Prediction Of Thermal Expansion Coefficients Research Articles

Prediction method of thermal expansion coefficient for 2D composite fabric

In this article, an analytical method for predicting the thermal expansion coefficient of 2D composite fabrics is established. First of all, different from the traditional prediction of thermal expansion coefficient by classical laminate theory, this paper extends the self-consistent method of micromechanics to 2D composite fabrics to improve the accuracy of thickness direction prediction. Analytical Prediction method of Thermal expansion coefficient of 2D Composite fabric (APTC)was established. In the x section of Representative Volume Element(RVE) model, the stress balance equation and deformation coordination equation are applied to derive the internal thermal expansion coefficient. In the z section, the coefficient of thermal expansion is derived from the Poisson effect. Secondly, the formula of thermal expansion coefficient is derived for plain fabric, twill fabric and satin fabric respectively, which is consistent with the APTC method, indicating that the method is representative. Finally, RVE finite element model is established according to the braided structure parameters of 5284/T800 composite fabric. Comparing analytical solution, numerical solution and experimental solution, the α 1 and α 2 of the numerical and experimental solutions is close enough to be considered equal, which is consistent with the transverse isotropy of the composite fabric in xOy plane predicted by theoretical analysis.When predicting α 1 and α 2 , the maximum error between numerical solution and analytical solution is 1.79%, the maximum error between numerical solution and experimental solution is 22.29%, and the error remains at about 4% during predicting α 3 . In general, the APTC method for predicting the thermal expansion coefficient of composite fabrics is accurate and effective, and the simplified formula is more convenient to popularize and apply.

High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides

Recent advances in machine learning and the expanding availability of materials data have enabled significant developments in materials science. In this study, novel configurations of high-entropy ceramic (HEC) materials were explored by predicting their coefficient of thermal expansion (CTE) using machine learning (ML) and high-throughput screening. A machine learning model was built using 3360 datasets containing the thermodynamic, elastic, and thermophysical properties of HEC with carbide configurations of (Ti0.2Ta0.2X0.2Y0.2Z0.2)C. The high correlation of the bulk and Young's moduli, and cohesive energy features with the CTE facilitated its prediction. The random forest (RF) and neural network (NET)-based models successfully reproduced the CTE reported in existing experimental and theoretical studies. Overall, first-principles calculation was implemented to configure a database for HEC and a new ML application method is proposed.

Prediction of thermal expansion coefficient of fiber-reinforced cement concrete based on micromechanics method

Prediction of thermal expansion coefficient of fiber-reinforced cement concrete based on micromechanics method

Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation.

Perovskite oxides have been of high-interest and relatively well studied over the last 20 years due to their various applications, specifically for solid oxide fuel cells (SOFCs) and solid oxide electrolysis cells (SOECs). One of the key properties for a perovskite to perform well as a component in SOFCs, SOECs, and other high-temperature applications is its thermal expansion coefficient (TEC). The use of machine learning (ML) to predict material properties has greatly increased over the years and has proven to be a very useful tool for materials screening. The process of synthesizing and testing perovskite oxides is laborious and costly, and the use of physics-based models is often highly computationally expensive. Due to the amount of elements able to be accommodated in the ABO3 structure and the ability for crystallographic mixing in both the A and B-sites, there are a massive amount of possible ABO3 perovskites. In this paper, a ML model for the prediction of the TECs of AA'BB'O3 perovskites is produced and applied to millions of potential compositions resulting in reliable TEC predictions for 150 451 of the compositions.

Prediction of Thermal Expansion Coefficients using the Second Phase Fraction and Void of Al-AlN Composites Manufactured by Gas Reaction Method

Prediction of Thermal Expansion Coefficients using the Second Phase Fraction and Void of Al-AlN Composites Manufactured by Gas Reaction Method

A Prediction of Thermal Expansion Coefficient for Compacted Bentonite Buffer Materials

A Prediction of Thermal Expansion Coefficient for Compacted Bentonite Buffer Materials

On the suitability of the volume-averaging approximation for the description of thermal expansion coefficient of nanofluids

Currently, volume-averaging approximation is in common use for the description of thermal expansion coefficient of nanofluids in terms of expansion coefficients of their constituents. The accuracy of this method is not, however, so clear since it ignores the dependence of density on temperature in the prediction of thermal expansion coefficient that may not be true in natural convection circumstances. In the current contribution, attention is focused to clarify how predictions of flow and thermal fields as well as heat transfer and entropy generation characteristics during natural convection of nanofluids may be influenced if one adopts the volume-averaging approximation for the description of thermal expansion coefficient. For this purpose, a porous enclosure saturated with several water-based nanofluids is simulated and results of the volume-averaged thermal expansion coefficient are compared with those of a recent correlation that takes into account the dependence of density on temperature.

Microstructure Modeling for Prediction of Thermal Expansion Coefficient of Plain Weave C/SiC Considering Manufacturing Porosities

The aim of this paper is to propose a microstructure modeling for prediction of thermal conductivity of plain weave C/SiC fibre bundles considering manufacturing flaws. Utilizing photomicrographs taken by scanning electron microscope (SEM), we established an accurate sub representative volume element (sub-RVE) model for carbon fiber bundles and RVE for the plain weave C/SiC composite with consideration of four classes of manufacturing porosity. The thermal expansion coefficient of carbon fibre bundles on axial and transverse coefficient of thermal expansion is calculated, respectively. Based on which thermal expansion coefficient of plain weave C/SiC is obtained with the value of 2.71×10-6 in-plain, which has a good correlation with experimental value. The influences of different manufacturing flaws on material’s thermal expansion coefficient are studied. The study shows that as the matrix porosity or crack volume fraction is increasing, thermal expansion coefficient of plain weave C/SiC is decreasing correspondingly while the speed gradually slows.

Prediction of thermal expansion coefficients of plain weave fabric composites

Three plain weave fabric composite analysis models are presented for the prediction of the on-axes thermal expansion coefficients. These are two-dimensional models in the sense that the actual strand cross-sectional geometry, strand undulation and the presence of a gap between the adjacent strands are taken into account. In the first two models, termed refined models, the representative unit cell is discretised into slices and elements and analysed. In the third method, a closed-form solution is presented. In this case, the representative unit cell is idealised as a crossply laminate and analysed. The relative merits and demerits of the models are also discussed. The predicted results are compared with the experimental values. A good correlation is observed.

Prediction of thermal expansion coefficients of sandwiches using finite elements methods validated by experimental test results

The new generation of telecommunication antennas for space applications working at frequencies above 20 GHz (which will be generally manufactured by sandwich structures) needs an accurate thermostructural design due to the envisaged increase of the dimensions of the reflectors and the related decrease of the required RMS thermal distortion. In an integrated design which takes into account the reciprocal influence of thermal and structural aspects, the thermal expansion coefficient (C.T.E.) of the structures which are part of the reflector assembly, is a very important parameter. A good prediction of this value is a contribution to an accurate estimate of the electrical performance of the antenna system in the space environment. This work describes a prediction method to evaluate the C.T.E. of a sandwich validated by some experimental results and checked by an analytical study. Numerical results illustrate the procedures; graphs and appendices complete the work.