Constituent Parameters Research Articles

بررسی ساختار و سامان‌بندی‌های به‌کار رفته مخمل‌های عثمانی کاخ موزه توپکاپی

بررسی ساختار و سامان‌بندی‌های به‌کار رفته مخمل‌های عثمانی کاخ موزه توپکاپی

Computational compensation of systematic errors accompanying non-equilibrium thermocouple measurements

Application of thermocouple sets of various complexity as temperature sensors in porous materials can be accompanied with systematic errors related to the very high differences between the thermal conductivities of thermocouple forming metals and measured samples. Since the higher measurement uncertainties can then make some presumed utilizations of measured temperature fields questionable, this paper introduces an approach which can overcome this drawback. Using a computational model, it is able to separate parameters of particular constituents of the sample-thermocouple system that helps to filter out the systematic errors by means of extracting the pure material parameters. The applicability of the approach is demonstrated on a one-sided high-temperature heating experiment, in which the temperature field in a sample is monitored and subsequently used for the determination of thermal conductivity. The effect of the computational compensation procedure is found to be very significant. The up to 64 °C temperature differences between the raw data and corrected values can cause systematic thermal conductivity errors of up to 46%. Depending on temperature, the corrected thermal conductivity is then by up to 0.89 W m−1 K−1 lower.

Experimentally derived trophic enrichment and discrimination factors for Chinook salmon, Oncorhynchus tshawytscha.

Stable isotope analysis (SIA) can provide important insights into food web structure and is a widely used tool in ecological conservation and management. It has recently been augmented by compound-specific stable isotope analysis of amino acids (CSIA-AA), an innovation that can provide greater precision when analyzing trophic level and food web connectivity. The utility of SIA rests on confidence in its constituent parameters such as the trophic enrichment factor (TEF). There is increasing emphasis on the need to experimentally derive species and tissue specific TEFs for studies utilizing SIA. Chinook salmon, Oncorhynchus tshawytscha, is a species with high potential for study using SIA due to the difficulty in observing its ecology during its marine phase and the significance of the conservation consequences of recent population declines. Bulk and amino acid-specific TEFs were determined for juvenile and adult Chinook salmon fed specific diets. Three controlled feeding studies were performed: adult salmon were fed a biofeed, juvenile salmon were fed a biofeed, and juvenile salmon were fed krill. Bulk and compound-specific stable isotope data were collected from diet samples and from salmon muscle tissue after a minimum of 8weeks of controlled feeding. Bulk isotope signatures were measured using EA-IRMS and CSIA-AA signatures using GC/C-IRMS, allowing the TEFs to be calculated. The bulk isotope TEFs were higher than those predicted for similar marine organisms and averaged 3.5‰ for ∆15 N and 1.3‰ for ∆13 C. The TEFs derived for nitrogen isotopes of amino acids were in line with expectations for this approach: the mean value for ∆15 NGlu - ∆15 NPhe was 7.06‰ and, using a multi-AA approach, the value for ∆15 NTrophic - ∆15 NSource was 6.67‰. For carbon isotopes of amino acids, the derived TEFs of Iso, Leu and Phe were near 0‰, as was that of Met, supporting their use of as source amino acids in future CSIA studies. This study presents Chinook salmon-specific TEFs for bulk and amino acid SIA. It supports the application of future research applying SIA to the study of Chinook salmon and validates previous research on species-specific TEFs.

Cumulative infiltration and infiltration rate prediction using optimized deep learning algorithms: A study in Western Iran

Study regionSixteen different sites from two provinces (Lorestan and Illam) in the western part of Iran were considered for the field data measurement of cumulative infiltration, infiltration rate, and other effective variables that affect infiltration process. Study focusSoil infiltration is recognized as a fundamental process of the hydrologic cycle affecting surface runoff, soil erosion, and groundwater recharge. Hence, accurate prediction of the infiltration process is one of the most important tasks in hydrological science. As direct measurement is difficult and costly, and empirical models are inaccurate, the current study proposed a standalone, and optimized deep learning algorithm of a convolutional neural network (CNN) using gray wolf optimization (GWO), a genetic algorithm (GA), and an independent component analysis (ICA) for cumulative infiltration and infiltration rate prediction. First, 154 raw datasets were collected including the time of measuring; sand, clay, and silt percent; bulk density; soil moisture percent; infiltration rate; and cumulative infiltration using field survey. Next, 70 % of the dataset were used for model building and the remaining 30 % was used for model validation. Then, based on the correlation coefficient between input variables and outputs, different input combinations were constructed. Finally, the prediction power of each developed algorithm was evaluated using different visually-based (scatter plot, box plot and Taylor diagram) and quantitatively-based [root mean square error (RMSE), mean absolute error (MAE), the Nash-Sutcliffe efficiency (NSE), and percentage of bias (PBIAS)] metrics. New Hydrological Insights for the RegionFinding revealed that the time of measurement is more important for cumulative infiltration, while soil characteristics (i.e. silt content) are more significant in infiltration rate prediction. This shows that in the study area, silt parameter, which is the dominant constituent parameter, can control infiltration process more effectively. Effectiveness of the variables in the present study, in the order of importance are time, silt, clay, moisture content, sand, and bulk density. This can be related to the fact that most of study area is rangeland and thus, overgrazing leads to compaction of the silt soil that can lead to a slow infiltration process. Soil moisture content and bulk density are not highly effective in our study because these two factors do not significantly change across the study area. Findings demonstrated that the optimum input variable combination, is the one in which all input variables are considered. The results illustrated that CNN algorithms have a very high performance, while a metaheuristic algorithm enhanced the performance of a standalone CNN algorithm (from 7% to 28 %). The results also showed that a CNN-GWO algorithm outperformed the other algorithms, followed by CNN-ICA, CNN-GA, and CNN for both cumulative infiltration and infiltration rate prediction. All developed algorithms underestimated cumulative infiltration, while overestimating infiltration rates.

Machine learning specific heat capacities of nanofluids containing CuO and Al2O3

AbstractEmpirical work and modeling approaches have shown that fundamental thermophysical parameters of constituents, nanoparticles, and base liquids have complex but synergistic effects on the specific heat capacity of nanofluids. In this work, we develop the Gaussian process regression model to investigate the statistical relationship among temperature, specific heat capacities of nanoparticles and base liquids, nanoparticle volume concentrations, and specific heat capacities of nanofluids. The model is developed with a dataset containing nanofluids with CuO and Al2O3 nanoparticles, and water and ethylene glycol (EG) as base liquids. The model also applies well to nanofluids containing mixtures of water and EG, and Al2O3 nanoparticles with different particle size distributions. The model is highly accurate and stable that contributes to fast and low‐cost estimations of specific heat capacities of nanofluids over a wide range of compositions and temperature.

Comparison of regulatory approaches for determining application limits for nitrogen fertilizer use in Germany

This study examined the suitability of three different indicators as entry points for agricultural regulation for limiting excess nitrogen (N) fertilizer inputs in Germany: net soil surface balance, gross farm-gate balance, and fertilization planning. Data on about 6000 farms in Germany were grouped into types for comparative analysis. The design of the regulatory approaches and the reliability of constituent parameters were then examined, and proportions of affected farms and mean N reduction requirements were identified. This revealed that: (a) design and purpose of the regulatory approaches differ, but the data requirements are very similar; (b) the parameters involved differ in reliability and integrity; and (c) the limits for maximum N fertilizer input at farm level vary with approach and farm type.

Simulasi Pembebanan Komponen Bender pada Desain Mesin Begel Fabricator Menggunakan Software Autodesk Inventor 2020

Stress analysis of the bender components in the design of the begel fabricator machine was carried out using FEA (Finite Element Analysis) with three variations of the constituent material parameters, namely 6061 aluminum, mild steel, and cast iron with a modulus of elasticity of 68.9 GPa, 220 GPa, 120.5 GPa, respectively. The test is carried out by a loading parameter 2520 MPa and fixed constraint. The maximum von misses stress and displacement obtained for each material parameter components using aluminum, mild steel, and cast iron are 17.78 MPa; 0.00765, 17.49 MPa; 0.00229, 17.62 MPa; 0.00427 respectively.

A micromechanics and machine learning coupled approach for failure prediction of unidirectional CFRP composites under triaxial loading: A preliminary study

This study presents a hybrid method based on artificial neural network (ANN) and micro-mechanics for the failure prediction of IM7/8552 unidirectional (UD) composite lamina under triaxial loading. The ANN is trained offline by numerical data from a high-fidelity micromechanics-based representative volume element (RVE) model using the finite element method (FEM). The RVE adopts identified constituent parameters from inverse analysis and calibrated interface strengths form uniaxial and biaxial tests. A hybrid loading strategy is proposed for the RVE under triaxial loading to obtain the failure points on sliced surfaces whilst maintaining the constant stress at different surfaces. It has been found that the ANN algorithm is robust in the failure prediction of the UD lamina when subjected to different triaxial loading conditions, with over 97.5% accuracy being achieved by the shallow ANN model, where only two hidden layers and 560 samples are used. The predicted 3D failure surface based on trained ANN model has an elliptical paraboloid shape and shows an extremely high strength in biaxial compression. The approach could be used to inform the modification of existing failure criteria and to propose ANN-based failure criteria.

Microscopic kinetics of isothermal sintering of Fe-20 % (mаs.) Mo alloy

Purpose of work. To investigate the features of microscopic kinetics of peritectoid transformation in Fe-Mo system alloys in an isothermal mode. Experimental part. Microscopic analysis of samples on light (Jenaphot 2000, K. Zeiss) and scanning electron (REM 106I, Selmi) microscopes, X-ray spectral microanalysis of the component’s concentrations distribution between the phases, X-ray phase analysis (Rigaku Ultima IV diffractometer). Results. Microstructure changes, phase composition and crystal lattices parameters of the phase constituents of the powder alloy during sintering at 920 °C were investigated. Variation in the phase constituents mass fraction during 7 hours of the isothermal exposure is analyzed. The formation of anomalous diffusion porosity at the beginning of the process, the nonmonotonic change in the phase constituents fraction and formation of intermediate phases with an unstable component’s concentration are the main features of the microscopic kinetics. The sintering mechanism is proposed. Scientific novelty. A local peritectoid transformation existence at the Fe/Mo interface was established by analyzing the local diffusion flows of components atoms. This transformation occurs upon isothermal supply of Mo atoms with the formation of a cooperative peritectoid structural constituents according to the α- Fe + Mo → α + μ scheme with residual Mo crystals. Formulation of the problem. This work aims to clarify the phenomenological theory of peritectoid transformation during isothermal α-Fe grains enrichment with molybdenum by studying the features of microscopic kinetics in the Fe-Mo system alloys. Practical value. Peritectoid (α + μ) with branched phase соnstituents of cooperative genesis forms a developed system of local diffusion flows of Mo atoms in α -Fe. This increases the molybdenum peritectoid transformation rate at a relatively low sintering temperature for these alloys and reduces the energy consumption in the technological process.

Patterns of Composite Soils Behavior under Shear and Compression

Clastic-silty-clayed (composite) soils of natural and artificial composition are the most widespread on construction sites in the Russian Far East, which makes the study of their strength and deformation properties relevant. Based on the results of laboratory and field studies, the relationship between the macrostructure and physical parameters of composite soil constituents and their mechanical properties was analyzed, and substantiation of patterns of composite soils behavior under shear and compression was made.

SABR for Baskets

We show that a basket of assets, each governed by the normal SABR model, is also governed by the normal SABR model. Explicit formulae for the SABR parameters of the basket are derived from the SABR parameters of the basket constituents.

A deep learning driven uncertain full‐field homogenization method

AbstractThis work is directed to uncertainty quantification of homogenized effective properties of composite materials with a complex, three dimensional microstructure. The uncertainties arise in the material parameters of the single constituents as well as in the fiber volume fraction. They are taken into account by multivariate random variables. Uncertainty quantification is carried out by an efficient surrogate model based on pseudospectral polynomial chaos expansion and artificial neural networks, which is trained on a fast Fourier transformation based homogenization method. The numerical example deals with the comparison of the presented method to Monte Carlo‐type simulation for uncertain homogenization of spherical inclusions in a matrix material.

Laboratory and numerical experimentation for masonry in compression

In this paper, the initial part of a laboratory and numerical experimental campaign dedicated to historical masonry is described. One leaf masonry panels with regular texture are built in order to simulate a historical material characterised by strong resisting elements and weak mortar joints. Laboratory tests are first dedicated to masonry components and then to the behaviour in compression of masonry panels, which is applied both orthogonal and parallel to bed joints, in order to highlight the orthotropic behaviour of the material. First of all, the mechanical parameters of masonry constituents are calibrated and then a heterogeneous finite element model is introduced and calibrated for reproducing the orthotropic behaviour of masonry, together with the initial elastic response and the initial nonlinear behaviour due to the first level of damage.

Kérdö Autonomic Index: Role of Initial Parameters, Areas, and Limitations of the Use

The Kerdo Autonomic Index (KAI) was developed by the Hungarian physician I. Kerdo and was used by Russian physiologists to assess the balance of tone between the sympathetic and parasympathetic nervous systems. KAI gives an indirect characteristic of the vagosympathetic balance in the body without revealing the mechanisms of this balance. It was found that in athletes and patients with hypertension, the correlation between KAI and heart rate (HR) is positive, and between KAI and diastolic blood pressure (BPd) negative, which confirms the ideas of I. Kerdo. A theoretical graphical analysis of the dependence of KAI on its constituent parameters showed that KAI has a decreasing rectilinear dependence on BPd and increasing inverted hyperbolic dependence on HR. KAI reflects vagosympathetic balance in the body to a greater extent in terms of HR and to a lesser extent in terms of BPd. KAI correctly characterizes the vagosympathetic balance in the body of athletes before and after physical activity. The use of KAI to assess vagosympathetic balance in patients with hypertension is not recommended. With high blood pressure in hypertensive patients, KAI values can lead to an erroneous conclusion about the vagosympathetic balance in the body.

Discrete-Fourier-Transform-Based Framework for Analysis and Synthesis of Cylindrical Omega -Bianisotropic Metasurfaces

This paper presents a framework for analyzing and designing cylindrical omega-bianisotropic metasurfaces, inspired by mode matching and digital signal processing techniques. Using the discrete Fourier transform, we decompose the the electromagnetic field distributions into orthogonal cylindrical modes and convert the azimuthally varying metasurface constituent parameters into their respective spectra. Then, by invoking appropriate boundary conditions, we set up systems of algebraic equations which can be rearranged to either predict the scattered fields of prespecified metasurfaces, or to synthesize metasurfaces which support arbitrarily stipulated field transformations. The proposed framework facilitates the efficient evaluation of field distributions that satisfy local power conservation, which is one of the key difficulties involved with the design of passive and lossless scalar metasurfaces. It represents a promising solution to circumvent the need for active components, controlled power dissipation, or tensorial surface polarizabilities in many state-of-the art conformal metasurface-based devices. To demonstrate the robustness and the versatility of the proposed technique, we design several devices intended for different applications and numerically verify them using finite element simulations.

Field measurements on alluvial watercourses in light of numerical modeling: case studies on the Danube River.

Adequate monitoring and data acquisition of proper hydraulic, sediment, and constituent parameters in alluvial watercourses have become crucial aspects of human interaction with the environment. Conducting well-organized, comprehensive, and meaningful field measurements on natural watercourses are of great importance when assessing its hydraulic, morphological, and ecological state. However, this paper presents a methodology for field measurements on alluvial watercourses in light of numerical modeling. The proposed methodology focuses on collecting field data sets to calibrate numerical models for flow, sediment, and heavy metal transport. The proposed approach targets the simultaneous measurement of hydraulic, sediment transport, and heavy metal transport parameters that are key for calibrating constants and exchange mechanisms in contemporary numerical models. Using the principles laid out in this paper, two sets of measurements were carried out on the Danube River, one on a reach near Mohács in Hungary and the other on a reach near Belgrade in Serbia. The first case study discusses the measurement and results of comprehensive hydraulic and sediment parameters. The second case study considers hydraulic and sediment measurements complemented with trace metal measurements for zinc, lead, and mercury. These measurements were used for calibrating numerical models for flow, sediment, and heavy metal transport, as a proof of concept. It has been demonstrated that the gathered data sets contain key parameters that are strongly linked through physical laws and are needed for calibration purposes, as well as parameters that can allow the newly calibrated coefficients to be confirmed through other measured phenomena. Therefore, the proposed methodology provides minimal data sets with detailed measurements for calibrating numerical models for flow, sediment, and heavy metal transport. Guidelines for future measurements that can suffice the increasing need for numerical modeling and monitoring of natural watercourses are also offered.

Constraint Deformation Behavior of Sand-EPS Beads Mixture Using Discrete Element Modeling (DEM)

Abstract Lightweight composite materials are increasingly used in geotechnical engineering projects. This paper presents experimental and numerical investigations on the compressibility characteristics and “at-rest” lateral earth pressure of the mixture of expanded polystyrene (EPS) beads and some virtual steel pellets that are completely rounded particulates. The mixtures were prepared by adding EPS beads to the steel pellets at 0, 0.1, 0.2, 0.3, 0.4, and 0.5 % by weight. The experiments were carried out using two types of oedometers: the standard Casagrande and a tall oedometer apparatus. The discrete element modeling (DEM), in turn, is conducted to simulate the compressibility characteristics and “at-rest” lateral earth pressure of the EPS composite particulates using the open-source framework YADE. To this end, the mechanical parameters of different constituents are calibrated based on the direct shear experiments on the pure steel and EPS beads. Comparison of the experimental and numerical results illustrates that the DEM model can properly predict the compression and lateral earth pressure behavior of the mixtures of EPS beads and sand.

An analytic model for effective mechanical properties and local contact stresses in lithium-ion porous electrodes

Explicit expressions for stress–strain relationships and swelling strains are derived for porous lithium-ion electrodes. Local contact stresses between neighboring electrode particles are also predicted. The analytical model is based on similar microstructural averaging techniques that previously have been applied to simulations of powder compaction. Both direct particle–particle and particle–binder–particlecontacts are considered. The model gives explicit dependencies of constituent material properties and parameters describing the microstructure geometry. Examples are presented for electrodes that have particle–particle contacts below the percolation limit. The prediction of the E-modulus shows good agreement with experimental results. Constrained swelling resulting from intercalation of lithium-ions has also been simulated. The resulting electrode stress state compares well with numerical predictions by the finite element and the discrete element method. Local particle–particle contact stresses of the order 1–6 GPa have as well been predicted. A simplified model using a model for rigid-plastic deformation of particles have shown that the stress–strain behavior during the first charging cycle may substantially differ from subsequent cycles, a phenomenon that also has been experimentally observed.

INFLUENCE OF BENZYLADENINE AND KRISTALON FERTILIZER TREATMENTS ON GROWTH AND CHEMICAL CONSTITUENTS OF LEMON CYPRESS (CUPRESSUS MACROCARPA L.) PLANT

A pot experimental trial was carried out during 2017 and 2018 seasons, to study the effect of 16 treatments, which were represented by the combinations between four concentrations of benzyladenine (BA), i.e. 0, 50, 75 and 100 ppm and kristalon fertilizer rates at 0, 3, 5 and 7 g/pot on the growth and chemical constituents of Cupressus macrocarpa plant. Results declared that all tested concentrations of BA increased plant height, No. of branches/plant, stem diameter, fresh, dry weights of shoots/plant, fresh, dry weights of roots/plant, as well as chemical constituents parameters i.e. N, P and K in the leaves, total carbohydrates, total chlorophylls and carotenoids contents, with an evident for the high rates of benzyladenine compared to untreated control plants in both seasons. On the other hand, all levels of kristalon chemical fertilizer significantly enhanced all the above-mentioned parameters, especially using the high level at 7 g/pot in both seasons. In addition, most of the applied combinations of benzyladenine and levels of kristalon fertilizer improved all the studied parameters in the two seasons, particularly using the combinations between BA at 100 ppm and kristalon fertilizer at 7 g/pot in both seasons. Conclusively, spraying Cupressus macrocarpa plant with benzyladenin at 100 ppm and supplemented with kristalon as chemical fertilizer at 7 g/pot produced the best growth and quality of Cupressus macrocarpa L. plant.

Predicting the mechanical properties of unidirectional fiber reinforced plastics from fiber and resin properties using data mining

Accurate prediction of unidirectional (UD) fiber reinforced plastics' (FRP) mechanical properties can significantly shorten the time from material design to structural application. Data mining, as a promising alternative approach to the traditional analytical micromechanics model and finite element model, can overcome their limitations of lack of certain constituent parameters and failure to differentiate processings. In order to establish a reliable predictive model for UD FRP properties based on fiber and resin properties by means of data mining, this paper created a mechanical property database of 203 kinds of FRPs and their constituents. Statistical tools were then applied to build and test structure-property correlation models. Results suggest that only longitudinal tensile modulus and longitudinal tensile strength can be credibly predicted, whereas the prediction of other six properties was quite unreliable or even infeasible. Overall, the study revealed that data mining, limited by incompleteness of data and complicated failure mechanisms, can only partially replace the actual testing of UD FRP properties.