An approach to predict the isobaric specific heat capacity of nitrides/ethylene glycol-based nanofluids using support vector regression

Challenges, limitations, and applications of nanofluids in solar thermal collectors-a comprehensive review

The daily increase in the demand for energy consumption is partly caused by the global population explosion and advancements in technology. Humanity relies on energy to fulfil its daily routines, such as electricity for lighting, heating, cooling, and running electronic devices. There are continuous attempts by researchers and industry experts to optimize and enhance the efficiency of various sustainable energy generation devices. Solar collectors play a critical role in the renewable energy sector, which is vital in helping the world achieve a clean, green, and sustainable environment. Over the last two decades, researchers have made significant efforts to explore various techniques for enhancing the effectiveness of solar thermal collectors. Their effort has been centered around improving the fluid thermal properties, which act as the heat transfer medium in solar collectors. The discovery of nanofluids will help resolve some of the challenges associated with conventional fluid used in solar collectors. Enhancement through nanofluids is influenced by several factors, which include nanoparticle types, nanoparticle concentration, base fluid, and the purpose of its application. This review provides a technical summary of the application of nanofluids in the two main types of collectors: non-concentrating and concentrated thermal collectors. Findings from this study showed that TiO2 + Cu hybrid nanofluids with a mass fraction of 0.03 augment heat transfer coefficient by 21% in parabolic trough collectors. The merits of employing nanofluids as heat transfer fluids in solar collectors are examined, while also outlining the obstacles and areas where further research is needed.

Thermophysical Properties of Hybrid Nanofluids and the Proposed Models: An Updated Comprehensive Study

Thermal performance of energy conversion systems is one of the most important goals to improve the system's efficiency. Such thermal performance is strongly dependent on the thermophysical features of the applied fluids used in energy conversion systems. Thermal conductivity, specific heat in addition to dynamic viscosity are the properties that dramatically affect heat transfer characteristics. These features of hybrid nanofluids, as promising heat transfer fluids, are influenced by different constituents, including volume fraction, size of solid parts and temperature. In this article, the mentioned features of the nanofluids with hybrid nanostructures and the proposed models for these properties are reviewed. It is concluded that the increase in the volume fraction of solids causes improvement in thermal conductivity and dynamic viscosity, while the trend of variations in the specific heat depends on the base fluid. In addition, the increase in temperature increases the thermal conductivity while it decreases the dynamic viscosity. Moreover, as stated by the reviewed works, different approaches have applicability for modeling these properties with high accuracy, while intelligent algorithms, including artificial neural networks, are able to reach a higher precision compared with the correlations. In addition to the used method, some other factors, such as the model architecture, influence the reliability and exactness of the proposed models.

Modeling and prediction of lattice parameters of binary spinel compounds (AM2X4) using support vector regression with Bayesian optimization

The lattice constants of spinel compounds AM2X4 are correlated with the constituent elemental properties using support vector regression (SVR) optimized with Bayesian optimization.

Modeling the viscosity of nanofluids using artificial neural network and Bayesian support vector regression

This study demonstrates the application of artificial neural networks (ANNs) and Bayesian support vector regression (BSVR) models for predicting the relative viscosity of nanofluids. The study examined 19 nanofluids comprising 1425 experimental datasets that were randomly split in a ratio of 70:30 as a training dataset and a testing dataset, respectively. To establish the inputs that will yield the best model prediction, we conducted a systematic analysis of the influence of volume fraction of nanoparticles, the density of nanoparticles, fluid temperature, size of nanoparticles, and viscosity of base fluids on the relative viscosity of the nanofluids. Also, we analyzed the results of all possible input combinations by developing 31 support vector regression models based on all possible input combinations. The results revealed that the exclusion of the viscosity of the base fluids (as a model input) leads to a significant improvement in the model result. To further validate our findings, we used the four inputs—volume fraction of nanoparticles, the density of nanoparticles, fluid temperature, and size of nanoparticles to build an ANN model. Based on the 428 testing datasets, the BSVR and ANN predicted the relative viscosity of nanofluids with an average absolute relative deviation of 3.22 and 6.64, respectively. This indicates that the BSVR model exhibits superior prediction results compared to the ANN model and existing empirical models. This study shows that the BSVR model is a reliable approach for the estimation of the viscosity of nanofluids. It also offers a generalization ability that is much better than ANN for predicting the relative viscosity of nanofluids.

Identification of host-guest systems in green TADF-based OLEDs with energy level matching based on a machine-learning study

Booming progress has been made in both the molecular design concept and the fundamental electroluminescence (EL) mechanism of thermally activated delayed fluorescence (TADF)-based organic light-emitting diodes (OLEDs) in recent years. One of the requirements for TADF-based OLEDs having high external quantum efficiency (EQE) is the favorable energy level alignment between the host and the guest to promote the energy transfer and improve the carrier balance. However, strategies to optimize the TADF-based OLED performance by selecting suitable host-guest systems in the light-emitting layer are far from enough. In this work, we investigated guest-host systems through the use of two machine-learning approaches (feature-based and similarity-based algorithms) from our recent effort for the optimization of TADF-based OLEDs. The Random Forest (RF) algorithm based on the features of electronic and photo-physical properties can accurately predict the EQE of green TADF-based OLEDs with average correlation coefficients of R2 = 0.85 for the training set and R2 = 0.74 for the testing set. Also, the Support Vector Regression (SVR) algorithm based on similarity metrics between pairs of materials (e.g., host and guest) in terms of electronic parameters can provide reasonable device performance prediction (R2 = 0.72) through the optimization procedure of the parameters. These results show that the predictive capability and model applicability of both machine-learning models can be used to identify suitable host-guest systems and explore complex relationships in green TADF-based OLEDs.