arXiv subjects

Yaomin Zhao

Publications and source records attributed to Yaomin Zhao.

At least 19 recordsLinked to original sources

arXiv2026

Direct numerical simulation of particle-laden flow in a linear compressor cascade: Unsteady boundary-layer effects on blade erosion

We perform point-particle direct numerical simulations (PP-DNS) of particle-laden flow through a linear compressor cascade subjected to synthetic freestream turbulence. Monodisperse particles are advanced in a one-way coupled Eulerian-Lagrangian framework with drag-only dynamics. We quantify blade-particle collisions and resulting blade erosion based on high-fidelity data, and the erosion hotspots are predicted near the leading edge and over the pressure side. On the pressure side, for intermediate Stokes numbers, the onset of collisions correlates with elevated boundary-layer intermittency associated with bypass transition, whereas for larger particles impacts occur farther upstream with a higher probability of multiple rebounds. On the suction side, sparse collisions appear only for the smallest particles and are phase-modulated by separation-induced vortex shedding. Joint distributions of impact velocity and angle show that leading-edge impacts are faster and span wider angles than pressure-side impacts, explaining their greater erosive severity. The present results highlight the role of unsteady boundary-layer dynamics in affecting erosion in compressor cascades.

physics.flu-dyn↗

arXiv2026

Asymmetric particle transport in turbulent flows within concentric annular ducts

We present the first direct numerical simulations of particle-laden turbulent flow in concentric annuli to investigate the effects of transverse curvature over a range of Stokes numbers. The results demonstrate that transverse curvature induces asymmetric radial transport, with particles preferentially drifting toward the outer wall. Unlike canonical planar flows where turbophoresis universally drives near-wall accumulation, the present study identifies a distinct physical regime at the convex inner wall where centrifugal effect competes with turbophoresis. As a consequence, significant particle depletion is observed near the inner wall under strong curvature, and the transient concentration field exhibits a non-monotonic evolution, with the overshoot generally being more evident at higher Stokes numbers. By deriving a transport equation and applying Sturm-Liouville modal analysis, we identify the competition between asymmetric transport modes with different decay rates as the physical mechanism driving this non-monotonic evolution, and establish a reduced-order model that captures the dynamics of the particle concentration near the walls.

physics.flu-dyn↗

arXiv2026

Physics-guided curriculum learning for the identification of reaction-diffusion dynamics from partial observations

Reaction-diffusion (RD) systems provide fundamental models for understanding self-organized spatiotemporal patterns across natural and engineered settings, yet reliable parameter estimation remains challenging, particularly when observations are sparse, noisy, and restricted to a subset of state variables. We introduce CLIP (Curriculum Learning Identification via PINNs), a physics-guided framework built on physics-informed neural networks for joint parameter inference and hidden-state reconstruction under partial observability. Leveraging the physical separability of RD systems, the CLIP training progresses from reaction-dominated regimes to full spatiotemporal dynamics using curriculum learning and an anchored widening transfer strategy. Across three canonical reaction-diffusion benchmarks, CLIP achieves more accurate and robust identification than baseline methods. Furthermore, the CLIP framework is successfully applied to infer the dynamics of the Min system in bacteria, where only membrane-bound species are observed and key kinetic rates span multiple orders of magnitude. Ablation experiments and loss-landscape visualizations demonstrate that both the curriculum stages and the anchored transfer are essential for stable convergence.

physics.comp-ph↗

arXiv2026

Boundary layer transition induced by surface roughness distributed over a low-pressure turbine blade

Direct numerical simulations of a low-pressure turbine with roughness elements distributed over the blade surface have been performed. A series of fifteen cases with varying roughness heights and streamwise wavenumbers are introduced to present a systematic study of the effect of roughness on the various transition phenomena in the suction-side boundary layer. For cases with large roughness heights, the boundary layer is violently disturbed by the wake of rough elements in the leading edge (LE) region, and maintains the turbulent state over the whole blade suction-side. For cases with small roughness heights, however, the disturbances induced by the LE roughness are suppressed by the favourable pressure gradient in the downstream boundary layer, and the relaminarized flow does not undergo transition until the separation near the blade trailing edge (TE). Furthermore, the streamwise wavenumber of the distributed roughness plays an important role in cases with intermediate roughness height. Specifically, cases with larger streamwise slope show earlier transition induced by strong shear layer instability, which manages to suppress the mean flow separation near the TE region. Overall, the combined effect of several factors, including the geometric effect at the blade LE and TE, the complex pressure gradient distribution across the turbine vane, and the various roughness configurations, is responsible for the intriguing boundary layer behaviours in the present study.

physics.flu-dyn↗

arXiv2026

Progressive Mixture-of-Experts with autoencoder routing for continual RANS turbulence modelling

Developing Reynolds-averaged Navier-Stokes (RANS) turbulence models that remain accurate across diverse flow regimes is a long-standing challenge. In this work, we propose a novel framework, termed the progressive mixture-of-experts (PMoE), designed to enable continual learning for RANS turbulence modelling. The framework employs a modular autoencoder-based router to associate each flow scenario with a specialised turbulence model, referred to as an expert. When a new flow regime cannot be adequately represented by the existing router and expert set, a new expert together with its routing component can be introduced at low cost, without modifying or degrading previously trained ones, thereby naturally avoiding catastrophic forgetting. The framework is applied to a range of flows with distinct physical characteristics, including airfoil wake, channel, periodic hill, and square duct flows. The resulting PMoE model effectively integrates multiple experts and achieves improved predictive accuracy across both seen and unseen test cases that differ in operating conditions or configurations. Owing to sparse activation, model expansion does not incur additional computational cost during inference. The proposed framework therefore provides a scalable pathway towards lifelong-learning turbulence models for industrial computational fluid dynamics.

physics.flu-dyn↗

arXiv2026

Low-wavenumber wall pressure fluctuations in turbulent flows within concentric annular ducts

Compressible direct numerical simulations of turbulent channel flows in concentric annular ducts of height $2\delta$ are performed to study the low-wavenumber wall pressure fluctuations (WPF) over cylindrical walls at a bulk Mach number $M_b = 0.4$ and bulk Reynolds number $Re_b=3000$. The radius of the inner cylinder $R$ is varied between $0.2\delta$, $\delta$, $2\delta$ and $\infty$. As $R$ decreases, the one-point power spectral density of the WPF decreases at intermediate but increases at high frequencies. When $R$ decreases, the 1D (streamwise) wavenumber-frequency spectrum of the WPF decreases at high wavenumbers. At low wavenumbers, however, as $R$ reduces to $0.2\delta$ the 1D wavenumber-frequency spectrum exhibits multiple spectral peaks whose strengths increase with frequency. Examination of the 2D wavenumber-frequency spectra shows that these represent acoustic duct modes that closely match theoretical predictions. The acoustic modes of higher radial orders exhibit increasingly high amplitude on the inner than on the outer walls. The low-wavenumber components of the $0$th-order (azimuthal) 2D wavenumber-frequency spectrum are of great importance in practice, and their magnitude increases as $R$ reduces; this increase is increasingly pronounced at higher frequencies. Analytical modelling and numerical validation show that this increase appears to arise from the ``geometric'' effects connected with the Green's function, and they are generated mainly by radial and azimuthal disturbances. Disturbances closer to the wall are shown to be increasingly important in WPF generation as $R$ reduces, which highlights a potential in WPF control using wall treatment on thin cylinders.

physics.flu-dyn↗

arXiv2025

Constructing wall turbulence using hierarchical hairpin vortices

Wall-bounded turbulence is characterized by coherent, worm-like structures such as hairpin vortices. The attached-eddy model provides a successful statistical framework for the log-law region, yet the complex geometry and multiscale nature of wall-turbulence vortices remain challenging for physics-based modelling. Here, we model wall turbulence as an ensemble of complex vortices, introducing a systematic approach to constructing turbulence fields enriched with hierarchically organized hairpin vortex packets. The geometry and organization of the vortex packets are calibrated to match observations, enabling the model to reproduce both attached and detached motions through a height-dependent core-size variation. Our model successfully reproduces the key statistical and structural features of wall turbulence, matching direct numerical simulations of turbulent channel flow at friction Reynolds numbers from 1,000 to 10,000. More importantly, it also reveals new insights into the coherent structures, emphasizing the role of vortex geometry, packet organization, and hierarchy in setting the attached/detached balance, meandering streaks and inclination angles, superstructure alignment, and the overall partition of contributions. Moreover, the constructed channel turbulence rapidly transitions into fully developed turbulence in direct numerical simulation, demonstrating its physical self-consistency and practical utility for initializing high-fidelity simulations. This approach significantly reduces computational costs associated with turbulence development while providing a flexible framework for testing and advancing turbulence models based on vortex structures.

physics.flu-dyn↗

arXiv2025

A high-fidelity and efficient framework for point-particle direct numerical simulation based on multi-block overset grids

In this work, we present a high-fidelity and efficient point-particle direct numerical simulation framework based on a multi-block overset curvilinear grid system, enabling large-scale Lagrangian particle tracking in complex geometries with high-order accuracy and low computational cost. To handle the multi-domain topological challenges inherent in such configurations, we develop an efficient particle storage and redistribution framework leveraging overset grid techniques. In particular, two optimization strategies have been proposed for particle redistribution: one is an innovative inter-block mapping within overlapping zones, and the other is a fast search-locate algorithm based on particle velocity. Together, these approaches significantly reduce the particle tracking overhead, especially for particles passing through interfaces between overlapping grid blocks. Moreover, the accuracy and robustness of the present framework are rigorously validated through various cases, including massless particle trajectories, one- and two-way coupled simulations. Specifically, we demonstrate the framework's applicability to the direct numerical simulation of particle-laden flow in a linear compressor cascade at engine-relevant conditions, showcasing its capability to resolve complex particle dynamics in turbomachinery configurations with low computational costs.

physics.flu-dyn↗

arXiv2025

Quantum implicit representation of vortex filaments in turbulence

Entangled vortex filaments are essential to turbulence, serving as coherent structures that govern nonlinear fluid dynamics and support the reconstruction of fluid fields to reveal statistical properties. This study introduces an quantum implicit representation of vortex filaments in turbulence, employing a level-set method that models the filaments as the intersection of the real and imaginary zero iso-surfaces of a complex scalar field. Describing the fluid field via the scalar field offers distinct advantages in capturing complex structures, topological properties, and fluid dynamics, while opening new avenues for innovative solutions through quantum computing platforms. The representation is reformulated into an eigenvalue problem for Hermitian matrices, enabling the conversion of velocity fields into complex scalar fields that embed the vortex filaments. The resulting optimization is addressed using a variational quantum eigensolver, with Pauli operator truncation and deep learning techniques applied to improve efficiency and reduce noise. The proposed quantum framework achieves a near-linear time complexity and a exponential storage reduction while maintaining a balance of accuracy, robustness, and versatility, presenting a promising tool for turbulence analysis, vortex dynamics research, and machine learning dataset generation.

physics.flu-dyn↗

arXiv2025

Data-driven quantum Koopman method for simulating nonlinear dynamics

Quantum computation offers potential exponential speedups for simulating certain physical systems, but its application to nonlinear dynamics is inherently constrained by the requirement of unitary evolution. We propose the quantum Koopman method (QKM), a data-driven framework that bridges this gap through transforming nonlinear dynamics into linear unitary evolution in higher-dimensional observable spaces. Leveraging the Koopman operator theory to achieve a global linearization, our approach maps system states into a hierarchy of Hilbert spaces using a deep autoencoder. Within the linearized embedding spaces, the state representation is decomposed into modulus and phase components, and the evolution is governed by a set of unitary Koopman operators that act exclusively on the phase. These operators are constructed from diagonal Hamiltonians with coefficients learned from data, a structure designed for efficient implementation on quantum hardware. This architecture enables direct multi-step prediction, and the operator's computational complexity scales logarithmically with the observable space dimension. The QKM is validated across diverse nonlinear systems. Its predictions maintain relative errors below 6% for reaction-diffusion systems and shear flows, and capture key statistics in 2D turbulence. This work establishes a practical pathway for quantum-accelerated simulation of nonlinear phenomena, exploring a framework built on the synergy between deep learning for global linearization and quantum algorithms for unitary dynamics evolution.

quant-ph↗

arXiv2025

A compressible Reynolds-averaged mixing model considering turbulent entropy and heat flux

In typical nature and engineering scenarios, such as supernova explosion and inertial confinement fusion, mixing flows induced by hydrodynamics interfacial instabilities are essentially compressible. Despite their significance, accurate predictive tools for these compressible flows remain scarce. For engineering applications, the Reynolds-averaged Navier-Stokes (RANS) simulation stands out as the most practical approach due to its outstanding computational efficiency. However, the majority of RANS mixing studies reported have concentrated on incompressible scenarios, with quite limited attention given to compressible cases. Moreover, most of the existing RANS mixing models demonstrate significantly inaccurate predictions for compressible mixing flow. This study develops a novel compressible RANS mixing model by incorporating physical compressibility corrections into the incompressible K-L-y mixing transition model recently proposed by Xie et al. (J. Fluid Mech., 1002, A31, 2025). Specifically, taking the density-stratified Rayleigh-Taylor mixing flows as representative compressible cases, we firstly analyze the limitations of the existing model for compressible flows, based on high-fidelity data and local instability criteria. Subsequently, the equation of state for a perfect gas and the thermodynamic Gibbs relation are employed to derive comprehensive compressibility corrections. The crucial turbulent entropy and heat flux are integrated into the closure of the key turbulent mass flux term of the turbulent kinetic energy equation. These corrections enable the model to accurately depict compressible mixing flows. Systematic validations confirm the efficacy of the proposed modeling scheme. This study offers a promising strategy for modeling compressible mixing flows, paving the way for more accurate predictions in complex scenarios.

physics.flu-dyn↗

arXiv2025

Simulating fluid vortex interactions on a superconducting quantum processor

Vortex interactions are commonly observed in atmospheric turbulence, plasma dynamics, and collective behaviors in biological systems. However, accurately simulating these complex interactions is highly challenging due to the need to capture fine-scale details over extended timescales, which places computational burdens on traditional methods. In this study, we introduce a quantum vortex method, reformulating the Navier--Stokes (NS) equations within a quantum mechanical framework to enable the simulation of multi-vortex interactions on a quantum computer. We construct the effective Hamiltonian for the vortex system and implement a spatiotemporal evolution circuit to simulate its dynamics over prolonged periods. By leveraging eight qubits on a superconducting quantum processor with gate fidelities of 99.97\% for single-qubit gates and 99.76\% for two-qubit gates, we successfully reproduce natural vortex interactions. This method bridges classical fluid dynamics and quantum computing, offering a novel computational platform for studying vortex dynamics. Our results demonstrate the potential of quantum computing to tackle longstanding challenges in fluid dynamics and broaden applications across both natural and engineering systems.

quant-ph↗

arXiv2025

Quantum lattice Boltzmann method for simulating nonlinear fluid dynamics

Quantum computing holds great promise to accelerate scientific computations in fluid dynamics and other classical physical systems. While various quantum algorithms have been proposed for linear flows, developing quantum algorithms for nonlinear problems remains a significant challenge. We introduce a novel node-level ensemble description of lattice gas for simulating nonlinear fluid dynamics on a quantum computer. This approach combines the advantages of the lattice Boltzmann method, which offers low-dimensional representation, and lattice gas cellular automata, which provide linear collision treatment. Building on this framework, we propose a quantum lattice Boltzmann method that relies on linear operations with medium dimensionality. We validated the algorithm through comprehensive simulations of benchmark cases, including vortex-pair merging and decaying turbulence on $2048^2$ computational grid points. The results demonstrate remarkable agreement with direct numerical simulation, effectively capturing the essential nonlinear mechanisms of fluid dynamics. This work offers valuable insights into developing quantum algorithms for other nonlinear problems, and potentially advances the application of quantum computing across various transport phenomena in engineering.

physics.flu-dyn↗

arXiv2023

A local intermittency based Reynolds-averaged transition model for turbulent mixing induced by interfacial instabilities

Accurate prediction of mixing transition induced by interfacial instabilities is vital for engineering applications, but has remained a great challenge for decades. For engineering practices, Reynolds-averaged Navier-Stokes simulation (RANS) is the most viable method. However, existing RANS models for mixing problems are mostly designed for fully developed turbulence, failing to depict the locally spatio-temporal-dependent characteristic of transition. In the present study, the idea of the intermittent factor (denoted as $γ$), which has been widely used in boundary layer transition in aerospace engineering, is extended to the mixing problems. Specifically, a transport equation for $γ$ is built based on local flow variables, which is used to describe the locally spatio-temporal-dependent characteristic of transition. Furthermore, $γ$ is coupled into the widely used K-L turbulent mixing model to constrain the two key product sources terms that dominate the evolution of mixing, i.e. the Reynolds stress and the buoyancy effect. Subsequently, the simulations of two reshocked Richtmyer-Meshkov mixing cases with remarkable transition effects confirm that the proposed model has a good performance for predicting mixing transition. To the best of our knowledge, it is the first study that an extra transport equation for intermittent factor has been proposed for a RANS mixing transition model. More importantly, the present modeling framework is flexible and has the potential to be applied to other RANS models. It provides a promising strategy for more advanced modeling for mixing transition.

physics.flu-dyn↗

arXiv2023

Turbulence Model Development based on a Novel Method Combining Gene Expression Programming with an Artificial Neural Network

Data-driven methods are widely used to develop physical models, but there still exist limitations that affect their performance, generalizability and robustness. By combining gene expression programming (GEP) with artificial neural network (ANN), we propose a novel method for symbolic regression called the gene expression programming neural network (GEPNN). In this method, candidate expressions generated by evolutionary algorithms are transformed between the GEP and ANN structures during training iterations, and efficient and robust convergence to accurate models is achieved by combining the GEP's global searching and the ANN's gradient optimization capabilities. In addition, sparsity-enhancing strategies have been introduced to GEPNN to improve the interpretability of the trained models. The GEPNN method has been tested for finding different physical laws, showing improved convergence to models with precise coefficients. Furthermore, for large-eddy simulation of turbulence, the subgrid-scale stress model trained by GEPNN significantly improves the prediction of turbulence statistics and flow structures over traditional models, showing advantages compared to the existing GEP and ANN methods in both a priori and a posteriori tests.

physics.comp-ph↗

arXiv2022

Gradient Information and Regularization for Gene Expression Programming to Develop Data-Driven Physics Closure Models

Learning accurate numerical constants when developing algebraic models is a known challenge for evolutionary algorithms, such as Gene Expression Programming (GEP). This paper introduces the concept of adaptive symbols to the GEP framework by Weatheritt and Sandberg (2016) to develop advanced physics closure models. Adaptive symbols utilize gradient information to learn locally optimal numerical constants during model training, for which we investigate two types of nonlinear optimization algorithms. The second contribution of this work is implementing two regularization techniques to incentivize the development of implementable and interpretable closure models. We apply $L_2$ regularization to ensure small magnitude numerical constants and devise a novel complexity metric that supports the development of low complexity models via custom symbol complexities and multi-objective optimization. This extended framework is employed to four use cases, namely rediscovering Sutherland's viscosity law, developing laminar flame speed combustion models and training two types of fluid dynamics turbulence models. The model prediction accuracy and the convergence speed of training are improved significantly across all of the more and less complex use cases, respectively. The two regularization methods are essential for developing implementable closure models and we demonstrate that the developed turbulence models substantially improve simulations over state-of-the-art models.

physics.comp-ph↗

arXiv2021

Multi-Objective CFD-Driven Development of Coupled Turbulence Closure Models

This paper introduces two novel concepts in data-driven turbulence modeling that enable the simultaneous development of multiple closure models and the training towards multiple objectives. The concepts extend the evolutionary framework by Weatheritt and Sandberg (2016), which derives interpretable and implementation-ready expressions from high-fidelity simulation data. By assigning a shared fitness value to the evolved closure models and utilizing the CFD-driven training approach by Zhao et al. (2020), the multi-expression training concept introduced here is able to account for the coupling between the trained models, i.e. Reynolds stress anisotropy, turbulent heat flux and turbulence production correction models. As a second concept, a multi-objective optimization algorithm is applied to the framework. The extension yields a diverse set of candidate models and allows a trade-off between the training objectives after analyzing the training results. In this study, the novel concepts are applied to a benchmark periodic hills case and a vertical natural convection flow. The predictions of mean flow quantities are improved compared to decoupled training strategies with distinct and robust improvements for strongly coupled momentum and thermal fields. The coupled training of closure models and the balancing of multiple training objectives are considered important capabilities on the path towards generalized data-driven turbulence models.

physics.flu-dyn↗

arXiv2020

RANS Turbulence Model Development using CFD-Driven Machine Learning

This paper presents a novel CFD-driven machine learning framework to develop Reynolds-averaged Navier-Stokes (RANS) models. The CFD-driven training is an extension of the gene expression programming method (Weatheritt and Sandberg, 2016), but crucially the fitness of candidate models is now evaluated by running RANS calculations in an integrated way, rather than using an algebraic function. Unlike other data-driven methods that fit the Reynolds stresses of trained models to high-fidelity data, the cost function for the CFD-driven training can be defined based on any flow feature from the CFD results. This extends the applicability of the method especially when the training data is limited. Furthermore, the resulting model, which is the one providing the most accurate CFD results at the end of the training, inherently shows good performance in RANS calculations. To demonstrate the potential of this new method, the CFD-driven machine learning approach is applied to model development for wake mixing in turbomachines. A new model is trained based on a high-pressure turbine case and then tested for three additional cases, all representative of modern turbine nozzles. Despite the geometric configurations and operating conditions being different among the cases, the predicted wake mixing profiles are significantly improved in all of these a posteriori tests. Moreover, the model equation is explicitly given and available for analysis, thus it could be deduced that the enhanced wake prediction is predominantly due to the extra diffusion introduced by the CFD-driven model.

physics.flu-dyn↗