High-fidelity Computational Fluid Dynamics Simulations Research Articles

CFD Prediction of Tip Vortex Aging in the Wake of a Multi-MW Wind Turbine

In the present study, prediction from high-fidelity Computational Fluid Dynamics (CFD) simulations of the tip vortex aging in the near-wake of a multi-MW wind turbine is evaluated and compared to in-situ measurements as well as results of a semi-empirical model. Optimized tip vortex refinement is also introduced to investigate the influence of the grid topology on the vortex evolution. The grid refinement affects only the vortex core size and a reduction of the core radius by a factor of 3.4 was achieved with the chosen parameters. On the refined setup, vortex core sizes and strength are comparable with in-situ Unmanned Aircraft System (UAS) based measurements at 0.5 rotor radius downstream of the wind turbine. A comparison of the aging function with a semi-empirical vortex helix model shows a good agreement with the refined CFD results, but the core size predicted by the model is smaller than in simulations and experiments.

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.

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.

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.

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.

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.

Application of an OCT-based 3D reconstruction framework to the hemodynamic assessment of an ulcerated coronary artery plaque

The rupture of a vulnerable plaque, known as ulceration, is the most common cause of myocardial infarction. It can be recognized by angiographic features, such as prolonged intraluminal filling and delayed clearance of the contrast liquid. The diagnosis of such an event is an open challenge due to the limited angiographic resolution and acquisition frequency. The treatment of ulcerated plaques is an open discussion, due to the high heterogeneity and the lack of evidences that support particular strategies. Therefore, the therapeutic decision should follow a detailed investigation with angiography and intravascular imaging, such as optical coherence tomography (OCT), to locate the lesion, besides its geometric features and the lumen occlusion severity.The aim of this study is the application of a framework for the in-silico analysis of the disrupted hemodynamics due to an ulcerated lesion. The study employed a validated OCT-based reconstruction methodology and computational fluid dynamics (CFD) simulations for the computation of local hemodynamic quantities, such as wall shear stress.The reported findings, such as disrupted pre-operative flow conditions, proved the applicability of the developed framework for CFD analyses on complicated patient-specific anatomies that feature ulcerated plaques. The prediction of lesion expansion and the clinical decision making can benefit from a reliable computation of wall shear stress distributions that result from the peculiar anatomy of the lesion. The application of intravascular OCT imaging, high fidelity 3D reconstructions and CFD simulations might guide the treatment of such pathology.

Enhancement of Unsteady and 3D Aerodynamics Models using Machine Learning

Unsteady aerodynamics will be an important part of the floating wind turbines of the future operating under high shear across the rotor disk coupled with platform motion and atmospheric turbulence. We develop an unsteady aerodynamics and dynamic stall model using a long short-term memory variant of recurrent neural networks. The neural network model is trained using the oscillating airfoil data set from Ohio State University. The predictions from our machine learning (ML)-based model show good agreement with the experimental data and other state-of-the-art dynamic stall models for a wide range of airfoils, Reynolds numbers and reduced frequencies. In some cases the predictions are better than the Beddoes-Leishman model implementation in OpenFAST, when using the default coefficients. The ML-based model is also able to capture the key physics associated with dynamic stall, such as the precedence of moment stall before lift stall and cycle-to-cycle variations in the aerodynamic response. The new unsteady aerodynamics model is expected to improve prediction of fatigue loads for yaw-based wake-steering control scenarios in actuator-line and actuator-disc simulations of wind farms. Our methodology for training the ML-model provides a pathway for improving design level tools using high-fidelity computational fluid dynamics (CFD) simulations in the future.

Data-driven prediction of vehicle cabin thermal comfort: using machine learning and high-fidelity simulation results

Predicting thermal comfort in an automotive vehicle cabin's highly asymmetric and dynamic thermal environment is critical for developing energy efficient heating, ventilation and air conditioning (HVAC) systems. In this study we have coupled high-fidelity Computational Fluid Dynamics (CFD) simulations and machine learning algorithms to predict vehicle occupant thermal comfort for any combination of glazing properties for any window surface, environmental conditions and HVAC settings (flow-rate and discharge air temperature). A vehicle cabin CFD model, validated against climatic wind tunnel measurements, was used to systematically generate training data that spanned the entire range of boundary conditions, which impact occupant thermal comfort. Three machine learning algorithms: linear regression with stochastic gradient descent, random forests and artificial neural networks (ANN) were applied to the simulation data to predict the Equivalent Homogeneous Temperature (EHT) for each passenger and the volume averaged cabin air temperature. The trained machine learning models were tested on unseen data also generated by the CFD model. Our best machine learning model was able to achieve a test error of less than 5% in predicting EHT and cabin air temperature. Predicted EHT can also yield thermal comfort metrics such as Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD), which can account for different passenger profiles (metabolic rates and clothing levels). Machine learning models developed in this work enable predictions of thermal comfort for any combination of boundary conditions in real-time without having to rely on computationally expensive CFD simulations.

Microdosimetric and Biokinetic Modelling of Alpha-Immuno-Conjugate Transport in Endothelial Cells

The characterization of the Alpha-Immuno-Conjugate (AIC) targeted delivery process under vascular environment is very challenging due to the small scale of AIC particles, decay chain of the labeled radionuclide and the complex in vivo vascular system. To understand such a complex system, computational biokinetic model and microdosimetric model have been developed at Canadian Nuclear Laboratories (CNL) to help understand the AIC targeted delivery process and efficacy. The overall aim is to assist in the optimization and personalization of the treatment of patients. 1) Biokinetic model based on computational fluid dynamics (CFD) method is developed to study the blood flow through a two-dimensional Endothelial Cell (EC) embedded in a solid tumour or a normal cell. This model includes the collision model to handle the interactions of multiple particles in an administered conjugate. Immersed boundary method was used to handle the multiple moving particles in the capillary after the intravenous injection of AIC. A mesoscale modeling is applied in order to gain an understanding of AIC transport in capillary so that the time-dependent location of the AICs can be predicted. The resulting locations of radiation sources can be used as an input by the dosimetric model to predict the absorbed dose. 2) Microdosimetic modeling based on alpha Monte Carlo simulation toolkit was developed in FORTRAN programming language to calculate the amounts of the energy deposited in a simplified nucleus model from internal moving emitters and evaluate the single-event spectra for alpha particle emitters. The model can handle various alpha kinetic energies based on emitters with long decay chains. 3)The coupled biokinetic and Monte Carlo models would be integrated into a generic coupling framework such as SALOME as reusable modules in order to capture the rapid changes involving the phenomenological interdependencies in biological effects to evaluate the efficacy and toxicity of the AIC. The resulting large amount of data calculated by the coupled high fidelity CFD simulation and microdosimetric analysis can be processed in real time by a visualization tool. A coupled model based on a simplified and the Geant4 Monte Carlo micro-dosimetry technique and Computational Fluid Dynamics analysis for multiple particle movement was established. The transient AIC delivery process and the absorbed dose to the EC cells in the capillary are investigated to determine the transient toxicity of the AIC. The model to implement the decay scheme for radionuclide such as 225Ac is under development. The Multiphysics model presented in this abstract demonstrates the feasibility of combined biokinetic and microdosimetric modeling to evaluate the efficacy of the TAT.

Active flow control for power enhancement of vertical axis wind turbines: Leading-edge slot suction

Vertical axis wind turbines (VAWTs) suffer from a poor power performance at low tip speed ratios, where their blade aerodynamics are dominated by unsteady separation and dynamic stall. Therefore, to enhance their aerodynamic performance, separation control is highly desired. The present study intends to suppress the flow separation on VAWTs using boundary layer suction through a slot located near the blade leading edge. High-fidelity computational fluid dynamics simulations extensively validated with experiments are employed. A characterization of the impact of the suction amplitude, 0.5% ≤ AS ≤ 10%, and the suction location, 8.5 ≤ XS/c ≤ 28.5, is performed. The dependency of the obtained power gain on operating conditions, i.e. tip speed ratio, 2.5 ≤ λ ≤ 3.5, Reynolds number, 0.51 × 105 ≤ Rec ≤ 2.78 × 105, and turbulence intensity, 1% ≤ TI ≤ 25%, is studied. The results show that applying suction along the chordwise extent of the laminar separation bubble (LSB) can prevent its bursting, eliminate/postpone its formation, avoid the formation of the dynamic stall vortex and trailing-edge roll-up vortex, and delay the incipient trailing-edge separation. This will significantly increase the blade lift force, decrease the drag force, delay the stall angle and suppress the aerodynamic load fluctuations. For the reference turbine and for AS = 0.5% and XS/c = 8.5%, the power coefficient at λ of 2.5, 3.0 and 3.5 is enhanced by 247%, 83% and 24%, respectively. The suction location is critical while a minimum amplitude, e.g. AS = 0.5%, suffices. The optimal suction location is insensitive to TI, weakly sensitive to λ while comparatively more sensitive to Rec.

Active control of vortex-induced vibration of a circular cylinder using machine learning

We demonstrate the use of high-fidelity computational fluid dynamics simulations in machine-learning based active flow control. More specifically, for the first time, we adopt the genetic programming (GP) to select explicit control laws, in a data-driven and unsupervised manner, for the suppression of vortex-induced vibration (VIV) of a circular cylinder in a low-Reynolds-number flow (Re = 100), using blowing/suction at fixed locations. A cost function that balances both VIV suppression and energy consumption for the control is carefully chosen according to the knowledge obtained from pure blowing/suction open-loop controls. By implementing reasonable constraints to VIV amplitude and actuation strength during the GP evolution, the GP-selected best ten control laws all point to suction-type actuation. The best control law suggests that the suction strength should be nonzero when the cylinder is at its equilibrium position and should increase nonlinearly with the cylinder’s transverse displacement. Applying this control law suppresses 94.2% of the VIV amplitude and achieves 21.4% better overall performance than the best open-loop controls. Furthermore, it is found that the GP-selected control law is robust, being effective in flows ranging from Re = 100 to 400. On the contrary, although the P-control can achieve similar performance as the GP-selected control at Re = 100, it deteriorates in higher Reynolds number flows. Although for demonstration purpose the chosen control problem is relatively simple, the training experience and insights obtained from this study can shed some light on future GP-based control of more complicated problems.

Surrogate-based aerodynamic shape optimization of hypersonic flows considering transonic performance

Aerodynamic shape optimization of a hypersonic transport aircraft over a wide Mach-number range is challenging. The difficulty is not only associated with the large computational cost of high-fidelity CFD (computational fluid dynamics) simulations but also linked to the reasonable compromise between aerodynamic performances of aircraft at different speed ranges. This article proposes to use efficient global optimization based on surrogate models to address this type of problems. A RANS (Reynolds averaged Navier-Stokes) flow solver is adopted to evaluate objective and constraint functions; Kriging surrogate model combined with a parallel infill-sampling method and a multi-round strategy are employed to find the global optimum. First, a profile optimization with baseline airfoil of NACA64A-204 is conducted to achieve high lift-to-drag ratio (L/D) at both hypersonic (Ma=6.0) and transonic (Ma=0.8) regimes with up to 18 design variables, and significant improvement has been observed. Then, multi-objective wing optimizations of maximizing both hypersonic and transonic L/Ds with up to 54 design variables are performed and multiple optimizations with various sets of weight coefficients are investigated. The optimized wings are further evaluated and compared with the baseline wing at the flow regimes from subsonic to hypersonic speeds. Results show that by using the optimized profile, hypersonic and transonic L/Ds of a typical wing configuration are increased by 13.85% and 7.32%, respectively. After 3-D optimizations, the L/Ds at hypersonic and transonic design points are further improved by 4.47% and 3.19%, respectively, and a better performance over a wide Mach-number range is obtained. It is shown that a multi-round surrogated-based optimization is feasible and effective for aerodynamic shape optimization of hypersonic transport aircrafts over a wide Mach-number range.

Variable-Fidelity Methodology for the Aerodynamic Optimization of Helicopter Rotors

The design of helicopter rotor blades is a challenging task. On the one hand, there are the demanding simulations, which are a multidisciplinary endeavor. On the other hand, tools for parametric studies or optimizations require many simulations, making the design process even more costly. In the rotorcraft community, two routes for the numerical optimization task are observed. The first route is based upon local gradient search algorithms, which exploit low-fidelity tools or adjoint-based computational fluid dynamics (CFD) simulations. The second route is surrogate-based optimization in combination with high-fidelity CFD simulations. These surrogate-based optimizations can be further accelerated, when knowledge from low-fidelity models is used. This paper presents a framework that is developed for the multi-objective aerodynamic optimization of helicopter rotor blades including surrogate models based on different fidelities. The individual components necessary for performing a variable-fidelity, multi-objective optimization are reviewed before being applied. A novel technique to deal with unsuccessful simulations referred to as a failure map is additionally presented. The gain of the variable-fidelity optimizations in contrast to the single-fidelity optimizations is quantified, and a reduction in computational resources of up to 69% is observed. The failure map requires 78% less resources in contrast to the classical failure handling.

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.