Large-scale Computational Fluid Dynamics Simulations Research Articles

Automated surgery planning for an obstructed nose by combining computational fluid dynamics with reinforcement learning

Septoplasty and turbinectomy are among the most common interventions in the field of rhinology. Their constantly debated success rates and the lack of quantitative flow data of the entire nasal airway for planning the surgery necessitate methodological improvement. Thus, physics-based surgery planning is highly desirable. In this work, a novel and accurate method is developed to enhance surgery planning by physical aspects of respiration, i.e., to plan anti-obstructive surgery, for the first time a reinforcement learning algorithm is combined with large-scale computational fluid dynamics simulations. The method is integrated into an automated pipeline based on computed tomography imaging. The proposed surgical intervention is compared to a surgeon’s initial plan, or the maximum possible intervention, which allows the quantitative evaluation of the intended surgery. Two criteria are considered: (i) the capability to supply the nasal airway with air expressed by the pressure loss and (ii) the capability to heat incoming air represented by the temperature increase. For a test patient suffering from a deviated septum near the nostrils and a bony spur further downstream, the method recommends surgical interventions exactly at these locations. For equal weights on the two criteria (i) and (ii), the algorithm proposes a slightly weaker correction of the deviated septum at the first location, compared to the surgeon’s plan. At the second location, the algorithm proposes to keep the bony spur. For a larger weight on criterion (i), the algorithm tends to widen the nasal passage by removing the bony spur. For a larger weight on criterion (ii), the algorithm’s suggestion approaches the pre-surgical state with narrowed channels that favor heat transfer. A second patient is investigated that suffers from enlarged turbinates in the left nasal passage. For equal weights on the two criteria (i) and (ii), the algorithm proposes a nearly complete removal of the inferior turbinate, and a moderate reduction of the middle turbinate. An increased weight on criterion (i) leads to an additional reduction of the middle turbinate, and a larger weight on criterion (ii) yields a solution with only slight reductions of both turbinates, i.e., focusing on a sufficient heat exchange between incoming air and the air-nose interface. The proposed method has the potential to improve the success rates of the aforementioned surgeries and can be extended to further biomedical flows.

Family of Skeletal Reaction Mechanisms for Methane–Oxygen Combustion in Rocket Propulsion

Analyzing methane–oxygen rocket propellant combinations requires suitable modeling of the major chemical reaction processes. Although several detailed kinetic mechanisms for methane oxidation in air exist, most do not reproduce the reaction pathways of high-pressure methane–oxygen combustion, typical of liquid rocket engines. Moreover, when large-scale computational fluid dynamics simulations are pursued, detailed reaction schemes are not computationally viable. In the present study, we identify a reliable detailed kinetic scheme for liquid rocket applications, and then we perform a wide reduction campaign leveraging computational singular perturbation theory. Enforcing various reduction targets, we obtain a family of seven skeletal schemes, including 11–39 species. Each mechanism targets different combustion modes, namely, homogeneous ignition, complex flows and flame extinction, premixed burning, reaction processes under intense turbulent mixing, and largely off-stoichiometric mixtures, typical of rocket engine preburners. We test the skeletal mechanisms against meaningful validation targets, attaining appreciable predictive accuracy compared with the detailed parent scheme. We expect the proposed family of skeletal schemes to offer a wide and flexible range of solutions—in terms of size, accuracy, and dominant combustion mode—for performing large-scale yet cost-affordable computational fluid dynamics of methane–oxygen flames under rocket-engine-relevant conditions.

Interactive steering on in situ particle-based volume rendering framework

The development of supercomputers and multi-scale computational fluid dynamics (CFD) models based on adaptive mesh refinement (AMR) enabled fast, large-scale, and high fidelity CFD simulations. Interactive in situ steering is an effective tool for debugging, searching for optimal solutions, and analyzing inverse problems in such CFD simulations. We propose an interactive in situ steering framework for large-scale CFD simulations on GPU supercomputers. This framework employs in situ particle-based volume rendering (PBVR), in situ data sampling, and a file-based control that enables interactive and asynchronous communication of steering parameters, compressed visualization particle data, and sampled monitoring data between supercomputers and user PCs. The parallelized PBVR is processed on the host CPU to avoid interference with CFD simulations on the GPU. We apply the proposed framework to a real-time plume dispersion analysis code CityLBM, which computes the lattice Boltzmann method on the block AMR grid using GPU supercomputers. In the numerical experiment, we address an inverse problem to find a pollutant source from the observation data at monitoring points and demonstrate the effectiveness of the human-in-the-loop approach via the in situ steering framework.Graphical abstract

Acoustic source characterization of simulated subsonic jet noise using spherical harmonics

As subsonic jets remain one of the major contributions to aircraft noise emissions, near-field flow simulations should be included in aircraft design at an early stage using quantitatively predicted sound pressure levels and the time-domain signal properties of the noise data. In this regard, the interface from the near-field data to the far-field radiation—under consideration of acoustic reflections from objects such as fuselage and wings—remains as bottleneck. This study presents the computation of a spherical equivalent source model of jet noise with minimal complexity by means of spherical harmonic (SH) coefficients. Using spherical Hankel extrapolation of sound pressure data from virtual, concentrical microphone arrays, the results of the determination for the radius, in which all acoustic sources of a flow field are confined, indicate the source radius around the end of the potential core to be equivalent to five times the nozzle diameter. The result of the SH transform shows the dominant energy contribution to be related to nine elementary sources. The resulting equivalent source model of jet noise provides a convenient format for further use in large-scale computational fluid dynamics simulations.

Experimental and numerical study of CO2 plume diffusion in a confined space

In this paper, heavy gas diffusion in a confined space has been investigated. The effects of barrier and source intensity on CO2 diffusion are explored by the small-scale experiments and computational fluid dynamics (CFD) methods. Six different turbulence models are selected to predict the gas concentrations. By comparing these experimental values with the simulated ones, it is found that all models can effectively predict the concentration variation with time, and SST k-ω model is most close to the ideal model compared with others. Three source-barrier distances and three CO2 flow rates have been set up for the study. In this confined space, the main flow is concentrated in the region near the ground. The existence of barriers in the space will have a dilution effect on the high-concentration plume near the ground of the near-source area and a barrier effect on the low-concentration plume in the far-source area. The changes in source intensity have notable impact on the gas concentrations. This study can provide an experimental basis for the risk assessment in the confined spaces, as well as an experimental and data reference for large-scale CFD simulations.

CFD based reactivity parameter determination for biomass particles of multiple size ranges in high heating rate devolatilization

This work presents a methodology that combines experimental measurements and Computational Fluid Dynamics (CFD) modeling to determine the global reaction kinetics of high heating rate biomass devolatilization. Three particle size ranges of woody biomass are analyzed: small (SF), medium (MF) and large (LF) fractions. Devolatilization mass loss is measured for each fraction in a laminar Drop-Tube Reactor (DTR) in nitrogen atmosphere, using two nominal reactor temperatures of 873 and 1173 K. Single First Order Reaction (SFOR) kinetics are determined by coupling an optimization routine with CFD models of the DTR. The global pre-exponential factors and activation energies for the SF, MF and LF particles are 5880 1/s and 42.7 kJ/mol, 48.1 1/s and 20.2 kJ/mol, and 102 1/s and 24.8 kJ/mol, respectively. These parameters are optimized for the isothermal heat transfer model available in CFD programs, and it is recommended that the specific heat capacity that was used in the optimization (1500 J/kgK) is used together with the parameters. Using the SF kinetics for small wood particles and either of the MF or LF kinetics for large particles, it is expected that more accurate devolatilization predictions can be obtained for the whole fuel size distribution in large scale CFD simulations.

A run time combustion zoning technique towards the EDC approach in large-scale CFD simulations

Purpose – This paper aims to present a novel run time combustion zoning (RTCZ) technique based on the working principle of eddy dissipation concept (EDC) for combustion modeling. This technique selectively chooses cells in which the full reaction mechanism needs to be solved. The selection criterion is based on the concept of differentiating between combustion and the non-combustion zone. With this approach, considerable reduction in computational load and stability of the solution was observed and even the number of iterations required to achieve a stable solution was significantly reduced. Design/methodology/approach – Computational fluid dynamics (CFD) simulations of real life combustion problems such as industrial scale flares, fuel fired furnaces and IC engines are difficult due to the strong interactions of chemistry with turbulence as well as the wide range distribution of time and length scales. In addition, comprehensive chemical mechanisms for hydrocarbon combustion may include hundreds of species and thousands of reactions that are known in detail for only a limited number of fuels. Even with the most advanced computers, accurate simulation of these problems is not easy. Hence, the modeler needs to have strategies to either simplify the chemistry or to improve the computational efficiency. Findings – The EDC turbulence model has been widely used for treating the interaction between turbulence and the chemistry in combustion problems. In an EDC model, combustion is assumed to occur in a constant pressure reactor, with initial conditions taken as the concentration of the current species and temperature in the cell. With these assumptions, EDC solves the full or simplified reaction mechanism in all the grid cells at all iterations. Originality/value – This paper presents a novel RTCZ technique for improving the computational efficiency, when the EDC model is used in CFD modeling. Considerable reduction in computational time and stability of the solution can be achieved. It was also observed that the number of iterations required to achieve a converged solution was significantly reduced.

Computational Fluid Dynamics Simulations of Particle Deposition in Large-Scale, Multigenerational Lung Models

Computational fluid dynamics (CFD) has emerged as a useful tool for the prediction of airflow and particle transport within the human lung airway. Several published studies have demonstrated the use of Eulerian finite-volume CFD simulations coupled with Lagrangian particle tracking methods to determine local and regional particle deposition rates in small subsections of the bronchopulmonary tree. However, the simulation of particle transport and deposition in large-scale models encompassing more than a few generations is less common, due in part to the sheer size and complexity of the human lung airway. Highly resolved, fully coupled flowfield solution and particle tracking in the entire lung, for example, is currently an intractable problem and will remain so for the foreseeable future. This paper adopts a previously reported methodology for simulating large-scale regions of the lung airway (Walters, D. K., and Luke, W. H., 2010, "A Method for Three-Dimensional Navier-Stokes Simulations of Large-Scale Regions of the Human Lung Airway," ASME J. Fluids Eng., 132(5), p. 051101), which was shown to produce results similar to fully resolved geometries using approximate, reduced geometry models. The methodology is extended here to particle transport and deposition simulations. Lagrangian particle tracking simulations are performed in combination with Eulerian simulations of the airflow in an idealized representation of the human lung airway tree. Results using the reduced models are compared with those using the fully resolved models for an eight-generation region of the conducting zone. The agreement between fully resolved and reduced geometry simulations indicates that the new method can provide an accurate alternative for large-scale CFD simulations while potentially reducing the computational cost of these simulations by several orders of magnitude.

New technologies for fluid dynamics experiments and optical diagnostics

Modern technologies offer new opportunities for experimentalists in a wide variety of research areas including hydrodynamics. A significant improvement in precision, dynamic range, reproducibility, motion control accuracy, data acquisition rate and information capacity of the experimental datasets over the current state-of-the-art are possible using new approaches and techniques, which may bring the quality of experiments to a new level of standards. Application of these new technologies in experimental diagnostics can help bridge the current quality gap between the observations and the large-scale computational fluid dynamics simulations allowing direct and unambiguous comparison of the data and the modeling results, which is crucial for the code validation. One of the new technologies which is described in this paper is ultra-high performance digital holographic data storage. The state-of-the-art motion control, electronics and optical imaging allow for realization of turbulent flows with very high Reynolds number (>107) in a relatively small laboratory-scale form-factor and quantification of their properties with extremely high spatio-temporal resolutions and bandwidth. Digital holographic technology can provide complete three-dimensional mapping of the flow velocity and density fields at high data rates (over 1000 fps) over large spatial area (∼50 cm) with high spatial (1–10 μm) and temporal (better than a few nanoseconds) resolutions and, therefore, can provide extremely accurate quantitative description of the fluid flows, including those of multiphase and unsteady conditions. These unique experimental and metrological capabilities enable the studies of spatial and temporal properties of the transport of momentum, angular momentum and energy, and the identification of scaling, invariants and statistical properties of the complex multiphase and unsteady turbulent flows. The technology can be applied for investigations of a large variety of hydrodynamic problems including the fundamental properties of non-Kolmogorov turbulence, flow–particle interactions, accelerating and rotating flows, boundary layers, Rayleigh–Taylor instability and turbulent mixing, magneto-hydrodynamics and laboratory astrophysics.