High-fidelity Numerical Simulations Research Articles

Neural Impedance Boundary (NeIB): a neural-network based framework for acoustic surface impedance estimation utilizing sparse measurement data

Amidst recent advancements in the 3D digital representation that have significantly enhanced the modeling of geometric attributes of pre-existing environments, accurate estimation of acoustic boundary conditions remains a complex challenge. This paper presents a novel way to determine what we refer to as a neural boundary field, using physics-informed neural networks (PINN). The aim is to estimate the surface impedance measured in-situ by utilizing several points of sound field pressure. This approach couples the Helmholtz equation with automatic differentiation in the PINN framework to estimate accurately the surface impedance using a hybrid modeling approach where measurement data and domain knowledge in the form of equations for the acoustic waves are combined. As a proof-of-concept, we train the neural networks using 2D sound field data obtained from high-fidelity numerical acoustical simulations that incorporate actual surface materials parameters. We discuss the measurement techniques associated with this method and outline our vision for its application to 3D scene reconstructions in the future.

Discovering an interpretable mathematical expression for a full wind-turbine wake with artificial intelligence enhanced symbolic regression

The rapid expansion of wind power worldwide underscores the critical significance of engineering-focused analytical wake models in both the design and operation of wind farms. These theoretically derived analytical wake models have limited predictive capabilities, particularly in the near-wake region close to the turbine rotor, due to assumptions that do not hold. Knowledge discovery methods can bridge these gaps by extracting insights, adjusting for theoretical assumptions, and developing accurate models for physical processes. In this study, we introduce a genetic symbolic regression (SR) algorithm to discover an interpretable mathematical expression for the mean velocity deficit throughout the wake, a previously unavailable insight. By incorporating a double Gaussian distribution into the SR algorithm as domain knowledge and designing a hierarchical equation structure, the search space is reduced, thus efficiently finding a concise, physically informed, and robust wake model. The proposed mathematical expression (equation) can predict the wake velocity deficit at any location in the full-wake region with high precision and stability. The model's effectiveness and practicality are validated through experimental data and high-fidelity numerical simulations.

A New Criterion for the Splashing of a Droplet on Dry Surface from High-Fidelity Simulations

In this study, a new criterion for the splashing of a droplet on a dry smooth surface is established from high-fidelity numerical simulations. The new criterion involves the Weber number, Reynolds number and contact angle. A new splashing mode, termed spreading splashing, is proposed, which predominates for contact angles below 120 degrees. For contact angles above 120 degrees, prompt splashing dominates. For contact angles above 90 degrees, there exists a critical Weber number of around 60, below which splashing does not occur.

Synthetic ground motions in heterogeneous geologies from various sources: the HEMEWS-3D database

Abstract. The ever-improving performances of physics-based simulations and the rapid developments of deep learning are offering new perspectives to study earthquake-induced ground motion. Due to the large amount of data required to train deep neural networks, applications have so far been limited to recorded data or two-dimensional (2D) simulations. To bridge the gap between deep learning and high-fidelity numerical simulations, this work introduces a new database of physics-based earthquake simulations. The HEterogeneous Materials and Elastic Waves with Source variability in 3D (HEMEWS-3D) database comprises 30 000 simulations of elastic wave propagation in 3D geological domains. Each domain is parametrized by a different geological model built from a random arrangement of layers augmented by random fields that represent heterogeneities. Elastic waves originate from a randomly located pointwise source parametrized by a random moment tensor. For each simulation, ground motion is synthesized at the surface by a grid of virtual sensors. The high frequency of waveforms (fmax⁡=5 Hz) allows for extensive analyses of surface ground motion. Existing and foreseen applications range from statistical analyses of the ground motion variability and machine learning methods on geological models to deep-learning-based predictions of ground motion that depend on 3D heterogeneous geologies and source properties. Data are available at https://doi.org/10.57745/LAI6YU (Lehmann, 2023).

Unveiling the layer-wise dynamics of defect evolution in laser powder bed fusion: Insights for in-situ monitoring and control

Lack of fusion (LOF) defects can significantly affect the mechanical properties of components manufactured by laser powder bed fusion (LPBF). The layer-wise evolution of LOF defects is complex and not yet thoroughly understood. This work explores the spatiotemporal variations of LOF defects under various conditions using in-situ monitoring, ex-situ surface topography and μ-CT porosity characterization, and high-fidelity multi-physics numerical simulation. The results show that LOF defects could exhibit typical self-healing characteristics for over five printing layers. The specific self-healing behaviors depend on the initial sizes of the defects and the LPBF process conditions. The evolution of LOF defects can be detected by in-situ monitoring using light intensity. However, the in-situ monitoring may miss detecting LOF defects buried below the healed layers, which were alternatively observed via μ-CT. For the defective area with a depth of 150 μm, the relative density increased from 61.7% to 95.7% for the first to the fifth printing layer. The optimization of process parameters demonstrated that the application of a 45° scanning angle could significantly enhance surface flatness and repair internal pores to a minimum of 36.0 μm. The findings highlight the ability of in-situ monitoring in detecting LOF defects and the potential of the controlled printing process to accelerate defect repair. These outcomes offer valuable insights for the industrial applications of in-situ monitoring and control during LPBF.

Adaptive formulation for probabilistic storm surge predictions through sharing of numerical simulation results across storm advisories

As a tropical storm/cyclone approaches landfall, real-time probabilistic predictions for the anticipated surge provide valuable information for emergency preparedness/response decisions. These probabilistic predictions are made through an uncertainty quantification process that involves: (i) generating a sufficiently large ensemble of storm scenarios based on the nominal storm advisory and the anticipated forecast errors; (ii) performing high-fidelity numerical simulations to obtain surge predictions for each storm scenario; and (iii) estimating surge statistics of interest by assembling the simulation results. This process is repeated whenever the nominal storm advisory is updated. The number of storm scenarios utilized in the analysis directly impacts the statistical accuracy of the probabilistic predictions; a larger ensemble improves accuracy but requires greater computational resources to provide predictions with the desired expediency to guide real-time decisions. This paper revisits two recently proposed Monte-Carlo (MC) frameworks that aim to improve accuracy without increasing the computational burden: adaptive importance sampling (AIS) and adaptive multi-fidelity Monte Carlo (AMFMC). The foundational concept behind them is similar: share numerical simulation results across the probabilistic predictions performed for different storm advisories to accelerate the MC estimation. This is achieved differently for each approach, through adaptive development of an importance sampling proposal density (for AIS) or a surrogate model (for AMFMC). Here, a direct comparison between these frameworks is established, focusing on the mechanisms for the information sharing and the challenges encountered in tuning the algorithm adaptive characteristics to provide probabilistic estimates across a large number of quantities of interest (QoIs), corresponding to the surge predictions for different locations within the coastal region of interest. As this large number results in conflicting choices for the adaptive characteristics, a compromise solution needs to be promoted. The efficacy of the two frameworks is examined in detail in this setting, comparing the accuracy of idealized implementations (adaptive decisions independently made for each QoI) to the accuracy of practical implementations (single, compromise decision within the MC implementation). The study also showcases the importance of information sharing across storm advisories in real-time probabilistic storm surge predictions and provides guidelines for an efficient adaptive MC formulation in such settings.

Impact of coagulation characteristics on the aggregation of microplastics in upper-ocean turbulence

The dynamics and aggregation of microplastics in marine environments are investigated through high-fidelity direct numerical simulations with Lagrangian point-particle tracking. The properties of microplastics and biogenic particles, including size, density, and concentration, align with scenarios typical of seawater systems. The stickiness nature of microplastics, induced by biofilm formation (biofouling), is modeled through coagulation efficiency (stickiness parameter), which represents the probability of aggregation following a collision event. Two main aspects are at the core of the present work: analyzing the mechanisms of collision and coalescence between microplastics and biogenic particles, along with their spatial distribution, and characterizing the emerging aggregates. The results indicate that particles stickiness, concentration and (especially) size impact on the collision and coalescence rates. Furthermore, microplastics exhibit a strong tendency to accumulate near biogenic particles, leading to the creation of hetero-aggregates whose tendency to sink supports the general hypothesis of “missing microplastics”. Particularly, in cases where microplastics and biogenic particles are evenly concentrated, microplastics primarily contribute to the formation of aggregates. The stickiness mainly determines the most complex and large aggregates, which are less than 1% of the total.

Comparison of Hybrid Machine Learning Approaches for Surrogate Modeling Part Shrinkage in Injection Molding.

Machine learning (ML) methods present a valuable opportunity for modeling the non-linear behavior of the injection molding process. They have the potential to predict how various process and material parameters affect the quality of the resulting parts. However, the dynamic nature of the injection molding process and the challenges associated with collecting process data remain significant obstacles for the application of ML methods. To address this, within this study, hybrid approaches are compared that combine process data with additional process knowledge, such as constitutive equations and high-fidelity numerical simulations. The hybrid modeling approaches include feature learning, fine-tuning, delta-modeling, preprocessing, and using physical constraints, as well as combinations of the individual approaches. To train and validate the hybrid models, both the experimental and simulated shrinkage data of an injection-molded part are utilized. While all hybrid approaches outperform the purely data-based model, the fine-tuning approach yields the best result in the simulation setting. The combination of calibrating a physical model (feature learning) and incorporating it implicitly into the training process (physical constraints) outperforms the other approaches in the experimental setting.

On the impact of operating condition and testing environment on the noise sources in an industrial engine cooling fan

Engine cooling fan noise is a relevant issue for manufacturers. It is well known that both the operating point and testing environment can affect the noise generation mechanisms and, consequently, the measured noise may change. Therefore, these aspects are investigated on a reference industrial fan, for which experimental data exists, using high-fidelity numerical simulations based on the lattice-Boltzmann method. Two operating conditions, namely the free blowing and the maximum efficiency ones, and three testing environments are analyzed: (i) a conventional semi-anechoic room, (ii) an ideal free field environment, and (iii) a testing environment resembling an anechoic aeraulic facility. For cases (i) and (ii) no pressure difference across the fan is imposed, while, for case (iii), a pressure difference across the fan can be imposed. For the latter, the impact of a fully reflective and fully absorbing wall separating the two regions upstream and downstream of the fan is analyzed. At free blowing conditions, the flow over the blades is largely separated. When the blade passes through a blockage region, because of the presence of a honeycomb-like structure needed for structural purposes, it experiences a prominent loading hump. The far-field noise, at a listener located along the axis of rotation, is therefore highly tonal, with a clear peak at the blade passing frequency tone. When the same fan is tested in a free field environment, it is found that there is a difference in the acoustic pressure at higher harmonics of the blade passing frequency due to the presence of flow recirculations in the anechoic room. Placing a thin wall across the fan increases the mass flow rate, for a given rotational speed, which results in a more severe flow separation over the blades and, therefore a higher tone prominence at the blade passing frequency. If the thin wall is modeled as a sound-absorbing wall, there is a drop of the overall sound pressure level of about 2 dBA. When the fan is tested at its maximum efficiency, i.e., nonzero pressure difference across the fan, it is found that the blockage effect is less relevant. The main noise generation mechanism is the back-flow vortex induced by the pressure difference across the fan interacting with the blade tip leading edge.

High-fidelity numerical simulation of longitudinal thermoacoustic instability in a high-pressure subscale rocket combustor

A detailed analysis of thermoacoustic combustion instability in a subscale rocket combustor at high pressure is reported in this paper using the high-fidelity large-eddy simulation (LES). Self-sustained longitudinal combustion instability is observed in the experiments for this combustor configuration. The computational setup follows the past experimental study of a rig called the Continuously Variable Resonance Combustor (CVRC). In this combustor, both stable combustion and unstable combustion dynamics have been observed by varying the oxidizer injector length. A combustor configuration with an oxidizer injector length of 12 cm is chosen for this study based on the experimental evidence of longitudinal combustion instability. An autonomous meshing using the modified cut-cell Cartesian grid generation approach, coupled with on-the-fly Adaptive Mesh Refinement (AMR) is employed in this study. Chemical reactions during turbulent combustion in this configuration are modeled by solving the species transport equations with a detailed chemistry solver using a kinetic mechanism with 21 species and 84 steps. Similar to the findings of the experiments, we observe a self-sustained combustion instability in the present study, which is characterized by a limit cycle behavior of the acoustic fluctuations. The spectral analysis of these acoustic fluctuations shows good agreement with experimental data for the frequency of the three dominant modes. We further analyze the features of time-averaged and instantaneous reacting flow to study the effects of combustion instability on the flame holding dynamics, vortex shedding, mixing, and combustion regime due to flame movement along the longitudinal direction of the combustor during a limit cycle. These phenomena are effectively captured through the integration of AMR with complex chemistry in the present study. A particular focus of the study is on understanding the role of minor species (OH, HO2, and CH2O) in the physical and state-space in sustaining the flame during the combustion instability. Additionally, the physical mechanisms responsible for the production and dissipation of enstrophy are examined to demonstrate that their contribution can create significant fluctuations in the reacting flow field, which can assist in sustaining the combustion instability.

Influence of bioinspired morphing on the flow field characteristics of UAV wings at low Reynolds number

The aerodynamic performance of unmanned aerial vehicles (UAV) can be improved by optimizing the surface flow characteristics over a wide range of angle of attack (AoA) through novel mechanisms. Recently, the bioinspired camber morphing concept has received greater attention because of the proven ability of nature species towards the retention of aerodynamic performance under different environmental conditions. In particular, birds like Eagles ( Accipitriformes) increase their wing camber in the course of flight to achieve maximum climbing altitude with good manoeuvring capability. The biomimetic designs such as the corrugated bone structure of Eel fish ( Anguilliformes) helps to achieve the wing camber morphing with optimal aerodynamic load distributions. The present work is focused on the bioinspired variable camber morphing (VCM) strategy to enhance the flow control behaviour and aerodynamic forces for a specific UAV wing configuration at various AoA. Here, NACA 4412 airfoil is used as a baseline wing configuration and the camber morphing mechanisms which are derived through Eel fish and Eagle are analysed. The model with Eagle wing morphing (EWM) mechanism is considered as a primary case of VCM and Eel fish’s corrugated structure is taken as a secondary case of VCM model. The coefficient of lift ( C L ), coefficient of drag ( C D), coefficient of pressure ( C p) and endurance factor are estimated for both morphed and baseline wing configurations through high fidelity numerical simulations. Interestingly, it is observed that the EWM wing configuration has excellent surface flow control characteristics than the CSM wing configuration and the results are presented with a detailed discussion.

The starting vortices generated by bodies with sharp and straight edges in a viscous fluid

A two-dimensional body that moves suddenly in a viscous fluid can instantly generate vortices at its sharp edges. Recent work using inviscid flow theory, based on the Birkhoff–Rott equation and the Kutta condition, predicts that the ‘starting vortices’ generated by the sharp and straight edges of a body – i.e. the vortices formed immediately after motion begins – can be one of three distinct self-similar types. We explore the existence of these starting vortices for a flat plate and two symmetric Joukowski aerofoils immersed in a viscous fluid, using high-fidelity direct numerical simulations (DNS) of the Navier–Stokes equations. A lattice Green's function method is employed and simulations are performed for chord Reynolds numbers ranging from 5040 to 45 255. Vortices generated at the leading and trailing edges of the flat plate show agreement with the derived inviscid theory, for which a detailed assessment is reported. Agreement is also observed for the two symmetric Joukowski aerofoils, demonstrating the utility of the inviscid theory for arbitrary bodies. While this inviscid theory predicts an abrupt transition between the starting-vortex types, DNS shows a smooth transition. This behaviour occurs for all Reynolds numbers and is related to finite-time effects – there is a maximal time for which the (self-similar) starting vortices exist. We confirm the inviscid prediction that the leading-edge starting vortex of a flat plate can be suppressed dynamically. This has implications for the performance of low-speed aircraft such as model aeroplanes, micro air vehicles and unmanned air vehicles.

Numerical simulation of wind turbine wake characteristics by flux reconstruction method

In the paper, a CFD method that employs the flux reconstruction (FR) method and the actuator line method (ALM) is proposed, to develop a novel numerical framework to improve the efficiency and accuracy of turbulent wake predictions of horizontal axis wind turbines (HAWT). The PyFR solver, which is an open-source program that uses the FR method is applied. The ALM is for the first time implemented in the FR solver to simulate HAWTs. To increase the fidelity of the wake prediction, the nacelle and tower of the HAWT are also modeled, and the proposed numerical simulation method is named as AL-PyFR(N&T) model. The NTNU “Blind Test 1” wind turbine is used to validate the proposed model. First, the mesh and time step dependence of the AL-PyFR(N&T) model are verified. Second, the accuracy of the proposed model is verified by comparing the velocity deficits and turbulent kinetic energy results at different downstream locations with the measured values and the results from other CFD studies. By comparison of the results with and without the effect of the nacelle and tower, it is found that tower wake plays a significant role in wake asymmetry. Crucially, the AL-PyFR(N&T) model demonstrates a capacity for accurate wake predictions with fewer mesh requirements compared to conventional CFD models. This efficiency marks a significant advancement for high-fidelity numerical simulations of offshore wind farms.

Investigations on the shock wave induced by collapse of a toroidal bubble.

When bubbles collapse near a wall, they typically experience an asymmetric deformation. This collapse leads to the creation of a jet that strikes the bubble interface, causing the formation of a toroidal bubble and the subsequent release of a water-hammer shock. In this study, we present a systematic analysis of the collapse of a toroidal bubble in an open field or adjacent to a flat wall using high-fidelity numerical simulation. To maintain the sharpness of the interface, we employ the interface compression technique and the boundary variation diminishing approach within the two-phase model. Our findings demonstrate that shock waves emitted from the toroidal bubble consistently propagate toward the central axis of the torus, resulting in significant pressure shocks along the axis, similar to the water-hammer shock formed during the collapse of a spherical bubble. In contrast, weak pressure waves are generated in the transverse directions, leading to relatively weaker pressure peaks. Furthermore, the wall-pressure peak induced by the toroidal bubble is approximately three times higher than that induced by the spherical bubble. Based on the directional characteristics of pressure wave propagation from collapsing toroidal bubbles, toroidal-shaped pressure vessels can be designed as buoyancy materials for deep submersibles. This design enables the focused release of energy in a specific direction, effectively minimizing the destructive chain reaction caused by the implosion.

Statistical analysis of microstructurally small fatigue crack growth in three-dimensional polycrystals based on high-fidelity numerical simulations

Microstructurally small cracks (MSCs) are sensitive to their local microstructural neighborhoods, resulting in highly variable 3D crack propagation within individual samples and among a population of samples. Statistically quantifying this complex spatiotemporal variability requires collecting and analyzing a large number of 3D MSC growth observations, which remains challenging due to experimental constraints that limit the availability of 3D observations of MSCs in the existing literature. We address this gap by leveraging virtual observations of MSC growth using a state-of-the-art, high-fidelity simulation framework to provide a statistical perspective on 3D MSC growth behavior. The MSC growth simulations were performed in 40 unique but statistically similar polycrystalline microstructures, and the data extracted from the virtual observations were collectively analyzed to quantify variability in geometric, microstructural, and micromechanical aspects of MSCs. Variability in tortuosity, crack surface crystallography, and MSC growth parameters (local crack extension and kink angle) were quantified statistically. The results show that 3D MSC surfaces do not necessarily propagate along {111} crystallographic planes despite crack traces on radial planes being closely aligned with {111} slip plane traces. Also, the 3D MSC surfaces exhibit increasingly tortuous propagation and spatially varying local crack growth rates (with variability as high as 59% among the population). Moreover, a correlation study revealed some of the highly influential microstructural and micromechanical features controlling MSC growth parameters. The quantified variability holds direct implications for advanced materials prognosis in engineering applications, and the identified features from the correlation study can be leveraged to train machine learning models for rapidly predicting MSC growth behavior in the future.

Numerical investigation on the enhanced heat transfer characteristics of non-Newtonian viscoelastic fluids in microchannel with chip arrays

Elastic turbulence is an efficient heat transfer method at microscale. In this work, we conduct a systematic investigation on the flow and heat transfer performances of viscoelastic polymer solutions in microchannels with chip arrays by means of high-fidelity numerical simulations. Focusing on the effects of fluid elasticity [i.e. polymer relaxation time (λ)] and flow Reynolds number (Re), we find that the synergistic interplay between inertial and elastic forces is strategically exploited to amplify convection, thereby facilitating an enhanced heat transfer performance. For highly elastic fluid, the Nusselt number (Nu) can be improved by 76.13 % compared to that of less elastic fluid, while the pressure drop is reduced appropriately. In addition, as the fluid elasticity increases, the pressure drop decreases and eventually converges to a constant. In the zone of elastic turbulence, the fluid reinforces the convective heat transfer, which can be ascribe to the deformation of long-chain polymer molecules. Particularly, we consider the effect of temperature-dependent λ on the heat transfer performance. Our results demonstrate that the increased temperature-dependent λ decreases the value of Nu, and the variation between values of variable and constant λ is reduced with increasing Re or λ. Hence, in some cases of large fluid elasticity or high Re, it is possible to safely ignore the impact of temperature-dependent λ with an acceptable accuracy. The present work provides important insights into the enhanced heat transfer via elastic turbulence, which could guide the applications in the field of chip cooling.

Multi-Scale Flow Estimation Within Stereoscopic PIV Processing Based On Deep Learning

Classical methods of particle-image velocimetry (PIV) based on state-of-the-art cross-correlation algorithms constitute a spatial filtering operation that leads to a loss of spatial resolution of the estimated velocity fields. This is associated with numerous limitations, e.g., when it comes to aligning high-fidelity numerical simulations, since the detection of small turbulent scales is often precluded. An optical flow neural network adapted for PIV processing, called RAFT-PIV, can overcome the drawback of reduced spatial resolution while maintaining the reliability and robustness of the classical approach. The present work aims to extend the application of the aforementioned deep learning based method by integrating it within a stereoscopic PIV processing workflow. Experimental data from an optically accessible single-cylinder combustion engine the flow field of which is characterized by complex, three-dimensional flow structures and a broad range turbulent scales are processed using RAFT-PIV. The 2D-3C vector fields obtained from stereoscopic reconstruction are compared against a classical cross-correlation based PIV evaluation, providing insight into instantaneous quantities and turbulent structures. A qualitative comparison of the instantaneous in-plane and out-of-plane velocity components reveals that the reconstructed flow obtained from the deep learning based estimation includes a wide range of resolved turbulent length scales. It outperforms the classical method. Furthermore, the findings of the present study demonstrate that the pixel accuracy has an effect on both physical quantities and turbulence characteristics. In particular, an increase in turbulent kinetic energy is observed across the engine cycles of the investigated engine flow states. Moreover, a higher fraction of three-dimensional isotropic turbulent structures is derived from RAFT-PIV compared to the results from the cross-correlation based technique.

Flame stabilization and pollutant emissions of turbulent ammonia and blended ammonia flames: A review of the recent experimental and numerical advances

Compared to traditional hydrocarbon fuels, ammonia presents significant challenges as a fuel, including high ignition energy, low reactivity, slow flame propagation, and high NOx emissions, which hinder its use as a renewable fuel. Blending ammonia with fossil fuels like natural gas improves its combustion reactivity and helps mitigate CO2 emissions. However, there is still much to understand about the complex dynamics of ammonia and its blends with hydrocarbons. Key areas such as reaction kinetics mechanisms, ignition properties, flame propagation behaviors, and methods for controlling combustion performance under various conditions require further elucidation. This paper reviews recent advancements in experiments and numerical simulations aimed at developing stable, and low-emission combustors for ammonia-fired power generation. Recent burner and flame configurations, including non-swirling jets, single-stage swirl burners, two-stage burners, and newly developed double-swirl burners are analyzed for their flame stability and pollutant emission potential when firing ammonia and ammonia blends. Chemical kinetic modeling of ammonia and its blends plays a crucial role in understanding combustion behavior and pollutant emissions, particularly for NOx. However, there are challenges in predicting NOx emissions accurately, with significant disparities among different models. High-fidelity numerical simulations using detailed and skeletal mechanisms, direct numerical simulation, and large eddy simulation, have helped uncover crucial operational conditions affecting combustion and pollutant emissions, such as combustor pressure, air dilution, wall cooling, fuel/air mixing, and fuel blending. Nonetheless, the accuracy of chemical kinetic models and their integration into turbulent flow simulations remain critical limitations for numerical simulations of ammonia combustion.

Weighted compact nonlinear scheme based on smooth scale separation for self-adaptive turbulence eddy simulations

Multiscale compressible turbulent flow is widely present in the aerospace field, and the co-existence of shock wave discontinuities and multi-scale turbulence in the flow field poses significant challenges for high-fidelity numerical simulation. To design high-order nonlinear scheme to improve the accuracy of complex turbulence simulation with stable shock wave capture, a weighted compact nonlinear scheme – targeted essentially non-oscillatory (WCNS-T) scheme is developed based on targeted essentially non-oscillatory scheme within the WCNS framework. Also, an improved variant of WCNS-T with smooth scale separation (denoted as WCNS-ST) is proposed from the perspective of both numerical convergence and accuracy. The numerical properties of different nonlinear weights are discussed in classical numerical problems, including spectral properties analysis, and two-dimensional Riemann problem. Then, the newly developed WCNS-ST scheme based on smooth scale separation was used to combine a unified turbulence model called Self-Adaptive Turbulence Eddy Simulation (SATES), which is applied to simulate subsonic flow past a 30P30N airfoil and supersonic base flow. The good convergence and numerical results are obtained using the newly developed WCNS-ST scheme, whereas WCNS-T shows worse convergence. In addition, adaptive dissipation control is further introduced for the WCNS-ST scheme, and the new variant (denoted as WCNS-STA) still shows good residual convergence and predicts more accurate results due to low numerical dissipation. The above results indicate that the proposed smooth scale separation and its WCNS-STA scheme have a good performance in SATES simulations of multiscale flow problems, making it more potential for complex engineering applications.

Numerical Investigation of the Reactive Impinging Jet Cooling – the role of vortices in heat and mass transfer intensification

While fundamental mechanisms of non-reactive jet impingement have been extensively researched, the reactive jet impingement is yet unexplored although it offers the potential to increase further the heat transfer. The present work fills this knowledge gap via high-fidelity numerical simulation of reactive jet impingement invoking the finite rate one-step chemistry. Endo-/exothermic reactions are found to have very limited influence on time-averaged fluid flow, while they induce substantial modifications in heat transfer characteristics. This influence is manifested as discernible changes in the momentary or instantaneous fluid dynamics. Specifically, the primary vortices are responsible for downwashing cold fluid (mostly N2O4) towards the hot plate, posing low local convective heat transfer. The secondary vortices are responsible for extracting heat from the hot plate via the dissociation reaction N2O4→2NO2, followed by transporting NO2 away from the hot plate and towards the upwashing side of the primary vortex. At the upside of the primary vortex, hot NO2 is cooled by the environment, therefore reforming N2O4 to give out energy. This intense and long-lasting heat absorption and heat release process explains the outstanding performance of reactive jet impingement.