Unsteady numerical simulations, coupled with the SST (Shear Stress Transport) k-ω turbulence model, were conducted to study the mixing-enhancement characteristics of the excited jet generated by the fluidic oscillator and the air tab in a single channel convergent nozzle with an inlet total pressure of 140−200 kPa. Compared with the steady air-tab jet, the sweeping jet generated by the fluidic oscillator has roughly the same penetration in the main flow, but it can induce streamwise vortices and planar vortices of larger scale and longer duration, which is beneficial to enhance jet mixing efficiency in the range of 1.0 D (D represents the diameter of the main nozzle outlet) downstream from the main nozzle. When x > 1.0 D, the jet mixing is mainly dominated by the shear layer between the main jet and the ambient. As the sweeping jet suppresses the expansion of the main jet, which reduces the contact area between the main jet and the ambient, its mixing efficiency is less than that of th... [more]

The smart grid concept is introduced to accelerate the operational efficiency and enhance the reliability and sustainability of power supply by operating in self-control mode to find and resolve the problems developed in time. In smart grid, the use of digital technology facilitates the grid with an enhanced data transportation facility using smart sensors known as smart meters. Using these smart meters, various operational functionalities of smart grid can be enhanced, such as generation scheduling, real-time pricing, load management, power quality enhancement, security analysis and enhancement of the system, fault prediction, frequency and voltage monitoring, load forecasting, etc. From the bulk data generated in a smart grid architecture, precise load can be predicted before time to support the energy market. This supports the grid operation to maintain the balance between demand and generation, thus preventing system imbalance and power outages. This study presents a detailed revie... [more]

This study addresses the issue of energy optimization by investigating solutions for the reduction of energy consumption in the diagnostics and monitoring of technological processes. The implementation of advanced process control is identified as a key approach for achieving energy savings and improving product quality, process efficiency, and production flexibility. The goal of this research is to develop a cost-effective system with a minimal number of ultrasound sensors, thus reducing the energy consumption of the overall system. To accomplish this, a novel method for obtaining high-resolution reconstruction in transmission ultrasound tomography (t-UST) is proposed. The method involves utilizing a convolutional neural network to take low-resolution measurements as input and output high-resolution sinograms that are used for tomography image reconstruction. This approach allows for the construction of a super-resolution sinogram by utilizing information hidden in the low-resolution m... [more]

The Taiwan Strait contains a vast potential for wind energy. However, the power grid balance is challenging due to wind energy’s uncertainty and intermittent nature. Wind speed forecasting reduces this risk, increasing the penetration rate. Machine learning (ML) models are adopted in this study for the short-term prediction of wind speed based on the complex nonlinear relationships among wind speed, terrain, air pressure, air temperature, and other weather conditions. Feature selection is crucial for ML modeling. Finding more valuable features in observations is the key to improving the accuracy of prediction models. The random forest method was selected because of its stability, interpretability, low computational cost, and immunity to noise, which helps maintain focus on investigating the essential features from vast data. In this study, several new exogenous features were found on the basis of physics and the spatiotemporal correlation of surrounding data. Apart from the conventiona... [more]

Artificial intelligence models have been widely applied for natural gas consumption forecasting over the past decades, especially for short-term consumption forecasting. This paper proposes a three-layer neural network forecasting model that can extract key information from input factors and improve the weight optimization mechanism of long short-term memory (LSTM) neural network to effectively forecast short-term consumption. In the proposed model, a convolutional neural network (CNN) layer is adopted to extract the features among various factors affecting natural gas consumption and improve computing efficiency. The LSTM layer is able to learn and save the long-distance state through the gating mechanism and overcomes the defects of gradient disappearance and explosion in the recurrent neural network. To solve the problem of encoding input sequences as fixed-length vectors, the layer of attention (ATT) is used to optimize the assignment of weights and highlight the key sequences. Apa... [more]

New-generation technologies on vehicles provide many advantages in terms of cost, time, and the environment in the transportation, logistics, freight, and delivery service sectors. This study aimed to measure the effect of using e-scooter vehicles in mail delivery on the energy cost and delivery time in Turkey. Considering the number of test drives in e-scooter applications of potential regions, the amount of energy consumption and driving time data were used. The number of test drives for each e-scooter was assumed as a package or postal delivery amount. The methodology of this study consisted of measuring the effect of input parameters on output variables using the linear response optimization regression method and minimizing the amount of energy consumed and delivery time. The nine input variables and two output variables based on the test drive were analyzed in this study. The distance to the delivery address, region where the delivery address was located, and temperature were foun... [more]

In this study, the effects of electric vehicles, whose usage rate is increasing day by day in the world, on the existing electricity grid have been studied. EV charging stations and similar non-linear loads cause various harmful effects on power systems such as phase imbalances, the effect of harmonic formation, energy quality, voltage, and current imbalance. The study focuses on the harmonic effects of EV charging stations at the point where they are connected to the grid and at lower voltage levels by using IEEE 6-, 14-bus, and 30-bus test power systems. In addition to the existing loads in these grid systems, the effects on the grid as a result of drawing electrical energy from the grid for charging electric vehicles are investigated. These effects have shown how these charging stations on the grid have changed, considering the fact that the number of electric vehicles and the number of charging stations increased over the years when a single electric vehicle provided energy from th... [more]

Energy price fluctuations pose a significant risk and uncertainty to financial investments for new developments in conventional power and freshwater cogeneration facilities. This study attempts to address the problem of making robust valuation for low-carbon energy project investments subject to multi-dimensional price risk, particularly looking at some key research questions: (a) how does the correlation structure, or independence, between the price risks affect the project value; and (b) does adding flexibility in investment enhance or worsen the project valuation, given (a). This study identified three price factors with significant fluctuations that impact conventional power generation, namely: wholesale electricity spot price, natural gas spot price, and CO2 market price. The price factors were used to construct a multidimensional risk model and evaluate investment decisions for cogeneration project expansion in the future based on a low-carbon energy mix. To this end, five cogene... [more]

There will be an increasing number of retired batteries in the foreseeable future. Retired batteries can reduce pollution and be used to construct a battery cycle ecosystem. To use retired batteries more efficiently, it is critical to be able to determine their State of Health (SOH) precisely and speedily. SOH can be estimated accurately through a comprehensive and inefficient charge-and-discharge procedure. However, the comprehensive charge and discharge is a time-consuming process and will make the SOH assessment for many retired batteries unrealistic. This paper proposes an accurate and efficient SOH Estimation (SOH-E) method using the actual data of retired batteries. A battery data acquisition system is designed to acquire retired batteries’ comprehensive discharge and charge data. The acquired discharge data are separated into various time interval-segregated sub-data. Then, the specially designed features for SOH-E are extracted from the sub-data. Neural Networks (NNs) are train... [more]

The bibliometric analysis investigated the impact of publications on trends in the literature and bioenergy research using artificial intelligence (AI) from 1991 to 2021. In this study, 1721 publications were extracted from the Web of Science, and an analysis of the countries, authorship, institutions, journals, and keywords was visualised. In the recent decades, this field has entered an outbreak phase. India was the most productive country in this area, followed by China, Iran, and the US. It also noted several notable differences between trends and subjects in developed and developing countries. The former led this field at the initial stage and later attached importance to using AI for research feedstock and impact assessment. Developing countries encouraged the advancement of this area and emphasised the feedstock usage of phase treatment and process optimisation. In addition, a co-authorship and institutes study revealed that authors and institutes in distant regions rarely colla... [more]

This article presents the research process and statistical analysis of the selection of an appropriate type of surfactant to be added to natural ester oil MIDEL eN 1204. The tested parameter was the breakdown voltage. The following surfactants were tested: Triton X, ROKwino l80, and oleic acid. With the obtained results, we can conclude that the surfactants with the best properties, compared to the basic oil sample, have oleic acid, and also that high levels of breakdown voltage characterize a sample of Triton X with a concentration of 2%. Statistical analysis was performed using the MATLAB program.

The main aim of this work was to provide heat transfer calculations of flow boiling in mini-channels with an application for the Trefftz functions. The test section comprised five parallel mini-channels with a depth of 1 mm, with a common heated wall. For the estimation of the temperature uncertainty, during the experiment the temperature measurement was performed with the use of K-type thermoelements and an infrared camera in two mini-channels simultaneously. According to the Guide to the Expression of Uncertainty in Measurement, the Monte Carlo method is a practical alternative to the GUM uncertainty framework. Since the uncertainty components are not approximately the same magnitude, the Monte Carlo method was indicated to estimate the uncertainty of the surface temperature measurement. The results obtained from this simulation method were compared with the results of the computation related to the uncertainty propagation method. The results of both methods of temperature measuremen... [more]

A new pumpless liquid cooling system for a single-stage two-cylinder reciprocating compressor has been developed from the analysis of work processes and cooling systems of reciprocating compressors, where one piston compresses and moves gas and coolant in the cooling system. The intensification of the coolant movement increases in the machine, which can reduce the temperature of the cylinder−piston group and increase the indicator efficiency and the compressor feed rate. A mathematical model of working processes in a reciprocating compressor and its cooling system has been developed on the basic fundamental laws of conservation of energy, mass and motion. A prototype was developed and tested to obtain new knowledge about the processes in the machine and confirm the assumptions made while developing the mathematical model. After a series of experiments, the influence of cooling on the working processes in a reciprocating compressor, the technical work carried out in each working process... [more]

Swirling hot air is a promising heat transfer enhancement technology for anti-icing applications in aircrafts, where the swirling flow is accompanied by pretty high turbulence and a quite thin boundary layer. It is of interest to investigate the compound heat transfer characteristics of the swirling air configuration combined with surface structures on the internal wall. This paper carries out a series of numerical computations to obtain the Nusselt number and pressure loss data in such a swirling air heat transfer system with four kinds of surface structures (trenches, ribs, dimples and bulges) on the wall and with different tangential inlet jets placed along the tube. At a tube Reynolds number from 10,000 to 50,000, the results show that the surface dimples and bulges are conducive to improving the Nusselt number, but the surface trenches and ribs show a Nusselt number deterioration relative to the smooth swirl tube. Among the four investigated surface structures, the surface bulges... [more]

Nuclear fusion is widely promoted as the ultimate environmentally friendly solution to the world’s energy demands. However, the medium/long-term nuclear weapons proliferation risks from a hypothetical fusion economy are rarely considered. Using risk assessment tools, this paper undertakes a trial scoping of proliferation hazards arising from fusion energy technologies, focused on the implications of a global ‘Mature Fusion Economy’ (MFE). In the medium term, an MFE could (1) facilitate construction of large, efficient, and reliable nuclear arsenals by producing tritium and the fissile materials Plutonium-239 and Uranium-233; and (2) erode the barriers constraining nuclear weapons acquisition by facilitating the spread of nuclear knowledge, technologies, and materials. Given the potential scale of a global MFE, management via monitoring of proliferation and diplomacy could become unworkable. Therefore, policy development must include independent and comprehensive expert and informed com... [more]

In this paper, we propose a mitigation method for reducing lightning-induced insulator voltages based on the installation of low-voltage surge arresters aligned parallelly to the insulator. The three-dimensional finite-difference time-domain (FDTD) method is applied to numerically model a real surge arrester residual voltage evaluation system. The application of a transient current pulse, typical of lightning discharges, is considered in our numerical model. We considered cases with one or two surge arresters installed per phase, in three different geometric and parametric configurations for installing distribution surge arresters. In addition to the Kirchhoff current division, which reduces both the absorbed energy and the thermal stress, the results associated with the installation of two surge arresters parallelly aligned to the insulator show that the interaction of magnetic fields generated by the surge arresters’ currents can produce an additional strong reduction in lightning-in... [more]

Anaerobic digestion (AD) is a promising way to produce renewable energy. The solid-state anaerobic digestion (SSAD) with a dry matter content more than 15% in the reactors is seeing its increasing potential in biogas plant deployment. The relevant processes involve multiple of evolving chemical and physical phenomena that are not crucial to conventional liquid-state anaerobic digestion processes (LSAD). A good simulation of SSAD is of great importance to better control and operate the reactors. The modeling of SSAD reactors could be realized either by theoretical or statistical approaches. Both have been studied to a certain extent but are still not sound. This paper introduces the existing mathematical tools for SSAD simulation using theoretical, empirical and advanced statistical approaches and gives a critical review on each type of model. The issues of parameter identifiability, preference of modeling approaches, multiscale simulations, sensibility analysis, particularity of SSAD o... [more]

Wind energy forecasting is a critical aspect for wind energy producers, given that the chaotic nature and the intermittence of meteorological wind cause difficulties for both the integration and the commercialization of wind-produced electricity. For most European producers, the quality of the forecast also affects their financial outcomes since it is necessary to include the impact of imbalance penalties due to the regularization in balancing markets. To help wind farm owners in the elaboration of offers for electricity markets, the EOLO predictor model can be used. This tool combines different sources of data, such as meteorological forecasts, electric market information, and historic production of the wind farm, to generate an estimation of the energy to be produced, which maximizes its financial performance by minimizing the imbalance penalties. This research study aimed to evaluate the performance of the EOLO predictor model when it is applied to the different Spanish electricity... [more]

A smart city is a city that binds together technology, society, and government to enable the existence of a smart economy, smart mobility, smart environment, smart living, smart people, and smart governance in order to reduce the environmental impact of cities and improve life quality. The first step to achieve a fully connected smart city is to start with smaller modules such as smart homes and smart buildings with energy management systems. Buildings are responsible for a third of the total energy consumption; moreover, heating, ventilation, and air conditioning (HVAC) systems account for more than half of the residential energy consumption in the United States. Even though connected thermostats are widely available, they are not used as intended since most people do not have the expertise to control this device to reduce energy consumption. It is commonly set according to their thermal comfort needs; therefore, unnecessary energy consumption is often caused by wasteful behaviors and... [more]

In recent years, there has been a significant increase in investment in renewable energy sources, leading to the decarbonization of the electricity sector. Accordingly, a key concern is the influence of this process on future electricity market prices, which are expected to decrease with the increasing generation of renewable power. This is important for both current and future investors, as it can affect profitability. To address these concerns, a long-term analysis is proposed here to examine the influence of the future electricity mix on Iberian electricity prices in 2030. In this study, we employed artificial intelligence forecasting models that incorporated the main electricity price-driven components of MIBEL, providing accurate predictions for the real operation of the market. These can be extrapolated into the future to predict electricity prices in a scenario with high renewable power penetration. The results, obtained considering a framework featuring an increase in the penet... [more]

Unlike the more prevalent alternating current transmission systems, the high voltage direct current (HVDC) electric power transmission system transmits electric power using direct current. In order to investigate the precise remedy for fault detection of HVDC, this research proposes a method for the HVDC fault diagnostic methodologies with their limits and feature selection-based probabilistic generative model. The main contribution of this study is using the wavelet transform based on ant colony optimization and ANN to detect the different types of faults in HVDC transmission lines. In the proposed method, ANN uses optimum features obtained from the voltage, current, and their derivative signals. These features cannot be accurate to use in ANN because they cannot give reliable accuracy results. For this reason, first, the wavelet transform applies to the fault and non-fault signals to remove the noise. Then the ACO reduces unimportant features from the feature vector. Finally, the opt... [more]

The dynamic behavior and energy transformation mechanism of the multi-period evolution of bubbles collapsing near a wall have been essential considerations in bubble dynamics research. In this study, a compressible two-phase solver considering thermodynamics and phase transitions is developed on OpenFOAM (version v2112). This model is validated via comparison with analytical solutions and experimental results. The dynamics of the multi-period evolution of bubbles collapse process at different dimensionless stand-off distances (γ) were accurately reproduced. The results indicate that the shock wave emitted by the collapse of cavitation bubbles impacts the wall, causing the fluid temperature along the wall to increase. Moreover, the liquid jet has a dual effect on the wall temperature increase, depending on the initial stand-off distance between the bubble and the wall. When γ is small, the jet carries the low-temperature fluid to occupy the high-temperature region, and when γ is large,... [more]

Oil production prediction plays a significant role in designing programs for hydrocarbon reservoir development, adjusting production operations and making decisions. The prediction accuracy of oil production based on single methods is limited since more and more unconventional reservoirs are being exploited. Artificial intelligence technology and data decomposition are widely implemented in multi-step forecasting strategies. In this study, a hybrid prediction model was proposed based on two-stage decomposition, sample entropy reconstruction and long short-term memory neural network (LSTM) forecasts. The original oil production data were decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN); then these IMFs with different sample entropy (SE) values were reconstructed based on subsequence reconstruction rules that determine the appropriate reconstruction numbers and modes. Following that, the highest-freque... [more]

Proton exchange membrane fuel cell (PEMFC) powered propulsion has gained increasing attention in urban air mobility applications in recent years. Due to its high power density, ultra-thin heat pipe technology has great potential for cooling PEMFCs, but optimizing the limited internal cavity of the heat pipe remains a significant challenge. In this study, a three-dimensional multiphase model of the heat pipe cooled PEMFC is built to evaluate the impact of three internal structures, layered, spaced, and composite, of ultra-thin heat pipes on system performance. The results show that the heat pipe cooling with the composite structure yields a lower thermal resistance and a larger operating range for the PEMFC system compared to other internal structures because of more rational layout of the internal cavity. In addition, the relationship between land to channel width ratio (LCWR) and local transport property is analyzed and discussed based on composite structural heat pipes. The heat pipe... [more]

With global warming, and internal combustion engine emissions as the main global non-industrial emissions, how to further optimize the power performance and emissions of internal combustion engines (ICEs) has become a top priority. Since the internal combustion engine is a complex nonlinear system, it is often difficult to optimize engine performance from a certain factor of the internal combustion engine, and the various parameters of the internal combustion engine are coupled with each other and affect each other. Moreover, traditional experimental methods including 3D simulation or bench testing are very time consuming or expensive, which largely affects the development of engines and the speed of product updates. Machine learning algorithms are currently receiving a lot of attention in various fields, including the internal combustion engine field. In this study, an artificial neural network (ANN) model was built to predict three types of indicators (power, emissions, and combustio... [more]