High-fidelity Computational Fluid Dynamics Research Articles

An experimental and numerical analysis of the dynamic variation of the angle of attack in a vertical-axis wind turbine

Simulation methods ensuring a level of fidelity higher than that of the ubiquitous Blade Element Momentum theory are increasingly applied to VAWTs, ranging from Lifting-Line methods, to Actuator Line or Computational Fluid Dynamics (CFD). The inherent complexity of these machines, characterised by a continuous variation of the angle of attack during the cycloidal motion of the airfoils and the onset of many related unsteady phenomena, makes nonetheless a correct estimation of the actual aerodynamics extremely difficult. In particular, a better understanding of the actual angle of attack during the motion of a VAWT is pivotal to select the correct airfoil and functioning design conditions. Moving from this background, a high-fidelity unsteady CFD model of a 2-blade H-Darrieus rotor was developed and validated against unique experimental data collected using Particle Image Velocimetry (PIV). In order to reconstruct the AoA variation during one rotor revolution, three different methods-detailed in the study-were then applied to the computed CFD flow fields. The resulting AoA trends were combined with available blade forces data to assess the corresponding lift and drag coefficients over one rotor revolution and correlate them with the most evident flow macro-structures and with the onset of dynamic stall.

On the feasibility of affordable high-fidelity CFD simulations for indoor environment design and control

Computational fluid dynamics (CFD) is a reliable tool for indoor environmental applications. However, accurate CFD simulations require large computational resources, whereas significant cost reduction can lead to unreliable results. The high cost prevents CFD from becoming the primary tool for indoor environmental simulations. Nonetheless, the growth in computational power and advances in numerical algorithms provide an opportunity to use accurate and yet affordable CFD. The objective of this study is to analyze the feasibility of fast, affordable, and high-fidelity CFD simulations for indoor environment design and control using ordinary office computers. We analyze two representative test cases, which imitate common indoor airflow configurations, on a wide range of different turbulence models and discretizations methods, to meet the requirements for the computational cost, run-time, and accuracy. We consider statistically steady-state simulations for indoor environment design and transient simulations for control. Among studied turbulence models, the no-model and large-eddy simulation with staggered discretizations show the best performance. We conclude that high-fidelity CFD simulations on office computers are too slow to be used as a primary tool for indoor environment design and control. Taking into account different laws of computer growth prediction, we estimate the feasibility of high-fidelity CFD on office computers for these applications for the next decades.

Deep Learning Based Reduced Order Model for Airfoil-Gust and Aeroelastic Interaction

This work aims to model transonic airfoil–gust interaction and the gust response on transonic aileron-buzz problems using high-fidelity computational fluid dynamics (CFD) and the Long Short Term Memory (LSTM) based deep-learning approach. It first explores the rich physics associated with these interactions, which show strong flowfield nonlinearities arising from the complex shock–boundary-layer interactions using CFD. In the transonic regime, most linear reduced-order models (ROMs) fail to reconstruct the unsteady global parameters such as the lift, moment, and drag coefficients and the unsteady distributive flow variables such as velocity, pressure, and skin friction coefficients on the airfoil or in the entire computational domain due to the nonlinear shock–gust interaction. As it is well known that a deep-learning framework creates several hypersurfaces to generate a nonlinear functional relationship between the gust or structural input and the unsteady flow variables, an algorithm is proposed to overcome the limitations of linear ROMs. This algorithm consists of two integral steps, namely, a dimensionality reduction where the Discrete Empirical Interpolation Method based linear data compression approach is applied and the reduced state is trained using the LSTM based Recurrent Neural Network for the reconstruction of unsteady flow variables. The present work shows its potential for predicting transonic airfoil gust response and the aileron-buzz problem demonstrating several orders of computational benefit as compared to high-fidelity CFD.

Geometrical Optimization of Pump-As-Turbine (PAT) Impellers for Enhancing Energy Efficiency with 1-D Theory

This paper presents a multi-objective optimization strategy for pump-as-turbines (PAT), which relies on one-dimensional theory and analysis of geometrical parameters. In this strategy, a theoretical model, which considers all possible losses incurred (mainly by the components of pipe inlet, impeller and volute), has been put forward for performance prediction of centrifugal pumps operating as turbines (PAT). With the established mathematical relationship between the efficiency of PAT (both at pump and turbine mode) and the impeller controlling variables, the geometric optimization of the PAT impeller is performed with constant rotational speed. Specifically, the optimization data consist of 50 sets of impellers generated from Latin Hypercube Sampling method with its corresponding efficiencies calculated. Subsequently, the pareto-based genetic algorithm (PBGA) was adopted to optimize the geometic parameters of the impellers through the theoretical model. To validate the theoretical optimization results, the high-fidelity Computational Fluid Dynamics (CFD) simulation and the experimental data are employed for comparison of the PAT performance. The findings show that the efficiencies of both the pump and PAT optimized variables increased by 0.27% and 16.3% respectively under the design flow condition. Based on the one-dimensional theoretical optimization results, the geometry of the impeller is redesigned to suit both pump and PAT mode operations. It is concluded that the chosen design variables (b2, β1, β2, and z) have a significant impact on the PAT efficiency, which demonstrates that the optimization scheme proposed in this study is practicable.

Computational Fluid Dynamics Based Mixing Prediction for Tilt Pad Journal Bearing TEHD Modeling—Part I: TEHD-CFD Model Validation and Improvements

AbstractThe core contributions of Part I (1) present a computational fluid dynamics (CFD)-based approach for tilting pad journal bearing (TPJB) modeling including thermo-elasto hydrodynamic (TEHD) effects with multi-mode pad flexibility, (2) validate the model by comparison with experimental work, and (3) investigate the limitations of the conventional approach by contrasting it with the new approach. The modeling technique is advanced from the author’s previous work by including pad flexibility. The results demonstrate that the conventional approach of disregarding the three-dimensional flow physics between pads (BP) can generate significantly different pressure, temperature, heat flux, dynamic viscosity, and film thickness distributions, relative to the high-fidelity CFD model. The uncertainty of the assumed mixing coefficient (MC) may be a serious weakness when using a conventional, TPJB Reynolds model, leading to prediction errors in static and dynamic performance. The advanced mixing prediction method for “BP” thermal flow developed in Part I will be implemented with machine learning techniques in Part II to provide a means to enhance the accuracy of conventional Reynolds based TPJB models.

On the spectrographic representation of cardiovascular flow instabilities

In the past decade, high-fidelity computational fluid dynamics (CFD) has uncovered the presence of high-frequency flow instabilities (on the order of 100 s of Hz) in a variety of cardiovascular applications. These fluctuations are typically reported as pulsatile velocity–time traces or fast-Fourier-transformed power-frequency spectra, often from a single point or at most a handful of points. Originally inspired by its use in spectral Doppler ultrasound, here we demonstrate the utility of the simplest form of time–frequency representation – the spectrogram – as a more comprehensive yet still-intuitive means of visualizing the potential harmonic complexity of pulsatile cardiovascular flows. After reviewing the basic theory behind spectrograms, notably the short-time Fourier transform (STFT), we discuss the choice of input parameters that inform the appearance and trade-offs of spectrograms. We show that spectrograms using STFT were able to highlight spectral features and were representative of those obtained from more complex methods such as the Continuous Wavelet transforms (CWT). While visualization properties (colourmap, filtering, smoothing/interpolation) are shown to affect the conspicuity of spectral features, the window properties (function, size, overlap) are shown to have the greatest impact on the resulting spectrogram appearance. Using a set of cerebral aneurysm CFD cases, we show that spectrograms can readily reveal the case-specific nature of the time-varying flow instabilities, whether broadband, suggesting intermittent turbulent-like flow, or narrowband, suggesting laminar vortex shedding, or some combination thereof.

Fly low: The ground effect of a barn owl (Tyto alba) in gliding flight

Birds take advantage of the ground effect to improve their flight performance by flying low over ground. In this paper, we created a high-fidelity computational fluid dynamics model of a barn owl ( Tyto alba) to study its ground effect in gliding flight. A computational fluid dynamics simulation shows that the ground effect leads to increases in the lift/drag ratio and span efficiency. Interestingly, the span efficiency exceeds one when the bird is below a certain ground clearance ( h/ c = 0.8). Such an estimation is under the condition of different weight supports; hence, there is no fair comparison for many parameters. Therefore, we used a vortex induction model validated by computational fluid dynamics to estimate the aerodynamics at different ground clearances under a constant weight support. As the ground blocks the downwash of the bird, an image wake system can equivalently replace the ground, forming a vortex system with four components: the wake vortex, bound vortex, image wake vortex and image bound vortex. The vortex induction model shows the vertical flows induced by these four vortex components. Such vertical flow can be used to estimate the drag production on the bird. As the ground clearance decreases, the drag due to the wake vortex and its image counterpart as a whole decreases, while that due to the bound vortex and its image counterpart increases slightly and then decreases. The remaining drag, namely, the zero-lift drag, undergoes a shallow “U” shape as a function of the ground clearance. We also analyzed the streamwise flow induction using this model and showed that the streamwise flow is reduced due to the ground effect, which might cause insufficient weight support at low speeds.

Comparative Study of CFD and LedaFlow Models for Riser-Induced Slug Flow

The goal of this study is to compare mainstream Computational Fluid Dynamics (CFD) with the widely used 1D transient model LedaFlow in their ability to predict riser induced slug flow and to determine if it is relevant for the offshore oil and gas industry to consider making the switch from LedaFlow to CFD. Presently, the industry use relatively simple 1D-models, such as LedaFlow, to predict flow patterns in pipelines. The reduction in cost of computational power in recent years have made it relevant to compare the performance of these codes with high fidelity CFD simulations. A laboratory test facility was used to obtain data for pressure and mass flow rates for the two-phase flow of air and water. A benchmark case of slug flow served for evaluation of the numerical models. A 3D unsteady CFD simulation was performed based on Reynolds-Averaged Navier-Stokes (RANS) formulation and the Volume of Fluid (VOF) model using the open-source CFD code OpenFOAM. Unsteady simulations using the commercial 1D LedaFlow solver were performed using the same boundary conditions and fluid properties as the CFD simulation. Both the CFD and LedaFlow model underpredicted the experimentally determined slug frequency by 22% and 16% respectively. Both models predicted a classical blowout, in which the riser is completely evacuated of water, while only a partial evacuation of the riser was observed experimentally. The CFD model had a runtime of 57 h while the LedaFlow model had a runtime of 13 min. It can be concluded that the prediction capabilities of the CFD and LedaFlow models are similar for riser-induced slug flow while the CFD model is much more computational intensive.

PadMesh: a parallel and distributed framework for interactive mesh generation software

Meshes are the input for computational fluid dynamics (CFD) analysis, whose size and quality have important impact on the simulation results. With the continuous advancement of high-fidelity CFD simulation, the scale of needed computational meshes has become larger and larger, which poses great challenges to the development of interactive mesh generation software. To address this issue, a parallel and distributed framework called PadMesh is proposed, which performs as the infrastructure for developing various interactive mesh generation software (e.g., structured, unstructured and Cartesian). First, the framework PadMesh is demonstrated, including the introduction of design principles, software architecture and key components, namely message-oriented middleware, client application, server application and parallel supporting module. Second, a parallel and distributed structured mesh generation software called PGridStar is developed based on PadMesh. Strategies are investigated on managing the visual data and distributed data for structured meshes. Finally, two functionalities of the preliminary PGridStar are presented, which validate the usability of PadMesh in developing interactive mesh generation software.

Body-force and mean-line models for the generation of axial compressor sub-idle characteristics

ABSTRACTThis paper describes the application of low-order models to the prediction of the steady performance of axial compressors at sub-idle conditions. An Euler body-force method employing sub-idle performance correlations is developed and presented alongside a mean-line approach employing the same set of correlations. The low-order tools are used to generate the characteristic lines of the compressor in the locked-rotor and zero-torque windmilling conditions. The results are compared against steady-state operating points from three-dimensional (3D) Reynolds-averaged Navier–Stokes (RANS) computational fluid dynamics (CFD) simulations. The accuracy of the low-order tools in reproducing the results from high-fidelity CFD is analysed, and the trade-off with the computational cost of each method is discussed. The low-order tools presented are shown to offer a fast alternative to traditional CFD which can be used to predict the performance in sub-idle conditions of a new compressor design during early development stages.

Comparative study on analytical and computational aerodynamic models for flapping wings MAVs

ABSTRACTA range of quasi-steady and unsteady aerodynamic models are used to predict the aerodynamic forces experienced by a flapping wing and a detailed comparison amongst these predictions in provided. The complexity of the models ranges from the analytical potential flow model to the computational Unsteady Vortex Lattice Method (UVLM), which allows one to describe the motion of the wake and account for its influence on the fluid loads. The novelty of this effort lies in a modification of the predicted forces as a generalisation of the leading edge suction analogy. This modification is introduced to account for the delayed stall mechanism due to leading edge flow separation. The model predictions are compared with two sets of independent experimental data and with computational fluid dynamics (CFD) simulation data available in the literature. It is found that both, the modified analytical model and the UVLM model can be used to describe the time history of the lift force, in some cases with better results than a high-fidelity CFD model. The models presented here constitute a useful basis for the aerodynamic design of bioinspired flapping-wings micro-air vehicles.

Data-driven modeling of the chaotic thermal convection in an annular thermosyphon

dentifying accurate and yet interpretable low-order models from data has gained a renewed interest over the past decade. In the present work, we illustrate how the combined use of dimensionality reduction and sparse system identification techniques allows us to obtain an accurate model of the chaotic thermal convection in a two-dimensional annular thermosyphon. Taking as guidelines the derivation of the Lorenz system, the chaotic thermal convection dynamics simulated using a high-fidelity computational fluid dynamics solver are first embedded into a low-dimensional space using dynamic mode decomposition. After having reviewed the physical properties the reduced-order model should exhibit, the latter is identified using SINDy, an increasingly popular and flexible framework for the identification of nonlinear continuous-time dynamical systems from data. The identified model closely resembles the canonical Lorenz system, having a similar structure and exhibiting the same physical properties. Finally, extensions to other flow configurations with or without control are discussed.

Automatic Shape Optimization of Patient-Specific Tissue Engineered Vascular Grafts for Aortic Coarctation.

This paper proposes a computational framework for automatically optimizing the shapes of patient-specific tissue engineered vascular grafts. We demonstrate a proof-of-concept design optimization for aortic coarctation repair. The computational framework consists of three main components including 1) a free-form deformation technique exploring graft geometries, 2) high-fidelity computational fluid dynamics simulations for collecting data on the effects of design parameters on objective function values like energy loss, and 3) employing machine learning methods (Gaussian Processes) to develop a surrogate model for predicting results of high-fidelity simulations. The globally optimal design parameters are then computed by multistart conjugate gradient optimization on the surrogate model. In the experiment, we investigate the correlation among the design parameters and the objective function values. Our results achieve a 30% reduction in blood flow energy loss compared to the original coarctation by optimizing the aortic geometry.

Numerical simulation and evaluation of spacer-filled direct contact membrane distillation module

Membrane fouling and temperature polarization are the most common issues that cause limitations to membrane distillation (MD) process. Integration of spacers has been proven to resolve those problems by inducing regions of turbulence and giving the required mechanical support to the membrane. In this work, a robust high-fidelity computational fluid dynamics simulation is carried out to assess and quantify the performance of spacer-filled DCMD module and compare it with a baseline spacer-free DCMD module. Mainly, simulations are done to delineate the problem of concentration polarization and by alternating spacers material with different thermal conductivities and different displacement configurations. The performance of these different models is demonstrated in terms of concentration boundary layer development, temperature distributions, temperature polarization coefficient (TPC), mass flux, heat flux, heat transfer coefficient, and thermal efficiency. Results show that concentration polarization can penalize mass flux by nearly 10%, and conductive spacers have favorable effect on the DCMD performance compared to spacer-free in terms of TPC by 50%, mass flux by 35%, heat flux by 31%, thermal efficiency by 1%, and top and bottom membrane surface heat transfer coefficients of, respectively, 19% and 62%. Meanwhile, the stride of the spacers in the range of 1.5–3.5 mm tends to achieve a measurable mass flux. Generally, spacers integration has confirmed the capability of reducing concentration polarization at the membrane surface. These attained improvements will accelerate industrial deployments of MD.

Inverse design of microfluidic concentration gradient generator using deep learning and physics-based component model

This paper presents a new paradigm of deep neural network (DNN) for the inverse design of microfluidic concentration gradient generators (µCGGs) with complex network topology. In this method, a concentration gradient (CG) and design parameters yielding the CG are, respectively, used as inputs and outputs of DNN, and the relationship between them is mapped. Several new elements are also proposed, including utilization of fast-running physics-based component model in the closed form to generate a large amount of data for DNN learning which otherwise is not available through computationally demanding computational fluid dynamics (CFD) simulation; and a divide-and-conquer strategy and DNN formulation combining classification and regression to mitigate many-to-one design complications for enhanced accuracy. Several DNN structures are investigated and developed, including single fully connected neural network (FCNN), convolutional neural network, and a new cascade FCNN for a divide-and-conquer implementation. Case studies are performed on a triple-Y µCGG to evaluate design performance of the proposed method in a six-dimensional space that only includes sample concentrations at inlet reservoirs as design parameters, and in a nine-dimensional design space, to which inlet flow pressures are also added. It is verified in high-fidelity CFD simulation that widely used CGs can be produced using DNN-predicted design parameters accurately with average error < 4% and < 8.5% relative to the prescribed CGs, respectively, in the six- and nine-dimensional design space. The learned design rules are packaged into the DNN model that allows to generate accurate µCGGs designs instantaneously (~ 3 ms) and eliminates requirements of simulation and optimization knowledge, facilitating distribution of the design capabilities to microfluidic end users.

Breakdown of aerodynamic interactions for the lateral rotors on a compound helicopter

Auxiliary lift and/or thrust on a compound helicopter can introduce complex aerodynamic interactions between the auxiliary lift and thrust components and the main rotor. In this study high-fidelity computational fluid dynamics analyses were performed to capture the various aerodynamic interactions which are occurring for the Airbus RACER compound helicopter, featuring a box-wing design for auxiliary lift in cruise and wingtip-mounted lateral rotors in pusher configuration for auxiliary thrust in cruise and counter-torque in hover. Although the study was limited to a specific geometry, the effects and phenomena are expected to be to some extent applicable in general for compound helicopters and wingtip-mounted rotors in pusher configuration. A quantitative indication of the aerodynamic interaction effects could be established by leaving away different airframe components in the simulations. The downwash of the main rotor was found to cause a small negative angle of attack in cruise for the wings and lateral rotors and impinged directly on the lateral rotors in hover, resulting in moderate to very significant sinusoidally varying blade loading. The wing increased lateral rotor propulsive efficiency in cruise through its wingtip rotational flowfield and to a lesser extent through its wake. An upstream effect of the lateral rotors on the wing loading was also found. In hover the wing caused a net increase in left lateral rotor thrust as the deflection of the main rotor flow towards the rotor resulted in a local thrust decrease and the low momentum inflow to the rotor from the wake of the wing resulted in a local thrust increase. A small thrust decrease for the right lateral rotor was found due to the wing disturbing its slipstream as this rotor produced reversed thrust. In general, very significant aerodynamic interaction effects can be expected when a main rotor, lateral rotors and wing are in proximity to each other.

Concept and Operation of a Wind Turbine Driven by Dynamic Stall

Dynamic stall is exploited to produce positive torque on a large-chord/radius-ratio () vertical-axis wind turbine. An elementary kinematic analysis shows that the blades experience large variations in angle of attack that produce deep dynamic stall, combined with large relative dynamic pressure variations, that are phase shifted by approximately 90 deg. The phase shift allows dynamic lift-overshoot effects associated with the formation of a dynamic stall vortex to drive the turbine when the relative dynamic pressure is high, whereas lift stall associated with shedding of the vortex occurs when the relative dynamic pressure is low. Blades also experience variable virtual camber (or “virtual morphing”) effects and favorable chordwise pressure gradients, where the former produces a virtual leading-edge droop that reduces the leading-edge angle of attack. Open jet wind-tunnel tests of a two-bladed model turbine revealed maximum power coefficients of 16% that were almost independent of . Flow visualization confirmed that dynamic stall, with the associated dynamic stall vortex, is the mechanism that drives the turbine to maximum power. Until reliable design tools are developed, either empiricism combined with dimensional analysis or high-fidelity computational fluid dynamics are proposed for optimization and analysis.

Open-Source Implementation and Validation of a 3D Inverse Design Method for Francis Turbine Runners

The hydraulic design of Francis turbines and pump-turbines is an expensive project-specific engineering effort that typically involves a direct iterative exploration of the design space. An inverse design method for turbomachinery has been previously introduced in the literature, and several recent applications have demonstrated its advantages; however, only a commercial implementation of the method is currently available. In this work, an open-source implementation of the inverse design method is introduced. First, the governing equations in cylindrical and curvilinear coordinate systems are derived, consolidating the somewhat inconsistent formulations that are available in the literature. Then, a convergence analysis of the method is performed in order to characterize the behavior of the discretization error and deduce the mesh resolution requirements. A validation of the method output with respect to high-fidelity computational fluid dynamics simulations is then presented; it is demonstrated that the velocity fields are well predicted, the pressure distribution on the blades is reasonably well approximated, and the flow angular momentum extraction is achieved in the prescribed manner. Possible improvements to the open-source implementation of the method are discussed.

Analytical model for the power–yaw sensitivity of wind turbines operating in full wake

Abstract. Wind turbines are designed to align themselves with the incoming wind direction. However, turbines often experience unintentional yaw misalignment, which can significantly reduce the power production. The unintentional yaw misalignment increases for turbines operating in the wake of upstream turbines. Here, the combined effects of wakes and yaw misalignment are investigated, with a focus on the resulting reduction in power production. A model is developed, which considers the trajectory of each turbine blade element as it passes through the wake inflow in order to determine a power–yaw loss exponent. The simple model is verified using the HAWC2 aeroelastic code, where wake flow fields have been generated using both medium- and high-fidelity computational fluid dynamics simulations. It is demonstrated that the spatial variation in the incoming wind field, due to the presence of wakes, plays a significant role in the power loss due to yaw misalignment. Results show that disregarding these effects on the power–yaw loss exponent can yield a 3.5 % overestimation in the power production of a turbine misaligned by 30∘. The presented analysis and model is relevant to low-fidelity wind farm optimization tools, which aim to capture the combined effects of wakes and yaw misalignment as well as the uncertainty on power output.

Combined scalar tracking, computational fluid dynamics, and multi-objective genetic algorithm method to accelerate optimization of film cooling systems

This paper presents a method to significantly accelerate optimization of film cooling systems. The method combines high-fidelity computational fluid dynamics with scalar tracking implemented, a proxy model (linear superposition model) initialized with the computational fluid dynamics solution, and a multi-objective evolutionary algorithm approach. The proposed method is structured as follows: the computational fluid dynamics solution is used to predict the (generally complex) flow domain for the film cooling system; the scalar tracking method identifies the contributions of individual holes to an overall cooling effectiveness distribution by associating a unique passive scalar variable to the flow associated with each hole, and solving an additional advection–diffusion (scalar transport) equation; the proxy model is a (generally linear) superposition model implemented – for example – in Matlab, which inherits the scalar values from the computational fluid dynamics solution, and allows extrapolation of solutions to new design points as part of an optimization process; the optimization process is handled with a multi-objective evolutionary algorithm approach which iterates the proxy model to optimize for a defined objective function. The process works with inner and outer convergence loops. The inner convergence loop is the multi-objective evolutionary algorithm interfacing with the proxy model, which achieves convergence against a design target. At the end of each inner loop cycle, a high-fidelity computational fluid dynamics simulation is run, and this is used to recalibrate the proxy model. Convergence for a given objective function is typically achieved with six outer-loop iterations (high-fidelity computational fluid dynamics runs) and 10,000 inner-loop iterations per outer-loop iteration. The significant advantage of the proposed method is that for certain optimization problems, the computational cost can be reduced by several orders of magnitude, replacing thousands of high-fidelity computational fluid dynamics runs with approximately six computational fluid dynamics runs. The process is demonstrated by applying the optimization method to the film cooling of a flat plate. In our example we have an objective function which maximizes the component life (related to the difference from an arbitrary target temperature distribution) and minimizes the mixing loss introduced by the films. The flow environment was moderately compressible. The optimization converged after six computational fluid dynamics runs. A 30% reduction in mixing loss, a 11% increase in component life, and a 30% reduction in cooling mass flow rate were achieved. The advantages and limitations of the proposed method are also discussed in detail.