Transverse effective thermal conductivity of the random unidirectional fibre-reinforced composite was studied. The geometry was circular with random patterns formed using random sequential addition method. Composite geometries for different volume fraction and fibre radii were generated and their effective thermal conductivities (ETC) were calculated. Influence of fibre-matrix conductivity ratio on composite ETC was investigated for high and low values. Patterns were described by a set of coordination numbers (CN) and correlations between ETC and CN were constructed. The correlations were compared with available formulae presented in literature. Additionally, symmetry of the conductivity tensor for the studied geometries of fibres was analysed.
An emerging ultrasonic technology aims to control high-pressure industrial processes that use liquids at pressures up to 800 MPa. To control these processes it is necessary to know precisely physicochemical properties of the processed liquid (e.g., Camelina sativa oil) in the high-pressure range. In recent years, Camelina sativa oil gained a significant interest in food and biofuel industries. Unfortunately, only a very few data characterizing the high-pressure behavior of Camelina sativa oil is available. The aim of this paper is to investigate high pressure physicochemical properties of liquids on the example of Camelina sativa oil, using efficient ultrasonic techniques, i.e., speed of sound measurements supported by parallel measurements of density. It is worth noting that conventional low-pressure methods of measuring physicochemical properties of liquids fail at high pressures. The time of flight (TOF) between the two selected ultrasonic impulses was evaluated with a cross-correlation method. TOF measurements enabled for determination of the speed of sound with very high precision (of the order of picoseconds). Ultrasonic velocity and density measurements were performed for pressures 0.1–660 MPa, and temperatures 3–30XC. Isotherms of acoustic impedance Za, surface tension #27; and thermal conductivity k were subsequently evaluated. These physicochemical parameters of Camelina sativa oil are mainly influenced by changes in the pressure p, i.e., they increase about two times when the pressure increases from atmospheric pressure (0.1 MPa) to 660 MPa at 30XC. The results obtained in this study are novel and can be applied in food, and chemical industries.
The aim of this work is the development of Cu-Al2O3 composites of copper Cu-ETP matrix composite materials reinforced by 20 and 30 vol.% Al2O3 particles and study of some chosen physical properties. Squeeze casting technique of porous compacts with liquid copper was applied at the pressure of 110 MPa. Introduction of alumina particles into copper matrix affected on the significant increase of hardness and in the case of Cu-30 vol. % of alumina particles to 128 HBW. Electrical resistivity was strongly affected by the ceramic alumina particles and addition of 20 vol. % of particles caused diminishing of electrical conductivity to 20 S/m (34.5% IACS). Thermal conductivity tests were performed applying two methods and it was ascertained that this parameter strongly depends on the ceramic particles content, diminishing it to 100 Wm-1K-1 for the composite material containing 30 vol.% of ceramic particles comparing to 400 Wm-1K-1 for the unreinforced copper. Microstructural analysis was carried out using SEM microscopy and indicates that Al2O3 particles are homogeneously distributed in the copper matrix. EDS analysis shows remains of silicon on the surface of ceramic particles after binding agent used during preparation of ceramic preforms.
The article presents the prototype of a measurement system with a hot probe, designed for testing thermal parameters of heat insulation materials. The idea is to determine parameters of thermal insulation materials using a hot probe with an auxiliary thermometer and a trained artificial neural network. The network is trained on data extracted from a nonstationary two-dimensional model of heat conduction inside a sample of material with the hot probe and the auxiliary thermometer. The significant heat capacity of the probe handle is taken into account in the model. The finite element method (FEM) is applied to solve the system of partial differential equations describing the model. An artificial neural network (ANN) is used to estimate coefficients of the inverse heat conduction problem for a solid. The network determines values of the effective thermal conductivity and effective thermal diffusivity on the basis of temperature responses of the hot probe and the auxiliary thermometer. All calculations, like FEM, training and testing processes, were conducted in the MATLAB environment. Experimental results are also presented. The proposed measurement system for parameter testing is suitable for temporary measurements in a building site or factory.
Main goal of the paper is to present the algorithm serving to solve the heat conduction inverse problem. Authors consider the heat conduction equation with the Riemann-Liouville fractional derivative and with the second and third kind boundary conditions. This type of model with fractional derivative can be used for modelling the heat conduction in porous media. Authors deal with the heat conduction inverse problem, which, in this case, consists in identifying an unknown thermal conductivity coefficient. Measurements of temperature, in selected point of the region, are the input data for investigated inverse problem. Basing on this information, a functional describing the error of approximate solution is created. Minimizing of this functional is necessary to solve the inverse problem. In the presented approach the Ant Colony Optimization (ACO) algorithm is used for minimization.
In the present article, we introduced a new model of the equations of general ized thermoelasticity for unbounded orthotropic body containing a cylindrical cavity. We applied this model in the context of generalized thermoelasticity with phase-lags under the effect of rotation. In this case, the thermal conductivity of the material is considered to be variable. In addition, the cylinder surface is traction free and subjected to a uniform unit step temperature. Using the Laplace transform technique, the distributions of the temperature, displacement, radial stress and hoop stress are determined. A detailed analysis of the effects of rotation, phase-lags and the variability thermal conductivity parameters on the studied fields is discussed. Numerical results for the studied fields are illustrated graphically in the presence and absence of rotation.
Heat and mass transfer stretched flow of an incompressible, electrically conducting Jeffrey fluid has been studied numerically. Nanoparticles are suspended in the base fluid and it has many applications such as cooling of engines, thermal absorption systems, lubricants fuel cell, nanodrug delivery system and so on. Temperature dependent variable thermal conductivity with Rosseland approximation is taken into account and suction effect is employed in the boundary conditions. The governing partial differential equations are first transformed into set of ordinary differential equations using selected similarity transformations, which are then solved numerically using Runge-Kutta-Felhberg fourth-fifth order method along with shooting technique. The flow, heat and mass transfer characteristics with local Nusselt number for various physical parameters are presented graphically and a detailed discussion regarding the effect of flow parameters on velocity and temperature profiles are provided. It is found that, increase of variable thermal conductivity, radiation, Brownian motion and thermophoresis parameter increases the rate of heat transfer. Local Nusselt number has been computed for various parameters and it is observed that, in the presence of variable thermal conductivity and Rosseland approximation, heat transfer characteristics are higher as compared to the constant thermal conductivity and linear thermal radiation.