Thermal conductivity modeling of nanofluids with ZnO particles by using approaches based on artificial neural network and MARS

Journal of Thermal Analysis and Calorimetry - Nanofluids are attractive alternatives for the current heat transfer fluids due to their remarkably higher thermal conductivity which leads to the...     

Thermal conductivity prediction of nanofluids containing CuO nanoparticles by using correlation and artificial neural network

Journal of Thermal Analysis and Calorimetry - Nanofluids are employed in different thermal devices due to their enhanced thermophysical features which lead to noticeable heat transfer augmentation....     

Performance evaluation of a U-shaped heat exchanger containing hybrid Cu/CNTs nanofluids: experimental data and modeling using regression and artificial neural network

Journal of Thermal Analysis and Calorimetry - In the current research, viscosity and thermal conductivity of hybrid Cu/CNTs water-based nanofluids were investigated at various concentrations of...     

Applying different types of artificial neural network for modeling thermal conductivity of nanofluids containing silica particles

Journal of Thermal Analysis and Calorimetry - Nanofluids are widely applicable in thermal devices with porous structures. Silica nanoparticles have been dispersed in different heat transfer fluids...     

Thermal resistance modeling of oscillating heat pipes filled with acetone by using artificial neural network

Journal of Thermal Analysis and Calorimetry - Oscillating heat pipes (OHPs) are applicable in different energy systems such as solar collectors, desalinations and fuel cells as thermal mediums or...     

Evaluation of a mathematical model using experimental data and artificial neural network for prediction of gas separation

In recent times, membranes have found wide applications in gas separation processes. As most of the industrial membrane separation units use hollow fiber modules, having a proper model for simulating this type of membrane module is very useful in achieving guidelines for design and characterization of membrane separation units. In this study, a model based on Coker, Freeman, and Fleming＇s study was used for estimating the required membrane area. This model could simulate a multicomponent gas mixture separation by solving the governing differential mass balance equations with numerical methods. Results of the model were validated using some binary and multicomponent experimental data from the literature. Also, the artificial neural network （ANN） technique was applied to predict membrane gas separation behavior and the results of the ANN simulation were compared with the simulation results of the model and the experimental data. Good consistency between these results shows that ANN method can be successfully used for prediction of the separation behavior after suitable training of the network     

QSPR modeling of decomposition temperature of energetic cocrystals using artificial neural network

Journal of Thermal Analysis and Calorimetry - The quantitative structure–property relationship for the decomposition temperature (Td) of energetic cocrystals was investigated. The artificial...     

Thermal conductivity of Al2O3/water nanofluids

A considerable number of studies can be found on the thermal conductivity of nanofluids in which Al₂O₃ nanoparticles are used as additives. In the present study, the aim is to measure the thermal conductivity of very narrow Al₂O₃ nanoparticles with the size of 5 nm suspended in water. The thermal conductivity of nanofluids with concentrations up to 5 % is measured in a temperature range between 26 and 55 °C. Using the experimental data, a correlation is presented as a function of the temperature and volume fraction of nanoparticles. Finally, a sensitivity analysis is performed to assess the sensitivity of thermal conductivity of nanofluids to increase the particle loading at different temperatures. The sensitivity analysis reveals that at a given concentration, the sensitivity of thermal conductivity to particle loading increases when the temperature increases.     

An artificial neural network approach for the prediction of dynamic viscosity of MXene-palm oil nanofluid using experimental data

Journal of Thermal Analysis and Calorimetry - Given the excellent thermal properties of MXene, MXene nanomaterials-based nanofluids may have the potential of being used as heat transfer fluids. In...     

Toward a modeling study of thermal conductivity of nanofluids using LSSVM strategy

Journal of Thermal Analysis and Calorimetry - In the present study, a comprehensive model based on least square support vector machine algorithm (LSSVM) was developed to estimate thermal...     

Characterization and modelling of density,thermal conductivity,and viscosity of TiN–W/EG nanofluids

Journal of Thermal Analysis and Calorimetry - Thermal conductivity, dynamic viscosity, and density of TiN nanofluids (NFs) with different base mediums have been characterized for the prospect of...     

Thermal conductivity and specific heat capacity measurements of CuO nanofluids

A study of thermal properties of CuO dispersed in water and ethylene glycol as a function of the particle volume fraction and at temperatures between 298 and 338 K has been performed. Thermal conductivities have been studied by the steady-state coaxial cylinders method, using a C80D microcalorimeter (Setaram, France) equipped with special calorimetric vessels. Heat capacities have been measured with a Micro DSC II microcalorimeter (Setaram, France) with batch cells designed in our laboratory and the “scanning or continuous method.” Results for thermal conductivities can be well justified using a classical model (Hamilton–Crosser), and experimental measurements of heat capacities can be justified with a model of particles in thermal equilibrium with the base fluid.     

Thermal conductivity of ethylene glycol-based nanofluid containing SiO2 nanoadditives: experimental data and modeling through curve fitting

Journal of Thermal Analysis and Calorimetry - The work focuses on an experimental evaluation of the changes of thermal conductivity (TC) of ethylene glycol-based nanofluid containing SiO2...     

Forecasting the thermal conductivity of a nanofluid using artificial neural networks

Journal of Thermal Analysis and Calorimetry - In this study, the influence of incorporating MWCNT on the thermal conductivity of paraffin was evaluated numerically. Input variables including mass...     

Selection of quasi-optimal inputs in chemometrics modeling by artificial neural network analysis

Instrumentation spectra used for chemometrics analysis are often too unwieldy to model, as many of the inputs do not contain important information. Several mathematical methods are used for reducing the number of inputs to the significant ones only. Artificial neural networks (ANN) modeling suffers from difficulties in training models with a large number of inputs. However, using a non-random initial connection weight algorithm and local minima avoidance and escape techniques can overcome these difficulties. Once the ANN model is trained, the analysis of its connection weights can easily identify the more relevant inputs. Repeating the process of training the ANN model with the reduced input set and the selection of the more relevant inputs can proceed until a quasi-optimal, small, set of inputs is identified. Two examples are presented—finding the minimal set of wavelengths in benchmark diesel fuel NIR spectra, and in spectra generated in a recent work, modeling of “artificial nose” sensor array. In the last example, 1260 inputs were reduced to optimal sets of <10 inputs. Causal index calculation can analyze the influence of each of selected wavelengths on the predicted property. Some of the resulting minimal sets are not unique, depending on the ANN architecture used in the training. The accuracy of the resulting ANN models is usually better, and more robust, than the original large ANN model.