Particles formation due to the wear of tires and measures for the wear reduction: A review

Tires are a very important part of a vehicle, necessary for the vehicle driving. During the vehicle exploitation, due to the friction which appears in the contact between the tire and the road, it comes to the formation of so called non-exhaust particles. The particles formed during the vehicle exploitation can be composed of heavy metals and they are harmful for people’s health and for the environment. Several factors influence the size of the wear of tires. The investigations about the tires behavior as well as the size of the wear can be conducted on the road, in the laboratory, as well as by application of modern software. The paper shows the applied methodologies applied by other researchers during the investigation of tires particles formation, the applied materials for the tires manufacturing, influential parameters on the wear of tires, as well as methods for the wear of tires reduction. After analyzing other researches in the subject field, the authors have come to the conclusions related to the factors which can reduce the wear of tires and these factors should be given proper attention in further studies.

Investigation of layer height influencing the impact strength of polylactic acid (PLA)

The aim of this study is to investigate the effect of layer height used during 3D printing on the impact strength, their standard deviations, and the printing time by using UNI EN ISO 180 unnotched specimens manufactured by FDM 3D printing technology. Every specimen is made of PLA, which is the most basic material of the FDM printing technology by using the same 3D printer. In this study it plays a key role to find out whether the layer height can be used to optimize the researched mechanical property within an economical framework or not. What is more, the possibly observable tendencies and crucial influential parameters will be analysed as well.

IDENTIFICATION OF INFLUENTIAL PARAMETERS IN A SHIP’S MOTION RESPONSES: A ROUTE TO MONITORING DYNAMIC STABILITY

Six degrees of freedom motion response tests of a Ro-Ro model have been carried out in irregular waves under intact conditions. A stationary model was tested in different sea states for following, astern quartering and beam seas. The investigation was limited to the effect of encountered frequency components and associated magnitude of energy of the ship’s motion responses. Analysis of heave, pitch and roll motions confirmed the vulnerability of the model to certain frequency ranges resulting in an adverse effect on the responses, and these were closely related to its natural frequencies. It was confirmed that the roll motion maintains its highest oscillation around the natural frequency in all sea conditions regardless of heading angles. However spectral analysis of the heave and pitch responses revealed the wave peak frequency. Roll is magnified when the peak frequency of wave approaches the natural roll frequency; therefore keeping them apart avoids a large motion response. It was concluded that peak frequency and associated magnitude are two important inherent characteristics of motion responses. Detection of influential parameters of encountered wave through heave and pitch responses could be utilised to limit a large ship’s motion at sea.

Optimized Degradation of Eosin Dye Through UV-ZnO NPs Catalyzed Reaction

The present study is set out to determine the photocatalytic degradation potential of ZnO nanoparticles for effective degradation of Eosin dye. The heterogeneous photocatalytic experiments were carried out by irradiating aqueous dye solutions with ultraviolet light. The influence of effective parameters like flow rate, pH, catalyst dose, and dye concentration was examined. The best degradation efficiency (66.82%) of ZnO Nanoparticles against Eosin dye was achieved within 90 min of reaction time. The Box–Behnken design under the Response Surface Methodology (RSM) was chosen as a statistical tool to obtain the correlation of influential parameters. The optimum values were recorded as follows: 0.59 g, 15.75 ppm and 136.12 ml/min for amount of catalyst, dye concentration and flow rate, respectively. The maximum percent degradation achieved at these conditions was 71.44%.

Global Sensitivity Analysis for CERES-Rice Model under Different Cultivars and Specific-Stage Variations of Climate Parameters

Global sensitivity analysis (SA) has become an efficient way to identify the most influential parameters on model results. However, the effects of cultivar variation and specific-stage variations of climate conditions on model outputs still remain unclear. In this study, 30 indica hybrid rice cultivars were simulated in the CERES-Rice model; then the Sobol’ method was used to perform a global SA on 16 investigated parameters for three model outputs (anthesis day, maturity day, and yield). In addition, we also compared the differences in the sensitivity results under four specific-stage variations (vegetative phase, panicle-formation phase, ripening phase, and the whole growth season) of climate conditions. The results indicated that (1) parameter Tavg, G4, and P2O are the most influential parameters for all model outputs across cultivars during the whole growth season; (2) under the vegetative-phase variation of climate parameters; the variability of model outputs is mainly controlled by parameter P2O and Tavg; (3) under the panicle-formation-phase or ripening-phase variation of climate parameters, parameter P2O was the dominant variable for all model outputs; (4) parameter PORM had a considerable effect (the total sensitivity index, STi; STi>0.05) on yield regardless of the various specific-stage variations of the climate parameters. Findings obtained from this study will contribute to understanding the comprehensive effects of crop parameters on model outputs under different cultivars and specific-stage variations of climate conditions.

Comprehensive Characterization of Spent Chemical Foundry Sand for Use in Concrete

The foundry industry generates large amounts of spent foundry sands, which are stored, available for recovery in other industrial sectors but unfortunately poorly exploited. Different authors have studied the possibility of recovering them in concretes, which would also allow production of more sustainable cementitious materials. The variability of their results highlights the importance of a better understanding of the potential influential parameters of the by-products. Unfortunately, exhaustive characterizations of the materials are rarely performed, especially for chemically bound foundry sands. This article presents a case study for the recovery of a spent chemical foundry sand with an exhaustive physicochemical characterization of the by-product and an analysis of its influence on the workability and mechanical strengths of cementitious materials. The tests recommended by the European standard for aggregates for concrete confirmed the suitability of the by-product. Associated with additional chemical tests (scanning electron microscopy, X-ray fluorescence, X-ray diffraction, etc.) as well as metallic particles characterization, they highlighted possible influential parameters. The workability and mechanical resistance tests carried out on mortars and concretes confirmed the influence of the fineness of the by-product associated with other parameters. Its use at a substitution rate of 30% results in a strength class C 30/37 concrete.

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.