reach places is main concept idea of terrain electric vehicle. This work is supported by the Slovak Research and Development Agency under the contract no. APVV-18-0457, Special Light Electric Vehicle from Unconventional Materials to Heavy Conditions and Terrain – LEV.