High-fidelity Flow Simulations Research Articles

Parametric modeling and analysis of transonic aeroelastic systems based on incremental learning

The aerodynamic reduced order model (ROM) is an effective tool for predicting unsteady aerodynamics and aeroelastic responses with high accuracy and low computational costs. However, traditional aerodynamic ROMs with respect to different flow conditions are lack of robustness. How to enhance dramatically the generalization capability of aerodynamic ROMs should be further studied. This paper presents three parametric ROMs based on the least squares support vector regression algorithm (LS-SVR) and two incremental learning algorithms. LS-SVR is used to establish the relationship between aerodynamic inputs and outputs based on training data from high-fidelity flow simulations. And the Mach number is considered as an additional system input to account for varying flow conditions. The main contribution of incremental learning algorithms based on LS-SVR is that it does not need to reconstruct the ROM when adding sample data. For incremental learning algorithm with forgetting mechanism, training samples are added in the manner of a time-window to improve training efficiency. To illustrate the performance of these ROMs, the aerodynamic and aeroelastic responses of a NACA 0012 airfoil with two degrees of freedom are investigated. The simulation results show that both unsteady aerodynamic results and aeroelastic responses computed by using the three ROM-based models agree well with the results of the high-fidelity simulation. The three proposed ROMs have better generalization capacity and modeling efficiency.

Accelerating CFD simulation with high order finite difference method on curvilinear coordinates for modern GPU clusters

A high fidelity flow simulation for complex geometries for high Reynolds number (Re) flow is still very challenging, requiring a more powerful HPC system. However, the development of HPC with traditional CPU architecture suffers bottlenecks due to its high power consumption and technical difficulties. Heterogeneous architecture computation is raised to be a promising solution to the challenges of HPC development. GPU accelerating technology has been utilized in low order scheme CFD solvers on the structured grid and high order scheme solvers on unstructured meshes. The high-order finite difference methods on structured grids possess many advantages, e.g., high efficiency, robustness, and low storage. However, the strong dependence among points for a high-order finite difference scheme still limits its application on the GPU platform. In the present work, we propose a set of hardware-aware technology to optimize data transfer efficiency between CPU and GPU, as well as communication efficiency among GPUs. An in-house multi-block structured CFD solver with high order finite difference methods on curvilinear coordinates is ported onto the GPU platform and obtains satisfying performance with a speedup maximum of around 2000x over a single CPU core. This work provides an efficient solution to apply GPU computing in CFD simulation with specific high order finite difference methods on current GPU heterogeneous computers. The test shows that significant accelerating effects can be achieved for different GPUs.

Assessment of a Discontinuous Galerkin Method for the Simulation of the Turbulent Flow around the DrivAer Car Model

The turbulent flow over the DrivAer fastback model is here investigated with an order-adaptive discontinuous Galerkin (DG) method. The growing need of high-fidelity flow simulations for the accurate determination of problems, e.g., vehicle aerodynamics, promoted research on models and methods to improve the computational efficiency and to bring the practice of Scale Resolving Simulations (SRS), like the large-eddy simulation (LES), to an industrial level. An appealing choice for SRS is the Implicit LES (ILES) via a high-order DG method, where the favourable numerical dissipation of the space discretization scheme plays directly the role of a subgrid-scale model. Implicit time integration and the p-adaptive algorithm reduce the computational cost allowing a high-fidelity description of the physical phenomenon with very coarse mesh and moderate number of degrees of freedom. Two different models have been considered: (i) a simplified DrivAer fastback model, without the rear-view mirrors and the wheels, and a smooth underbody; (ii) the DrivAer fastback model, without rear-view mirrors and a smooth underbody. The predicted results have been compared with experimental data and CFD reference results, showing a good agreement.

Extremum Seeking Control Applied to Airfoil Trailing-Edge Noise Suppression

Extremum seeking control (ESC) and its slope seeking generalization are applied in a high-fidelity flow simulation framework for reduction of acoustic noise generated by a NACA0012 airfoil. Two Reynolds numbers are studied for which different noise generation mechanisms are excited. For a low Reynolds number flow, the scattering of vortex shedding at the airfoil trailing edge produces tonal noise, while for a moderate Reynolds number case, boundary-layer instabilities scatter at the trailing edge, leading to noise emission at multiple tones superimposed on a broadband hump. Different control setups are investigated, and they are configured to either find an optimal steady actuator intensity or an optimal position for a blowing/suction device. Implementation details are discussed regarding the control modules and design of digital filters. Analyses of physical phenomena as well as of relevant behavior of the actuated plant are conducted to understand how the extremum seeking loop leverages the flow physics to control noise. Depending on the flow configuration studied and the control setup, results demonstrate that the ESC can provide considerable airfoil noise reductions.

On the ice-nucleating potential of warm hydrometeors in mixed-phase clouds

Abstract. The question as to whether or not the presence of warm hydrometeors in clouds may play a significant role in the nucleation of new ice particles has been debated for several decades. While the early works of Fukuta and Lee (1986) and Baker (1991) indicated that it might be irrelevant, the more recent study of Prabhakaran et al. (2020) suggested otherwise. In this work, we attempt to quantify the ice-nucleating potential using high-fidelity flow simulation techniques around a single hydrometeor and use favorable considerations to upscale the effects to a collective of ice particles in clouds. While we find that ice nucleation may be significantly enhanced in the vicinity of a warm hydrometeor and that the affected volume of air is much larger than previously estimated, it is unlikely that this effect alone causes the rapid enhancement of ice nucleation observed in some types of clouds, mainly due to the low initial volumetric ice concentration. Furthermore, it is demonstrated that the excess nucleation rate does not primarily depend on the rate at which cloud volume is sampled by the meteors' wakes but is rather limited by the exposure time of ice-nucleating particles to the wake, which is estimated to be of the order of few microseconds. It is suggested to further investigate this phenomenon by tracking the trajectories of ice-nucleating particles in order to obtain a parametrization which can be implemented into existing cloud models to investigate second-order effects such as ice enhancement after the onset of glaciation.

Optimizing wind farm control through wake steering using surrogate models based on high-fidelity simulations

Abstract. This paper aims to develop fast and reliable surrogate models for yaw-based wind farm control. The surrogates, based on polynomial chaos expansion (PCE), are built using high-fidelity flow simulations coupled with aeroelastic simulations of the turbine performance and loads. Developing a model for wind farm control is a challenging control problem due to the time-varying dynamics of the wake. The wind farm control strategy is optimized for both the power output and the loading of the turbines. The optimization performed using two Vestas V27 turbines in a row for a specific atmospheric condition suggests that a power gain of almost 3%±1% can be achieved at close spacing by yawing the upstream turbine more than 15∘. At larger spacing the optimization shows that yawing is not beneficial as the optimization reverts to normal operation. Furthermore, it was also identified that a reduction in the equivalent loads was obtained at the cost of power production. The total power gains are discussed in relation to the associated model errors and the uncertainty of the surrogate models used in the optimization, as well as the implications for wind farm control.

A data-driven surrogate to image-based flow simulations in porous media

The objective for this work is to develop a data-driven surrogate to high-fidelity numerical flow simulations using digital images of porous media. The proposed model can capture the pixel-scale velocity vectors in a large verity of digital porous media created by random two-dimensional (2D) circle packs. To develop the model, images of the 2D media (binary images of solid grains and void spaces) along with their corresponding velocity vectors at the pixel level computed using lattice Boltzmann simulation runs are used to train and to predict the solutions with a high accuracy in much less computational time. The velocity vector predictions made by the surrogate models are used to compute the permeability tensor for samples that have not been used in the training. The results show high accuracy in the prediction of both velocity vectors and permeability tensors. The proposed methodology harness the enormous amount of generated data from high-fidelity flow simulations to decode the often under-utilized patterns in simulations and to accurately predict solutions to new cases. The developed model can truly capture the physics of the problem and enhance the prediction capabilities of the simulations at a much lower cost. These predictive models, in essence, do not spatially reduce the order of the problem. They, however, possess the same numerical resolutions as their Lattice Boltzmann simulations equivalents do with the great advantage that their solutions can be achieved by a significant reduction in computational costs (speed and memory).

Dimension reduction in heterogeneous parametric spaces with application to naval engineering shape design problems

We present the results of the first application in the naval architecture field of a methodology based on active subspaces properties for parameter space reduction. The physical problem considered is the one of the simulation of the hydrodynamic flow past the hull of a ship advancing in calm water. Such problem is extremely relevant at the preliminary stages of the ship design, when several flow simulations are typically carried out by the engineers to assess the dependence of the hull total resistance on the geometrical parameters of the hull, and others related with flows and hull properties. Given the high number of geometric and physical parameters which might affect the total ship drag, the main idea of this work is to employ the active subspaces properties to identify possible lower dimensional structures in the parameter space. Thus, a fully automated procedure has been implemented to produce several small shape perturbations of an original hull CAD geometry, in order to exploit the resulting shapes and to run high fidelity flow simulations with different structural and physical parameters as well, and then collect data for the active subspaces analysis. The free form deformation procedure used to morph the hull shapes, the high fidelity solver based on potential flow theory with fully nonlinear free surface treatment, and the active subspaces analysis tool employed in this work have all been developed and integrated within SISSA mathLab as open source tools. The contribution will also discuss several details of the implementation of such tools, as well as the results of their application to the selected target engineering problem.

Efficient collective swimming by harnessing vortices through deep reinforcement learning

Fish in schooling formations navigate complex flow fields replete with mechanical energy in the vortex wakes of their companions. Their schooling behavior has been associated with evolutionary advantages including energy savings, yet the underlying physical mechanisms remain unknown. We show that fish can improve their sustained propulsive efficiency by placing themselves in appropriate locations in the wake of other swimmers and intercepting judiciously their shed vortices. This swimming strategy leads to collective energy savings and is revealed through a combination of high-fidelity flow simulations with a deep reinforcement learning (RL) algorithm. The RL algorithm relies on a policy defined by deep, recurrent neural nets, with long-short-term memory cells, that are essential for capturing the unsteadiness of the two-way interactions between the fish and the vortical flow field. Surprisingly, we find that swimming in-line with a leader is not associated with energetic benefits for the follower. Instead, "smart swimmer(s)" place themselves at off-center positions, with respect to the axis of the leader(s) and deform their body to synchronize with the momentum of the oncoming vortices, thus enhancing their swimming efficiency at no cost to the leader(s). The results confirm that fish may harvest energy deposited in vortices and support the conjecture that swimming in formation is energetically advantageous. Moreover, this study demonstrates that deep RL can produce navigation algorithms for complex unsteady and vortical flow fields, with promising implications for energy savings in autonomous robotic swarms.

Neurofuzzy-Model-Based Unsteady Aerodynamic Computations Across Varying Freestream Conditions

This paper presents a reduced-order modeling approach based on recurrent local linear neurofuzzy models for predicting generalized aerodynamic forces in the time domain. Regarding aeroelastic applications, the unsteady aerodynamic loads are modeled as a nonlinear function of structural eigenmode-based disturbances. In contrast to established aerodynamic input/output model approaches trained by high-fidelity flow simulations, the Mach number is considered as an additional model input to account for varying freestream conditions. To train the relationship between the input parameters and the corresponding flow-induced forces, the local linear model tree algorithm is adopted in this work. The proposed method is tested exemplarily with respect to the AGARD 445.6 configuration in the subsonic, transonic, and supersonic flight regimes. It is shown that good conformity is obtained between the reduced-order model results and the respective full-order computational-fluid-dynamics solution. A further comparative analysis in the frequency domain in conjunction with a classical flutter analysis confirms the validity of the approach. Finally, the method’s potential for reducing the computational effort of aeroelastic analyses is demonstrated.

Low Energy Direct Contact Membrane Desalination: Conjugated Heat and High Fidelity Flow Simulation

The steady state performance of Low Energy Direct Contact Membrane Distillation (DCMD) is numerically investigated. A case of uniform flow is considered entering both the feed side at a temperature above the permeate side. The developed fluid model is governed by the Navier-Stokes flow and energy equation in a coupled conjugate heat transfer formulation to the flow and the solid membrane. Across the membrane and depending on the membrane parameters including permeability, thickness, pore size and conductivity the local temperature difference creates a driving pressure gradient responsible of evaporation part of the feed adjacent to the membrane surface, transport it through the pores, and condense it at permeate side through the hydrophobic membrane. The membrane's coefficients of DCMD membrane is evaluated along with the mass flux, heat flux, temperature polarization factor, and thermal efficiency. In this paper, two flow configurations are studied: Counter and Parallel flow. A parametric study is conducted incorporating velocity combinations and concluding an optimum configuration in terms of DCMD efficiency, mass flux. In view of these plausible results, a sensitivity study to the flow rates is carried out to gain better insight to the temperature polarization, heat flux including convective, conductive and the associated latent heat as well as in understanding its effect on the process metrics and yield.

Comparative study of different numerical approaches in space–time CESE framework for high-fidelity flow simulations

With the advancement of computer hardware, the trend of research in computational fluid dynamics is moving towards development of highly accurate, unstructured-mesh compatible, robust and efficient numerical methods for simulating problems involving strong transient effects and relatively complex geometries as well as physics. The space–time conservation element and solution element method is a genuinely multi-dimensional, unstructured-mesh compatible numerical framework, which was built from a consistent and synergetic integration of conservation laws in the space–time domain to avoid the limitations of conventional schemes, such as the use of 1-D flux reconstruction with a Riemann solver. It has been shown that the framework can be used for time-accurate simulations of a variety of problems involving unsteady waves, strong flow discontinuities, and their interactions with remarkable accuracy. However, this method at its current state has encountered the challenge in balancing the robustness and numerical accuracy when highly stretched meshes were used in viscous flow simulation. In this paper, we briefly discuss various numerical approaches developed for this framework thus far as well as their strengths and weaknesses, and conduct a comparative study of their numerical accuracies using some 2-D viscous benchmark test cases. The application of this method in realistic, complex 3-D problems is also included here to demonstrate its computational efficiency in large-scale computing.