Real-time Conditions Research Articles

An experimental analysis and deep learning model to assess the cooling performance of green walls in humid climates

Introduction: Amidst escalating global temperatures, increasing climate change, and rapid urbanization, addressing urban heat islands and improving outdoor thermal comfort is paramount for sustainable urban development. Green walls offer a promising strategy by effectively lowering ambient air temperatures in urban environments. While previous studies have explored their impact in various climates, their effectiveness in humid climates remains underexplored.Methods: This research investigates the cooling effect of a green wall during summer in a humid climate, employing two approaches: Field Measurement-Based Analysis (SC 1: FMA) and Deep Learning Model (SC 2: DLM). In SC 1: FMA, experiments utilized data loggers at varying distances from the green wall to capture real-time conditions. SC 2: DLM utilized a deep learning model to predict the green wall’s performance over time.Results: Results indicate a significant reduction in air temperature, with a 1.5°C (6%) decrease compared to real-time conditions. Long-term analysis identified specific distances (A, B, C, and D) contributing to temperature reductions ranging from 1.5°C to 2.5°C, highlighting optimal distances for green wall efficacy.Discussion: This study contributes novel insights by determining effective distances for green wall systems to mitigate ambient temperatures, addressing a critical gap in current literature. The integration of a deep learning model enhances analytical precision and forecasts future outcomes. Despite limitations related to a single case study and limited timeframe, this research offers practical benefits in urban heat island mitigation, enhancing outdoor comfort, and fostering sustainable and climate-resilient urban environments.

A Performance Evaluation Method for Long and Steep Uphill Sections of Heavy-Haul Railway Lines

Any system for evaluating the safety service performance of heavy-haul railway lines must effectively reflect the real-time service status of the line. The working conditions of heavy-load lines are complex and diverse, particularly on uphill sections. Existing evaluation systems struggle to accurately reflect the service conditions of long and steep uphill sections bearing heavy loads, posing a significant threat to the safe operation of these lines. To address this problem, we propose a new method for evaluating the safety service performance of long and steep uphill sections of heavy-haul railway lines by establishing a scoring system based on the Analytic Hierarchy Process (AHP). First, damage indicators for heavy-haul lines are categorized into three groups: track geometry status indicators, track structure status indicators, and track traffic status indicators. Using data from existing heavy-haul lines and maintenance experiences, we determine a score deduction standard, classifying lines into four levels based on their safety service quality. Next, we establish a coefficient table for the service performance of long and steep uphill sections after the corresponding scores are deducted. Using data for the length and elevation grade of the actual uphill section, we adjust the deducted scores of the track structure status indicators, enhancing the evaluation system’s accuracy in describing the working conditions. Finally, we verify the stability of the entire system by conducting a sensitivity analysis of the indicator evaluation results using the One-At-a-Time (OAT) method. This method fills a critical gap in the safe operation and maintenance of heavy-haul railways and provides a safety guarantee for the operation of long uphill sections of heavy-haul railways.

Photodiode-based porosity prediction in laser powder bed fusion considering inter-hatch and inter-layer effects

Laser powder bed fusion, while promising, faces hurdles in certifying fabricated parts due to cost and complexity, with in-process monitoring emerging as a potential solution. Existing models focus on predicting defects at a given location using the monitoring signals from solely that same location. Hence, these models treat each track or layer independently of the previous and subsequent ones, neglecting potential interdependencies. This study proposed an in-situ, photodiode-based monitoring approach considering inter-hatch and inter-layer effects on porosity formation - factors often overlooked in existing research. Two Ti-6Al-4 V cuboids (10×10×5 mm3) were built with optimized process parameters, with the melt pool continuously monitored at 20 kHz via a co-axially mounted photodiode. The monitoring system captured the integral radiation in the near-infrared spectrum within a field of view centered on the melt pool. The porosity is assessed by X-ray computed tomography (X-CT), serving as ground truth to build supervised machine learning (ML) models. This study considered physical phenomena occurring during the printing process, including remelting of lack of fusion pores by the subsequent layer, keyholes penetrating the current layer hence introducing pores in the layer below, and overlap between adjacent scan tracks. These considerations are critical for a holistic understanding of pore formation mechanisms. Photodiode signals and computed tomography volumes were cropped using windows of four sizes to test the model's pore localization capability. A machine learning model, specifically a Convolutional Neural Network (CNN) - Long Short-Term Memory (LSTM) network, was trained to predict porosities using these window sequences. The CNN extracted spatial features from photodiode signals, addressing inter-hatch effects, while the LSTM captured temporal dependencies across layers, addressing inter-layer effects. The results, with the Area Under the Receiver Operating Characteristic curve (AUC) of 0.91 for pores exceeding 8000 μm3 in volume and 100 μm2 in cross-sectional area, demonstrate the feasibility of the proposed model in detecting pores-level defects. This high defect prediction and positioning accuracy are essential for process control, providing real-time status of the region of interest and informing the controller of pore positions, thus facilitating intra-layer or inter-layer correction.

Hybrid-timescale optimal dispatch strategy for electricity and heat integrated energy system considering integrated demand response

An integrated energy system realizes multi-energy management and multi-load integrated supplement. However, because of the uncertainties of the renewable energy and loads and different dynamic characteristics of multi-energy, the efficiency of the system is constrained. To ascend the flexibility, a hybrid-timescale dispatch strategy considering integrated demand response is proposed. Firstly, a deep integrated demand response strategy is proposed using price demand response and ground source heat pump. Secondly, a linear dynamic model of the heat network is adopted to calculate the real-time energy transfer status in the heat pipeline, reflecting the thermal delay. Thirdly, to embed the linear dynamic model into the optimal scheduling process, a hybrid-timescale optimal dispatch framework for electricity and heat integrated energy system is proposed. Simulation results on MATLAB show that compare to the system that does not consider integrated demand response, the wind desertion, user cost and operation cost are reduced by 16.4 %, 0.85 %, and 2.72 %, respectively. And compared with the system only using energy storage, the planning cost is reduced by 47.0 %. By introducing the IDR strategy, and adopting the dynamic model of heat system, the flexibility of the system is released and the balance between economic cost and energy efficiency are achieved.

The impact of intermediate option availability on consumers' choice: The moderating role of price roundness

Given the surge in consumer demand and rapid inflation, businesses have turned their attention to premiumization as a means of market segmentation. Building upon previous studies that highlight the significance of the compromise effect in gaining market share for intermediate options, this research examines the feasibility of revenue management strategies using 'psychological pricing' in hospitality and tourism products. By investigating choice behaviors related to intermediate options, we explore how subtle pricing manipulations can enhance the compromise effect. Through five experiments, we validate our hypotheses. Our findings not only extend the literature on pricing behavior and hospitality concerning rounded pricing but also provide insights on how to strategically adjust psychological pricing to align with real-time market conditions.

Erosion Induced Flow Changes in Pelton Bucket: A Numerical Approach

Abstract The hydro-turbines working in sediment-laden conditions often experience some material loss from their surface, which modifies the surface design. This material loss alters the mechanical strength of the turbine component as well as the corresponding flow phenomenon. These changes in the flow pattern can lead to efficiency losses, but they can also be exploited to determine the actual condition of the component in real time. In fact, if detected, they can be useful for developing maintenance strategies to predict the turbine conditions in real time. To compare the flow parameters, a numerical simulation has been performed for a 2D stationary Pelton bucket under both eroded and uneroded scenarios considering clean water as the working fluid. The 2D middle section of a reference Pelton bucket has been considered in two configurations: the uneroded bucket case and eroded, whose uneven surface patterns were created taking inspiration from real erosion patterns inside Pelton buckets observed in sediment-laden power plants. The CFD simulations have been carried out using OpenFOAM multiphase solver interFoam. The two cases were then compared based on several flow parameters such as water film thickness, velocity, and exit flow angle. The result analysis confirmed flow disturbances due to erosion causes the decrement in the overall flow velocity and the increment in water film thickness. The velocity reduction effect, as well as the water film thickness increment effect, strengthens approaching the outlet of the bucket. The results also showed a significant alteration of exit flow angle due to erosion. Specifically, for the erosion pattern considered in this study, the exit flow angle was observed to be larger compared to the uneroded bucket case.

Innovative technologies in the logistics system

This article highlights the importance of the latest technologies and innovative logistics as the most effective element of the logistics activities of enterprises. The essence of the most modern and progressive technologies is described in detail, their advantages and features of implementation in real-time conditions are determined. The impact of these technologies on logistics and supply chains is analyzed. It has been established that the participants of the logistics chain are able to create a transparent and effective system of transaction recording, asset tracking and documentation management using the most promising innovative technologies in the logistics field. The effects that the subjects of the logistics chain receive from the introduction of the latest technologies are outlined. It has been proven that the use of progressive innovations in logistics can ensure the growth of the effective activity of any enterprise, reduce the total costs for the implementation of flow processes and improve the quality of customer service as a whole.

Research on risk data analysis of hazardous chemical vessels based on stream computing

Abstract To effectively improve the safety supervision and information management level of vessels carrying hazardous chemicals, the maritime management department is promoting the construction of hazardous chemical vessel monitoring systems based on artificial intelligent technology for hazardous chemical terminals and shipping companies in their jurisdiction. It can identify specific risks and transmit the risk data in real-time to the data center. To analyze data in real-time, a risk data analysis platform for hazardous chemical vessels based on stream computing is designed and implemented. The platform acquires vessel automatic identification system (AIS), risk, basic information, and other data. Based on the stream computing of Flink, it evaluates the risk status of vessels in real time, counts the risk data, and displays the results in the form of charts. The analysis of risk data for hazardous chemical vessels helps maritime management departments to track the real-time navigation status of key vessels, timely identify potential safety hazards, and effectively improve the level and efficiency of safety supervision.

A hybrid physical and co-simulation modern adaptive power protection testbed for testing the resilience of smart grids under cyber-physical threats

Power protection systems play a critical role in ensuring the safe and reliable operation of modern power grids. With the increasing complexity of grid topologies and the integration of Distributed Energy Resources (DERs), traditional protection schemes face challenges in maintaining effective coordination among relay systems. This paper presents a new adaptive Overcurrent Relay (OCR) protection and investigates the resilience of different traditional and new adaptive OCR protection schemes against various cyber-physical threats in a smart grid environment. Key contributions include the implementation of a novel adaptive protection scheme designed to dynamically adjust OCR settings based on real-time grid conditions, improving flexibility and responsiveness in managing grid variations induced by DER integration and operational changes. The proposed scheme is validated and implemented using the Multifunction Protection Relay SIEMENS 7SJ62, following to the programming standards of IEC 61131–3, ensuring the reliability and practical applicability of the adaptive OCR scheme in real-world scenarios. Additionally, a dedicated testbed is developed to simulate real-time cyber-physical interactions, incorporating hardware components such as the SIEMENS 7SJ62 and OMICRON-256 test device, along with high-performance computers and communication networks to facilitate comprehensive evaluations of adaptive OCR schemes under varying operational conditions. The study also examines the implications of real-time cyber-attacks on power system operations and the effectiveness of adaptive OCR protection schemes, addressing cybersecurity concerns and proposing mitigation strategies to the system resilience.

Development of Remote Condition Monitoring System for Panauti Hydropower Station

Abstract Condition monitoring is the process of predicting faults in machine through the measurement and analysis of pre-identified parameters. This paper aims to present the features and process of development of a real time condition monitoring system installed at Panauti Hydropower station. In total, seven different sensors including Pressure, Temperature, Guide Vane Position and Tri-axial Vibration sensor were installed at the power plant. The locations of the pressure sensors are at the inlet of the turbine and the outlet of the draft tube. Tri-axial vibration sensor was installed at the turbine shaft to capture the turbine vibrations at different operating conditions. The temperature sensors provide the temperature data from the bearings and housing of a generator. The sensors were connected to the NI-DAQ (data acquisition system) hardware including current measuring module and sound and vibration module integrated with the LabVIEW program. The data acquisition system is capable of recording the pressure pulsations at the outlet of draft tube. The monitoring system processes the data from sensors and uploads the relevant information to the Kathmandu University’s central server. A remote monitoring program in LabVIEW has been developed which uses an Application Programming Interface (API) to retrieve the information from the central server in the real time, enabling the monitoring of the power plant from any location.

Priority-based scheduling in residential energy management systems integrated with renewable sources using adaptive Salp swarm algorithm

With the remarkable growth and implementation of communication technology, sensors, and measurement equipment in the Smart Grid (SG) environment, demand side management (DSM) and demand response (DRs) can be easily implementable in residential energy systems integrated with renewable energy sources (RES). Looking at this perspective, this paper suggests an intelligent and dynamic load-priority-based scheduling optimal smart residential energy management system (REMS). The objectives to achieve through priority-based scheduling in the case of a residential energy management system are multi-focussed in terms of peak load reduction, consumer choice of consumption according to priority basis, and cost-effectiveness towards electricity price savings. The issues related to uncertainties with RES due to environmental dependency must be incorporated into the DSM. A single objective discrete formulation based on the Adaptive Salp Swarm Algorithm (ASSA) has been done on modelling and optimizing the crucial system parameters for scheduling, ideally the operation of residential appliances, along with the sources and prioritized-based loads available. System constraints, consumer priorities, energy source availability, uncertainties, and objectives are considered in the formulation to justify the approach that is feasible in real-time conditions. To enhance the search capabilities of SSA, the control parameters vary optimally in both the exploration and exploitation stages of searching. Comparative results with genetic algorithms (GA), particle swarm optimization (PSO), and conventional SSA are presented in different cases, such as (1) traditional homes without REMS, (ii) smart homes with REMS (iii) smart homes using REMS with RES.

Synergistic integration of a rhodamine-labelled tripeptide into AIE-active fluorogenic probe: Enabling nanomolar detection of Al3+ ions through test strips, thin films, and Arduino-assisted optosensing platform

Peptide-fluorophore conjugates (PFCs) have been expeditiously utilized for metal ion recognition owing to their distinctive characteristics. Selective detection and quantification of aluminum is essential to minimize health and environmental risks. Herein, we report the synthesis and characterization of a new chemoprobe with aggregation-induced emission characteristics by chemically conjugating rhodamine-B fluorophore with a tripeptide. The probe revealed β-sheet secondary conformation in both solid and solution states, as confirmed by FT-IR, PXRD, and CD experiments. AIE characteristics of the probe in water-MeCN mixtures revealed the formation of spherically shaped nanoaggregates with an average size of 353 ± 7 nm, as confirmed by SEM, TEM, and DLS studies. The probe exhibited a large stokes shift (175 nm) and displayed selective colorimetric and fluorometric responses towards Al3+ ions with an extremely low detection limit (51 nm) and a fast response time (≤15 s). Comparative NMR studies confirmed the cleavage of spirolactam ring upon aluminum binding. The probe’s practicality was enhanced through integration into test strips and thin films, allowing solid-phase detection of Al3+ ions. Furthermore, an RGB-Arduino enabled optosensing device has been developed to enable instant quantifiable analysis of aluminum concentrations in real-time conditions.

Optimization of microgrid operations using renewable energy sources

Microgrids, comprising localized energy systems capable of operating independently or in conjunction with the main grid, are increasingly being recognized as vital components of modern energy infrastructure. The integration of renewable energy sources (RES) into microgrids offers significant potential for enhancing operational efficiency, sustainability, and resilience. This paper presents an overview of recent advancements and methodologies for optimizing microgrid operations utilizing renewable energy sources. The optimization of microgrid operations involves the strategic coordination and management of diverse energy resources, including solar photovoltaic (PV) systems, wind turbines, and energy storage systems (ESS). Key objectives include minimizing operational costs, reducing greenhouse gas emissions, ensuring reliable power supply, and maintaining system stability. Advanced optimization techniques, such as model predictive control (MPC), mixed-integer linear programming (MILP), and heuristic algorithms, play a crucial role in achieving these objectives by enabling the dynamic adjustment of energy generation and distribution in response to real-time conditions. A critical aspect of microgrid optimization is the accurate forecasting of renewable energy generation and load demand. Machine learning (ML) and artificial intelligence (AI) algorithms have been effectively employed to enhance prediction accuracy, thereby improving decision-making processes. Furthermore, the integration of ESS, such as batteries and flywheels, helps to address the intermittency of RES, providing a buffer that can store excess energy during periods of high generation and release it during peak demand. The implementation of demand response (DR) strategies within microgrids further contributes to optimization efforts. By incentivizing consumers to adjust their energy usage patterns in response to price signals or grid needs, DR programs help to balance supply and demand, reduce peak loads, and enhance overall grid reliability. Case studies and field implementations demonstrate the practical benefits of optimized microgrid operations. For instance, microgrids incorporating high shares of RES have been shown to achieve significant cost savings, improved energy security, and reduced environmental impacts. These successes underline the importance of ongoing research and development in optimization techniques and the need for supportive policy frameworks to facilitate the broader adoption of microgrids. In conclusion, optimizing microgrid operations using renewable energy sources presents a promising pathway toward a more sustainable and resilient energy future. Continued advancements in optimization algorithms, predictive analytics, and integrated system design are essential for unlocking the full potential of microgrids, ensuring they can effectively meet the evolving energy demands and environmental challenges of the 21st century. Keywords: Optimization, Microgrid, Operations, Renewable Energy, Energy Sources.

Leveraging the Integration of IoT Devices for Energy Optimization in Office Blocks

The integration of Internet of Things (IoT) devices in office buildings has emerged as a promising solution for enhancing energy efficiency and optimizing resource utilization. In the context of modern workplaces, the adoption of smart technologies offers a transformative approach towards achieving energy optimization goals. Internet connected smart devices are ubiquitous and hence opens new opportunities for optimising energy consumption within office blocks. This research paper explores the integration of IoT devices within an office environment to improve energy efficiency and sustainability. The research paper aims to review a simple and low-cost smart workplace model that employs IoT technology, cloud storage, and data analytics for real-time monitoring and analysis of energy consumption patterns within an office environment. The study examines the implementation of an integration of IoT sensors within an office environment enabling HVAC and lighting systems to dynamically adjust energy use based on real-time conditions such as occupancy levels, ambient temperature, and daylight availability. Through the identification of trends, anomalies, and opportunities for optimization, the study seeks to qualify adaptive control strategies that can effectually reduce energy waste and improve general operational productivity in office buildings. The findings from this research underwrite to the growing field of smart offices by demonstrating the potential of integrating IoT devices for energy optimization in modern office blocks.

Fuzzy adaptive cruise control with model predictive control responding to dynamic traffic conditions for automated driving

Traditional Adaptive Cruise Control (ACC) systems often struggle to dynamically adapt to rapidly changing traffic conditions, resulting in suboptimal performance. Additionally, with fuel consumption emerging as a critical consideration alongside safety, there is a pressing need for more advanced solutions. This paper presents a novel approach to address these challenges by integrating Fuzzy ACC with Model Predictive Control, denoted as FACMPC. This integration aims to enhance both the longitudinal safety of AVs and fuel efficiency by considering real-time traffic conditions. The FACMPC system utilizes fuzzy logic inside the MPC, adaptively generates controller's weighting factors, allowing the system to adapt instantly to varying traffic environments and driving circumstances. The findings show that this adaptation improves the balance between driving safety, efficiency, and comfort. Additionally, three interruption scenarios, Alpha, Beta and Gama, are examined. In Alpha, the study evaluates the sensitivity of the FACMPC to disturbances by applying band-limited white noise to the lead vehicle velocity. In Beta, the AV experiences a loss of the lead vehicle velocity signal for a defined period, prompting safety considerations and assumptions. The Gama scenario includes a sensitivity analysis to account for variations and uncertainties in parameters by considering a range of ±5% around the nominal values for four key parameters: road slope, wind speed, wind direction, and rolling resistance. The findings indicate that the proposed controller's mean fuel consumption is 8.110, only a 3.21% increase over the nominal, compared to a 7.03% increase for the conventional ACC, demonstrating greater robustness against uncertainties.

Intelligent Decision-Making Algorithm for UAV Swarm Confrontation Jamming: An M2AC-Based Approach

Unmanned aerial vehicle (UAV) swarm confrontation jamming offers a cost-effective and long-range countermeasure against hostile swarms. Intelligent decision-making is a key factor in ensuring its effectiveness. In response to the low-timeliness problem caused by linear programming in current algorithms, this paper proposes an intelligent decision-making algorithm for UAV swarm confrontation jamming based on the multi-agent actor–critic (M2AC) model. First, based on Markov games, an intelligent mathematical decision-making model is constructed to transform the confrontation jamming scenario into a symbolized mathematical problem. Second, the indicator function under this learning paradigm is designed by combining the actor–critic algorithm with Markov games. Finally, by employing a reinforcement learning algorithm with multithreaded parallel training–contrastive execution for solving the model, a Markov perfect equilibrium solution is obtained. The experimental results indicate that the algorithm based on M2AC can achieve faster training and decision-making speeds, while effectively obtaining a Markov perfect equilibrium solution. The training time is reduced to less than 50% compared to the baseline algorithm, with decision times maintained below 0.05 s across all simulation conditions. This helps alleviate the low-timeliness problem of UAV swarm confrontation jamming intelligent decision-making algorithms under highly dynamic real-time conditions, leading to more effective and efficient UAV swarm operations in various jamming and electronic warfare scenarios.

Application of polyethylene glycol 8000 as plasticizer for the development of ellagic acid and Eudragit® EPO based solid dispersions using hot melt extrusion.

Solid dispersion formation by Hot melt extrusion (HME) is a widely used formulation strategy to improve the solubility and bioavailability of poorly water-soluble drugs. Despite this, they are limited by various factors such as drug-excipient miscibility, poor stability, limited drug loading and extrudability of physical drug-excipient mixtures. In this work, polyethylene glycol 8000 (PEG 8000) was used as a plasticizer for the manufacture of ellagic acid solid dispersions (EASD) with high drug loading. Indeed, EA is a polyphenolic active compound with antimalarial and other promising therapeutic activities. However, its low solubility and low permeability limit its therapeutic use. Solid dispersions formation may overcome this challenge, but its high melting point negatively influences the extrudability of its binary physical mixtures with a high drug loading rate, hence the need to use a plasticizer. Thus, five formulations consisting of EA, Eudragit® EPO and PEG 8000 in the ratio of 15:75:10 (F1), 20:70:10 (F2), 25:65:10 (F3), 15:80:5 (F4) and 20:85:5 (F5) % w/w, respectively, have been extruded, four of which were successful. The extrudates were evaluated by X-ray powder diffraction, FTIR spectroscopy and in vitro dissolution tests. Based on the results of these tests, the F5 formulation was identified as the most promising. Indeed, after 15 min of dissolution test, the dissolution rate of ellagic acid from the formulations was 62.67±3.10%, 58.74±7.23 %, 88.75±3.02% and 83.47±4.40% respectively for formulation F1, F2, F4 and F5. Moreover, the results of the FTIR spectroscopy analyses showed stronger interactions between the different constituents in the F4 and 5 formulations compared to the F1 and 2 formulations. Extruded materials of the F5 formulation, characterized by solid state nuclear magnetic resonance (ssNMR) spectroscopy and subjected to stability studies, showed good physical stability for twelve months under real-time stability study conditions and for six months under accelerated conditions.

State of Health Estimation of Electric Vehicle Batteries Using Transformer-Based Neural Network

Abstract Electric vehicles (EVs) are considered an environmentally friendly option compared to conventional vehicles. As the most critical module in EVs, batteries are complex electrochemical components with nonlinear behavior. On-board battery system performance is also affected by complicated operating environments. Real-time EV battery in-service status prediction is tricky but vital to enable fault diagnosis and prevent dangerous occurrences. Data-driven models with advantages in time-series analysis can be used to capture the degradation pattern from data about certain performance indicators and predict the battery states. The transformer model can capture long-range dependencies efficiently using a multi-head attention block mechanism. This paper presents the implementation of a standard transformer and an encoder-only transformer neural network to predict EV battery state of health (SOH). Based on the analysis of the lithium-ion battery from the NASA Prognostics Center of Excellence website's publicly accessible dataset, 28 features related to the charge and discharge measurement data are extracted. The features are screened using Pearson correlation coefficients. The results show that the filtered features can improve the model's accuracy and computational efficiency. The proposed standard transformer shows good performance in the SOH prediction.

Digital Twin System of Substation Equipment

This paper adopts the following technical solutions to achieve: a digital twin system for substation, including: database, database configuration module, data warehousing module, background data processing module and front-end browsing module; The front-end browsing module is used to display the data sent by the background data processing module in the corresponding form according to the user's selection; The form includes building the three-dimensional model of the substation based on BIM technology, and displaying the corresponding real-time data and status in the three-dimensional components of the three-dimensional model. Compared with the existing technology, it organically integrates the three-dimensional model and panoramic monitoring data of the substation, realizes the three-dimensional panoramic display of substation assets and the acquisition of substation monitoring data, improves the spatial experience of daily operation and maintenance comprehensive monitoring, and provides a solid data platform for the subsequent in-depth mining and intelligent analysis of multidimensional data of the substation.

Coated boron layers by boronization and a real-time boron coating using an impurity powder dropper in the LHD

In the Large Helical Device (LHD), diborane (B2H6) is used as a standard boron source for boronization, which is assisted by helium glow discharges. In 2019, a new Impurity Powder Dropper (IPD) system was installed and is under evaluation as a real-time wall conditioning technique. In the LHD, which is a large-sized heliotron device, an additional helium (He) glow discharge cleaning (GDC) after boronization was operated for a reduction in hydrogen recycling from the coated boron layers. This operational time of 3 h was determined by spectroscopic data during glow discharges. A flat hydrogen profile is obtained on the top surface of the coated boron on the specimen exposed to boronization. The results suggest a reduction in hydrogen at the top surface by He-GDC. Trapped oxygen in coated boron was obtained by boronization, and the coated boron, which has boron-oxide, on the first wall by B-IPD was also shown. Considering the difference in coating areas between B2H6 boronization and B-IPD operation, it would be most effective to use the IPD and B2H6 boronization coating together for optimized wall conditioning.