Computational Fluid Dynamics (CFD) frameworks of supercritical cryogenic fluids need to employ Real Fluid models such as cubic Equations of State (EoS) to account for thermal and inertial driven mechanisms of fluid evolution and disintegration. Accurate estimation of the non-linear variation in density, thermodynamic and transport properties is required to computationally replicate the relevant thermo and fluid dynamics involved. This article reviews the availability, performance and the implementation of common Real Fluid EoS and data-based models in CFD studies of supercritical cryogenic fluids. A systematic analysis of supercritical cryogenic fluid (N2, O2 and CH4) thermophysical property predictions by cubic (PR and SRK) and non-cubic (SBWR) Real Fluid EoS, along with Chung’s model, reveal that: (a) SRK EoS is much more accurate than PR at low temperatures of liquid phase, whereas PR is more accurate at the pseudoboiling region and (b) SBWR EoS is more accurate than PR and SRK desp... [more]

In this paper, the applicability of advanced heat transfer enhancement technology to a paddle dryer was discussed. A computational fluid dynamics (CFD) method was used to simulate condensation heat transfer on the inner surface of a dryer paddle. The effect of surface wettability and rotation on condensation heat transfer and droplet behavior was studied. The results showed that the present CFD model could properly simulate the condensation process on a vertical surface. With a decrease in the contact angle, the filmwise condensation turned into a dropwise condensation, which resulted in a significant increase in heat transfer coefficient and provided an approximately 5% increase in evaporation rate for the paddle dryer by changing the wettability of the inner surface of the paddle. Additionally, with a change in rotational angular velocity, heat transfer performance was almost unchanged under the filmwise condensation condition. However, rotational motion might cause a decrease in wal... [more]

The efficiency of rim-driven thrusters (RDT) has always been the focus of attention in the context of energy conservation and environmental protection. A multi-parameter collaborative optimization framework is proposed to improve the efficiency of RDT based on the response surface method (RSM). The common structural parameters of RDT, including pitch ratio, disk ratio and rake angle, are selected as design variables to carry out the Box−Behnken experimental design combined with the simulation data obtained through CFD calculations. The response surface second-order model is employed to evaluate the extent to which different parameters can affect the target variable and obtain the optimal hydraulic efficiency. The results show that the established model has high precision, good reproducibility and strong anti-interference ability. The influence of the pitch ratio, rake angle and disk ratio on open water efficiency decreases in sequence. Compared with the prototype RDT, the maximum effic... [more]

Shell and tube heat exchangers are used to transfer thermal energy from one medium to another for regulating fluid temperatures in the processing and pasteurizing industries. Enhancement of a heat transfer rate is desired to maximize the energy efficiency of the shell and tube heat exchangers. In this research work, we performed computational fluid dynamics (CFD) simulations and experimental analysis on the shell and tube heat exchangers using round and hexagonal tubes for a range of flow velocities using both parallel flow and counter flow arrangements. In the present work, the rate of heat transfer, temperature drop, and heat transfer coefficient are computed using three turbulence models: the Spalart−Allmaras, the k-epsilon (RNG), and the k-omega shear stress transport (SST). We further utilized the logarithmic mean temperature difference (LMTD) method to compute the heat transfer and mass flow rates for both parallel and counter flow arrangements. Our results show that the rate of... [more]

A hybrid feature selection (HFS) algorithm to obtain the optimal feature set to attain optimal forecast accuracy for short-term load forecasting (STLF) problems is proposed in this paper. The HFS employs an elitist genetic algorithm (EGA) and random forest method, which is embedded in the load forecasting algorithm for online feature selection (FS). Using selected features, the performance of the forecaster was tested to signify the utility of the proposed methodology. For this, a day-ahead STLF using the M5P forecaster (a comprehensive forecasting approach using the regression tree concept) was implemented with FS and without FS (WoFS). The performance of the proposed forecaster (with FS and WoFS) was compared with the forecasters based on J48 and Bagging. The simulation was carried out in MATLAB and WEKA software. Through analyzing short-term load forecasts for the Australian electricity markets, evaluation of the proposed approach indicates that the input feature selected by the HFS... [more]

In most cases, internal insulation is the only solution to improve the energy efficiency of historic buildings. However, it is one of the most challenging and complex energy efficiency measures due to changes in boundary conditions and hygrothermal behavior of the wall, particularly in cold climates. This study presents the long-term monitoring of the hygrothermal performance of an internally insulated historic stone wall building. The study aimed to assess the hygrothermal behavior of the dolomite wall if mineral wool insulation is applied internally on the north-east wall in the rooms with and without high internal moisture load. The measurements included temperature, relative humidity, water content, and heat flux. Monitoring results are compared with 1D hygrothermal simulations and a building energy consumption simulation. The in situ measurement results and hygrothermal assessment shows energy consumption decreased by 55% with relative humidity under the insulation staying belove... [more]

Computer modelling and digitalization are integral to the wind energy sector since they provide tools with which to improve the design and performance of wind turbines, and thus reduce both capital and operational costs. The massive sensor rollout and increase in big data processing capacity over the last decade has made data collection and analysis more efficient, allowing for the development and use of digital twins. This paper presents a methodology for developing a hybrid-model-based digital twin (DT) of a power conversion system of wind turbines. This DT allows knowledge to be acquired from real operation data while preserving physical design relationships, can generate synthetic data from events that never happened, and helps in the detection and classification of different failure conditions. Starting from an initial physics-based model of a wind turbine drivetrain, which is trained with real data, the proposed methodology has two major innovative outcomes. The first innovation... [more]

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such a formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving significant speed up.

The aim of the paper is to analyze the effectiveness of smoke exhaust ventilation for the protection of metro technical personnel. Therefore, the specified technological corridor as a part of the underground station was chosen. The Fire Dynamics Simulator (FDS) was used to carry out numerical simulations. Due to the low fire hazard, the heat release rate (HRR) was set at 1 MW after 250 s. Four cases were analyzed: three differing in the location of the fire source and a reference case in which the smoke exhaust ventilation is turned off. The analysis took into account temperature distributions and gas flow speeds, and qualitative verification of visibility. It was shown that the variant in which the fire source was located in the middle of the corridor turned out to be the most unfavorable variant in terms of the effectiveness of smoke exhaust ventilation. The operation of the smoke exhaust ventilation improved visibility, and reduced the temperature from 270 °C to 120−155 °C, dependin... [more]

The cooling circuit is an important component of the magnetic drive pump because it prevents demagnetization of the permanent magnet and damage to the containment shell owing to a high temperature increase. In this paper, the flow field and losses of the cooling circuit of the magnetic pump are discussed and experimentally verified based on numerical simulation methods. Five different lengths of magnetic couplings were designed, and the flow field distribution, cooling flow rate, and loss variation laws of the cooling circuit were analyzed. The results show that the pump flow rate and magnetic coupling length have a minimal effect on the velocity distribution in the cooling circuit. When the magnet length increases from 30 mm to 55 mm, the temperature rise of the cooling circuit and the pressure drop at the gap increase by 23.1% and 25.3%, respectively. When the length of the magnetic coupling remains constant, the cooling flow rate of the cooling circuit falls with an increasing pump... [more]

Due to micro landforms and climate, the 110 kV transmission lines crossing the mountain areas are exposed to severe icing conditions for both their high voltage (HV) conductors and shield wires during the winter. Ice accumulation on the shield wire causes excessive sag, which leads to a reduced clearance between earth and HV wires, and could eventually result in tripping of the line due to phase-to-ground flashover. Due to the lack of effective de-icing techniques for the shield wires, removing them completely from the existing overhead line (OHL) structure becomes a reasonable solution to prevent icing accidents. Nevertheless, the risk of exposure to lightning strikes increased significantly after the shield wires were removed. In order to cope with this, the anti-thunder and anti-icing composite insulator (AACI) is installed on the OHLs. In this article, the 110 kV transmission line without shield wire is considered. The shielding failure after installation of the AACIs is studied us... [more]

Current-limiting reactors are widely used in industrial electrical grids to reduce the current amplitude in the equipment and stabilize the voltage on the busbar during short circuits. Their application is distinguished by high technical and economic efficiency. However, mechanical damage to the reactors has been observed within extensive industrial grids with many induction motors and internal synchronous generators. The article analyses a case study of the reactor damage in the true industrial grid during a short circuit. An analysis of the damaged reactor’s previous operation had shown that there was a weakening of the fastenings in the reactor design, caused by the repeated starting currents of the grid motors and generators. A study of grid transients during short circuits was carried out by Matlab/Simulink software. The simulation results showed that the reactor could be damaged by a critical peak current in an unfavourable combination of the grid configuration and the short circ... [more]

The time-varying, dynamic, nonlinear, and other characteristics of lithium-ion batteries, as well as the capacity regeneration phenomenon, leads to the low accuracy of the traditional deep learning models in predicting the remaining useful life of lithium-ion batteries. This paper established a sequence-to-sequence model for remaining useful life prediction by combining the variational modal decomposition with bi-directional long short-term memory and Bayesian hyperparametric optimization. First, variational modal decomposition is used for noise reduction processing to maximize the retention of the original information of capacity degradation. Second, the capacity declining trend after noise reduction is modeled and predicted by the combination of bi-directional long-short term memory and temporal attention mechanism. In addition, a Bayesian optimizer is used to adaptively adjust the hyperparameters while training the model. Finally, the model was validated on NASA and CALCE data sets,... [more]

The natural gas quality fluctuates in complex natural gas pipeline networks, because of the influence of the pipeline transmission process, changes in the gas source, and fluctuations in customer demand in the mixing process. Based on the dynamic characteristics of the system with large time lag and non−linearity, this article establishes a deep−learning−based dynamic prediction model for calorific value in natural gas pipeline networks, which is used to accurately and efficiently analyze the dynamic changes of calorific value in pipeline networks caused by non−stationary processes. Numerical experiment results show that the deep−learning model can effectively extract the effects of non−stationary and large time lag hydraulic characteristics on natural gas calorific value distribution. The method is able to rapidly predict the dynamic changes of gas calorific value in the pipeline network, based on real−time operational data such as pressure, flow rate, and gas quality parameters. It h... [more]

The Société Nationale des Chemins de fer Français (SNCF) is facing a significant challenge to meet the growth in rail traffic while maintaining continuous service, particularly in densely populated areas such as Paris. To tackle this challenge, the SNCF has implemented several electrification projects. These projects aim to reduce line losses and decrease voltage drops on the railway network. Amongst the possible technological choices, high temperature superconductor (HTS) cables have been evaluated, since they offer greater energy density at lower electrical losses than conventional cables. This feature is advantageous in order to transmit more electrical energy at a lesser footprint than conventional cable, therefore avoiding costly modifications of the existing infrastructures. In the present work, the electromagnetic response of two HTS cables topologies, unipolar and bipolar, was analyzed, and their impact on a direct current (DC) railway network under load was assessed. A commerc... [more]

Fractured reservoirs are highly heterogeneous in both matrix and fracture properties, which results in significant variations in well production. Assessing and quantifying the influence of fractures on fluid flow is essential for developing unconventional reservoirs. The complicated effects of fractures in unconventional fractured reservoirs on fluid flow highly depend on fracture geometry, fracture distribution, and fracture properties, which can be reflected in pressure transient testing. The biggest challenge lies in delineating the pre-existing natural fracture distribution pattern, density, azimuth, and connectivity. Using the advanced finite element method, this paper builds a finely characterized near-wellbore model to numerically simulate the pressure transient testing process in naturally fractured reservoirs and further evaluates fracture-related effects to obtain a more accurate solution. First, the numerical program is benchmarked by the analytical solutions and numerical r... [more]

In this study, calibrated hot box and finite element simulation methods were used to study the influence of a slotted web on the thermal performance of a lightweight steel stud composite wall. By comparing the results from the simulations and experiments, the accuracy of the finite element method was verified; this method was then used for parameter analyses. The results showed that the wall’s thermal transfer coefficient is inversely proportional to increases in the length of the slot and height of the stud web, leading to improvements in the thermal insulation effect; vice versa, the wall thermal transfer coefficient increases when the slot transverse spacing and stud thickness increase, and the insulation effect correspondingly worsens. The stud spacing influences the insulation performance of the wall by changing the proportion of studs within a certain wall. The greater the proportion of studs, the greater the stud thermal bridging, the faster the thermal loss, and the worse the i... [more]

The effect of gathering pipeline pressure, temperature, and key components on the solubility of monomeric sulfur in high-sulfur-content natural gas is directly related to the prediction and prevention of sulfur deposition in surface gathering pipelines. Based on our previous study on a prediction model of sulfur solubility in gas with a new formula for the binary interaction coefficient between sulfur and H2S, a new gas−solid thermodynamic phase equilibrium solubility prediction model for monomeric sulfur in high-sulfur-content natural gas was improved based on the gas−solid phase equilibrium principle considering both physical and chemical solution mechanisms. Two new expressions for binary interaction coefficients between sulfur and CO2 and CH4, considering both temperature and solvent density, are proposed in this new solubility prediction model. In this paper, the main factors, such as the gathering pipeline pressure, gathering pipeline temperature, H2S, and CO2, affecting the solu... [more]

The need for a greener and more sustainable energy system evokes a need for more extensive energy system transition research. The penetration of distributed energy resources and Internet of Things technologies facilitate energy system transition towards the next generation of energy system concepts. The next generation of energy system concepts include “integrated energy system”, “multi-energy system”, or “smart energy system”. These concepts reveal that future energy systems can integrate multiple energy carriers with autonomous intelligent decision making. There are noticeable trends in using the agent-based method in research of energy systems, including multi-energy system transition simulation with agent-based modeling (ABM) and multi-energy system management with multi-agent system (MAS) modeling. The need for a comprehensive review of the applications of the agent-based method motivates this review article. Thus, this article aims to systematically review the ABM and MAS applica... [more]

Compared with film condensation, dropwise condensation based on droplet growth can significantly improve the condensing equipment’s water collection and thermal efficiency in the vapor condensate system. Therefore, as a critical behavior affecting the evolution of dropwise condensation, research on droplet growth is of great significance to further understanding the evolutionary characteristics and heat transfer mechanism of dropwise condensation. In this paper, a model for simulating the entire evolution process of dropwise condensation is improved and constructed, and the evolution process of dropwise condensation with different condensation nucleus densities on the vertical wall is simulated based on certain assumptions. Moreover, parameters such as evolution rate and size contribution are proposed to measure droplet growth’s influence on the evolution process of dropwise condensation. In the simulation, the Cassie model was used to describe the condensation growth of droplets. The... [more]

Lightning represents one of the most critical issues for electrical infrastructure. In dealing with overhead distribution line systems, indirect lightning strikes can lead to induced voltages overcoming the critical flashover value of the line, thus damaging the insulators. The computation of lightning-induced voltages requires the modeling of the lightning current, the evaluation of the lightning electromagnetic fields and the solution of the field-to-line coupling equations. The numerical calculation of the lightning electromagnetic fields is time-consuming and is strongly dependent on the lightning channel modeling and soil properties. This article presents a review of the most widely adopted methods to calculate the lightning electromagnetic fields, starting from the classical formulation, which requires numerical integration, and highlighting the most effective approaches that have been developed to reduce computational effort. This is done first for the case of a perfectly conduc... [more]

Most of the practical inductive power transfer (IPT) systems are the ones with variable coupling coefficients and loads. The output voltage, current and power are affected by the variation in coupling coefficient and load. In this paper, a novel approach based on a nonlinear resonator is proposed to obtain stable output voltage, which is independent of coupling coefficient and load variation. First, the theory and properties of nonlinear resonators are analyzed by Duffing equation. Then, a nonlinear IPT system with a magnetic saturation inductor is proposed, and the saturable inductor modeling and its effect on system performance are further studied. Finally, the experimental prototype is built to validate the effectiveness of the nonlinear IPT system. The experimental results show that when the coupling coefficient varies from 0.32 to 0.24 and the load resistance varies from 80Ω to 120Ω, the system works in a nonlinear state, the output voltage ripple is 1.77%, and the overall efficie... [more]

The perovskite solar cell has recently gained momentum within the renewable energy industry due to its unique advantages such as high efficiency and cost-effectiveness. However, its instability remains a challenge to its commercialization. In this study, a singlet fission material, namely tetracene, is coupled with the perovskite solar cell to simulate its effect on the solar cell. The amount of thermalization loss and the temperature of the perovskite layer are simulated and analyzed to indicate the mechanism’s effectiveness. We found that coupling the tetracene layer resulted in a drastic reduction in thermalization loss and a slower slope in perovskite layer temperature. This indicates that tetracene would stabilize the perovskite solar cell and minimize its potential losses. The thickness of the solar cell layers is also analyzed as a factor of the overall effectiveness of singlet fission on solar cells.

Earth−rock dams are typical soil−rock mixtures with high heterogeneity. Mastering the effect of dam heterogeneity on seismic wavefields is the premise of accurately detecting hidden risks in dams. In this paper, based on the soil−rock mixture characteristics of actual dams, a soil−rock mixture model that can reflect the heterogeneity of dams is established through local segmentation and reassignment of random disturbances. The influence of local area size on model heterogeneity is described. The seismic wavefield in a soil−rock mixture dam is numerically simulated through a staggered-grid finite-difference algorithm with second-order accuracy in time and sixth-order accuracy in space. Then, the effect of dam heterogeneity on effective wavefields is analyzed. The results show that the heterogeneity of the earth−rock dam can lead to scattered waves in the seismic wavefield, and the scattered waves are mainly generated by Rayleigh surface waves. In the seismic record, scattered waves with... [more]

Since P.O. Fanger proposed PMV, it has been the most widely used index to estimate thermal comfort. However, in some cases, it is challenging to measure all six parameters within indoor spaces, which are essential for PMV estimation; a couple of parameters, such as Clo or Met, tend to show a large deviation in accuracy. For these reasons, several studies have suggested methods to estimate PMV but their accuracies were significantly compromised. In this vein, this study proposed a way to reduce the dimensions of parameters for PMV prediction utilizing the machine learning method, in order to provide fast PMV calculations without compromising its prediction accuracy. Throughout this study, the most influential features for PMV were pinpointed using PCA, Best Subset, and the Gini Importance, with each model compared to the others. The results showed that PCA and ANN achieved the highest accuracy of 89.70%, and the combination of Best Subset and Random Forest showed the fastest prediction... [more]