High-fidelity Computational Fluid Dynamics Simulations Research Articles

Multiple boundary layer suction slots technique for performance improvement of vertical-axis wind turbines: Conceptual design and parametric analysis

Vertical-axis wind turbines (VAWTs) are gaining attention for urban and offshore applications. However, their development is hindered by suboptimal power performance, primarily attributable to the complex aerodynamic characteristics of the blades. Flow control techniques are expected to regulate the flow on the blade surface and improve blade aerodynamics. In the present study, an effective active flow control technique, multiple boundary layer suction slots (MBLSS), is designed for VAWTs performance improvement. The impact of MBLSS on the aerodynamic performance of VAWTs is examined using high-fidelity computational fluid dynamics simulations. The response surface methodology is employed to identify the relatively optimal configuration of MBLSS. Three key parameters are considered, i.e., number of slots (n), distance between slots (d), and slot length (l), which vary from 2 to 4, 0.025c to 0.125c, and 0.025c to 0.075c, respectively. The results show that MBLSS positively affects the power performance and aerodynamics of VAWTs. Parameter n has the most significant effect on VAWT power performance and the importance of d and l is determined by tip speed ratios (TSRs). Tight and loose slot arrangements are recommended for high and low TSRs, respectively. The relatively optimal configuration (n = 2, d = 0.025c, l = 0.05c) results in a remarkable 31.02% increase in the average net power output of the studied TSRs. The flow control mechanism of MBLSS for VAWT blade boundary layer flow has also been further complemented. MBLSS can prevent the bursting of laminar separation bubbles and avoid the formation of dynamic stall vortices. This increases the blade lift-to-drag ratio and mitigates aerodynamic load fluctuations. The wake profiles of VAWTs with MBLSS are also investigated. This study would add value to the application of active flow control techniques for VAWTs.

Multiple boundary layer suction slots technique for performance improvement of vertical-axis wind turbines: Conceptual design and parametric analysis

Vertical-axis wind turbines (VAWTs) are gaining attention for urban and offshore applications. However, their development is hindered by suboptimal power performance, primarily attributable to the complex aerodynamic characteristics of the blades. Flow control techniques are expected to regulate the flow on the blade surface and improve blade aerodynamics. In the present study, an effective active flow control technique, multiple boundary layer suction slots (MBLSS), is designed for VAWTs performance improvement. The impact of MBLSS on the aerodynamic performance of VAWTs is examined using high-fidelity computational fluid dynamics simulations. The response surface methodology is employed to identify the relatively optimal configuration of MBLSS. Three key parameters are considered, i.e., number of slots (n), distance between slots (d), and slot length (l), which vary from 2 to 4, 0.025c to 0.125c, and 0.025c to 0.075c, respectively. The results show that MBLSS positively affects the power performance and aerodynamics of VAWTs. Parameter n has the most significant effect on VAWT power performance and the importance of d and l is determined by tip speed ratios (TSRs). Tight and loose slot arrangements are recommended for high and low TSRs, respectively. The relatively optimal configuration (n = 2, d = 0.025c, l = 0.05c) results in a remarkable 31.02% increase in the average net power output of the studied TSRs. The flow control mechanism of MBLSS for VAWT blade boundary layer flow has also been further complemented. MBLSS can prevent the bursting of laminar separation bubbles and avoid the formation of dynamic stall vortices. This increases the blade lift-to-drag ratio and mitigates aerodynamic load fluctuations. The wake profiles of VAWTs with MBLSS are also investigated. This study would add value to the application of active flow control techniques for VAWTs.

Efficient aerodynamic optimization of turbine blade profiles: an integrated approach with novel HDSPSO algorithm

PurposeThis paper delves into the aerodynamic optimization of a single-stage axial turbine employed in aero-engines.Design/methodology/approachAn efficient integrated design optimization approach tailored for turbine blade profiles is proposed. The approach combines a novel hierarchical dynamic switching PSO (HDSPSO) algorithm with a parametric modeling technique of turbine blades and high-fidelity Computational Fluid Dynamics (CFD) simulation analysis. The proposed HDSPSO algorithm introduces significant enhancements to the original PSO in three pivotal aspects: adaptive acceleration coefficients, distance-based dynamic neighborhood, and a switchable learning mechanism. The core idea behind these improvements is to incorporate the evolutionary state, strengthen interactions within the swarm, enrich update strategies for particles, and effectively prevent premature convergence while enhancing global search capability.FindingsMathematical experiments are conducted to compare the performance of HDSPSO with three other representative PSO variants. The results demonstrate that HDSPSO is a competitive intelligent algorithm with significant global search capabilities and rapid convergence speed. Subsequently, the HDSPSO-based integrated design optimization approach is applied to optimize the turbine blade profiles. The optimized turbine blades have a more uniform thickness distribution, an enhanced loading distribution, and a better flow condition. Importantly, these optimizations lead to a remarkable improvement in aerodynamic performance under both design and non-design working conditions.Originality/valueThese findings highlight the effectiveness and advancement of the HDSPSO-based integrated design optimization approach for turbine blade profiles in enhancing the overall aerodynamic performance. Furthermore, it confirms the great prospects of the innovative HDSPSO algorithm in tackling challenging tasks in practical engineering applications.

Optimizing Ship Airwake Database for Predicting Autospectra Using Deep Learning

Airwakes are shed behind a ship’s superstructure, and they represent a highly turbulent and rapidly distorting flow field. Because of this complexity, they require a baseline: A database of flow velocity points, preferably through experiments and also through high-fidelity computational fluid dynamics simulations. However the database is prohibitively costly, and in situ measurements, at a constant wind angle, are often affected by the weather. A previously developed perturbation scheme helps to generate the autospectrum in closed form from a given wind database, specifically, from the numerically extracted autospectrum for this database. The primary objective of this paper is to demonstrate how deep neural networks can be fruitfully exploited to reduce the dependence on a database of flow velocity points in generating the autospectra, by reducing the amount of in situ data required to achieve the same accuracy as established methods. Two machine learning approaches are developed to predict the autospectrum for a data set when provided with a subset of the wind data set at the input and are assessed using cross-validation. In the first, a neural network is applied to predict the perturbation scheme. Though this method did not predict the autospectrum better than the previously developed perturbation scheme, it laid the foundation for the second method. In the second, a neural network is applied to the autospectra which can be numerically extracted from a database. An improvement is found in the average error computed over all data sets for the model-predicted autospectrum compared to the average error in the autospectrum predicted using the conventional method. The model provided better results for 51.4% of the data sets compared to the conventional method, with many data sets showing marked improvement. The database measured from the Naval Academy YP676 training vessel is used for training and testing the neural network.

Investigation of Surface-Catalycity Effects on Hypersonic Glide Vehicle Trajectory Optimization

The optimization of a hypersonic glide vehicle’s reachable domain is studied using different models to constrain the stagnation-point heat transfer. The vehicle is modeled as a high-lift common aero vehicle that uses bank-angle modulation for crossrange maneuvering with a leading-edge thermal protection system made of an ultra-high-temperature ceramic. A constrained nonlinear optimization problem is formulated to determine the range of feasible trajectories that satisfy aerodynamic heating and loading constraints. The optimal control profile is determined using a modified particle swarm optimization routine that employs a penalty function approach to handle path and terminal constraints. Baseline trajectories are determined using an existing convective heat-flux correlation, and the effect of surface catalycity on the optimized trajectories is investigated by applying a correction factor, derived from high-fidelity computational fluid dynamics simulations, to the heat-flux correlation. Results demonstrate a high sensitivity of the optimized trajectories to the underlying aerothermodynamics model such that accounting for surface catalycity expands the reachability by an order of magnitude.

Influence of rotor blade flexibility on the near-wake behavior of the NREL 5 MW wind turbine

Abstract. High-fidelity computational fluid dynamics (CFD) simulations of the National Renewable Energy Laboratory (NREL) 5 MW wind turbine rotor are performed, comparing the aerodynamic behavior of flexible and rigid blades with respect to local blade quantities as well as the wake properties. The main focus has been set on rotational periodic quantities of blade loading and fluid velocity magnitudes in relation with the blade tip vortex trajectories describing the development of those quantities in the near wake. The results show that the turbine loading in a quasi-steady flow field is mainly influenced by blade deflections due to gravitation. Deforming blades change the aerodynamic behavior, which in turn influences the surrounding flow field, leading to non-uniform wake characteristics with respect to speed and shape.

Performance characterization of a slotted wind turbine airfoil featuring passive blowing

Renewable energy is crucial for a sustainable future, and wind energy holds significant potential as a viable solution to meet global demands. The current study employs high-fidelity Computational Fluid Dynamics (CFD) simulations to investigate the unsteady aerodynamics of an S809 wind turbine airfoil carved with an inclined contracting curved slot at the mid-chord location. The numerical analyses cover a wide range of angles of attack, α = 0°− 20°, at a chord-based Reynolds number of Rec = 5 × 105. The slotted airfoil exhibits superior aerodynamic performance over moderate-to-high angles of attack (α > 6°), with increased lift, reduced drag, and enhanced glide ratio of up to 1.3×, 0.5×, and 3.7× respectively, compared to the baseline. The slot-induced flow control significantly suppresses flow transition and separation across the airfoil surfaces. The attached-flow regime impedes the formation of flow anomalies, resulting in enhanced aerodynamic performance. Comparative analyses reveal significant reductions in velocity fluctuations, Reynolds shear, vorticity, and turbulent kinetic energy, across the slotted airfoil surface and wake region. The aeroacoustic analysis of the slotted airfoil exhibits substantial reduction in the far-field noise over moderate-high angles of attack, with an overall noise level reduction of approximately 14 decibels recorded at α = 17°. Overall, this study highlights the potential of slot-induced flow control as an effective strategy for enhancing the aerodynamic performance of wind turbines.

Acoustic Metamaterials for Electronics Cooling Fan Noise Reduction

Acoustic noise is a significant obstacle that limits the performance of data centre Hard Disk Drive (HDD) storage systems. The primary noise source of these systems has been identified as cooling fans that are used for their thermal management. New acoustic metamaterial (AM) designs can be used as a solution to this problem. AMs can be custom-designed to address specific tonal frequencies that HDD servo systems show high sensitivity to. This paper focuses on developing a 3D-printed annular AM treatment that can be easily attached to the inlet or outlet of an axial fan. Initial design optimization was conducted using the Finite Element Method (FEM). Further FEM simulations were carried out using more realistic fan sources from high-fidelity Computational Fluid Dynamics (CFD) simulations. The AM geometry was tested experimentally with a fan that is commonly used in a data centre storage system, where the fan speed was set to induce an acoustic tone that negatively affects the performance of HDDs. Then a full scale Finite Element Method simulation was ran using fan acoustic source data extracted from a high fidelity CFD simulation. Results indicate significant noise reduction at the targeted frequency.

Data-Driven Modeling for Transonic Aeroelastic Analysis

Aeroelasticity in the transonic regime is challenging because of the strongly nonlinear phenomena involved in the formation of shock waves and flow separation. In this work, we introduce a computationally efficient framework for accurate transonic aeroelastic analysis. We use dynamic mode decomposition with control to extract surrogate models from high-fidelity computational fluid dynamics (CFD) simulations. Instead of identifying models of the full flowfield or focusing on global performance indices, we directly predict the pressure distribution on the body surface. The learned surrogate models provide information about the system’s stability and can be used for control synthesis and response studies. Specific techniques are introduced to avoid spurious instabilities of the aerodynamic model. We use the high-fidelity CFD code SU2 to generate data and test our method on the benchmark supercritical wing. Our Python-based software is fully open source and will be included in the SU2 package to streamline the workflow from defining the high-fidelity aerodynamic model to creating a surrogate model for flutter analysis.

A computational framework for the lifetime prediction of vertical-axis wind turbines: CFD simulations and high-cycle fatigue modeling

A novel computational framework is presented for the lifetime prediction of vertical-axis wind turbines (VAWTs). The framework uses high-fidelity computational fluid dynamics (CFD) simulations for the accurate determination of the aerodynamic loading on the wind turbine, and includes these loading characteristics in a detailed 3D finite element method (FEM) model to predict fatigue cracking in the structure with a fatigue interface damage model. The fatigue interface damage model allows to simulate high-cycle fatigue cracking processes in the wind turbine in an accurate and robust fashion at manageable computational cost. The FEM analyses show that the blade-strut connection is the most critical structural part for the fatigue life of the VAWT, particularly when it is carried out as an adhesive connection (instead of a welded connection). The sensitivity of the fatigue response of the VAWT to specific static and fatigue modeling parameters and to the presence of a structural flaw is analyzed. Depending on the flaw size and flaw location, the fatigue life of the VAWT can decrease by 25%. Additionally, the decrease of the fatigue resistance of the VAWT appears to be mainly characterized by the monotonic reduction of the tensile strength of the adhesive blade-strut connection, rather than by the reduction of its mode I toughness, such that fatigue cracking develops in a brittle fashion under a relatively small crack opening. It is emphasized that the present computational framework is generic; it can also be applied for analyzing the fatigue performance of other rotating machinery subjected to fluid–structure interaction, such as horizontal-axis wind turbines, steam turbine generators and multistage pumps and compressors.

Hemodynamics in AVF over time: A protective role of vascular remodeling toward flow stabilization.

The mechanisms underlying vascular stenosis formation in the arteriovenous fistula (AVF) for hemodialysis (HD) remain mostly unknown. Several computational fluid dynamics (CFD) studies have suggested a potential role for unsteady flow in inducing intimal hyperplasia and AVF stenosis, but the majority of these observations have been limited to a single time point after surgical creation. The aim of the present study was to investigate the relation between hemodynamic conditions and AVF vascular remodeling through a CFD longitudinal study. Non contrast-enhanced MR images and Doppler Ultrasound (US) examinations were acquired at 3 days, 40 days, 6 months, 1 year, and 1.5 years after surgery in a 72-year male referred for native radio-cephalic AVF. Three-dimensional AVF models were generated and high fidelity CFD simulations were performed using pimpleFoam, setting patient-specific boundary conditions derived from US. Morphological and hemodynamic changes over time were then analyzed. Analysis of vessel morphology and hemodynamics during follow-up showed that the AVF had a successful maturation process, characterized by a massive arterial and venous dilatation within the 6 months after surgery, a corresponding increase in blood flow volume and important flow instabilities. Between 6 months and 1 year, a stenosis developed in the juxta-anastomotic vein and caused AVF failure at 1.5 years. The development of stenosis was paralleled by the regularization of blood flow velocity pattern and consequent decrease in the near-wall disturbed flow metrics. These results suggest that development of intimal hyperplasia and vessel stenosis, triggered by unsteady flow, could be the result of vascular inward remodeling toward regularization of turbulent-like flow.

Performance, Aerodynamics, and Aeroacoustics of Side-by-Side Rotors Using High-Fidelity Computational Fluid Dynamics

This paper investigates the performance, aerodynamics, and aeroacoustics of NASA’s side-by-side urban air mobility (UAM) aircraft in hover conditions, utilizing high-fidelity computational fluid dynamics (CFD) simulations. High Performance Computing Modernization Program CREATE™-AV Helios is employed for the CFD simulations, while acoustic predictions are performed by PSU-WOPWOP. The simulations apply overset structured grids, the Spalart–Allmaras detached eddy simulation turbulence model, and adaptive mesh refinement (AMR) on the off-body mesh. The investigation covers four rotor overlap scenarios and five collective pitch angles, all maintaining a consistent rotational speed. Results indicate that as rotor overlap distance increases, a minor improvement in the figure of merit is noticed. Nevertheless, a substantial reduction in blade loading within the overlap region becomes apparent. The blade–vortex interaction (BVI) intensifies between both rotors as the overlap region broadens. The 25% overlap configuration is shown to yield the highest overall sound pressure level (OASPL) among all configurations due to the more pronounced BVIs at the entrance and exit of the overlap area. It is observed that the OASPL exceeds 62 dBA at an altitude of 500 ft (152.4 m) for all overlap cases, exceeding the suggested UAM aircraft noise guideline. Additionally, noise generated by all overlap scenarios is compared against various background noise levels. Results suggest that the noise produced by the side-by-side rotor configuration is not fully masked by any of the different background noise levels at an altitude of 500 ft (152.4 m).

Interactive steering on in situ particle-based volume rendering framework

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

Strategy for mitigating wake interference between offshore vertical-axis wind turbines: Evaluation of vertically staggered arrangement

Wake interference between wind turbines potentially decreases the power output of offshore wind farms. While the staggered arrangement shows promise for mitigating this interference, the understanding of the impact of vertically staggered setup remains ambiguous, especially in the case of vertical-axis wind turbines (VAWTs). Also, the difference between horizontally and vertically staggered arrangements needs to be clarified. This study aims to shed light on these ambiguities, systematically evaluating the effectiveness of vertically staggered arrangement in mitigating wake interference between offshore VAWTs. High-fidelity computational fluid dynamics simulations are used to investigate the wake interference between two vertically staggered MW-class VAWTs. Typical separation distances (3D ≤ LS ≤ 7D) and different vertically staggered distances (−0.25H ≤ LV ≤ 0.75H) are considered, where D and H denote the turbine diameter and blade span length, respectively. Employing the Taguchi method, the effectiveness of horizontally and vertically staggered arrangements is compared, and the relatively optimal layout of the turbine array is identified. The results show that the vertically staggered arrangement significantly improves the power performance of the downstream turbine, e.g., the power output increases by 75.96% when LS = 7D and LV = 0.25H. When dealing with smaller LS (e.g., 3D), a negative LV is suggested, which reduces the tower cost while preserving the performance improvement. The vertically staggered arrangement has a greater impact on wake interference than the horizontally staggered arrangement. In terms of the relatively optimal layout, the power output of the turbine array is increased by 46.27%. These findings would contribute to the layout design of offshore wind farms.

Assessment of icing effects on the wake shed behind a vertical axis wind turbine

To shed light on the effect of the icing phenomenon on the vertical-axis wind turbine (VAWT) wake characteristics, we present a high-fidelity computational fluid dynamics simulation of the flow field of H-Darrieus turbine under the icing conditions. To address continuous geometry alteration due to the icing and predefined motion of the VAWT, a pseudo-steady approach proposed by Baizhuma et al. [“Numerical method to predict ice accretion shapes and performance penalties for rotating vertical axis wind turbines under icing conditions,” J. Wind Eng. Ind. Aerodyn. 216, 104708 (2021)] was implemented, which enables the utilization of appropriate approaches for handling turbine rotation and turbulence prediction for each solver. Proper orthogonal decomposition (POD) was utilized to perform a deep analysis of the wake and aerodynamics of the wind turbine for the clean and iced turbines with large eddy simulation turbulence method. Icing causes the leading edge vortex and trailing edge vortex to separate faster than the clean case resulting in a steeper drop in the power coefficient. As for POD modes, those of the streamwise component of velocity illustrated more difference in the amount of modal energy especially at the first modes proving that the icing phenomenon mainly affects the vortex shedding of the flow structures with larger energy and size. The modes of the transversal component of velocity of the clean and iced cases demonstrated more similarity in essence, which could also be understood from the accumulated energy curve.

Sensitivity of hostile hemodynamics to aneurysm geometry via unsupervised shape interpolation

Background and ObjectiveVessel geometry and hemodynamics are intrinsically linked, whereby geometry determines hemodynamics, and hemodynamics influence vascular remodeling. Both have been used for testing clinical outcomes, but geometry/morphology generally has less uncertainty than hemodynamics derived from medical image-based computational fluid dynamics (CFD). To provide clinical utility, CFD-based hemodynamic parameters must be robust to modeling errors and/or uncertainties, but must also provide useful information not more-easily extracted from shape alone. The objective of this study was to methodically assess the response of hemodynamic parameters to gradual changes in shape created using an unsupervised 3D shape interpolation method. MethodsWe trained the neural network NeuroMorph on 3 patient-derived intracranial aneurysm surfaces (labelled A, B, C), and then generated 3 distinct morph sequences (A→B, B→C, C→A) each containing 10 interpolated surfaces. From high-fidelity CFD simulation of these, we calculated a variety of common reduced hemodynamic parameters, including many previously associated with aneurysm rupture, and analyzed their responses to changes in shape, and their correlations. ResultsThe interpolated surfaces demonstrate complex, gradual changes in branch angles, vessel diameters, and aneurysm morphology. CFD simulation showed gradual changes in aneurysm jetting characteristics and wall-shear stress (WSS) patterns, but demonstrated a range of responses from the reduced hemodynamic parameters. Spatially and temporally averaged parameters including time-averaged WSS, time-averaged velocity, and low-shear area (LSA) showed low variation across all morph sequences, while parameters of flow complexity such as oscillatory shear, spectral broadening, and spectral bandedness indices showed high variation between slightly-altered neighboring surfaces. Correlation analysis revealed a great deal of mutual information with easier-to-measure shape-based parameters. ConclusionsIn the absence of large clinical datasets, unsupervised shape interpolation provides an ideal laboratory for exploring the delicate balance between robustness and sensitivity of nominal hemodynamic predictors of aneurysm rupture. Parameters like time-averaged WSS and LSA that are highly “robust” may, as a result, be effectively redundant to morphological predictors, whereas more sensitive parameters may be too uncertain for practical clinical use. Understanding these sensitivities may help identify parameters that are capable of providing added value to rupture risk assessment.

Heat exchange performance evaluation inside a lattice panel using CFD analysis for an innovative aerospace anti-icing system

As the global push for sustainable development intensifies, the aerospace industry is exploring innovative solutions to reduce greenhouse gas emissions and optimize resource consumption. This paper investigates the performance of an advanced anti-icing system that integrates an onboard function within the primary structure of an aircraft, offering potential reductions in weight, cost, and energy consumption. To evaluate the heat exchange performance of this innovative system, we examine various lattice structures featuring different cell topologies, sizes, and densities. High-fidelity Computational Fluid Dynamics (CFD) simulations are utilized to assess the performance of the constituent cells within the lattice panel, with the goal of identifying the optimal solution that meets design requirements for heat transfer efficiency, pressure, and overall mass of the components. A statistical approach based on Design of Experiments (DoE) is employed to elucidate the relationship between the cell design parameters and the resulting thermal properties. Our findings contribute valuable insights for the development and implementation of advanced aerospace anti-icing systems, supporting the ongoing evolution of sustainable and efficient aviation technologies.

Critical effect of fore-aft tapering on galloping triggering for a trapezoidal body

The critical effect of the windward interior angles of elastically mounted trapezoidal bodies on a galloping instability is numerically investigated in this paper using two methodologies of high-fidelity computational fluid dynamics simulations and data-driven stability analysis using the eigensystem realization algorithm. A micro exploration of the dynamical response is processed to understand the mechanism underpinning the structural amplification at the initial stage of the galloping instability and the competition between wake and structural modes. It is observed that very small changes in the windward interior angle of an isosceles-trapezoidal body can provoke or suppress galloping – indeed, a small decrease or increase (low to $1^\circ$ ) of the windward interior angle from a right angle ( $90^\circ$ ) can result in a significant enhancement and complete suppression, respectively, of the galloping oscillations. This supports our hypothesis that the contraction and/or expansion (viz., fore-aft tapering and/or widening) of the cross-section in the streamline direction has potential influences on galloping triggering from the geometrical perspective. The data-driven stability analysis is also applied to verify and analyse this phenomenon from the perspective of modal analysis. The experimental measurements are also conducted in the wind tunnel to support this hypothesis.

Unsteady reduced order model with neural networks and flight-physics-based regularization for aerodynamic applications

Numerical simulation of unsteady fluid flow plays an important role in several areas of the aeronautical industry. Since high-fidelity computational fluid dynamics simulations could be prohibitive in terms of computational cost, data-driven reduced order models become a suitable alternative for efficiently predicting flow variables as long as the accuracy of such models is comparable to that obtained by the full order model counterpart. This is especially important for iterative design purposes, where a few target variables must be evaluated on a large number of possible parameters. Therefore, we propose a neural network based methodology to develop an unsteady reduced order model of the subsonic/transonic flow field on 2D aerodynamic profiles trained on high-fidelity computational fluid dynamics data. For the purpose of dimensionality reduction, either proper-orthogonal decomposition or autoencoders are employed. For the regression task, a gated recurrent unit neural network is used to map an unsteady Schroeder multi-sine signal of angle of attack along with its first and second time-derivatives to the solution of surface variables, such as coefficients of pressure and friction. In order to shed light on the inner workings of data-driven methods so it could be employed in the aircraft design process, we introduce a flight-physics-based regularization term to incorporate information about the calculation of integral coefficients, like drag and lift, into the machine learning training workflow. Using our method, airfoil flow variables of interest can be predicted at a fraction of the cost of classical methods without any considerable accuracy loss. We also provide a comparison between reduction methods and we show evidence that supports the use of the proposed flight-physics-based regularization for building unsteady reduced order models based on machine learning.

Investigation on the influence of vortex generator on particle resuspension

The vortex generator (VG) and its well-known effect in flow optimization are widely studied and employed across different engineering sectors. However, while the same working principles of VG may be well suited for the applications on surface-cleaning technologies, such promising potential is hardly, if any, explored in the published literature. Therefore, in the present study, the influence on flow-induced particle resuspension brought by a rectangular VG in a channel flow is investigated with the help of high-fidelity computational fluid dynamics simulations. Substantial increases of particle removal forces and resuspension rates are discovered in long, strip-like regions with reduced boundary-layer thickness resulted from the VG-induced vortices, and the enhancement effect is especially significant for configurations with the VG installed at a greater angle of attack. It is also shown that while the resuspension enhancements on the lower and the upper surfaces of the channel exhibit distinct statistical characteristics, having a VG in the channel improves the overall particle-removing capability of the channel flow by introducing higher surface-averaged removal forces and particle resuspension rates. Last but not least, the increase of resuspension rate is especially significant for the smaller, micron-scale particles which are otherwise hardly disturbed by a VG-less channel flow, and such resuspension-enhancement effect generally subsides with increasing particle size.