Operational Scenarios Research Articles

Test Stand for Microjet Engine Prototypes

To investigate the functionality and performance of a prototype microjet engine, we constructed a versatile test stand tailored to the specifications of a 400 N prototype. This test stand facilitated a comprehensive study by enabling real-time recording of 45 essential parameters for analysis, encompassing temperatures, pressures, speed, fuel flow, thrust, vibration, and various other monitored metrics. All parameters and control elements were seamlessly integrated via a data acquisition and control system, utilizing a compactDAQ (Data Acquisition) system from National Instruments and a custom Virtual Instrument programmed with graphical language. The test stand offers both manual and automated operation modes, with the flexibility for hybrid operation. For instance, following the idle regime, manual control using a potentiometer can seamlessly transition from automated control via a proportional control (P control) mechanism. Before the experimental campaign, rigorous verification and validation tests were conducted to ensure the reliability and accuracy of the setup. The experimental campaign comprised a series of manual tests focusing on the fuel system and automated tests covering starting, idle, working, and stopping regimes. This structured approach allowed for a comprehensive evaluation across different operational scenarios, providing insights into the engine’s behavior and performance under varying conditions.

Optimising LPG Bottling Plant With DES Using Flexsim Simulation Tool

Purpose: This study explores the application of Discrete Event Simulation (DES) using FlexSim software to enhance the operational efficiency of a Liquefied Petroleum Gas (LPG) bottling plant. The primary goal is to ensure that the LPG plant can safely and efficiently meet escalating market demands, thereby prioritising all stakeholders' safety. Design/Methodology/Approach: The research design focused on empirical research and experimental simulation modelling. The study began with collecting and analysing one month of LPG plant data, laying the foundation for developing a simulation model. Verification and validation processes ensured the model's accuracy, enabling the investigation of various operational scenarios. The key performance indicators like First Time to Failure (FTTF), Time Between Failures (TBF), and Time to Repair (TTR) were analysed. The availability rates were 77% from actual data and 76% from simulations, showing that the model is suitable for real-world use. Findings: This study's findings underscore the potential impact of proactive maintenance strategies and operational enhancements as practical and applicable approaches to optimising performance. The analysis also revealed significant improvement opportunities through what-if scenarios: increasing MTBF by 100%, reducing MTTR by 50%, and raising conveyor speed by 15%. Research Limitation: The study's dependence on a systematic literature review could restrict its ability to capture the industry's real-time dynamics. Practical Implication: Implementing proactive maintenance strategies and operational enhancements as practical approaches can reduce downtime and costs and promote productivity and safety. Social Implication: Optimising plant operations will help maintain supply chain stability with the growing demand for LPG. Originality/Value: This work contributes valuable insights and recommendations, establishing a foundation for informed decision-making in the LPG bottling sector.

Prospects of autonomous railway transport development

The implementation of unmanned train control systems offers a number of advantages, such as increasing the capacity of railway lines and traffi c volumes, reducing the ‘human factor’, as well as reducing operating costs. The paper considers the challenges arising from the use of automatic train control systems, and presents the prospects for the implementation of automatic technologies in railway transport for various railway systems. The social and economic aspects of changing professional activities in railway transport are described. Grades of automation applicable to surface urban railwaytransport are presented. The issues of proving the functional safety of machine vision systems as part of the train traffic control system and determining their level of safety completeness are discussed. Examples of railway transport automation in Russia and other countries are given. Basic scenarios of automatic control system operation describing normal and abnormal situations are formulated. In conclusion, the levels of technological readiness of the reviewed solutions in the fi eld of train traffic automation are defi ned. The tasks faced by railway companies in implementing these technologies are outlined, and possible ways of overcoming obstacles to the introduction of automatic systems are proposed, taking into account the current political situation.

A numerical procedure for calculating ice-induced fatigue damage for ship hulls in level ice

Ship hulls advancing in level ice are exposed to considerable cyclic ice loadings, which might cause fatigue damage, threatening the structural integrity and endangering the crews’ lives. Ice-induced fatigue problem is of significance to ice-going ships. In this paper, a fatigue analysis for ship structures in level ice is performed based on a dedicated 6-degree-of-freedom (6-DOF) numerical model of ice-ship interaction, founded on a circumferential crack assumption. The numerical procedure considers the time-varying instantaneous waterline and the couplings between ship motions and external forces. The local ice loads used for fatigue assessment can be obtained from numerical simulations, and the random load peaks are represented by Weibull distribution. A case study of fatigue calculation for designated transverse frames is carried out, of which the applied stress is converted from ice loads by means of structural beam theory. Eventually, according to a proper S-N curve, the fatigue damages are evaluated on the basis of Palmgren–Miner rule, respectively under the operational scenarios of laterally symmetrical and asymmetrical initial ice edges.

Advanced Data Augmentation Techniques for Enhanced Fault Diagnosis in Industrial Centrifugal Pumps

This study presents an advanced data augmentation framework to enhance fault diagnostics in industrial centrifugal pumps using vibration data. The proposed framework addresses the challenge of insufficient defect data in industrial settings by integrating traditional augmentation techniques, such as Gaussian noise (GN) and signal stretching (SS), with advanced models, including Long Short-Term Memory (LSTM) networks, Autoencoders (AE), and Generative Adversarial Networks (GANs). Our approach significantly improves the robustness and accuracy of machine learning (ML) models for fault detection and classification. Key findings demonstrate a marked reduction in false positives and a substantial increase in fault detection rates, particularly in complex operational scenarios where traditional statistical methods may fall short. The experimental results underscore the effectiveness of combining these augmentation techniques, achieving up to a 30% improvement in fault detection accuracy and a 25% reduction in false positives compared to baseline models. These improvements highlight the practical value of the proposed framework in ensuring reliable operation and the predictive maintenance of centrifugal pumps in diverse industrial environments.

Enhancing wind turbine load response tool through geometric non-linearities correction: Case study on DEFlex and design load comparison

This paper introduces a rapid correction technique applied in DEFlex, Ørsted’s in-house aero-servo-hydro-elastic solver for offshore wind turbines. The technique addresses geometric non-linearities from large wind turbine blade deflections, focusing specifically on correcting the angle of attack for torsional deflections. To assess the implementation, comparisons were made with the literature and HAWC2, using the IEA 15-MW wind turbine blade subjected to static and simplified dynamic loading. Additionally, simulations of normal operation scenarios, both with and without turbulence, were performed on a real wind turbine exceeding 10 MW rated power. The results were compared with Siemens Gamesa Renewable Energys tool BHawC, which utilizes a co-rotational formulation for capturing large blade deflections. The findings reveal that coupling DEFlex with the correction method accurately captures the angle of attack dynamics and significantly improves the agreement of fatigue loads.

Adaptive quadrotor control using online dynamic mode decomposition

Precision control of Unmanned Aerial Vehicles (UAVs) is essential for deployment in a wide range of applications. However, real-world flight conditions often deviate from ideal operating scenarios, presenting uncertainties such as external disturbances and unmodeled dynamics. These can dramatically impact tracking accuracy and stability. This study proposes a novel adaptive control technique for quadrotors based on Windowed Dynamic Mode Decomposition (DMDc) techniques. This techniques efficiently identifies dynamic models directly from data, and updates this model in real-time, allowing the controller to compensate for changing conditions. To facilitate realistic validation, the proposed system is integrated within a Hardware-in-the-Loop (HiL) framework. In a series of simulated experiments, the adaptive controller demonstrates improvement in trajectory tracking under disturbances when compared to a conventional inverse dynamics approach. This research underscores the promise of DMDc-based techniques combined with adaptive control to enhance UAV operation, enabling safer and more robust performance in demanding scenarios.

Investigation of DG units influence on 66 kV sub-transmission system network considering region load growth: a case study

Abstract Voltage breakdown in sub-transmission networks resulting from an increase in load demand can be addressed by incorporating distributed generation (DG) units such as hydro, wind, photovoltaic, and geothermal in some buses. This approach can lower power losses and the price of the primary generated energy while increasing network dependability and efficiency. Feasibility of dispersed generation integration & its effect on sub-transmission system operation were investigated using Ben Walied 66 kV sub-transmission network in Libya as a state. This study presents modified particle swarm optimization (MOPSO) technique to select the best option with dispersed generation units under various operating conditions. The Ben Walied 66 kV sub-transmission network has been subject of effects research on both normal operation and load growth scenarios. The goal of this study was to find best way to adjust penetration level of the three DGs in response to changes in the network loading. Three DG units have been refitted to study the effects on test networks for sub-transmission, which consist of 30 and 51 buses. A comparison analysis shows that while the ideal locations of DG units remain constant with load expansion, the optimal solution for the penetration level percentage of three DG units was boosted by new optimal sizes. Research has been done on how the power factor of distributed generators (DGs) affects the practical performance of 66 kV networks in steady-state conditions. The integration of DG units in regulating these losses and voltage variations is demonstrated by the results, which indicate that the ideal solution for DG unit’s power factor was at a value of 0.87 lagging.

Intelligent control of the Magnus anti-rolling device: A co-simulation approach

In this study, we present an artificial intelligence control method specifically designed for the Magnus anti-rolling device. The core of our approach is the development of a co-simulation framework that integrates an intelligent algorithm with a three-dimensional numerical computational programme within a complex hydrodynamic environment. This integration is achieved through the use of a deep reinforcement learning algorithm, which allows for intelligent adaptation of the anti-rolling device's rotating speed in real time. Our research focuses on evaluating the performance of the intelligent anti-rolling control algorithm under a variety of conditions, including different ship-model roll angles, column geometry models, and varying swinging or slewing speeds. Through numerical studies, we compare the effects of these variables on the effectiveness of the control method. The co-simulation technology provides a platform for testing the intelligent control method. It allows a detailed examination of how the intelligent algorithm interacts with the hydrodynamic responses of the Magnus anti-rolling device, ensuring that the control method can adapt to a wide range of operational scenarios. This adaptability is crucial for maintaining stability and improving the performance of the anti-rolling device in real maritime environments. This study highlights the potential for integrating artificial intelligence with traditional maritime engineering solutions.

Stability Study and Strengthening Strategy of Spiral Case-Encased Concrete Structure of Pumped Storage Power Station

With the development of global hydropower, the scale of hydropower stations is increasing, and the operating conditions are becoming more complex, so the stable operation of hydropower stations is very important. The vibration of the turbine unit will cause resonance in the powerhouse, and the structural stability of the powerhouse will be affected. Many scholars pay attention to the stability of the turbine unit operation, and there are few studies on the powerhouse of the hydropower station. Therefore, this paper relies on the Weifang Hydropower Station project to study key issues such as the tensile strength of concrete and how to arrange steel bars to increase the structural stability by changing the material properties through FEA. Three schemes are designed to evaluate the safety of the powerhouse structure when the turbine unit is running through the safety factor. Our findings indicate that the stress variation patterns observed on the inner surface of the powerhouse remain consistent across different operating scenarios. Notably, along the spiral line of the worm section, we observed that the stress levels on the vertical loop line decrease gradually with increasing distance from the inlet. Conversely, stress concentrations arise near the inlet and the tongue. Additionally, it has been noted that the likelihood of concrete cracking increases significantly at the tongue region.

Bud-YOLO: A Real-Time Accurate Detection Method of Cotton Top Buds in Cotton Fields

Cotton topping plays a crucial and indispensable role in controlling excessive growth and enhancing cotton production. This study aims to improve the operational efficiency and accuracy of cotton topping robots through a real-time and accurate cotton top bud detection algorithm tailored for field operation scenarios. We propose a lightweight structure based on YOLOv8n, replacing the C2f module with the Cross-Stage Partial Networks and Partial Convolution (CSPPC) module to minimize redundant computations and memory access. The network’s neck employs an Efficient Reparameterized Generalized-FPN (Efficient RepGFPN) to achieve high-precision detection without substantially increasing computational cost. Additionally, the loss calculation of the optimized prediction frame was addressed with the Inner CIoU loss function, thereby enhancing the precision of the model’s prediction box. Comparison experiments indicate that the Bud-YOLO model is highly effective for detecting cotton top buds, with an AP50 of 99.2%. This performance surpasses that of other YOLO variants, such as YOLOv5s and YOLOv10n, as well as the conventional Faster R-CNN model. Moreover, the Bud-YOLO model exhibits robust performance across various angles, occlusion conditions, and bud morphologies. This study offers technical insights to support the migration and deployment of the model on cotton topping machinery.

Ameliorating the sodium storage performance of hard carbon anode through rational modulation of binder

Hard carbon anodes have emerged as promising candidates for sodium-ion batteries due to their inherent advantages. Nevertheless, the surface imperfections in these materials often culminate in irreversible electrolyte consumption, fostering the development of a heterogeneous and fragile solid electrolyte interface (SEI), thereby compromising the initial Coulombic efficiency (ICE). Drawing inspiration from the catalytic potential of C=O (carbonyl) bonds in directing preferential salt reduction, we introduce a novel strategy that leverages the modulation of the binder, a long-term overlooked pivotal components in the electrode process. Specifically, Polymethyl methacrylate (PMMA), abundant in C=O groups, is partially substituted for PVDF, ensuring robust adhesion of the electrode material to the current collector while preserving superior mechanical properties. The accurate combination of two binders with delightful compatibility in the state-of-art electrode process, can promote a uniform formation of the SEI on the hard carbon surface enriched in inorganic components, which can ensure long-term interfacial stability and suppresses excessive solvent decomposition and facilitates Na+ transfer at the interface. Consequently, the initial Coulombic efficiency of the hard carbon anode with 70 %PMMA binder achieves 86 %, with prominent cycling stability (88 % capacity retention over 500 cycles) at a high current density of 1.2 A g−1. When paired with high loading cathodes to assemble the pouch cell, it also demonstrates stable operational scenarios.

Operational status effect on the seismic risk assessment of oil refineries

The operational status of an oil refinery (type and scale of operations that take place at any time instance) largely determines the amount of fuel produced, circulated within the facility, and stored in tanks. This status is affected by seasonality, periods of peak or low demand, as well as periods of routine maintenance. However, it is an aspect that is typically neglected even though it stands out among the factors that determine the seismic performance of several critical industrial assets, such as the storage tanks, as well as the consequences of any potential failure. An open-source refinery testbed is employed herein to demonstrate the effect of the refinery's operational status on the seismic risk estimates. Alternative realistic operational scenarios are developed following typical industry practices and are arranged over a time period between two refinery major maintenance shutdown events. The most probable damage state is selected for each asset to identify the most vulnerable ones. Based on the type and importance of the impacted assets, the potential consequences are determined at the facility level. Resulting estimates are very different if an earthquake strikes during a regular/high/low-demand period, or a maintenance period. The framework can be utilized to identify the locations within the refinery that may trigger cascading failures and secondary damages, should their assets be damaged by a seismic event. The outcomes can be exploited by stakeholders, risk engineers, and emergency action planners for developing customized and businesslike procedures to enhance the seismic resilience of the facility.

Investigation of operational settings, environmental conditions, and faults on the gas turbine performance

Abstract Gas turbine engines are complex mechanical marvels widely employed in diverse applications such as marine vessels, aircraft, power generation, and pumping facilities. However, their intricate nature renders them susceptible to numerous operational faults, significantly compromising their performance and leading to excessive emissions, consequently incurring stringent penalties from environmental regulatory bodies. Moreover, the deterioration of gas turbine performance is exacerbated by variations in working conditions based on operational settings and environmental conditions. Past studies have focused on certain working conditions that limit effectiveness in real-world applications where operational settings and environmental conditions vary during operations. The influence of these working conditions on the performance of gas turbines also needs to be assessed, as they can lead to different fault patterns resulting in unplanned maintenance, unnecessary maintenance costs, unsafe conditions and stringent penalties. This study uses the gas turbine simulation program to simulate a high-bypass turbofan engine inspired by Pratt & Whitney PW-4056, analysing the combined effects of operational settings and environmental conditions on engine performance while also incorporating simulations of common gas turbine faults like fouling and erosion in various locations and severities along the gas path. The model’s accuracy is confirmed by low mean absolute percentage errors of 0.004% of thrust at the cycle reference point and 0.15% and 0.28% at 2 km and 7 km altitudes, respectively, demonstrating the model’s robustness across varying operational scenarios. In conclusion, this research highlights the significant effects of operational settings and environmental factors on gas turbine performance, particularly impacting specific fuel consumption and thrust. The study reveals that operational settings and environmental factors significantly impact fuel consumption and thrust. Specifically, compressor fouling and low-pressure turbine erosion increase nitrogen oxide (NOx) emissions by 4.5% and 11.1%, while fouling of nozzle guide vanes and high-pressure turbine erosion raise unburned hydrocarbon by 10.0% and 20.2%, and carbon monoxide (CO) by 3.2% and 5.2%, respectively, compared to a healthy engine. These insights highlight the importance of component-specific degradation in influencing gas turbine performance and emissions.

Modeling and simulation study of end around taxiway operation of Shanghai Hongqiao Airport

Abstract With the rapid expansion of the aviation industry, an increasing number of Close Spaced Parallel Runway (CSPR) airports are either planning or constructing End Around Taxiways (EAT) to alleviate field operation pressures and enhance safety. Taking Shanghai Hongqiao Airport’s typical CSPR EAT configuration as a case study, this research integrates the airport’s current operational status with the anticipated requirements for future structural renovations and increased flight volumes. Various operational scenarios are established, and simulation research on optimising EAT operations is conducted in advance. The simulation study proceeds as follows: first, an AirTOP simulation model is constructed based on Hongqiao Airport’s actual operational construction. Subsequently, leveraging existing operational scenarios, five simulation scenarios are devised by activating EATs at the departure and approach ends of the eastern zone. The merits and drawbacks of these scenarios are thoroughly analysed. The findings indicate that, with escalating flight volumes, the utilisation of EAT for larger aircraft can curtail their holding duration by nearly 8 min, consequently reducing overall arrival holding duration by 6 min. Departures from gates proximate to T1 experience a 3-min reduction in holding duration through the adoption of EAT at the approach end. Despite an increase in taxi distance due to a higher proportion of aircraft taxiing around, the overall taxi time is diminished. Activating EATs at the departure and approach ends of the eastern zone effectively mitigates the adverse effects of heightened flight volumes on field operational efficiency.

Development and comprehensive analyses of a hybrid solar–geothermal driven polygeneration system: Thermodynamic, exergoeconomic, and emergoenvironmental aspects

The present research proposed an innovative polygeneration system that uses solar, geothermal, and natural gas energy to produce power, heat, steam, and freshwater. The system consists of a proton exchange membrane fuel cell, an organic Rankine cycle, and a multi-effect thermal desalination system. The study included thermodynamic, exergy, exergoeconomic, exergoenvironmental, emergy-based exergoeconomic, and emergy-based exergoenvironmental factors in its comprehensive evaluation of the system. Results underscored the financial aspect of the organic Rankine cycle and cogeneration system, incurring costs of 0.4518 $/s and 1.054 $/s respectively, while also highlighting the system's capability to produce 6 kg/s of freshwater. The environmental impact rates were quantified for the organic Rankine cycle and cogeneration system at 0.1417 and 0.4814 pts/s respectively, situating the system within its ecological context. Further, the study detailed the total efficiency and net power output of the organic Rankine cycle and cogeneration system, ranging between 32.45–33.51% and 43.25–45.83% for efficiency, and 56956–56322 kW and 50174–51898 kW for net power output r espectively, showcasing the system's operational capacity. A parametric analysis was also integral to the study, examining the impact of key parameters on the functionality of the proposed system, thereby providing a nuanced understanding of the system's performance under varying operational scenarios.

Stability optimization of energetic particle driven modes in nuclear fusion devices: the FAR3d gyro-fluid code

The development of reduced models provide efficient methods that can be used to perform short term experimental data analysis or narrow down the parametric range of more sophisticated numerical approaches. Reduced models are derived by simplifying the physics description with the goal of retaining only the essential ingredients required to reproduce the phenomena under study. This is the role of the gyro-fluid code FAR3d, dedicated to analyze the linear and nonlinear stability of Alfvén Eigenmodes (AE), Energetic Particle Modes (EPM) and magnetic-hydrodynamic modes as pressure gradient driven mode (PGDM) and current driven modes (CDM) in nuclear fusion devices. Such analysis is valuable for improving the plasma heating efficiency and confinement; this can enhance the overall device performance. The present review is dedicated to a description of the most important contributions of the FAR3d code in the field of energetic particles (EP) and AE/EPM stability. FAR3d is used to model and characterize the AE/EPM activity measured in fusion devices as LHD, JET, DIII-D, EAST, TJ-II and Heliotron J. In addition, the computational efficiency of FAR3d facilitates performing massive parametric studies leading to the identification of optimization trends with respect to the AE/EPM stability. This can aid in identifying operational regimes where AE/EPM activity is avoided or minimized. This technique is applied to the analysis of optimized configurations with respect to the thermal plasma parameters, magnetic field configuration, external actuators and the effect of multiple EP populations. In addition, the AE/EPM saturation phase is analyzed, taking into account both steady-state phases and bursting activity observed in LHD and DIII-D devices. The nonlinear calculations provide: the induced EP transport, the generation of zonal structures as well as the energy transfer towards the thermal plasma and between different toroidal/helical families. Finally, FAR3d is used to forecast the AE/EPM stability in operational scenarios of future devices as ITER, CFETR, JT60SA and CFQS as well as possible approaches to optimization with respect to variations in the most important plasma parameters.

A Method for Assessing the Reliability of the Pepper Robot in Handling Office Documents: A Case Study.

Humanoid robots are increasingly being utilized in various activities involving humans, as they can facilitate certain tasks and provide benefits to users. Humanoid service robots possess capabilities akin to human performance, often proving advantageous due to their operational speed and immunity to fatigue. Within the scope of this study, Pepper, a humanoid robot renowned for its fidelity in mimicking human gestures and behavior, serves as the focal point. Tasked with aiding office occupants in object manipulation and relocation, Pepper underwent a targeted reliability assessment. This assessment encompassed the development of a reliability block diagram (RBD), alongside meticulous analyses of individual components and system functionality across diverse operational scenarios.

An agent-based simulation modeling framework for Mobility-as-a-Service (MaaS)

This study develops an agent-based intelligent Mobility-as-a-Service (MaaS) simulation model consisting of three types of agents (i.e., MaaS fleet unit, travelers, and central intelligent mobility assignment module) to assess mobility service assignment processes balancing conflicting entities (e.g., demand and supply) within the MaaS ecosystem. The study follows a two-level optimization method (i.e., lower, and upper levels). It employs artificial intelligence and multicriteria decision making to solve two-dimensional mobility assignment problems (demand vs. supply). The novelty of this assignment process is that it implements an intelligence module accounting for the past system performance to make a future assignment decision in favor of both sides of the operation. This study processes 24-hour trip requests extracted from Nova Scotia Travel Activity (NovaTRAC) survey data in real-time. Two scenarios (1-D and 2-D assignments) are compared using the cost criteria, such as total waiting time, empty time, and idle time. The 1-D scenario refers to mobility assignment that emphasizes the demand side only, and the 2-D scenario formulates mobility assignment by balancing the demand and supply sides of the MaaS ecosystem. Experimental results indicate that a MaaS fleet of 350 units is the most balanced fleet in both scenarios. However, the 2-D optimization method reduces the overall supply cost by 25%. Moreover, 2-D operation demonstrates a higher fleet utilization over a considerable period, whereas the 1-D design guarantees a higher fleet utilization only during the peak period. Results of this study provide us with a benchmark for assessing more complex MaaS operation scenarios which will further aid in advancing the operational MaaS ecosystem. Our findings can help policymakers implement cost-effective MaaS solutions supporting sustainable urban mobility and SDG 13: Climate Action.

Hydrogen storage planning robust to year‐round net load fluctuation

AbstractLong‐term hydrogen storage systems are considered a solution to the long‐term supply imbalance caused by different seasonal characteristics in renewable energy output and load. However, most existing planning models assume the whole year‐round net load series is known in advance and the optimal operation sequence of long‐term storage is derived accordingly. This assumption underestimates the necessary long‐term storage capacity because of the difficulty of accurate fine‐grained long‐term forecasting. This work proposes a long‐term hydrogen storage planning framework that is robust to year‐round net load fluctuation. The daily average component from the historical net load series is first derived to formulate the long‐term operation scenario set. On this basis, a robust planning model is developed where the trend of long‐term storage operation is forced to be identical for all long‐term operation scenarios and not to respond to one specific scenario with full information of the net load sequence. The results show that the planning results given by the proposed framework are more robust in real‐time operation with less load shedding.