Application Of Fluid Dynamics Research Articles

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

Exploration of soliton structures in the Hirota–Maccari system with stability analysis

In this research, the modified extended tanh-function (METF) and the extended Jacobi elliptic function expansion (EJEFE) techniques are used to investigate the generation and detection of soliton structures in the Hirota–Maccari (HM) model. Consequently, we obtain soliton solutions with advanced structures, including singular bright soliton, dark soliton, periodic waves, breather waves, periodic breather waves, and multiple bright and dark breather waves. In addition, a lump-type breather wave is also included in the presented solutions. Stability analysis of the obtained solutions is addressed by employing the Hamiltonian technique. 3D surfaces and 2D visuals of the outcomes are represented with the help of a computer application. These findings contribute to understanding nonlinear wave phenomena with potential applications in optics, fluid dynamics, and plasma physics.

Unlocking massively parallel spectral proper orthogonal decompositions in the PySPOD package

We propose a parallel (distributed) version of the spectral proper orthogonal decomposition (SPOD) technique. The parallel SPOD algorithm distributes the spatial dimension of the dataset preserving time. This approach is adopted to preserve the non-distributed fast Fourier transform of the data in time, thereby avoiding the associated bottlenecks. The parallel SPOD algorithm is implemented in the PySPOD library and makes use of the standard message passing interface (MPI) library, implemented in Python via mpi4py. An extensive performance evaluation of the parallel package is provided, including strong and weak scalability analyses. The open-source library allows the analysis of large datasets of interest across the scientific community. Here, we present applications in fluid dynamics and geophysics, that are extremely difficult (if not impossible) to achieve without a parallel algorithm. This work opens the path toward modal analyses of big quasi-stationary data, helping to uncover new unexplored spatio-temporal patterns. Program summaryProgram Title: PySPODCPC Library link to program files:https://doi.org/10.17632/jf5bf26jcj.1Developer's repository link:https://github.com/MathEXLab/PySPODLicensing provisions: MIT LicenseProgramming language: PythonNature of problem: Large spatio-temporal datasets may contain coherent patterns that can be leveraged to better understand, model, and possibly predict the behavior of complex dynamical systems. To this end, modal decomposition methods, such as the proper orthogonal decomposition (POD) and its spectral counterpart (SPOD), constitute powerful tools. The SPOD algorithm allows the systematic identification of space-time coherent patterns. This can be used to understand better the physics of the process of interest, and provide a path for mathematical modeling, including reduced order modeling. The SPOD algorithm has been successfully applied to fluid dynamics, geophysics and other domains. However, the existing open-source implementations are serial, and they prevent running on the increasingly large datasets that are becoming available, especially in computational physics. The inability to analyze via SPOD large dataset in turn prevents unlocking novel mechanisms and dynamical behaviors in complex systems.Solution method: We provide an open-source parallel (MPI distributed) code, namely PySPOD, that is able to run on large datasets (the ones considered in the present paper reach about 200 Terabytes). The code is built on the previous serial open-source code PySPOD that was published in https://joss.theoj.org/papers/10.21105/joss.02862.pdf. The new parallel implementation is able to scale on several nodes (we show both weak and strong scalability) and solve some of the bottlenecks that are commonly found at the I/O stage. The current parallel code allows running on datasets that was not easy or possible to analyze with serial SPOD algorithms, hence providing a path towards unlocking novel findings in computational physics.Additional comments including restrictions and unusual features: The code comes with a set of built-in postprocessing tools, for visualizing the results. It also comes with extensive continuous integration, documentation, and tutorials, as well as a dedicated website in addition to the associated GiHub repository. Within the package we also provide a parallel implementation of the proper orthogonal decomposition (POD), that leverages the I/O parallel capabilities of the SPOD algorithm.

An Application of Computational Fluid Dynamics to Predict Shock Loss Factors at Raise Junctions in Underground Mine Ventilation Systems

An Application of Computational Fluid Dynamics to Predict Shock Loss Factors at Raise Junctions in Underground Mine Ventilation Systems

Aerodynamic Characterization of Glider Lift-to-drag Ratio and its Wing Shape

As a type of fixed-wing aircraft without power devices, gliders have important value in aviation measurement, cargo transportation, and other scenarios. This article studies the application of aerodynamics and fluid dynamics in gliders, but the wing shape of a glider is not sufficient to provide sufficient aerodynamic characteristics for the glider. Therefore, based on the basic theory of aerodynamics, this article studies the relationship between the lift-to-drag ratio of a glider and the aerodynamic characteristics of its wing shape in the aviation field, and changing the shape of the wings to alter the difference in air pressure between the upper and lower air during the glider's flight. Furthermore, the force exerted on the glider during flight is altered to maintain its altitude and flight time. Different airfoils to find out which one can make the glider fly farther, safer, and at higher altitudes were tested. Finally, based on this study, the conclusion drawn is that the double convex wing shape can provide higher overall efficiency and stability for gliders during flight, despite its high cost. This study provides important references for the study of aerodynamic efficiency and stability of gliders.

Application of computational fluid dynamics in the evaluation of left ventricular function in cardiomyopathies and coronary disease

Computational fluid dynamics (CFD) is an emerging technology applied in the field of cardiovascular medicine, which can obtain hemodynamic data by simulating the blood flow in the patient's heart for cardiac function assessment and disease diagnosis. Left ventricular function plays a key role in the occurrence and development of cardiomyopathies and coronary disease. CFD can reconstruct the left ventricular anatomic structures of patients to clarify pathophysiologic mechanisms and analyze hemodynamic parameters to evaluate left ventricular function, verify surgical efficacy, and guide surgical strategy, which has a positive effect on achieving early diagnosis and reducing mortality from cardiomyopathies and coronary disease. At present, there are still technical limitations in the large-scale clinical application of CFD, and various solutions are being developed and tested, and further improvement and refinement are needed.

Factors affecting pedestrian-level ship pollution in port areas: CFD in the service of policy-making

Examining the impact of various factors associated with plume dispersion is essential for quantifying ship pollution in port areas, particularly adjacent to urban environments. This paper concerns the application of Computational Fluid Dynamics (CFD) modelling to study the effect of such factors on air pollution at the passenger port of Marseille, France. Pedestrian-level pollution maps are created for several emission scenarios involving variables such as wind conditions, docking location, emission reduction technology, different emission sources, as well as parameters related to the vessel. Depending on wind direction, docking location and vessel's engine power, different parts of the studied area are exposed to ship pollution. Within a typical examined range, funnel exhaust temperature, funnel height and wind speed deviate up to 78 %, 45 % and 59 % from the baseline scenario, respectively, indicating significant sensitivity of ship pollution to changes in these factors. The lower buoyancy effects of using a scrubber lead to a 17 % larger area exposed to increased ship pollution instead of using Ultra Low Sulphur Fuel Oil (ULSFO). Additionally, it is estimated that an individual vessel may contribute up to six times more to the NOx pollution of a roundabout located 800 m downwind, compared to the local road traffic. The current work can provide valuable information to various stakeholders such as port authorities, decision-makers, or even air quality modelers regarding the impact of shipping emissions on air pollution in port areas, constituting a useful assessment and advisory tool.

The Important Role of Fluid Mechanics in the Engineering Field

Fluid mechanics, a fundamental discipline within engineering, has gained increasing significance across various fields due to continuous advancements in engineering science and technology. Its principles and methodologies find widespread application in aerospace, energy development, environmental protection, biomedicine, and beyond, serving as a cornerstone for technological progress. However, despite the expanding applications of fluid dynamics, challenges persist in integrating theory with practice in certain domains. This paper aims to synthesize exemplary applications of fluid mechanics in diverse engineering fields, illustrating its crucial role in addressing practical challenges. Additionally, it explores current problems and limitations in fluid dynamics applications, such as numerical simulation accuracy, multiphase flow complexity, and fluid-structure interaction nonlinearity. Furthermore, recommendations are provided for future fluid mechanics development, emphasizing interdisciplinary collaboration, advanced computational methods, and the seamless integration of experimental techniques and theoretical research. Through these discussions, this paper endeavors to offer insights into the ongoing development of fluid mechanics, fostering its broader application and deeper exploration within engineering disciplines.

Determination of Maneuvering Force Coefficients for a Destroyer Model with OpenFOAM

_ Ship maneuvering performance can be predicted using various methods, including physical model tests, rapid simulations using coefficient-based forces, and high-fidelity computational fluid dynamics. This article considers the application of computational fluid dynamics for the prediction of hull force coefficients to be used as inputs to rapid maneuvering simulations. The open-source software OpenFOAM was used to simulate forces on the destroyer model DTMB 5415 for steady drift, oscillatory pure sway motion, and oscillatory yaw motion. Recommendations are provided regarding best practices in a number of areas, including domain size, mesh refinement, time step size, and turbulent modeling. Predicted forces for steady drift angles up to 12 degrees are typically within 10%of experimental values. For oscillatory sway and yaw motions, predicted forces are typically within 25%of experimental values. Keywords maneuvering; OpenFOAM; best modeling practices

CFD Analysis of Coolant Flow Characteristics in Reactor Pressure Vessel: A Comprehensive Review

This comprehensive review explores the application of Computational Fluid Dynamics (CFD) in analyzing coolant flow within Reactor Pressure Vessels (RPVs) of nuclear power plants. By synthesizing existing research, methodologies, and advancements specific to RPVs, the paper offers in-depth insights into critical aspects such as boundary conditions, turbulence modeling, heat transfer mechanisms, and validation techniques. Examining a range of studies encompassing various reactor types from Pressurized Water Reactors (PWRs) to Integral Pressurized Water Reactors (IPWRs), the review underscores CFD's pivotal role in enhancing safety, efficiency, and performance optimization in nuclear reactors. Through systematic exploration, this study underscores the critical importance of precise modeling in facilitating safety assessments, operational optimization, and design enhancements across various reactor systems. Accurate modeling serves as a cornerstone for informed decision-making processes aimed at maximizing reactor performance while ensuring the highest standards of safety and reliability. The paper navigates through challenges such as computational limitations and turbulence modeling intricacies, while also discussing emerging trends like the porous media method aimed at improving computational efficiency. By offering a comprehensive understanding of thermal-hydraulic behavior in nuclear reactors, the review underscores CFD's contribution to enhancing safety and reliability in nuclear power generation. Overall, this review underscores the indispensable role of CFD in advancing our understanding of nuclear reactor dynamics, thereby contributing significantly to the overarching goals of improved safety and reliability in nuclear power generation.

Numerical study on magnetic control of boundary layers in non-Newtonian flows over stretching cylinders using Keller box analysis

This article presents an analysis of the magnetic field’s effects on two-dimensional, two-directional, incompressible, and steady third-grade fluid flow over a stretched circular cylinder. A mathematical model describing the behavior of third-grade fluid in the cylindrical coordinate system is developed, accounting for nonlinear differential conditions. To simplify the analysis, appropriate transformations are applied to convert the fractional differential conditions into ordinary differential conditions. The resulting nonlinear differential framework is solved using the Keller Box method. The influences of several novel parameters on the velocity are depicted and examined. Furthermore, the expression for the skin-friction coefficient is computed and provided. The comparison of the obtained results with existing literature is made and found in good accordance. Through comprehensive numerical simulations and analytical derivations, this study contributes to the understanding of magnetic field control in boundary layers of third-grade fluid over stretching cylinders, with implications for a wide range of practical applications in engineering and fluid dynamics. The stronger influence of the magnetic field, indicating an increase in the Hartmann number, corresponds to suppression of thermal and solutal transport, thereby leading to a decrease in the temperature and concentration gradients. Conversely, the velocity profile exhibits an increase, indicating enhanced fluid motion under the influence of the magnetic field. This behavior is consistent with the magnetohydrodynamic effects, where the Lorentz force induced by the magnetic field alters the fluid flow, resulting in changes in the velocity distribution while impacting temperature and concentration gradients.

Computational fluid dynamics (CFD) modeling and application for cleaning of food‐contact surfaces: A review

AbstractIn the food industry, ensuring clean production equipment is essential for maintaining product quality and safeguarding consumer health. For optimal geometric cleaning, it is crucial to consider the specific interactions between flow, dirt, and cleaning fluids. Computational fluid dynamics (CFD) is a powerful tool for simulating fluid flow behavior, especially in terms of momentum, mass, and energy transfer. It has been widely used to model elimination processes in food sanitation. The construction of CFD model and the cleaning of food contact surface were explored. Additionally, an overview of existing CFD studies on fouling and cleaning mechanisms was provided in this manuscript. The goal of this review is to discuss the present state of CFD modeling in cleaning soil from surfaces and to offer a valuable reference for food cleaning process.

Optothermal Microparticle Oscillator Induced by Marangoni and Thermal Convection.

The light-fueled microparticle oscillator, exemplifying sustained driving in a static light source, potentially holds applications in fundamental physics, cellular manipulation, fluid dynamics, and various other soft-matter systems. The challenges of photodamage due to laser focusing on particles and the control of the oscillation direction have always been two major issues for microparticle oscillators. Here, we present an optical-thermal method for achieving a 3D microparticle oscillator with a fixed direction by employing laser heating of the gold film surface. First, the microparticle oscillation without direction limitation is studied. The photothermal conversion originates from the laser heating of a gold film. The oscillation mechanism is the coordination of the forces exerted on the particles, including the thermal convective force, thermophoresis force, and gravity. Subsequently, the additional Marangoni convection force, generated by the temperature gradient on the surface of a microbubble, is utilized to control the oscillation direction of the microparticle. Finally, a dual-channel oscillation mode is achieved by utilizing two microbubbles. During the oscillation process, the microparticle is influenced by flow field forces and temperature gradient force, completely avoiding optical damage to the oscillating microparticle.

Overview on GRS CFD activities related to containment applications

Abstract In the case of a severe accident in a nuclear power plant, hydrogen could be produced in the superheated reactor core. The hydrogen could flow through a leak in the primary circuit into the containment and a hydrogen deflagration could occur. A consequence of such an event was the Fukushima accident in 2011. For the analysis of such events, different phenomena in the reactor containment must be simulated correctly such as the distribution of steam and hydrogen, steam condensation, thermal radiation, conjugated heat transfer, hydrogen recombination and deflagration. The application of Computational Fluid Dynamics (CFD) methods for such analyses is becoming more relevant. Selected models needed to simulate the different containment phenomena with OpenFOAM are currently being tested, validated or improved and further developed by a network of German research institutes and universities.

SIMULATION-DRIVEN STRATEGIES FOR ENHANCING WATER TREATMENT PROCESSES IN CHEMICAL ENGINEERING: ADDRESSING ENVIRONMENTAL CHALLENGES

Water treatment processes in chemical engineering play a critical role in addressing environmental challenges and ensuring the sustainability of water resources. This paper examines simulation-driven strategies aimed at enhancing water treatment processes within the domain of chemical engineering. By leveraging advanced simulation techniques and methodologies, engineers can optimize the design, operation, and performance of water treatment systems, thereby mitigating environmental impacts and improving overall efficiency. The review highlights the importance of addressing environmental challenges through innovative approaches in water treatment processes. It underscores the role of simulation-driven strategies in chemical engineering to achieve sustainable solutions for water management. Through a comprehensive review of simulation techniques and case studies, this paper elucidates how simulation-driven approaches can enhance the effectiveness and sustainability of water treatment processes. Furthermore, the review emphasizes the interdisciplinary nature of this research, bridging chemical engineering principles with environmental science and technology. By integrating simulation tools with knowledge of water chemistry, fluid dynamics, and process engineering, engineers can develop robust strategies for optimizing water treatment processes while minimizing environmental footprints. Key topics covered include the application of computational fluid dynamics (CFD), process simulation software, and advanced modeling techniques in the analysis and design of water treatment systems. Case studies illustrating the successful implementation of simulation-driven strategies in various water treatment applications are presented to provide practical insights and demonstrate the potential benefits. Overall, this paper underscores the pivotal role of simulation-driven strategies in advancing water treatment processes in chemical engineering. It advocates for the adoption of innovative approaches to address environmental challenges and promote sustainability in water management practices within the oil and gas industry and other sectors reliant on chemical engineering processes.
 Keywords: Water treatment, Environmental Challenges, Chemical Engineering, Processes, Simulation Driven Strategies.

Exact wave solutions of new generalized Bogoyavlensky–Konopelchenko model in fluid mechanics

In this paper, we examine a novel generalized [Formula: see text]-dimensional Bogoyavlensky–Konopelchenko (gBK) model analytically via application of five mathematical methods, namely called Extended Simple Equation (ESE) method, Modified Extended Auxiliary equation mapping (MEAEM) method, [Formula: see text]-expansion method, [Formula: see text]-expansion method and modified F-expansion method, respectively, to obtain different types solitary wave solutions in the form of trigonometric, hyperbolic, rational and exponential functions as compared to solutions exist in [S. T. Chen and W. X. Ma, Math. China 13 (2018) 525; A. Sonia, A. Jamshad, S.-U. Rehman and A. Ali, Int. J. Appl. Comput. Math. 9 (2023)]. The research focuses on physical systems, physiology, mathematical applications, engineering fields, and the chemical processes of species in the porous nanoparticles, examining the impact of various parameters on the nonlinear model. The graphs of some solutions are plotted in the form of two-dimensional and three-dimensional by assigning the particular values to the paraments with the assistance of mathematica software which illustrated the physical behavior of the concern model. The derived solution has numerous applications in fluid dynamics and the interaction of Riemann waves and long waves.

Deep learning architecture for sparse and noisy turbulent flow data

The success of deep learning models in fluid dynamics applications will depend on their ability to handle sparse and noisy data accurately. This paper concerns the development of a deep learning model for reconstructing turbulent flow images from low-resolution counterparts encompassing noise. The flow is incompressible through a symmetric, sudden expansion featuring bifurcation, instabilities, and turbulence. The deep learning model is based on convolutional neural networks, in a high-performance, lightweight architecture. The training is performed by finding correlations between high- and low-resolution two-dimensional images. The study also investigates how to remove noise from flow images after training the model with high-resolution and noisy images. In such flow images, the turbulent velocity field is represented by significant color variations. The model's peak signal-to-noise ratio is 45, one of the largest achieved for such problems. Fine-grained resolution can be achieved using sparse data at a fraction of the time required by large-eddy and direct numerical simulation methods. Considering its accuracy and lightweight architecture, the proposed model provides an alternative when repetitive experiments are complex and only a small amount of noisy data is available.

Design and Analysis of Biomass Gasification Reactor

Abstract: Biomass can be utilized as a renewable source of energy through various processes, and its theory involves understanding the principles of biomass composition, conversion technologies, and environmental implications. The objective of current research is to evaluate the thermal and fluid flow characteristics of gasification reactor using CFD tool. From the CFD tool, the temperature distribution at the combustion zone, mixing zone and exit zone are evaluated. The effect of turbulent mixing in gasification is evaluated. The application of Computational Fluid Dynamics (CFD) analysis in the study of biomass gasification reactors has proven to be a valuable tool for understanding and optimizing the complex processes involved. CFD simulations provide insights into the intricate interactions between biomass particles and gasifying agents, offering a detailed understanding of fluid flow patterns, temperature distributions, and species concentrations within the reactor.

Quasi-Global Sensitivity Analysis of Supersonic Computational Fluid Dynamics Simulations

Determining the sensitivity of model outputs to input parameters is an important precursor to developing informative parameter studies, building surrogate models, and performing rigorous uncertainty quantification. Determining parameter sensitivities over a range of parameter values, termed global sensitivity analysis, requires many model evaluations sampled over the parameter space, which is intractable for many large-scale computational fluid dynamics (CFD) applications. For moderate parameter dimensions, we propose the use of Morris screening, a one-at-a-time quasi-global method for estimating parameter sensitivities over a range of parameter values for CFD simulations. The Morris method is implemented within a CFD framework that utilizes adaptive grid refinement, thereby enabling its application to state-of-the-art production-level problems. The method is shown to be viable for a model problem of supersonic flow over an axisymmetric capsule geometry with both physical and nonphysical (i.e., simulation-informing) input parameters. It is shown that Morris screening identifies relative parameter sensitivities consistent with those of more costly experimental and computational studies that were previously performed on this geometry.

Reduced-order modelling of unsteady cavitating flow around a Clark-Y hydrofoil

This paper proposes a novel approach that combines Proper Orthogonal Decomposition (POD) reduced-order system with Long Short-Term Memory (LSTM) neural network to predict flow velocity. Large Eddy Simulation (LES) is used to simulate the cavitating flow around a NACA66 hydrofoil. POD is adopted to reduce the dimensionality of the high-dimensional data. It was found that 66.81% of the flow field energy and dominant coherent structures can be captured with first eight POD modes. The LSTM network model was further used to predict the temporal data of the POD mode coefficients, and the error of the predicted coefficients was within an acceptable range. The reconstructed flow field agrees well with the real flow field and the cavitation development has also been well illustrated. This method provides a promising and efficient alternative for flow prediction and has potential for applications in fluid dynamics, aerospace engineering, and hydrodynamics.