Computational Fluid Dynamics Algorithms Research Articles

Experimental investigation and numerical simulation of intense plume impingement effects from a 10 N bipropellant thruster

The intense interaction between thruster plumes and spacecraft surfaces presents substantial challenges for the advancement of space technologies. The impingement effects from a 10 N bipropellant thruster plume on a vertical instrumented plate were experimentally and numerically investigated in this study. Impingement pressure and heat flux were measured using micro differential pressure transducers and Gardon gauges, respectively, while the spatial distribution of the plume was captured using the high-speed camera. The phenomena and the distribution characteristics of plume impingement have been summarized, thereby providing a comprehensive validation dataset for subsequent predictions. Numerical simulations are conducted utilizing the coupled hybrid Computational Fluid Dynamics and Direct Simulation Monte Carlo (CFD-DSMC) algorithm. The dependence of the simulation outcomes on grid resolution was critically assessed, and the accuracy of the results was validated by comparison with experimental data. An integrated methodology, combining experimental and numerical approaches, was developed to calculate the maximum heat flux under realistic grid configurations. This method has proven to be effective and supports the accurate and efficient evaluation of intense plume impingement effects for engineering applications.

Coronary computed tomographic angiography-derived anatomic and hemodynamic plaque characteristics in prediction of cardiovascular events.

This study investigated the association of anatomic and hemodynamic plaque characteristics based on deep learning coronary computed tomography angiography (CCTA) with high-risk plaques that caused subsequent major adverse cardiovascular events (MACE). A retrospective analysis was conducted on patients who underwent CCTA between 1month and 3years prior to the occurrence of a MACE. Deep learning and computational fluid dynamics algorithms based on CCTA were applied to extract adverse plaque characteristics (low-attenuation plaque, positive remodeling, napkin-ring sign, and spotty calcification), and hemodynamic parameters (fractional flow reserve derived by coronary computed tomographic angiography [FFRCT], change in FFRCT across the lesion [△FFRCT], wall shear stress [WSS], and axial plaque stress [APS]). Correlation analysis, logistic regression, and Cox proportional risk analysis were conducted to understand the relationship between these measures and the occurrence of MACE and assess the value of hemodynamic parameters in predicting the incidence of MACE events and their prognosis. Our study included 86 patients with a total of 134 vessels exhibiting plaque formation and 83 culprit vessels with a subsequent coronary event. Culprit vessels had percent diameter stenosis [%DS] (0.54 ± 0.16 vs. 0.62 ± 0.13, P = 0.003), larger non-calcified plaque volume (45.8 vs. 101.7, P < 0.001), larger low-attenuation plaque volume (3.6 vs. 14.5, P < 0.001), more lesions with ≥ 3 adverse plaque characteristics (APC) (4 vs.26, P = 0.002), and worse hemodynamic features of adverse plaque. FFRCT demonstrated better visualization of maximum achievable flow in the presence of coronary stenosis and better correlation with the stenosis severity, while maximum of wall shear stress (WSSmax) was highly correlated with low-attenuation plaques and APC. The inclusion of hemodynamic parameters improved the efficacy of the predictive model, and a high WSS suggested a higher probability of MACE. Hemodynamic parameters based on CCTA are significantly correlated with plaque morphology. Importantly, integrating CCTA-derived parameters can refine the predictive performance of MACE occurrence.

Operator decision-making in angiography-only guided revascularization for lesions not indicated for FFR: a QFR-based functional assessment in chronic coronary syndrome.

Discordance between coronary angiographic findings and invasive functional significance is well-established. Yet, the prevalence of this mismatch in an era increasingly utilizing invasive functional assessments, such as fractional flow reserve (FFR), remains unclear. This study examines the extent of such discrepancies in current clinical practice. This single-center prospective registry included consecutive patients with chronic coronary syndrome (CCS) who underwent elective coronary angiography, with or without revascularization. Coronary angiograms deemed not requiring FFR due to clear anatomical distinctions, either anatomically severe indicating a need for revascularization or mild suggesting no need for intervention, were selected for evaluation. These were then subjected to post-hoc analysis by three independent operators who were blinded to the definitive treatment strategies. Importantly, the post-hoc analysis was conducted in two distinct phases: firstly, a re-evaluation of coronary stenosis, and secondly, a separate functional assessment, each carried out independently. Coronary stenosis severity was assessed visually, while functional relevance was determined by quantitative flow ratio (QFR), calculated using a computational fluid dynamics algorithm applied to angiographic images. Analysis focused on discrepancies between QFR-based functional indications and revascularization strategies actually performed. In 191 patients, 488 vessels were analyzed. Average diameter stenosis (DS) was 37 ± 34%, and QFR was 0.87 ± 0.15, demonstrating a moderate correlation (r = -0.84; 95% CI: -0.86 to -0.81, p < 0.01). Agreement with QFR at conventional anatomical cutoffs was 88% for 50% DS and 91% for 70% DS. Mismatches between revascularization decisions and QFR indications occurred in 10% of cases. Discrepancies were more frequent in the left anterior descending artery (14%) compared to the left circumflex (6%) and the right coronary artery (9%; p = 0.07). In a cardiac-center where FFR utilization is high, discordance between coronary angiography and functional significance persists, even when operators are confident in their decisions not to use functional interrogation. This gap, most evident in the left anterior descending artery, highlights the potential need for integrated angiography-based functional assessments to refine revascularization decisions in CCS.

Harnessing CFD Modeling Techniques for Optimizing Automotive Streamlines and Reducing Aerodynamic Drag

Computational fluid dynamics (CFD) has emerged as an indispensable tool in automotive design, significantly enhancing vehicle performance through aerodynamic optimization. This paper offers a comprehensive exploration of the application of CFD modeling techniques in refining automotive streamline designs, with the overarching goal of mitigating aerodynamic drag and amplifying vehicle efficiency. To contextualize the discussion, the author commences with a historical overview of fluid dynamics, segueing into a detailed exposition of core CFD algorithms, numerical methodologies, and advanced modeling approaches encompassing boundary layer dynamics, turbulence modeling, meshing strategies, and coupling techniques. Through illustrative case studies, the author elucidates the transformative impact of CFD in streamlining the design process and achieving tangible drag reduction outcomes. The discourse extends to the implications of CFD on fuel economy, vehicular speed, stability, and holistic design considerations. In conclusion, the author addresses the challenges inherent in transposing CFD insights to tangible automotive applications and charts prospective trajectories for CFD's continued evolution in automotive engineering, underscoring its pivotal role in steering the future of vehicular design.

3D computational fluid and particle dynamics simulations: metrics of aerosol capture by impaction filters * *Contribution of NIST; not subject to U.S. copyright. Certain commercial equipment, instruments, software, or materials are identified in this paper in order to specify the experimental procedure and virtual experimentation adequately. Such identification is not intended to imply recommendation or endorsement by NIST, nor is

Human studies provide valuable information on components or analytes recovered from exhaled breath, but there are limitations due to inter-individual and intra-individual variation. Future development and implementation of breath tests based on aerosol analysis require a clear understanding of how human factors interact with device geometry to influence particle transport and deposition. The computational fluid and particle dynamics (CFPD) algorithm combines (i) the Eulerian approach to fluid dynamics and (ii) the Lagrangian approach to single particle transport and deposition to predict how particles are carried in fluids and deposited on surfaces. In this work, we developed a 3D multiscale CFPD model to provide insight into human factors that could be important to control or measure during sampling. We designed the model to characterize the local transport, spatial distribution, and deposition of polydisperse particles in a single impaction filter of a commercial aerosol collection device. We highlight the use of decoupling numerical strategies to simultaneously quantify the influence of filter geometry, fluid flowrate, and particle size. Our numerical models showed the remarkable effect of flowrate on aerosol dynamics. Specifically, aerosol mass deposition, spatial distribution, and deposition mechanisms inside the filter. This work as well as future studies on the effect of filter geometry and human factors on aerosol collection will guide the development, standardization, and validation of breath sampling protocols for current and emerging breath tests for forensic and clinical applications.

Systematic computational methods of unsteady flow simulation based on computational fluid dynamics

Unsteady flow is the dominant flow state in real life; thus, the simulation of it is of vital importance, especially in engineering, for example, the flutter or buffeting of the aerofoil. In the past decades, the progress in computational science greatly paced the development of computational fluid dynamics (CFD), providing powerful tools for simulating unsteady flow via numerical methods. However, the unsteady flow state depends on more variables than a steady flow, including the external conditions in different time moments and the flow's properties that vary with time. The calculation is still too massive, even using CFD. Therefore, CFD algorithms with higher efficiency and less reduction in accuracy are still needed to optimize the technique. This paper reviews the main CFD computational methods that have been maturely developed and proven effective, including direct numerical simulation (DNS), classic turbulence models and reduced order model (ROM), illustrating the main mechanisms and displaying their features. The paper also sheds light on these methods' latest research progress.

Optimization for Pipeline Corrosion Sensor Placement in Oil-Water Two-Phase Flow Using CFD Simulations and Genetic Algorithm.

Internal corrosion is a major concern in ensuring the safety of transmission and gathering pipelines in Structural Health Monitoring (SHM). It usually requires numerous sensors deployed inside the piping system to comprehensively cover the locations with high corrosion rates. This study presents a hybrid modeling strategy using Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA) to improve the sensor placement scheme for corrosion detection and monitoring. The essence of the proposed strategy harnesses the well-validated physical modeling capability of the CFD to simulate the oil-water two-phase flow and the stochastic searching ability of the GA to explore better solutions on a global level. The CFD-based corrosion rate prediction was validated through experimental results and further used to form the initial population for GA optimization. Importantly, fitness was defined by considering both sensing effectiveness and cost of sensor coverage. The hybrid modeling strategy was implemented through case studies, where three typical pipe fittings were used to demonstrate the applicability of the sensor layout design for corrosion detection in pipelines. The GA optimization results show high accuracy for sensor placement inside the pipelines. The best fitness of the U-shaped, upward-inclined, and downward-inclined pipes were 0.9415, 0.9064, and 0.9183, respectively. Upon this, the hybrid modeling strategy can provide a promising tool for the pipeline industry to design the practical placement.

Fourth-order accurate numerical modeling of the multi-fluid plasma equations with adaptive mesh refinement

This work develops a multi-fluid plasma modeling capability in extension of a parallel, adaptive, fourth-order, finite-volume method, computational fluid dynamics algorithm. The governing equations of the multi-fluid plasma model are derived from moments of the Boltzmann equation, under the assumption of local thermodynamic equilibrium. Maxwell's equations describe the electromagnetic field evolution and are coupled to the fluid equations of charged species. The present work concentrates on two, charged, collisionless fluids as the first step, although the algorithm is designed to accommodate more fluids, both charged and neutral, and collisions between fluids; the extension to multiple fluids is straightforward. The numerical algorithm featuring parallel, high-order (≥4 for smooth flows), adaptive mesh refinement is expected to help cope with the inherent stiffness of the physics involved. The standard, fourth-order Runge-Kutta scheme is used to evolve the solution in time. The current study is primarily focused on verification and testing of the new, adaptive algorithm; we demonstrate 4th-order accuracy using problems with exact solutions and benchmark our solutions for common plasma simulations against references from literature. Using the 4th-order scheme, our multi-fluid model is shown to provide accurate solutions to plasma problems using half the cell count as traditional, 2nd-order methods. Furthermore, adaptive mesh refinement is successfully applied to three, plasma test problems, providing a significant reduction in mesh size. In subsequent work, an optimized version of our multi-fluid plasma algorithm will be applied to plasma test cases with more practical configurations, with a rigorous assessment of the improvements to computational efficiency afforded by our adaptive, higher-order scheme.

Multi-Objective Optimisation of a Novel Bypass Graft with a Spiral Ridge.

The low long-term patency of bypass grafts is a major concern for cardiovascular treatments. Unfavourable haemodynamic conditions in the proximity of distal anastomosis are closely related to thrombus creation and lumen lesions. Modern graft designs address this unfavourable haemodynamic environment with the introduction of a helical component in the flow field, either by means of out-of-plane helicity graft geometry or a spiral ridge. While the latter has been found to lack in performance when compared to the out-of-plane helicity designs, recent findings support the idea that the existing spiral ridge grafts can be further improved in performance through optimising relevant design parameters. In the current study, robust multi-objective optimisation techniques are implemented, covering a wide range of possible designs coupled with proven and well validated computational fluid dynamics (CFD) algorithms. It is shown that the final set of suggested design parameters could significantly improve haemodynamic performance and therefore could be used to enhance the design of spiral ridge bypass grafts.

A hybrid CFD – Deep learning methodology for improving the accuracy of pressure drop prediction in cyclone separators

Cyclone separators are widely used in many industries to separate solid particles from the fluid flow. As same as separation efficiency, pressure drop is considered as a primary parameter to evaluate performance of cyclone separators. Computational Fluid Dynamics (CFD) is a powerful method to predict the pressure drop of cyclone separators. However, it is indisputable that CFD technique is mathematically complicated and computationally expensive. This study has proposed a more accurate, computationally efficient hybrid method to predict cyclone pressure drop by merging CFD and Deep Learning algorithms. Its generalization has been validated by k-fold cross validation. The proposed hybrid method has also been proved to be more superior than the DNN regression method. The results verify that the proposed method not only predicts cyclone pressure drop accurately (with a maximum error less than 5% in comparison with experimental data), but also requires less computational time than traditional CFD. In other words, it leverages the potential of novel approaches to decrease CFD computational cost while increasing the accuracy level.

Optimization of Photovoltaic Panel Array Configurations to Reduce Lift Force Using Genetic Algorithm and CFD

Aerodynamic lift force acting on the solar structure is important while designing the counterweight for rooftop-mounted solar systems. Due to their unique configuration, the load estimated for solar structures using international building codes can be either higher or lower than the actual. Computational Fluid Dynamics(CFD) simulations haveproven to be an efficient tool for estimating wind loads on solar panels for design purposes and identifying critical design cases. Computational Fluid Dynamics (CFD) simulations usually require high computation power, and slight changes in geometry to find optimum configuration can be time-consuming. An optimization method to minimize lift force effects on solar photovoltaic (PV) arrays installed on rooftops usesthe Computational Fluid Dynamics (CFD)and genetic algorithms proposed in this paper. The tilt angle and pitch between two rows of solar panels were parameterized, and a genetic algorithm was used to search for aconfiguration resulting in minimum wind lift force acting on the solar photovoltaic plant. Only combinations with a performance ratio &gt;80% were considered. Three different rooftopphotovoltaic (PV) plant layout configurations were analyzed in this research. Two rows of photovoltaic (PV) panel arrays wereconsidered for optimization in the 2D domain using ANSYS Fluent. Results showed that the difference in wind-liftforce between optimized configurations against that with maximum lift force configuration for all three cases above was fifty percent.

Investigation of Supercritical Power Plant Boiler Combustion Process Optimization through CFD and Genetic Algorithm Methods

One of the main energy sources utilized to produce power is coal. Due to the lack of combustion enhancement, the main issue with coal-based power plants is that they produce significant amount of pollutants. The major problem of slagging formation within the boiler; it sticks to the water tube walls, superheater, and reheater. Slagging might decrease the heat transferred from the combustion area to the water or steam inside the tubes, increasing the amount of coal and extra air. The abrupt fall of slag on the tube surface into the water-filled seal-trough at the bottom of the furnace might occasionally cause boiler explosions. In order to maximize heat transmission to the water and steam tubes by reducing or eliminating slag formation on the tube surface, the work presented here proposes an appropriate computational fluid dynamics (CFD) technique with a genetic algorithm (GA) integrated with conventional supercritical power plant operation. Coal usage and surplus air demand are both decreased concurrently. By controlling the velocity and temperatures of primary air and secondary air, the devised technique could optimize the flue gas temperature within the furnace to prevent ash from melting and clinging to the water and steam tube surfaces. Heat transmission in the furnace increased from 5945.876 W/m2 to 87,513.9 W/m2 as a result of the regulated slag accumulation. In addition to reducing CO2 emissions by 8.55 tonnes per hour and saving close to nine tonnes of coal per hour, the boiler’s efficiency increased from 82.397% to 85.104%.

Application of Artificial intelligence in Computational fluid dynamics

With the continuous development of artificial intelligence (AI) and computer, the further improvement of computational fluid dynamics (CFD) algorithm and software, artificial intelligence technology has shown its advantages in many fields.AI is becoming increasingly common in engineering applications and is significant in reducing human labor. The purpose of this paper is to summarize the AI technology in the field of CFD, the application of artificial intelligence can through machine learning geometry model parameters, the grid generation technique, the turbulence model calculation, reduce manual intervention, improve the meshing degree, improve the predictive accuracy, rapid turbulence data visualization analysis, bring so much convenient for computational fluid dynamics.

Numerical Investigation of Bladeless Compressor on Different Disk Spaces and Diffuser Configurations

Abstract The cost-effectiveness of turbomachinery is a key aspect within the small-size compressor market. For this reason, Tesla turbomachinery, invented by Nikola Tesla in 1913, could be a good solution, particularly for low volumetric flow applications, where volumetric compressors are usually used. It consists of a bladeless rotor that stands out for its ease of construction and its ability to maintain almost the same performance as size decreases. One of its advantages is that it can run either as a turbine or as a compressor with minor modifications at the stator. The objective of this paper is to investigate a 3 kW Tesla compressor, which design was derived from an analogous Tesla expander prototype (58% isentropic efficiency from the numerical study), by conducting a computational fluid dynamic analysis for different disk gaps and diffuser configurations. The potential of the Tesla compressor is shown to be quite promising, with a peak isentropic efficiency estimated at 53%. Although bladeless compressor is a simple turbomachinery device, different parts, i.e., diffuser, tip clearance, and volute need to be optimized. Utilizing computational fluid dynamics algorithms, different disk gaps and different diffusers are simulated in order to increase the overall performance of the compressor and understand the flow dynamic behavior behind this technology. The dimensionless Ekman number is used to express the optimum disk space of the compressor rotor. Thus, the overall performance of the Tesla compressor is improved by 5–10% points compared to the initial model. Simultaneously, diffuser optimization strategies are applied and proved that there is a direct impact on the optimum design conditions, improving the pressure ratio at high mass flow rates.

Multiple-objective optimization of Jet Controlled Compression Ignition (JCCI) mode with dual-direct injection in a high-speed light-duty engine

Jet Controlled Compression Ignition (JCCI) mode with dual-direct injection of diesel and gasoline has been demonstrated by the engine experiments to provide an effective control on the combustion process without sacrificing the advantages on efficiency and emissions performance. The multiple-objective optimization of JCCI mode based on the 186FA engine at 75 % engine load with an engine speed of 3000 r/min was carried out in this research by the combination of Three-Dimensional (3D) Computational Fluid Dynamics (CFD) and Genetic Algorithm (GA). The simulation results indicate that the ignition timing and the combustion phasing can be effectively controlled by the jet-injection diesel, and the pre-injection gasoline mainly affects the combustion stage from CA50 to CA90. With the increase of the pre-injection energy ratio, higher initial in-cylinder temperature is required to ensure the engine performance. The optimal results present that JCCI mode can achieve high efficiency and low emissions simultaneously when the combustion phasing is at 5 ∼ 9 °CA ATDC. The low EISFC, NOx and soot emissions can be realized when SOIpre and SOIjet are retarded to −63 and −28 °CA ATDC, respectively, which prepares the appropriate premixed charge considering the equivalence ratio and reactivity. The comparisons of the typical cases reveal that the high-temperature combustion process gradually translates to the two-stage characteristics when the SOIjet retards from −27.77 to −18.88 °CA ATDC, the combustion duration is prolonged slightly and the in-cylinder temperature is decreased, resulting in the reduction of pmax by 0.42 MPa, NOx emissions by 0.07 g/kWh, associating with the lower heat transfer loss and exhaust loss. In addition, the NOx emissions in JCCI mode are mainly generated within the high-temperature combustion region of the jet-injection diesel spray target location.

Design and optimization of rudder bulb based on CFD and genetic algorithm

With the mandatory implementation of Energy Efficiency Design Index (EEDI), higher requirements are put forward on the energy-saving capacity of ships. The fact that 15–30% propulsion energy is lost in the wake inspires people to exploit the measures for improving energy-saving capacity of ships to meet the EEDI requirements by recovering the lost energy, such as through rudder bulb. Thus, the design and optimization of rudder bulb appears to be particularly important. This paper aims to propose a design and optimization framework for rudder bulb to find a high-performance energy-saving device suitable for the propulsion system of a ship. A Computational Fluid Dynamics (CFD) method is applied to predict the hydrodynamic performance of the propulsion system with rudder bulbs, and validation and gird uncertainty analysis are conducted for simulation of propeller open-water test. Then, a Design of Experiment (DOE) method is used to conduct sensitivity analysis of design variables, and a Genetic Algorithm (GA) approach is adopted in search of the optimal design of rudder bulb. The results of the propulsion system with different rudder bulbs are presented and analysed. It shows that a 3.66% increase in efficiency is achieved by the optimal rudder bulb design via GA approach.

JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows

Physical systems are governed by partial differential equations (PDEs). The Navier-Stokes equations describe fluid flows and are representative of nonlinear physical systems with complex spatio-temporal interactions. Fluid flows are omnipresent in nature and engineering applications, and their accurate simulation is essential for providing insights into these processes. While PDEs are typically solved with numerical methods, the recent success of machine learning (ML) has shown that ML methods can provide novel avenues of finding solutions to PDEs. ML is becoming more and more present in computational fluid dynamics (CFD). However, up to this date, there does not exist a general-purpose ML-CFD package which provides 1) powerful state-of-the-art numerical methods, 2) seamless hybridization of ML with CFD, and 3) automatic differentiation (AD) capabilities. AD in particular is essential to ML-CFD research as it provides gradient information and enables optimization of preexisting and novel CFD models. In this work, we propose JAX-Fluids: a comprehensive fully-differentiable CFD Python solver for compressible two-phase flows. JAX-Fluids is intended for ML-supported CFD research. The framework allows the simulation of complex fluid dynamics with phenomena like three-dimensional turbulence, compressibility effects, and two-phase flows. Written entirely in JAX, it is straightforward to include existing ML models into the proposed framework. Furthermore, JAX-Fluids enables end-to-end optimization. I.e., ML models can be optimized with gradients that are backpropagated through the entire CFD algorithm, and therefore contain not only information of the underlying PDE but also of the applied numerical methods. We believe that a Python package like JAX-Fluids is crucial to facilitate research at the intersection of ML and CFD and may pave the way for an era of differentiable fluid dynamics. Program summaryProgram title: JAX-FluidsCPC Library link to program files:https://doi.org/10.17632/pzvkwn5s6p.1Developer's repository link:https://github.com/tumaer/JAXFLUIDSCode Ocean capsule:https://codeocean.com/capsule/6819679Licensing provisions: GNU GPLv3Programming language: PythonSupplementary material: Source code; Examples; Videos: Moving solid bodies, Taylor-Green vortex, Rising bubble, Shock-bubble interaction.Nature of problem: The compressible Navier-Stokes equations describe continuum-scale fluid flows. These flows often involve highly complex flow phenomena such as shocks, material interfaces, and turbulence. The intrinsic nonlinear dynamics render the numerical simulation of the these equations challenging. Machine learning provides novel avenues for describing partial differential equations. Machine learning models rely on gradient information provided by automatic differentiation and are often implemented in Python. In contrast, existing high-performance computational fluid dynamics codes are typically written in Fortran or C++ and do not offer inherent automatic differentiation capabilities. These discrepancies hinder the advance of machine-learning-supported computational fluid dynamics. Up to this day, a general-purpose fully-differentiable computational fluid dynamics solver for compressible two-phase flows is missing.Solution method: We introduce JAX-Fluids: a general-purpose three-dimensional fully-differentiable computational fluid dynamics solver for compressible two-phase flows. JAX-Fluids is a simulation framework intended for machine-learning-supported computational fluid dynamics research. Our framework is written entirely in JAX, a high-performance numerical computing library with automatic differentiation capabilities. We have used an object-oriented programming style and a modular design philosophy. This allows the straightforward exchange of numerical methods. We provide a wide variety of state-of-the-art high-order computational methods for compressible flows. The modularity of our framework additionally facilitates the integration of custom subroutines. We use the sharp-interface level-set method to model two-phase flows. The software package can easily be installed as a Python package. We have build the source code around the JAX NumPy API. This makes JAX-Fluids accessible and performant. JAX-Fluids runs on CPUs, GPUs, and TPUs. We use HDF5 in combination with XDMF for writing output quantities. The Python packages Haiku and Optax are used for implementation and training of machine learning methods.Additional comments including restrictions and unusual features: JAX-Fluids relies on open-source third-party Python libraries. These are automatically installed. In the current version, JAX-Fluids only runs on a single accelerator (CPU/GPU/TPU). Future versions will include support for parallel execution. JAX-Fluids has been tested on Linux and macOS operating systems.

A Multi-Region CFD Model for Aircraft Ground Deicing by Dispersed Liquid Spray

The aircraft ground deicing (AGD) process is a mandatory step before taking off in a cold climate. The development of CFD (computational fluid dynamics) tools to simulate AGD could help the industry reduce its costs and limit pollution. Previous works have modelled some parts of the AGD process. Building on these previous works, this paper presents a three-dimensional (3D) CFD algorithm to simulate the process in full scale. The algorithm comprises a multi-region model where a Lagrangian method solves the spray particle equations, and an enthalpy–porosity approach with an Eulerian method simulates the ice melting. The multi-region approach is verified in this paper through a spray-tip penetration (STP) test. The STP predicted using the multi-region model had 99% agreement with the STP predicted using a Lagrangian method. Therefore, the multi-region technique correctly modeled the particle momentum between the two regions. This paper also presents a numerical calibration of the permeability coefficient for the extended enthalpy–porosity technique in the context of AGD. The numerical calibration of the permeability coefficient will enable future parametric studies of the AGD process.

Adjoint node-based shape optimization of free-floating vessels

The paper is concerned with a node-based, gradient-driven, continuous adjoint two-phase flow procedure to optimize the shapes of free-floating vessels and discusses three topics. First, we aim to convey that elements of a Cahn–Hilliard formulation should augment the frequently employed Volume-of-Fluid two-phase flow model to maintain dual consistency. It is seen that such consistency serves as the basis for a robust primal/adjoint coupling in practical applications at huge Reynolds and Froude numbers. The second topic covers different adjoint coupling strategies. A central aspect of the application is the floating position, particularly the trim and the sinkage, that interact with a variation of hydrodynamic loads induced by the shape updates. Other topics addressed refer to the required level of density coupling and a more straightforward—yet non-frozen—adjoint treatment of turbulence. The third part discusses the computation of a descent direction within a node-based environment. We will illustrate means to deform both the volume mesh and the hull shape simultaneously and at the same time obey technical constraints on the vessel’s displacement and its extensions. The Hilbert-space approach provides smooth shape updates using the established coding infrastructure of a computational fluid dynamics algorithm and provides access to managing additional technical constraints. Verification and validation follow from a submerged 2D cylinder case. The application includes a full-scale offshore supply vessel at mathrm{Re} = 3 times 10^8 and mathrm{Fn} = 0.37. Results illustrate that the fully parallel procedure can automatically reduce the drag of an already pre-optimized shape by 9–13% within approx,{mathcal{O}}(10,000-30,000) CPUh depending on the considered couplings and floatation aspects.

Prediction of fractional flow reserve based on reduced-order cardiovascular model

Fractional flow reserve (FFR) is the “gold standard” for diagnosis of myocardial ischemia. The existing 3D computational fluid dynamics (CFD) algorithm based on coronary computed tomography angiography (CTA) has realized the non-invasive calculation of FFR (FFRCT), but the CFD simulation in 3D coronary arteries is time consuming. The purpose of this study was to develop a reduced-order (0D) cardiovascular model for rapid computation of pressure and flow through coronary arteries and stenosis and to ensure accuracy in prediction of FFR (FFR0D). One hundred patients (118 lesions) who had undergone invasive FFR were enrolled retrospectively. Based on coronary CTA, a patient-specific lumped parameter model of the cardiovascular system and coronary system was constructed. In addition, an experimentally validated, theoretical calculation model of stenosis resistance was introduced. Stenosis resistance and flow rate were quantified by coupling the two models, and FFR0D was calculated according to the coronary pressure distribution. The overall modeling time of FFR0D was approximately 41.2 min while FFRCT was reported to be 3.5 h. Using the clinical cut-off value of FFR (0.8), the accuracy, sensitivity, specificity, positive predictive value and negative predictive value of FFR0D were 89.8%, 79.2%, 92.6%, 73.1% and 94.6%, respectively, while that of FFRCT were reported to be 84.3%, 87.9%, 82.2%, 73.9% and 92.2%, respectively. We successfully proposed a reduced-order cardiovascular model for rapid and accurate prediction of FFR. Compared with FFRCT, FFR0D reduced computational cost and ensured computational accuracy in the case of our limited samples. These advantages represent an advance in the diagnosis and treatment of myocardial ischemia.