Two-phase flows are ubiquitous in nature, exhibiting complex fluid-fluid interactions that challenges numerical simulators. To accurately and efficiently solve two-phase flows, grid-based methods have been widely adopted. Navier-Stokes (NS) solvers consume a small memory footprint, but simultaneously achieving both high performance and low numerical dissipation remains a significant challenge. In contrast, lattice Boltzmann solvers are efficient and have low numerical dissipation, yet they remain memoryintensive, even with state-of-the-art moment-encoding schemes. To date, the simultaneous attainment of high accuracy, exceptional efficiency, and a low memory footprint remains a major challenge in the field. In this paper, we propose a novel two-phase flow solver that achieves this objective. Our work is motivated by extending gas-kinetic scheme (GKS), which is adapted to handle nearly incompressible flows. To allow stable and accurate two-phase flow simulations, we systematically derive a coupled formulation of the GKS method and the phase-field model, incorporating novel mathematical constructs. Combined with robust boundary treatments and specialized techniques for handling turbulent flows, this results a unified framework capable of efficiently simulating two-phase flows, even those with large density contrasts and high Reynolds numbers. Since our formulation is explicit, it achieves exceptional performance when optimized on GPU, making it the fastest kinetic two-phase flow solver to date. Additionally, as it is derived from GKS, it obviates the need to store distribution functions. Thus, it has a small memory footprint, competitive with, or even lower than, that of many NS solvers. As a result, our solver can efficiently simulate complex two-phase flow dynamics at high resolutions using a single commodity GPU. We validate the accuracy of our solver via several benchmark tests, compare its performance with recent methods in various aspects, and demonstrate its capability to replicate a broad range of two-phase flow phenomena, encompassing both typical and large-scale scenarios.

Turbulent boundary layer represents one of the most complex but interesting phenomena in fluid flows. While the generation and alteration of sheared vortices in various interacting scales near the boundary are visually appealing, it is difficult to correctly replicate such phenomena by simulation, especially at high Reynolds numbers. Practical methodologies typically incorporate empirical wall modeling to substantially curtail the computational expenses while retaining physical consistency. Nevertheless, these are predominantly applicable to steady-state flow solvers. While complex scenarios involving dynamic fluid-solid interaction and its application to create time-dependent flow phenomena invariably necessitate unsteady flow solvers, the underlying wall modeling techniques are imprecise, leading to a different formation of near-wall vortices, especially for the highly efficient lattice Boltzmann solver operating on Cartesian grids. In this paper, we propose a novel hybrid near-wall model for the lattice Boltzmann solver, which can handle turbulent boundary layer flows in a simple and efficient manner, inspired by measuring the degree of boundary layer separation. Our model comprises both macroscopic and mesoscopic algebraic models, which collaborate to let the low dissipation lattice Boltzmann solver naturally form the turbulent boundary layer appropriately. By leveraging the multi-resolution technique, accurate simulation outcomes can be obtained. Our model is parameterized to approximate different physical attributes of the solid surface that can potentially influence the boundary layer distribution, and comparable boundary layer flow behaviors can be simulated at various grid resolutions. Rigorous benchmark tests are carried out to validate our model at different grid resolutions by comparing with experimental data and visualizations. We showcase the applications of our new model in both facilitating computational design and generating visual animations, accompanied by specific examples and comparisons with actual experimental setups and photographic images. All demonstrations affirm the physical consistency of our solver even when simulated with a relatively coarse grid resolution.

Simulating the coupled dynamics between rigid bodies and two-phase fluids, especially those with a large density ratio and a high Reynolds number, is computationally demanding but visually compelling with a broad range of applications. Traditional approaches that directly solve the Navier-Stokes equations often struggle to reproduce these flow phenomena due to stronger numerical diffusion, resulting in lower accuracy. While recent advancements in kinetic lattice Boltzmann methods for two-phase flows have notably enhanced efficiency and accuracy, challenges remain in correctly managing fluid-rigid boundaries, resulting in physically inconsistent results. In this article, we propose a novel kinetic framework for fluid-rigid interaction involving two fluid phases. Our approach leverages the idea of an overset grid, and proposes a novel formulation in the two-phase flow context with multiple improvements to handle complex scenarios and support moving multi-resolution domains with boundary layer control. These new contributions successfully resolve many issues inherent in previous methods and enable physically more consistent simulations of two-phase flow phenomena. We have conducted both quantitative and qualitative evaluations, compared our method to previous techniques, and validated its physical consistency through real-world experiments. Additionally, we demonstrate the versatility of our method across various scenarios.

Abstract Makeup transfer (MT) aims to transfer the makeup style from a given reference makeup face image to a source image while preserving face identity and background information. In recent years, MT has attracted the attention of many scholars, and it has a wide range of application prospects and research value. Since then, many methods have been proposed to accomplish MT, most of which are based on Generative Adversarial Network methods. A taxonomy of existing algorithms in the field of MT is first proposed. Then, evaluation methods are proposed, existing methods are analysed, and existing datasets are introduced. This paper finally discusses the current problems in the field of MT and the trend of future research.

Virtual wind tunnel testing is a key ingredient in the engineering design process for the automotive and aeronautical industries as well as for urban planning: through visualization and analysis of the simulation data, it helps optimize lift and drag coefficients, increase peak speed, detect high pressure zones, and reduce wind noise at low cost prior to manufacturing. In this paper, we develop an efficient and accurate virtual wind tunnel system based on recent contributions from both computer graphics and computational fluid dynamics in high-performance kinetic solvers. Running on one or multiple GPUs, our massively-parallel lattice Boltzmann model meets industry standards for accuracy and consistency while exceeding current mainstream industrial solutions in terms of efficiency --- especially for unsteady turbulent flow simulation at very high Reynolds number (on the order of 10 7 ) --- due to key contributions in improved collision modeling and boundary treatment, automatic construction of multiresolution grids for complex models, as well as performance optimization. We demonstrate the efficacy and reliability of our virtual wind tunnel testing facility through comparisons of our results to multiple benchmark tests, showing an increase in both accuracy and efficiency compared to state-of-the-art industrial solutions. We also illustrate the fine turbulence structures that our system can capture, indicating the relevance of our solver for both VFX and industrial product design.

Bionic underwater robots have demonstrated their superiority in many applications. Yet, training their intelligence for a variety of tasks that mimic the behavior of underwater creatures poses a number of challenges in practice, mainly due to lack of a large amount of available training data as well as the high cost in real physical environment. Alternatively, simulation has been considered as a viable and important tool for acquiring datasets in different environments, but it mostly targeted rigid and soft body systems. There is currently dearth of work for more complex fluid systems interacting with immersed solids that can be efficiently and accurately simulated for robot training purposes. In this paper, we propose a new platform called “FishGym”, which can be used to train fish-like underwater robots. The framework consists of a robotic fish modeling module using articulated body with skinning, a GPU-based high-performance localized two-way coupled fluid-structure interaction simulation module that handles both finite and infinitely large domains, as well as a reinforcement learning module. We leveraged existing training methods with adaptations to underwater fish-like robots and obtained learned control policies for multiple benchmark tasks. The training results are demonstrated with reasonable motion trajectories, with comparisons and analyses to empirical models as well as known real fish swimming behaviors to highlight the advantages of the proposed platform.

Simulating turbulent smoke flows with fine details is computationally intensive. For iterative editing or simply faster generation, efficiently upsampling a low-resolution numerical simulation is an attractive alternative. We propose a novel learning approach to the dynamic upsampling of smoke flows based on a training set of flows at coarse and fine resolutions. Our multiscale neural network turns an input coarse animation into a sparse linear combination of small velocity patches present in a precomputed over-complete dictionary. These sparse coefficients are then used to generate a high-resolution smoke animation sequence by blending the fine counterparts of the coarse patches. Our network is initially trained from a sequence of example simulations to both construct the dictionary of corresponding coarse and fine patches and allow for the fast evaluation of a sparse patch encoding of any coarse input. The resulting network provides an accurate upsampling when the coarse input simulation is well approximated by patches present in the training set (e.g., for re-simulation), or simply visually plausible upsampling when input and training sets differ significantly. We show a variety of examples to ascertain the strengths and limitations of our approach and offer comparisons to existing approaches to demonstrate its quality and effectiveness.

Predicting the fine and intricate details of a turbulent flow field in both space and time from a coarse input remains a major challenge despite the availability of modern machine learning tools. In this paper, we present a simple and effective dictionary-based approach to spatio-temporal upsampling of fluid simulation. We demonstrate that our neural network approach can reproduce the visual complexity of turbulent flows from spatially and temporally coarse velocity fields even when using a generic training set. Moreover, since our method generates finer spatial and/or temporal details through embarrassingly-parallel upsampling of small local patches, it can efficiently predict high-resolution turbulence details across a variety of grid resolutions. As a consequence, our method offers a whole range of applications varying from fluid flow upsampling to fluid data compression. We demonstrate the efficiency and generalizability of our method for synthesizing turbulent flows on a series of complex examples, highlighting dramatically better results in spatio-temporal upsampling and flow data compression than existing methods as assessed by both qualitative and quantitative comparisons.

Kinetic approaches, i.e., methods based on the lattice Boltzmann equations, have long been recognized as an appealing alternative for solving incompressible Navier-Stokes equations in computational fluid dynamics. However, such approaches have not been widely adopted in graphics mainly due to the underlying inaccuracy, instability and inflexibility. In this paper, we try to tackle these problems in order to make kinetic approaches practical for graphical applications. To achieve more accurate and stable simulations, we propose to employ the non-orthogonal central-moment-relaxation model, where we develop a novel adaptive relaxation method to retain both stability and accuracy in turbulent flows. To achieve flexibility, we propose a novel continuous-scale formulation that enables samples at arbitrary resolutions to easily communicate with each other in a more continuous sense and with loose geometrical constraints, which allows efficient and adaptive sample construction to better match the physical scale. Such a capability directly leads to an automatic sample construction which generates static and dynamic scales at initialization and during simulation, respectively. This effectively makes our method suitable for simulating turbulent flows with arbitrary geometrical boundaries. Our simulation results with applications to smoke animations show the benefits of our method, with comparisons for justification and verification.

In this paper we consider distributed detection in ultra-wideband(UWB) wireless sensor networks with asynchronous transmissions over frequency-selective channels. Three amplify-and-forward schemes with different requirements on channel state information (CSI) are investigated. Performances are studied and compared by using the large deviation principle and simulations.

In this paper, we study the effectiveness of the multipath energy combining for the coarse acquisition of UWB signals. The performances of different detectors, including single pulse correlator (SPC), energy detector (ED), and transmittedreference (TR), are derived and compared in terms of the mean acquisition time (MAT) and the false acquisition rate (FAR) using flow graph analysis in a two-step random search framework. The results show that both the ED and the TR can achieve a lower acquisition time than the SPC under the same FAR constraint due to their multipath energy combining capabilities. Simulation results also reveal that the ED scheme is more vulnerable to multiuser interference than the TR and the SPC schemes.

We consider multi-access control for the uplink in OFDMA networks. Assuming that subcarriers are grouped into clusters, we investigate multichannel random access based on local channel state information, and propose an opportunistic multichannel Aloha scheme to maximize the system throughput. A key step is to build a mapping from a user's channel state information to its transmission probability and channel allocation. For the sake of comparison, we also characterize the throughput corresponding to the optimal centralized scheduling by using the extreme-value theory of order statistics. We show that the opportunistic multichannel Aloha scheme is asymptotically order-optimal, in the sense that the only performance loss compared to the optimal centralized scheduling is due to the contention inherent in random access. In addition, we generalize the study to heterogeneous cases. Our findings show that when each user behaves as if it were in homogeneous systems, the proposed scheme can provide proportional fairness among the users.