Coupling of BGK lattice Boltzmann method and experimental rheological/thermal behavior of Al2O3–oil nanolubricant for modeling of a finned thermal storage

Purpose The purpose of the present work is to investigate the hydrodynamic and thermal performance of a thermal storage based on the numerical and experimental approaches using the lattice Boltzmann method and the experimental observation on the thermo-physical properties of the operating fluid. Design/methodology/approach For this purpose, the Al2O3 nanoparticle is added to the lubricant with four nanoparticle concentrations, including 0.1, 0.2, 0.4 and 0.6Vol.%. After preparing the nanolubricant samples, the thermal conductivity and dynamic viscosity of nanolubricant are measured using thermal analyzer and viscometer, respectively. Finally, the extracted data are used in the numerical simulation using provided correlations. In the numerical process, the lattice Boltzmann equations based on Bhatnagar–Gross Krook model are used. Also, some modifications are applied to treat with the complex boundary conditions. In addition, the second law analysis is used based on the local and total views. Findings Different types of results are reported, including the flow structure, temperature distribution, contours of local entropy generation, value of average Nusselt number, value of entropy generation and value of Bejan number. Originality/value The originality of this work is combining a modern numerical methodology with experimental data to simulate the convective flow for an industrial application.

The approximate solution of nonlinear Fredholm implicit integro-differential equation in the complex plane

This paper’s purpose is to study the nonlinear Fredholm implicit integro-differential equation in the complex plane, where the term implicit integro-differential means that the derivative of unknown function is founded inside of the integral operator. Initially, according to Banach fixed point theory, we ensure that the equation has a unique solution under particular conditions. However, we exhibit a numerical process based on the conjunction between Nyström and Picard methods, for the sake of approximating solutions of this equation. In addition to that, the convergence analysis of this numerical process is demonstrated, and some illustrated numerical examples are presented.

Stacked sparse autoencoders that preserve the local and global feature structures for fault detection

Although the model based on an autoencoder (AE) exhibits strong feature extraction capability without data labeling, such model is less likely to consider the structural distribution of the original data and the extracted feature is uninterpretable. In this study, a new stacked sparse AE (SSAE) based on the preservation of local and global feature structures is proposed for fault detection. Two additional loss terms are included in the loss function of SSAE to retain the local and global structures of the original data. The preservation of the local feature considers the nearest neighbor of data in space, while that of the global feature considers the variance information of data. The final feature is not only a deep representation of data, but it also retains structural information as much as possible. The proposed model demonstrates remarkable detection performance in case studies of a numerical process and the Tennessee Eastman process.

Strengths of Struts and Nodal Zones for Strut-and-Tie Model Design of Reinforced Concrete Corbels

The strut-and-tie model (STM) method is useful for the limit state design of reinforced concrete (RC) corbels. However, for the rational design of RC corbels, designers must accurately determine the strengths of concrete struts and nodal zones to check the strength conditions of a selected STM and the anchorage of reinforcing bars in nodal zones. In this study, the authors suggested a numerical process for determining the strengths of concrete struts and nodal zones in RC corbel STMs. The technique incorporates the state of two-dimensional (2-D) stresses at the strut and nodal zone locations, 2-D failure envelope of concrete, deviation angle between the strut orientation and compressive principal stress trajectory, and the effect of concrete confinement by reinforcing bars. The authors also proposed the strength equations of struts and nodal zones that apply to the typical determinate and indeterminate STMs of RC corbels. The authors considered the effects of the shear span-to-effective depth ratio, the horizontal-to-vertical load ratio, and the primary tensile and horizontal shear reinforcement ratios in developing the strength equations. The authors predicted the failure strengths of 391 RC corbels tested to examine the appropriateness of the proposed numerical process and strength equations. The predicted failure strength compares very well with experimental results, proving that the rational analysis and design of RC corbels are possible by using the present study's strut and nodal zone strengths. Doi: 10.28991/cej-2021-03091725 Full Text: PDF

Grey Box Modelling of Decanter Centrifuges by Coupling a Numerical Process Model with a Neural Network

Continuously operating decanter centrifuges are often applied for solid-liquid separation in the chemical and mining industries. Simulation tools can assist in the configuration and optimisation of separation processes by, e.g., controlling the quality characteristics of the product. Increasing computation power has led to a renewed interest in hybrid models (subsequently named grey box model), which combine parametric and non-paramteric models. In this article, a grey box model for the simulation of the mechanical dewatering of a finely dispersed product in decanter centrifuges is discussed. Here, the grey box model consists of a mechanistic model (as white box model) presented in a previous research article and a neural network (as black box model). Experimentally determined data is used to train the neural network in the area of application. The mechanistic approach considers the settling behaviour, the sediment consolidation, and the sediment transport. In conclusion, the settings of the neural network and the results of the grey box model and white box model are compared and discussed. Now, the overall grey box model is able to increase the accuracy of the simulation and physical effects that are not modelled yet are integrated by training of a neural network using experimental data.

Thermal performance of parabolic-trough solar collector using double-population LBM with single-node/curved scheme and experimental evaluation on properties of SiO2-TiO2/EG nanofluid

Purpose This paper aims to analyze the effect of absorber’s geometry and operating fluid on the thermal and hydrodynamic behaviors of a solar collector. Two different profiles are proposed for the absorber which is wavy and flat. Also, the inner tube of HTF (i.e. heat transfer fluid) is considered as single and double. The solar collector is filled with hybrid nanofluid of SiO2-TiO2/ ethylene glycol (EG) which its thermal conductivity and dynamic viscosity are measured using KD2 Pro and Brookfield LVDV III Ultra; respectively, in the temperature range of 30°C to 80°C and nanoparticle concentration in the range of 1.5% to 3.5%. Design/methodology/approach Among the solar collector, the parabolic-trough solar collector is one of the most efficient models for extracting solar thermal power. A parabolic trough solar collector with two different models of absorbers and included with two models of inner HTF tube is proposed. Findings The corresponding regression equations are derived versus temperature and volume fraction and used in the numerical process. For the numerical process, the thermal lattice Boltzmann method manipulated with a single-node curved scheme is used. Also, in the final step, the second law analysis is carried out in local and volumetric forms. The influential factors are Rayleigh number, the concentration of hybrid nano-powder and the structure of absorber profile. Originality/value The originality of the present work is combining a modern numerical method (i.e. double-population lattice Boltzmann method) with experimental observation on characteristics of SiO2-TiO2/EG nanofluid to analyze the thermal performance of parabolic trough solar collector.

Exploring the most stable aptamer/target molecule complex by the stochastic tunnelling-basin hopping-discrete molecular dynamics method

The stochastic tunnelling-basin hopping-discrete molecular dynamics (STUN-BH-DMD) method was applied to the search for the most stable biomolecular complexes in water by using the MARTINI coarse-grained (CG) model. The epithelial cell adhesion molecule (EpCAM, PDB code: 4MZV) was used as an EpCAM adaptor for an EpA (AptEpA) benchmark target molecule. The effects of two adsorption positions on the EpCAM were analysed, and it is found that the AptEpA adsorption configuration located within the EpCAM pocket-like structure is more stable and the energy barrier is lower due to the interaction with water. By the root mean square deviation (RMSD), the configuration of EpCAM in water is more conservative when the AptEpA binds to EpCAM by attaching to the pocket space of the EpCAM dimer. For AptEpA, the root mean square fluctuation (RMSF) analysis result indicates Nucleobase 1 and Nucleobase 2 display higher flexibility during the CGMD simulation. Finally, from the binding energy contour maps and histogram plots of EpCAM and each AptEpA nucleobase, it is clear that the binding energy adsorbed to the pocket-like structure is more continuous than that energy not adsorbed to the pocket-like structure. This study has proposed a new numerical process for applying the STUN-BH-DMD with the CG model, which can reduce computational details and directly find a more stable AptEpA/EpCAM complex in water.

Dropstone deposition: Results of numerical process modeling of deformation structures, and implications for the reconstruction of the water depth in shallow lacustrine and marine successions

ABSTRACT Dropstones in lacustrine and marine sediments show a wide range of sizes: from less than a millimeter to many meters. Their size and shape determine the velocity and the acceleration when they settle through the water column, and this, in turn, determines in principle the imprint that they make in the bottom sediment. Although these parameters are crucial for dropstone deposition, the unknown material (sediment) properties (like strength, porosity, pore-water content, viscosity, etc.) of the bottom sediment play a just as important role in this process as the water depth, which can physically be understood as the length of the pathway traveled vertically through a dense medium before the impact. Reconstruction of the principal environmental conditions at the time of dropstone fall and deposition consequently requires considering the variety of factors affecting the final imprint depth of a dropstone, the combination of several numerical methods. Here, we show the results of numerical modeling of dropstones with different sizes that settle through water columns with different depths. Our results show how environmental factors control the deformation structures formed at the sedimentary surface during the impact of a dropstone, and how deep the imprint caused by the settling dropstone will be.