At present, computational fluid dynamics (CFD) is an inherent component of the development procedure of a majority of technological processes involving fluid flows and/or heat and mass transfers. Practicing engineers and investigators employ different commercial CFD software, open-source codes and even develop their own computational codes (in house) for solving tasks, requiring accounting for nonstandard effects.

This work shows an alternative method to determine the Voltage Unbalance Factor in a power grid by using both the mean value of the line voltages and Current Unbalance Factor in induction motors. Twenty unbalanced voltage points on three induction motors were used in order to compare the two methods. The influence of the measurement error of both the voltmeters and the ammeters on the resulting Voltage Unbalance Factor was studied, and the validation was made with laboratory data for one of the three motors analyzed, in addition to the simulations carried out. The proposed Voltage Unbalance Factor was compared with the most typical method in the standards to obtain this factor, showing that the proposed factor has a better approach than the standard factor to determine the value of the Voltage Unbalance Factor in an unbalanced power system.

Recent advancements in the Internet of Things and Machine Learning techniques have allowed the deployment of sensors on a large scale to monitor the environment and model and predict individual thermal comfort. The existing techniques have a greater focus on occupancy detection, estimations, and localization to balance energy usage and thermal comfort satisfaction. Different sensors, actuators, and analytic data methods are often non-invasively utilized to analyze data from occupant surroundings, identify occupant existence, estimate their numbers, and trigger the necessary action to complete a task. The efficiency of the non-invasive strategies documented in the literature, on the other hand, is rather poor due to the low quality of the datasets utilized in model training and the selection of machine learning technology. This study combines data from camera and environmental sensing using interactive learning and a rule-based classifier to improve the collection and quality of the dat... [more]

The global trend is constantly increasing investments in strategic sectors of the economy, for example the electric power industry, which, in many countries, is becoming diversified and dispersed due to the multitude of entities investing in energy production and renewable resources, which leads to an increase in the heterogeneity of investment decisions. There is an urgent need to control the movement of investments, budget funds, as well as their development in the process of implementing investment programs of energy companies. The control of the movement of investments is the most promising direction of studying the subject of finance and audit. The increasing volume of public and private targeted investments in the energy sector and the lack of control over the effectiveness of investment projects (since each program contains several thousand lists of projects) necessitated the introduction of additional regulation of budget spending. The development of a mathematical apparatus fo... [more]

Electric vehicles (EVs) are a promising option to reduce air pollution and shipping costs, especially in urban areas. To provide scientific guidance for the growing number of logistics companies using EVs, we investigated an electric-vehicle-routing problem with simultaneous pickup and delivery that also considers non-linear charging and load-dependent discharging (EVRPSPD-NL-LD). The objective was to minimize the total number of EVs and the total working time, including travel time, charging time, waiting time, and service time. We formulated the problem as a mixed integer linear program (MILP), and small-size problems could be solved to optimality in an acceptable amount of time using the commercial solver IBM ILOG CPLEX Optimization Studio (CPLEX). In view of the fact that the problem is NP-hard, an adaptive large neighborhood search (ALNS) metaheuristic method was proposed to solve large-size problems. Meanwhile, new operators and a time priority approach were developed to provide... [more]

The performance of air conditioning systems deteriorate due to the natural aging and wear caused by operating the devices. This is termed “aging degradation,” and it results from a lack of appropriate maintenance which accelerates the degree of performance degradation. The performance degradation of an air conditioning system can cause problems such as increased energy consumption, deteriorated indoor heating environment, and shortened lifespan of air conditioning equipment. To prevent such problems, it is important to establish a long-term maintenance plan to recover degraded performance, such as predicting an appropriate maintenance time by identifying the real-time performance degradation rate based on a system’s operation data. In this study, the performance degradation rate, according to the operating time, was estimated using long-term operation data for devices constituting a heat source system, and the effect of performance degradation of the heat source system’s operation and... [more]

This paper presents a characteristic analysis and experimental verification for predicting the electromagnetic losses in high-speed permanent magnet synchronous motors. To predict the operating characteristics (such as speed and input current), dynamic modeling is conducted that combines models for the space vector pulse width modulation (SVPWM) inverter and high-speed permanent magnet synchronous motor (HPMSM). By applying the predicted harmonic currents to the electromagnetic analysis, DC and AC copper losses of the stator winding, and eddy current loss of the rotor sleeve and rotor permanent magnet, are comprehensively analyzed using the finite element (FE) method. In particular, by analyzing the magnetic field behavior of magnetic flux density according to harmonics, a core loss analysis technique was presented. The validity of the hybrid analysis, which combines the stator copper loss and rotor eddy current loss derived from the FE analysis and the proposed core loss analysis, was... [more]

With more stringent IEC energy efficiency standards, electrical machine industry increasingly focuses on new motor technologies. Amongst others, the line-start permanent magnet synchronous machine (LSPMSM) is considered as an attractive alternative to induction machine, especially for low power and fixed-speed applications. However, the design of LSPMSMs is rather complex as both steady-state and transient synchronization performances need to be considered. The synchronization capability determination of a LSPMSM design usually relies on time-consuming transient finite-element simulations, which is impractical for use in an iterative design optimization process. This paper compares and evaluates various existing analytical synchronization analysis methods in an attempt to identify most suitable equations and methods for fast synchronization analysis. Using the selected methods, a software tool is developed that can seamlessly work with ANSYS Electronics Desktop to perform rapid transie... [more]

The present study established two different models based on the convolutional neural network (CNN) and the encoder−decoder (ED) to predict the characteristics of the flow and heat transfer around the NACA sections. The established CNN predicts the aerodynamic coefficients and the Nusselt number. The established ED model predicts the velocity, pressure and thermal fields to explain the performances of the aerodynamics and heat transfer. These two models were trained and tested by the dataset extracted from the computational fluid dynamics (CFD) simulations. The predictions mostly matched well with the true data. The contours of the velocity components and the pressure coefficients reasonably explained the variation of the aerodynamic coefficients according to the geometric parameter of the NACA section. In order to physically interpret the heat transfer performance, more quantitative and qualitative information are needed owing to the lack of the correlation and the resolution of the th... [more]

Building-integrated photovoltaic (BIPV) façades are a promising technique for improving building energy performance. This study develops energy simulation models of different photovoltaic-integrated shading devices (PVSDs) in single-story and multi-story office buildings. A cross-region study in China is carried out to explore the energy performance of PVSDs in five climate zones. The shading effect of the upper PVSDs is taken into account. The results show that (1) PVSDs can be applicable in hot and cold climates; shading effects lead to a notable difference in the optimal PVSDs style. The average comprehensive energy saving ratios of different PVSDs ranged from 16.12% (fixed PV louvres in the vertical plane) to 51.95% (lower single panel). The most rewarding PVSDs are for single-story buildings in Kunming and the least suitable are for multi-story buildings in Guangzhou. (2) In climate zones with little air-conditioning energy consumption, avoiding considerably increased lighting con... [more]

Cyclic supercritical multi-thermal fluid stimulation (CSMTFS) is a novel technology that can efficiently recover heavy oil, while the heating effect, production and heat loss characteristics of CSMTFS have not been discussed. In this study, a physical simulation experiment of CSMTFS is conducted with a three-dimensional experimental system. The results of the study indicate that the whole process of CSMTFS can be divided into four stages, namely, the preheating stage, production increase stage, production stable stage and production decline stage, of which the production stable stage is the main oil production stage, and the production decline stage is the secondary oil production stage. In the first two stages of the CSMTFS process, there is no supercritical multi-thermal fluid chamber, and only a relatively small supercritical multi-thermal fluid chamber is formed in the last stage of the CSMTFS process. Out of the supercritical multi-thermal fluid chamber, supercritical water in the... [more]

The process of carbon neutrality does have economic costs; however, few studies have measured the cost and the economic neutral opportunities. This paper uses a dynamic computable general equilibrium (CGE) model to simulate China’s carbon neutrality path from 2020 to 2060 and analyzes its economic impact. This paper innovatively adjusts the CGE modeling technology and simulates the boundary of the Porter hypothesis on the premise of economic neutrality. The results show that the carbon neutrality target may reduce the annual GDP growth rate by about 0.8% in 2020−2060. To make the carbon pricing method under the carbon neutrality framework meet the strong version of the Porter hypothesis (or economic neutrality), China must increase its annual total factor productivity by 0.56−0.57% in 2020−2060; this is hard to achieve. In addition, the study finds that China’s 2030 carbon target has little impact on the economy, but the achievement of the 2060 carbon neutrality target will have a sign... [more]

Field tests indicate that temporary well shut-ins may enhance oil recovery from a shale reservoir; however, there is currently no systematic research to specifically guide such detailed operations in the field, especially for the design of the shut-in scheme and multiple rounds of shut-ins. In this study, the applicability of well shut-in operations for shale oil reservoirs is studied, and a numerical model is built using the finite element method. In order to simulate the production in a shale oil reservoir, two separate modules (i.e., Darcy’s law and phase transport) were two-way coupled together. The established model was validated by comparing its results with the analytical Buckley−Leverett equation. In this paper, the geological background and parameters of a shale oil reservoir in Chang-7 Member (Chenghao, China) were used for the analyses. The simulation results show that temporary well shut-in during production can significantly affect well performance. Implementing well shut-... [more]

Ethanol is considered an alternative fuel to power fuel cells, especially due to its ease of transport and storage and renewable production on a large scale. However, its use in direct ethanol fuel cells (DEFC) is still limited by incomplete electro-oxidation and slow reaction kinetics. Modeling approaches have focused on investigating different reaction mechanisms, but so far, no formal analysis of model parameter sensitivity has been conducted. This work modeled and identified sensitive parameters for different types of Pt−Sn catalysts previously prepared by our research group that displayed good performance in the 5−15 mW/cm2 range (relative to a performance of 12 mW/cm2 achieved by a commercial ETEK catalyst). Analyses to study the effect of these parameters on coverage fraction distribution, reaction rates and possible correlations were also performed. The model was developed based on Butler−Volmer kinetics and on a reaction mechanism previously reported in the literature. Statist... [more]

Bearing in mind European Green Deal assumptions regarding a significant reduction of green house emissions, electricity generation from Renewable Energy Sources (RES) is more and more important nowadays. Besides this, accurate and reliable electricity generation forecasts from RES are needed for capacity planning, scheduling, managing inertia and frequency response during contingency events. The recent three years have proved that Machine Learning (ML) models are a promising solution for forecasting electricity generation from RES. In this review, the 8-step methodology was used to find and analyze 262 relevant research articles from the Scopus database. Statistic analysis based on eight criteria (ML method used, renewable energy source involved, affiliation location, hybrid model proposed, short term prediction, author name, number of citations, and journal title) was shown. The results indicate that (1) Extreme Learning Machine and ensemble methods were the most popular methods used... [more]

One of the relevant factors in smart energy management is the ability to predict the consumption of energy in smart households and use the resulting data for planning and operating energy generation. For the utility to save money on energy generation, it must be able to forecast electrical demands and schedule generation resources to meet the demand. In this paper, we propose an optimized deep network model for predicting future consumption of energy in smart households based on the Dipper Throated Optimization (DTO) algorithm and Long Short-Term Memory (LSTM). The proposed deep network consists of three parts, the first part contains a single layer of bidirectional LSTM, the second part contains a set of stacked unidirectional LSTM, and the third part contains a single layer of fully connected neurons. The design of the proposed deep network targets represents the temporal dependencies of energy consumption for boosting prediction accuracy. The parameters of the proposed deep network... [more]

Absorption cooling technologies converting excess heat and renewable heat resources to cooling energy have shown progress in recent years. In this study, two 400 kW LiBr solution absorption chiller types with series and parallel connected are analyzed over a range of parameter values to better understand their applicability for different uses. Thermodynamic models for the components were constructed and validated. The performance of the chillers related to heat transfer, energy, exergy, and economy performance was comprehensively analyzed. The operating performance was investigated by considering the external variables, including inlet cooling water, chilled water, and inlet steam temperatures and the solution allocation ratio. The results indicate that the parallel connected chiller reaches higher energy and exergy performance than the series-connected chiller, but the heat transfer and economic performance was lower. The coefficient of performance and the exergy efficiency of the par... [more]

Power system simulations with long-term data typically have large time steps, varying from one second to a few minutes. However, for PV inverter semiconductors in grid-connected applications, the minimum thermal stress cycle occurs over the fundamental grid frequency (50 or 60 Hz). This requires the time step of the fatigue simulation to be approximately 100 μs. This small time step requires long computation times to process yearly power production profiles. In this paper, we propose a fast fatigue simulation for inverter semiconductors using the quasi-static time-series simulation concept. The proposed simulation calculates the steady state of the semiconductor junction temperature using a fast Fourier transform. The small thermal cycling during a switching period and even over the fundamental waveform is disregarded to further accelerate the simulation speed. The resulting time step of the fatigue simulation is 15 min, which is consistent with the solar dataset. The error of the prop... [more]

A monopile is the most conventional structure foundation for offshore wind turbines (OWTs) in the world. However, the Korean offshore wind industry has mostly been using the jacket type of foundation. The main reason for the current situation in Korea is that most of the marine soil consists of weak layers of sand and clay. Thus, the monopile foundation depth has to be deep enough to satisfy the intended serviceability design requirement of the monopile and the rotation limit at the seabed; a conventional monopile design concept alone might be insufficient in Korean offshore conditions, or otherwise could be very expensive, e.g., resulting in a rock socket installation at the tip of the monopile. The main objective of this paper is to introduce a novel hybrid monopile that is composed of a monopile and a supplemental support with three buckets, followed by assessing the lateral resistance of the hybrid system through physical experiments and finite element (FE) simulations. Namely, 1/6... [more]

Unconventional oil and gas formations are abundant, have become an increasingly important part of the global energy supply, and are attracting increasing attention from the industry. Predicting key reservoir properties plays a significant role in both geological science and subsurface engineering workflows. With the advent of horizontal well drilling and multiple-stage hydraulic fracturing, the Montney Shale formation is one of the most promising and productive shale plays in Canada. However, very few academic papers discuss its in situ stress, reservoir pressure, and permeability, which are essential for the development of the Montney Shale. The objective of this study is to analyze the geo-stress, the pore pressure, and several key reservoir properties by using diagnostic fracture injection test (DFIT) data from the Montney Shale. One horizontal well from the Wapiti field has been analyzed with a set of DFIT data, and its results show that the general pressure and Gdp/dG responses fr... [more]

One of the main energy sources utilized to produce power is coal. Due to the lack of combustion enhancement, the main issue with coal-based power plants is that they produce significant amount of pollutants. The major problem of slagging formation within the boiler; it sticks to the water tube walls, superheater, and reheater. Slagging might decrease the heat transferred from the combustion area to the water or steam inside the tubes, increasing the amount of coal and extra air. The abrupt fall of slag on the tube surface into the water-filled seal-trough at the bottom of the furnace might occasionally cause boiler explosions. In order to maximize heat transmission to the water and steam tubes by reducing or eliminating slag formation on the tube surface, the work presented here proposes an appropriate computational fluid dynamics (CFD) technique with a genetic algorithm (GA) integrated with conventional supercritical power plant operation. Coal usage and surplus air demand are both de... [more]

Helical tube steam generators are often used in nuclear power plants because of their compact structure and high heat transfer efficiency. The impact of the internal fluid causes the vibration of the tube bundle, which leads to the failure of the integrity of the safety structure. Aiming at flow-induced vibration (FIV) of helical tube arrays, a finite element model of the helical tube was established to consider the constraint of the support structure. The computational fluid dynamics (CFD)/computational structural dynamics (CSD) coupling calculation method based on the superposition of three modes was used to study the FIV characteristics of helical tube arrays at different flow velocities. The influence of adjacent helical tubes’ vibration on the vibration of the target tube was also investigated. The results show that when FIV occurs in the helical tube, with the increase of inlet velocity, the axial amplitude will be greater than the radial at the same velocity. When some tubes vib... [more]

The structural design and operating strategy of a free piston expander−linear generator (FPE−LG) has a major impact on performance. In this paper, the simulation model of single−piston FPE−LG was built and verified by combining the structural parameters of the existing test rig with a set of kinetic and thermodynamic equations. On this basis, the influence of the design and operating parameters of the device on the performance was studied, while keeping other parameters fixed. Then, a sensitivity analysis of power output and operating frequency was carried out. The results show that within a certain range of external load and intake beginning position, increasing the diameter of the intake and exhaust pipes, or reducing the piston rod diameter can improve the power output. Within a certain range of frictional coefficient and intake time, increasing the cylinder diameter and intake pressure, or reducing the piston assembly mass and back electromotive force (EMF) constant can increase th... [more]

The primary objective of this investigation was to explore the aerodynamic impact of adding trailing edge serrations to a Wells turbine. The baseline turbine consists of eight NACA 0015 blades. The blade chord length was 0.125 m and the span was 0.100 m. Two modified serrated blade configurations were studied: (1) full-span, and (2) partial-span covering 0.288c of the trailing edge. The numerical simulations were carried out by solving the three-dimensional, incompressible steady-state Reynolds Averaged Navier-Stokes (RANS) equations using the k-ω SST turbulence model in ANSYS™ (CFX). The aerodynamic performance of the modified Wells turbine was compared to the baseline by calculating non-dimensional parameters (i.e., torque coefficient, pressure drop coefficient, and turbine efficiency). A comparison of the streamlines was performed to analyze the flow topology around the turbine blades for a flow coefficient range of 0.075 ≤ ϕ ≤ 0.275, representing an angle of attack range of 4.29° ≤... [more]

The paper reports the results of experimental tests and numerical simulations related to the pressure drop during two-phase air-water mixture flow through a pipe containing metal foam packing. Aluminium foam with 40 PPI open cells was used in the tests. A horizontal pipe with an internal diameter of 10 mm was used, and the foam only occupied a section of the pipe length equal to 240 mm. In the section of the pipe upwards of the foam, stratified flow pattern was generated, i.e., the most characteristic type for the gas-liquid flow. The results of the experimental research were compared with the values derived on the basis of the empirical method, which was developed for several different metal foams and two-phase systems. The values derived from measurements and calculations were subsequently applied to validate one numerical simulation method that is known to be particularly applicable for two-phase gas-liquid flow through metal foams. As a final result, the phenomena resulting from th... [more]