bluuebunny/crossref_metadata_2025_split huggingface dataset:
Prediction of transport in the JET DTE2 discharges with TGLF and NEO models using the TGYRO transport code
Authors: Shi N. , Staebler G.M. , Belli E.A. , McClenaghan J. , Kim H-T. , Auriemma F. , Kirov K. , Frigione D. , Garzotti L. , Zotta V.K. , Rimini F. , Van Eester D. , Lomas P. ,   JET Contributors
Published: June 2024

Abstract
The JET Deuterium-Tritium-Experiment Campaign 2 (DTE2) has demonstrated the highest-ever fusion energy production. To forecast the transport dynamics within these discharges, the TGLF and NEO models within the TGYRO transport code were employed. A critical development in this study is the new quasilinear transport model, TGLF-SAT2, specifically designed to resolve discrepancies identified in JET deuterium discharges. This model accurately describes the saturated three-dimensional (3D) fluctuation spectrum, aligning closely with a database of nonlinear CGYRO turbulence simulations, thereby enhancing the predictive accuracy of TGYRO simulations. In validating against the JET DTE2 discharges across two primary operating scenarios, TGYRO effectively predicted the temperature profiles within a broad radial window (ρ ∼ 0.2–0.85), though with minor ion temperature discrepancies near the core. However, a consistent underprediction of electron density profiles by 20% across the simulation domain was noted, indicating areas for future refinement. To achieve a self-consistent steady-state solution based on the JET DTE2 discharges, an integrated modeling workflow TGYRO-STEP within the OMFIT framework was introduced. This workflow iterates among the core transport, the pedestal pressure and the MHD equilibrium, ultimately yielding a converged solution that significantly reduces dependence on experimental boundary conditions for temperature and density profiles. The integrated simulation results show negligible differences in electron density and temperature profiles compared to standalone TGYRO modeling, while the ion temperature profile is lower due to the updated boundary condition in TGYRO-STEP. The application of the TGYRO-STEP workflow to JET DTE2 discharges serves as a crucial test to validate its robustness and highlights its limitations, providing valuable insights for its potential future application in ITER and Fusion Power Plant deuterium and tritium prediction modeling.
Material characterization by surface wave parameters extraction using 3D vibrometer and ellipsometry
Authors: Bouzzit A. , Arciniegas A. , Martinez L. , Serfaty S. , Wilkie-Chancellier N. 
Published: June 2024

Abstract
This work focuses on the monitoring of the 3D propagation of ultrasonic surface waves used in non-destructive testing and evaluation. The ellipticity of the movement related to the Rayleigh wave, specifically the H/V parameter, is studied analytically, numerically, and experimentally on an aluminum block. The study involves generating signals from the theoretical harmonic expression of particle displacement issued from Rayleigh wave, carrying out numerical simulations using a finite element method, and generating the Rayleigh wave using a shear contact transducer with a 1 MHz central frequency in the experimental study. The work also involves appropriate filtering of signals to extract components with specific polarization and suppress other modes with low polarization energy. The obtained results for aluminum show a good agreement between the analytical, numerical, and experimental studies of Rayleigh wave ellipticity. Moreover, these H/V ratios are consistent with the theoretical values described in the literature. As a proof of concept, this method is also applied to simulation data to study orthotropic materials (wood/bone), obtaining promising results.
Towards an understanding of the mechanisms of therapeutic ultrasound on biomimetic models of cancer
Authors: Silva D J D C , Cheema U , Gélat P 
Published: June 2024

Abstract
Therapeutic ultrasound is transforming the treatment of a range of malignancies in a non-invasive and non-systemic manner. Low-intensity ultrasound (LIUS) has been proposed to selectively eradicate cancer cells but the underlying biological mechanisms remains unknown. To gain an understanding of this phenomenon, 2D breast cancer monocultures were sonicated at varying acoustic intensities (0.1–0.5 W·cm−2) and excitation times (1–10 minutes). Additionally, 2D monocultures consisting of healthy cell lines were sonicated at varying acoustic intensities (0.1–0.5 W·cm−2) to determine any distinguishing biological responses. To begin recapitulating in vivo conditions, breast cancer cells were also seeded into 3D collagen hydrogels. At a 1 MHz frequency, 20% duty cycle, 100 Hz pulse repetition frequency, a significant drop in cancer cell viability is observed at a sonication intensity of 0.5 W·cm−2 and over 10 minute excitation time. Healthy counterparts subjected to the same parameters revealed no distinguishing effects. Sonication of breast cancer cells seeded in 3D collagen hydrogels revealed no effect in cell viability compared to non-sonicated controls. The acoustic wave propagation software OptimUS was used to determine the influence culturing plates have on ultrasound propagation, revealing these materials can significantly vary the acoustic field at frequencies relevant to LIUS.
Improved skull bone acoustic property homogenization for fast transcranial ultrasound simulations
Authors: Angla Célestine , Larrat Benoit , Gennisson Jean-Luc , Chatillon Sylvain 
Published: June 2024

Abstract
Transcranial ultrasound simulations are crucial to optimize and secure ultrasound interventions in brain therapy, depending on the patient skull. When performing such simulations, accurate modeling of the skull is essential, although very challenging, because of the inter/intra sample property variability. Simulations based on semi-analytical methods require a homogeneous description of the skull. Averaging the acoustic property maps derived from the CT scan does not modify the focus shift, but it leads to an overestimation of the pressure field amplitude. The purpose of this work is to provide a homogenization method that compensates for this amplitude overestimation. First, the skull acoustic property maps are segmented into a three-layer medium to represent the different types of skull bone (cortical – trabecular – cortical). Then, equivalent properties are computed so as to minimize the time of flight and transmission coefficient errors between the three-layer medium and the one-layer equivalent medium. This method was validated using 3D simulations with CIVA Healthcare and k-Wave and has proven to be very efficient.
Evaluation of the benefits of tradable peak energy use rights products
Authors: Zhao Haibo , Zhang Zhi , Wang Yao , Wang He , Wang Huaqing , Tan Zhongfu 
Published: June 2024

Abstract
Environmental degradation and energy security issues require our country to accelerate the transition of its energy consumption structure towards decarbonization. Clarifying the impact of the pilot trading system of peak energy use rights products on the low-carbon transformation of energy consumption structure and the path of its effect is an important policy revelation for the construction of market-based environmental rights and interests regulation. In this paper, we study the implementation effect of the pilot provinces of peak energy use right product trading from the perspective of carbon emission reduction benefit and analyze the carbon emission reduction benefit generated by the peak energy use right product according to the pilot trading situation.
Study on the carbon footprint impact analysis method of integrated energy system operation in low-carbon park considering multi-domain dynamic characteristics
Authors: Wang Yang , Qiao Biao , Tan Zhukui , Sun Zongyu , Fu Yu , Liang Shukui , Li Yue 
Published: June 2024

Abstract
Low-carbon parks are an important battlefield for China to achieve the goal of peaking carbon neutrality. Accurate and efficient analysis and tracking of park carbon footprint is a necessary measure to reduce carbon and save energy in parks. Aiming at the problems of less research on the carbon footprint of low-carbon parks at home and abroad and the lack of tracking and analysis methods of dynamic influencing factors of carbon footprint, this paper focuses on the carbon footprint of typical low-carbon parks during operation and proposes a dynamic tracking method of the carbon footprint of low-carbon parks that considers the dynamic characteristics of carbon emissions in various fields. In this paper, the hierarchical impact structure of the carbon footprint of low-carbon parks was first constructed, including the impact of population, economy, construction, energy, transportation, and industry. LMDI (Logarithmic Mean Divisia Index) was used to decompose the macro-level impact of the carbon footprint of low-carbon parks. Secondly, this paper establishes a dynamic simulation model of carbon emissions of low-carbon parks in the fields of building, energy, transportation, and industry, which can realize 8760 hours of dynamic simulation calculation throughout the year. Based on dynamic simulation, this paper carries out single factor sensitivity analysis and combines the Monte Carlo simulation method to complete quantitative calculation of dynamic second-order influence weight of carbon footprint. Finally, a multi-domain impact analysis and tracking method for the carbon footprint of low-carbon parks is obtained.
Study on optimal scheduling of energy storage participation in power market considering source-load uncertainty
Authors: Zhang Weijun , Guo Wei , Huang Jinfu , Chen Dawei , Deng Chaoping 
Published: June 2024

Abstract
With the increasing penetration of new energy sources such as wind and light in the power grid, the selection of new energy-storage joint scheduling and operation methods can reduce the phenomenon of wind and light abandonment, but the new energy-storage joint scheduling and operation methods need to take into account the market factors, characteristics, and other aspects of a variety of problems. Given these difficulties, this paper takes into account the uncertainty of wind power, adopts the Latin hypercube sampling method to generate and consider the probabilistic distance fast reduction method, selects the typical scenarios, establishes the energy storage market operation model by minimizing the comprehensive operation cost, and responds to the source-load demand through the integrated response of the energy storage and the power grid. Finally, the effectiveness of the proposed method is demonstrated through simulation examples.
Design of maximum power point energy storage and inverter for photovoltaic power generation
Authors: Fan Yaohua , Chen Jianli , Bian Qingqing , Wu Yuzhen , Tong Jie , Zhan Chunyi 
Published: June 2024

Abstract
With the increasing tension of fossil energy in the world and the damage to the environment caused by the use of fossil energy, new energy led by solar energy has been paid much more attention by countries around the world. If the efficiency of solar energy is improved, it will bring breakthrough changes to the world’s energy structure. Based on the related applications of solar photovoltaic power generation, this paper designs an independent photovoltaic power generation system with energy storage device. In the form of DC/DC conversion, the system uses the maximum power point tracking technology of photovoltaic cells to realize the efficient use of solar energy during the charging process. The system inverts the DC voltage after the storage, realizing the transformation of DC voltage to 220 V AC voltage, to realize the utilization of energy in various aspects.
Soft coal drilling technology and application based on air directional drilling
Authors: Song Yubo 
Published: June 2024

Abstract
In China, there are many layers and a wide distribution of crushed soft coal seams, which are commonly characterized by complex structural stress, high gas pressure, and low mechanical strength of coal bodies. In response to the problems of hole collapse and spray during the drilling process of such coal seams, such as hole wall instability, drilling difficulties, and the existence of blind spots for extraction, a dual tube and dual action air directional drilling method was proposed for coal mine soft and fractured coal seams. The grading of protective casing and inner tube directional drilling tools was studied, the parameters of spiral casing blades were optimized, and air supply parameters such as air volume and pressure were studied. Numerical simulation and on-site testing methods were used to study the law of powder discharge and the optimal drilling process parameters. Research and field experiments have shown that the construction depth of dual pipe and dual action drilling can reach over 300 meters, effectively improving the hole formation rate and drilling construction accuracy and providing a new process method for precise drilling of deep holes while drilling in fragmented soft coal seams.
Multi-Task neural network model considering low power output risk for short-term photovoltaic forecasting
Authors: Song Weiye , Jiao Meining , Han Shuang , Yan Jie , Wang Han , Liu Yongqian 
Published: June 2024

Abstract
As the proportion of installed photovoltaic power generation continues to increase, low output under the influence of large-scale weather systems has an increasingly significant influence on the power grid. There is an urgent need to improve photovoltaic power generation forecasting accuracy and reduce the risk of insufficient output in forecast results. To this end, a multi-task neural network model considering low power output risk (LPOMTN) for short-term photovoltaic forecasting is proposed. First, a numerical weather forecast encoder based on multi-scale CNN-Attention is established, which can extract multi-time scale photovoltaic output characteristics. Then a low-output day prediction module based on parallel CNN decoding and a photovoltaic low-output process prediction module based on the clear-sky model and LSTM were established. The latter uses the prediction results of the former as input, and the two modules perform training modeling in a multi-task learning manner to strengthen the model’s sensitivity to low-output states and improve the accuracy of low-output process predictions. The calculation example results show that LPOMTN has higher average forecasting accuracy for power output processes compared to methods such as XGBoost.
Performance prediction of a novel solar power tower system based on spectral beam splitting
Authors: Zhang Miaomiao , Qiu Zhongzhu , Zhang Jingkui , Zhu Qunzhi , Zhang Tao , Cai Jingyong , Ni Xingrui , Xu Ning , Huang Qinqing , Liao Chenqi 
Published: June 2024

Abstract
Photovoltaic technology is currently the most widely used solar generation technology in engineering. The particular spectral response band of PV cells cannot convert all solar radiation into electricity. Meanwhile, some radiation will become heat, leading to an increase in cell temperature and a decrease in the conversion efficiency of photovoltaic cells. Spectral splitting technology is an effective technology to improve electrical generation efficiency and can achieve full spectrum utilization of solar energy. The current work involved (1) proposing a novel solar power tower comprehensive utilization system based on spectral splitting; (2) developing a spectral splitting thin film configuration by using the Needle method whose transmittance is higher than 90% against the band range of 510 nm-1, 020 nm; (3) setting up a spectral splitting PV panel whose cover glass is coated the developed film, and replacing the heliostat of SPT system by using such spectral splitting PV panels; (4) predicting the electrical power generation of the spectral splitting-based tower type concentrating solar power system. The results demonstrate that the power generation efficiency of the proposed system is 5.5% and 3.46% higher than PV power generation and SPT systems respectively.
Harmonic impedance estimation based on improved rank estimation
Authors: Wang Dianlang , Chen Jing , Yin Qi , Ding Hanlin , Liu Qingsong , Miao Hong 
Published: June 2024

Abstract
In response to the issue of harmonic data anomalies affecting harmonic impedance estimation, a method based on improved rank regression is proposed, building upon the foundation of rank estimation. This method utilizes harmonic data sampled from the point of common coupling, treating harmonic impedance as regression parameters. Initially, the least squares method is employed to solve for regression parameters. Subsequently, Bayesian optimization is applied to refine these parameters. The optimized parameters are then incorporated into the rank estimation function derived from the residual rank matrix for weighted iteration. The final calculation results are determined through this iterative process. Simulation analysis demonstrates that this method can effectively mitigate the impact of outliers, yielding more accurate harmonic impedance values.
The design of the insulation layer and energy consumption optimization of the injection mould for the motor commutator
Authors: Wang Yuhui , Han Lei 
Published: June 2024

Abstract
The injection molding of the motor commutator requires a long-term high-temperature operation, which poses a problem of high energy consumption. The heating zone is located behind the mold, and there is thermal radiation and convection during heating, resulting in uneven temperature distribution. To solve the above problems, an automatic lifting and insulation mechanism is proposed. An insulation frame is built with an aerogel insulation layer inside. It is connected up to the upper table of the machine, moving up and down during processing, and connected down to the crank slider mechanism. Simulation software analyzes its insulation and temperature distribution and, through experimental verification, compares the temperature distribution and energy consumption of injection molds before and after the installation of insulation mechanisms. The experimental and simulation results show that using the automatic lifting insulation layer design scheme can effectively reduce energy consumption, improve temperature distribution unevenness, and increase the yield rate.
Centralized and decentralized combined voltage control for distribution network with high penetration PV connected
Authors: Xi Haikuo , Chen Can , Bai Xuesong , Ma Yuan , Ding Jiachao , Chen Qifang 
Published: June 2024

Abstract
With the high penetration of distributed photovoltaics (DPVs) integrated into the distribution network, the voltage problem becomes a critical problem. At present, the resources in the current distribution network hardly satisfy the requirements of voltage regulation. However, the huge amount of DPVs in the distribution network could be a potential voltage regulation resource due to the controllable active power and reactive power. In this paper, a day-ahead centralized and intraday decentralized combined voltage control method is proposed for high penetration DPVs connected distribution Networks. In the part of day-ahead control, centralized compensation of the distribution network voltage is achieved by controlling the gear ratio of the capacitor/reactor group on an hourly scale. The intraday control part consists of decentralized local control based on DPVs and centralized control based on energy storage (ES) equipment. The reactive and active power of DPVs is controlled in a decentralized way by using local priority sensitivity control based on the coordination with capacitor optimization. Finally, simulations are performed to verify the performance of the method. The results indicate that this method can effectively solve the voltage problem caused by DPVs.
Bases for optimising stabiliser decompositions of quantum states
Authors: de Silva Nadish , Yin Ming , Strelchuk Sergii 
Published: June 2024

Abstract
Stabiliser states play a central role in the theory of quantum computation. For example, they are used to encode computational basis states in the most common quantum error correction schemes. Arbitrary quantum states admit many stabiliser decompositions: ways of being expressed as a superposition of stabiliser states. Understanding the structure of stabiliser decompositions has significant applications in verifying and simulating near-term quantum computers. We introduce and study the vector space of linear dependencies of n-qubit stabiliser states. These spaces have canonical bases containing vectors whose size grows exponentially in n. We construct elegant bases of linear dependencies of constant size three. Critically, our sparse bases can be computed without first compiling a dictionary of all n-qubit stabiliser states. We utilise them to explicitly compute the stabiliser extent of states of more qubits than is feasible with existing techniques. Finally, we delineate future applications to improving theoretical bounds on the stabiliser rank of magic states.
Advances and challenges in direct additive manufacturing of dense ceramic oxides
Authors: Fan Zhiqi , Tan Qiyang , Kang Chengwei , Huang Han 
Published: June 2024

Abstract
Ceramic oxides, renowned for their exceptional combination of mechanical, thermal, and chemical properties, are indispensable in numerous crucial applications across diverse engineering fields. However, conventional manufacturing methods frequently grapple with limitations, such as challenges in shaping intricate geometries, extended processing durations, elevated porosity, and substantial shrinkage deformations. Direct additive manufacturing (dAM) technology stands out as a state-of-the-art solution for ceramic oxides production. It facilitates the one-step fabrication of high-performance, intricately designed components characterized by dense structures. Importantly, dAM eliminates the necessity for post-heat treatments, streamlining the manufacturing process and enhancing overall efficiency. This study undertakes a comprehensive review of recent developments in dAM for ceramic oxides, with a specific emphasis on the laser powder bed fusion and laser directed energy deposition techniques. A thorough investigation is conducted into the shaping quality, microstructure, and properties of diverse ceramic oxides produced through dAM. Critical examination is given to key aspects including feedstock preparation, laser-material coupling, formation and control of defects, in-situ monitoring and simulation. This paper concludes by outlining future trends and potential breakthrough directions, taking into account current gaps in this rapidly evolving field.
Accelerating multilayer frequency selective surface absorber design by combining equivalent circuit models with machine learning
Authors: Zhao Yong , Cao Zhaowang , Cao Qiyang , Li Haoran , Ji Xiao , Miao Ling , Jiang Jianjun 
Published: June 2024

Abstract
Designing high-performance electromagnetic functional materials and artificial structures is of great significance for electromagnetic wave modulation applications. Optimizing performance in the high-dimensional parameter space of electromagnetic functional materials has posed a challenging problem. This paper proposes the use of reinforcement and transfer learning methods to facilitate the quick and accurate design of wideband circuit analog absorbers (CAA). A trained reinforcement learning network is utilized to comprehend the relationship between reflectivity performance and changes in impedance parameter. Furthermore, the transfer learning method is applied to accelerate the training process, leading in a 20-fold reduction in the number of simulations. To validate the effectiveness of this approach, a double-layer absorber aiming for reflectivity less than −20 dB at 3–10 GHz is designed, requiring only 50 simulations. This demonstrates that the proposed method provides a flexible strategy for the interactive design of equivalent circuit and electromagnetic structural parameters. Moreover, it presents a novel and efficient solution for microwave absorber design, with potential applications across various microwave design fields.