bluuebunny/crossref_metadata_2025 huggingface dataset:
Exploring the Interplay between Conceptualizing and Realizing Inquiry—The Case of One Mathematics Teacher’s Trajectory
Authors: Bråtalien Marte , Naalsund Margrethe , Eriksen Elisabeta
Published: August 2023

Inquiry, an approach that departs from traditional mathematics teaching, empowers students through active participation and increased accountability in exploration, argumentation, evaluation, and communication of mathematical ideas. There is broad research consensus on the benefits of inquiry-based approaches to teaching and learning mathematics, including their potential to support equitable mathematics classrooms. While research has separately explored teachers’ conceptions of inquiry and their efforts to enact the practice, little is known about the interplay between mathematics teachers’ conceptions and enactment, and how it could be harnessed in professional development. In this study, we follow Alex, an experienced upper secondary mathematics teacher unfamiliar with inquiry, as he participates in a one-semester professional development course that draws on inquiry in multiple ways. His trajectory towards learning to teach through inquiry is revealed through patterns and shifts in his reflections and classroom actions. Our findings reveal significant developments in Alex’s conception of inquiry and in how he realizes it in his classroom, identifying three paths that illuminate his inquiry trajectory: the teacher’s role in inquiry interactions, a growing idea of inquiry, and orchestrating whole-class situations. In the interplay between enacting and reflecting, he moves from distributing authority separately between himself and ‘the students’ (as one unit) to fostering shared authority, a key aspect of empowerment, between himself and his students (as multiple voices) in both groupwork and whole-class episodes.
Testing the Psychometric Properties of an Arabic Version of the Brunel Mood Scale among Physical Education Students
Authors: Sahli Hajer , Sahli Faten , Saidane Mouna , Rebhi Mahmoud , Guelmami Noomen , Trabelsi Khaled , Jahrami Haitham , Ammar Achraf , Terry Peter C. , Zghibi Makram
Published: August 2023

In our study, we translated and tested the psychometric properties of an Arabic version of the Brunel Mood Scale (BRUMS), referred to as the Arabic Mood Scale (ARAMS), among physical education university students. A total of 681 participants completed the ARAMS in exploratory and confirmatory phases. Exploratory analyses were conducted on data from 253 students between the ages of 19 and 25 years (M = 21.14 ± 1.65 years) of whom 132 were women (52.2%) and 121 were men (47.8%). Confirmatory analyses were conducted on data from 428 students between the ages of 19 and 25 years (M = 20.93 ± 1.55 years) of whom 203 were women (52.6%) and 225 were men (47.4%). The measurement model of the ARAMS was initially evaluated using exploratory factor analysis (EFA) and was subsequently tested via confirmatory factor analysis (CFA). EFA identified a 24-item, 6-factor structure that aligned with the original BRUMS measurement model, and CFA demonstrated congruence between the two models. Internal consistency of the six subscales exceeded adequacy levels with good Cronbach’s alpha and McDonald’s Omega values respectively for anger (0.811; 0.812), confusion (0.830; 0.830), depression (0.858; 0.859), fatigue (0.823; 0.825), and tension (0.824; 0.825), and an acceptable value for vigor (0.749; 0.748). Findings support the factorial validity and internal consistency of the ARAMS, which appears to be a suitable measure for use in Arabic physical education contexts. Further validation studies are required before the ARAMS is used in other Arabic-language contexts.
Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network
Authors: Liu Jingtong , Yi Huawei , Gao Yixuan , Jing Rong
Published: August 2023

Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.
5G and Beyond: Channel Classification Enhancement Using VIF-Driven Preprocessing and Machine Learning
Authors: Zaki Amira , Métwalli Ahmed , Aly Moustafa H. , Badawi Waleed K.
Published: August 2023

The classification of wireless communication channel scenarios is vital for modern wireless technologies. Efficient data preprocessing for identification, especially starting from 5G and beyond, where multiple scenario transitions occur, is crucial. Machine Learning (ML) is employed for scenario identification. Moreover, accurate ML classification is required to enhance the decision-making process in each communication layer. The proposed model in this study utilizes an enhanced preprocessing phase. The proposed model proves that adding the variance inflation factor (VIF) elimination layer has a significant effect in eliminating the residual noise after regularization. By evaluating the VIF, the high multi-collinear features are removed after adding a regularization penalty. Consequently, the total explained variance (TEV) was enhanced by 5% and reached 76%. Thus, the classification accuracy of the identification processes of different rural and urban scenarios was increased by 3%, on average, compared with previous work for each algorithm: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Gaussian Mixture model (GMM).
A High-Gain DC-DC Converter with a Wide Range of Output Voltage
Authors: Zhang Shaoru , Li Huixian , Duan Shuchun , Du Xiuju , Liu Shengli , Wang Pingjun , Zhang Jielu
Published: August 2023

In fuel-cell-powered electric vehicles, the output characteristics of the fuel cell are relatively soft, and the output voltage is unstable. Therefore, a DC-DC converter is required between the fuel cell and the inverter to transform the output voltage of the fuel cell into a suitable voltage for the motor drive. Existing non-isolated DC-DC converters cannot meet the requirements of high voltage gain, high efficiency and a wide range of output voltage simultaneously. To improve these performances, a high-gain DC-DC converter with a wide range of output voltage, based on a switched capacitor structure, is proposed in this paper. The converter supplies power to the load by connecting multiple capacitors with the input source in series in switch-on states, while the input source charges the capacitors through a series connection with an inductor in switch-off states. In comparison to existing converters, the proposed converter maintains high voltage gain at lower duty ratios and offers a wide range of output voltage. The operating principles, key waveforms and parameter design of the topology in Continuous Conduction Mode (CCM) are described and analyzed in detail, and the voltage gain of the proposed converter is compared with some other DC-DC converters. Finally, the results of simulations using Simulink and hardware experiments that were conducted to validate the theoretical analysis are described.
Evaluation of the Improved Extreme Learning Machine for Machine Failure Multiclass Classification
Authors: Surantha Nico , Gozali Isabella D.
Published: August 2023

The recent advancements in sensor, big data, and artificial intelligence (AI) have introduced digital transformation in the manufacturing industry. Machine maintenance has been one of the central subjects in digital transformation in the manufacturing industry. Predictive maintenance is the latest maintenance strategy that relies on data and artificial intelligence techniques to predict machine failure and remaining life assessment. However, the imbalanced nature of machine data can result in inaccurate machine failure predictions. This research will use techniques and algorithms centered on Extreme Learning Machine (ELM) and their development to find a suitable algorithm to overcome imbalanced machine datasets. The dataset used in this research is Microsoft Azure for Predictive Maintenance, which has significantly imbalanced failure classes. Four improved ELM methods are evaluated in this paper, i.e., extreme machine learning with under-sampling/over-sampling, weighted-ELM, and weighted-ELM with radial basis function (RBF) kernel and particle swarm optimization (PSO). Our simulation results show that the combination of ELM with under-sampling gained the highest performance result, in which the average F1-score reached 0.9541 for binary classification and 0.9555 for multiclass classification.
Particle-in-Cell Simulations on High-Efficiency Phase-Locking Millimeter-Wave Magnetrons with Unsynchronized High-Voltage Pulses
Authors: Song Minsheng , Meng Lin , Wang Bin , Bi Liangjie , Qin Yu , Liu Haixia , Chen Liangpin , Yin Yong , Li Hailong
Published: August 2023

Phase locking is an essential choice for building a coherent array, and a system of phase-locked magnetrons is relatively compact and cheaper than other microwave sources. Previous theoretical and experimental studies on phase locking are conducted using synchronized high-voltage pulses. Here, we investigate the characteristics of two phase-locked magnetrons using particle-in-cell (PIC) simulation software (CST STUDIO SUITE 2020) when two high-voltage pulses have delays. The results show that the magnetrons produced two-level RF signals because the operation could be divided into two stages. The first stage happened when one cathode emitted electrons; then, the electrons formed one spoke, traveling in synchronism with the 0-phase difference mode. Two output ports both produced half the output power of a free-running magnetron. The second stage happened after another cathode started to emit electrons, which were instantly pre-modulated by the electromagnetic field of the 0-phase difference mode produced during the first stage. In the second stage, simulations showed that pre-modulation accelerated the process of electron bunching. Eventually, two magnetrons were phase-locked, and the total output power of the two identical magnetrons nearly doubled the output power of the free-running magnetron, which demonstrated that the magnetrons were phase-locked in the high-efficiency phase-locking regime.
Multimodal Sentiment Analysis in Realistic Environments Based on Cross-Modal Hierarchical Fusion Network
Authors: Huang Ju , Lu Pengtao , Sun Shuifa , Wang Fangyi
Published: August 2023

In the real world, multimodal sentiment analysis (MSA) enables the capture and analysis of sentiments by fusing multimodal information, thereby enhancing the understanding of real-world environments. The key challenges lie in handling the noise in the acquired data and achieving effective multimodal fusion. When processing the noise in data, existing methods utilize the combination of multimodal features to mitigate errors in sentiment word recognition caused by the performance limitations of automatic speech recognition (ASR) models. However, there still remains the problem of how to more efficiently utilize and combine different modalities to address the data noise. In multimodal fusion, most existing fusion methods have limited adaptability to the feature differences between modalities, making it difficult to capture the potential complex nonlinear interactions that may exist between modalities. To overcome the aforementioned issues, this paper proposes a new framework named multimodal-word-refinement and cross-modal-hierarchy (MWRCMH) fusion. Specifically, we utilized a multimodal word correction module to reduce sentiment word recognition errors caused by ASR. During multimodal fusion, we designed a cross-modal hierarchical fusion module that employed cross-modal attention mechanisms to fuse features between pairs of modalities, resulting in fused bimodal-feature information. Then, the obtained bimodal information and the unimodal information were fused through the nonlinear layer to obtain the final multimodal sentiment feature information. Experimental results on the MOSI-SpeechBrain, MOSI-IBM, and MOSI-iFlytek datasets demonstrated that the proposed approach outperformed other comparative methods, achieving Has0-F1 scores of 76.43%, 80.15%, and 81.93%, respectively. Our approach exhibited better performance, as compared to multiple baselines.
Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique
Authors: Jenkinson George , Abbasi Muhammad Ali Babar , Molaei Amir Masoud , Yurduseven Okan , Fusco Vincent
Published: August 2023

This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot.
DoseFormer: Dynamic Graph Transformer for Postoperative Pain Prediction
Authors: Zhang Cao , Zhao Xiaohui , Zhou Ziyi , Liang Xingyuan , Wang Shuai
Published: August 2023

Many patients suffer from postoperative pain after surgery, which causes discomfort and influences recovery after the operation. During surgery, the anesthetists usually rely on their own experience when anesthetizing, which is not stable for avoiding postoperative pain. Hence, it is essential to predict postoperative pain and give proper doses accordingly. Recently, the relevance of various clinical parameters and nociception has been investigated in many works, and several indices have been proposed for measuring the level of nociception. However, expensive advanced equipment is required when applying advanced medical technologies, which is not accessible to most institutions. In our work, we propose a deep learning model based on a dynamic graph transformer framework named DoseFormer to predict postoperative pain in a short period after an operation utilizing dynamic patient data recorded in existing widely utilized equipment (e.g., anesthesia monitor). DoseFormer consists of two modules: (i) We design a temporal model utilizing a long short-term memory (LSTM) model with an attention mechanism to capture dynamic intraoperative data of the patient and output a hybrid semantic embedding representing the patient information. (ii) We design a graph transformer network (GTN) to infer the postoperative pain level utilizing the relations across the patient embeddings. We evaluate the DoseFormer system with the medical records of over 999 patients undergoing cardiothoracic surgery in the Fourth Affiliated Hospital of Zhejiang University School of Medicine. The experimental results show that our model achieves 92.16% accuracy for postoperative pain prediction and has a better comprehensive performance compared with baselines.
Recent Advances in the Use of eXplainable Artificial Intelligence Techniques for Wind Turbine Systems Condition Monitoring
Authors: Astolfi Davide , De Caro Fabrizio , Vaccaro Alfredo
Published: August 2023

There is a good probability that wind turbines will emerge as one of the predominant technologies for electricity production in the upcoming decades [...]
Lithofacies Characteristics and Methodology to Identify Lacustrine Carbonate Rocks via Log Data: A Case Study in the Yingxi Area, Qaidam Basin
Authors: Tian Mingzhi , Liu Zhanguo , Zhu Chao , Wu Kunyu , Wang Yanqing , Song Guangyong , Xia Zhiyuan , Li Senming
Published: August 2023

Lacustrine carbonate reservoirs, extensively distributed in China, have extensive oil and gas exploration potential. However, such reservoirs are characterized by high content of terrigenous debris and complex lithofacies, and the resultant high difficulty in lithofacies identification severely restrains exploration expansion and efficient development, especially for the Upper Member of the Paleogene Lower Ganchaigou Formation (E32) of the Yingxi area in the Qaidam Basin, with burial depths generally greater than 4000 m. This research targets this area and develops a methodology for detailed lithofacies identification, after systematically investigating the characteristics of lithofacies and well log responses of lacustrine carbonate rocks, on the basis of a massive volume of data of cores, thin sections, and experiments of the study area. The analysis identified lithofacies in the Upper Member of the Paleogene Lower Ganchaigou Formation of the Yingxi area, namely, pack-wackestone, mudstone, laminated carbonate, muddy gypsum, and limy claystone. The analysis of well log response characteristics suggested that natural gamma ray, matrix density, and bulk density were sensitive to lithofacies. Then, for the first time, the rock fabric factor (RFF) method was proposed, and the lithofacies identification plot was based on the calculated RFF and high-definition spectroscopy log. The presented methodology was applied to 55 wells in the study area. The average accuracy of lithofacies interpretation in 14 cored wells reached 82.4%, indicating good application performance. This method improves the lithofacies identification accuracy of lacustrine carbonate rocks, which is of great significance for investigating the reservoir distribution law and guiding exploration and development.
Survey of Novel Architectures for Energy Efficient High-Performance Mobile Computing Platforms
Authors: O’Connor Owen , Elfouly Tarek , Alouani Ali
Published: August 2023

There are many real-world applications that require high-performance mobile computing systems for onboard, real-time processing of gathered data due to latency, reliability, security, or other application constraints. Unfortunately, most existing high-performance mobile computing systems require a prohibitively high power consumption in the face of the limited power available from the batteries typically used in these applications. For high-performance mobile computing to be practical, alternative hardware designs are needed to increase the computing performance while minimizing the required power consumption. This article surveys the state-of-the-art in high-efficiency, high-performance onboard mobile computing, focusing on the latest developments. It was found that more research is needed to design high-performance mobile computing systems while minimizing the required power consumption to meet the needs of these applications.
Assessing the Dynamic Performance and Energy Efficiency of Pure Electric Car with Optimal Gear Shifting
Authors: Zhao Qiang , Zhou Shengming , Yue Yongheng , Liu Bohang , Xie Qin , Zhang Na
Published: August 2023

Traditional pure electric cars generally adopt single-speed transmission for cost consideration. However, with the renewal and iteration of technology, small electric cars are all developed in the direction of power performance and environmental protection. Gear shifting makes it possible for the motor to work in a more efficient range, which possibly improves the performance of the entire powertrain. In this paper, a small electric car is designed, its power parameters are matched, and the energy-saving space and effect brought by adding multiple-gear shifting transmissions are discussed. To begin, the power-matching design was carried out, and then the transmission ratio was determined by particle swarm optimization. Finally, the power performance and fuel economy of this designed car equipped with different types of transmissions were analyzed and compared through simulation experiments. The results show that the electric car equipped with two-speed transmission has improvements in most important indicators, among which the acceleration time of 0 to 100 km/h is decreased by 17.7%, and the power consumption is reduced by 1.8%. To sum up, the feasibility of applying multiple-gear shifting to small electric cars is verified, and the experimental results provide a valuable reference for the development of electric cars.
Romanian Energy System Analysis (Production, Consumption, and Distribution)
Authors: Fratita Michael , Popescu Florin , Rusu Eugen , Ion Ion V. , Mahu Răzvan
Published: August 2023

The aim of this paper is to provide a comprehensive overview of how electricity is produced from different sources and the electricity needs of the consumer. According to the Green Deal pact, the objective is to gradually reduce polluting emissions in the energy sector by 2050. In the meantime, the CO2 intensive thermal power plants (those using coal and heavy oil) will be closed and strategic investments in green energy sources will be pursued. This paper presents a detailed analysis of the profile of the Romanian electricity producer over a period of one year. Furthermore, this paper explores the possibility for the energy producer to adapt to consumer demand to reduce electricity imports, strategic locations for the installation of farms using renewable energy, and the current capacity of the national distribution system. This paper analyses electricity only quantitatively and not qualitatively, as well as the way the supplier follows consumers’ need. The paper concludes on possible problems that may arise from the case of green energy.
Coupled Monte Carlo and Thermal-Hydraulics Modeling for the Three-Dimensional Steady-State Analysis of the Xi’an Pulsed Reactor
Authors: Jiang Duoyu , Xu Peng , Hu Tianliang , Jiang Xinbiao , Wang Lipeng , Li Da , Zhang Xinyi , Cao Lu
Published: August 2023

The Xi’an Pulsed Reactor (XAPR) is characterized by its small core size and integrated fuel moderator structure, which results in a non-uniform core power and temperature distribution. Consequently, a complex coupling relationship exists between its core neutronics and thermal hydraulics, necessitating the assurance for the operational safety of the XAPR. To optimize the experimental scheme in the reactor, a refined three-dimensional steady-state nuclear-thermal coupling analysis is imperative. This study focuses on investigating the coupling calculation of a three-dimensional steady-state neutronics and thermal-hydraulics model for the XAPR by utilizing an open-source multi-physical coupling framework known as Cardinal. The neutron transport equation is effectively solved using OpenMC, while a three-dimensional heat conduction model is employed to compute the heat conduction of the fuel elements. Furthermore, a parallel multi-channel model is utilized to determine the fluid heat transfer. The research is centered on the XAPR, whereby Monte Carlo and thermal-hydraulics coupling calculations of the core under steady-state full-power conditions are conducted, specifically at an operational capacity of 2 MW. The results demonstrate a strong agreement between the simulation and experimental outcomes. The maximum temperature recorded for the thermometric fuel element in the XAPR is 795.1 K, with a deviation of approximately −5.7% from the measured value. Moreover, the outlet fluid temperature of the thermal channel is observed to be 360 K, exhibiting a deviation of around −2.7% from the measured value.
A FEM-Based Comparative Study of the Effect of Rotor Bar Designs on the Performance of Squirrel Cage Induction Motors
Authors: Ocak Cemil
Published: August 2023

Induction motors (IM) are the most frequently used type of motor in the industry. The number of rotor slots, bar geometry, and conductivity of bar material have a strong impact on the torque profile and efficiency characteristics of induction motors. This study focused on investigating the effect of different rotor bar designs on motor performance by the finite element method (FEM). The IMs have been designed using the same stator core, winding, and core lengths. The total rotor bar cross-section areas are also fixed throughout all designs. In addition to the change in the number of rotor bars and geometry, the effect of copper and aluminum bar materials on motor performance was also investigated, both for single and double-layered squirrel-cage structures. The results of the study indicate that the starting torque of the motor in a 36/30-slot aluminum single-cage structure was obtained as 96.26 Nm, while the starting torque of a 36/46-slot aluminum double-cage structure was found to be 115.34 Nm. It is also found that the starting torque of the initial design can be increased by up to 19.82% by changing only the rotor bar numbers and material with the same stator and rotor size. The efficiency of the motors was determined as 86.6% for both designs. In addition to efficiency, the output torque ripple has been decreased to 2.63, which equals a 67.32% decrease in the ripple of the initial design. The improved design has an approximately 8 °C lower T2 due to better cooling performance as a result of a higher number of rotor slots.
Holistic Evaluation of Demand Response Events in Real Pilot Sites: From Baseline Calculation to Evaluation of Key Performance Indicators
Authors: Andreadou Nikoleta , Thomas Dimitrios , De Paola Antonio , Kotsakis Evangelos , Fulli Gianluca
Published: August 2023

Explicit demand response plays a significant role in the future energy grid transition, as it involves end consumers in smart grid activities and, at the same time, exploits the potential of flexibility, giving the opportunity to grid operators to accommodate a total amount of energy without the need to reinforce the grid infrastructure. For evaluating the successfulness of a demand response program, thus, evaluating its advantages, it is fundamental to have an accurate baseline curve consumption along with meaningful key performance indicators. In this work, we propose a novel way of calculating the baseline consumption using artificial intelligence techniques. In particular, regression models have been applied to a database of historical data. In order to present a complete evaluation of demand response programs, we present five key performance indicators (KPIs). The KPIs have been selected so as to depict the successfulness of the explicit demand response program. We suggest a novel way of evaluating two of the five KPI using a quantitative approach. We also apply the proposed methodology for baseline calculation and KPIs evaluation in a practical example: two pilot sites have been used and real-life scenarios of demand response events have been applied for this scope to actual nonindustrial consumers and especially residential consumers. The baseline has been calculated for these pilot sites and the KPIs have been evaluated for them. The presented results complete the picture of evaluating a real-life demand response program and show the effectiveness of the selected approach. The proposed schemes for baseline calculation and KPI evaluation can be used by the scientific community for evaluating future demand response programs, especially in the residential sector.
Effect of Rotating Channel Turning Section Clearance Size on Heat Transfer Characteristics of Supercritical Pressure Hydrocarbon Fuel
Authors: Dong Mengqiang , Huang Hongyan
Published: August 2023

For the problem of power generation turbine blade ablation in hypersonic vehicles, hydrocarbon fuel carried by the vehicle is used to cool the turbine blades. In order to fully utilize the cooling capacity of hydrocarbon fuel, the structure of the cooling channels needs to be optimized. In this study, a variable clearance hydrocarbon fuel cooling channel is applied for the first time to the rotating turbine blades of a hypersonic vehicle to enhance the heat transfer ability of hydrocarbon fuel. The effect of clearance size on the heat transfer performance of hydrocarbon fuel under rotating conditions is investigated. The accuracy of the calculations is verified by comparison with experimental data. The results of the study show that the heat transfer performance can be significantly improved by changing the clearance of the turning section. The clearance size 2.5 D channel has the highest thermal performance with a maximum improvement of 1.8 times. The law of change of thermal performance is affected by crossing the critical temperature point, as it is different before and after the crossing. Thermal performance changes from decreasing then increasing to increasing then decreasing as the clearance size increases for high rotation speed conditions as the temperature of the entrance straddles the critical temperature. The Nusselt number first increases and then decreases for all channels with different clearance sizes with an increasing rotational speed. The friction factor changes from first increasing and then decreasing to decreasing and then increasing as the clearance size increases for high rotation speed conditions as the temperature of the entrance straddles the critical temperature.
Thermodynamic and Economic Simulation of an Organic Rankine Cycle for the Utilization of Combustion Gas Produced in Small Landfills in Antioquia, Colombia
Authors: Rocha-Meneses Lisandra , Zea Jhojan , Martínez Brandon , Arrieta Carlos , Risco Mario Luna-del , Villegas Sebastián , Arredondo Carlos
Published: August 2023

This study presents a simulation of an organic Rankine cycle (ORC) for the utilization of combustion gas produced in small landfills in Antioquia, Colombia, with a focus on the municipality of Angostura. This municipality has been chosen as the focus of this study due to its growing population and industrial and tourism development, bringing with them the need for sustainable waste and energy management solutions. The proposed ORC system includes two evaporators, two turbines, a condenser, a pump, and a generator, similar to successful systems reported in the literature. A sensitivity analysis was performed to investigate the impact of pressure, temperature, and mass flow on the system’s net power output and thermodynamic efficiency. The results showed that the system reached a net power output of 64.33 kW with an overall power plant efficiency of 13.03% and an investment cost of 192,340 USD based on a reference cost of 2990 USD/kW. The study also found that the system’s use in Angostura is economically feasible, with a net present value of 31,208 USD over a 10-year investment study. The sensitivity analysis revealed that temperature and pressure have direct effects on the system’s performance and economic viability. The municipality’s geomorphological characteristics were found to reduce the risk of groundwater contamination, while surface waters may still be vulnerable to contamination from leachates. Overall, this study highlights the feasibility and potential benefits of utilizing an ORC system, in which the combustion heat from methane gas produced in small landfills in Antioquia, Colombia, is harnessed for energy production.
Energy Storage Potential Needed at the National Grid Scale (Poland) in Order to Stabilize Daily Electricity Production from Fossil Fuels and Nuclear Power
Authors: Olczak Piotr , Matuszewska Dominika
Published: August 2023

With the increasing share of renewable sources in the energy mix, there is a need to balance energy production from weather-dependent sources, such as wind turbines and photovoltaics. This is also a current global trend associated with climate policy. In Poland, there has been a significant increase in energy production from renewable sources, leading to a duck curve phenomenon mainly in the case of photovoltaics, which requires balancing this production through various measures. One possible way to achieve this is energy storage installation. This article identifies the need for energy storage to ensure the stability of electricity production from low-flexibility sources like coal-based power plants. For this purpose, a methodology has been developed to determine the daily minimum energy storage capacities which would also allow for the integration of other stable (though less flexible) energy sources, such as nuclear power. In the case of Poland, energy storage has been estimated to require, as a median value, approximately 6 GWh of additional storage capacity, which is equivalent to twice the planned capacity of the Młoty Pumped Storage Power Plant.
Sand-Carrying Law and Influencing Factors in Complex Fractures of Nano-Clean Fracturing Fluid
Authors: Han Hongkai , Shi Zhaoqi , Zuo Weiqin , Wu Shengjie , Liu Yanwei , Xie Kunrong , Long Liqun , Mitri Hani
Published: August 2023

Nano-clean fracturing fluids have broad application potential in coalbed methane reservoir fracturing owing to their high stability, good temperature resistance, low filtration loss, and strong frictional resistance reduction. However, the sand-carrying regularity of nano-clean fracturing fluids in coalbed methane reservoirs is unclear, especially for complex fractures with variable directions. This study established a sand transport model that considers proppant collision, wall friction blocking, fracture fluid filtration loss, and the fracture branching angle to study the sand-carrying law of nano-clean fracturing fluids and its influencing factors in complex fractures. The degrees of influence on equilibrium height and placement rate from high to low were the proppant particle size, proppant density, fracturing fluid properties, sand ratio, and pumping discharge volume, and the correlation degrees obtained by grey correlation analysis are 0.862, 0.861, 0.855, 0.854, and 0.832, respectively. As the complexity of the fractures deepens and the resistance increases, the flow rate of the fracturing fluid is reduced, making it difficult for the proppant to enter the branching joints. The sand-carrying performance of a nano-clean fracturing fluid is better than that of a common clear-water fracturing fluid. The fluid-structure coupling model of a nano-clean fracturing fluid can accurately characterize the sand-carrying law of nano-clean fracturing fluids, providing a research basis for optimizing high efficiency sand-carrying fracturing fluid parameters in coalbed methane reservoir fracturing construction.
A Review of the CFD Method in the Modeling of Flow Forces
Authors: Domagala Mariusz , Fabis-Domagala Joanna
Published: August 2023

Hydraulic valves are key components of fluid power systems. They control the flow rate and pressure in hydraulic lines, actuator motion, and direction. Valves that control flow rate or pressure can be divided into two main categories: spool-type valves, where control components are similar to the piston inside a sleeve with control orifices; and seat-type valves, in which a poppet inside a seat opens and closes the flow. Forces induced on valve components during oil flow are crucial to the valve’s operational capabilities. They can be calculated using a formula originating from the momentum conservation equation for a two-dimensional control volume. Increasing demands for flow rate and pressure control accuracy cause flow forces to be calculated much more accurately than when using the analytical formula. Therefore, computational fluid dynamics (CFD) simulations are the only effective tool for their calculation. This paper reviews the CFD approaches used for calculating flow forces inside hydraulic valves. It presents typical approaches used for evaluating flow forces inside hydraulic valves. The oldest and most common are conducted for a fixed position of valve components for defined flow conditions, which do not cover all components of flow forces. The dynamic flow forces can be calculated using more complex CFD models using fluid–structure interaction (FSI) techniques. This paper presents available FSI techniques for the simulation of transient flow forces, mainly for valves whose component position is determined by the forces occurring during oil flow.
Development of Adaptive Model and Occupant Behavior Model in Four Office Buildings in Nagasaki, Japan
Authors: Genjo Kahori , Nakanishi Haruna , Oki Momoka , Imagawa Hikaru , Uno Tomoko , Saito Teruyuki , Takata Hiroshi , Tsuzuki Kazuyo , Nakaya Takashi , Nishina Daisaku , Hasegawa Kenichi , Mori Taro , Rijal Hom Bahadur
Published: August 2023

A field survey of indoor environmental measurements and questionnaires on thermal sensation, overall comfort, and behaviors was conducted in four office buildings in Japan by visiting each office every month over a duration of more than a year during the coronavirus disease 2019 (COVID-19) pandemic. The indoor environment was measured concurrently. We obtained 1047 votes from office workers in their 20s to 60s. The regression and Griffiths’ methods were used to calculate the indoor comfort temperature. A logistic regression analysis was used to develop the occupant behavior model. Over 70% of the occupants found the indoor environment comfortable at a mean comfort temperature of 23.2 to 25.9 °C. Gender differences were observed in thermal sensation and overall comfort, but a gender difference was observed only in the cooling mode for the indoor comfort temperature. An adaptive model was developed for the office buildings in Nagasaki city to predict the indoor comfort temperature from the outdoor air temperature. The proportions of heating, cooling, and fan usage can be predicted from the outdoor air temperature using a logistic regression analysis. The adaptive model and occupant behavior model are useful for the indoor temperature control of the existing buildings and thermal simulation of the new building design.
Variable Blade Inertia in State-of-the-Art Wind Turbine Structural-Dynamics Models
Authors: Alhrshy Laurence , Lippke Alexander , Jauch Clemens
Published: August 2023

This paper presents a comparison of two methods to represent variable blade inertia in two codes for aero-servo-elastic simulations of wind turbines: the nonlinear aeroelastic multi-body model HAWC2 and the nonlinear geometrically exact beam model BeamDyn for OpenFAST. The main goal is to enable these tools to simulate the dynamic behavior of a wind turbine with variable blade inertia. However, current state-of-the-art load simulation tools for wind turbines cannot simulate variable blade inertia, so the source code of these tools must be modified. The validity of the modified codes is proven based on a simple beam model. The validation shows very good agreement between the modified codes of HAWC2, BeamDyn and an analytical calculation. The add-on of variable blade inertias is applied to reduce the mechanical loads of a 5-megawatt reference wind turbine with an integrated hydraulic-pneumatic flywheel in its rotor blades.
Evaluation Method of the Incremental Power Supply Capability Brought by Distributed Generation
Authors: Hao Yi , Huang Zhigang , Ma Shiqian , Huang Jiakai , Chen Jiahao , Sun Bing
Published: August 2023

More and more distributed generation (DG) and energy storage (ES) devices are being connected to the distribution network (DN). They have the potential of maintaining a stable supply load during failure periods when using islanding operations. Therefore, DG and ES have capacity value, i.e., improving the power supply capability of the system. However, there are strong fluctuations in DG outputs, and the operations of ES devices have sequential characteristics. The same capacity of DG has different load-bearing capabilities compared to conventional thermal or hydroelectric units. This paper proposes a method for evaluation of power supply capability improvement in DNs. First, the temporal fluctuation in both power source and load demand during fault periods is considered. A DN island partition model considering the secondary power outage constraint is established. Then, a modified genetic algorithm is designed. The complex island partition model is solved to achieve accurate power supply reliability evaluation. And the incremental power supply capability associated to DG and ES devices is calculated. Finally, a case study is conducted on the PG&E 69-bus system to verify the effectiveness of the proposed method. It is found that with a 20% configuration ratio of ES devices, the power supply capability improvement brought about by 6 MW DG can reach about 773 kW.
The Effect of Carbon Sink Afforestation Projects on China’s Forest Product Export Trade: Empirical Evidence from Chinese Microenterprises
Authors: Gao Xiaoxin , Zhang Caihong
Published: August 2023

Forestry carbon sinks are an important measure for enabling China to cope with global climate change and realize its “double carbon” goal. Carbon sink afforestation projects (CSAPs) are a type of forestry carbon sink projects, and the question of whether China’s forest products with an export orientation can adapt to the requirements of high-quality development is important for balancing the relationship between emissions reduction and development. We use the micro-data of forest product export enterprises provided by the China Customs Database and construct a difference-in-difference (DID) model to investigate the impact of CSAPs on China’s forest product export and its heterogeneity. The results show that CSAPs significantly increase the export scale of China’s forest products. CSAPs lead to significant heterogeneity regarding the forest product export scale in relation to regions, production factor intensity, trade pattern, enterprise ownership, and export destination countries. We further find that although the implementation of CSAPs significantly reduces the export types of forest products, it significantly promotes an increase in the export price and the expansion of the export quantity of forest products, and it optimizes the export structure of forest products to some extent. The “Belt and Road” initiative (BRI) has played a significant positive role in regulating the expansion of the forest product export scale with CSAPs. The implementation of CSAPs reduces the risk of termination of trade relations among forest product export enterprises and extends the export duration. The conclusions provide implications for policy makers and managers of forest product enterprises.
Light Induction of Seed Culture Accelerates Lutein Accumulation in Heterotrophic Fermentation of Chlorella protothecoides CS-41
Authors: Fu Yunlei , Yi Lanbo , Yang Shufang , Lu Xue , Liu Bin , Chen Feng , Huang Junchao , Cheng Kawing , Sun Han , Wu Xiaolei
Published: August 2023

Microalgae are recognized as a promising and valuable source of lutein. However, the current two-stage method for lutein production has drawbacks, such as complex operations and a long cultivation time. Additionally, utilizing heterotrophic fermentation to cultivate microalgae for lutein production leads to low lutein content due to the absence of light. In this study, we proposed a novel cultivation method that involves light induction of the seed culture to enhance lutein production during the heterotrophic cultivation phase of Chlorella protothecoides CS-41. To gain comprehensive insights into the underlying mechanisms of this method, we conducted qualitative and quantitative analyses of specific metabolites related to central carbon metabolism. The results revealed that low-light induction of seeds exhibited higher carbon efficiency compared to cells continuously subjected to heterotrophic cultivation, which may explain the observed increase in biomass and lutein content in cultures. Cultures after low-light induction of seed exhibited significantly higher lutein content (2.71 mg/g), yield (66.49 mg/L) and productivity (8.59 mg/L/d) compared to those consistently cultivated under heterotrophic conditions (2.37 mg/g, 37.45 mg/L, 4.68 mg/L/d). This cultivation strategy effectively enhances lutein yields, reduces production costs and holds the potential for broader application in other algal species for pigment production.
Assessment of Remediation of Municipal Wastewater Using Activated Carbon Produced from Sewage Sludge
Authors: Mudzanani Khuthadzo , Iyuke Sunny , Daramola Michael O.
Published: August 2023

This study evaluates the potential to synthesize an adsorbent for wastewater remediation applications from an anaerobic digestion by-product synthesized using biomaterials and a less energy-intensive process. The synthesized sludge-based granular activated carbon (GAC) was used to adsorb Cr(VI) and Cd(II) in a batch reactor stirred for 24 h at 25 °C. The surface chemistry of the material was assessed porosity with BET, SEM for morphology, EDS-XRF for elemental analysis, and functional groups on these materials using FTIR and TGA for thermal profile. SBET of the SAC was discovered to be 481.370 m2/g with a VT of 0.337 cm3/g, respectively 9.02 and 2.23 times greater than raw sludge. The modification to SAC shows a dramatic increase in performance from 40% to 98.9% equilibrium adsorption rate. The maximum or equilibrium removal (99.99%) of Cr(VI) and Cd(II) was achieved by 0.8 and 1.4 g SAC dosage, respectively. Thus, it can be concluded that activation of sewage sludge was effective in enhancing the surface area and pore volume which made it suitable for AMD remediation application.