machinery
Not a member yet
8395 research outputs found
Sort by
Current Developments in the Field of Sound Monitoring in the Beehives
No full textIn the past ten years, beekeepers and academics have become increasingly concerned about honeybee mortality due to their critical role in pollination. As a result, reliable and accurate monitoring of beehives has become an effective method for tracking honeybee health. The bees use sounds and vibrations to interact within the hive, and with the help of microphones and a recording system, their analysis can provide important information about the colony’s health and help identify any sudden abnormalities. Even though plenty of these signals have been recognized and examined, research on using vibration and acoustics to monitor population health and behavior continuously is still in its early stages. Literature shows most bee sounds are emitted at frequencies lower than 1000 Hz. One potential method for identifying changes in beehive noises is to apply acoustic categorization to recordings of those sounds. The presence of a queen and the readiness for swarming are two signs that beekeepers should consider when evaluating the health of their colonies. These factors can cause colony collapse or diminished honey yield. The research presented here attempts to review the fascinating approaches that have been put together over time for analysis of the sound made by honeybees and the information that may be gathered from their recordings
Multi-Objective Hybrid Sailfish Optimization Algorithm for Planetary Gearbox and Mechanical Engineering Design Optimization Problems
No full textThis paper introduces a hybrid multi-objective optimization algorithm, designated HMODESFO, which amalgamates the exploratory prowess of Differential Evolution (DE) with the rapid convergence attributes of the Sailfish Optimization (SFO) algorithm. The primary objective is to address multi-objective optimization challenges within mechanical engineering, with a specific emphasis on planetary gearbox optimization. The algorithm is equipped with the ability to dynamically select the optimal mutation operator, contingent upon an adaptive normalized population spacing parameter. The efficacy of HMODESFO has been substantiated through rigorous validation against established industry benchmarks, including a suite of Zitzler-Deb-Thiele (ZDT) and Zeb-Thiele-Laumanns-Zitzler (DTLZ) problems, where it exhibited superior performance. The outcomes underscore the algorithm’s markedly enhanced optimization capabilities relative to existing methods, particularly in tackling highly intricate multi-objective planetary gearbox optimization problems. Additionally, the performance of HMODESFO is evaluated against selected well-known mechanical engineering test problems, further accentuating its adeptness in resolving complex optimization challenges within this domain
Renewable Energy and Ai Implementation as Sustainable Strategy for Agriculture Resilience to Climate Change
No full textClimate change has a major impact on all human activities. One of the most important is agriculture from the point of view of food production, as the starting point of survival on the globe. The negative consequences of climate change are reflected in increasingly pronounced droughts and catastrophic floods, and are also reflected in the increase in the frequency of these extremes. Multidisciplinary teams of engineers and scientists are engaged in the development of sustainable strategies, with the aim of increasing the resilience of agriculture to climate change. The application of renewable energy sources and artificial intelligence plays a significant role in the management of sustainable strategies for the protection and improvement of smart agriculture. Wind turbines and photovoltaic panels raised above agricultural land at an appropriate distance form a synergy of agricultural development and renewable energy. Their implementation in itself reduces the emission of greenhouse gases and contributes to mitigating climate change. At the same time, this energy can be used both in the system and locally for pumping water for irrigation. Hydropower and the construction of water acumulations also provide increased opportunities for solving droughts and floods and thereby improving and stabilizing agricultural production and higher resilience of agriculture to climate change. Agricultural residues can be used as a resource for renewable bioenergy. Artificial intelligence, supported by contemporary solutions of sensor technology, helps us in the optimal management of all these complex processes. This research also contains a positive case studies from international practice on the implementation of renewable energy sources and artificial intelligence and their concrete contribution to increasing the resilience of agriculture to climate change
COMPARISON OF NOISE SPECTRA OF THE FLOW PAST A CYLINDER COMPUTED BY DIFFERENT TURBULENCE MODELS
No full textAero-acoustic features are a contemporary and important topic for many mechanical systems,
and include various flow phenomena, such as “singing” of wires, cables, metal rods, antennas,
etc. as well as wake shedding from different kinds of blades. The aim of the current
computational study is to compare and estimate the reliability of available turbulence and acoustic
models in ANSYS FLUENT. A spatial, incompressible, transient flow around a cylinder at
relatively low Reynolds number Re = 90000 and Mach number M = 0.2 is used as a benchmark
and investigated in detail. Both unsteady Reynolds-averaged Navier-Stokes (URANS) and
filtered Navier-Stokes (large eddy simulation, LES) equations are solved and resulting flow fields
are compared. In both cases, Ffowcs-Williams and Hawkings (FWH) acoustic model is
employed. The presented results include oscillating aerodynamic coefficients, instantaneous and
averaged flow field visualizations, noise spectra and overall noise levels. Comparisons between
the tested turbulence models confirm that the simpler, computationally less expensive URANS
approach captures fewer flow features than LES, as well as the resulting noise frequency
components, but is applicable for initial studies. On the other hand, both tested turbulence models
underestimate the generated noise by roughly 25%. In can be concluded that the agreement with
the corresponding, available experimental data seems acceptable for preliminary analyses.
Aerodynamic coefficients can be estimated with more reliability than acoustic quantities
Enhancing impact toughness of additive manufactured pet-g using short carbon fibers
No full textPolyethylene terephthalate glycol (PET-G) material is used in additive manufacturing (AM) for the production of moderately loaded polyester-based parts. Scientific papers show that build orientation and printing direction angle substantially influence the mechanical properties of additively manufactured specimens. This paper explores the influence of adding short carbon fibers (SCF) to PET-G material where the effect of build orientations (flat, on edge and upright) and raster angles (0° and 90°) on the impact toughness was investigated. In this study, a low-energy instrumented Charpy Instron Ceast 9050 test was used as an experimental method to evaluate the impact behavior of those two different materials. An experimental evaluation of impact toughness in the case of AM impact specimens fabricated with different build orientations and printing angles was performed. The impact tests were carried out on five specimens per group (a total of 60 specimens for this research). The results obtained from an instrumented pendulum were compared between different specimen groups and different material to gain insight into enhancing impact toughness of additive manufactured PET-G using short carbon fibers. It was confirmed that addition of short carbon fibers enhance impact toughness but the different build orientations and raster angles have a greater influence on the final mechanical properties.under the agreement number 451-03-136/2025-03/200105, date February 4, 2025
Influence of dynamic behavior of excavator steel structure on correction of human vibrations: operator cabin case study
No full textIn this paper, the investigation and the influence of the dynamic behavior of the
structure of the structural part on the correction of human vibrations of the cabin of the unloading
boom of a bucket wheel excavator are performed. Diagnostic analysis using the finite element
method influenced the reconstruction of the local part of the structure to increase the first natural
frequency of the given structure, i.e. to reduce the human vibrations of the unloading boom booth.
This way, the lifespan of structural parts is extended, but also the health of the operator and better
working conditions are affected. By monitoring the state of human vibrations in a certain time
interval, before and after the reconstruction, this correct approach was prove
Stochastic reliability optimization of a controlled nonlinear energy sink under random excitation using GA-GRBFNN algorithm
No full textDesigning a control strategy to enhance the reliability of mechanical systems under random loads is crucial for maintaining system stability, resilience, performance, and safety. The primary challenge lies in optimizing the controller parameters while determining the reliability indexes. To overcome this difficulty, we have developed a novel intelligent algorithm to estimate optimal reliability of a kind of mechanical systems subjected to random loads by using a time-delay controller. This algorithm integrates a Gaussian Radial Basis Function Neural Network (GRBFNN) into a Genetic Algorithm (GA), taking the reliability function with unknown controlling parameters as the objective function, meanwhile the Backward Kolmogorov (BK) equation governing the reliability function with boundary condition and initial condition as constraints. In this algorithm, the neural network is employed to solve the BK equations at each iteration step of the GA to derive a fitness function, then the GA is utilized to obtain the optimal controlling parameters. Our algorithm enables the simultaneous optimization of implicit objectives and the solution of time-dependent BK equations. The influence of key parameters, such as population size, maximum iteration times in GA and the number of nodes in the neural network, on reliability performance is discussed in detail. The effectiveness of the proposed algorithm is testified through numerical comparisons and Monte Carlo simulations. The control strategy presented in this paper provides theoretical guidance to enhance reliability performance in mechanical engineering and shows great promise for practical applications
EFFECT OF CAVITATION EROSION ON MATERIAL MECHANICAL PROPERTIES AND MACHINE ELEMENTS PERFORMANCE
No full textCavitation, a frequent phenomenon caused by the implosion of vapour or vapour-gas bubbles in fluid, commonly occurs in mechanical systems. Cavitation erosion leads to surface damage, which can reduce machine performance or even completely disrupt its operation. In addition to fluid characteristics and the geometry of machine elements, the selected material plays a significant role in cavitation erosion resistance. Previous research has concluded that cavitation erosion has a substantial impact on changes in the mechanical properties of materials. With the emergence and rapid development of additive manufacturing technologies, challenges related to the fabrication of complex geometry components have been effectively overcome. However, the impact of cavitation erosion on 3d-printed materials, particularly metals, remains largely unexplored. In addition to reviewing existing research on the influence of cavitation erosion on material properties, especially the load-bearing capacity and functionality of machine components, this study presents fundamental characteristics of cavitation erosion tested according to the ASTM G32 standard on specimens made from MS1 metal powder
Internality of Two-Measure-Based Generalized Gauss Quadrature Rules for Modified Chebyshev Measures II
No full textGaussian quadrature rules are commonly used to approximate integrals with respect to a non-negative measure ������������̂
. It is important to be able to estimate the quadrature error in the Gaussian rule used. A common approach to estimating this error is to evaluate another quadrature rule that has more nodes and higher algebraic degree of precision than the Gaussian rule, and use the difference between this rule and the Gaussian rule as an estimate for the error in the latter. This paper considers the situation when ������������̂
is a Chebyshev measure that is modified by a linear factor and a linear divisor, and investigates whether the rules in a recently proposed new class of quadrature rules for estimating the error in Gaussian rules are internal, i.e., if all nodes of the new quadrature rules are in the interval (−1,1)
. These new rules are defined by two measures, one of which is a modified Chebyshev measure ������������̂
. The other measure is auxiliary
Real working process of a supercharged direct-injection spark-ignition engine with multiple injection: method for calculating the effective mixture composition in the angular domain
No full textTo evaluate the effective mixture composition in a direct-injection spark-ignition (DISI) engine, a numerical simulation was developed incorporating models for fuel injection, primary and secondary liquid fuel breakup and evaporation. The primary objective is to assess the fuel evaporation state prior to combustion - a critical factor influencing heat release and pollutant formation. The Wave-Breakup Model was employed for simulating the primary breakup of liquid fuel into droplets, providing input to the Arcoumanis model for secondary breakup. A chi-squared distribution was applied to model the distribution of Sauter Mean Diameters (SMD) of the resulting atomized droplets, while breakup time parameters were derived directly from the breakup models. These parameters were then used to solve a linearly implicit system of ordinary differential equations governing the evaporation process. Initially, a single injection event was simulated as a baseline, followed by simulations of dual injection strategies. Comparative analysis was conducted on the impact of fuel split ratios, with one set of cases maintaining fixed start of injection (SOI) crank angles and another set maintaining fixed end of injection (EOI) timings