High-fidelity Computational Fluid Dynamics Research Articles

Modeling of ash formation and deposition processes in coal and biomass fired boilers: A comprehensive review

This paper presents a comprehensive review on the development of the modelling of ash deposition with particle combustion, sticking, rebound and removal behaviors. The modelling of ash deposit morphomology is also included. Ash deposition in coal and biomass fired boilers will induce many ash-related issues (such as slagging, fouling and corrosion) which will reduce the boiler efficiency and capacity. Some traditional prediction methods have been proposed to evaluate ash deposition. However, these methods are based on chemical compositions of ash deposits and the operating temperatures in boilers, which are unable to fully predict the complex ash deposition process. Great efforts have been made to develop mechanistic models to predict ash deposition processes in nature. The behavior of ash formation and deposition in the boilers plays a key role in the design of boilers and the selection of fuels. The ash formation process is primarily due to the fragmentation and coalescence of mineral matters in fuels and only a small portion of ash is formed. When ash particles impact the heat transfer surfaces, only a few particles will deposit on these surfaces and several ash deposition mechanisms have been identified to predict their behaviors, such as inertial impaction (for large particles), thermophoresis (for fine particles) and condensation (for vapors). The ash deposition mechanisms used in the experimental, numerical and mechanistic studies coupled with the fuels and investigated systems are summarized in this paper.Numerous attempts have been made to develop different models to overcome the shortcomings of traditional methods. As such, various numerical attempts have been made to predict the growth behavior of ash deposition in furnaces and boilers by employing comprehensive combustion models coupled with high fidelity computational fluid dynamics (CFD) modelling methods for different types of fuels. Furthermore, several combustion codes have been incorporated into the ash deposition models, including the fuel combustion process (the release of volatiles, devolatilization and char combustion), wall reaction and consumption sub-models as well as the packed bed and overbed combustion sub-models. Moreover, several ash deposition sub-models have been developed to predict the ash deposition growth behavior using computational fluid dynamics (CFD) methods in combustors of different scales. In order to better understand the impact behavior of ash particles, some analytical models such as dynamic models and kinetic models have been developed. For accurate prediction of impaction efficiency, an impaction correction factor has been proposed to reduce the effect of coarse meshes on the impaction efficiency. Also, the stickiness of ash particles which is determined by kinetic energy, viscosity and molten degree of ash particles plays a key role in the ash formation and deposition processes and determines whether ash particles stick on the surfaces or rebound from the surfaces. Briefly, three main types of particle adhesion theories are used to evaluate if an impacting particle bounces off or sticks to the surface, namely, the viscosity-based empirical model, the critical velocity model and the melt fraction model.When the particles impact the heat transfer surfaces, they may rebound from the surface or remove the ash deposit. The particle surface energy with its static contact angle is also important in determining the sticking efficiency. In addition, several rebound criteria have been proposed to predict the particle rebound behavior, which include the critical rebound velocity, energy balance, excess energy, bouncing potential and critical impact angle on a flat or oblique plate or on a heat transfer tube. Tremendous efforts have also been made to develop the theories and mechanisms of ash deposition as well as removal on the surfaces of heat exchangers, some of which were based on the Kern-Seaton theory. Furthermore, several removal sub-models have been proposed to predict the particle removal behavior, including the energy balance, moment conservation, energy dissipation, critical moment theory, critical shear velocity and critical impact angle. In the actual fouling process, the growth behavior of fouling on the tube surfaces changes the original tube shape continuously. The fly ash deposited on the surface alters the boundary of heat transfer and affects the distribution of the temperature, flow field as well as the deposition rates. Various theoretical methods have been proposed to predict the ash deposit morphology, including the lattice Boltzmann method (LBM), dynamic meshing method, and time and mass magnification factors. In addition, many investigations have focused on developing models for inter-particle thermal conductivity by studying the ash deposit microstructure to characterize the thermal and morphological changes through an ash deposit. Several ash deposition layer models have been experimentally and theorectically studied in details, including the two-layer, three-layer, four-layer and six-layer sub-models.

Radical Guide Vane Design and Optimization

Turbofan engines are connected to the airframe by a pylon that crosses the bypass duct downstream of the fan. The presence of the pylon generates a potential field that influences the upstream flowfield. Distortion generated by the pylon can be detrimental to the fan stability and integrity, and as such it must be controlled. This work presents a new method for the two-dimensional design of fan outlet guide vane cascades in the presence of the potential flow distortion caused by the pylon. The method, based on surface singularities, is capable of mitigating the distortion while preserving the performance of the nominal cascade. Design targets are achieved by manipulating both airfoil geometry and position within the cascade. Because the distortions caused by the pylon obey essentially inviscid fluid mechanics, the variation in load and loss parameters across the cascade can be approximated surprisingly well with a singularity method. The design rationale of controlling the incompressible load distribution leads to cascades with uniform load and loss even when viscous and compressible effects are considered using high-fidelity computational fluid dynamics. The new approach provides a significant speed-up over methods based on computational fluid dynamics and is valuable as an initial design tool.

Dynamic inflow effects in measurements and high-fidelity computations

Abstract. A wind turbine experiences an overshoot in loading after, for example, a collective step change in pitch angle. This overshoot occurs because the wind turbine wake does not immediately reach its new equilibrium, an effect usually referred to as dynamic inflow. Vortex cylinder models and actuator disc simulations predict that the time constants of this dynamic inflow effect should decrease significantly towards the blade tip. As part of the NASA Ames Phase VI experiment, pitch steps have been performed on a turbine in controlled conditions in the wind tunnel. The measured aerodynamic forces from these experiments seemed to show much less radial dependency of the dynamic inflow time constants than expected when pitching towards low loading. Moreover the dynamic inflow effect seemed fundamentally different when pitching from low to high loading, and the reason for this behavior remained unclear in previous analyses of the experiment. High-fidelity computational fluid dynamics and free-wake vortex code computations yielded the same behavior as the experiments. In the present work these observations from the experiments and high-fidelity computations are explained based on a simple vortex cylinder wake model.

Component-Based Geometry Manipulation for Aerodynamic Shape Optimization with Overset Meshes

Mesh generation for high-fidelity computational fluid dynamics simulations and aerodynamic shape optimization is a time-consuming task. Complex geometries can be accurately modeled using overset meshes, whereby multiple high-quality structured meshes corresponding to different aircraft components overlap to model the full aircraft configuration. However, from the standpoint of geometry manipulation, most methods operate on the entire geometry rather than on separate components, which diminishes the advantages of overset meshes. To address this issue, a geometry module is introduced that operates on individual components and automatically computes their intersections to update overset meshes during optimization. Reverse-mode automatic differentiation is applied to compute partial derivatives across this geometry module so that it fits into an optimization framework that uses a hybrid adjoint method (known as ADjoint) to efficiently compute gradients for a large number of design variables. By using these automatically updated meshes and the corresponding derivatives, the aerodynamic shape of the DLR-F6 geometry is optimized while allowing changes in the wing–fuselage intersection. Sixteen design variables control the fuselage shape, and 128 design variables determine the wing surface. Under transonic flight conditions, the optimization reduces drag by 15 counts (5%) as compared with the baseline design.

An Automatic Aerodynamic Shape Optimisation Framework Based on DAKOTA

Aerodynamic design is a must and fundamental aspect for better performance of an aircraft. This work describes a framework for aerodynamic shape optimisation (ASO) based on high-fidelity Computational Fluid Dynamics (CFD) analyses and an open source optimisation toolkit DAKOTA. The free form deformation (FFD) technique is adopted for parameterization of geometries, followed by a transfinite interpolation (TFI) method for grid deformation. CFD simulations are performed by full Navier-Stokes equations in conjunction with turbulence model. CFD, FFD and TFI modules are integrated with DAKOTA by a black-box interface, a pre-processing and a post-processing. The transonic RAE2822 airfoil is employed for demonstration of the effectiveness and capability of the developed framework. Several cases with different optimisation algorithms, design variables and turbulence models are performed. Optimum solutions with significant improvement of lift-drag ratio are presented to demonstrate the capability of the framework in this paper.

Transfer learning for efficient meta-modeling of process simulations

In chemical engineering applications, computational efficient meta-models have been successfully implemented in many instants to surrogate the high-fidelity computational fluid dynamics (CFD) simulators. Nevertheless, substantial simulation efforts are still required to generate representative training data for building meta-models. To solve this problem, in this research work an efficient meta-modeling method is developed based on the concept of transfer learning. First, a base model is built which roughly mimics the CFD simulator. With the help of this model, the feasible operating region of the simulated process is estimated, within which computer experiments are designed. After that, CFD simulations are run at the designed points for data collection. A transfer learning step, which is based on the Bayesian migration technique, is then conducted to build the final meta-model by integrating the information of the base model with the simulation data. Because of the incorporation of the base model, only a small number of simulation points are needed in meta-model training.

Contactless Space Debris Detumbling: A Database Approach Based on Computational Fluid Dynamics

This paper aimed to investigate the feasibility of a contactless method to detumble space debris by using thruster plume impingement during proximity operation. To detumble the rotational debris, the force and torque applied to the debris must be precisely estimated by an onboard computer. The high-fidelity computational fluid dynamics (CFD) approach is one of the options for estimating the high precision force and torque applied on the debris, although it entails a huge computational cost. In this study, CFD results, under representative conditions with several relative attitudes and distances, were plotted on a multidimensional map. Then force and torque were derived from the interpolated discrete mapped data, thus reducing the computational time. The present study designed a guidance and control algorithm for debris removal spacecraft, in order to achieve safe and efficient detumbling using estimated force and torque. Dynamics simulations were conducted using this approach to detumble large space debris objects, assuming an upper-stage rocket. The results showed that the contactless detumbling of space debris can be achieved with reasonable fuel consumption and time.

A POD reduced-order model for wake steering control

Wake steering by active yawing of upstream wind turbines is a promising wind plant control technique. To enable the development of model-based wind plant control methods, there is a need for models that can marry the contrasting requirements of good fidelity and low computational cost. This paper presents a reduced-order model (ROM) obtained by directly compressing high-fidelity computational fluid dynamics (CFD) simulation data using the proper orthogonal decomposition (POD) method. At first, simulations of wake-interacting wind turbines are obtained for time-varying yaw settings using the lifting-line large-eddy simulation (LES) code SOWFA. Next, a ROM is synthesized from the CFD transient simulations, obtaining a discrete-time state-space model that captures the dominant dynamics of the underlying high-fidelity model with only a reduced number of states. The ROM is optionally augmented with a Kalman filter, which feeds back turbine power measurements from the plant to the model, enhancing its accuracy. Results obtained in realistic turbulent conditions show a good agreement between high-fidelity CFD solutions and the proposed POD-based ROM in terms of wake behavior and power output of waked turbines. Additionally, the ROM presents acceptable results when compared to wind tunnel experiments, including the capability of the model to partially correct for an intentionally built-in model mismatch.

Combining induction control and wake steering for wind farm energy and fatigue loads optimisation

Turbine power and yaw set points can be adjusted across a wind farm to minimise the overall power losses and the additional fatigue loads caused by wake interactions. Detailed modelling is required to understand the complex flows in sufficient detail to allow a realistic practical control design. High-fidelity computational fluid dynamics requires enormous computational resources, so simpler engineering models are needed which capture the most important effects while running fast enough to allow sufficient testing. This paper describes a steady-state optimisation tool which has been extended to optimise all the power reduction set-points and yaw offsets simultaneously for different wind conditions. It also describes a fast time-domain simulation model which captures turbine and wake dynamic effects, so that wind farm controllers of all kinds can be tested in realistic and time-varying conditions. To demonstrate its application for controller testing, the performance of the combined power and yaw controller is tested during changing conditions of wind speed, direction and turbulence derived from measured site data. Finally, the need for validation is discussed, as many uncertainties still need to be resolved in order to obtain sufficient confidence that the potential benefits of such wind farm control schemes can be realised in practice.

Multi-fidelity aerodynamic design trade-off exploration using point-by-point Pareto set identification

Aerodynamic design is inherently a multi-objective optimization (MOO) problem. Determining the best possible trade-offs between conflicting aerodynamic objectives can be computationally challenging when carried out directly at the level of high-fidelity computational fluid dynamics simulations. This paper presents a computationally cheap methodology for exploration of aerodynamic design trade-offs. In particular, point-by-point identification of a set of Pareto-optimal designs is executed starting in the neighborhood of a single-objective optimal design, and using a trust-region-based, multi-fidelity optimization algorithm as well as locally constructed response surface approximations (RSAs). In this work, the RSAs are constructed using second-order polynomials without mixed terms, multi-fidelity models, and adaptive corrections. The application of the point-by-point MOO algorithm is demonstrated through MOO of transonic airfoil shapes using the Reynold–Averaged Navier Stokes equations and the Spalart–Allmaras turbulence model. The results demonstrate that the Pareto front in the neighborhood of an initial design can be obtained at a low cost when considering up to 12 design variables. The results also indicate that the computational cost of the optimization process grows slowly with the number of the design variables, and the repeatability of the algorithm is very good when starting the search from different initial points.

Characterization of aerodynamic performance of vertical axis wind turbines: Impact of operational parameters

Vertical axis wind turbines (VAWTs) have received growing interest for off-shore application and in the urban environments mainly due to their omni-directional capability, scalability, robustness, low noise and costs. However, their aerodynamic performance is still not comparable with their horizontal axis counterparts. To enhance their performance, the impact of operational parameters such as tip speed ratio (λ), Reynolds number (Rec) and turbulence intensity (TI) on their power performance and aerodynamics needs to be deeply understood. The current study, therefore, intends to systematically investigate the effect of these parameters in order to provide a deeper insight into their impact on the aerodynamic performance of VAWTs. For this investigation, a Darrieus H-type VAWT has been employed. A wide range of the parameters is considered: λ = 1.2–6.0, Rec = 0.3 × 105–4.2 × 105 and TI = 0%–30% to analyze the turbine performance, turbine wake and dynamic loads on blades. High-fidelity computational fluid dynamics (CFD), extensively validated with experimental data, are employed. The results show that (i) variable-speed operation maintaining the optimal λ at different wind speeds improves the turbine power coefficient, e.g. up to 168% at 4 m/s, while keeping an almost constant thrust coefficient, (ii) the turbine performance and wake are Re-dependent up to the highest Rec studied, (iii) large TI (> 5%) improves the turbine performance in dynamic stall by promoting the laminar-to-turbulent transition and delaying stall on blades, however it deteriorates the optimal performance by introducing extra skin friction drag. The findings of the current study can support more accurate performance prediction of VAWTs for various operating conditions and can help the improvement of the aerodynamic performance of VAWTs.

Reduced-order models for aerodynamic applications, loads and MDO

This article gives an overview of reduced-order modeling work performed in the DLR project Digital-X. Parametric aerodynamic reduced-order models (ROMs) are used to predict surface pressure distributions based on high-fidelity computational fluid dynamics (CFD), but at lower evaluation time and storage than the original CFD model. ROMs for steady aerodynamic applications are built using proper orthogonal decomposition and Isomap, a manifold learning method. Approximate solutions in the so-obtained low-dimensional representations of the data are found with interpolation techniques, or by minimizing the corresponding steady flow-solver residual. The latter approach produces physics-based ROMs driven by the governing equations. The steady ROMs are used to predict the static aeroelastic loads in a multidisciplinary design and optimization context, where the structural model is to be sized for the (aerodynamic) loads. They are also used in a process where an a priori identification of the critical load cases is of interest and the sheer number of load cases to be considered does not lend itself to high-fidelity CFD. An approach to correct a linear loads analysis model using steady CFD solutions at various Mach numbers and angles of attack and a ROM of the corrected aerodynamic influence coefficients is also shown. This results in a complete loads analysis model preserving aerodynamic nonlinearities while allowing fast evaluation across all model parameters. The different ROM methods are applied to a 3D test case of a transonic wing-body transport aircraft configuration.

Analysis and predictive modeling of nanofluid-jet impingement cooling of an isothermal surface under the influence of a rotating cylinder

In this paper, numerical study and thermal prediction for a nanofluid jet impingement cooling of an isothermal hot surface with an adiabatic rotating cylinder were performed. Finite volume method was used for the solution of resulting governing equations along with the boundary conditions. Influence of various pertinent parameters such as Reynolds number (between 100 and 400), angular rotational velocity of the cylinder (between −0.1 and 0.1), horizontal location of the cylinder (between 0 and 3.75w) and solid particle volume fraction (between 0 and 0.04) on the fluid flow thermal characteristics were examined. It was observed that cylinder rotation and its location affect the cooling performance of the hot surface. It can be used as control element for heat and fluid flow. At the highest angular rotational speed as compared to motionless cylinder case, average Nusselt number reduces by about 20.16% for clockwise rotation. Solid particle addition to the base fluid affects the variation of first and secondary peaks in the Nusselt number along the hot wall. At the highest solid when the cylinder is away from the inlet slot and average Nusselt number enhancement is by about 8.08% at the highest volume fraction. An efficient modeling strategy was developed based on proper orthogonal decomposition and radial basis neural networks for thermal predictions. Accurate and fast results were achieved as compared to high fidelity computational fluid dynamics simulation results.

DDES Analysis of the Wake Vortex Related Unsteadiness and Losses in the Environment of a High-Pressure Turbine Stage

In this work, the flows inside a high-pressure turbine (HPT) vane and stage are studied with a delayed detached eddy simulation (DDES) code. The fundamental nozzle/blade interaction is investigated with special attention paid to the development and transportation of the vane wake vortices. There are two motivations for this work. First, the extreme HPT operation conditions, including both transonic Mach numbers and high Reynolds numbers, impose a great challenge to modern computational fluid dynamics (CFD), especially for scale-resolved simulation methods. An accurate and efficient high-fidelity CFD solver is very important for a thorough understanding of the flow physics and the design of more efficient HPT. Second, the periodic wake vortex shedding is an important origin of turbine losses and unsteadiness. The wake and vortices not only cause losses themselves, but also interact with the shock wave (under transonic working condition), pressure waves, and have a strong impact on the downstream blade surface (affecting boundary layer transition and heat transfer). Based on one of our previous DDES simulations of a HPT vane, this work further investigates the development and length characteristics of the wake vortices, provides explanations for the length characteristics, and reveals the transportation of the wake vortices in the downstream rotor passages along with its impact on the downstream aero-thermal performance.

Multi-objective optimization of cyclone separators in series based on computational fluid dynamics

The pressure drop and collection efficiency are generally the most relevant parameters when evaluating the performance of gas-solid cyclone separators. In optimization procedures, mathematical cyclone models are used to find the best possible compromise between the pressure drop and the collection efficiency, based on the design preferences. Although several geometry optimization studies on single cyclone configurations have been reported, multiple cyclones in series remain relatively unexplored in this regard. Highly sophisticated models are described in the literature, but researchers mostly use lower fidelity models due to the shorter computational times involved. Also, when more sophisticated models are used, the authors tend to employ surrogate modeling approaches, which reduce the computational times but add another layer of uncertainty. In addition, several authors have optimized either the efficiency or the pressure drop, usually by adding a constraint on the other parameter. In contrast, herein, we describe the multi-objective optimization of three cyclones in series based on high fidelity computational fluid dynamics (CFD) cyclone modeling. A fully automated methodology solving this problem is evaluated, with the COBYLA optimization method and an Eulerian-Eulerian six-phase two-way gas-solid CFD approach simultaneously minimizing the pressure drop and maximizing the efficiency. No surrogate modeling is used here, which means that the results of the CFD simulations are directly used in the optimization procedures. The results obtained indicate that the optimized trios of cyclones outperform the conventional Stairmand (high efficiency) and Lapple (moderate pressure drop) geometries. The minimization of the emission resulted in 30 times less solids being emitted compared with the Stairmand trio. When minimizing the energy consumption, the pressure drop was 50% that of the Lapple geometry. When balancing the pressure drop and emission several results were considerably better than both geometries simultaneously (e.g. 33% less emission than Stairmand combined with 30% less pressure drop than Lapple). This demonstrates that the multi-objective optimization of cyclones in series delivers excellent results and is highly feasible in industrial timescales without compromising the fidelity of the mathematical cyclone model used.

Comparison of Three Rotor Designs for Rubber Mixing Using Computational Fluid Dynamics

ABSTRACT In recent years, there has been an increasing demand for efficient mixers with high-quality mixing capabilities in the rubber product industry, with the focus of producing fuel-efficient tires. Depending on the functional characteristics of the tire and thus the compounding ingredients, different types of mixers can be used for the rubber mixing process. Hence, the choice of an appropriate mixer is critical in achieving the proper distribution and dispersion of fillers in rubber and a consistent product quality, as well as the attainment of high productivity. With the availability of high-performance computing resources and high-fidelity computational fluid dynamics tools over the last two decades, understanding the flow phenomena associated with complex rotor geometries such as the two- and four-wing rotors has become feasible. The objective of this article is to compare and investigate the flow and mixing dynamics of rubber compounds in partially filled mixing chambers stirred with three types of rotors: the two-wing, four-wing A, and four-wing B rotors. As part of this effort, all the 3D simulations are carried out with a 75% fill factor and a rotor speed of 20 rpm using a computational fluid dynamics (CFD) code. Mass flow patterns, velocity vectors, particle trajectories, and other mixing statistics, such as cluster distribution index and length of stretch, are presented here. All the results showed consistently that the four-wing A rotor was superior in terms of dispersive and distributive mixing characteristics compared with the other rotors. The results also helped to understand the mixing process and material movement, thereby generating information that could potentially improve productivity and efficiency in the tire manufacturing process.

Aerodynamics and flow features of a damselfly in takeoff flight

Flight initiation is fundamental for survival, escape from predators and lifting payload from one place to another in biological fliers and can be broadly classified into jumping and non-jumping takeoffs. During jumping takeoffs, the legs generate most of the initial impulse. Whereas the wings generate most of the forces in non-jumping takeoffs, which are usually voluntary, slow, and stable. It is of great interest to understand how these non-jumping takeoffs occur and what strategies insects use to generate large amount of forces required for this highly demanding flight initiation mode. Here, for the first time, we report accurate wing and body kinematics measurements of a damselfly during a non-jumping takeoff. Furthermore, using a high fidelity computational fluid dynamics simulation, we identify the 3D flow features and compute the wing aerodynamics forces to unravel the key mechanisms responsible for generating large flight forces. Our numerical results show that a damselfly generates about three times its body weight during the first half-stroke for liftoff. In generating these forces, the wings flap through a steeply inclined stroke plane with respect to the horizon, slicing through the air at high angles of attack (45°–50°). Consequently, a leading edge vortex (LEV) is formed during both the downstroke and upstroke on all the four wings. The formation of the LEV, however, is inhibited in the subsequent upstrokes following takeoff. Accordingly, we observe a drastic reduction in the magnitude of the aerodynamic force, signifying the importance of LEV in augmenting force production. Our analysis also shows that forewing-hindwing interaction plays a favorable role in enhancing both lift and thrust production during takeoff.

Data assimilation-based reconstruction of urban pollutant release characteristics

The development of efficient methodologies for the reconstruction of pollutant source characteristics in urban areas is a challenging issue due to the complexity of the flow in such environments, requiring the use of expensive computational fluid dynamics (CFD) solvers for the numerical estimation of the flow. In the present study, it is proposed to apply data assimilation (DA) techniques to the identification of both pollutant source characteristics and wind parameters from observations of the gas concentration at discrete locations in an urban environment. A non-intrusive ensemble-based variational DA scheme enhanced by proper orthogonal decomposition (POD) is deployed in conjunction with a Large Eddy Simulation (LES) solver, which provides high-fidelity estimation of the urban flow. The proposed methodology is proved to be able to accurately identify both pollutant source and wind characteristics with a computational cost of O(10) LES simulations. In addition, a sensor placement procedure based on global sensitivity analysis techniques is proposed in order to improve the performances of the assimilation process, which appear to be very sensitive to the choice of the measurement locations, in terms of computational cost and quality of the reconstructed parameters. Despite of several limitations in the use of the present approach for real time applications due to computational costs, this study demonstrates the potentiality of deploying sensitivity analysis and DA methodologies to solve inverse problems for urban flows in conjunction with high-fidelity CFD solvers.

Optimization of Flap Position Using a Modified Discrete-Vortex Method

The work proposes the use of a low-fidelity discrete-vortex-method-based model for function evaluation in genetic-algorithm-based optimization cycles toward optimizing the flap position of a high-lift configuration. The proposed methodology is a two-step strategy. In the first step, a modified discrete-vortex method is employed to obtain a vortex distribution for a given reference configuration for which the aerodynamic coefficients are known from a high-fidelity computational fluid dynamics solver. A constrained minimization procedure is employed to match the aerodynamic coefficients as computed using the modified discrete-vortex method with those obtained using the high-fidelity solver. The use of drag panels in the modified discrete-vortex method allows drag predictability even though the underlying procedure is based on a potential flow theory. In the second step, the model based on the discrete-vortex method is employed to obtain aerodynamic coefficients for any new configuration that can be considered as a perturbation around a reference configuration. The reference configuration is chosen based on a proximity criterion. The cost effectiveness of the proposed strategy allows the use of multiple optimization cycles, each with a large population size, with the high-fidelity reference data getting enriched at the end of every optimization cycle, particularly in the region of optimality. As the optimization cycles evolve, the model becomes progressively more accurate. The effectiveness of the present optimization strategy is demonstrated for the flap-position optimization of a high-lift airfoil corresponding to a takeoff configuration of a conceptual Regional Transport Aircraft.

Active Power Control of Waked Wind Farms

Active power control can be used to balance the total power generated by wind farms with the power consumed on the electricity grid. With the increasing penetration levels of wind energy, there is an increasing need for this ancillary service. In this paper, we show that the tracking of a certain power reference signal provided by the transmission system operator can be significantly improved by using feedback control at the wind farm level. We propose a simple feedback control law that significantly improves the tracking behavior of the total power output of the farm, resulting in higher performance scores. The effectiveness of the proposed feedback controller is demonstrated using high-fidelity computational fluid dynamics simulations of a small wind farm.