Dynamic Operation Research Articles

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy, with its abundant reserves, green and low-carbon characteristic, high energy density, diverse sources, and wide applications, is gradually becoming an important carrier in the global energy transformation and development. In this paper, the off-grid wind power hydrogen production system is considered as the research object, and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell, including underload, overload, variable load, and start- stop are analyzed. On this basis, the characteristic extraction of wind power output data after noise reduction is carried out, and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study, and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems, and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.

Optimization of the pipe diameters and the dynamic operation of a district heating network using a robust initialization strategy

District heating networks (DHN) are systems offering a substantial solution for the decarbonization of heat production. As these systems involve significant costs, the development of digital tools for their optimization becomes a necessity. In the literature, there is a lack of studies optimizing the sizing of the DHN while considering a dynamic operation of the system with a detailed physics. In this work, we developed a Non-Linear Programming (NLP) optimization used in a case study of a DHN where the pipe sizing and the operation are optimized with a precise modeling of the distribution network. The method of Orthogonal Collocation on Finite Elements (OCFE) was employed to discretize the partial differential equation (PDE) governing the temperature evolution in the pipes. In addition, the pressure drops were modeled using the Darcy–Weisbach equation. Three optimization criteria were used: investment cost of the pipes, operational cost, and the global cost. The optimization provided the pipe diameters that best fit each optimization criterion. Furthermore, several initializations were tested to verify if the obtained optimum was a confidence optimum and if the resolution was robust. Finally, the optimization provided a coherent response to the variation in the cost of heat production.

Optimization of a Boom Height Ultrasonic Detecting Model for the Whole Growth Cycle of Wheat: Affected by the Oscillation of the Three-Section Boom of the Sprayer

In the dynamic operation of a boom sprayer, the boom oscillation will cause the detection value of the boom height to fluctuate greatly, resulting in failures of the control system. Based on the previously developed static boom height detection model for the entire wheat growth cycle, this study aimed to optimize the model to reduce the impact of boom oscillation on the accuracy of boom height detection. Three ultrasonic sensors were installed on each section boom of a three-section boom sprayer, and dynamic boom height detection tests were conducted at vehicle speeds of 4 to 8 km/h across six growth stages of winter wheat in Beijing, a total detection area within a single fixed operational row of approximately 14 ha. The test results showed that as vehicle speed increased, boom oscillations intensified across all sections. By setting the boom oscillation correction parameters, the detecting value of each section of boom height is corrected. The results show that the fluctuation and deviation degree of the boom height-detecting value are obviously reduced, and the correction effect is obvious. Further analysis of the detecting value of the boom height after the correction shows that the previously established detection model still maintains high detection accuracy under dynamic conditions; that is, the detection position of the ultrasonic sensor does not downward shift. This paper provides a low-cost technical method that can be directly applied to the dynamic detection of boom height.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Future Projection of Water Resources of Ruzizi River Basin: What Are the Challenges for Management Strategy?

This study investigates the impact of climate change on hydrological dynamics in the Ruzizi River Basin (RRB) by leveraging a combination of observational historical data and downscaled climate model outputs. The primary objective is to evaluate changes in precipitation, temperature, and water balance components under different climate scenarios. We employed a multi-modal ensemble (MME) approach to enhance the accuracy of climate projections, integrating historical climate data spanning from 1950 to 2014 with downscaled projections for the SSP2-4.5 and SSP5-8.5 scenarios, covering future periods from 2040 to 2100. Our methodology involved calibrating and validating the SWAT model against observed hydrological data to ensure reliable simulations of future climate scenarios. The model’s performance was assessed using metrics such as R2, NSE, KGE, and PBIAS, which closely aligned with recommended standards. Results reveal a significant decline in mean annual precipitation, with reductions of up to 37.86% by mid-century under the SSP5-8.5 scenario. This decline is projected to lead to substantial reductions in surface runoff, evapotranspiration, and water yield, alongside a marked decrease in mean monthly stream flow, critically impacting agricultural, domestic, and ecological water needs. The study underscores the necessity of adaptive water resource management strategies to address these anticipated changes. Key recommendations include implementing a dynamic reservoir operation system, enhancing forecasting tools, and incorporating green infrastructure to maintain water quality, support ecosystem resilience, and ensure sustainable water use in the RRB. This research emphasizes the need for localized strategies to address climate-driven hydrological changes and protect future water resources.

Dynamic responses of locomotive axle box bearings under varying operating conditions

Axle box bearings (ABBs) are a vital component of a railway locomotive, ensuring their reliable performance requires an in-depth understanding of their dynamic behaviour. This work theoretically derives a locomotive–track longitudinal–vertical coupled dynamics model that takes detailed dynamics of the ABBs into account. The model considers a number of complex interactions, such as non-linear contact and friction forces within the ABB, wheel–rail interactions, and track irregularities. The motions of ABB are coupled with the wheelset and bogie frame, allowing detailed investigation of the ABB dynamic behaviour during operation. The correctness of the proposed model is verified in both the time and frequency domains by comparing with field test. The dynamic characteristics of ABBs under varying operating conditions were comprehensively investigated. The results demonstrate that track irregularities have a non-negligible influence on the dynamic forces of the locomotive ABB. Furthermore, the axle load transfer occurs during loading conditions, which leads to significant differences in vertical and longitudinal dynamic interactions within the ABBs across wheelsets. It is clear that the operating conditions of the locomotive should be suitably considered in the dynamic assessment, operation, and maintenance of ABBs.

A Novel Parameter-Variabled and Coupled Chaotic System and Its Application in Image Encryption with Plaintext-Related Key Concealment.

The design of a chaotic system and pseudo-random sequence generation method with excellent performance and its application in image encryption have always been attractive and challenging research fields. In this paper, a new model of parameter-variabled coupled chaotic system (PVCCS) is established by interaction coupling between parameters and states of multiple low-dimensional chaotic systems, and a new way to construct more complex hyperchaotic systems from simple low-dimensional systems is obtained. At the same time, based on this model and dynamical DNA codings and operations, a new pseudo-random sequence generation method (PSGM-3DPVCCS/DNA) is proposed, and it is verified that the generated pseudo-random sequence of PSGM-3DPVCCS/DNA has excellent random characteristics. Furthermore, this paper designs a novel pixel chain diffusion image encryption algorithm based on the proposed parameter-variabled coupled chaotic system (PVCCS) in which the hash value of plaintext image is associated with the initial key to participate in the encryption process so that the encryption key is closely associated with plaintext, which improves the security of the algorithm and effectively resists the differential cryptanalysis risk. In addition, an information hiding method is designed to hide the hash value of plaintext image in ciphertext image so that the hash value does not need to be transmitted in each encryption, and the initial key can be reused, which solves the key management problem in application and improves the application efficiency of the encryption algorithm. The experimental analysis shows that the chaotic system constructed in this paper is creative and universal and has more excellent chaotic characteristics than the original low-dimensional system. The sequence generated by the pseudo-random sequence generation method has excellent pseudo-random characteristics and security, and the image encryption algorithm can effectively resist differential cryptanalysis risk, showing advanced encryption performance.

Standalone Photovoltaic Water Pumping System using Induction Motor Drive

This paper presents a simple and efficient standalone photovoltaic (PV) water pumping system employing an induction motor drive. This system uses two power conversion stages linked by a DC-bus capacitor. The first is a DC-DC boost converter controlled by using a perturb-and-observe (P&O) maximum power point tracking (MPPT) technique; used to extract the available maximum power of the PV array. The second is a three-phase Pulse-Width Modulated Voltage Source Inverter (PWM-VSI) coupled to an induction motor for driving the centrifugal pump. This drive is controlled by the indirect field oriented control (FOC) which ensures high dynamic and static performances. The DC-bus voltage is regulated at a required level by using a proportional-integral (PI) controller. The reference speed is estimated from the affinity law and the DC-bus voltage control. The performances of the system are verified by digital simulation under Matlab/Simulink environment in steady-state and dynamic operating conditions.

Real-Time Identification of Strawberry Pests and Diseases Using an Improved YOLOv8 Algorithm

Strawberry crops are susceptible to a wide range of pests and diseases, some of which are insidious and diverse due to the shortness of strawberry plants, and they pose significant challenges to accurate detection. Although deep learning-based techniques to detect crop pests and diseases are effective in addressing these challenges, determining how to find the optimal balance between accuracy, speed, and computation remains a key issue for real-time detection. In this paper, we propose a series of improved algorithms based on the YOLOv8 model for strawberry disease detection. These include improvements to the Convolutional Block Attention Module (CBAM), Super-Lightweight Dynamic Upsampling Operator (DySample), and Omni-Dimensional Dynamic Convolution (ODConv). In experiments, the accuracy of these methods reached 97.519%, 98.028%, and 95.363%, respectively, and the F1 evaluation values reached 96.852%, 97.086%, and 95.181%, demonstrating significant improvement compared to the original YOLOv8 model. Among the three improvements, the improved model based on CBAM has the best performance in training stability and convergence, and the change in each index is relatively smooth. The model is accelerated by TensorRT, which achieves fast inference through highly optimized GPU computation, improving the real-time identification of strawberry diseases. The model has been deployed in the cloud, and the developed client can be accessed by calling the API. The feasibility and effectiveness of the system have been verified, providing an important reference for the intelligent research and application of strawberry disease identification.

Dynamic characteristics of the hydrogen injector-ejector unit in a PEM fuel cell system

The ejector, as a fluid-dynamics controlled passive component, is a promising hydrogen recirculation device in proton exchange membrane (PEM) fuel cell systems. Nevertheless, its performance is significantly affected by dynamic operating conditions and the behavior of the injector. This study investigates the dynamic performance of an ejector integrated with a hydrogen injector in a 100 kW PEM fuel cell system. A dynamic computational fluid dynamics (CFD) model of the integrated injector-ejector unit is developed using a dynamic mesh method and validated through theoretical analysis and experimental data. A thorough analysis of the global performance and local fluid flow characteristics of the injector-ejector unit is conducted. The results show that the hydrogen injector, with a throat diameter of 2.00 mm, exhibits an adjustable linear flow range from 0 to 1.62 g/s under an inlet pressure of 2.0 MPa. Additionally, the primary mass flow rate can be linearly regulated by adjusting the valve gap, while the secondary mass flow rate exhibits asynchronous behavior with the primary flow. The velocity and temperature distributions within the injector-ejector unit undergo significant changes under dynamic conditions. These findings contribute to optimizing the design and control strategies of hydrogen supply components in PEM fuel cell systems.

Dynamic operating envelopes embedded peer-to-peer-to-grid energy trading

A novel decentralized peer-to-peer-to-grid (P2P2G) trading mechanism considering distribution network integrity is proposed. In order to direct prosumers’ peer-to-peer (P2P) trading behavior to be grid-friendly, the proposed method incorporates Dynamic Operating Envelopes (DOEs) into the existing P2P2G trading. Moreover, DOEs are determined through negotiations between the distribution system operator (DSO) and prosumers alongside the process of P2P trading, avoiding compromising prosumers’ privacy and network parameters leakage. To reduce communication costs during P2P trading, a variant of the alternating direction method of multipliers (ADMM), i.e., communication-censored ADMM (COCA) is used to solve the P2P2G trading problem. Finally, the DOE price is shown to be comprised of several economically interpretable components. Simulations validate the effectiveness of the proposed mechanism.

Asan Cups: navigating blue oceans in menstrual hygiene sustainability

Learning outcomes After completion of the case study, students will be able to understand the format of for-profit social enterprise working for menstrual hygiene sustainability and its contribution toward U.N. Sustainable Development Goals, to appreciate the company’s alignment with the triple bottom line framework, to analyze the blue ocean mechanism that the company has developed to create an impact and to critique the strategies the Asan Cups company could adopt to increase its market share and growth. Case overview/synopsis Asan Cups was a for-profit social enterprise founded by Ira Guha in 2021. The company crafted reusable menstrual cups from liquid silicone, sporting a patented design in India, the UK, Europe and the USA. Successfully retailing its products in India, the UK and Europe, Asan Cups operated on a bootstrap model with a compact team of four, led by its visionary founder. From the get-go, the company embraced a compelling 1-for-1 donation initiative. For every cup sold, Asan Cups generously donated another to women who could not afford it. Collaborating with nongovernmental organizations, schools, educational institutions and social workers, the company spearheaded campaigns to heighten menstrual hygiene awareness. This proactive approach aimed to boost the acceptance of menstrual cups among rural women and championed the cause of environmental sustainability. The company did not just stop at providing an eco-friendly alternative. Asan Cups fervently educated the masses on the detrimental environmental impact of traditional disposable period products like sanitary pads and tampons. Fast-forwarding to 2023, Asan Cups had garnered approximately 30,000 users, with the adoption rate steadily climbing. The company strategically used an education-intensive model to foster awareness about period products in collaboration with partners nationwide. However, being a for-profit entity, the founder, Guha, was at a crossroads. Balancing the need for profitability, there was mounting pressure to explore additional revenue streams and expand operations and market reach. The dilemma loomed large: opt for a quicker marketing strategy or stay true to the company’s foundational education-centered approach. This case study delves into the dynamic strategies, impactful operations and growth scenarios Asan Cups navigated since its inception. It examines the pivotal choices faced by the founder and explores potential strategies for sustained growth. Complexity academic level This case study can be used at both undergraduate and master’s levels. The case study will be handy for strategic management and business strategy courses and can also be used for social entrepreneurship, marketing and entrepreneurship courses. Supplementary materials Teaching notes are available for educators only. Subject code CSS 11: Strategy.

Day-ahead dynamic operating envelopes using stochastic unbalanced optimal power flow

Driven by the energy transition, distribution networks are dealing with increasing uptake of distributed energy resources, including solar photovoltaic generation. Residential rooftop solar allows customers to minimize exposure to price increases in the market, which has led to high penetration of PV in regions with high amounts of solar hours such as Australia. Eventually, this leads to congestion in the network, either due to voltage rise, or due to overcurrent in lines or transformers. Distribution utilities are now moving on from static export limits for customers in congested networks, to dynamic limits that are based on the state of the network. It is considered thoughtful to give customers advance warning, e.g. day-ahead, of the moments and degrees of export limitation, despite uncertainty surrounding the future state of the network. Therefore, in this paper, we consider the application of general polynomial chaos expansion based stochastic unbalanced optimal power flow to the day-ahead determination of dynamic export limits. We perform a numerical study on a European style low voltage feeder and illustrate the impact of fairness principles on chance-constrained stochastic nonlinear optimization without the need of sampling, linearizing the power flow equations, or applying relaxations. Case studies show the necessity of considering unbalanced study of distribution system. It was observed that equality measures reduce the overall output of the system in an attempt to achieve equal relative injection, while alpha fairness with a higher value of alpha is a compromise between the efficiency and fairness in DOEs.

EXPRESS: Impact of Dynamic Operations Complexity on Performance: the Role of National Culture

We build on the national culture, complex adaptive systems, and operations complexity literature to develop hypotheses about the moderating role of national culture on the negative relationship between dynamic operations culture and operational complexity. Monthly performance data were collected from 179 plants in a multinational corporation’s global plant network that spans all national culture clusters. We use random effects time series modeling to incorporate the transitory nature of this relationship and control unobserved heterogeneity over time. The results indicate that the relationship between dynamic operations complexity and operational performance is moderated by every dimension of national culture except gender egalitarianism and institutional collectivism. We contribute to the literature on complexity by introducing the dynamic operations complexity construct and showing the moderating effect of national culture. We contribute to the national culture literature by comprehensively examining the role of national culture supporting the national culture divergence thesis. Managerial implications relate to locating new plants in regions whose national culture is supportive of key goals and allocating tasks and products accordingly.

Online Trajectory Replanning for Avoiding Moving Obstacles Using Fusion Prediction and Gradient-Based Optimization

In this study, we introduce a novel method for an online trajectory replanning approach for fixed-wing Unmanned Aerial Vehicles (UAVs). Our method integrates moving obstacle predictions within a gradient-based optimization framework. The trajectory is represented by uniformly discretized waypoints, which serve as the optimization variables within the cost function. This cost function incorporates multiple objectives, including obstacle avoidance, kinematic and dynamic feasibility, similarity to the reference trajectory, and trajectory smoothness. To enhance prediction accuracy, we combine physics-based and pattern-based methods for predicting obstacle movements. These predicted movements are then integrated into the online trajectory replanning framework, significantly enhancing the system’s safety. Our approach provides a robust solution for navigating dynamic environments, ensuring both optimal and secure UAV operation.

Impact of Nickel on Iridium-Ruthenium Structure and Activity for the Oxygen Evolution Reaction under Acidic Conditions.

Proton exchange membrane water electrolysis (PEMWE) is a promising technology to produce hydrogen directly from renewable electricity sources due to its high power density and potential for dynamic operation. Widespread application of PEMWE is, however, currently limited due to high cost and low efficiency, for which high loading of expensive iridium catalyst and high OER overpotential, respectively, are important reasons. In this study, we synthesize highly dispersed IrRu nanoparticles (NPs) supported on antimony-doped tin oxide (ATO) to maximize catalyst utilization. Furthermore, we study the effect of adding various amounts of Ni to the synthesis, both in terms of catalyst structure and OER activity. Through characterization using various X-ray techniques, we determine that the presence of Ni during synthesis yields significant changes in the structure of the IrRu NPs. With no Ni present, metallic IrRu NPs were synthesized with Ir-like structure, while the presence of Ni leads to the formation of IrRu oxide particles with rutile/hollandite structure. There are also clear indications that the presence of Ni yields smaller particles, which can result in better catalyst dispersion. The effect of these differences on OER activity was also studied through rotating disc electrode measurements. The IrRu-supported catalyst synthesized with Ni exhibited OER activity of up to 360 mA mgPGM -1 at 1.5 V vs RHE. This is ∼7 times higher OER activity than the best-performing IrO x benchmark reported in the literature and more than twice the activity of IrRu-supported catalyst synthesized without Ni. Finally, density functional theory (DFT) calculations were performed to further elucidate the origin of the observed activity enhancement, showing no improvement in intrinsic OER activity for hollandite Ir and Ru compared to the rutile structures. We, therefore, hypothesize that the increased activity measured for the IrRu supported catalyst synthesized with Ni present is instead due to increased electrochemical surface area.

Utilizing serverless framework for dynamic visualization and operations in geospatial applications

ABSTRACT While substantial efforts have been invested in the development of Discrete Global Grid Systems (DGGS) spatial operations and their potential applications in the geospatial domain, it has become evident that there is a demand for an efficient and scalable system to handle the visualization of large-scale DGGS data. This study demonstrated the potential of DGGS in conjunction with the serverless framework for dynamic visualization at various resolutions, which is based on data storage and effective querying using PostgreSQL integrated into Amazon Aurora Serverless. The use of Amazon Web Services (AWS) Lambda for on-the-fly generation of hexagon geometries significantly reduced the storage requirements and improved the speed of the visualization process. In addition, we implemented on-the-fly spatial operations including point binning, thresholding, aggregation, and neighborhood operations in the DGGS, highlighting the capabilities of DGGS in vector and raster processing. The proposed system has shown promising results in terms of efficiency, scalability, and adaptability, making it a viable solution for large-scale geospatial data processing and visualization. Case studies using flood risk data and terrain data further illustrate the system’s practical applicability in on-the-fly spatial operations and rapid visualization.

The transient thermomechanical behavior of solid oxide fuel cell during the operation condition variations

Solid oxide fuel cell (SOFC) is a promising energy conversion technology with high efficiency, low emission and fuel flexibility. In practical and dynamic operation, the thermal stress at the interface of different layers can cause cracks or even thermal mechanical failure. This study investigates the transient thermomechanical behavior of SOFC under changing operating conditions. A three-dimensional dynamical model is developed by considering the electrochemical reaction, electron and ion transport, gas diffusion, heat and momentum transfer, and thermal-mechanical interaction. When the operating conditions vary, the time it takes for the thermal stress to reach a steady state is consistent with the time required for heat transfer to stabilize. The electrolyte layer is subjected to the maximum gradient of thermal stress change after the voltage is switched. With an increase in the inlet gas temperature, the change in the cell temperature can be divided into two stages: a rapid rise and a slow rise. Appropriate prolongation of the temperature rise time is beneficial to the thermal stability of SOFC. The increase in inlet gas temperature changes the distribution trend of cell temperature and thermal stress, while the 50 % reduction in cathode and anode inlet velocity changes its magnitude.

Transit Dynamic Operation Optimization Using Combination of Stop-Skipping Strategy and Local Route Optimization

The efficient operation and intelligent upgrading of public transportation can effectively enhance the attractiveness of conventional public transportation. In order to improve the delicacy management level of bus operations, this study designed a new dynamic optimization model for single-line bus operations with the dual optimization objectives of the lowest passenger travel cost and lowest operation cost, using a combination of the strategy of stop-skipping control and local route optimization. Simulated annealing (SA) was introduced into the genetic algorithm (GA) to design a hybrid heuristic algorithm for model solving. The effectiveness of the optimization model and the hybrid algorithm were verified and evaluated by using the No. 115 bus line in Ganzhou City as an example. The results showed that the proposed optimization model had a good usability, which can effectively improve the average vehicle speed, shorten the overall waiting time of passengers, and enhance the operational efficiency of the line. The hybrid algorithm saw significant improvement in terms of the iteration speed and the quality of the optimal solution compared with the conventional genetic algorithm.

Construction of multiple interpenetrati ng network electric field-assisted healing hydrogel composite damage-resistant membrane

Inspired by the intrinsic self-healing ability of organisms, this study proposes to incorporate healing properties into separation membranes that are susceptible to physical damage during installation, operation, maintenance, and cleaning. The polyacrylic acid (PAA) hydrogel-modified layer was first obtained by in situ cross-linking and free radical polymerization in this research. After that, the construction of multiple interpenetrating three-dimensional (3D) network hydrogel-modified layers was realized via inserting amininated carbon nanotubes (CNTs-NH2) and Fe3+. Under the external effect of the electric field, the reversible dynamic linkage bonded in the conductive network constructed by CNTs-NH2 and Fe3+ benefited the rearrangement and diffusion of PAA molecular chains at the fractured interface. The dye separation performance of the composite membrane was restored to ca. 90 % of the initial level without affecting the dynamic operation after damage. The hydrogel-modified layer can be structurally stabilized for as long as 100 days. The Fe3+/CNTs-NH2/PAA/PES hydrogel composite membrane maintained a pure water flux of as high as 124.66 L m−2 h−1∙bar−1. This coating also exhibited excellent electrical conductivity (714.33 Ω/sq), mechanical strength (4.31 Mpa), repair, and screening capabilities. It provides a theoretical basis for achieving industrial scale-up and demonstrates good application prospects and empowerment potential.