bluuebunny/crossref_metadata_2025 huggingface dataset:
On Magnetic Models in Wavefunction Ensembles
Authors: De Carlo Leonardo , Wick William D.
Published: March 2023

In a wavefunction-only philosophy, thermodynamics must be recast in terms of an ensemble of wavefunctions. In this perspective we study how to construct Gibbs ensembles for magnetic quantum spin models. We show that with free boundary conditions and distinguishable “spins” there are no finite-temperature phase transitions because of high dimensionality of the phase space. Then we focus on the simplest case, namely the mean-field (Curie–Weiss) model, in order to discover whether phase transitions are even possible in this model class. This strategy at least diminishes the dimensionality of the problem. We found that, even assuming exchange symmetry in the wavefunctions, no finite-temperature phase transitions appear when the Hamiltonian is given by the usual energy expression of quantum mechanics (in this case the analytical argument is not totally satisfactory and we relied partly on a computer analysis). However, a variant model with additional “wavefunction energy” does have a phase transition to a magnetized state. (With respect to dynamics, which we do not consider here, wavefunction energy induces a non-linearity which nevertheless preserves norm and energy. This non-linearity becomes significant only at the macroscopic level.) The three results together suggest that magnetization in large wavefunction spin chains appears if and only if we consider indistinguishable particles and block macroscopic dispersion (i.e., macroscopic superpositions) by energy conservation. Our principle technique involves transforming the problem to one in probability theory, then applying results from large deviations, particularly the Gärtner–Ellis Theorem. Finally, we discuss Gibbs vs. Boltzmann/Einstein entropy in the choice of the quantum thermodynamic ensemble, as well as open problems.
Face Database Protection via Beautification with Chaotic Systems
Authors: Wang Tao , Zhang Yushu , Zhao Ruoyu
Published: March 2023

The database of faces containing sensitive information is at risk of being targeted by unauthorized automatic recognition systems, which is a significant concern for privacy. Although there are existing methods that aim to conceal identifiable information by adding adversarial perturbations to faces, they suffer from noticeable distortions that significantly compromise visual perception, and therefore, offer limited protection to privacy. Furthermore, the increasing prevalence of appearance anxiety on social media has led to users preferring to beautify their faces before uploading images. In this paper, we design a novel face database protection scheme via beautification with chaotic systems. Specifically, we construct the adversarial face with better visual perception via beautification for each face in the database. In the training, the face matcher and the beautification discriminator are federated against the generator, prompting it to generate beauty-like perturbations on the face to confuse the face matcher. Namely, the pixel changes produced by face beautification mask the adversarial perturbations. Moreover, we use chaotic systems to disrupt the order of adversarial faces in the database, further mitigating the risk of privacy leakage. Our scheme has been extensively evaluated through experiments, which show that it effectively defends against unauthorized attacks while also yielding good visual results.
Digital Innovation in Language Teaching—Analysis of the Digital Competence of Teachers according to the DigCompEdu Framework
Authors: Rubio-Gragera María , Cabero-Almenara Julio , Palacios-Rodríguez Antonio
Published: March 2023

The utilization of technology in the process of teaching and learning, as well as the influence of the COVID-19 crisis on education, are widely recognized and interconnected factors. This investigation is primarily focused on a group of formal education teachers who have received little attention to date, teachers from Official Language Schools, which are a part of the Special Regime Education system of Andalucía, a southern region of Spain, which provides foreign language education. Specifically, we aim to assess their level of digital proficiency in relation to their experience and use of information and communication technology (ICT) in the classroom. We also analyze how the March 2020 lockdown impacted their confidence levels in utilizing ICT in their teaching practices. One hundred and four teachers took part in the study and answered the DigCompEdu check-in questionnaire. The findings indicate that teachers’ overall self-assessment of their digital competence is low, with particular attention needed in the least developed areas, which is the facilitation of digital proficiency to students. Additionally, there are noteworthy differences in the variables of ICT experience and confidence. For example, the amount of time spent utilizing ICT in teaching does not necessarily correlate with teaching proficiency. Based on these results, we discuss potential strategies for enhancing digital competence in this educational group and propose some curriculum content for teacher training in digital competence.
Diverse Development and Future Challenges of Game-Based Learning and Gamified Teaching Research
Authors: Hou Huei-Tse
Published: March 2023

Play is one of the most important activities in human life [...]
Blockchain and Interplanetary File System (IPFS)-Based Data Storage System for Vehicular Networks with Keyword Search Capability
Authors: N. Sangeeta  , Nam Seung Yeob
Published: March 2023

Closed-circuit television (CCTV) cameras and black boxes are indispensable for road safety and accident management. Visible highway surveillance cameras can promote safe driving habits while discouraging moving violations. According to CCTV laws, footage captured by roadside cameras must be securely stored, and authorized persons can access it. Footages collected by CCTV and Blackbox are usually saved to the camera’s microSD card, the cloud, or hard drives locally but there are concerns about security and data integrity. These issues may be addressed by blockchain technology. The cost of storing data on the blockchain, on the other hand, is prohibitively expensive. We can have decentralized and cost-effective storage with the interplanetary file system (IPFS) project. It is a file-sharing protocol that stores and distributes data in a distributed file system. We propose a decentralized IPFS and blockchain-based application for distributed file storage. It is possible to upload various types of files into our decentralized application (DApp), and hashes of the uploaded files are permanently saved on the Ethereum blockchain with the help of smart contracts. Because it cannot be removed, it is immutable. By clicking on the file description, we can also view the file. DApp also includes a keyword search feature to assist us in quickly locating sensitive information. We used Ethers.js’ smart contract event listener and contract.queryFilter to filter and read data from the blockchain. The smart contract events are then written to a text file for our DApp’s keyword search functionality. Our experiment demonstrates that our DApp is resilient to system failure while preserving the transparency and integrity of data due to the immutability of blockchain.
A Software Defect Prediction Method Based on Program Semantic Feature Mining
Authors: Yao Wenjun , Shafiq Muhammad , Lin Xiaoxin , Yu Xiang
Published: March 2023

As the size and complexity of software systems grow, knowing how to effectively judge whether there are defects in the programs has attracted extensive attention in research. However, current software defect prediction methods only extract semantic information at the syntactic level and lack features to mine defect manifestations at the semantic level of code, because defective software is incomplete or defective in semantic representation. Defective software exhibits incomplete or flawed semantic behavior. This paper proposes a software defect prediction method based on the program semantics feature mining (PSFM) method. Specifically, the semantic information is first extracted from the code grammatical structure information and code text information. Then, the defect feature is mined through the semantic information. Finally, software defects are predicted by using the mined defect features. The experimental results show that, compared with the existing software defect prediction methods, the method in this paper (PSFM method) obtained a higher F-measure value.
DFEN: Dual Feature Enhancement Network for Remote Sensing Image Caption
Authors: Zhao Weihua , Yang Wenzhong , Chen Danny , Wei Fuyuan
Published: March 2023

The remote sensing image caption can acquire ground objects and the semantic relationships between different ground objects. Existing remote sensing image caption algorithms do not acquire enough ground object information from remote-sensing images, resulting in inaccurate captions. As a result, this paper proposes a codec-based Dual Feature Enhancement Network (“DFEN”) to enhance ground object information from both image and text levels. We build the Image-Enhancement module at the image level using the multiscale characteristics of remote sensing images. Furthermore, more discriminative image context features are obtained through the Image-Enhancement module. The hierarchical attention mechanism aggregates multi-level features and supplements the ground object information ignored due to large-scale differences. At the text level, we use the image’s potential visual features to guide the Text-Enhance module, resulting in text guidance features that correctly focus on the information of the ground objects. Experiment results show that the DFEN model can enhance ground object information from images and text. Specifically, the BLEU-1 index increased by 8.6% in UCM-caption, 2.3% in Sydney-caption, and 5.1% in RSICD. The DFEN model has promoted the exploration of advanced semantics of remote sensing images and facilitated the development of remote sensing image caption.
Piecewise Hybrid System with Cross-Correlation Spectral Kurtosis for Fault Diagnosis in Rolling Bearing of Wind Power Generator
Authors: Wang Shan , Qiao Zijian , Niu Pingjuan
Published: March 2023

As the core equipment of wind turbines, rolling bearings affect the normal operation of wind power generators, resulting in huge economic losses and significant social impacts in the case of faults. Most faults are not easily found because of the small vibration response of these rolling bearings that operate in harsh conditions. To address the problem that the fault identifications of rolling bearings are disturbed by the strong noise in wind power generators, an adaptive nonlinear method based on a piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis is proposed. Then, the vibration signals collected from the fault point of the outer and inner rings are used to clarify the outstanding capability of the proposed method when compared with the maximum cross-correlation-kurtosis-based unsaturated stochastic resonance method. Furthermore, the machine learning method based on the medium tree was adopted to further prove the excellent performance of the piecewise hybrid stochastic resonance system with a novel cross-correlation spectral kurtosis for realizing the efficient detection of rolling bearing faults in wind power generators, which has important innovation significance and practical engineering value for ensuring the safe and stable operation of wind turbines.
Artifact Detection in Lung Ultrasound: An Analytical Approach
Authors: Hliboký Maroš , Magyar Ján , Bundzel Marek , Malík Marek , Števík Martin , Vetešková Štefánia , Dzian Anton , Szabóová Martina , Babič František
Published: March 2023

Lung ultrasound is used to detect various artifacts in the lungs that support the diagnosis of different conditions. There is ongoing research to support the automatic detection of such artifacts using machine learning. We propose a solution that uses analytical computer vision methods to detect two types of lung artifacts, namely A- and B-lines. We evaluate the proposed approach on the POCUS dataset and data acquired from a hospital. We show that by using the Fourier transform, we can analyze lung ultrasound images in real-time and classify videos with an accuracy above 70%. We also evaluate the method’s applicability for segmentation, showcasing its high success rate for B-lines (89% accuracy) and its shortcomings for A-line detection. We then propose a hybrid solution that uses a combination of neural networks and analytical methods to increase accuracy in horizontal line detection, emphasizing the pleura.
Symmetric Two-Workshop Heuristic Integrated Scheduling Algorithm Based on Process Tree Cyclic Decomposition
Authors: Zhou Wei , Zhou Pengwei , Yang Dan , Cao Wangcheng , Tan Zhenjiang , Xie Zhiqiang
Published: March 2023

The existing research on the two-workshop integrated scheduling problem with symmetrical resources does not consider the complex product attribute structure and the objective situation of plant equipment resources. This results in the prolongation of the product makespan and the reduction of the utilization rate of the general equipment in the workshop. To solve the above problems, a two-workshop integrated scheduling algorithm based on process tree cyclic decomposition (STHIS-PTCD) was proposed. First, a workshop scheduling scheme based on the sub-tree cyclic decomposition strategy was proposed to improve the closeness of continuous processing further. Second, an operation allocation scheme based on the principle of workshop processing balance was presented. On the basis of ensuring the advantages of parallel processing, it also effectively reduces the idle time of equipment resources and then optimizes the overall effect of the integrated scheduling of both workshops. Through the comparison and analysis of all the existing resource-symmetric two-workshop integrated scheduling algorithms, the scheduling effect of the proposed algorithm is the best.
Adaptive Task Scheduling Algorithm for Multifunction Integrated System with Joint Radar–Communications Waveform
Authors: Rong Juan , Liu Feifeng , Miao Yingjie , Zhu Huizhu , Wu Chuanzhang
Published: March 2023

Joint radar–communications (JRC) waveform could enable simultaneously radar sensing and communication functions in a multifunction integrated system (MFIS). Because of the special JRC waveform characteristics of MFIS, the traditional task scheduling algorithm for multifunction phased array radar cannot be applied to the MFIS to release its full potential. Therefore, a novel adaptive task scheduling algorithm for JRC waveform enabled MFIS is proposed in the paper. Firstly, the modified JRC waveform scheduling criteria for MFIS are remodeled after establishing the multi-task models. Secondly, the task scheduling optimization problem, which can reflect the scheduling capability of the JRC waveform, is reformulated, and the proposed novel adaptive task scheduling algorithm based on the JRC waveform effectively schedules different kinds of tasks. Finally, the simulation results show that the proposed algorithm not only has a high JRC waveform scheduling capability but also provides better performance compared with the traditional ones.
Recent Developments and Prospects of Fully Recessed MIS Gate Structures for GaN on Si Power Transistors
Authors: Fernandes Paes Pinto Rocha Pedro , Vauche Laura , Pimenta-Barros Patricia , Ruel Simon , Escoffier René , Buckley Julien
Published: March 2023

For high electron mobility transistors (HEMTs) power transistors based on AlGaN/GaN heterojunction, p-GaN gate has been the gate topology commonly used to deplete the two dimensional electron gas (2-DEG) and achieve a normally-OFF behavior. But fully recessed MIS gate GaN power transistors or MOSc-HEMTs have gained interest as normally-OFF HEMTs thanks to the wider voltage swing and reduced gate leakage current when compared to p-GaN gate HEMTs. However the mandatory AlGaN barrier etching to deplete the 2-DEG combined with the nature of the dielectric/GaN interface generates etching-related defects, traps, and roughness. As a consequence, the threshold voltage (VTH) can be unstable, and the electron mobility is reduced, which presents a challenge for the integration of a fully recessed MIS gate. Recent developments have been studied to solve this challenge. In this paper, we discuss developments in gate recess with low impact etching and atomic layer etching (ALE) alongside surface treatments such as wet cleaning, thermal or plasma treatment, all in the scope of having a surface close to pristine. Finally, different interfacial layers, such as AlN, and alternative dielectrics investigated to optimize the dielectric/GaN interface are presented.
A Cost–Benefit Analysis for Utility-Scale Agrivoltaic Implementation in Italy
Authors: Di Francia Girolamo , Cupo Paolo
Published: March 2023

Utility-scale photovoltaic plants can take up areas as wide as several tens of hectares, often occupying spaces normally used for other purposes. This “land competition” issue might become particularly relevant for agriculture since, similarly to the production of photovoltaic electricity, farming uses the sun as a primary energy source. Thus, there is increasing interest in investigating agrivoltaic plants that allow the coexistence of agricultural activity and the production of electricity from photovoltaics. Such solutions are more complex and expensive than standard ground-mounted photovoltaic plants, so it is questionable whether the economic revenues produced by the agrivoltaic choice and resulting from both the cropland activity and electricity production can compensate for the high costs involved. The problem is further complicated by the fact that both crop revenues and photoelectricity costs depend, in general, on the geographical location. In this study, a cost/benefit methodology was developed to investigate the conditions under which the installation of an agrivoltaic utility plant can be economically advantageous compared with a standard ground-mounted photovoltaic plant. The analysis relies on the evaluation of both the extra cost related to the agrivoltaic choice and the performance benefit related to the crop revenues. By fixing the capacity of PV utility plants to be installed in all Italian regions, results were validated, considering crops such as wheat, corn, soybean, potato, and sunflower that make use of wide areas. It was determined that the higher infrastructural costs of agrivoltaic plants seriously hamper their installation, even for high-revenue croplands, unless suitable supporting policies in the form of public subsidies are conceived. In this context, it would be useful to evaluate whether such financial aids conceived to support agrivoltaic implementation in productive agricultural areas could be better used to support agrivoltaic installations in croplands at risk of abandonment or even already abandoned croplands, recovering otherwise unproductive agricultural lands.
Off-Grid Hybrid Renewable Energy System Operation in Different Scenarios for Household Consumers
Authors: Pop Teodor , Ungureanu Constantin , Pentiuc Radu Dumitru , Afanasov Ciprian , Ifrim Visarion Catălin , Atănăsoae Pavel , Milici Laurențiu Dan
Published: March 2023

Off-grid hybrid renewable energy systems represent the most modern and flexible solutions that can cover a wide range of energy efficiency needs for household consumers. In the current context, these systems must be taken into account by most household consumers, given the fact that the price of energy has increased. This paper proposes an experimental analysis of the behavior of such a system, in real operating conditions, considering two renewable energy sources, wind and photovoltaic sources, using an experimental stand. It can be considered a testing platform for hybrid energy production systems, in that the power installed in the two sources can be scaled up. The platform has the advantage of allowing the implementation of different load and wind curves through a programmable logic controller; in this way, it is possible to evaluate the degree of coverage of the energy consumption produced from renewable sources, in the north-east of Romania, in the Suceava County region. The experimental study also involves an analysis of the storage capacity in relation to the consumption and the electricity produced by the two renewable sources. In this regard, three scenarios differentiated by the state of charge (30%, 50% and 70%) have been established. The results indicate that, for each of the imposed scenarios, the energy required to cover consumption is produced by renewable energy sources (42%, 47% and 53%), to which the energy stored in batteries (39%, 28% and 18%) is added.
Foreign Object Shading Detection in Photovoltaic Modules Based on Transfer Learning
Authors: Liu Bin , Kong Qingda , Zhu Hongyu , Zhang Dongdong , Goh Hui Hwang , Wu Thomas
Published: March 2023

As a representative new energy source, solar energy has the advantages of easy access to resources and low pollution. However, due to the uncertainty of the external environment, photovoltaic (PV) modules that collect solar energy are often covered by foreign objects in the environment such as leaves and bird droppings, resulting in a decrease in photoelectric conversion efficiency, power losses, and even the “hot spot” phenomenon, resulting in damage to the modules. Existing methods mostly inspect foreign objects manually, which not only incurs high labor costs but also hinders real-time monitoring. To address these problems, this paper proposes an IDETR deep learning target detection model based on Deformable DETR combined with transfer learning and a convolutional block attention module, which can identify foreign object shading on the surfaces of PV modules in actual operating environments. This study contributes to the optimal operation and maintenance of PV systems. In addition, this paper collects data in the field and constructs a dataset of foreign objects of PV modules. The results show that the advanced model can significantly improve the target detection AP values.
A Model Predictive Control Approach for Heliostat Field Power Regulatory Aiming Strategy under Varying Cloud Shadowing Conditions
Authors: Zhu Ruidi , Ni Dong
Published: March 2023

Weather conditions have significant impacts on the solar concentration processes of the heliostat fields in solar tower power plants. The cloud shadow movements may cause varying solar irradiance levels received by each heliostat. Hence, fixed aiming strategies may not be able to guarantee the solar concentrating performance. Dynamic aiming strategies are able to optimize the aiming strategy based on real-time shadowing conditions and short-term forecast, and, therefore, provide much more robust solar concentration performance compared to fixed strategies. In this work, a model predictive control approach for s heliostat field power regulatory aiming strategy was proposed to regulate the total concentrated solar flux on the central receiver. The model predictive control method obtains the aiming strategy, leveraging real-time and forecast shadowing conditions based on the solar concentration model of the heliostat field. The allowable flux density of the receiver and the aiming angle adjustment limits are also considered as soft and hard constraints in the aiming strategy optimization. A Noor III-like heliostat field sector was studied with a range of shadow-passing scenarios, and the results demonstrated the effectiveness of the proposed method.
Analysis of the Impact of Design Parameters on the Power Density of the New Design of the Cogging Machine
Authors: Lerch Tomasz
Published: March 2023

The cogging machine is a new design of a machine with permanent magnets, which combines the advantages of a machine with transverse flux (TFM), eliminating its disadvantages. The biggest advantage of TFM machines is their high torque density, which varies with the design parameters. The developed machine retains these advantages while simplifying the construction of the stator core, which is the biggest disadvantage of TFM machines. This article presents the results of an analysis of the power density of the developed cogging machine as a function of a number of pole pairs and the basic design parameters. Analyses were carried out on the basis of the derived analytical equations, which make the generated torque depend on the construction parameters. Thanks to the derived dependencies, one can easily examine the effects of modifying the machine structure. The results presented in the article prove that the developed design of the cogging machine is competitive in relation to machines with permanent magnets existing on the market.
Multi-State Online Estimation of Lithium-Ion Batteries Based on Multi-Task Learning
Authors: Bao Xiang , Liu Yuefeng , Liu Bo , Liu Haofeng , Wang Yue
Published: March 2023

Deep learning-based state estimation of lithium batteries is widely used in battery management system (BMS) design. However, due to the limitation of on-board computing resources, multiple single-state estimation models are more difficult to deploy in practice. Therefore, this paper proposes a multi-task learning network (MTL) combining a multi-layer feature extraction structure with separated expert layers for the joint estimation of the state of charge (SOC) and state of energy (SOE) of Li-ion batteries. MTL uses a multi-layer network to extract features, separating task sharing from task-specific parameters. The underlying LSTM initially extracts time-series features. The separated expert layer, consisting of task-specific and shared experts, extracts features specific to different tasks and shared features for multiple tasks. The information extracted by different experts is fused through a gate structure. Tasks are processed based on specific and shared information. Multiple tasks are trained simultaneously to improve performance by sharing the learned knowledge with each other. SOC and SOE are estimated on the Panasonic dataset, and the model is tested for generalization performance on the LG dataset. The Mean Absolute Error (MAE) values for the two tasks are 1.01% and 0.59%, and the Root Mean Square Error (RMSE) values are 1.29% and 0.77%, respectively. For SOE estimation tasks, the MAE and RMSE values are reduced by 0.096% and 0.087%, respectively, when compared with single-task learning models. The MTL model also achieves reductions of up to 0.818% and 0.938% in MAE and RMSE values, respectively, compared to other multi-task learning models. For SOC estimation tasks, the MAE and RMSE values are reduced by 0.051% and 0.078%, respectively, compared to single-task learning models. The MTL model also outperforms other multi-task learning models, achieving reductions of up to 0.398% and 0.578% in MAE and RMSE values, respectively. In the process of simulating online prediction, the MTL model consumes 4.93 ms, which is less than the combined time of multiple single-task learning models and almost the same as that of other multi-task learning models. The results show the effectiveness and superiority of this method.
Lifetime Limitations in Multi-Service Battery Energy Storage Systems
Authors: Ohrelius Mathilda , Berg Magnus , Wreland Lindström Rakel , Lindbergh Göran
Published: March 2023

A reliable power grid system based on renewable energy sources is a crucial step to restrict the climate crisis. Stationary battery energy storage systems (BESS) offer a great potential to repel power fluctuations in the grid at different timescales. However, for a reliable operation and cost estimation, the degradation in the batteries needs to be understood. We present an accelerated battery degradation study, on single as well as multi-service applications, of NCM532/Gr lithium-ion battery cells. Frequency regulation (FR) was the least harmful for the battery, with an expected lifetime of 12 years, while peak shaving (PS) resulted in an expected lifetime of 8 years. The combined cycle (FRPS) accelerated the capacity loss, and degradation of the positive electrode was induced from the start of cycling, causing power limitations after only 870 equivalent full cycles (EFC). Tracking the 1C-rate discharge capacity was proven to be a good indication of the accelerated cell polarization, and it can serve as a useful method to evaluate the internal battery state of health (SOH).
Analysis of Investments in RES Based on the Example of Photovoltaic Panels in Conditions of Uncertainty and Risk—A Case Study
Authors: Wojewnik-Filipkowska Anna , Filipkowski Paweł , Frąckowiak Olaf
Published: March 2023

The aim of this study is to examine the profitability of investment in a photovoltaic microinstallation, to analyze the impact of legal changes on its profitability, and to perform a sensitivity analysis of the investment profitability to energy price changes. The novelty of the research applies to the financial analysis of two legal systems of discount called net-metering and net-billing. The two systems and the change in energy prices present legal and macroeconomic risks. Climate neutrality strategy implementation is the analysis background. The authors formulate the hypothesis that, firstly, the solar panel installations in Poland are aimed at reducing the operating costs of the building; secondly, the investment motivation is environmental. The main research conclusion is that taking into consideration the solar panel ‘boom’ in Poland, the ‘regulator’ has achieved its intended goal connected with progress toward climate neutrality. This research used the method of logical design, experiment, and comparative analysis. The tools applied to calculate project profitability included the internal rate of return (IRR) and net present value (NPV). The case study method has been applied. The analysis uses real-world assumptions and is conducted for weather conditions in Gdańsk, Poland.
Low- and High-Fidelity Aerodynamic Simulations of Box Wing Kites for Airborne Wind Energy Applications
Authors: Eijkelhof Dylan , Buendía Gabriel , Schmehl Roland
Published: March 2023

High aerodynamic efficiency is a key design driver for airborne wind energy systems as it strongly affects the achievable energy output. Conventional fixed-wing systems generally use aerofoils with a high thickness-to-chord ratio to achieve high efficiency and wing loading. The box wing concept suits thinner aerofoils as the load distribution can be changed with a lower wing span and structural reinforcements between the upper and lower wings. This paper presents an open-source toolchain for reliable aerodynamic simulations of parameterized box wing configurations, automating the design, meshing, and simulation setup processes. The aerodynamic tools include the steady 3D panel method solver APAME and the CFD-solver OpenFOAM, which use a steady Reynolds-Averaged Navier–Stokes approach with k-ω SST turbulence model. The finite-volume mesh for the CFD-solver is generated automatically with Pointwise using eight physical design parameters, five aerofoil profiles and mesh refinement specifications. The panel method provided accurate and fast results in the linear lift region. For higher angles of attack, CFD simulations with high- to medium-quality meshes were required to obtain good agreement with measured lift and drag coefficients. The CFD simulations showed that the upper wing stall lagged behind the lower wing, increasing the stall angle of attack compared to conventional fixed-wing kites. In addition, the wing tip boundary layer separation was delayed compared to the wing root for the straight rectangular box wing. Choosing the design point and operational envelope wisely can enhance the aerodynamic performance of airborne wind energy kites, which are generally operated at a large angle of attack to maximise the wing loading and tether force, and through that, the power output of the system.
On the Adoption of Rooftop Photovoltaics Integrated with Electric Vehicles toward Sustainable Bangkok City, Thailand
Authors: Jittayasotorn Thiti , Sadidah Muthiah , Yoshida Takahiro , Kobashi Takuro
Published: March 2023

Realizing urban energy systems with net-zero CO2 emissions by 2050 is a major goal of global societies in building sustainable and livable cities. Developing cities hold a key to meeting this goal, as they will expand rapidly in the next decades with increasing energy demand, potentially associated with rising CO2 emissions and air pollution if fossil fuels continue to be utilized. Therefore, identifying equitable, cost-effective, and deep decarbonization pathways for developing cities is essential. Here, we analyzed Bangkok City, Thailand, using the System Advisor Model (SAM) for techno-economic analysis to evaluate the decarbonization potential of rooftop photovoltaics (PV) integrated with electric vehicles (EVs) as batteries on a city scale. The analyses took into consideration hourly local weather conditions, electricity demand, electricity tariffs, feed-in-tariffs, degradation, declining costs of PV and EV, etc., specific to Bangkok. As the prices of PV and EVs decrease over the next several decades, the “PV + EV” system may provide a basis for new urban power infrastructure with high energy efficiency, low energy cost, and large CO2 emission reduction. The results show that the “PV + EV” scenario in 2030 has the highest CO2 emission reduction of 73% from electricity and vehicle usage, supplying 71% of the electricity demand of the city. The “PV + EV” system may reduce energy costs by 59% with estimated technology costs in 2030. Most of the energy generated from rooftop PV is consumed owing to large EV battery capacities, which can contribute to the rapid decarbonization of Bangkok City by 2050.
Characterization of Agrivoltaic Crop Environment Conditions Using Opaque and Thin-Film Semi-Transparent Modules
Authors: Uchanski Mark , Hickey Thomas , Bousselot Jennifer , Barth Kurt L.
Published: March 2023

Agrivoltaics (APV), the co-location of agriculture and photovoltaics (PV), addresses an inherent competition for land usage. Taking the same dual-use concept to the urban landscape, rooftop APV can provide locally grown food in areas of need while providing distributed energy generation. In this multi-year investigation, different APV plots in northern Colorado, USA, were studied for crop metrics, light transmission, air temperature, soil/substrate temperature and moisture. Crops were grown under different solar panel types including opaque silicon and opaque and semi-transparent (ST) thin-film CdTe technologies. Growth conditions were characterized showing generally improved conditions and moderated temperatures under the panels. The ST-CdTe panels had increased photosynthetically active radiation (PAR) compared to both opaque panel types without a significant corresponding increase in temperature.
Distributed Energy Management for Networked Microgrids with Hardware-in-the-Loop Validation
Authors: Liu Guodong , Ferrari Maximiliano F. , Ollis Thomas B. , Sundararajan Aditya , Olama Mohammed , Chen Yang
Published: March 2023

For the cooperative operation of networked microgrids, a distributed energy management considering network operational objectives and constraints is proposed in this work. Considering various ownership and privacy requirements of microgrids, utility directly interfaced distributed energy resources (DERs) and demand response, a distributed optimization is proposed for obtaining optimal network operational objectives with constraints satisfied through iteratively updated price signals. The alternating direction method of multipliers (ADMM) algorithm is utilized to solve the formulated distributed optimization. The proposed distributed energy management provides microgrids, utility-directly interfaced DERs and responsive demands the opportunity of contributing to better network operational objectives while preserving their privacy and autonomy. Results of numerical simulation using a networked microgrids system consisting of several microgrids, utility directly interfaced DERs and responsive demands validate the soundness and accuracy of the proposed distributed energy management. The proposed method is further tested on a practical two-microgrid system located in Adjuntas, Puerto Rico, and the applicability of the proposed strategy is validated through hardware-in-the-loop (HIL) testing.
Critical Review of Ca(OH)2/CaO Thermochemical Energy Storage Materials
Authors: Feng Yupeng , Li Xuhan , Wu Haowen , Li Chaoran , Zhang Man , Yang Hairui
Published: March 2023

Thermal energy storage is an essential technology for improving the utilization rate of solar energy and the energy efficiency of industrial processes. Heat storage and release by the dehydration and rehydration of Ca(OH)2 are hot topics in thermochemical heat storage. Previous studies have described different methods for improving the thermodynamic, kinetic, and structural stability of Ca(OH)2 to improve energy storage density, energy storage rate, and cycle stability, respectively. Here, the mechanisms and effects of different techniques on the performance improvement of Ca(OH)2 and some common problems were reviewed. Specific problems were also clarified based on the characteristics of different technologies. Finally, suggestions for the future development of Ca(OH)2 heat storage materials were provided.
Critical Comparison of Li-Ion Aging Models for Second Life Battery Applications
Authors: Ganesh Sai Vinayak , D’Arpino Matilde
Published: March 2023

Lithium-ion batteries (LIBs) from electrified vehicles (EVs) that have reached the automotive end of life (EoL) may provide a low-cost, highly available energy storage solution for grid-connected systems, such as peak shaving and ancillary services. There are several issues related to the integration of second life batteries (SLBs) in power systems, such as the variability of the pack design and cell chemistry, in-field assessments of the state of health (SoH), and estimations of the expected lifetimes of SLBs in different power system applications. Model-based approaches are commonly used in the automotive industry for estimating/predicting the capacity and power fade trajectories of LIBs during their life. However, a large variety of models are available with different fidelities, complexities, and computational costs. The accuracy of these estimations is critical for the derivation of business models for SLB applications. This paper presents a qualitative and quantitative assessment of the performance of two well-accepted, state-of-the-art aging models, initially developed for automotive applications and here applied to different SLB applications to predict both the capacity and power fade. These models are evaluated with respect to several performance metrics, such as fidelity of estimation and capability of extrapolation outside the calibration data range. The considered models are classified as semi-empirical physics-based and empirical models, respectively. Three different SLB power profiles, bulk energy for DC fast charge stations and two frequency regulation profiles, are considered, corresponding to different ranges of the SoC, C-rates, and battery temperatures, with the aim of exciting different aging mechanisms. The numerical results provide insight for the selection of aging models for SLB applications based on their performances and limitations.
A Comparative Perspective of the Effects of CO2 and Non-CO2 Greenhouse Gas Emissions on Global Solar, Wind, and Geothermal Energy Investment
Authors: Ghezelbash Azam , Khaligh Vahid , Fahimifard Seyed Hamed , Liu J. Jay
Published: March 2023

Greenhouse gas emissions, including carbon dioxide and non-CO2 gases, are mainly generated by human activities such as the burning of fossil fuels, deforestation, and agriculture. These emissions disrupt the natural balance of the global ecosystem and contribute to climate change. However, by investing in renewable energy, we can help mitigate these problems by reducing greenhouse gas emissions and promoting a more sustainable future. This research utilized a panel data model to explore the impact of carbon dioxide and non-CO2 greenhouse gas emissions on global investments in renewable energy. The study analyzed data from 63 countries over the period from 1990 to 2021. Firstly, the study established a relationship between greenhouse gas emissions and clean energy investments across all countries. The findings indicated that carbon dioxide had a positive effect on clean energy investments, while non-CO2 greenhouse gas emissions had a negative impact on all three types of clean energy investments. However, the impact of flood damage as a representative of climate change on renewable energy investment was uncertain. Secondly, the study employed panel data with random effects to examine the relationship between countries with lower or higher average carbon dioxide emissions and their investments in solar, wind, and geothermal energy. The results revealed that non-CO2 greenhouse gas emissions had a positive impact on investments only in wind power in less polluted countries. On the other hand, flood damage and carbon dioxide emissions were the primary deciding factors for investments in each type of clean energy in more polluted countries.
Advanced Utilization Technologies of Secondary Energy and Resources from Energy-Intensive Industries
Authors: Chen Ziwei , He Beini , Wang Xidong
Published: March 2023

Energy-intensive industries (EIIs) refer to industries that are highly dependent on energy (fuel), which are primarily responsible for industrial energy consumption [...]
The Effect of Renewable Energy and Corporate Social Responsibility on Dual-Channel Supply Chain Management
Authors: Kar Sumi , Pal Anita , Basu Kajla , Sarkar Achyuth , Sarkar Biswajit
Published: March 2023

Global energy demand has unquestionably increased significantly in recent years. Nowadays, industries are very aware of global warming, and to save the environment, they produce green products with energy consumption. Day by day, energy use is increasing due to population, end-use markets of construction, transportation, industry, etc. But the energy limit is finite, whereas the daily use is rising, so the price is increasing. In this study, two situations have been shown in two models with renewable energy consumption. Model 1 analyzes the manufacturer and retailer’s optimal green quality and sales price in two-echelon supply chain systems with centralized and decentralized cases. In this case, the retailer sells their products through three different channels: online, offline, and buy-online-pickup-in store, with three different selling prices. In Model 2, Manufacturer 1 and Manufacturer 2 produce green and regular products with renewable energy consumption. In this case, both manufacturers sell their products through three different channels: online, offline, and buy-online-pickup-in store, with three different selling prices. There is competition between substitutable products with respect to green quality and the selling price of the products. A hybrid channel policy is studied here to maximize the total profit with considering corporate social responsibility under renewable energy consumption. The study has been analyzed mathematically. The classical optimization approach and game theory are applied here to find the optimal values of procurement cost, selling price, and green quality development cost. A numerical study shows that the centralized system gives a better result to the manufacturer than the decentralized system. When the demand is a power function of the selling price, the manufacturer producing eco-friendly products gains 0.99% more profit than the conventional product. This result shows that manufacturers creating eco-friendly products motivate other manufacturers to make eco-friendly products.
Quantitative Evaluation of the Effects of Heat Island on Building Energy Simulation: A Case Study in Wuhan, China
Authors: Pei Long , Schalbart Patrick , Peuportier Bruno
Published: March 2023

The climate data used for dynamic energy simulation of buildings located in urban regions are usually collected in meteorological stations situated in rural areas, which do not accurately represent the urban microclimate (e.g., urban heat island effect), and this might affect the simulation accuracy. This paper aims at quantitatively evaluating the effects of heat island on a high-rise building’s energy performance based on the microclimate simulation tool ENVI-met and the building energy simulation tool COMFIE. However, the computation of microclimate models is time consuming; it is not possible to simulate every day of a year in a reasonable time. This paper proposes a method that generates hourly “site-specific climate data” to avoid long microclimate simulation times. A coupling method of ENVI-met and COMFIE was developed for more precise building energy simulation, accounting for the heat island effect. It was applied to a high-rise building in Wuhan, China. The results showed that the yearly average urban heat island effect intensity at the height of 3 m was estimated to be 0.55 °C and decreased with height. Compared to the simulation considering the outdoor temperature variation with the height and orientation, using the original climate data collected in rural areas led to an overestimation of the heating load by around 5.8% and an underestimation of the cooling load by around 8.7%. Compared to the weather file at the height of 3 m near the north facade neglecting the temperature variation along the height, the heating load was overestimated by 8.2% and the cooling load was underestimated by 10.8%. The methods proposed in this paper can be used for the more precise application of urban building energy simulation.
A Novel Solar System of Electricity and Heat
Authors: Mamykin Sergii , Shneck Roni Z. , Dzundza Bohdan , Gao Feng , Dashevsky Zinovi
Published: March 2023

Thermoelectric devices may have an essential role in the development of fuel-saving, environmentallyfriendly, and cost-effective energy sources for power generation based on the direct conversion of heat into electrical energy. A wide usage of thermoelectric energy systems already exhibits high reliability and long operation time in the space industry and gas pipe systems. The development and application of solar thermoelectric generators (TEGs) arelimited mainly by relatively low thermoelectric conversion efficiency. Forthe first time, we propose to use the direct energy conversion of solar energy by TEGs based on the high-performance multilayer thermoelectric modules with electric efficiency of ~15%. Solar energy was absorbed and converted to thermal energy, which is accumulated by a phase-change material (aluminum alloys at solidification temperature ~900 K). The heat flow from the accumulator through the thermoelectric convertor (generator) allows electrical power to be obtained and the exhaust energy to be used for household purposes (heating and hot water supply) or for the operation of a plant for thermal desalination of water.