Dynamic Flow Research Articles

Optimization on the peroxide ratio control strategy of PEMFC system based on immune algorithm

The air supply system is an essential component of a proton exchange membrane fuel cell (PEMFC), and proper control of the oxygen stoichiometry ratio(λo2) is crucial to prevent oxygen starvation in the PEMFC while maintaining efficient and stable performance output. In response to the issues of poor dynamic performance and slow response in traditional λo2 control strategies, this study was conducted to establish a dynamic oxygen flow model of the PEMFC and proposed a λo2 control strategy based on an immune algorithm-fuzzy PID (IAFPID). The performance of the IAFPID control strategy in λo2 control was analyzed under different load requirements, simulated vehicle dynamic loads, and temperature disturbances. The results demonstrate that the IAFPID control strategy has advantages such as fast convergence speed, strong robustness, and good dynamic performance. The efficiency of achieving the optimal stoichiometry ratio for high power output in PEMFC under disturbances was nearly 50% higher with IAFPID control compared to traditional PID, Fuzzy-PID, Genetic Algorithm PID, and Immune Algorithm PID strategies. Furthermore, this method ensures a lower average error in maintaining stable λo2 under dynamic disturbances compared to the IAPID and PID control strategies, with reductions of 12.93% and 37.75%, respectively.

Evolving cobalt cycle and urban mines in the EU, 1988–2020

Urban mines are vital for resource security and resilience, but the intricacies of their recycling processes are often overlooked. This study uses dynamic Material Flow Analysis to delve into the EU's cobalt cycle from 1988 to 2020, focusing on urban cobalt mining potential. We observed a significant shift in EU cobalt consumption patterns, with the demand for electric vehicle batteries surpassing that for superalloys, thereby positioning batteries as the primary cobalt consumers. Moreover, batteries increasingly dominate the cobalt in-use stock. The recycling process experiences substantial losses, especially in collection and pretreatment stages, reaching up to 87 %. Our findings indicates that urban cobalt mines could account for 57–66 % of the EU's total cobalt consumption, amounting to 166–193 kt in a high scenario. These insights emphasize the changing dynamics of the EU cobalt cycle and the importance of efficient urban cobalt mine management.

A novel design of layered recurrent neural networks for fractional order Caputo–Fabrizio stiff electric circuit models

Electrical engineering models often rely on complex circuit configurations that facilitate the dynamic flow of electrically charged particles within a closed conductive network. These circuits serve as essential tools for simulating and analyzing diverse electrical systems and components. This paper introduces a study on nonlinear fractional circuits modeling through the development of a stochastic neuro-computational artificial intelligent-based solver to address mathematical models governing the Fractional order Caputo–Fabrizio stiff electric circuit model (FO-CFSECM) by manipulating the knacks of layered recurrent neural networks (LRNNs) trained with Gradient-based local search algorithm (GLA). In fractional calculus, the Caputo–Fabrizio (CF) fractional order derivative (FOD) emerges as a powerful instrument, binding its capabilities to deliver remarkably accurate solutions for fractional stiff systems. The objective of this work is to exploit the numerical treatment comprehensively for the dynamics of fractal Resistor–Capacitor (RC) and fractal Resistor–Inductor (RL) circuit models by introducing the CF fractional operator. Through the application of artificial intelligence-based soft computing and advanced back-propagative deep neural networks, a deeper understanding of the behavior and distinctive characteristics inherent in these models is sought. The Levenberg–Marquardt optimizer serves as an efficient training GLA tool for learning of LRNNs weights of fractal RL/RC circuit models. The comparative studies on variants of FO-CFSECM demonstrate that LRNNs achieve an impressive mean square error (MSE) ranging from 10[Formula: see text] to 10[Formula: see text] and absolute error (AE) within 10[Formula: see text] to 10[Formula: see text]. The accuracy, reliability, and efficiency of LRNNs for solving the FO-CFSECM were further validated through MSE, AE, controlling parameters of state transitions, error histograms, and correlation measures.

On the potential of vehicle-to-grid and second-life batteries to provide energy and material security

The global energy transition relies increasingly on lithium-ion batteries for electric transportation and renewable energy integration. Given the highly concentrated supply chain of battery materials, importing regions have a strategic imperative to reduce their reliance on battery material imports through, e.g., battery recycling or reuse. We investigate the potential of vehicle-to-grid and second-life batteries to reduce resource use by displacing new stationary batteries dedicated to grid storage. Based on dynamic material flow analysis, we show that equipping around 50% of electric vehicles with vehicle-to-grid or reusing 40% of electric vehicle batteries for second life each have the potential to fully cover the European Union’s need for stationary storage by 2040. This could reduce total primary material demand from 2020–2050 by up to 7.5% and 1.5%, respectively, which could ease geopolitical risks and increase the European Union’s energy and material security. Any surplus capacity could be used as a strategic reserve to increase resilience in the face of emergencies such as blackouts or adverse geo-political events.

Traffic Flow Optimization at Toll Plaza Using Proactive Deep Learning Strategies

Global urbanization and increasing traffic volume have intensified traffic congestion throughout transportation infrastructure, particularly at toll plazas, highlighting the critical need to implement proactive transportation infrastructure solutions. Traditional toll plaza management approaches, often relying on manual interventions, suffer from inefficiencies that fail to adapt to dynamic traffic flow and are unable to produce preemptive control strategies, resulting in prolonged queues, extended travel times, and adverse environmental effects. This study proposes a proactive traffic control strategy using advanced technologies to combat toll plaza congestion and optimize traffic management. The approach involves deep learning convolutional neural network models (YOLOv7–Deep SORT) for vehicle counting and an extended short-term memory model for short-term arrival rate prediction. When projected arrival rates exceed a threshold, the strategy proactively activates variable speed limits (VSLs) and ramp metering (RM) strategies during peak hours. The novelty of this study lies in its predictive and adaptive capabilities, ensuring efficient traffic flow management. Validated through a case study at Ravi Toll Plaza Lahore using PTV VISSIMv7, the proposed method reduces queue length by 57% and vehicle delays by 47% while cutting fuel consumption and pollutant emissions by 28.4% and 34%, respectively. Additionally, by identifying the limitations of conventional approaches, this study presents a novel framework alongside the proposed strategy to bridge the gap between theory and practice, making it easier for toll plaza operators and transportation authorities to adopt and benefit from advanced traffic management techniques. Ultimately, this study underscores the importance of integrated and proactive traffic control strategies in enhancing traffic management, minimizing congestion, and fostering a more sustainable transportation system.

Dynamic demand patterns in the profit optimisation of bike-sharing station locations: an agent-based analysis of the greater Vienna region

ABSTRACT This study employs the MATSim agent-based simulation model to analyse bike-sharing station locations by examining dynamic trip flows and individual behavioural changes. It explores demand on a microscopic scale, capturing the behaviour of multi-segment trips. A protocol was established to evaluate different station configurations’ impact on profitability and demand. Findings suggest that optimising the station count from 219 to 66 strategic locations can significantly enhance both revenue and operational efficiency. The simulation produces data on the number of users arriving and departing from each station in different configurations, indicating the size of each station. This data allows stations to be classified into three types: generator, attractor, and interchange; displaying their changes across different configurations. This quantification offers operators insights for predicting bike distribution and planning operational strategies. Considering spatial and built environment factors, the findings underscore the potential of bike-sharing stations to evolve into mobility hubs, offering valuable insights for policymakers.

Dynamic stress prediction for a Pump-Turbine in Low-Load Conditions: Experimental validation and phenomenological analysis

To meet the increasing demand for higher flexibility in hydropower, an operating range extension in an existing hydroelectric powerplant is considered. To this end, the dynamic flow and structural behavior of the low-head pump-turbine is investigated in turbine mode in partial load and deep partial load conditions, to evaluate the feasibility of this flexibilization due to the increased high cycle fatigue damage. In a first step, different numerical simulation approaches that combine fluid dynamics and structural dynamics are applied and compared to in-situ dynamic strain and pressure measurements on the impeller. As a result, the modelling requirements for a numerical workflow that leads to unmatched accuracy in predicting the broad-band dynamic stresses of deep partial load are identified. In a second step, the operating-point-dependent fatigue crack initiation spots are identified based on dynamic stresses. Herein, the emergence of an additional dominant critical spot near the blade leading edge is observed, which is unexpected for low-head pump turbines. Thanks to the detailed numerical flow and structural analyses, the root cause for the dynamic stress concentration is studied and the emergence of the new critical spot is understood. It turns out in deep partial load, that the main source of pressure oscillations on the impeller blade surfaces is the vortex-dominated flow detachment zone around the blade leading edge, which, in combination with the geometry of the investigated impeller, translates into the main stress oscillations. The results of this work were later used to extend the operating range of the hydroelectric powerplant from initially 25% to 100% power output to now 0% to 100%.

Deep Trans-Omic Network Fusion for Molecular Mechanism of Alzheimer's Disease.

There are various molecular hypotheses regarding Alzheimer's disease (AD) like amyloid deposition, tau propagation, neuroinflammation, and synaptic dysfunction. However, detailed molecular mechanism underlying AD remains elusive. In addition, genetic contribution of these molecular hypothesis is not yet established despite the high heritability of AD. The study aims to enable the discovery of functionally connected multi-omic features through novel integration of multi-omic data and prior functional interactions. We propose a new deep learning model MoFNet with improved interpretability to investigate the AD molecular mechanism and its upstream genetic contributors. MoFNet integrates multi-omic data with prior functional interactions between SNPs, genes, and proteins, and for the first time models the dynamic information flow from DNA to RNA and proteins. When evaluated using the ROS/MAP cohort, MoFNet outperformed other competing methods in prediction performance. It identified SNPs, genes, and proteins with significantly more prior functional interactions, resulting in three multi-omic subnetworks. SNP-gene pairs identified by MoFNet were mostly eQTLs specific to frontal cortex tissue where gene/protein data was collected. These molecular subnetworks are enriched in innate immune system, clearance of misfolded proteins, and neurotransmitter release respectively. We validated most findings in an independent dataset. One multi-omic subnetwork consists exclusively of core members of SNARE complex, a key mediator of synaptic vesicle fusion and neurotransmitter transportation. Our results suggest that MoFNet is effective in improving classification accuracy and in identifying multi-omic markers for AD with improved interpretability. Multi-omic subnetworks identified by MoFNet provided insights of AD molecular mechanism with improved details.

Mechanism and deterioration pattern of sandstone surrounding rock voiding at bottom of heavy-haul railway tunnel

This study combines laboratory experiments and discrete element simulation methods to analyze the mechanism and deterioration patterns of sandstone surrounding rock voiding the bottom of a heavy-haul railway tunnel. It is based on previously acquired measurement data from optical fiber grating sensors installed in the Taihangshan Mountain Tunnel of the Wari Railway. By incorporating rock particle wastage rate results, a method for calculating the peak strength and elastic modulus attenuation of surrounding rock is proposed. Research indicates that the operation of heavy-haul trains leads to an instantaneous increase in the dynamic water pressure on the bottom rock ranging 144.4–390.0%, resulting in high-speed water flow eroding the rock. After 1–2 years of operation, the bottom water and soil pressures increase by 526.5% and 390.0%, respectively. Focusing on sandstone surrounding rock with high observability, laboratory experiments were conducted to monitor the degradation stages of infiltration, particle loss, and voiding of rock under the action of dynamic water flow. The impact of water flow on the “cone-shaped” bottom rock deformation was also clarified. The extent of rock deterioration and voiding was determined using miniature water and soil pressure sensors in conjunction with discrete element numerical simulations. The measured rock particle loss was used as a criterion. Finally, a fitting approach is derived to calculate the peak strength and elastic modulus attenuation of surrounding rock, gaining insight into and providing a reference for the maintenance and disposal measures for the bottom operation of heavy-haul railway tunnels.

Mapping Plastic and Plastic Additive Cycles in Coastal Countries: A Norwegian Case Study.

The growing environmental consequences caused by plastic pollution highlight the need for a better understanding of plastic polymer cycles and their associated additives. We present a novel, comprehensive top-down method using inflow-driven dynamic probabilistic material flow analysis (DPMFA) to map the plastic cycle in coastal countries. For the first time, we covered the progressive leaching of microplastics to the environment during the use phase of products and modeled the presence of 232 plastic additives. We applied this methodology to Norway and proposed initial release pathways to different environmental compartments. 758 kt of plastics distributed among 13 different polymers was introduced to the Norwegian economy in 2020, 4.4 Mt was present in in-use stocks, and 632 kt was wasted, of which 15.2 kt (2.4%) was released to the environment with a similar share of macro- and microplastics and 4.8 kt ended up in the ocean. Our study shows tire wear rubber as a highly pollutive microplastic source, while most macroplastics originated from consumer packaging with LDPE, PP, and PET as dominant polymers. Additionally, 75 kt of plastic additives was potentially released to the environment alongside these polymers. We emphasize that upstream measures, such as consumption reduction and changes in product design, would result in the most positive impact for limiting plastic pollution.

Corrosion kinetics of T91 steel under low oxygen conditions (10−7 wt% O) in lead-bismuth eutectic at 500 °C

Among the advanced reactor designs of the fourth generation, the Lead-cooled Fast Reactor (LFR) stands out as one of six distinct types. The corrosion caused by the liquid Lead-Bismuth Eutectic (LBE) on structural materials is recognized as a significant obstacle hindering the development of LFR. The corrosion kinetics of T91 steel in static LBE with 10−7 wt% oxygen at 500 ºC is analyzed in this study. The formed oxide scale consists of an outer (Fe, Cr)3O4 layer with a Fe-Cr spinel structure and an inner internal oxidation zone (IOZ). The thickness evolution of the oxide layers follows a parabolic rule, with rate constants determined for different layers. The dissolution rate of metals into the LBE decreases over time, indicating diminishing the concentration gradient of Fe across the oxide layer-LBE interface. The observed deviations from expected norms may be attributed to dynamic flow conditions and non-isothermal loop experiments, which accelerate Fe diffusion into LBE.

Expert Elicitation for the Resilient Design and Optimisation of Ultra-long Ore Passes for Deep Mass Mining

AbstractExtension of ore pass length has become increasingly critical for optimising energy-efficient underground mining operations. Long and ultra-long ore passes, spanning from 300 to 700 m, can significantly improve the functionality and viability of underground mass mining operations though suboptimal performance has an extremely adverse impact on production. The public domain lacks substantial information regarding the primary engineering, geological, and geotechnical risks and challenges associated with the design, implementation, operation, and maintenance of such long ore passes. Therefore, the aggregation of past experiences and the insights of experts assume paramount significance. An innovative methodology is introduced to address this evident data deficiency and to establish comprehensive guidelines for the resilient design of such lengthy ore passes — combining gap analysis with expert elicitation techniques. This equips design engineers with the necessary tools to formulate and adapt strategies for assessing the numerous challenges and uncertainties that invariably accompany their projects. Expert elicitation techniques are summarised, and a gap analysis is conducted with subject matter experts, from various countries, collating their extensive ore pass design experience, to create a comprehensive list of effective parameters and key risks that must be considered. Quantitative analysis of the survey results enabled the identification and ranking of the numerous factors affecting the design, operation, and maintenance of long and ultra-long ore passes and highlights the complex technical challenges (substantial damage from rock particle impact, increased dynamic mining stresses leading to failure, air-blasts and back blasts, dust, preferential flow, turbulent and dynamic material flow) that are uncommon in shorter ore passes. Additionally, increasing length heightens the probability of intersecting weak rock or discontinuities, leading to a higher risk of structural failure and instabilities. Faulting, folding, and large-scale structures are also critical geological factors to be considered in the design of such structures. The key geotechnical factor is also the rock type surrounding the pass. Experts highlighted the lack of clear guidelines for decision-making, resilient design, and construction so this work suggests future investigations to determine the complex interaction between the effective parameters, using approaches like the rock engineering system, discovery of cascading hazards, and optimal controls.

A passenger flow spatial–temporal distribution model for a passenger transit hub considering node queuing

Passenger transit hub is a quintessential complex system, characterized by intricate interactions among humans, facilities, and the surrounding environment. External disturbances often precipitate crowd congestion and safety risks. Modeling the spatial–temporal distribution of passenger flow within the hub is an important element in operational management. Prevailing research predominantly focuses on static models or monitoring data to assess the spatial–temporal characteristics of passenger flow throughout the hub. Nevertheless, few studies have been found in modeling passenger flow distribution for passenger transit hub from the vantage point of traveler behavior in the intricate ‘human-facility-environment’ complex network. Thus, this paper proposes a computational model for the passenger flow spatial–temporal distribution based on traveler behavior. First, combining consideration of critical spatial facilities and passenger flow streamlines, a passenger flow network is established. Second, the instantaneous travel times of link and node are defined while considering queuing and congestion at crucial facilities in the hub. Then, a passenger flow spatial–temporal distribution model for the passenger transit hub is constructed, which consists of dynamic route choice model and dynamic passenger flow loading model. A solution algorithm is simultaneously designed. Finally, the effectiveness of the model and algorithm are verified by a numerical example. The results show that the proposed model can effectively capture real-time congestion and the dynamic distribution of passenger flow in the hub. Therefore, this study contributes to the safety management and layout optimization of the hub, holding significant importance for improving hub operational efficiency and service levels.

Assessment of nitrogen, phosphorus and potassium flows in China's food system during 2010–2019: Implications for integrated management

Nitrogen (N), phosphorus (P), and potassium (K) are essential nutrients for food production. However, the mismanagement of these elements has resulted in numerous environmental problems and poses a threat to resource security. This study aims to measure the flows of N, P, and K through China's food system during 2010–2019 by applying a dynamic flow accounting method. Our results show that China's food system is characterized by substantial nutrient inputs and significant nutrient losses, with moderate levels of nutrient recycling. The nutrient management had been improved during this study period. However, there were certain issues that should be further addressed, which were identified in this study. More efforts should be made to control nutrient inputs, minimize nutrient losses, and enhance nutrient recycling. By considering both synergies and trade-offs among N, P, and K management, we propose a holistic and integrated approach to nutrient management.

Effect of polyvinylpyrrolidone and lithium chloride composite desiccant-coated heat exchangers on dehumidification studies

Cooling systems are necessities that remain energy intensive, particularly in tropical regions with elevated humidities and temperatures. Conventional vapor-compression air-conditioners are often inefficient because the sensible and latent heat loads are intrinsically coupled. To realize more energy-efficient cooling, solid desiccant-coated devices can be introduced to mitigate the latent heat load. Among such devices, desiccant-coated heat exchangers (DCHEs) are attractive for enabling nearly isothermal dehumidification. Hence, desiccants with high sorption capacity and rapid kinetics are essential. Solid polymeric desiccants are well-positioned to meet these requirements but comprehensive studies on the dehumidification performance of solid polymeric desiccant-coated heat exchangers are currently lacking. In this study, we investigated the potential of polyvinylpyrrolidone (PVP) and lithium chloride (LiCl) composites as desiccant materials at both material and system levels in terms of gravimetric water uptake. Specifically, LiCl is a hygroscopic salt bound by the moisture-sorbing PVP matrix. This work also demonstrates how the Elovich model accurately predicted the PVP-LiCl composite’s sorption kinetics. The results show that the optimum PVP-LiCl composite ratio is 66.7 wt%, with an equilibrium sorption capacity of 280 % at 75 % RH and 25 °C, compared to 29 % for silica gel under the same condition. With much superior sorption kinetics and capacity, the PVP-LiCl-coated prototype heat exchanger demonstrated a moisture removal capacity eight times higher than a silica gel-coated one under dynamic flow towards energy savings in air-conditioning systems.

SURGICAL DEMONSTRATION OF PRESSURE DIFFERENTIAL THROUGH AN OPTIC DISK PIT.

To demonstrate intraoperative dynamic flow through an optic disk pit. Retrospective interventional case report. Surgical management of an optic disk pit involved standard, 25-gauge, pars plana vitrectomy, induction of posterior hyaloid separation with triamcinolone stain, temporal peripapillary laser, and SF 6 gas. Intraoperatively, a remnant of stained vitreous at the optic disk head prolapsed into the optic disk pit with higher intraocular pressure but was drawn back into the vitreous cavity as the intraocular pressure was lowered. This clearly demonstrated a pressure differential across the pit. Communication between the vitreous cavity and the perineural space of the optic nerve is described histologically. Our case demonstrated dynamic flow through the optic disk pit with intraoperative fluctuation of intraocular pressure.

Analytical Dynamic Energy-Carbon Flow Model and Application in Cost Allocation for Integrated Energy Systems

Analytical Dynamic Energy-Carbon Flow Model and Application in Cost Allocation for Integrated Energy Systems

A multiscale hydro-elastoplastic model for large floating structures in two-dimension

A novel fluid–structural model was presented considering hydro-elastoplastic behavior, which coupled multiple hydro-structural-material scales. A large floating sandwich structure (LFSS) comprising upper and lower high-strength panels and a low-weight core was considered as an illustration. The mesoscale characteristics of materials and elastoplastic parameters of the low-weight perforated components were coupled by utilizing the representative volume element (RVE) method. Through the parameterized relations from RVE analysis, the flexure dynamics model for the floating sandwich structure with an equivalent homogenized core was deduced. With the flexure dynamic equation, yield criterion, and potential flow model, a multiscale hydro-elastoplasticity theoretical model was established, which combined the wave action, hierarchical component, material configuration, and structural behavior. The dynamic responses of the large floating structure under fluid–structure interaction were calculated, and the internal deformation (i.e., core strain) was set as the determining variable for the plastic region. The initially intact floating structure became hinged multi-modules after plastic cracking, and the high-strength layers at the cracking positions behaved as flexible hinges, which was defined as a hydro-elastoplastic process. The elastoplastic state evolutions of the LFSSs with different structural parameters and material configurations were solved for practical optimization. The results indicated that the multiscale coupled calculation model can provide great scientific guidance for designing large floating structures.

Rapid calculation for avalanche maps by GPGPU-based snow avalanche model

The required time for producing snow avalanche maps is influenced by computation speed of simulations. Commonly, integrating terrain assessment with dynamic flow simulation aids in mapping dangerous areas for human and structural threats. This approach enables the evaluation of avalanche paths, as well as the assessment of flow rate and thickness during avalanche movement. However, the substantial computational cost of the simulation results in long calculation times when using the Central Processing Unit (CPU). In this study, a new rapid snow avalanche simulator was developed by applying massively parallel computation with the General-Purpose computing on Graphics Processing Unit (GPGPU) technique. By avoiding slower data transfer and utilizing faster memory, computational speed could be accelerated up to 80 times faster than conventional simulation using a CPU. Additionally, the rapid calculation models were validated based on the Mt. Nasu event in 2017, and pilot studies of the avalanche map of Mt. Nasu in Japan demonstrated the usefulness of the developed model for vulnerability evaluation. A total of 123 simulations were conducted for each susceptible source area, and all simulations were completed within only 6.5 h. This high-performance calculation can significantly reduce the time cost of producing and expanding conventional avalanche maps.

Investigation of dynamic characteristics of hydrofoil with leading-edge tubercles

The convex structure of a humpback whale flipper is included in the airfoil design to investigate the control principle of the dynamic stall process. The effect of two different types of tubercles on the dynamic stall process of airfoils was investigated. The distribution of dynamic hysteresis loops and pressure fields with streamlines was used to explain the properties of the dynamic flow field and the emergence of dynamic stall vortices. The results demonstrate that the airfoils with tubercles have stronger hysteretic characteristics in the pre-stall region, and the ζ CM of mod-1 is increased by 51.33%, indicating stronger torsional hydroelastic stability. In the post-stall region, mod-2 is always in a condition of significant flow separation, which impairs the torsional hydrodynamic stability of the airfoil and has the potential to cause flutter. These findings suggest that leading-edge tubercles offer a negligible advantage in reducing airfoil dynamic stall, and their significance warrants further investigation.