Dynamics Simulations Of Flow Research Articles

Long-Duration Dynamic Numerical Simulation of Combustion and Flow in Hybrid Rocket Motors Considering Nozzle Erosion

Long-Duration Dynamic Numerical Simulation of Combustion and Flow in Hybrid Rocket Motors Considering Nozzle Erosion

Turbulent spot formation in three-dimensional yukawa liquids using large-scale molecular dynamics simulation—effect of system size

We have performed classical ‘first principles’ 3D Molecular Dynamics (MD) simulation of plane-Couette flow (PCF) in a 3D Yukawa liquid. The main aim of the present investigation is to study the effect of stream-wise (flow-direction) and span-wise (transverse direction to the flow) width on the subcritical transition to turbulence in plane Couette flow (PCF), separately. In the past, taking very large-aspect ratio systems, subcritical transition to turbulence in PCF has been studied in great detail. However, the effect of stream-wise and span-wise width on the turbulent dynamics separately, have not been studied in detail. In the present work, we have investigated the effect of stream-wise and span-wise width or length and found that the stream-wise length enhances the large-scale energy, which gives rise to strong large-scale flow, and span-wise width enhances the small-scale energy, which gives rise to strong small-scale structures. In other words, topology of the turbulent spot is observed to change with the change in system size. The spectral separation between the modes governing the large and small-scale dynamics improves with the increase in system sizes. The connection between the stream-wise vortices and the stream-wise velocity streaks is investigated in details. We have found that the number stream-wise velocity streaks is one unit larger than the number of stream-wise vortices. A qualitative comparison between the results obtained from the MD simulation and the hydrodynamics experiment is presented in this article. The behavior of the system was observed to vary with the range of interactions among the dust grains.

Enhancing Recovery of Ultra-Fine Magnetite from Low-Iron-Grade Cyanidation Tailings by Optimizing Flow Field Parameters of Low-Intensity Magnetic Separation (LIMS)

The characteristics of iron minerals in cyanidation tailings with a low iron grade were determined via chemical composition analysis, iron phase analysis, and mineral liberation analysis (MLA). The results showed that the cyanidation tailings contained 15.68% iron, mainly occurring in the form of magnetite (19.66%) and limonite (79.91%), in which 16.52% magnetite and 65.90% limonite particles were fully liberated. Most ultra-fine magnetite grains were adjacent and wrapped with limonite to form complex intergrowths, which resulted in low-efficiency magnetite recovery in low-intensity magnetic separation (LIMS) and adversely affected the downstream high-gradient magnetic separation (HGMS) process. Thus, in this work, the optimization of the flow field was proposed to enhance the separation of ultra-fine magnetite from the cyanidation tailings using pilot-scale LIMS separation, and the controllable parameters (including feed flow, separation gap, drum rotating speed, and solid weight) affecting ultra-fine magnetite capture were investigated. Under optimized conditions, a high-grade magnetite concentrate assaying 63.31% Fe with 86.46% magnetite recovery was produced, which, respectively, increased by 0.76% and 15.22%, compared with those obtained from industrial production. In addition, from the flow dynamics simulation, it was found that the magnetite particles in the −6 µm ultra-fine fraction were lost much more easily than those of coarser fractions due to the relatively enhanced hydrodynamic drag force acting on the particles compared with the magnetic force. However, this loss would be effectively reduced with the regulation and control of the flow field. The iron recoveries in the −16~+6 µm and −6 µm fractions of magnetite concentrate increased by 3.66% and 4.42%, respectively, under optimized hydrodynamic conditions. This research outcome provides a valuable reference for the economic and effective utilization of iron resources from such solid wastes.

Predicting Quantum-Mechanical Partial Charges in Arbitrarily Long Boron Nitride Nanotubes to Accurately Simulate Nanoscale Water Transport.

Single-walled boron nitride nanotubes (BNNTs) have been explored for various applications, ranging from water desalination to osmotic power harvesting. However, no simulation work so far has modeled the changes in the partial charge distribution when a flat sheet is rolled into a tube, hindering the ability to perform accurate molecular dynamics (MD) simulations of water flow through BNNTs. To address this knowledge gap, we employ electronic density functional theory (DFT) calculations to precisely estimate quantum-mechanically derived partial charges on boron (B) and nitrogen (N) atoms in BNNTs of varying lengths and diameters. We observe a spatially varying charge distribution inside both armchair and zigzag nanotubes of finite lengths. Performing DFT calculations for longer BNNTs is computationally intractable, even with state-of-the-art computing resources. To solve this issue, we devise a charge assignment scheme to predict partial charges for longer BNNTs using DFT data for shorter nanotubes, thus overcoming the need to perform more expensive DFT calculations. We show that these charges reproduce the electrostatic potential predicted from first-principles simulations. Subsequently, we carried out MD simulations to predict the effect of the charge distribution inside BNNTs on water flow enhancement via them. We find that using uniform charges leads to an underprediction in flow enhancement, as compared to using quantum-mechanical charges for both armchair and zigzag BNNTs. We also incorporate atomic vibrations into our simulations and show that these vibrations lead to a reduction in the water flow through aperiodic BNNTs. Our work demonstrates the requirement of a quantum-mechanical charge assignment scheme for BNNTs and evolves a framework to assign charges to nanotubes of arbitrary length, thus allowing realistic MD simulations of long BNNTs using accurate partial charges.

A new reduced geometric model for simulation and design of NETmix reactors: LoopNUB

NETmix is a mesosized static mixer comprising a network of mixing chambers and channels, which has experienced a fast industrial uptake. The flow inside this mixer has been previously simulated using the NETmix Unit Block (NUB) - a vertical subdomain of the network that uses lateral Periodic Boundary Conditions (PBCs). A new model is introduced, enabling further reduction of the simulated space – the LoopNUB – that considers PBCs in the sidewalls and between the inlets and outlets, representing an infinite network. The LoopNUB with four rows proved suitable for simulating flow dynamics in NETmix for distances larger than 8 unit cells from inlet channels. LoopNUB was verified, and the results were validated from NUB simulations, which had been validated from experiments. LoopNUB enables the accurate simulation of flow dynamics and heat transfer while decreasing simulation time and computational resources such as disk and memory usage.

Transient and Dynamic Simulation of the Fluid Flow through Five-Way Electric Coolant Control Valve of a 100 kW Fuel Cell Vehicle by CFD with Moving Grid Technique

In order to maintain the performance of a fuel cell vehicle, it is essential to maintain a constant temperature of the stack. Therefore, it is very important to distribute the optimal coolant flow rate to each major component under very diverse and rapidly changing dynamic operating conditions. The part responsible for this is a five-way electric coolant valve. Therefore, this study aims to investigate transient dynamic flow characteristics of the fluid flow through a five-way electric coolant valve (PCCV: Penta-Control Coolant Valve). To achieve this goal, this paper attempts a three-dimensional dynamic simulation of the fluid flow through the valve using a commercial CFD solver with moving mesh technique to consider flow inertia and dynamic flow in the opening and closing stages of the ball valve rotating motion. The dynamic flow characteristics and the thermal mixing inside the PCCV ball valve during the opening and closing stages are analyzed. It was found that the discrepancies between dynamic and steady-state simulations are remarkable when fluxes with different levels of enthalpy and momentum flow into the PCCV, leading to strong flow interference and flow inertia, while the discrepancies are relatively small at low rotation speed and weak flow interference. Subsequently, the effect of the dynamic flow characteristics of the valve on the dynamic thermal mixing characteristics at two different ball valve rotation speeds and rotation directions are investigated. It was found that the dynamic flow and thermal mixing characteristics inside the PCCV are greatly affected by the rotation speed, rotation direction, and degree of flow interference between fluxes. It also helps design better coolant control strategies and improves the FCEV thermal management system.

The identification of the generalised Maxwell fluid for n-hexadecane liquids via non-equilibrium molecular dynamics simulations

ABSTRACT Linear viscoelasticity is very important for chain-molecular fluids. The generalised Maxwell constitutive equation is classical in which the linear viscoelastic fluids consist of a series of spring and dashpot. It is significant to identify the generalised Maxwell fluid of linear viscoelasticity for n-hexadecane liquids through non-equilibrium molecular dynamics simulations of small-strain amplitude oscillatory shear flows. For describing a characteristic of material viscoelasticity, the predicted relaxation modulus curves at different temperatures exhibit three regions: transition, rubbery, and flow. Additionally, the Cox-Merz rule is verified in which the complex oscillation-shear viscosity almost equals the steady-state shear viscosity with respect to shear rates.

A vertebrobasilar junction aneurysm successfully treated with a combination of surgical clipping and flow diverter placement based on the results of computational fluid dynamics analysis: illustrative case.

The treatment of vertebrobasilar junction (VBJ) aneurysms is challenging. Although flow diverters (FDs) are a possible treatment option, geometrical conditions hinder intervention. VBJ aneurysms possess dual inflow vessels from the bilateral vertebral arteries (VAs), one of which is ideally occluded prior to FD treatment. However, it remains unclear which VA should be occluded. A 75-year-old male with a growing VBJ complex aneurysm exhibiting invagination toward the brainstem and causing perifocal edema required intervention. Preoperative computational fluid dynamics (CFD) analysis demonstrated that left VA occlusion would result in more stagnant flow and less impingement of flow than right VA occlusion. According to the simulated strategy, surgical clipping of the left VA just proximal to the aneurysm was performed, followed by FD placement from the basilar artery trunk to the right VA. The patient demonstrated tolerance of the VA occlusion, and follow-up computed tomography angiography at 18 months after FD treatment confirmed the disappearance of the aneurysm. Preoperative flow dynamics simulations using CFD analysis can reveal an optimal treatment strategy involving a hybrid surgery that combines FD placement and direct surgical occlusion for a VBJ complex aneurysm.

Mechanism analysis and dynamic simulation of landslide-generated debris flow influenced by forest fire prevention road

Mechanism analysis and dynamic simulation of landslide-generated debris flow influenced by forest fire prevention road

Statistical moments for simulation calibration with model-bridge

Computer simulations are actively used for analyzing complex phenomena, especially in fields where access to their real-world counterparts is not feasible, such as physics, chemistry, material science, and others. The key to executing successful simulations is making sure that the parameters of a simulator reflect real-world scenarios, a tedious and error-prone effort addressed through simulation calibration. Recently, several methods have been proposed to automatize this task by learning from previously calibrated simulations using a model-bridge paradigm: a complex simulation is replaced by a simpler surrogate model, which can then be bridged to the calibrated simulation parameters. However, designing the surrogate model is a non-trivial problem involving trade-offs between simplicity of representation, interpretability and calibration accuracy, as well as the complexity of the bridge model required to map the surrogate to calibrated parameters. Further, while effective, such approaches can be non-intuitive for practitioners due to their distance from the simulation. In this paper, we view a simulation as a distribution of output variables, which can be easily represented by statistical moments. This yields a very simple and interpretable surrogate that can be bridged to calibrated parameters with a simple linear regression. We show that our method outperforms previous approaches, in terms of calibration accuracy and time, through experiments on simulations of turbulent flow dynamics and synthetic signals.

Physically constrained eigenspace perturbation for turbulence model uncertainty estimation

Aerospace design is increasingly incorporating design under uncertainty-based approaches to lead to more robust and reliable optimal designs. These approaches require dependable estimates of uncertainty in simulations for their success. The key contributor of predictive uncertainty in computational fluid dynamics (CFD) simulations of turbulent flows are the structural limitations of Reynolds-averaged Navier–Stokes models, termed model-form uncertainty. Currently, the common procedure to estimate turbulence model-form uncertainty is the eigenspace perturbation framework (EPF), involving perturbations to the modeled Reynolds stress tensor within physical limits. The EPF has been applied with success in design and analysis tasks in numerous prior works from the industry and academia. Owing to its rapid success and adoption in several commercial and open-source CFD solvers, in-depth verification and validation of the EPF is critical. In this work, we show that under certain conditions, the perturbations in the EPF can lead to Reynolds stress dynamics that are not physically realizable. This analysis enables us to propose a set of necessary physics-based constraints, leading to a realizable EPF. We apply this constrained procedure to the illustrative test case of a converging-diverging channel, and we demonstrate that these constraints limit physically implausible dynamics of the Reynolds stress tensor, while enhancing the accuracy and stability of the uncertainty estimation procedure.

Random Fourier Surrogate for simulation calibration

In this paper we propose Random Fourier Surrogate (RFS) as a method for simulation calibration. Computer simulations are actively used in various fields to model and analyze complex real-world phenomena, enabling practitioners to explore situations inaccessible by other means. To create a simulation that accurately reflects the desired real-world scenario requires calibrating the simulation parameters, a non-trivial process that requires domain expertise and iterative trial-and-error. The model-bridge paradigm has been proposed to automatize this process, where a more computationally efficient surrogate model is used in lieu of the real simulation, and a bridge model is used to map the noninterpretable surrogate parameters to calibrated simulation parameters, which are physically interpretable. Recently an efficient solution called tangent slope-intercept (TSI) descriptors has been proposed, where tangent lines of a principal curve are used to represent the simulation in low dimensionality. However, we find that it has two key problems: it is prone to overfitting and is sensitive to initialization. We address these issues using Random Fourier Features to construct a surrogate model, thereby reducing calibration time by avoiding complex computations, while retaining high accuracy. We evaluate the effectiveness of our method with different experiments on synthetic signal simulations and physical simulations of turbulent flow dynamics.

In silico hemodynamical simulations show additional benefits of artery wall softening induced by antihypertensive drugs

Background and objectivesAge-related arterial stiffening increases peripheral resistance and decreases arterial distensibility, thus contributing to hypertension, an important risk factor of atherosclerosis. It causes abnormal blood flow, endothelial dysfunction, higher pulse wave velocity, and consequently elevated pressure wave amplitude. MethodsThis paper presents the influence of these changes via multiscale 3D-0D transient computational fluid dynamics simulations of blood flow in five patient-specific geometries of human carotid bifurcation using archetypal flow waveforms for young and old subjects. ResultsThe proposed model shows a significant decrease in the time-averaged wall shear stress (TAWSS) for the old archetypal flow waveform. This is in good agreement with clinical data on a straight segment of common carotid arteries available for young and old subjects. Moreover, our study showed that the decrease of area-averaged TAWSS related to the old flow waveform is much more pronounced (2.5 ÷ 4.5 times higher) at risk areas (areas showing TAWSS below its threshold value of 0.48 Pa) than in straight segments commonly considered in clinical studies. ConclusionsSince arterial stiffness can be lowered through long-term usage of any of the five basic groups of antihypertensives, possible benefits of such medical therapy could be not only lowering blood pressure and peripheral resistance but also in increasing the TAWSS and thus attenuating an important mechanism of the atherosclerotic process.

VISSIM Based Traffic Flow Simulation Analysis on Road Network

The urban transport system is a heterogeneous system and is in serious trouble. Traffic congestion has become a major socio-economic problem in both urban and rural areas. Traffic congestion is managed through signage, interchanges, and road widening. Traffic analysis remains a difficult task, especially in countries like India where the number of vehicles is constantly increasing and the roads are increasingly busy, because it is difficult to generate alternatives. Traffic simulation systems such as PTV VISSIM are tools dedicated to the analysis of traffic problems. VISSIM is a simulation software that is widely used to create simulations in dynamic traffic situations before evaluating traffic conditions and creating a realistic plan. Dynamic simulation of traffic flow using VISSIM and appropriate techniques to reduce traffic congestion. The main objective is to examine the study area and prepare a sample for it to find out the root causes of the traffic problem and give the necessary suggestions to solve the problem.

Estimating water transport in carbon nanotubes: a critical review and inclusion of scale effects.

The quasi-frictionless water flow across graphitic surfaces offers vast opportunities for a wide range of applications from biomedical science to energy. However, the conflicting experimental results impede a clear understanding of the transport mechanism and desired flow control. Existing literature proposes numerous modifications and updated boundary conditions to extend classical hydrodynamic theories for nanoflows, yet a consensus or definitive conclusion remains elusive. This study presents a critical review of the proposed modifications of the pressure driven flow or the Hagen-Poiseuille (HP) equations to estimate the flow enhancement through carbon nanotubes (CNTs). For such a case, we performed (semi-)classical molecular dynamics simulations of water flow in various sizes of CNTs, applied the different forms of boundary definitions from the literature, and derived HP equation models by implementing these modifications. By aggregating seven distinct experimental datasets, we tested various flow enhancement models against our measurements. Our findings indicate that including the interfacial layering-based dynamic slip-definition in the proposed HP equations yields accurate estimations. While considering interfacial viscosity predicts the individual CNT experiments well, using the experimental viscosity yields better estimations of measurements for the water flow enhancement through membranes of CNTs. This critical review testing existing literature demonstrates how to refine continuum fluid mechanics to predict water flow enhancement at the nanoscale providing holistic multiscale modeling.

Transformer Outage Contingency in the Southern Interconnected Grid of Cameroon Based on Dynamic Power Flow Simulation

The power system is a network of components having specific operational limits such that any violation of these limits during their operation could lead to more damage in the grid. The grid in Cameroon has been facing several undesired loading events at some sections of the network and the utility company has over the years, employed measures to mitigate these issues. This study has used the ETAP software to conduct a contingency analysis on the largest independent grid in Cameroon, supplying 6 regions in Cameroon. Seventeen transformer outage events in the network were used to conduct the N‐1 scenario‐based contingency, so that the vulnerability of the network can be evaluated. The fast decoupled power flow method was continuously applied on the network, and the different contingency scenarios were ranked using a combination of 4 performance indices. These four performance indices gave information on the impact of the various transformer outage scenarios on the grid. The four indices were added to give a combined value, and the outages were ranked in order of severity based on this combined index. A major contribution of this paper is the use of four scalar indices to classify the critical elements in the grid of a developing country. The initial load flow simulation of the network showed that 12 nodes had under voltages, while 3 transformers and one transmission line were overloaded. The Ngousso corridor suffered one of the worst under voltage situation during the initial power flow study. This is an indication that the utility company needs to undertake intensive network upgrade and implore measures to increase power generation to support the growing loads. It was observed that all the contingency scenarios experienced convergence. The Oyomabang transformer 1 had the highest combined index of 71.20, showing a huge negative impact on the network as a result of the outage of the transformer. This was followed by the Oyomabang transformer 2 with a combined index of 48.63. The results will be very useful to utility operators in prioritizing transformers in the network with high risk of causing huge impact on the network in case of an outage.

Modeling Multiphase Debris Floods Down Straight and Meandering Channels

Natural debris floods travel in straight and meandering courses. The flow behaviour greatly depends on the volume fractions of solid and fluid, as well as on their dynamic interactions with the channel geometry. For the quasi three-dimensional simulations of flow dynamics and mass transport of these floods through meandering and straight channels, we employ a two-phase debris flow model to carry out simulations for debris floods within straight and sine-generated meandering channels of different amplitudes. The results for different sinuous meandering paths are compared with that in the straight one in terms of phase velocity, downslope advection and dispersion, depths of the maxima, deposition of mass, position of front and rear parts of the solid and fluid phases, and also the flow dynamics out of the conduits. The results reveal the slowing of the flow and increase of momentary deposition of the mixture mass in the vicinity of the bends along with the increasing sinuosity. The numerical experiments are useful to better understand the dynamics of debris floods down meandering channels as seen in the natural paths of the rivers as well as already existing channels like episodic rivers in hilly regions. The results can be extended to propose some appropriate mitigation strategies.

Graph Neural Network-based surrogate model for granular flows

Accurate simulation of granular flow dynamics is crucial for assessing geotechnical risks, including landslides and debris flows. Traditional numerical methods are limited by their computational cost in simulating large-scale systems. Statistical or machine learning-based models offer alternatives. Still, they are largely empirical, based on limited parameters. Due to their permutation-dependent learning, traditional machine learning-based models require huge training data to generalize. To resolve these problems, we use a graph neural network (GNN), a state-of-the-art machine learning architecture that learns local interactions. Graphs represent the state of dynamically changing granular flows and their interactions. We implement a multi-Graphics Processing Units (GPU) GNN simulator (GNS) capable of handling different material types. We demonstrate the capability of GNS by modeling granular flow interactions with barriers. GNS takes the granular flow’s current state and predicts the next state using Euler explicit integration by learning the local interaction laws. We train GNS on different granular trajectories. We then assess its performance by predicting granular column collapse and interaction with barriers. GNS accurately predicts flow dynamics for column collapses with different aspect ratios and interaction with barriers with configurations unseen during training. GNS is up to a few thousand times faster than high-fidelity numerical simulators.

Computational Fluid Dynamics Simulation of Internal Compressible Flow in Transfer Lines and Structural Optimization

Computational Fluid Dynamics Simulation of Internal Compressible Flow in Transfer Lines and Structural Optimization

Intelligent lighting design of cloud computing platform based on digital twin historic landscape blocks—Take Yu Garden commercial street in Shanghai as an example

SummaryIn recent years, with the development of the Digital Revolution, information technologies such as digital twins and virtual reality are gradually penetrating people's daily learning and living environment. The digital twin historic landscape street has the performance advantages of virtual reality integration, real‐time interaction, independent iteration, and dynamic perception. Taking the historical district of Yu Garden in Shanghai as an example, this paper builds a travel simulation model of the historical district based on the basic elements of the scene service of the digital twin district under the empowerment of “cloud, data and intelligence”. With the digital twin lamps as the physical entity, the passenger flow dynamic simulation data model is constructed. The effectiveness of the model was verified through simulation of the impact of passenger flow changes on passenger flow, and improvements and enhancements were made to smart lighting fixtures in the block. This study explores the rich application scenarios of digital twin historic blocks and creates a scenario‐based service model, which will fully activate various online and offline elements of historic landscape blocks, bringing users a brand‐new immersive experience.