Operational Scenarios Research Articles

Core design and neutronics analysis of lead liquid metal-based heat pipe cooled reactor for undersea purpose

Abstract Various heat pipe-cooled reactors have been conceptualized and designed in recent years, ranging from kilowatts to megawatts, suitable for remote and extreme environments. However, existing designs employ monolith cores, which are saddled with heat transfer deteriorations due to thermal expansion and subsequent deformations are some of the key challenges facing monolith heat pipe reactors. Addressing this, a novel 300 kW heat pipe has been designed by introducing lead liquid instead of the monolith. A comprehensive Monte Carlo neutronics analysis of the designed core has been conducted including its thermal analysis. The core design comes with the following parameters: fresh core excess reactivity of 2.9914 $ that can support a 10-year life cycle, and a negative temperature coefficient of −0.1648 pcm/K. The axial and radial peak power factors at critical states were calculated to be 1.67 and 1.35 respectively. Preliminary thermal hydraulics analysis shows maximum temperatures of 1044 K under normal operation and worst-case accident scenarios (3 heat-pipe failures) of 1,149.9 K. These temperatures are lower than the melting point of uranium nitride fuel and lower than the possible heat pipe boiling failure limit at 1,300 K. Thus, the possibility of fuel melting/heat pipe boiling accidents is avoided. A hypothetical criticality accident was simulated, assuming the core was flooded with water, leading to an increased fission rate. However, the implementation of three emergency rods in the core ensures the subcritical of the reactor.

Analysis of ship electrical load management to reduce exhaust gas emissions in landing platform dock (LPD) vessel class

This paper investigates the impact of electrical load management on reducing exhaust gas emissions in Landing Platform Dock (LPD) class vessels. The study focuses on the implementation of Integrated Fully Electric Propulsion (IFEP) systems and Variable Speed Drives (VSD) to enhance energy efficiency and minimize environmental impact. The conventional Diesel Engine Propulsion system in LPD vessels often results in high fuel consumption and significant emissions, particularly under non-optimal operating conditions. By transitioning to an IFEP system and optimizing electrical load management using VSD, the study demonstrates a substantial reduction in carbon dioxide (CO₂), nitrogen oxides (NOₓ), and sulfur oxides (SOₓ) emissions. The results indicate a potential emission reduction of up to 40%, alongside a 20% improvement in overall energy efficiency. Additionally, the operational benefits of these technologies include increased flexibility, reduced maintenance costs, and extended equipment lifespan. The findings underscore the importance of adopting advanced propulsion and load management technologies in naval vessels to meet stricter environmental regulations and support sustainable maritime operations. This study provides a framework for future research and practical applications in expanding these technologies across different ship types and operational scenarios.

Design and Uncertainty Evaluation of a Calibration Setup for Turbine Blades Vibration Measurement

Turbomachinery engines face significant failure risks due to the combination of thermal loads and high-amplitude vibrations in turbine and compressor blades. Accurate stress distribution measurements are critical for enhancing the performance and safety of these systems. Blade tip timing (BTT) has emerged as an advanced alternative to traditional measurement methods, capturing blade dynamics by detecting deviations in blade tip arrival times through sensors mounted on the stator casing. This research focuses on developing an analytical model to quantify the uncertainty budget involved in designing a calibration setup for BTT systems, ensuring targeted performance levels. Unlike existing approaches, the proposed model integrates both operational variability and sensor performance characteristics, providing a comprehensive framework for uncertainty quantification. The model incorporates various operating and measurement scenarios to create an accurate and reliable calibration tool for BTT systems. In the broader context, this advancement supports the use of BTT for qualification processes, ultimately extending the lifespan of turbomachinery through condition-based maintenance. This approach enhances performance validation and monitoring in power plants and aircraft engines, contributing to safer and more efficient operations.

Thermo-Mechanical Coupled Analysis of Electric Vehicle Drive Shafts

With the growing concerns over global warming and abnormal weather patterns, the development of eco-friendly technologies has emerged as a critical research area in the transportation industry. In particular, the global automotive market, one of the most widely used sectors, has witnessed a surge in research on electric vehicles (EVs) in line with these trends. Compared to traditional internal combustion engine vehicles, EVs require components with high strength and durability to achieve optimal performance. This study focuses on the development of a constant velocity (CV) joint, a critical component for reliably transmitting the maximum output of an electric vehicle motor. Unlike conventional numerical methods, the proposed thermo-mechanical coupled analysis simultaneously accounts for thermal and mechanical interactions, providing more realistic operational performance predictions. This analysis, conducted using the thermal modules of Ls-Dyna and ANSYS Mechanical, effectively simulated field operation scenarios. Prototype testing under simulated conditions showed a 6% discrepancy compared to numerical predictions, validating the high accuracy and reliability of the proposed method. This robust thermo-mechanical coupled analysis is expected to improve the durability and reliability of CV joint designs, advancing electric vehicle component development.

Comprehensive benefit optimization method for photovoltaic inverters participating in distribution network loss reduction by reactive compensation

When a large number of distributed photovoltaic (PV) systems are integrated into the distribution network, power flow becomes bidirectionally fluctuating, resulting in variable line losses. In the scenario of reverse flow, the increase in line loss is particularly significant. The bidirectional reactive power regulation of photovoltaic inverters is an effective approach to reduce losses in the distribution network. However, despite the benefits of reducing losses, reactive power regulation by photovoltaic inverters also incurs additional costs. Therefore, there is a need for research into a comprehensive benefit optimization method for inverters participation in reducing reactive power losses in distribution networks. Firstly, the cost quantification models for the investment, transformation, operation, and lifespan loss of the photovoltaic inverters involved in reactive power loss reduction are established. Secondly, the benefit quantification models for loss reduction and power factor improvement are developed. Thirdly, considering various operational scenarios of both photovoltaic systems and loads, a comprehensive benefit optimization method for photovoltaic inverters participating in reactive power loss reduction in distribution networks is proposed. Finally, through example analysis, the cost and benefit are calculated and fitted in different scenarios, and the optimization calculation is carried out. Compared to the scenario where the photovoltaic inverter operates at the maximum reactive power regulation capacity, the optimized comprehensive benefit is increased by 21.20%. The proposed method is validated to effectively enhance the comprehensive benefits of inverters participation in reactive power loss reduction.

Modeling for underground logistics system in the Korean metropolitan area

This study evaluated the development and validation of an integrated operational model for the Underground Logistics System (ULS) in South Korea’s metropolitan area, aiming to address challenges in urban logistics and freight transportation by highlighting the potential of innovative logistics systems that utilize underground spaces. This study used conceptual modeling to define the core concepts of ULS and explored the system architecture, including cargo handling, transportation, operations and control systems, as well as the roles of cargo crews and train drivers. The ULS operational scenarios were verified through model simulation, incorporating both logical and temporal analyses. The simulation outcomes affirm the model’s logical coherence and precision, emphasizing ULS’s pivotal role in boosting logistics efficiency. Thus, ULS systems in Korea offer prospects for elevating national competitiveness and spurring urban growth, underscoring the merits of ULS in navigating contemporary urban challenges and championing sustainability.

An In Situ UV Cross-Linking Asymmetric Adhesive Hydrogel for Noncompressible Hemostasis and Postoperative Adhesion Prevention.

Noncompressible hemorrhage control is vital for clinical outcome after surgical treatment and prehospital trauma injuries. Meanwhile, wound bleeding and tissue damage could induce postoperative adhesions, leading to a severe threat to the health of patients. Considerable research had been conducted on the development of hemostatic and antiadhesive materials. However, it was still a great challenge to realize hemostasis and antiadhesion simultaneously especially in inaccessible and irregular wound sites. In this study, a kind of fluid hemostatic agent composed of gelatin methacryloyl/sulfobetaine methacrylate/oxidized konjac glucomannan (termed GOS) was developed, which spread immediately upon contacting the hepatic trauma surface and turned into hydrogels under UV radiation within 5 s, resulting in rapid hemostasis and firm adhesion to tissues (shear strength 486.08 kPa). Importantly, the surface of the as-formed GOS hydrogel exhibited lubricious and nonadhesive properties, exhibiting excellent anti-postoperative adhesion performance in a rat liver hemostasis model and a rat abdominal wall-cecum adhesion model. In addition, the GOS hydrogel reduced the postoperative secretion of inflammatory factors TNF-α and IL-6, facilitating the tissue repair. Therefore, the asymmetrical adhesive GOS hydrogel could fulfill the requirements for simultaneously rapid hemostasis, tissue adhesion, and subsequent excellent antiadhesion, which demonstrated significant potential for diverse clinical surgical operation scenarios.

Fault diagnosis of inter‐turn short circuits in PMSM based on deep regulated neural network

AbstractPermanent Magnet Synchronous Machine (PMSM) is widely utilised in numerous industrial applications due to its precise control capabilities. However, these motors frequently encounter operational faults, potentially leading to severe safety and performance issues. Consequently, effective health monitoring techniques for early fault detection are essential to maintain optimal performance and extend the lifespan of these systems. This study presents a qualification‐based methodology for diagnosing faults in three‐phase PMSMs through vibration–current data fusion analysis. The stator faults, specifically inter‐turn short circuits (ITSC) induced via bypassing resistances, were investigated using experimental data from a custom‐built test rig. The collected current and vibration signals were transformed into statistical features. Various operating scenarios were diagnosed utilising a deep regulated neural network (RegNet), an improved convolutional neural network based on an enhanced residual architecture. The proposed approach was assessed through various metrics including training efficiency, precision, recall, f1‐score, and accuracy, and compared against several neural network methods. The findings reveal that the proposed RegNet model achieves perfect accuracy, attaining 100%. This research highlights the efficacy of data fusion analysis and deep learning in fault diagnosis, facilitating proactive maintenance strategies and improving the reliability of PMSMs in diverse industrial applications and renewable energy systems.

Energy-Efficient Green Information Centric Networking for Future Wireless Communications

Energy-efficient green information-centric networking (EEGICN) is proposed in this paper for advancing future wireless communication networks by addressing the challenge of energy consumption. This model can adapt the power consumption of network nodes to optimized values according to the associated link utilization. The model aimed to reduce energy consumption and increase network performance and stability. The EEGICN model incorporates efficient routing mechanisms, content caching strategies, and energy-intelligent communication protocols to improve resource utilization across the network infrastructure. EEGICN minimizes large data transmission, reduces power consumption, and reduces network congestion. Popular resources are cached in key network locations, and proximity data is exchanged to achieve this. The model also includes dynamic power management algorithms that adapt to changing traffic demand and network conditions to provide consistent performance across a variety of operational scenarios. In comparison to current wireless network systems that employ various forms of cache, the evaluation findings demonstrated that EEGICN can increase network efficiency by dramatically lowering the number of hops and energy consumption. Future networks may find this application to be a quick and easy way to transmit content.

A Neural Controller Design for Enhancing Stability of a Single Machine Infinite Bus Power System

This paper examines the formulation and implementation of a neuro-controller for the excitation system of synchronous generators in a Single-Machine Infinite Bus (SMIB) power system. The SMIB model is employed as a fundamental model of a power system, thereby facilitating the assessment and comparison of disparate control strategies with the objective of enhancing system stability. The goal of this study is to enhance the stability of the SMIB power system through the implementation of an Artificial Neural Network (ANN) neuro-controller, providing a comparison of its performance to that of a Power System Stabilizer (PSS) and a Proportional-Integral-Derivative (PID) controller. The proposed neuro-controller will be integrated into the generator's excitation system and will be designed to regulate the excitation voltage in response to fluctuations in the system's operational parameters. To this end, an ANN is calibrated to account for the singularity of the generator's excitation level and terminal voltage. The Levenberg-Marquardt algorithm is employed to ascertain the optimal weight coefficients for the ANN. To assess the performance of the neuro-controller, simulations were conducted using MATLAB/Simulink. The simulations encompass a comprehensive range of operational scenarios, including diverse disturbances and alterations in the reference voltage level. Subsequently, the neuro-controller's outputs are evaluated in comparison to the PSS and PID controllers, as these are the prevailing controllers used to enhance voltage regulation and transient stability in power systems. This paper presents the results of an analysis of the neuro-controller's impact on the system's robustness, voltage variation amplitude, and generator dynamic performance during faults. Simulation results demonstrate that the application of an ANN-based neuro-controller yields superior outcomes in voltage regulation and transient stability compared to the conventional controllers PSS and PID. Furthermore, the neuro-controller is distinguished by accelerated response times and enhanced precision in voltage level regulation. The neuro-controller represents a superior approach to the control of a power system, particularly in the context of SMIB, which would ultimately result in enhanced performance and stability.

The LLCL filter-based synchronverter with adaptive fuzzy logic controller

Global warming has emerged as a significant issue, primarily due to the use of fossil fuels for power generation. Renewable energy adoption has surged. However, stability issues arise when integrating the primary source and inertia-less nature of converter-interfaced renewable energy sources (RESs) into the traditional power system, due to its intermittent and uncertain nature. The proposed solution involves controlling converter-interfaced RES to emulate the characteristics of a conventional synchronous generator (SG), commonly known as a virtual synchronous generator or synchronverter. As it fundamentally functions as a power converter, it is crucial to consider the appropriate filter design for the synchronverter. Additionally, by emulating the characteristics of SG, the values of “virtual” inertia and droop coefficients must be chosen accurately to provide a robust response under different operating scenarios of the synchronverter. Hence, this paper proposes a novel LLCL filter-based synchronverter system along with an adaptive control strategy using a fuzzy logic controller (FLC). The system is simulated under different operating scenarios in MATLAB/Simulink. The proposed system demonstrated improved frequency response and reduced total harmonic distortion compared to the conventional synchronverter model for all the different operating scenarios, thus enhancing grid stability and power quality.

Real-Time Structural Health Monitoring of Bridges Using Convolutional Neural Network-Based Loss Factor Analysis for Enhanced Energy Dissipation Detection

This study introduces a novel method for real-time structural health monitoring (SHM) of bridges using a convolutional neural network (CNN) model that leverages loss factor analysis. As bridge structures deteriorate over time, often accelerated by operational loads, maintaining structural integrity and safety becomes critical. The proposed approach utilizes the concept of a loss factor, which represents the process of energy dissipation across different vibration states, as a key indicator of structural health. This factor is computed from vibration energy spectra, which include amplitude and frequency components, processed through the CNN model for high sensitivity to structural changes. The results demonstrate that the energy dissipation of the bridge during operation can be categorized into signals from three distinct sources: structural responses, defects-related indicators, and noise interference. By monitoring variations in the loss factor over time, the model effectively identifies early signs of structural deterioration, which is critical for timely maintenance interventions. The study also highlights the adaptability to different load conditions and environmental factors, ensuring robust performance in various operational scenarios. The findings underscore the potential of the CNN model to transform SHM practices by enhancing early defect detection, supporting preventive maintenance, and ultimately extending the lifespan of bridge infrastructure.

PREDICTION OF THE MAIN ENVIRONMENTAL AND ENERGY CHARACTERISTICS OF DIESEL ENGINES USING AN ARTIFICIAL NEURAL NETWORK FOR PURE DIESEL FUEL, PURE HVO, AND A MIXTURE OF THESE FUELS IN THE RATIO 50/50 BY VOLUME

This research assesses the influence of different quantities of hydrotreated vegetable oil (HVO) in diesel fuel on the performance of the engine and the emissions it produces. The particular areas of interest are the level of smoke emitted and the brake thermal efficiency (BTE). A series of engine experiments were undertaken to quantify emissions and performance characteristics under various operating regimes using three distinct fuel blends: D100 (pure diesel), HVO50 (50% HVO mixture), and HVO100 (pure HVO). The results show a tendency to reduce soot emissions when the amount of HVO in the mixture reaches 50%, but in order to accurately determine the correlation between the amount of HVO and emissions, additional studies with various concentrations of HVO and diesel mixtures are necessary. Similarly, the results show a slight improvement in BTE stability with a 50% HVO blend, but more studies with different percentages of HVO diesel blends are needed to reliably determine changes in BTE. This indicates a trend of change in engine performance with increasing HVO concentration in diesel fuel. In order to forecast emissions and performance indicators under different operating scenarios, we used artificial neural networks ANNs, which demonstrated excellent prediction accuracy, exhibiting robust linear correlations between the expected and real values for all fuel types. This research emphasizes the advantages of utilizing HVO in diesel engines for both environmental impact and performance. It also emphasizes the usefulness of ANNs in optimizing engine settings to improve efficiency and minimize emissions. The results endorse the further use of HVO as a viable substitute for conventional diesel, leading to less ecological consequences and enhanced engine efficiency.

Initial design concepts for solid boron injection in ITER

As part of ITER’s consideration to change its first wall material from beryllium to tungsten, the ITER Organization has proposed studying the feasibility of real-time solid boron injection (SBI) into the plasma to coat the walls and divertor to supplement glow discharge boronization (GDB) [1]. Boron deposits getter oxygen and reduce sputtering of tungsten from plasma facing components (PFCs). Particularly in areas with significant plasma wall interactions, boron coatings are expected to be short-lived under high performance plasma conditions. The proposed SBI system aims to maintain boron layers in these areas to avoid excessive radiation from tungsten in the plasma as a risk mitigation to ensure ITER will be able to reach and sustain Q = 10 conditions. The system will be used sparingly, as redeposition of boron can lead to significant tritium retention, which must be minimized in ITER to comply with nuclear safety concerns. SBI is proposed to limit and precisely control the amount of boron injected in real-time during plasma operation. Here, some of the design requirements and initial concepts for an SBI system in ITER are presented based on previous results carried out with SBI systems on a number of tokamaks and stellarators around the world [2–18].Previous results using SBI systems installed by PPPL have injected boron particles from 5 µm–2 mm diameter at calibrated rates of 2–200 mg/s in real-time during plasma operation on AUG [4], DIII-D [5], EAST [6], KSTAR [7], LHD [8], TFTR [9], WEST [10], and W7-X [11,12], leading to improved wall conditions with reduced plasma impurity concentrations and radiated power and improved plasma performance. The boron is ionized in the plasma edge and then deposited on plasma-wetted surfaces. On AUG [4], EAST [6] and WEST [10] reduced tungsten sputtering sources were observed following several discharges with SBI. Extrapolation of these SBI results are presented to estimate the amount of boron needed for wall conditioning in ITER. Real-time SBI control requirements and plasma operation scenarios for ITER are also described.

Multi-step optimization with operational scenarios for hull form and propulsor design for pod-driven cruise ships

Multi-step optimization with operational scenarios for hull form and propulsor design for pod-driven cruise ships

Development of ITER first wall heat load feedback control

Real-time monitoring and control of thermal loads on tungsten plasma-facing components is mandatory for ITER to avoid their overheating during plasma discharges. For the Start of Research Operations (SRO) phase, dedicated First Wall Heat Load Control (FWHLC) functions are included in the ITER Plasma Control System (PCS) to prevent the development of excessive plasma heat loads on the inertially cooled first wall (FW). In this work, we propose a FWHLC design with a model-based approach, which features a simplified thermal model for the FW armour. This control-oriented model simulates the thermal response of ITER FW to slow changes in plasma equilibrium and energy, which during ITER operation will be monitored by the real-time wide angle infrared camera system. This allows computationally efficient runs for rapid controller performance assessment but can also assist operation scenario design by pre-empting potential heat load issues. FWHLC is designed to react to measured and predicted FW temperatures overcoming predesigned limits with real-time variations of the plasma shape or auxiliary power references, aiming to reduce thermal loads before a premature plasma termination is required to protect the FW. The new scheme is tested in closed loop simulations, where perturbations to nominal ITER low current scenarios are introduced in the wall thermal model to trigger the response of FWHLC, supporting the effectiveness of the proposed policy.

A novel linear programming-based predictive control method for building battery operations with reduced cost and enhanced computational efficiency

Battery energy storage systems can be readily integrated with buildings to enhance renewable energy self-consumptions while leveraging time-variant electricity tariffs for possible operation cost reductions. The extensive variability in building operating conditions presents significant challenges in developing universally applicable methods for optimal controls. To ensure reliable and robust controls, this study integrates predictive control with efficient linear programming to effectively fine-tune battery controls for real-time operations. An adaptive time aggregation scheme has been proposed to streamline the optimization process by accounting for unique changes in energy balances and tariffs. Comprehensive data experiments, based on measurements from 95 unique building operation scenarios, have been conducted to quantify the control performance given different optimization formulations, varying types and levels of prediction uncertainties in building energy demands and PV generations. The results validate the value of the method proposed, leading to 11.75% to 34.63% operation cost reductions on average, while reducing computation steps by 87.75% to 92.60% compared with conventional linear programming approaches. The insights obtained are useful for developing flexible building control strategies with improved computation efficiency and robustness, while providing extensible optimization frameworks for buildings with various energy patterns and storage systems.

Integrated model and automatically designed solver for power system restoration

Power system restoration strategies schedule the power plants to re-energize the power network and loads after a blackout. These strategies are of great significance to ensure a resilient power system. Most power system restoration models consider either certain restoration stage(s) or all stages, with each or some of the stages managed independently. They destroy the essential characteristics of the interaction among the restoration stages and lead to sub-optimal solutions. To address this issue, in this paper, we first propose an improved integrated power system restoration (IPSR) model covering the entire restoration process without decoupling and sectionalization. We then develop both mathematical and metaheuristic solvers for the proposed model. In particular, we introduce the automated solver design paradigm to the highly non-linear constrained restoration problem-solving. By the paradigm, we automatically design the metaheuristic solver that gains enhanced performance beyond handcrafted solvers without algorithmic expertise. It is suggested that the automatically designed metaheuristic solver is a promising new option in variable and complicated power system operation scenarios without algorithmic expertise. Finally, a comprehensive case study demonstrates the proposed model’s significance and the proposed solver’s efficiency. Numerical tests show that the proposed IPSR model and solver obtained over 20% lower restoration time than current restoration methods, demonstrating IPSR’s and the proposed solver’s efficiency and effectiveness.

Optimizing Maritime Energy Efficiency: A Machine Learning Approach Using Deep Reinforcement Learning for EEXI and CII Compliance

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships, car carriers, oil tankers, bulk carriers, and container ships, under six different operational scenarios, such as varying cargo loads and weather conditions. Traditional fuels, like marine gas oil (MGO) and intermediate fuel oil (IFO), challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm, which dynamically adjusts engine parameters, demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL, fuel efficiency increased by up to 10%, while EEXI values decreased by 8% to 15%, and CII ratings improved by 10% to 30% across different scenarios. Specifically, under heavy cargo loads, the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally, the DRL approach consistently outperformed traditional optimization methods, demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels, integrating additional machine learning techniques to enhance optimization.

Mechanical Characterization and Modeling of Large-Format Lithium-Ion Battery Cell Electrodes and Separators for Real Operating Scenarios

This study presents a novel application-oriented approach to the mechanical characterization and subsequent modeling of porous electrodes and separators in lithium-ion cells to gain a better understanding of their real mechanical operating behavior. An experimental study was conducted on the non-linear stiffness of LiNi0.8Co0.15Al0.05O2 and graphite electrodes as well as PE separators, harvested from large-format lithium-ion cells, using compression tests. The mechanical response of the components was determined for different operating conditions, including nominal stress levels, mechanical loading rates, and mechanical cycles. The presented work describes the test procedure, the experimental setup, and an objective evaluation method, allowing for a detailed summary of the observed mechanical behavior. A distinct nominal stress level and mechanical cycle dependency of the non-linear stiffnesses of the porous materials were found. However, no clear dependency on compression rate was observed. Based on the experimental data, a poroelastic mechanical model was utilized to predict the non-linear behavior of these porous materials under real mechanical operating scenarios with a normalized root-mean-squared error less than 5.5%. The results provide essential new insights into the mechanical behavior of porous electrodes and separators in lithium-ion cells under real operating conditions, enabling the accelerated development of high-performing and safe batteries for various applications.