High-fidelity Numerical Simulations Research Articles

Simulation of nuclear fuel clad high-temperature ballooning under loss-of-coolant accident conditions considering anisotropic creep

Zirconium (Zr) alloys are standard clad material in commercial light water reactors. However, it is vulnerable to ballooning and burst under high-temperature conditions such as LOCA (Loss-Of-Coolant Accident), due to large internal/external pressure difference. Considering that clad ballooning is detrimental in terms of nuclear reactor safety especially for high-burnup fuel rod, high-fidelity numerical simulation of Zircaloy clad ballooning is desirable. This paper presented a finite-element based implementation for the simulation of Zircaloy clad ballooning. The developed simulation capability predicts progressive clad ballooning up to failure given empirical burst limits. The material models coupled together are phase transformation and high-temperature creep. Especially, anisotropic creep was considered for α phase Zircaloy. The implemented algorithm for stress update is implicit in time integration, which allows relatively large time step size to efficiently simulate the process that can last for hundreds of seconds. An analytic benchmark, the PUZRY ballooning and burst test, and the Hardy stress-rupture test were utilized to verify and validate the simulation capability. The anisotropic creep calculation conforms to the analytical solution. The comparison with PUZRY and Hardy test data shows that considering anisotropic creep in the α phase region is essential. 3D ballooning was also simulated by considering circumferential temperature difference for both conventional and novel annular clad designs. Interestingly, it is the first time to note that the risk of buckling failure is predicted for the double-cooled annular nuclear fuel clad during the accident condition, only after considering the anisotropic creep algorithm.

Revisiting the modelling framework for the unresolved scalar variance

The unresolved scalar variance in large-eddy simulations of turbulent flows is a fundamental physical and modelling parameter. Despite its importance, relatively few algebraic models have been developed for this important variable with the most prominent models to date being the classic scale-similarity and gradient models. In this work a new generalized modelling framework based on reconstruction has been developed, which in contrast to classic modelling approaches allows the construction of base static variance models of arbitrary accuracy. It is demonstrated that higher-order reconstructions naturally lead to base static variance models of increased accuracy, and that the classic scale-similarity and gradient models are subsets of more general and higher-order models. The classic scale-similarity assumption for developing dynamic models is also revisited, and it is demonstrated that this can essentially be reinterpreted as a two-level reconstruction approach. Based on this result, a new general methodology is proposed that allows the construction of dynamic models for any given base static model, and a corresponding general reconstruction operator, algebraic or iterative. Consequently, improved static and dynamic models for the scalar variance are developed. The newly developed models are then thoroughly tested a priori using two high-fidelity direct numerical simulation databases corresponding to two substantially different flame and flow configurations, and are shown to outperform classic algebraic models for the variance.

Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow

In this study, we conduct a parametric analysis to evaluate the sensitivities of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS) models, mesh resolution, wall boundary conditions and mesh anisotropy. While such investigations have been conducted for attached/flat-plate flow configurations, systematic studies specifically targeting turbulent flows with separation are notably sparse. To bridge this gap, our study focuses on the flow over a two-dimensional Gaussian-shaped bump at a moderately high Reynolds number, which involves smooth-body separation of a turbulent boundary layer under pressure-gradient and surface-curvature effects. In the simulations, the no-slip condition at the wall is replaced by three different forms of boundary condition based on the thin boundary layer equations and the mean wall-shear stress from high-fidelity numerical simulation to avoid the additional complexity of modeling the wall-shear stress. Various statistics, including the mean separation bubble size, mean velocity profile, and dissipation from SGS model, are compared and analyzed. The results reveal that capturing the separation bubble strongly depends on the choice of SGS model. While simulations approach grid convergence with resolutions nearing those of wall-resolved LES meshes, above this limit, the LES predictions exhibit intricate sensitivities to mesh resolution. Furthermore, both wall boundary conditions and the anisotropy of mesh cells exert discernible impacts on the turbulent flow predictions, yet the magnitudes of these impacts vary based on the specific SGS model chosen for the simulation.

A LAPACK Implementation of the Dynamic Mode Decomposition

The Dynamic Mode Decomposition (DMD) is a method for computational analysis of nonlinear dynamical systems in data driven scenarios. Based on high fidelity numerical simulations or experimental data, the DMD can be used to reveal the latent structures in the dynamics or as a forecasting or a model order reduction tool. The theoretical underpinning of the DMD is the Koopman operator on a Hilbert space of observables of the dynamics under study. This paper describes a numerically robust and versatile variant of the DMD and its implementation using the state-of-the-art dense numerical linear algebra software package LAPACK . The features of the proposed software solution include residual bounds for the computed eigenpairs of the DMD matrix, eigenvectors refinements and computation of the eigenvectors of the Exact DMD, compressed DMD for efficient analysis of high dimensional problems that can be easily adapted for fast updates in a streaming DMD. Numerical analysis is the bedrock of numerical robustness and reliability of the software, that is tested following the highest standards and practices of LAPACK . Important numerical topics are discussed in detail and illustrated using numerous numerical examples.

Turbulent wake characteristics for a circular cylinder in proximity to a moving wall

This study investigates the scenario of flow past a circular cylinder in proximity to a moving wall (or equally a body translating in still fluid parallel to a stationary wall). Fifty high-fidelity three-dimensional direct numerical simulations are performed over a parameter space of turbulent Reynolds numbers (Re) of 300 to 1000 combined with gap-to-diameter ratios (G/D) of 0.2 to 3. The flow, hydrodynamic and turbulence characteristics over the (Re, G/D) parameter space are examined in detail. Small-scale rib-like mode B structures and alternate vortex shedding are observed for all cases. The streamwise location for the vortex shedding (quantified by the wake recirculation length Lr) varies strongly with both Re and G/D. The variation of Lr with Re and G/D can be explained by the spanwise circulation $\varGamma_z$ fed into the wake, where the variation trends of Lr and $\varGamma_z$ are inversely correlated. The variations of the mean drag and fluctuating lift coefficients are also governed by the variations in Lr and $\varGamma_z$ . The total kinetic energy in the wake region reduces drastically as G/D reduces below 0.8, which is contributed collectively by (i) reduction in the strength of the shed vortices, (ii) downstream movement of the location of vortex shedding and (iii) associated delayed generation of streamwise vortices. The present results on a moving wall also help to explain several flow and hydrodynamic characteristics reported in the literature for a stationary wall, because the moving wall eliminates the complex wall boundary layer and retains a ‘clean’ near-wall effect.

Turbomachinery Noise Review

The present paper is aimed at providing an updated review of prediction methods for the aerodynamic noise of ducted rotor–stator stages. Indeed, ducted rotating-blade technologies are in continuous evolution and are increasingly used for aeronautical propulsion units, power generation and air conditioning systems. Different needs are faced from the early design stage to the final definition of a machine. Fast-running, approximate analytical approaches and high-fidelity numerical simulations are considered the best-suited tools for each, respectively. Recent advances are discussed, with emphasis on their pros and cons.

The breakup and instability structures of liquid jets in subsonic crossflows

The breakup of water and Jet A fuel jets injected transverse into elevated temperature (up to 425 °C), combustion pre-heated, subsonic crossflows was investigated by internally fluorescing the liquid jet column. The fluorescent dye seeded test liquids were illuminated by a pulsed laser light that was transmitted within the injector using a quartz rod light-guide. High-resolution images isolating the intact liquid column from light scattered by the surrounding dense spray facilitated precise measurement of the liquid column breakup position and the size and distribution of windward column instability waves. The injector geometry consisted of a 1 mm circular orifice with an orifice length of 20 mm with a tapered inlet. Liquid jet to gaseous crossflow momentum flux ratios were varied from 6 to 266 and the gas Weber number was varied from 10.7 to 228. Empirical correlations were derived for the liquid column breakup coordinates and breakup time in addition to the average and local maximum instability wavelength and amplitude on the liquid column windward surface. The reported correlations provided key insight into atomization physics and aim to support the quantitative validation of high-fidelity numerical simulations of the liquid jet in subsonic crossflow.

A two-field formulation for surfactant transport within the algebraic volume of fluid method

Surfactant transport plays an important role in many technical processes and industrial applications such as chemical reactors, microfluidics, printing and coating technology. High fidelity numerical simulations of two-phase flow phenomena reveal rich insights into the flow dynamics, heat, mass and species transport. In the present study, a two-field formulation for surfactant transport within the algebraic volume of fluid method is presented. The slight diffuse nature of representing the interface in the algebraic volume of fluid method is utilized to track the concentration of surfactant at the interface as a volumetric concentration. Transport of insoluble and soluble surfactants is investigated by tracking two different concentrations of the surfactant, one within the bulk of the liquid and the other one at the interface. These two transport equations are in turn coupled by source terms considering the ad-/desorption processes at a liquid–gas interface. Appropriate boundary conditions at a solid–fluid interface are formulated to ensure surfactant conservation, while also enabling to study the ad-/desorption processes at a solid–fluid interface. The developed numerical method is verified by comparing the numerical simulations with well-known analytical and numerical reference solutions and applied to a surfactant-laden drop in shear flow. The presented numerical methodology offers a seamless integration of surfactant transport into the algebraic volume of fluid method, where the latter has many advantages such as volume conservation and an inherent ability of handling large interface deformations and topological changes.

Vertical-axis turbine performance enhancement with physics-informed blade pitch control. Basic principles and proof of concept with high-fidelity numerical simulation

In this study, we developed active physics-informed turbine blade pitch control methods to conquer the inconsistent energy harvesting efficiency challenges encountered by the vertical-axis turbines (VATs) technology. Specifically, individual turbine blades were pitched by actuators following commands from the physics-informed controllers, and the turbine performance improvements as a result of the blade pitch control mechanism and the associated flow physics were studied. The aim of the blade pitch control was to maintain constant effective angles of attack (AoAs) experienced by turbine blades through active blade pitch, and the constant AoA function was designed to facilitate control mechanism implementation into real-world VATs. To gain in-depth understanding of the capability of the control, flow physics was studied for different constant AoA control strategies across a wide range of tip speed ratios and wind speeds and was compared with that from the corresponding baselines without control, and that from the sinusoidal AoA control strategy. The comparison between the turbine performance with constant AoA control and that without control showed a consistent increase in the time-averaged net power coefficient, a measure of energy harvesting efficiency taking out of the actuator loss, ranging from 27.4% to 704.0% across a wide spread of wind speeds. The superior turbine performance with constant AoA control was largely attributed to blade dynamic stall management during the blade upstream and downstream cycles and the transition between the two cycles.

Aerodynamic performance increase over an A320 morphing wing in transonic regime by numerical simulation at high Reynolds number

PurposeThis study aims to investigate the morphing concepts able to manipulate the dynamics of the downstream unsteadiness in the separated shear layers and, in the wake, be able to modify the upstream shock–boundary layer interaction (SBLI) around an A320 morphing prototype to control these instabilities, with emphasis to the attenuation or even suppression of the transonic buffet. The modification of the aerodynamic performances according to a large parametric study carried out at Reynolds number of 4.5 × 106, Mach number of 0.78 and various angles of attack in the range of (0, 2.4)° according to two morphing concepts (travelling waves and trailing edge vibration) are discussed, and the final benefits in aerodynamic performance increase are evaluated.Design/methodology/approachThis article examines through high fidelity (Hi-Fi) numerical simulation the effects of the trailing edge (TE) actuation and of travelling waves along a specific area of the suction side starting from practically the most downstream position of the shock wave motion according to the buffet and extending up to nearly the TE. The present paper studies through spectral analysis the coherent structures development in the near wake and the comparison of the aerodynamic forces to the non-actuated case. Thus, the physical mechanisms of the morphing leading to the increase of the lift-to-drag ratio and the drag and noise sources reduction are identified.FindingsThis study investigates the influence of shear-layer and near-wake vortices on the SBLI around an A320 aerofoil and attenuation of the related instabilities thanks to novel morphing: travelling waves generated along the suction side and trailing-edge vibration. A drag reduction of 14% and a lift-to-drag increase in the order of 8% are obtained. The morphing has shown a lift increase in the range of (1.8, 2.5)% for angle of attack of 1.8° and 2.4°, where a significant lift increase of 7.7% is obtained for the angle of incidence of 0° with a drag reduction of 3.66% yielding an aerodynamic efficiency of 11.8%.Originality/valueThis paper presents results of morphing A320 aerofoil, with a chord of 70cm and subjected to two actuation kinds, original in the state of the art at M = 0.78 and Re = 4.5 million. These Hi-Fi simulations are rather rare; a majority of existing ones concern smaller dimensions. This study showed for the first time a modified buffet mode, displaying periodic high-lift “plateaus” interspersed by shorter lift-decrease intervals. Through trailing-edge vibration, this pattern is modified towards a sinusoidal-like buffet, with a considerable amplitude decrease. Lock-in of buffet frequency to the actuation is obtained, leading to this amplitude reduction and a drastic aerodynamic performance increase.

High-fidelity numerical simulations in Eulerian/Lagrangian framework for liquid fuel jets in crossflow with atomization and evaporation: Effect of aerodynamic Weber number

The effect of the aerodynamic Weber number Weg on the atomization-evaporation process of liquid fuel jets in crossflow is investigated by detailed numerical simulations accounting for evaporation from both the gas–liquid interface and dispersed particles in the Eulerian/Lagrangian framework. The accuracy of the evaporation model on the gas–liquid interface treated in the Eulerian manner is verified by applying two-dimensional (2D) numerical simulations to the evaporation of a single droplet and comparing the computational results with the theoretical solution and psychrometric data. The results show that the present detailed numerical simulations reasonably reproduce the primary and secondary atomization characteristics of the liquid jets in crossflow under different breakup modes. Additionally, considering both evaporations from the Eulerian gas–liquid interface and Lagrangian dispersed particles is essential for precisely predicting the fuel mixing with the oxidizer and the ignition. As Weg increases, the Sauter mean diameter (SMD) of generated fuel droplets decreases in the whole region. Furthermore, as Weg increases, the tendency that the fuel vapor mass fraction is high in the upstream region and decreases downstream becomes more evident. However, the temperature behind the liquid column do not decrease despite the increasing heat loss due to evaporation. These behaviors can be well explained in terms of the differences in the atomization, evaporation, and heat transfer between the liquid jet and hot air crossflow against Weg.

Adaptive multi-fidelity Monte Carlo for real-time probabilistic storm surge predictions

Real-time, probabilistic predictions of the expected storm surge represent an important information source for guiding emergency response decisions during landfalling tropical storms/cyclones. The uncertainty quantification in these predictions is accomplished as follows: an ensemble of sample storm scenarios is generated based on the nominal storm advisory and a probabilistic description of the corresponding forecast errors; subsequently high-fidelity numerical simulations are performed to predict the surge for each of these scenarios; the simulation results are finally leveraged to estimate the statistics of interest. This process is repeated whenever a new storm advisory is issued. The need to establish predictions in real-time, to support emergency planning decisions, creates incentives for high computational efficiency for this probabilistic estimation. This paper investigates an adaptive Multi-Fidelity Monte Carlo (MFMC) framework for achieving such an objective. As a lower-fidelity model within the MFMC setup, a surrogate model is adaptively developed based on high-fidelity numerical simulations from the current or past storm advisories, achieving information sharing across them. MFMC leverages the correlation between the high- and low-fidelity models to establish unbiased predictions with high statistical accuracy, significantly improving abilities to provide timely and informative estimates. To facilitate the development of the low-fidelity model using only a small number of high-fidelity numerical simulations, a combination of physics-based and data-driven dimensionality reduction techniques are introduced for the input and the output, respectively, of the surrogate model. To accommodate the use of high-fidelity simulations from the current advisory in the surrogate model calibration, the MFMC implementation is established using leave-one-out surrogate model predictions. Finally, the challenge of establishing MFMC predictions for a large number of quantities of interest (QoIs), corresponding to the surge at different geographic locations, is discussed. These QoIs may advocate conflicting decisions for the optimal MFMC implementation, and an efficient search for a compromise solution is introduced. The efficiency of the proposed MFMC framework is showcased by considering its application to different historical storms.

Data-driven turbulence modeling for fluid flow and heat transfer in peripheral subchannels of a rod bundle

This study presents a comparison of the performance of machine learning (ML) techniques, specifically multi-dimensional gene expression programming (MGEP), tensor basis neural network (TBNN), and also proposes a novel universally interpretable machine learning architecture to model the turbulent scalar flux (UIML-s) to enhance turbulence models for fluid flows at different Prandtl numbers in channels with complex shapes of walls in the channel cross section. In particular, peripheral subchannels of rod bundles are of primary interest. However, the accuracy of mean velocity and scalar distributions predicted by commonly used turbulence models still poses a challenge compared to data extracted from high-fidelity eddy-resolving numerical simulations, particularly for engineering applications involving complex geometry flows. In the present study, by utilizing an explicit algebraic expression for the nonlinear Reynolds-stress term obtained through both the evolutionary MGEP optimization and TBNN, the secondary flow structure has been adequately predicted in the cross-wise mean velocity distributions in the square duct and the rectangular channel with three longitudinal rods. This structure is also observed in the data from the concurrent runs performed by direct numerical simulation (DNS) but is completely absent in the results produced by a baseline Reynolds-averaged Navier–Stokes (RANS) closure, which employs the linear eddy viscosity model for the Reynolds stress tensor. Comparison of MGEP and TBNN has shown their nearly equal performance in a square duct flow; however, MGEP works better for the more complex geometry channel with three rods. Furthermore, based on the velocity field produced by the RANS-MGEP model, the ML modification of the gradient diffusion hypothesis, integrated into the aforementioned novel RANS-ML model called as UIML-s, significantly improves the mean scalar distributions in a flow with three bumps serving as a prototype for the peripheral subchannel of rod bundle. The normalized root mean squared error decreases from 13.5% to 7.6%, bringing the predicted distributions closer to the DNS data, particularly in the near-wall region. Another approach, MGEP-s, also yields the acceptable results, which are nearly identical to those from UIML-s. These findings highlight the potential of using data-driven calibration of turbulence models with nonlinear closures to enhance the predictability for RANS simulations of fluid flows, heat, and mass transfer in channels with complex geometry.

The Secular Dynamical Evolution of Binary Asteroid System (65803) Didymos Post-DART

The successful impact of NASA’s DART mission with Dimorphos, the secondary body of binary asteroid system (65803) Didymos, altered the attitude, shape, and orbit of Dimorphos. In addition to perturbing the immediate short-term dynamics of the system, these changes have major implications for the binary Yarkovsky–O’Keefe–Radzievskii–Paddack (BYORP) effect and resulting long-term secular evolution. In this study, we assess the range of possible reshaping-induced changes in BYORP. We produce high-fidelity numerical simulations of the orbit-attitude coupled tidal-BYORP dynamical evolution of the Didymos system to constrain how the secular evolution changed from its preimpact behavior. We find that the nature of the dynamics is highly dependent on a variety of initial conditions and assumptions, and it is difficult to fully predict how the system will secularly evolve following the impact. Rather, we provide a range of feasible possibilities within the bounds of observations and current best estimates of the Didymos system parameters. ESA’s Hera mission will help reduce uncertainties surrounding the postimpact state and shape of Dimorphos in 2027, allowing our predictions of the secular evolutionary effects and long-term fate of the Didymos system to be better refined.

Multi-fidelity reduced-order surrogate modelling

High-fidelity numerical simulations of partial differential equations (PDEs) given a restricted computational budget can significantly limit the number of parameter configurations considered and/or time window evaluated. Multi-fidelity surrogate modelling aims to leverage less accurate, lower-fidelity models that are computationally inexpensive in order to enhance predictive accuracy when high-fidelity data are scarce. However, low-fidelity models, while often displaying the qualitative solution behaviour, fail to accurately capture fine spatio-temporal and dynamic features of high-fidelity models. To address this shortcoming, we present a data-driven strategy that combines dimensionality reduction with multi-fidelity neural network surrogates. The key idea is to generate a spatial basis by applying proper orthogonal decomposition (POD) to high-fidelity solution snapshots, and approximate the dynamics of the reduced states—time-parameter-dependent expansion coefficients of the POD basis—using a multi-fidelity long short-term memory network. By mapping low-fidelity reduced states to their high-fidelity counterpart, the proposed reduced-order surrogate model enables the efficient recovery of full solution fields over time and parameter variations in a non-intrusive manner. The generality of this method is demonstrated by a collection of PDE problems where the low-fidelity model can be defined by coarser meshes and/or time stepping, as well as by misspecified physical features.

A bootstrap strategy to train, validate and test reduced order models of coupled geomechanical processes

This paper presents an approach for developing Reduced Order Models (ROMs) of complex geomechanical systems from high-fidelity numerical simulations. The approach uses proper orthogonal decomposition of the state variables for the model reduction, combined with long short-term memory neural networks to update the simulations in response to changing boundary conditions. A key challenge in creating these models is the need to efficiently sample the system state during training and testing. Too few (or poorly distributed) training simulations yields inaccurate models, while too many results in wasted effort. However, the exact number required is difficult to determine ahead of time, as it is both context and model dependent. Here, we present a bootstrap strategy to estimate the number of simulations required to develop the reduced order model. The strategy uses an iterative approach in conjunction with a quasi-random sampling technique to ensure consistent coverage of the sample space. We demonstrate the approach by developing a ROM describing the coupled hydro-mechanical response of a section of an open-cut coal mine. Excellent agreement is achieved between the resulting reduced order model and the original high-fidelity simulation for significantly less computational effort.

Numerical investigation on the viscoelastic polymer flow in material extrusion additive manufacturing

In this paper, high-fidelity direct numerical simulations (DNSs) are carried out to explore the behavior of viscoelastic polymer flow in direct ink writing (DIW) additive manufacturing. The impact of ink elasticity on the stress for the strand deposition process has been examined. The exponential Phan-Thien Tanner (PTT) model is used to characterize the shear-thinning viscoelastic rheological behavior of polymeric fluids, and the gas-polymer interface is captured by the volume of fluid (VOF) method. Focusing on the extrusion and deposition of polymer, the effects of ink elasticity represented by the Weissenberg number (Wi), normalized printing speed and gap height on the formation of the printed strand have been studied. The results indicate that the polymer flow out of the nozzle is quite stable with time, and the polymeric stress balance is disturbed in the middle of the printed strand. For high Wi, the strand has significant deformation and an obvious large head (the earliest printed bulge in the deposited strand), whereas a larger normalized printing speed leads to a large stress region. In particular, a small normalized gap height has a squeezing effect on the deposited strand, and a visible depression in the middle of the strand can be obtained for large heights. Additionally, the degree of polymer deformation exhibits a major monotonic dependence on the normalized printing speed and gap height. The nonlinear relationships between the maximum stress components and process parameters show that the maximum stress decreases with Wi, but different printing speeds and gap heights have almost no effect on the stress components in the build platform-normal direction. The above analysis contributes to the inference of the deformation effects in different flow states, which can provide guidance to DIW additive manufacturing.

Domain Decomposition Strategy for Combining Nonlinear and Linear Reduced-Order Models

Increasingly ubiquitous reliance on expensive, high-fidelity numerical simulations has led to the emergence of reduced-order modeling as an effective method to predict high-dimensional, spatially discretized field outputs in an efficient manner. This study presents a method to construct parametric, nonintrusive reduced-order models (ROMs) by leveraging domain decomposition (DD) to enable using multiple dimension reduction (DR) techniques within different spatial subdomains of the field to construct a single predictive ROM. To enforce smoothness of solutions across subdomains, points at domain interfaces are reconstructed using the data-driven gappy proper orthogonal decomposition method. The method is assessed on three high-dimensional test cases with shocks, including one with two-dimensional turbulent flow around the RAE2822. Results show that fully nonlinear ROMs predict fields more accurately in the vicinity of discontinuous features compared to their linear counterparts, which are more accurate in regions that are far from these discontinuities. Furthermore, irrespective of the DR method, DD-based ROMs outperformed their global counterparts in all test cases. Finally, models that used a combination of DR methods simultaneously showed superior performance near shockwaves and a significant improvement in total error compared to existing approaches that employ a single DR method in all subdomains.

An efficient deep learning-based workflow for CO2 plume imaging considering model uncertainties with distributed pressure and temperature measurements

Monitoring CO2 plumes throughout the operation of geologic CO2 sequestration projects is essential to environmental safety. The evolution of underground CO2 saturation can be predicted using high-fidelity numerical simulations. However, high-fidelity simulations can be prohibitively expensive to compute. As a result of recent developments in data-driven models, rapid predictions of the CO2 plume can now be made using readily available pressure and temperature measurements. This study presents a novel deep learning-based workflow for efficiently visualizing CO2 plumes in near real-time while considering their uncertainties.In our deep learning workflow, we visualize the CO2 plume images in the reservoir as a propagating saturation front, represented by ‘onset time’, using field measurements, including downhole pressure and temperature. At a given location, the ‘onset time’ is the calendar time when the CO2 saturation exceeds a certain threshold. Therefore, a single image of the CO2 front propagation is captured using the ‘onset time’ rather than storing multiple CO2 saturation images at different time steps. The use of ‘onset time’ significantly reduces memory and computational cost of deep learning-based framework, enabling large-scale field applications.We use a variational autoencoder-decoder (VAE) network to compress high dimensional ‘onset time’ images into low dimensional latent variables while considering uncertainties of the predicted images. The use of VAE and onset time, simplifies the overall neural network architecture and significantly enhances the training efficiency. To estimate the latent variables of the VAE network, we train a feed forward neural network model that incorporates available monitoring data such as downhole pressure and temperature measurements. The estimated latent variables are then fed into a trained decoder network to generate 3D onset time images, visualizing the propagation of CO2 plume in near real time.The proposed workflow is applied to both synthetic and field cases, where the field application is a large-scale geological carbon storage project in a carbonate reef reservoir in the Northern Niagaran Pinnacle Reef Trend in Michigan, USA. The field measurements include distributed temperature sensing (DTS) and distributed pressure responses from down-hole gauges along the monitoring well. The predicted CO2 plume images provided by the proposed workflow are shown to be consistent with the simulation results of traditional history matched model using genetic algorithm. Our deep learning-based framework can predict the CO2 front propagation in terms of onset time in seconds, making it well-suited for real time decision-making and operational optimization.

Supercritical combustion instabilities of oxygen/kerosene mixture in a swirl coaxial injector

This study focuses on the fundamental combustion instability issues in staged combustion for liquid oxygen/kerosene rocket engines. A comprehensive and high-fidelity numerical simulation is conducted based on the Peng-Robinson real gas model. This involves combining the large eddy simulation (LES) method with the eddy-dissipation-concept (EDC) model. This study focuses on both the mixing and combustion processes within the gas-centered liquid-swirl coaxial injector and the influence of the swirl number. The results show that the numerical method can accurately predict the change of thermodynamic properties at transcritical conditions. For the injector under investigation, the dominant frequencies of near-field pressure, axial velocity, and radial velocity all coincide at 1,066 Hz. This indicates that these three parameters are coupled, but no periodic fluctuation processes are evident for tangential velocity and temperature. The time-domain pressure characteristics at monitoring points in various directions are compared and analyzed. These comparisons reveal a robust coupling relationship between pressure and axial velocity. Additionally, the numerical simulation results show that the change of the swirl number conditions at the fuel inlet has negligible effect on the evolutionary characteristics of the injector. It is confirmed that tangential velocity is not the cause of pressure fluctuations.