Dynamic Flow Model Research Articles

Failure analysis and flow dynamic modeling using a new slow-flow functionality: the 2022 Jiaokou (China) tailings dam breach

Failure analysis and flow dynamic modeling using a new slow-flow functionality: the 2022 Jiaokou (China) tailings dam breach

Design of High Power Density MVDC Cables for Wide-Body All Electric Aircraft

Aircraft electrification yields the next generation of aircraft such as more electric aircraft (MEA) and all electric aircraft (AEA). These aircraft require high-power-density and low-system-mass electric power systems (EPS). To this end, the voltage of the system must be enhanced to reach medium voltage levels in a few kV ranges. However, using medium voltage (MV) EPS for aircraft exacerbates the challenges of designing aircraft cables such as arc and arc tracking, partial discharges (PD), and thermal management. In this paper, several novel multi-layer based insulation systems for MVDC power cables are proposed to resolve aircraft cables’ challenges while maintaining low weight and size. The proposed cables are thermally and electrically analyzed and compared to cable systems designed based on IEC60502 and AS50881 standards. We use a coupled electrical-thermal-fluid flow dynamic model, for simulations where all possible heat transfer approaches, including conduction, convection, and radiation, to transfer mainly the joule heating generated in the core conductor to ambient with a low pressure of 18.8 kPa which is the air pressure at cruising height of wide-body aircraft (12.2 km) are considered and modeled. To compare the proposed cables to the designs based on IEC 60502 and AS50881, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">J</i> is introduced, which is the product of cables’ overall mass in a unit length and diameter. The results show that besides benefiting multi-layer, multi-function insulation systems to resolve aircraft cables’ challenges, the overall diameter and weight of the designed cables are lower than single-layer insulation system designs based on AS50881 and IEC60502.

A multiclass simulation-based dynamic traffic assignment model for mixed traffic flow of connected and autonomous vehicles and human-driven vehicles

Connected and Autonomous Vehicles (CAVs) may exhibit different driving and route choice behaviours compared to Human-Driven Vehicles (HDVs), which can result in a mixed traffic flow with multiple classes of route choice behaviour. Therefore, it is necessary to solve the Multiclass Traffic Assignment Problem (TAP) for mixed traffic flow. However, most existing studies have relied on analytical solutions. Furthermore, simulation-based methods have not fully considered all of CAVs’ potential capabilities. This study presents an open-source solution framework for the multiclass simulation-based TAP in mixed traffic of CAVs and HDVs. The proposed model assumes that CAVs follow system optimal with rerouting capabilities, while HDVs follow user equilibrium. It also considers the impact of CAVs on road capacity at both micro and meso scales. The proposed model is demonstrated through three case studies. This study provides a valuable tool that can consider several assumptions for better understanding the impact of CAVs on mixed traffic flow.

Two-Leak Case Diagnosis Based on Static Flow Model for Liquid Transmission Pipelines.

The article deals with a diagnosis of multiple leaks from liquid transmission pipelines using analytical methods. Such solutions, based on advanced mathematical models of pipeline flow dynamics, usually turn out to be very complex and time-consuming. However, under certain operating conditions, a simpler approach may also be useful. Such an idea is presented in this paper, proposing two simplified methods for diagnosing double leakages. In principle, these methods apply to both simultaneous and non-simultaneous leaks. The first one uses a static model of a pipeline involving two leaks and takes advantage of the minimization of the objective function defined as the squared deviation of the modeled pressures from the pressures measured on the pipeline. The second method uses a pipeline flow model of a static type in combination with a gradient indicator aimed at detecting leaks and employing algorithms assigned to determining the location and size of leaks. The results of methods' validation, based on tests carried out with the use of measurement data obtained from an experimental water pipeline, were also presented. The outcomes of the performed tests proved the methods' effectiveness in terms of detection, isolation, localization, and intensity estimation of both simultaneous and non-simultaneous double leakages.

Flow stress and dynamic recrystallization behavior and modeling of GH4738 superalloy during hot compression

GH4738 superalloy is a key material for gas turbine engines, yet its relatively poor thermoplasticity and narrow temperature range for hot working deteriorate grain structure uniformity and thereafter service performance. Employing hot compression experiments and finite element analysis (FEA), the present study examined the thermoplasticity of the GH4738 alloy and proposed a fast approach to establishing its dynamic recrystallization (DRX) model. The hot working conditions were considered in the 1000–1160 °C temperature range under strain rates of 2–26 s−1 up to a true compressive strain of 0.69. Friction- and temperature-corrected stress-strain curves were obtained, based on which the efficiency of power dissipation and constitutive equations were constructed. It reveals that DRX is the dominant softening mechanism in the GH4738 alloy, where the sensitivity of flow softening positively correlates with lower temperatures and higher strain rates. The appropriate hot working condition for the alloy is temperatures around 1040–1080 °C and a strain rate larger than 8 s−1. The final grain sizes (d) and fractions of DRX (Xdyn) were minimized using the stochastic gradient descent algorithm based on 120 sets of data within the framework of the Avrami equation. It is shown that the formulated model and its integration into the FEA not only yield satisfactory agreement with the experiment but effectively avoid the complexity involved in the traditional method. The study provides key inputs for predicting the flow behavior and grain structure of the GH4738 superalloy, which shall be critical for process development and optimization of industrial manufacturing processes.

The Polishing of Inner Wall on Medical Device Hole by Shear Thickening Abrasive Flow

To improve medical device hole inner wall quality and overcome issues of traditional abrasive flow methods—limited fluidity in small holes causing deformation due to high inner wall pressure, and slow processing with low viscosity abrasives—a new method called shear thickening abrasive flow polishing is suggested. It uses shear thickening fluid as the medium. By leveraging the Preston equation and fluid dynamics theory, this study establishes both an abrasive flow dynamics model and a material removal model for the shear thickening abrasive flow machining of small titanium alloy hole workpieces in medical instruments. Utilizing the COMSOL software, the flow field state of shear thickening fluid within small holes is examined under varying flow behavior indexes and flow velocities. The findings demonstrate that shear thickening fluid yields superior polishing effects compared to Newtonian fluid. Elevating the flow behavior indexes facilitates a higher material removal rate on the inner wall surface; however, excessively large flow behavior indexes diminish the uniformity of material removal, thereby hindering the attainment of a high-quality polished surface. Furthermore, excessively large flow behavior indexes can reduce fluidity and consequently lower the efficiency of the polishing process. Conversely, while maintaining a constant flow behavior index, increasing the flow velocity contributes to an enhanced material removal rate and improved polishing efficiency. Nevertheless, as the flow velocity rises, the uniformity of inner wall surface roughness diminishes, posing challenges in achieving a high-quality polished surface.

High-Speed On-Site Deep Learning-Based FFR-CT Algorithm: Evaluation Using Invasive Angiography as the Reference Standard.

BACKGROUND. Estimation of fractional flow reserve from coronary CTA (FFR-CT) is an established method of assessing the hemodynamic significance of coronary lesions. However, clinical implementation has progressed slowly, partly because of off-site data transfer with long turnaround times for results. OBJECTIVE. The purpose of this study was to evaluate the diagnostic performance of FFR-CT computed on-site with a high-speed deep learning-based algorithm with invasive hemodynamic indexes as the reference standard. METHODS. This retrospective study included 59 patients (46 men, 13 women; mean age, 66.5 ± 10.2 years) who underwent coronary CTA (including calcium scoring) followed within 90 days by invasive angiography with invasive fractional flow reserve (FFR) and/or instantaneous wave-free ratio measurements from December 2014 to October 2021. Coronary artery lesions were considered to have hemodynamically significant stenosis in the presence of invasive FFR of 0.80 or less and/or instantaneous wave-free ratio of 0.89 or less. A single cardiologist evaluated the CTA images using an on-site deep learning-based semiautomated algorithm entailing a 3D computational flow dynamics model to determine FFR-CT for coronary artery lesions detected with invasive angiography. Time for FFR-CT analysis was recorded. FFR-CT analysis was repeated by the same cardiologist in 26 randomly selected examinations and by a different cardiologist in 45 randomly selected examinations. Diagnostic performance and agreement were assessed. RESULTS. A total of 74 lesions were identified with invasive angiography. FFR-CT and invasive FFR had strong correlation (r = 0.81) and, in Bland-Altman analysis, bias of 0.01 and 95% limits of agreement of -0.13 to 0.15. FFR-CT had AUC for hemodynamically significant stenosis of 0.975. At a cutoff of 0.80 or less, FFR-CT had 95.9% accuracy, 93.5% sensitivity, and 97.7% specificity. In 39 lesions with severe calcifications (≥ 400 Agatston units), FFR-CT had AUC of 0.991 and at a cutoff of 0.80, 94.7% sensitivity, 95.0% specificity, and 94.9% accuracy. Mean analysis time per patient was 7 minutes 54 seconds. Intraobserver agreement (intraclass correlation coefficient, 0.85; bias, -0.01; 95% limits of agreement, -0.12 and 0.10) and interobserver agreement (intraclass correlation coefficient, 0.94; bias, -0.01; 95% limits of agreement, -0.08 and 0.07) were good to excellent. CONCLUSION. A high-speed on-site deep learning-based FFR-CT algorithm had excellent diagnostic performance for hemodynamically significant stenosis with high reproducibility. CLINICAL IMPACT. The algorithm should facilitate implementation of FFR-CT technology into routine clinical practice.

Problem feature based meta-heuristics with Q-learning for solving urban traffic light scheduling problems

An urban traffic light scheduling problem (UTLSP) is studied by using problem feature based meta-heuristics with Q-learning. The goal is to minimize the network-wise total delay time within a time window by finding a high-quality schedule of traffic lights. First, a dynamic flow model is used to describe the UTLSP in a scheduling framework. Second, four improved meta-heuristics combining Q-learning are proposed, including harmony search (HS), water cycle algorithm (WCA), Jaya, and artificial bee colony (ABC) algorithms. Five problem feature based local search operators are constructed. During the iterative process, Q-learning is employed to select the local search operators with strong competitiveness. Two ensemble strategies are proposed to combine meta-heuristics and Q-learning. Finally, experiments are conducted based on real traffic data. The performance of the improved meta-heuristics with Q-learning is verified by solving eighteen cases with different scales. Numerical results and comparisons show that the proposed algorithms have statistical improvements over their peers. The proposed feature-based ABC with Q-learning has the strongest competitiveness among all compared ones.

Risk Assessment of Single-Gully Debris Flow Based on Dynamic Changes in Provenance in the Wenchuan Earthquake Zone: A Case Study of the Qipan Gully

After the Wenchuan earthquake on 12 May 2008, a huge amount of loose deposits was generated on the mountain surface in the earthquake zone, and vegetation was severely damaged, providing a rich source of material for debris flow, greatly increasing the danger. For many years, researchers have mainly considered the recovery of slope vegetation in assessing the risk of debris flow post-earthquake. However, field investigations have found that large amounts of the dynamic reserve materials in the gully have an important impact on the risk. Thus, based on field survey data, this paper takes the Qipan gully in Wenchuan County as an object and uses multi-source and multi-scale monitoring methods (Landsat series, Quickbird, and Unmanned Air Vehicle) to analyze and statistically study the provenance of the slope and gully both pre- and post- the earthquake. By comprehensively using game theory combination weighting and the cloud model, a dynamic risk assessment model for debris flow was constructed to evaluate the risk of debris flow from 2005 to 2019. The results show that the slope provenance post-earthquake was 7.7 times that of pre-earthquake, and by 2019 the slope provenance had recovered to the pre-earthquake level. Based on the statistical estimation of the amount of debris flow outbreak and the dredging of the blocking dam recorded in relevant data, the dynamic provenance of debris flow had decreased by about 781.3 × 104 m3 by 2019. Compared with considering slope provenance only, the assessment result of debris flow risk considering both slope and gully provenance is more realistic. The results are expected to provide reference and guidance for dynamic assessment of the risk of debris flow faced by increasing projects in high-seismic-intensity mountainous areas and to ensure the safety of people’s lives and property effectively.

Combining dynamic material flow analysis and life cycle assessment to evaluate environmental benefits of recycling – A case study for direct and hydrometallurgical closed-loop recycling of electric vehicle battery systems

We conduct a life cycle assessment (LCA) for two recycling processes, a hydrometallurgical and a direct recycling route. Both show ecological benefits compared to production with virgin material (between 2.76–4.55 kg CO2e/ kg battery for NMC111 and NMC811 less greenhouse gas emissions). In contrast to previous works, we combine the LCA results with a dynamic material flow model. This allows the evaluation of the influence on ecological benefits of admixture limits for directly recycled cathode material in a closed-loop recycling system over a time in which the newly produced battery systems and the amount of end-of-life traction batteries grows. We show that for such a closed-loop recycling system, the choice of different allocation methods, namely cut-off or avoided burden approach, may lead to significantly varying results of up to 85% in ecological benefits. We further conclude that combining production with both recycling routes can achieve the lowest greenhouse gas emissions for our closed-loop scenarios.

Microchannel reactors for Fischer-Tropsch synthesis: Experimental investigation and mathematical modeling

The Fischer-Tropsch synthesis is a significant technology for converting coal, natural gas, and biomass into synthetic fuels. In recent years, the use of microchannel reactors for the Fischer-Tropsch synthesis has attracted significant attention. Fischer-Tropsch synthesis experiments were carried out in a microchannel reactor and the influences of reaction conditions on the experimental results were investigated in this study. Based on the experimental data, a dynamic multi-component pseudo-homogeneous variable-volume flow model of microchannel reactors for the Fischer-Tropsch synthesis was built to determine the pressure-, velocity-, conversion- and (component-wise) concentration-distributions in reaction channels. The model takes into account the combined effects of gas volume expansion caused by the frictional pressure drop and gas volume contraction caused by reaction consumption. A novel effective method for calculating the pressure and superficial gas velocity values in microchannel reactors was proposed in the model. Besides that, two sets of experimental data were selected from references to evaluate the validity and accuracy of the model. The reaction performances in the microchannels were analyzed carefully based on the calculated results.

Diverse reactivity model for traffic flow dynamics in Eulerian scope

Diverse reactivity model for traffic flow dynamics in Eulerian scope

Numerical simulations and modeling of MHD boundary layer flow and heat transfer dynamics in Darcy-forchheimer media with distributed fractional-order derivatives

In this study, we extend the application of an existing Cattaneo heat flux model integrated with magnetohydrodynamic (MHD) Maxwell boundary layer flow using distributed fractional-order derivatives. This offers a novel approach for the use of disturbed order along with the midpoint quadrature rule, not previously considered in the literature for such phenomena. Moreover, the application of a variable heat flux with distributive order fractional constitutive equation, also an unexplored area in the literature, adds to the novelty of our study. We investigate the dynamics of the model over a flat surface in the presence of variable heat flux and Darcy-Forchheimer media. The governing equations, along with their respective initial and boundary conditions, are derived and solved using the finite difference method employing the L1 scheme. A benchmark 'exact solution' is obtained for a simplified version of the problem and a numerical solution is provided for a more complex, realistic version. The accuracy of our numerical scheme is validated by comparing results with the benchmark solution, and a mesh independence study further validates our code. Our study uncovers unique insights into the influence of various physical parameters on the velocity and temperature boundary layer thickness, as well as the flow and heat transfer mechanisms, elucidating the rheological characteristics of the system.

Effect of thermal capacitance on microchannel heat sink response to pressure drop oscillations

The efficient heat transfer resulting from flow boiling in microchannel heat sinks can help dissipate high heat fluxes from high-density electronic devices across a small temperature difference. However, practical implementation challenges unique to two-phase flow boiling, as compared to single-phase liquid cooling, have prevented its widespread adoption. A primary challenge is the occurrence of dynamic two-phase flow instabilities, such as pressure drop oscillations (PDO), that have the potential to degrade heat transfer performance or trigger premature critical heat flux (CHF) under some conditions. Under other conditions, PDOs are observed to have little to no impact on performance. One factor proposed by modeling studies to be responsible for this discrepancy in observations of the effect of dynamic instabilities on performance is the thermal capacitance of the heat sink, though this has not been confirmed by experiments. In this study, the effect of thermal capacitance on the transient thermal response of a heat sink experiencing PDOs is examined through use of a dynamic two-phase flow model and experiments. Flow boiling experiments are performed with a controlled compressible volume upstream of parallel-microchannel heat sinks having either a large or a small thermal capacitance. In accordance with the behavior predicted by our model, when thermal capacitance is reduced, the pressure drop oscillation frequency is found to decrease and temperature swings in the heat sink become more severe. Additionally, the experimentally measured CHF limit is diminished in the heat sink at smaller thermal capacitance. These results reveal thermal capacitance as a critical parameter that determines how much dynamic instabilities degrade flow boiling performance in a microchannel heat sink.

Increase in demand for critical materials under IEA Net-Zero emission by 2050 scenario

Increasing the deployment of low-carbon technologies on a global scale is critical for mitigating climate change and achieving net-zero emissions (NZE) by 2050. Implementing such technologies requires a rapidly increasing supply of both critical and non-critical materials. We developed a scenario-based dynamic material stock flow model to calculate the critical material requirements of major low-carbon technologies under the newly released International Energy Agency (IEA) NZE by 2050 scenario. Our results show that most of the metal demand related to photovoltaics would reach its peak around 2035 and then gradually decline, whereas the demand for rare earth elements (REEs) continue to surge. Over the past decade, wind turbines have mainly been responsible for the consumption of neodymium iron boron (NdFeB) magnets containing REEs; however, this will soon be superseded by electric motors (EMs) primarily used in for electric vehicles (EVs). In the short term, increased recycling will have a relatively low impact on the demand for primary resources. With current recycling rates, the secondary supply of REEs will contribute <1% to the demand in 2050, however, aggressive recycling strategies could increase the contribution to 35%. Furthermore, we contend that alternative technologies based on non-critical materials could address the mismatch between supply and demand to resolve the issues presented by material scarcity.

A design method for edge–cloud collaborative product service system: a dynamic event-state knowledge graph-based approach with real case study

ABSTRACT Product service system (PSS) is an effective approach to achieve win–win situation between service providers and consumers, and the design of PSS is the first step of its application. However, on the one hand, PSS is relatively an abstract concept, and what exactly should be considered when designing a PSS still require further exploration; on the other hand, the fast development of information and web technologies bring both opportunities and challenges for PSS design. For example, how to efficiently design the smart & connected products that support remote monitoring and control of service operation, how to build the dynamic service activity flow model that can be intuitively and visually read by human engineers and also can be conveniently deployed on computers, and how to realise edge–cloud collaboration between service providers and consumers. In this regard, a service requirement-oriented four-step generic PSS design method is established, including service mode selection and structured service order generation → smart & connected service product configuration → dynamic event-state knowledge graph-based service activity flow and service resource network configuration → industrial internet-based edge–cloud collaborative service delivering. Finally, a real design case of carbon block grinding and polishing PSS is used for verification. ABBREVIATIONS: AS: assembly service; C: controlling targets or objectives; I: input; M: enabling methods or mechanisms; MRO: maintenance, repair, and operation; MS: maintenance service; O: output; OEE: overall equipment effectiveness; PSS: product service system; PV: processing volume; R x E y -S z : the relation from Event y to the State z of Service activity x; R x S z -E y : the relation from the State z of Service activity x to Event y; StateAi Current: the current state of Service activity i; StateAi Futurek : the kth possible future state of Service activity i; StateAi Historyj : the jth historical state of service activity i; StateEy : the state of Event y; Tq : the qth time point in the time line.

Modeling and comparison of the powder flow dynamics for tilted annular and discrete-outlet nozzles in laser directed energy deposition

The Laser Directed Energy Deposition (L-DED) process is gradually maturing as an Additive Manufacturing (AM) technique. It provides substantial advantages for the refurbishment, redesign or coating of high-value components, which in general, are also large in volume and mass. For such components, it becomes necessary the use of a positioner which keeps the nozzle in a normal orientation as it is displaced over the different components’ surfaces. There is little understanding on the implications of tilting the nozzle on the process efficiency, as well as on the impact of using different nozzle configurations. This work presents a detailed Computational Fluid Dynamics (CFD) analysis of the powder stream generated by two widely used types of powder-fed coaxial nozzles: annular and discrete three-outlet nozzle. For different cases of nozzle orientation, the effect of mass flow and volumetric gas flow on particle velocity and powder convergence, are analyzed. This is crucially contrasted when the two nozzle types are taken into account. Further to analyzing stream behavior, this work also presents process maps for predicting powder usage rates. It was found that the powder usage is significantly affected by the nozzle type, mass and gas setting. For instance, for the case of a narrow 1.0 mm–wide melt pool in a 90°tilt, the annular nozzle is able to achieve up to 75.0% of powder capture efficiency, whereas for the three-outlet nozzle, this usage drops to 36.0%.

Deep Koopman Traffic Modeling for Freeway Ramp Metering

Ramp metering has been considered as one of the most effective approaches of dealing with the traffic congestion on the freeways. The modelling of the freeway traffic flow dynamics is challenging because of its non-linearity and uncertainty. Recently, Koopman operator, which transfers a non-linear system to a linear system in an infinite-dimensional space, has been studied for modelling complex dynamics. In this paper, we propose a data-driven modelling approach based on neural networks, denoted by deep Koopman model, to learn a finite-dimensional approximation of the Koopman operator. To consider the sequential relations of the ramps and main roads on the freeway, a long short-term memory network is applied. Furthermore, a model predictive controller with the trained deep Koopman model is proposed for the real-time control of the ramp metering on the freeway. To validate the performance of the proposed approach, experiments based on the simulation in the traffic simulation software Simulation of Urban MObility (SUMO) environment are conducted. The results demonstrate the effectiveness of the proposed approach on both the dynamics prediction and the real-time control of the ramp metering.

Modeling and simulation of gas vortex flow dynamics to understand the nature of mass transfer enhancement

Vortex flow has been demonstrated to be an effective way of process intensification for interphase mass transfer. However, the underlying principles of this phenomenon are not yet fully known. To understand the nature of gas vortex flow in improving process intensification from a fluid flow perspective, this work conducts an experimental investigation and numerical simulations to compare the differences in energy loss, static pressure, main velocity, and turbulent kinetic energy between the vortex and the axial gas flow inside a countercurrent contactor operated at a flow rate of 50–250 L/min. The results indicate that the energy loss increases with increasing gas flow rate, while the Euler number remains stable at 2.49 ± 0.17, which is 21.05% higher than the conventional axial flow. The vortex flow displays higher values and gradients in the distribution of static pressure, tangential, axial velocity, and turbulent kinetic energy, which is directly linked to the enhanced interphase contact, mixing, and mass transfer processes. Additionally, the tangential velocity of vortex flow exhibits a decaying behavior, but it also has an extra tangential dimension, which was a critical factor for process enhancement compared to conventional axial flow. Moreover, semi-empirical models are developed to characterize the parameters of the maximum tangential velocity and its radial position for the vortex flow with R2 = 0.892 and 0.919, respectively. The results may provide a positive reference for the design, optimization, and operation of countercurrent vortex contactors.

Quantifying the impact of the urban karst on infiltrated stormwater

Abstract Urbanization alters the flow regime of streams, including increasing the frequency and magnitude of storm flows, along with reducing baseflows. An increasingly common management strategy is stormwater infiltration, which is thought to reduce surface runoff and recharge groundwater and thus restore lost baseflows to streams. Recent research has pointed to considerable uncertainty on the fate of infiltrated stormwater, particularly due to the presence of human-made underground infrastructure – e.g. sewer and water supply pipes and telecommunication cables. Such infrastructure is commonly housed in trenches partially filled with highly permeable material which can cause urban karst like flow conditions. We used a dynamic subsurface flow model (HYDRUS-3D) to predict the impact of the urban karst on the fate of infiltrated stormwater. The model was constructed with the presence of a sewer pipe situated between an infiltration basin and a stream. The model predicted that the impact of the urban karst on infiltrated stormwater increases with higher groundwater levels, and greater contrast between hydraulic conductivity of regional soil and gravel which surrounds the sewer pipe. Results suggest that it is important to consider the impact of the urban karst in cases where the goal of stormwater infiltration is baseflow restoration.